IMPACTS OF MAN-MADE STRUCTURES ON MARINE
BIODIVERSITY AND SPECIES STATUS - NATIVE & NONNATIVE SPECIES
BY
SONALI PAWASKAR
A thesis submitted to
Victoria University of Wellington
in fulfilment of the requirements for the degree of
Doctor of Philosophy in Marine Biology.
2020
This thesis was conducted under the supervision of:
Professor Jonathan P. A. Gardner (Primary supervisor)
Victoria University of Wellington,
Wellington, New Zealand
Professor Chad Hewitt (Secondary supervisor)
Murdoch University,
Perth, Western Australia
And
Professor Marnie Campbell (Secondary supervisor)
Deakin University,
Melbourne, Victoria, Australia
Abstract
Coastal environments are exposed to anthropogenic activities such as frequent marine traffic
and restructuring, i.e., addition, removal or replacing with man-made structures. Although
maritime shipping and coastal infrastructures provide socio-economic benefits, they both cause
varied perturbations to marine ecosystems. The ports and marinas receiving a high frequency
of international vessels, act as ‘hot-spots’ for marine invasions. The disturbed and modified
habitats found in harbours and ports provide opportunities for non-native species to settle due
to their competitive traits. Once established, the non-native species may spread to neighbouring
habitats, thereby modifying the adjacent natural environment, its biodiversity, ecosystem
structure and functioning.
Up to 70% of coastlines around the world have now been modified and is expected to
rise in future. New bioinvasions are still being reported even with various biosecurity and
management approaches across the globe. It is essential to understand the potential factors
influencing the bioinvasions to have effective biosecurity measures and management plans.
The overall aim of this thesis is to determine the influence of man-made structures on the
marine biodiversity and presumptive fitness of native and non-native species on these
structures. This thesis investigates ports and harbours as man-made environments, their
impacts on marine biodiversity and the species status – native, non-native and cryptogenic, and
the factors facilitating the spread of non-native species.
Chapter 2 focussed on two large national-scale baseline port surveys; a) Australian Port
Survey (APS), and b) New Zealand Port Survey (NZPS). The two datasets were analysed to
determine the community structure and species status, i.e., native, non-native and cryptogenic
as a function of the surveyed ports, port type (major vs minor ports) (based on the volume of
vessels) and latitudinal groups. A) APS: The results for community composition indicated
significant effects as a function of surveyed ports, port type and latitudinal group. The
community composition was relatively more abundant at major ports than at minor ports. The
factor, the latitudinal group indicated significant results, and a distinct separation in community
composition was observed between low (15, 20oS) and high (35, 40oS) latitudes. The species
status showed a significant and positive relationship between native vs non-native, indicating
with an increase in the number of native species there was an increase in the number of nonnative species. The species status indicated significant results for the factors; surveyed ports,
port type and latitudinal group. The native species were abundant throughout the study.
i
However, the non-native species were relatively abundant at major ports compared to minor
ports. Regarding the latitudinal groups, the abundance of non-native species was observed to
increase at higher latitudes (latitudinal gradients). B) NZPS: The community composition and
species status showed significance among the 27 surveyed ports; however, no significant
results were observed for the factor port type (major vs minor). The community composition
significantly varied as a function of latitudinal groups, with species at higher latitudes (45oS)
being better discriminator explaining the differences. Latitudinal groups, however, highlighted
sub-groupings of ports with similar community composition (e.g. Bluff and Dunedin; Nelson,
Wellington and Picton; Lyttelton and Timaru; Whangarei, Tauranga and Taranaki; Auckland,
Gulf Harbour Marina and Opua Marina). The ports in question are within close proximity of
each other (distance). This suggests the possibility of natural dispersal of species between ports
on top of the human-mediated dispersal. The responses in Australia were very different from
those in New Zealand, which suggests that the responses are regional or country-specific and
not global.
Chapter 3 describes fieldwork using settlement tile arrays to examine the effects of
man-made built structures and natural rocky reefs on marine biological community
composition and successional patterns over two years. The work also tests the preference of
native and non-native species in terms of habitat type (natural reef vs man-made habitat) and
substratum type (PVC vs slate tile). The results showed a rapid increase in species settlement
on bare tiles as the available bare space was 30% just after 3 months of submersion. The
community composition significantly differed as a function of the interaction of factors, habitat
× substratum × sample interval. However, differences between the habitat types and substratum
types, respectively, were explained by the difference in abundance of the same suite of species.
The species were abundant at marina sites compared to reef sites; however, in terms of
substrata, the species were abundant on slate (natural) tiles than on PVC tiles.
The succession patterns of species over time (8 sample intervals) showed a similar trend
on both the habitat type and substratum type, with differences in the average abundances of
each species. The differences in abundances highlight the influence of species dispersal
patterns, recruitment patterns and post-settlement processes of species between habitat type
and substratum type, respectively. Subsequently, the species status indicated significance as a
function of habitat type, substratum type and sample intervals. The cryptogenic species were
abundant throughout the study. The cryptogenic species, however, decreased in abundance
over time, with an increase in abundance of native and non-native species. Subsequently, the
ii
non-native species significantly varied between habitat type, with relatively higher abundance
at marina (man-made) sites compared to reef (natural) sites. However, the non-native species
did not show significant variation as a function of substratum type (PVC vs slate). The results
are discussed in the context of the recruitment of species on a new barren substrate, and the
preference of habitat type and substratum type by native, non-native and cryptogenic species.
In Chapter 4, the reproduction output (gonadosomatic index, GSI) of the Southern
hemisphere, native (SHMg) and Northern hemisphere, non-native (NHMg) lineages of the blue
mussel, Mytilus galloprovincialis were measured. The GSI and shell length of NHMg and
SHMg were compared between habitat type; reef (natural) vs marina (man-made) sites. This
study aimed to identify reproductive patterns (i.e., timing and magnitude of spawning events)
and differences in performance (presumptive fitness) of the native and non-native blue mussel
lineages at the natural and man-made habitats. The results for shell length indicated
significance for habitat type and no significance as a function of lineage. The mussels were
relatively bigger mussels at marina sites compared to reef sites; however, the differences were
trivial. The GSI values as a function of habitat type, lineage and sampling time showed a
significant difference between habitat type, with high GSI values at reef sites than at marina
sites. However, this indicates that the blue mussels at marina sites had comparatively higher
spawning activity than at reef sites. The temporal variation of GSI of NHMg and SHMg showed
a similar reproductive trend (i.e., spawning and gametogenesis) at both habitats. However,
significant spawning activity was observed in July and November when compared between
reef and marina habitats. The results are discussed in the context of management implications
and strategies regarding the establishment and success of non-native M. galloprovincialis
lineage and whether their eradication is necessary or even possible.
The findings of this research are summarised and discussed in relation to our
understanding of biological community composition and diversity on man-made habitats and
the subsequent invasion in the neighbouring natural habitats. This study, from an ecoengineering perspective, highlights the importance of complex habitats and surfaces, and not
just material type. However, from a biosecurity and management approach, even though
Australia and New Zealand have one of the strong international biosecurity country-specific
legislation; the continuous arrival of non-native species in these countries indicates that such
marine legislation is not sufficiently compelling on its own. This study highlights the
interaction of non-native species at proximity ports, and it provides recommendations towards
regional-scale management measures concentrating on intra-coastal transfer of invaders
iii
through domestic maritime traffic or natural dispersal. The life-history traits, recruitment
timing and post-settlement processes, plays an essential role in determining long term patterns.
Lastly, this research indicated that native and non-native species with ecologically similar
responses lead to limited management options to some extent. Therefore, from a manager’s
perspective, the eradication of non-native species may not be necessary if it does not cause any
negative impacts to the biodiversity or the environment.
iv
Acknowledgements
Completion of my thesis was made possible through the support of many people that have
contributed to my journey of self-development as a person and a scientist. First of all, I am
deeply grateful to my supervisor, Prof. Jonathan P.A. Gardner, for all his guidance, patience
and encouragement throughout this work. It has always been a pleasure and an honour to work
under him and keep me steered along the steady track towards completion of this thesis. Thanks
to Prof. Chad Hewitt and Prof. Marnie Campbell for providing me with the data for the
Australian Port surveys and valuable advice in the early stage of my study. I would not have
been able to start my PhD let alone finish without funding; therefore, I am grateful for the New
Zealand Educated International Doctoral Scholarship, Victoria University Wellington and
Victoria PhD Submission Scholarship.
Many thanks to all the staff in the School of Biological Sciences, Faculty of Sciences and
Faculty of Graduate Research, especially Mary Murray, Paul Marsden, Sandra Taylor, Mark
Stephen, Patricia Stein and Barry Lewis for all the advice and support when needed. The field
experimental study would not be possible without the technicians at the Victoria University
Coastal Ecology Laboratory (VUCEL) John, Snout and Dan for helping me with diving sample
retrievals even in Wellington's cold, windy weather. Special thanks to John for helping with
the design and set-up of my experimental study. Special thanks to Neville for helping me cut
the settlement tiles. I thank the marina managers at Seaview Marina, Chaffers Marina and
Evans Bay Marina for facilitating access to the marina and berths as my sampling locations to
deploy my settlement tile setups.
Many people have been there, day-to-day, throughout my PhD who have been more help than
they realise. Thank you, Jo, for helping me set-up and helping me with different techniques in
the lab, Ruojin for being a great colleague and a friend. Many thanks to the 'Gardner Lab', Ian,
Lorenzo, Giulia, Calvin and Jana for all the encouragement, support and distractions when 'I've
needed a break. I am grateful to my friends who have coped with me throughout this journey
and kept me sane outside the lab, Rohan, Zeineb, Vaibhav, Ashish, Garima, Aileen, Manu,
Lucy, Lee and all my friends back home for checking up on me. Special thanks to Rohan
without whom my field collections and settlement tile drillings would not have been fun, thanks
for your immense support throughout this journey. Finally, I would like to thank my family,
dad Rajan, mom Bhagyashree and my brother Akshay. Thank you for your unwavering love
and support. You are all important to me in so many ways. Thank you!
v
Table of contents
Abstract .................................................................................................................................... I
Acknowledgements ................................................................................................................. V
Table of Contents .................................................................................................................. VI
List of Tables ......................................................................................................................... XI
List of Figures ....................................................................................................................... XV
Chapter 1. General Introduction ............................................................................................ 1
1.1 Modification of marine ecosystems ................................................................................ 1
1.2 Man-made habitat characteristics and impacts ............................................................... 3
1.3 Bioinvasions .................................................................................................................... 5
1.3.1 Invasion ecology ...................................................................................................... 5
1.3.2 Invasion stages ......................................................................................................... 7
1.3.3 Non-native species - characteristics and impacts..................................................... 8
1.4 Management and Mitigation ......................................................................................... 10
1.4.1 Marine port management approaches .................................................................... 10
1.4.2 Marine ecosystem monitoring................................................................................ 11
1.4.3 Mitigation approach – Eco-engineering ................................................................ 13
1.5 Thesis aim and objectives ............................................................................................. 16
1.6 Chapter outline and hypothesis testing ......................................................................... 17
Chapter 2. Port areas as introduction foci for non-native species .................................... 20
2.1 Background ................................................................................................................... 20
2.1.1 Marine shipping and port pressures ....................................................................... 20
2.1.2 Biosecurity strategies ............................................................................................. 21
2.1.3 Monitoring strategies ............................................................................................. 22
2.1.4 Port Surveys ........................................................................................................... 24
A) Australian Port Survey .................................................................................... 24
B) New Zealand Port Biological Baseline Survey ............................................... 24
C) Overview of the sampling 'CRIMP protocols' ................................................ 25
2.1.5 Limitations of large surveys .................................................................................. 26
2.2 Methods......................................................................................................................... 26
vi
2.2.1 Data analysis .......................................................................................................... 26
A) Australian Port Survey (APS) ......................................................................... 26
B) New Zealand Port Biological Baseline Survey (NZPS) ................................. 29
2.2.2 Statistical analysis .................................................................................................. 31
2.3 Results (A) – Australian Port Survey............................................................................ 33
2.3.1 Presence/absence data ............................................................................................ 33
2.3.2 Variations in total presence of species as a function of replicates of each surveyed
port ................................................................................................................................... 34
2.3.3 Variations in community composition as a function of the surveyed port, port type
and latitudinal groups....................................................................................................... 35
a) Surveyed ports ................................................................................................. 35
b) Port type ........................................................................................................... 41
c) Latitudinal groups ............................................................................................ 42
2.3.4 Variations in species status - native, non-native and cryptogenic species as a
function of the surveyed port, port type and latitudinal groups ....................................... 48
a) Surveyed ports ................................................................................................. 48
b) Port type ........................................................................................................... 52
c) Latitudinal groups ............................................................................................ 53
2.4 Results (B) – New Zealand Port Biological Baseline Survey ....................................... 57
2.4.1 Presence/absence data ............................................................................................ 57
2.4.2 Variations in total presence of species as a function of replicates of each surveyed
port ................................................................................................................................... 58
2.4.3 Occurrences of species ........................................................................................... 59
2.4.4 Variations in the community composition as a function of the surveyed port, port
type, latitudinal groups .................................................................................................... 60
a) Surveyed ports ................................................................................................. 60
b) Port type ........................................................................................................... 63
c) Latitudinal groups ............................................................................................ 64
2.4.5 Variations in species status - native, non-native and cryptogenic species as a
function of the surveyed port, port type and latitudinal groups ....................................... 67
a) Surveyed ports ................................................................................................. 67
b) Port type ........................................................................................................... 69
vii
c) Latitudinal groups ............................................................................................ 70
2.5 Discussion ..................................................................................................................... 70
2.5.1 Background ............................................................................................................ 70
2.5.2 Australian Port Surveys (APS) .............................................................................. 71
2.5.3 New Zealand Port Biological Baseline Survey (NZPS) ........................................ 72
2.5.4 Defining pathways ................................................................................................. 73
2.5.5 Management implications ...................................................................................... 75
2.5.6 Pros and cons of large-scale studies ...................................................................... 77
Chapter 3. Community development and structure on natural and man-made substrata
in natural and man-made environments.............................................................................. 79
3.1 Background ................................................................................................................... 79
3.2 Methods......................................................................................................................... 87
3.2.1 Study sites .............................................................................................................. 87
3.2.2 Substratum ............................................................................................................. 88
3.2.3 Experimental design .............................................................................................. 88
3.2.4 Field sampling and sampling intervals .................................................................. 89
3.2.5 Tile processing ....................................................................................................... 91
3.2.6 Data analyses ......................................................................................................... 91
a) Preliminary analyses ........................................................................................ 91
b) Bare space availability ..................................................................................... 92
c) Fouling community composition ..................................................................... 92
d) Species status (native, non-native, cryptogenic)............................................... 93
3.3 Results ........................................................................................................................... 95
3.3.1 Diversity of the fouling community....................................................................... 95
3.3.2 Species accumulation ............................................................................................. 97
3.3.3 Bare space availability ........................................................................................... 97
3.3.4 Fouling community ordination ............................................................................ 100
3.3.5 Variations in fouling community composition as a function of habitat, site(habitat),
substratum and time ....................................................................................................... 102
i) Habitat ..................................................................................................... 102
ii) Substratum ............................................................................................... 103
iii) Habitat × Substratum.............................................................................. 106
viii
iv) Sample interval…………………………………………………………106
3.3.6 Species status (native, non-native and cryptogenic) ............................................ 110
a) Species status and their occurrences .............................................................. 110
b) Status of species as a function of habitat, substratum and sample interval ... 110
i) Habitat ..................................................................................................... 111
ii) Substratum ............................................................................................... 112
iii) Sample interval ....................................................................................... 114
3.3.7 Correlation between native and non-native species as a function of sample interval
....................................................................................................................................... 116
3.4 Discussion ................................................................................................................... 117
3.4.1 Availability of bare space .................................................................................... 117
3.4.2 Fouling community composition as a function of habitat and substratum .......... 117
3.4.3 Species status – native, non-native, cryptogenic species .................................... 120
Chapter 4. Do native Mytilus galloprovincialis lineage, and its non-native lineage congener
differ in reproductive response to man-made habitats? .................................................. 123
4.1 Introduction ................................................................................................................. 123
4.1.1 Importance of habitat type ................................................................................... 123
4.1.2 Impacts of man-made structures on bioinvasions ................................................ 124
4.1.3 Study species........................................................................................................ 125
4.2 Methods....................................................................................................................... 129
4.2.1 Study site.............................................................................................................. 129
4.2.2 Field sampling...................................................................................................... 130
4.2.3 Shell length .......................................................................................................... 130
4.2.4 Laboratory analysis .............................................................................................. 131
4.2.5 Lineage identification .......................................................................................... 131
a) DNA extraction .............................................................................................. 131
b) PCR ................................................................................................................ 131
c) DNA Digest ................................................................................................... 132
d) Gel Electrophoresis ........................................................................................ 132
4.2.6 Statistical analyses ............................................................................................... 132
a) Shell length .................................................................................................... 132
b) Gonadosomatic index (GSI) .......................................................................... 133
ix
4.3 Results ......................................................................................................................... 134
4.3.1 Shell length .......................................................................................................... 134
4.3.2 Regression - GSI as a function of shell length for the lineages, NHMg and SHMg
between habitat type (reef vs marina) ............................................................................ 135
4.3.3 GSI as a function of habitat type and lineage ...................................................... 136
4.4 Discussion ................................................................................................................... 139
4.4.1 Background .......................................................................................................... 139
4.4.2 Abundances .......................................................................................................... 139
4.4.3 Shell length as a function of habitat type and lineage ......................................... 140
4.4.4 GSI as a function of habitat type and lineage ...................................................... 141
4.4.5 Reproductive cycle............................................................................................... 142
4.4.6 Management implications .................................................................................... 144
Chapter 5. General Discussion............................................................................................ 146
5.1 Background ................................................................................................................. 146
5.2 Chapter syntheses........................................................................................................ 146
5.3 Statistical vs Biological significance .......................................................................... 149
5.4 Limitations of the monitoring study ........................................................................... 150
5.5 Management implications ........................................................................................... 151
5.6 Future work ................................................................................................................. 152
5.7 Conclusion ................................................................................................................... 153
References ............................................................................................................................. 154
Appendix ............................................................................................................................... 191
x
List of Tables
Table 2.1.1 Sampling methods for port surveys as per CRIMP protocols……………………26
Table 2.2.1. Ports sampled for the Australian Port Surveys with latitudinal and longitudinal
groups, port type and replicates (sampling effort) ……………… ……………………….… 28
Table 2.2.2. Ports sampled for the New Zealand Port Biological Baseline Surveys with
latitudinal and longitudinal groups, port type and replicates (sampling effort) ………………30
Table 2.3.1. The total number of species noted in Australian Port Surveys grouped to Phylum
and status (native, non-native and cryptogenic) ……………………………………………..33
Table 2.3.2. The total number, sampling effort (replicates) and relative number of species as a
function of replicates at each surveyed port………………………………………………….34
Table 2.3.3. Results of the PERMANOVA test performed on the presence/absence of species
as a function of the surveyed port. Significance marked in bold……………………………. 36
Table 2.3.4. The average similarity of the presence/absence of species as a function of the
surveyed port…………………………………………………………………………………36
Table 2.3.5. SIMPER analysis: average similarity in presence/absence of species as a function
of the surveyed port…………………………………………………………………………..38
Table 2.3.6. Results of the PERMANOVA and pairwise test performed as a function of port
type (2 levels) ……………………………………………………………………………….41
Table 2.3.7. SIMPER analysis: average similarity in the community composition as a function
of port type…………………………………………………………………………………...42
Table 2.3.8. Results of the PERMANOVA test performed as a function of latitude………..43
Table 2.3.9. Results of pairwise PERMANOVA test performed on the community composition
as a function of latitude………………………………………………………………………44
Table 2.3.10. SIMPER analysis: average dissimilarity for the community composition as a
function of latitude……………………………………………………………………………45
Table 2.3.11. The list of species that indicated variations as a function of the port type and
latitudinal groups …………………………………………………………………………….47
Table 2.3.12. The total number of species, species status, replicates (sampling effort) and the
relative number of native, non-native and cryptogenic species - across 27 surveyed
ports.…………………………………………………..……………………………………...48
Table 2.3.13. Results of the PERMANOVA test performed on the species status as a function
of the surveyed port………………………………………………………………………….51
Table 2.3.14. Results of the PERMANOVA test and pairwise test performed on the species
status as a function of port type………………………………………………………………52
xi
Table 2.3.15. SIMPER analysis: percent contribution of species status as a function of port
type…………………………………………………………………………………………...53
Table 2.3.16. Results of the PERMANOVA test performed on the status of the species as a
function of latitude……………………………………………………………………………54
Table 2.3.17. Results of the pairwise PERMANOVA test performed on the presence of species
as a function of latitude……………………………………………………………………….54
Table 2.3.18. SIMPER analysis: percent contribution (C%) of species status as a function of
the latitudinal groups…………………………………………….…………………………...55
Table 2.3.19. SIMPER analysis: species status contributing (C%) to the average dissimilarity
as a function of the latitude…………………………………………………………………...56
Table 2.4.1. The total number of species noted in New Zealand Port Surveys grouped as per
their Phyla and species status (native, non-native and cryptogenic) …………………………57
Table 2.4.2. The total relative number of species as a function of replicates (sampling effort) at
each surveyed port…………………………………………………………………………....58
Table 2.4.3. Total occurrences of top 10 species across 15 surveyed ports……………….….59
Table 2.4.4. Results of the PERMANOVA test performed on the presence/absence of species
as a function of the surveyed port…………………………………………………………….60
Table 2.4.5. SIMPER analysis: average similarity in the presence/absence of species as a
function of the surveyed port…………………………………………………………………61
Table 2.4.6. Results of the PERMANOVA analysis and pairwise test performed as a function
of port type……………………………………………………………………………………63
Table 2.4.7. Results of the PERMANOVA test performed as a function of latitude…………64
Table 2.4.8. Results of the PERMANOVA and pairwise test as a function of port type……..65
Table 2.4.9. SIMPER analysis: average dissimilarity of community composition as a function
of latitude……………………………………………………………………………………..65
Table 2.4.10. The list of species that indicated variations as a function of surveyed ports and
latitudinal groups…………………………………………………………………………….66
Table 2.4.11. The total number of species as a function of status - native, non-native and
cryptogenic species - across 15 surveyed ports……………………………………………….67
Table 2.4.12. Results for regression for species status - native, non-native and cryptogenic
species………………………………………………………………………………………..68
Table 2.4.13. Results of the PERMANOVA test performed on the species status as a function
of surveyed port……………………………………………………..….................................68
Table 2.4.14. SIMPER analysis: percent contribution of species status - native, non-native and
cryptogenic species, as a function of the surveyed port……………………………………..69
xii
Table 2.4.15. The PERMANOVA analysis used to determine differences in the species status
as a function of port type………………………………………………..……………………69
Table 2.4.16. The PERMANOVA analysis used to determine differences in the species status
as a function of latitude…………………………………………………………..…...............70
Table 3.2.1. List of sampling sites with habitat types, codes, latitude and longitudes……….87
Table 3.2.2. List of sampling intervals, months, seasons and sampling year examined in this
study, following set-up and deployment in August 2017…………………………………….89
Table 3.3.1. List of species and major groups recorded at the six sampling sites for all 8 sample
intervals in Wellington Harbour……………………………………………………………...95
Table 3.3.2. Results for PERMANOVA to determine the availability of bare space as a function
of habitat, substratum and sample intervals with their interactions………………………….98
Table 3.3.3. Results of pairwise PERMANOVA test for the availability of bare space as a
function of habitat type, substratum type and habitat × sample interval …………………….98
Table 3.3.4. Permutational ANOVA (PERMANOVA) analysis used to determine differences
in community composition between factors: habitat type, site (habitat), substratum type and
sampling intervals…….……………………………………………………………………..102
Table 3.3.5. Results of pairwise PERMANOVA test performed on fouling communities as a
function of habitat and substratum with the average similarities between groups………….103
Table 3.3.6. SIMPER results for major species contributing to the average similarity between
habitat type and substratum type…………………….............................................................104
Table 3.3.7. Results for SIMPER analysis. Average similarities within groups and average
dissimilarities in fouling communities between habitat and substratum…………………….105
Table 3.3.8. Results of pairwise PERMANOVA test performed on fouling communities as a
function of the interaction between habitat × substratum with the average similarities between
groups……………………………………………………………………………………….106
Table 3.3.9. SIMPER results for ten major consistent species contributing to the average
similarity within each sampling interval……………………………………………………108
Table 3.3.10. Permutational ANOVA (PERMANOVA) analysis used to determine differences
in the status of the species between factors: habitat type, site (habitat), substratum type and
sampling interval………………………………………………….…...................................110
Table 3.3.11. Results of pairwise PERMANOVA test performed on species status as a function
of habitat type and substratum type…………………………………………………………111
Table 3.3.12. Results of pairwise PERMANOVA test performed on non-native species as a
function of habitat type and substratum type……………………………………………….112
xiii
Table 3.3.13. SIMPER results for species status contributing to the average within-group
similarity and dissimilarity between a) habitat type (Reef vs Marina and b) substratum type
(PVC vs Slate)……………………………………………….……………………………..113
Table 3.3.14. Major species contributing to the average similarity within each sampling
interval…………………………………………………………….... ……………………..114
Table 3.3.15. The temporal variation of species status (percent contribution) as per SIMPER
results for habitat type (reef vs marina) and substratum type (slate vs PVC)………………115
Table 4.3.1. Two-way ANOVA for shell length (cm) as a function of habitat type and
lineage………………………………………………………………………………………135
Table 4.3.2. Paired t-tests for shell length as a function of habitat type and lineage and Cohen’s
d effect size ‘r’ of shell length for habitat type (reef vs marina) and lineage (NHMg vs
SHMg).……………………………………………………………………………………...135
Table 4.3.3. Results for regression for Gonadosomatic index (GSI %) as a function of shell
length for NHMg and SHMg at the marina and reef habitats……………………………….135
Table 4.3.4. Results of ANCOVA for GSI values and shell length (cm; Co-variate) as a function
of Month × Lineage x Habitat type………………………………………………………….136
Table 4.3.5. Tukey HSD post hoc tests for GSI values as a function of significant factors as per
ANCOVA, i.e. habitat type and month and Cohen’s d effect size of GSI for habitat type (reef
vs marina) for significant effects…………………………………………………………….137
Table 4.3.6. Monthly variations in Gonadosomatic Index (%) ± standard deviation (SD) of
SHMg and NHMg at reef and marina habitats during June 2017-May 2018…......................138
Table A1. SIMPER analysis: average similarity in the status of species as a function of the
surveyed port………………………………………………………………………………..191
Table A2. Species identified in the 27 port surveys around Australia and species status – native,
non-native and cryptogenic species………….……………………………………………..193
Table A3. Species identified in the 15 port surveys around New Zealand and species status –
native, non-native and cryptogenic species…………………………………………………224
Table A4. Pairwise PERMANOVA test for the community composition for the interaction
factors; Habitat × Sample interval and Substratum × Sample interval. Significance marked in
bold (P < 0.05)………………………………………………………………………………237
xiv
List of Figures
Figure 2.2.1. Map of Australia with commercial shipping ports surveyed for Australian Port
Survey study………………………………………………………………………………….29
Figure 2.2.2. Map of Australia with commercial shipping ports surveyed for New Zealand Port
Biological Baseline Survey study…………………………………………………………….31
Figure 2.3.1. The total relative number of species across all 27 surveyed ports…………......34
Figure 2.3.2. Multidimensional Scaling (MDS) plot. The proximity of surveyed ports to each
other indicates similarity in species (based on presence/absence data) ……………………..35
Figure 2.3.3. Multidimensional Scaling (MDS) plot. The proximity of surveyed ports to each
other indicates similarity in community composition as a function of port type…................41
Figure 2.3.4. Multidimensional Scaling (MDS) plot. The proximity of surveyed ports to each
other indicates similarity in community composition as a function of latitudinal groups…..43
Figure 2.3.5. The total relative number of species status - native, non-native and cryptogenic
species across 27 surveyed ports……………………………………………………………...48
Figure 2.3.6. Correlation between relative native and non-native species across 27 surveyed
ports; a) Native vs Cryptogenic species, b) Cryptogenic vs Non-native species and c) Native
vs Non-native species.………….…………………………………………………………….50
Figure 2.3.7. SIMPER analysis: Percent contribution of species status- native, non-native,
cryptogenic to the average similarity as a function of surveyed port………………………….51
Figure 2.3.8. Multidimensional Scaling (MDS) plot. The proximity of surveyed ports to each
other indicates similarity in species status (native, non-native, cryptogenic) as a function of
port type………………………………………………………………………………………52
Figure 2.3.9. Multidimensional Scaling (MDS) plot. The proximity of surveyed ports to each
other indicates similarity in species status (native, non-native, cryptogenic) as a function of
latitudinal groups……………………………………………………………………………..54
Figure 2.4.1. The total relative number of species across all 15 surveyed ports…………......58
Figure 2.4.2 Multidimensional Scaling (MDS) plot. The proximity of surveyed ports to each
other indicates similarity in community composition as a function of port type…………….63
Figure 2.4.3. Multidimensional Scaling (MDS) plot. The proximity of latitudes to each other
indicates similarity in the community composition………………………………………….64
Figure 2.4.4. The total relative number of species status - native, non-native and cryptogenic
species across 15 surveyed ports……………………………………………….……………..67
xv
Figure 3.2.1. Map of Wellington Harbour, New Zealand, indicating natural reef and man-made
habitats (marinas) as sampling sites for the study…………………………………………..87
Figure 3.2.2. Set-up of settlement tiles deployed in the a) marinas and at b) natural reef sites;
c) front view and d) side view of the PVC and slate substrata………………………………..90
Figure 3.2.3. Representative PVC and slate tiles collected in the study, showing the community
growth at a first sampling interval (Nov 2017) and last sampling interval (Aug 2019)……….94
Figure 3.3.1. Species accumulation curves for observed species (Sobs) and non-parametric
estimators of species count richness (Chao1, Jacknife1, Bootstrap)………………................97
Figure 3.3.2. Temporal variation of average bare space availability (%) and species richness
(%) for the 2-year study………………………………………………………………………99
Figure 3.3.3. Two-dimensional MDS plot based on community composition between a) habitat
type (marina and reef) b) substratum type (PVC and slate), and c) sampling interval (1-8)…100
Figure 3.3.4. MDS ordination of temporal variation of the fouling community on three marina
sites and reef sites ………………………………………………………………………….101
Figure 3.3.5. The percent cover of top 8 major consistent species contributing to within-group
similarity for each sample interval indicating variations with time at a) man-made habitat b)
natural habitat c) man-made substratum and d) natural substratum…………………………109
Figure 3.3.6. Temporal change in the number of species with respect to - species status (native,
non-native and cryptogenic) as a function of habitat type (Reef vs Marina)…......................111
Figure 3.3.7. Temporal change in the number of species with respect to the species status
(native, non-native and cryptogenic) as a function of substratum type (Slate vs
PVC)………………………………………………………………………………………...112
Figure 3.3.8. Correlation of number of native and non-native species as a function of time at a)
habitat type (Reef vs Marina); b) substratum type (Slate vs PVC)………………………….116
Figure 4.2.1. Map of Wellington Harbour, New Zealand. Points denoting marinas as man-made
structure sites and natural rocky reefs as natural sites for this study………………………....129
Figure 4.2.2. Illustration representing the measurements taken for shell length, height and
width; a) frontal view; b) lateral view……………………………………………………….131
Figure 4.3.1. Total number of Northern hemisphere (NHMg- non-native) and Southern
hemisphere (SHMg- native) Mytilus galloprovincialis at the marina and reef habitats……...134
Figure 4.3.2. Monthly variations of the GSI values of SHMg and NHMg lineages at a) marina
and b) reef habitats in Wellington Harbour from June 2017 - May 2018……………………138
xvi
CHAPTER 1
GENERAL INTRODUCTION
1.1. Modification of marine ecosystems
Marine ecosystems provide a large number of ecological benefits, including complex habitats
for marine biodiversity and socio-economic benefits, including tourism, fisheries, mariculture
and trade (Costanza et al. 1997, 2014). More than 40% of the world's population lives along
the coastline leading to exploitation of marine resources and altering the coastline by building
coastal infrastructures (Dafforn et al. 2015a; Dafforn et al. 2015b). With an increase in the
human population, an increase in demand for food and energy production is evident. More than
90% of global trade is dependent on marine shipping routes, resulting in the building of
numerous shipping ports and berths on the coasts (Kaluza et al. 2010; Cope et al. 2015).
Additionally, natural phenomena such as storms, flooding, erosion, and sea-level rise due to
climate change have led to the building of coastal defence structures such as seawalls, groynes
and breakwaters (Naylor et al. 2012; Hinkel et al. 2014). Subsequently, the profound changes
to the coastlines due to the dominance of man-made structures are called 'ocean sprawl' (Duarte
et al. 2013; Firth et al. 2016). This proliferation of 'ocean sprawl' has already modified up to
70% of coastlines around the world and a rise in this percentage can be expected in the future
(Bulleri & Airoldi 2005; Dafforn et al. 2015b).
Studies focussing on changes to the biological species assemblage following the
changes in habitats have been fundamental in ecology studies. Early studies concentrated on
how the man-made structures modify coastal marine communities (Connell & Glasby 1999;
Bacchiocchi & Airoldi 2003; Chapman & Bulleri 2003; Moschella et al. 2005). These studies
further developed to examine if man-made structures can be surrogates for the natural rocky
reefs (Bulleri & Chapman 2010; Perkol-Finkel et al. 2012; Carvalho et al. 2013; Pastro et al.
2017). The results clearly show the alteration of the local habitats as well as local community
(Browne & Chapman 2011; Perkol-Finkel et al. 2012; Firth et al. 2013; Serrano et al. 2013).
Furthermore, man-made habitats are often shown to support different marine communities
compared to natural rocky reefs (Moschella et al. 2005; Perkol-Finkel et al. 2006; Clynick et
al. 2007; Lam et al. 2009; Bulleri & Chapman 2010; Airoldi & Bulleri 2011; Chapman &
1
Underwood 2011; Bulleri & Chapman 2015; Lai et al. 2018). For example, relatively low
numbers of individuals (with associated low species diversity), high abundances of early
colonisers, opportunistic and non-native species colonised man-made structures.(e.g. Glasby
et al. 2007; Vaselli et al. 2008; Dafforn et al. 2009; Bulleri & Chapman 2010; Airoldi & Bulleri
2011; Chapman & Underwood 2011; Firth et al. 2011, 2015; Dafforn et al. 2012; Floerl et al.
2012; Mineur et al. 2012; Bracewell et al. 2013; Airoldi et al. 2015; Pastro et al. 2017). The
realisation of facilitation of non-natives due to proliferation of man-made coastal structures has
only been relatively recent (Thompson et al. 2002; Airoldi et al. 2005; Chapman & Underwood
2011; Kueffer & Kaiser-Bunbury 2014; Firth et al. 2016; Bishop et al. 2017; Dafforn et al.
2017).
Commercial shipping ports, harbours, seawalls and other man-made structures play an
important role in providing suitable habitats ‘hot-spots’ and act as ‘stepping stones’ for the
introduction of species (Apte et al. 2000; Moschella et al. 2005; Clark & Johnston 2009; Bulleri
& Chapman 2010; Dumont et al. 2011; Firth et al. 2013, 2015; Rivero et al. 2013; Firth et al.
2016; Johnston et al. 2017). For example, hard coastal structures along the North Adriatic, Italy
(Bacchiocchi & Airoldi 2003; Airoldi et al. 2005) and along the coast of the Yangtze River,
China (Ma et al. 2014; Huang et al. 2015) create corridors for species expansion. The high
supply of introductions (propagule pressure) at port areas due to receiving marine traffic (e.g.
biofouling and ballast water discharge) results in a high probability of successful establishment
of non-native species in port areas (Lockwood et al. 2005; Clark & Johnston 2009; Johnston et
al. 2009; Lo et al. 2012).
Marine trade around the world is thought to have transported thousands of species
through accidental introductions such as ballast water tanks and attachment on ship hulls
(Gollasch et al. 2002; Hewitt et al. 2009). Attachment and detachment of species on the hulls
of vessels and exchange of large volumes of ballast water holding numerous planktonic species,
larvae and egg masses are major marine pathways for marine introductions (Hewitt and Martin
2001; Campbell et al. 2007; Hewitt et al. 2009; Lo et al. 2012). The marine vessels form
connectivity pathways linking all the major shipping ports and harbours, thereby exchanging
marine species from their native habitat to a new environment (Apte et al. 2000; Wyatt et al.
2005; Clark & Johnston 2009; Clarke et al. 2011; Hopkins et al. 2011a; O'Brien et al. 2017).
Marine transport facilitates a steppingstone model for the spread of species by overcoming
biogeographical barriers and by providing direct introductions (Adams et al. 2014). Regional
domestic marine traffic and recreational boating may then act as a secondary source of non2
native species dispersal within a country (e.g. Forrest et al. 2009; Clarke & Johnston 2011;
Hänfling et al. 2011). For example, the non-native tunicate, Didemnum vexillum, was first
introduced in New Zealand in 2001 and has spread over various harbours and ports on marine
vessel hulls and ballast water (Coutts & Forrest 2007).
1.2. Man-made habitat characteristics and impacts
Development of coastal man-made structures has led to the displacement of natural habitats
resulting in habitat loss and fragmentation, thereby altering physical, chemical and biological
environments (Dugan et al. 2012; Firth et al. 2016; Todd et al. 2019). Man-made structures
differ in terms of physical aspects to adjacent natural reefs, factors including structure materials
(e.g. wood, plastic, cement) (Andersson et al. 2009; Burt et al. 2009; Chapman & Blockley
2009; Spagnolo et al. 2014; Tan et al. 2015; Cacabelos et al. 2016; Johnston et al. 2017; Albano
& Obenat 2019), vertical orientation (e.g. seawalls, breakwaters) (Andersson et al. 2009;
Perkol-Finkel et al. 2012; Albano & Obenat 2019; O'Shaughnessy et al. 2019), surface
complexity (e.g. smooth flat surfaces) (Perkol-Finkel et al. 2012; Ferrario et al. 2016),
movement (e.g. floating pontoons) (Holloway & Connell 2002) and age (newly built structures)
(Perkol-Finkel et al. 2006; Burt et al. 2011; Dong et al. 2016). There is growing evidence of
non-native species taking advantage of these built structures, which is not the case for local
species (Bulleri & Airoldi 2005; Glasby et al. 2007; Tyrrell & Byers 2007; Vaselli et al. 2008;
Ruiz et al. 2009; Dafforn et al. 2012; Mineur et al. 2012; Duarte et al. 2013; Airoldi et al. 2015;
Ferrario et al. 2016).
Man-made structures may form physical barriers obstructing ecological connectivity
(e.g. larval dispersal and movement of mobile species), nutrient flow, altering genetic and
trophic connectivity (Moschella et al. 2005; Airoldi et al. 2010; Duarte et al. 2013; Adams et
al. 2014; Firth et al. 2016; Moss 2017). Loss of connectivity between species results in biotic
homogenisation, thereby altering ecosystem functioning and ecosystem services (PerkolFinkel et al. 2011; Macdonald & King 2018; Mayer-Pinto et al. 2018a; Mayer-Pinto et al.
2018b). For instance, species with low dispersal potential may fail to overcome physical
barriers and settle at short distances, however, with time the genetic diversity decreases
resulting in a reduced gene pool (Fauvelot et al. 2009; Sammarco et al. 2012). This limitation
of the spread of species to regional scales may lead to changes in the biogeographic ranges of
species (Schiel 2011). Construction of offshore structures provides a steppingstone for the
spread of species across biogeographical boundaries (Adams et al. 2014). Therefore, it is
3
essential to evaluate the ecological impacts of man-made structures on the local marine
ecosystem at local, regional and national scales (Airoldi, et al. 2005; Ma et al. 2014).
Movement of marine vessels in marinas and the building of new structures along the
coast cause physio-chemical disturbances and may affect both the marine biodiversity and the
invasibility of communities (Clark & Johnston 2011; Ceccherelli et al. 2014). Physical
disturbances can cause sedimentation, displacement of the intertidal community, and provide
bare substrata (Guerra-García et al. 2004; Oricchio et al. 2016; Pastro et al. 2017). Studies have
shown a subsequent increase in occupancy of bare substratum by non-native species and
opportunistic species (Sousa 1979; Paine & Levin 1981; Sousa 1985; Airoldi et al. 2000;
Erlandsson et al. 2006). For example, physical removal of seagrass due to anchoring and
dredging led to the fragmentation of seagrass habitat, which promoted the spread of the nonnative green alga (Ceccherelli et al. 2014). Several manipulative experiments, for example,
applying disturbance (e.g. dredging) to reduce the already existing local species cover resulted
in the habitat to be susceptible to invasions (Valentine & Johnson, 2003; Airoldi & Bulleri
2011; Clark & Johnston 2011; Bulleri et al. 2016).
The modification of environments may result in changes to biological interactions,
distribution of species and ecosystem functioning, at both natural and man-made environments
(Bulleri & Chapman 2010; Boström et al. 2011; Knights et al. 2012; Johnston et al. 2017). For
instance, limited resource availability can alter prey-predation interactions (Kimbro et al.
2009). Therefore, species-specific interactions and life-history traits of species determine the
negative and positive connectivity between lower and higher trophic levels and genetic transfer
between species (Boudouresque & Verlaque 2012; van de Koppel et al. 2015; Firth et al. 2017).
Ocean sprawl and alteration of natural habitats may cause changes to the energy allocation of
species for reproduction, growth or survival (Mayer-Pinto et al. 2018). At disturbed habitats
and low resource availability, the species tend to allocate/ exert more energy towards survival
and feeding instead of reproduction and growth (reviewed in Bishop et al. 2017). For instance,
seawalls exhibit relatively reduced reproductive output of limits with fewer and small egg
masses compared to adjacent natural rocky reefs (Moreira et al. 2006). However, it is still not
evident if there is a difference observed in the energy exertion by native and invasive species
on man-made or disturbed habitats.
4
1.3. Bioinvasions
Biological invasion, i.e. bioinvasions, in a broad sense, means the movement of local species
from their native habitat to a new environment (Olenin et al. 2017). Bioinvasions are ubiquitous
and universally accepted as one of the significant threats to marine biodiversity and the global
economy (Ojaveer et al. 2015; Gestoso et al. 2017; Olenin et al. 2017; Simpson et al. 2017).
Apart from human-mediated spread of species through maritime transport, the species show
natural range expansion due to changing global climate (Occhipinti-Ambrogi 2007). With
changing climates, the species have been showing anti-equatorial shift, i.e., moving from
highly diverse, warm equatorial regions to slightly cooler, less diverse temperate regions
(higher latitudes) ‘Latitude diversity groups hypothesis’ (Darwin 1860; Sax 2001). Biological
stresses are important in natural selection and evolution of species (Lockwood et al. 2005). The
colder environmental temperatures in the temperate regions may hinder the natural selection
processes. However, this is not the case in terms of tropical regions (Currie et al. 2004). The
introduction of species in higher latitudes can lead to competition among species (introduced
and local species) for habitat and food, change predator-prey interactions or cause co-existence
(Shea & Chesson 2002; Jeschke et al. 2012; Marraffini & Geller 2015; Papacostas et al. 2017).
There are many terminological ambiguities around the definition of non-native species.
Throughout the invasion process, the species are called ‘introduced species’, once established
they are called exotic, non-native, non-indigenous or alien species. However, when the species
causes negative impacts in the marine habitats they are termed ‘invasive’ or ‘unwanted
species’ but when the species is introduced without causing any adverse effects in the habitat
are ‘naturalised’ species (Lockwood et al. 2005). There have been emerging studies related to
'invasion biology', non-native species and their impacts on the native assemblages and
permanent destruction of marine ecosystems where they are introduced (Johnston et al. 2015).
Knowledge about mitigation and management approaches towards marine invasions, and
human impacts have also been growing in recent years (Moschella et al. 2005; Pyšek &
Richardson 2010; Airoldi & Bulleri 2011; Airoldi et al. 2015; Dafforn et al. 2015; Dafforn
2017).
1.3.1. Invasion ecology
Invasion ecology is the study of establishment, spread and ecological impact of species
translocated from their natural geographical boundaries (Lockwood et al. 2007). Specific
individuals within a species or group of species which are successful colonisers and immigrants
that expand their native range are called invasive species. However, not all invasive species
5
can survive in new environments. The invasive species tend to disrupt the already existing
species community therein to affect the species interaction and further poses a threat to the
marine ecosystems (Shea & Chesson 2002). Consequently, studies related to biological
invasions, also called as 'invasion ecology' has gained much attention to investigating the
ecology and evolution of populations and their interactions to maintain the natural ecosystems
(Ricciardi 2007).
Elton (1958) first identified biological invasions as an ecological threat, since then
ecologists around the world have made efforts to understand the patterns of non-native species,
their establishment – successful or failed, spread and impacts on natural ecosystems once
established. Considerable progress has been made in understanding the species traits such as
life history, genetic and other biological characteristics promoting invasion (Ehrlich 1984;
Sakai et al., 2001; Callaway & Ridenour 2004; Snyder & Evans 2006). Recently much work
has been focussed on developing methods to predict and even quantify the impacts of nonnative species (Hewitt et al. 2009; Ruiz et al. 2011). Invasion ecology thus far could guide
managers and policymakers to identify the risks of invaders and undertake appropriate
management plans.
To understand and to reduce the impact of invasive species on ecosystem functioning,
it is crucial to identify the key mechanisms which contribute to the invasion success of a
species. Most of the invasion conceptual framework theories are dependent on factors such as
a) human-mediated activities move species overcoming environmental, physical or biological
barriers (Campbell & Hewitt 1999; Carlton & Rutz 2005; Hewitt et al. 2009; Ruiz et al. 2011),
b) despite the increase in invasive species into novel environments , invasion success is low
and variable (Williamson 1996; Sala et al. 2000), c) the number and quality of offspring
released into a novel environment i.e. 'invasion pressure' or the 'propagule pressure' is important
for the establishment and success of non-native species (Lockwood et al. 2009), d) the traits of
non-native species determine the rate of spread and invasion success (competitive abilities,
ecological and evolutionary species history), e) biotic interaction hypotheses- 'biotic
acceptance' by native species to establishment and coexistence of non-native species,
'competitive release hypothesis' where non-native species are released from competition with
the native competitors or no competitors for food and space (Sorte et al. 2010), 'biotic
homogenisation' of replacement of native species by non-native species (McKinney &
Lockwood 1999), 'biotic resistance' by native species to the establishment of non-native species
(diversity-invasibility hypothesis) (Elton 1958), 'invasional meltdown' is where the non-native
6
facilitates establishment and spread of other non-native species (Simberloff & Von Holle 1999;
Simberloff 2006).
1.3.2. Invasion stages
The typical biological invasion process includes steps such as introduction,
establishment, spread and impact (Hulme 2006). The non-native species are introduced through
human mediation (e.g. shipping) or natural processes (e.g. rafting) from their native
environment to a new environment. The invasion through human mediation helps the nonnative species to overcome biogeographical barriers (Gribben et al. 2013; Adams et al. 2014).
However, once introduced to a new environment, the non-native species must overcome
environmental barriers (e.g. temperature, salinity) to the successful establishment (reviewed by
Olenin et al. 2017). The probability of the species' successful establishment in a new
environment is influenced by the species' life-history traits and the environmental conditions
of the receiving habitat (Glasby et al. 2007; Petes et al. 2008; Piola et al. 2009; van de Koppel
et al. 2015; Fava et al. 2016; Firth et al. 2017; Purroy et al. 2019). There is evidence of nonnative species being relatively competitive and adaptable to harsh and disturbed environments.
These competitive and adaptable traits of non-native species help them overcome biological
(e.g. predation) and environmental stressors (e.g. turbidity) (Leppäkoski et al. 2002; Bownes
& McQuaid 2010; Marraffini & Geller 2015).
Propagule pressure plays an important role in the successful establishment of nonnative species, i.e. potential for introduction (Johnston et al. 2009). The 'propagule pressure'
includes not only the number of arriving individuals in an area (propagule supply) but also the
rate at which they arrive (propagule frequency) (Lockwood et al. 2005). For instance, ports and
marinas are areas that are susceptible to invasions due to marine traffic bringing with it
increased propagule pressure (e.g. biofouling and ballast water exchange) (Lockwood et al.
2005; Clark & Johnston 2009; Lo et al. 2012). It is often presumed that of the vast number of
introductions, only a few introductions are successful (Leppäkoski et al. 2002). However, it is
difficult to determine the number of successful introductions and more challenging to quantify
the number of failed introductions due to the lag phase, i.e. the time between introduction and
spread/ expansion of the non-native species (Mack et al. 2000). This lag phase might last for
years or decades due to differences in environmental conditions, genetic factors and habitat
heterogeneity (Melbourne & Hastings 2009; Zaiko et al. 2016). For instance, where
introductions are relatively frequent, the invasive species may show overall positive effects
after establishment, i.e. 'equilibrium state' (Clark & Johnston 2011).
7
Once a non-native species has established, it may spread at regional levels to new
environments (Forrest et al. 2009), i.e. the expansion phase. The spread of the species may be
due to domestic, commercial ships or recreational boats and spread may also be via larval
dispersal which is highly dependent on larva dispersal potential (Forrest et al. 2009; Fava et al.
2016). Species with a low larval dispersal rate will settle at semi-enclosed ports and harbours
and can only spread to regional distances through human mediation (Johnston et al. 2011).
Following the successful introduction, establishment and spread of the non-native species,
there may be possibilities for negative and positive impacts on the marine ecosystem and
economic services (Hänfling et al. 2011; Oliver et al. 2015). For example, a recent metaanalysis reported that 35% of non-native species have a positive impact on other species
(Katsanevakis et al. 2014). Many non-native species may facilitate other non-native species
which may be harmful, and that can impact the native environment, native species and the
economy (Simberloff & Von Holle 1999; Leppäkoski et al. 2002; Hänfling et al. 2011;
Simberloff et al. 2013; Oliver et al. 2015).
1.3.3. Non-native species - characteristics and impacts
Most of the non-native species have r-selected life-history traits, where the species have
high fecundity, small-sized, have early maturity, competitive, phenotypic plasticity and can
live in a novel low-quality environments (Glasby et al. 2007; Petes et al. 2008; Piola et al.
2009; Fava et al. 2016; Purroy et al. 2019). Few experimental studies comparing the native and
non-natives and their response to pollution (e.g. metal pollution, turbidity) indicated high
tolerance to pollutants and increased non-native species dominance with increased pollution
(Piola & Johnston 2008; Crooks et al. 2011; Johnston & Keough 2011). These traits help the
non-native species to rapidly colonise the available substratum even in disturbed coastal
habitats and thereby contribute to invasion success (Marraffini & Geller 2015). However, the
success of their introduction is dependent on the abundances and competitive abilities of native
species. For example, there is evidence of relatively less occupancy of native species on manmade structures, i.e. absence of competitors or predators of non-native species, thereby
resulting in successful invasion 'enemy release hypothesis' (Shea & Chesson 2002; Bulleri &
Airoldi 2005; Glasby et al. 2007; Tyrrell & Byers 2007; Vaselli et al. 2008; Ruiz et al. 2009;
Dafforn et al. 2012; Jeschke et al. 2012; Mineur et al. 2012; Duarte et al. 2013; Airoldi et al.
2015; Ferrario et al. 2016; Papacostas et al. 2017).
Studies have suggested that man-made habitats have a negative impact on the adjacent
natural habitats especially in terms of modifications to water flow, sedimentation, nutrient
8
transport, light availability and turbidity (Dugan et al. 2012; Dafforn et al. 2015a; Heery et al.
2018) and native community structure (Byers 2000; Ojaveer et al. 2002; Rodriguez 2006;
Browne & Chapman 2014; Airoldi et al. 2015; Tan et al. 2015; Ferrario et al. 2017).
Competition for resources and food utilisation between native and non-native species can lead
to 'competitive exclusion' of native species as non-native species have high levels of phenotypic
plasticity (Griffen et al. 2011). However, with time, displacement/replacement of native species
impairs 'biotic resistance' by reducing species diversity, species interactions and alter trophic
level (top-down or bottom-up trophic cascades) at natural habitats (reviewed by Johnston et al.
2011; Bulleri et al. 2016; Johnston et al. 2017; Skein et al. 2020). 'Biotic resistance' resulting
from an increased number of native species and/or individuals, competition between native and
non-native species and/or predation pressure by native species on non-native species may
contribute to invasion failure (reviewed by Johnston et al. 2017; Skein et al. 2020). Recent
management approaches consider monitoring the performance of the key species at different
trophic levels, reduction of key species and alterations in species interactions may aid as
predictors of possible invasions as the native species may not support biotic resistance (Bulleri
et al. 2016; Skein et al. 2020).
Multiple introductions, i.e., increased propagule pressure, may result in changes to the
evolutionary and adaptation processes at genetic levels (Roman & Darling 2007; Hänfling
2007; Hänfling et al. 2011). The multiple introductions influence hybridisation between native
and non-native species (Hänfling 2007; Chan & Briski 2017). The increased exchange of alleles
makes the non-native more adaptable whereas there are some instances where the hybrid
individuals perform better than the parent lineages (Williams & Grosholz 2008; Hänfling et al.
2011; Franscisco et al. 2018). Hybridisation further raises concerns of genotypic displacement
of native species by non-native species (reviewed by Geller et al. 2010; Johnston et al. 2011;
Saarman & Pogson 2015; Bulleri et al. 2016; Johnston et al. 2017; Skein et al. 2020). Molecular
genetic studies have been used for many years to identify the origin, pathways of introductions
and interactions of closely related native and non-native species (Daguin & Borsa 2000;
Goldstien et al. 2013; Westfall & Gardner 2013; Tay et al. 2015; Gardner et al. 2016; Oyarzún
et al. 2016; Larraín et al. 2018). However, the question remains whether the rate of native
species displacement increases or decreases with the interbreeding of native and non-native
genotypes.
9
1.4. Management and Mitigation
With changing global climate and the vastness of marine systems, it is difficult to detect,
investigate, manage marine bioinvasions, and predict future impacts on the marine environment
and economy (Firth et al. 2016). Growing evidence of adverse effects of marine urbanisation
and bioinvasions has raised the need for ecologically-driven planning and long-term mitigation
and management approaches (Airoldi et al. 2005; Airoldi & Beck 2007; Bulleri & Chapman
2010; Airoldi & Bulleri 2011; Browne & Chapman 2011; Perkol-Finkel et al. 2012; Firth et al.
2014).
1.4.1. Marine port management approaches
The most critical approach in the management of non-native species is to prevent
introductions (Hulme 2006). Pre-border, at-border and post-border management measures have
been undertaken in different countries around the globe (e.g. Australia and New Zealand).
These measures help to reduce successful introductions, establishment and spread of nonnative species in the recipient environment (Hewitt & Campbell 2007; Forrest at al. 2009;
Hopkins et al. 2011b; Ojaveer et al. 2015). Effective preventive measures are mainly conducted
on the human-mediated vectors that may involve ballast water treatments and hull maintenance
'anti-fouling' to avoid propagule pressure (Secord 2003; Hewitt and Campbell 2007; Coutts et
al. 2010a, 2010b; Tamelander et al. 2010; Hopkins et al. 2011b. For example, the New Zealand
Government developed a preventive management action, Craft Risk Management standard to
minimise the arrival and spread of non-native biofouling species through marine vectors (MPI
2014). Such management approaches to limit their spread has been undertaken by many
countries and have shown gradual effectiveness (GloBallast 2014 and GloFouling 2017).
The International Marine Organisation's (IMO) International Convention for the
Control and Management of Ships' Ballast Water and Sediments" implemented ballast water
management plans and recorded the per tank basis onboard mid-ocean ballast water exchange
as the intermediate solution (IMO 2017). IMO created the GloBallast and GloFouling
programmes that provide training, technical support and help with ballast water management
and antifouling methods. However, no international regulations have been established to
prevent the regional spread of non-native species through domestic marine trade or recreational
boating (Forrest et al. 2009; Sinner et al. 2013; Inglis et al. 2014). There have been management
ideas to establish contingency de-ballasting zones to discharge the ballast water safely (Hobday
et al. 2002); however, establishing suitable areas for the same is still a struggle. Goldsmit et al.
10
(2019), examined two alternative de-ballasting zones in eastern Canada through these zones
were formed without any scientific ecological assessments. The authors described the locations
to be the primary route for marine traffic in the eastern Canadian Arctic, thereby risking the
establishment of non-native species at coastal areas (Goldsmit et al. 2019).
New introductions of non-native species are still likely as some introductions go
undetected and establish before they are reported (Ruiz et al. 1999; Hewitt et al. 2004; Floerl
et al. 2009). It is important to have surveillance or risk-assessment to detect non-native species
early on to enable fast and more effective eradication response (Hewitt & Campbell 2007;
Gardner et al. 2016; Zaiko et al. 2016). For example, successful eradication of the invasive
black striped mussel in Darwin Harbour occurred within 6-7 months of its detection (Willan et
al. 2000). However, such successful detection and eradication of non-native species are
infrequent (Hopkins et al. 2011a). For instance, eradication programmes of the fouling pest,
Didemnum vexillum, failed miserably in New Zealand due to its widespread - first recorded in
2001 in Whangamata, North Island after its establishment but quickly spread to the South
Island on barges, recreational vessels and moorings (Coutts & Forrest 2007). The attempt to
eradicate D. vexillum from New Zealand cost NZ$2.2 million (Biosecurity New Zealand 2010).
Therefore, cost-effective and timely eradication approaches are necessary for positive
ecological and economic impacts. Even though a species has been eradicated, it may be
required to have a continuous survey and monitoring of high-risk pests to enable quick
detection of re-introductions (Hewitt et al. 2004, de Rivera et al. 2005; Campbell et al. 2007;
Brockerhoff et al. 2010; Tobin et al. 2014). Local fishers, local boat owners and commercial
vessel operators should be made aware of high-risk marine pest species through educational or
awareness programmes (Pollard & Pethebridge 2002).
1.4.2. Marine ecosystem monitoring
In the light of increasing pressures on the marine ecosystem globally, the impacts on the
ecological and environmental conditions and the conservation and management of marine
biodiversity is a complex issue. Monitoring is a crucial component in marine management; it
provides a more consistent spatial and temporal analysis of different chemical, physical and
biological parameters in a marine system. These programmes offer integrated knowledge of
the current functioning of the ecological systems and the disturbances caused by human
impacts. Appropriate evaluations of data can determine the effectiveness of the surveys and
monitoring programmes, report key gaps and weaknesses for future monitoring (Miller et al.
2019). The monitoring reports provide scientific advice to the stakeholders and managers to
11
take adequate conservation planning and successful management measures (Hewitt et al. 2004;
Hewitt & Campbell 2007; Ojaveer et al. 2018).
Monitoring programmes' key role is to provide an understanding of community
structure (species abundance, species diversity, spatial distribution, species status), the health
of the habitat, environmental conditions and socio-economic characteristics and impacts.
Different monitoring techniques provide the different type and quality of information.
Standardised protocols and consistent survey designs will help achieve the purpose of the
surveys (Hewitt & Martin 2001). Baseline assessment of an area provides information about
the overall species diversity for temporal and spatial variations in species composition. Species
diversity is one of the indicators of the functioning of trophic levels in marine ecosystems.
Species diversity affects the top-down (prey-predators) and bottom-up (space and nutrients)
interactions which are fundamental in the functioning of marine ecosystems (Paine 1966).
Species diversity can be quantified by species richness, species abundances, species
composition and species interactions. These assessments provide an opportunity to understand
the changes in ecosystems and species diversity due to human impacts. Long-term surveys
detect effects of environmental variables on species diversity, composition, abundances and
spread of non-native species (Branch et al. 2008; Goldstien et al. 2013). Species-specific
surveys focus on a particular species or taxa and its ecosystem functioning; in contrast, highrisk pest surveys concentrate on detection and quantification of high-risk invasive species to
form effective management and eradication plans (Hewitt et al. 2004, de Rivera et al. 2005;
Campbell et al. 2007; Brockerhoff et al. 2010; Tobin et al. 2014).
Monitoring surveys act as tools to detect invaders and implement rapid management
and eradication strategies (Hewitt & Campbell 2007). Early detection is critical for effective
control and eradication. Several countries in recent years have adopted national scale surveys
to identify the already entered non-native species as well as new arrivals (Simberloff 2003;
Olenin et al. 2014; Ojaveer et al. 2016). Monitoring surveys provide a baseline for native and
non-native biodiversity, which will further inform about the impacts of invasions on the
biodiversity. For instance, nationwide based Australian port surveys with standardised CRIMP
protocols and designs aimed to examine the marine biological invasions in Australian waters
(Hewitt & Martin 1999, 2001). In addition, the New Zealand government implemented marine
biosecurity monitoring strategies focussed on nationwide baseline surveys, New Zealand Port
Biological Baseline Survey Marine (NZPS). Development of NZPS led to form monitoring
surveys for target species, i.e. High-Risk Site Surveillance Programmes (Woods et al. 2015).
12
The high dispersal rate of marine species has implications for the geographic
distribution and genetic connectivity among marine populations. Therefore, the dispersal
potential of various species can be tested using genome divergence patterns. Development of
molecular techniques for genetic analyses helps analyse the genetic connectivity among
populations (Palumbi 2000). However, the use of molecular tools in invasion ecology have
been adopted in recent years (Holland 2000; Ruiz & Fofnoff 2000). The molecular techniques
(PCR tools and genetic markers) examine the genetic structure of native and non-native species
and their geographic distribution (Ruiz & Fofnoff 2000; Holland et al. 2004; Goldstein et al.
2011). Several molecular studies now are focussing on early detection of non-native species
and provide optimal identification of non-native species especially congeners which are
morphologically similar to their native species (Darling & Blum 2007; Pochon et al. 2013).
Several countries, including the USA, Canada, New Zealand, and Australia have already
adopted molecular techniques to survey non-native species and aid management decisions
(Geller et al. 2020; Ruis et al. 2015; Zaiko et al. 2015, 2018). For successful monitoring and
management plans, it is crucial to have a robust taxonomic foundation. Current limited
taxonomic knowledge and decline in taxonomic specialists are a big concern to have effective
monitoring and management programmes. Misidentification of introduced species may lead to
cryptic invasions, and most of the non-native species remain unnoticed. Molecular analyses,
together with large-scale surveys, form the basis of rapid and effective monitoring
programmes, however, are costly and time-consuming.
1.4.3. Mitigation approach - Eco-engineering
Human-mediated vectors are mostly responsible for initial transport of non-native
species from their native ranges, whilst anthropogenic urbanised habitats facilitate invasion
success, establishment and further spread (Mineur et al. 2012; Airoldi et al. 2015; Johnston et
al. 2015; Ferrario et al. 2017). Recent management attention has focussed on re-designing and
engineering coastal structures to have multifunctional uses, i.e. benefit ecosystems and
economic services via 'eco-engineering' or 'green engineering' (Chapman & Underwood 2011;
Dafforn et al. 2015; Morris et al. 2019). Marine urbanisation and bioinvasions are ubiquitous
and are predicted to increase in the coming years. Studies adopting eco-engineered coastal
structures have revealed enhanced habitats and enhancing the biodiversity of different
functioning species groups (Morris et al. 2017, 2019; Strain et al. 2018). Hence, there is a
strong argument to focus on the design of the structures not just to enhance assemblages but to
target specific species or taxa (Correia et al. 2013). For example, increased micro-habitats on
13
seawalls reduced fish predation on native mussel species (Strain et al. 2018b) and reduced
shading improved native species growth (Dafforn et al. 2012). Studies have shown increased
colonisation of benthic encrusting species such as algae, barnacles, mussels and oysters to
promote a 'bioprotective effort', i.e. contributing to the structure's stability and longevity. For
instance, to increase protection against extreme environmental conditions, e.g. fluctuating
temperature or wave action (Coombes et al. 2013; Coombes et al. 2015). Another factor to
consider whilst re-assessing man-made structures and the ecological processes that occur on
them is to preserve the natural marine biodiversity (Perkol-Finkel et al. 2012; Dafforn et al.
2015). An increase in native biodiversity will facilitate enhanced biotic resistance and may help
to reduce or avoid invasions, i.e. 'bio-control' (reviewed by Johnston et al. 2017; Skein et al.
2020).
Increased complexity on the surfaces of coastal man-made structures such as the
addition of rockpools, crevices, and cracks provides refuges for organisms, thereby increasing
biodiversity and modifying community structure (Moreira et al. 2007; Chapman & Blockley
2009; Loke et al. 2014; Loke & Todd 2016; Morris et al. 2019). Addition of overhangs or
flowerpots increased species assemblage by 65% out of which 25 species were not previously
observed (Browne & Chapman 2011). Engineering of cost-effective hybrid structures such as
revetments, tetra-pods, and geo-tubes can provide substrata where natural habitats have
declined (Moschella et al. 2005; Chapman & Underwood 2011; Browne & Chapman 2014;
Firth et al. 2014; Loke et al. 2014). For example, the endangered seahorse, Hippocampus whitei
inhabits specially designed eco-engineered artificial habitats' Seahorse Hotels' in Port
Stephens, New South Wales, Australia due to significant decline in natural habitats (Simpson
et al. 2019). The field of eco-engineering requires stakeholders, engineers and scientists to
work together and produce new strategies for the design and deployment of coastal structures
that will reduce the opportunities for non-native species (Dafforn et al. 2009; O’Shaughnessy
et al. 2019).
To summarise, it is well established that marine built systems have a negative impact
on marine biodiversity and facilitate non-native species ( Bulleri & Chapman 2010; Airoldi &
Bulleri 2011; Chapman & Underwood 2011; Clark & Johnston 2011; Firth et al. 2011, 2015;
Dafforn et al. 2012; Airoldi et al. 2015; Firth et al. 2016; Dafforn et al. 2017; Bishop et al.
2017; Pastro et al. 2017). High introduction rates of non-native species at port areas through
marine traffic have resulted in the spread of non-native species from port to port, and also to
natural (i.e., not modified by human activities) areas. International and domestic shipping plays
14
a vital role in transporting non-native species at regional to national scales (Hewitt, 2002;
Lockwood et al. 2005; Clark & Johnston 2009; Johnston et al. 2009; Ruiz et al. 2011; Lo et al.
2012). For example, the economies of Australia and New Zealand are dependent on
international maritime trade (Piola & McDonald 2012). With their unique coastlines and given
the high levels of endemism amongst Australia and New Zealand’s marine biota, many
preventive marine biosecurity and management strategies have been developed and
implemented by both countries to control marine invasion risks (Hewitt & Campbell 2007;
DoE 2015; DAWR 2017, 2019; MPI 2018). However, there is no infallible solution to the
invasion problem.
The first major step before attempting to control the introduction and spread of nonnative species is to determine the current distribution and abundance of non-native species
(Hewitt & Campbell 2007; Coutts & Forrest 2007; Kaiser & Burnett 2010; Simberloff et al.
2013). Surveys at heavily disturbed and modified port areas help keep a record of the native
species present and to detect non-native species so that further action (e.g., eradication or
containment) may be carried out. It is necessary to understand better how such non-native
species may respond to challenges raised by the ongoing development of the built environment
at local, regional and national scales. Baseline monitoring surveys are critical to answer such
questions, which will help planners and managers better understand how built structures in
heavily developed areas such as ports influence native biodiversity, contribute to bioinvasions
and modify the health of the marine environment. Increased maritime traffic with the
proliferation of construction of marine harbours, ports, seawalls, and breakwaters along the
coasts raise the risk of bioinvasions and its subsequent spread (Floerl et al. 2009; Seebens et
al. 2013). Bioinvasions can lead to devastating and unforeseen impacts on new environments.
The effect of marine built systems on marine biodiversity and determining the influence on
native and invasive species is increasingly important. In this thesis, I will investigate the ports
and harbours as man-made environments, their impacts on the marine biodiversity with regard
to the species status – native, non-native and cryptogenic – and the factors facilitating the
spread of non-native species.
15
1.5. Thesis aim and objectives
The general aim of this thesis is to determine the influence of man-made structures on
ecosystem structure and function in natural and man-made habitats with regard to native and
non-native species. Whilst there has been quite a bit of this sort of work done around the world,
there has been very little done in New Zealand. This study focusses on marine biology and
invasion ecology but extends into biosecurity and conservation. The potential impact of this
thesis will be as a contribution to new knowledge about the marine built environment, thereby
supporting management decisions to improve marine coastal environmental health and ecoengineering of modified habitats.
The following objectives were developed to provide a more specific indication of the purpose
of this research:
1) To assess community composition with regard to species status - native, non-native and
cryptogenic species in Australian ports and New Zealand ports, and how this varies
between ports as a function port type (major vs minor ports) and latitudinal groups.
2) To evaluate the effect of natural and man-made substrata (PVC vs slate tiles) and habitat
type (marina vs reef) on the ecological successional patterns (temporal variation of
species) and presence of native, non-native and cryptogenic species.
3) To assess if/how natural and man-made habitats influence energy allocation to
reproductive output and reproduction patterns by two closely related congeneric blue
mussels, native (SHMg) and non-native (NHMg) lineages.
16
1.6. Chapter outline and hypothesis testing
Chapter 1 is a general literature review, to help set the scene, and to make clear the state of
knowledge. As such, there is no hypothesis testing in this chapter.
Chapter 2 focusses on two large nationwide datasets to observe and interpret patterns of
distribution of species status - native, non-native and cryptogenic species. Also, to understand
the potential impacts on the marine biodiversity as a correspondence between ports, port types
(major vs minor ports), latitudinal and longitudinal groups. The analyses were performed on
presence/absence data for community composition and frequency data for species status (i.e.
tally of the presence of species as per their status-native, non-native and cryptogenic). The data
was analysed to observe patterns of species composition and species status as a function of
ports, port type and latitudinal groups. The results obtained in this chapter will help highlight
the patterns of distribution of species status and help understand the impacts on the marine
biodiversity as a correspondence between ports, port types (major vs minor ports), and
latitudinal and longitudinal groups. The patterns observed in this study among ports, port types
and latitudinal groups will help managers to take cost-effective measures to analyse the impacts
of the target (i.e. high occurring/contributing) non-native species and their spread with respect
to the above-stated factors.
A. Australian port survey – a national port survey program commissioned by the Australian
Association of Port and Marine Authorities (AAPMA) and carried out by the CSIRO Centre
for Research on Introduced Marine Pests (CRIMP) in 1995. This program aimed to report
the occurrences of non-native species in Australian ports. Identification of non-native
species at the national level will help understand the spread of the non-native species from
port-to-port at a local, regional and national scale.
I hypothesised that;
H1: Occurrences of non-native and cryptogenic species will be relatively greater at major
commercial ports than at minor ports because of increased international marine traffic at the
major ports.
H2: Frequencies of non-native and cryptogenic species increases with an increase in latitude
(15-40°S) as there is evidence of high invasibility at temperate climates compared to tropical
climates.
17
B. New Zealand Port Biological Baseline Survey (NZPS), New Zealand Government initiated
a nationwide biological baseline survey in 2000 at 13 international shipping ports and 3
marinas in New Zealand. The principle aim of this survey was to record the native, nonnative and cryptogenic species in New Zealand port areas and to identify new invasions.
I hypothesised that;
H1: Occurrences of non-native and cryptogenic species will be relatively greater at major
commercial ports than at minor ports because of increased international marine traffic at the
major ports.
H2: Frequencies of non-native and cryptogenic species increases with an increase in latitude
(35-45°S) because there is evidence of high invasibility at temperate climates compared to
tropical climates.
Chapter 3 aimed to compare the ecological successional patterns (temporal variation of
species), community composition and species status (native, non-native, cryptogenic) in both
natural and man-made habitats (marinas vs reefs) using natural and man-made substrata, i.e.,
settlement tiles (PVC vs slate). This study was carried out in Wellington Harbour at 3 marinas
(man-made habitat) and 3 neighbouring rocky reef sites (natural habitat). The data of this
experimental study compares the differences in community structure and status of the species,
at the natural and man-made habitats/substrata. Results highlight the settlement preferences of
native, non-native and cryptogenic species for habitat and substratum types, and differences in
community structure on natural and man-made substrata/habitats.
I hypothesised that;
H1: Community composition at man-made habitat (marina) is less diverse than at adjacent
natural habitat (rocky reef).
H2: Community composition on the man-made substratum (PVC) is less diverse than that on
the natural substratum (slate).
H3: Non-native species are more abundant at the man-made habitat and on man-made
substratum relative to natural habitat and substratum.
Chapter 4 aimed to compare the reproductive timing and output of the closely related native
and non-native blue mussels, Mytilus galloprovincialis (invasive North hemisphere lineage;
native Southern hemisphere lineage) by analysing the gonadosomatic index (GSI) of mussels
18
to determine the energy expenditure and changes in reproductive patterns at natural (reef) and
man-made habitats (marinas). I also measured mussel shell length (size), Molecular assays
were employed to distinguish the native and non-native species, and estimates of GSI helped
determine the timing of gametogenesis and spawning events, as well as the energy invested
into reproduction, for the two lineages. This study was carried out for a year to examine an
entire reproduction cycle. Fieldwork was conducted at 3 marina sites (man-made) and 3
neighbouring rocky reef sites (natural) in Wellington Harbour, which is a semi-enclosed
harbour receiving high volumes of commercial shipping traffic and highly developed coastline.
The output of this chapter will help understand the impact of man-made structures on the
performance of native and non-native species and if these structures facilitate the non-native
species.
I hypothesised that;
H1: Mussels on natural habitat will have a greater reproductive output (GSI) than those on manmade habitat
H2: NHMg will have greater reproductive output on man-made habitat than on natural habitats.
H3: SHMg will have greater reproductive output than NHMg on natural habitats.
Finally, Chapter 5 summarises the thesis findings and the analyses carried out for this study
whilst answering the research objectives. I further explain the significance of my findings, the
contribution my work makes to biosecurity science and management decision making, and I
present specific recommendations to improve the changing habitat which impact the marine
biodiversity. I conclude by addressing the limitations of this study and by reviewing future
research that may build on my research.
19
CHAPTER 2
PORT AREAS AS INTRODUCTION FOCI FOR NON-NATIVE SPECIES
2.1. Background
2.1.1. Marine shipping and port pressures
The 21st century is known as the era of globalisation (Ehrenfeld 2003). Global interactions
facilitating marine trade has transported thousands of species from their native regions around
the world has immensely increased, and a future rise is expected (UNCTAD 2014), this raises
the risk for a high volume of marine introductions. Transport of marine organisms associated
with ships’ hull and ballast water is the primary source of vessel-based introduction of nonnative species (Hewitt & Campbell 2007; Hewitt et al. 2009; Seebens et al. 2016; Ziako et al.
2016; O’Brien et al. 2017). Inglis et al. (2016) reported that more than 65% of 187 non-native
species in New Zealand arrived as biofouling on international vessels.
Furthermore, regional domestic trade, cargo vessels or pleasure crafts ‘intra-coastal
shipping’ transfer the non-native species from major shipping port areas to minor port/marinas
(Forrest et al. 2009; Clarke & Johnston 2011; Hänfling et al. 2011). The ports are classified
into major and minor ports as per the annual cargo volumes. Major commercial ports manage
at least 1 million tonnes whilst minor ports operate annual cargo volume of fewer than 1 million
tonnes (Department for Transport Statistics United Kingdom 2016). There is evidence of
relatively higher densities of non-native species at major commercial ports (e.g. San Francisco
and Los Angeles) (Foss et al. 2007). With major ports being the focal point of entry for
international vessels, it is also a focal point of transfer of non-native species to minor ports and
marinas (Floerl et al. 2009; Firth et al. 2016; Olenin et al. 2016; Johnston et al. 2017).
The disturbed environment and modified substrata that harbours provide might be less
attractive for native species; however, it provides opportunities for the non-native species to
settle on ‘unoccupied spaces’ and proliferate (Johnston & Keough 2002; Dafforn et al. 2015;
Olenin et al. 2016). The high supply of introductions, i.e. propagule pressure at port areas due
to receiving marine traffic results in a high probability of successful establishment of nonnative species in port areas (Lockwood et al. 2005; Johnston et al. 2009; Clark & Johnston
2011; Lo et al. 2012; Seebens et al. 2016; O’Brien et al. 2017).
20
Another aspect to consider the successful invasion is the suitability of environmental
conditions of the receiving habitat (Lockwood et al. 2005; Petes et al. 2008; Piola et al. 2009;
van de Koppel et al. 2015; Fava et al. 2016; Firth et al. 2017; Purroy et al. 2019). There is
evidence of anti-equatorial and latitudinal shifts in species' distributions due to changing global
climate (Herbert et al. 2007; Keith et al. 2011; Poloczanska et al. 2016). For example,
Occhipinti-Ambrogi (2007) highlighted the range expansion of non-native species due to
increasing water temperatures. It is evident from early studies that the tropics have a greater
level of species diversity than temperate or polar climates ‘Latitudinal diversity groups’
(Darwin 1860). Diverse species interactions may reduce invasion success into species-rich
compared to species-poor areas (e.g., Sax 2001; Freestone et al. 2013). However, this is not the
case in higher latitudes, where studies have indicated relatively lower species diversity (Sax
2001). Thus, marine shipping carrying trade from lower to higher latitudinal regions may pose
more risk of bioinvasion at high latitudes (Hewitt, 2002; Ruiz et al. 2011). However, the
success of invaders also depends on their life-history traits and species interactions (Shea &
Chesson 2002; Jeschke et al. 2012; Marraffini & Geller 2015; Papacostas et al. 2017).
2.1.2. Biosecurity strategies
Management of spread of non-native species is problematic due to the complex marine
ecosystems (Rilov & Crooks 2009). Once the non-native species have established, it is
challenging to eradicate the invader (Kaiser & Burnett 2010). Early detection and timely
eradication measures prove as a successful management approach (Ricciardi et al. 2017).
Numerous international, national and regional agreements and regulations have been adopted
by many countries to minimise the spread of non-native species (Lehtiniemi et al. 2015).
Australia and New Zealand, for example, have established among the world’s strongest
biosecurity and management measures, i.e. a comprehensive pre-border, at-border and postborder management responses (Hewitt & Campbell 2007; Ojaveer et al. 2015). Australia and
New Zealand Environment and Conservation Council (ANZECC), reported introduced nonnative species as one of the key threats to the biodiversity. The International Maritime
Organisation (IMO), introduced many policies and management guidelines to avoid the
introduction of non-native species through ballast waters and biofouling on shipping vessels
(IMO 2017).
Ballast water loaded in other countries are not allowed to discharge waters in national
territorial waters without permissions and permissions are granted to those vessels which have
evidence of the mid-ocean ballast exchange and should follow guidelines to manage ballast
21
water under the Biosecurity Act. New Zealand is one of the few countries in the world to have
specific topics of legal legislation for biosecurity control “an act to restate and reform the law
relating to the exclusion, eradication, and effective management of pests and unwanted
organisms” known as the ‘Biosecurity Act 1993’. Regarding Australia, ‘Biosecurity Act 2015,
replaced the Quarantine Act 1908 and aims to strengthen and manage the existing framework
for biosecurity risks "managing diseases and pests that may cause harm to human, animal or
plant health or the environment" in Australia. Therefore, to eradicate the biofouling issue,
frequent hull maintenance and cleaning are required, and vessels with evidence of biofouling
maintenance and record books are allowed into national-territorial regions (Ministry of Primary
Industries 2018; Australian Government Department of Agriculture and Water Resources
2019). Recreational vessels posing risks on intra-coastal species transfer are required to follow
Craft Risk Management Standard (CRMS) and IMO guidelines (i.e. Biofouling Management
Plan and BioFouling Record Book) (MPI 2014; IMO 2015).
The NZ’s Ministry of Fisheries and Australian National System for the Prevention and
Management of Marine Pest Incursions are a government-controlled institution which deals
with rapid detection and management of non-native species with this many regional councils
also hold responsibilities regarding introduction management (Wotton & Hewitt 2004).
2.1.3. Monitoring strategies
To have effective biosecurity measures, it is crucial to have accurate identification of species,
i.e., native, or non-native species. For example, the invasive northern Pacific seastar, Asterias
amurensis, was misidentified as a native species for nearly 10 years (Wotton & Hewitt 2004).
Some introductions go unnoticed leading to cryptic invasions. Therefore, to apprehend these
challenges, there is a need for extensive surveillance and monitoring for non-native species
(Hewitt & Campbell 2007; Kaiser & Burnett 2010; Zaiko et al. 2016; Ricciardi et al. 2017).
Early detection of the invasive species (non-native species which have adverse impacts on the
environment) enables the managers to take effective eradication response (Hewitt & Campbell
2007; Gardner et al. 2016; Zaiko et al. 2016). However, managers are often faced with technical
and financial constraints to undertake extensive monitoring surveys (Hewitt & Martin 2001;
Campbell et al. 2007). To overcome this problem, prioritising harbours to monitor non-native
species can be of great importance (Peters et al. 2017). As stated earlier, harbours as port areas
(vessel berths) are ‘hotspots’ for bio-invaders and can serve as the perfect invasion study areas.
22
The last decade has seen an immense increase in harbour surveys all around the world
to record non-native species (Pollard & Pethebridge 2002; Campbell et al. 2007; Russel et al.
2008; Inglis et al. 2016; Woods et al. 2018). Harbour surveys attempt to establish an optimal
sampling design to increase the species detection ability to form baseline records of the
presence of species and help monitor the spread of non-native species (Campbell et al. 2007).
The baseline surveys can feed into risk assessments as a tool to monitor the introductions,
whether it be species, pathway or vectors. The assessment of baseline surveys has given rise to
many cost-effective nationwide surveillance programs targeting high-risk pest species whereas
only a few focussed on the underlying factors influencing the introductions of non-native
species (Campbell et al. 2007; Woods et al. 2018). The first step at risk assessment is speciesspecific assessments to identify the potential of the introduced species to cause harm to the
environment or economy (Andersen et al. 2004; Pyšek & Richardson 2010; Hayes et al. 2019).
The species attributes such functional traits (e.g. grazers, predators) and native biodiversity
may help understand the invasion success (Hayes et al. 2019). However, this is a difficult task
to practice due to complex species interactions (Simberloff 2006) and are rarely explored in
monitoring surveys.
The above-stated text gave an overview of human-mediated bioinvasions, and the
strategies developed to manage their spread. Baseline port surveys provide a comprehensive
record of the presence of non-native species on a site-by-site basis. However, the evaluated
reports of port surveys have only highlighted the high-risk pest species and the need for further
surveillance and eradication response (Inglis et al. 2006, 2008 MPI Technical Reports). Rarely
do they consider the factors promoting invasions, whether it be the suitability of habitats or
native biodiversity. For this reason, I have used Australian Port Survey (APS) and New Zealand
Port Biological Baseline Survey (NZPS) as base datasets to identify the factors promoting the
spread of non-native species at local, regional and national scale across the port areas. The twoport surveys (APS and NZPS) have covered numerous ports; major commercial and minor
shipping ports and ports expanding across latitudinal groups. This study provides separate
comparative analyses on presence/ absence data across all surveyed ports in APS and NZPS.
The frequencies of species status, i.e., native, non-native and cryptogenic species will be
indicated as a function of the port type (major vs minor ports) and latitudinal groups.
23
2.1.4. Port Surveys
A. Australian Port Survey
In 1995, the Australian Association of Port and Marine Authorities (AAPMA) and the CSIRO
Centre for Research on Introduced Marine Pests (CRIMP) commenced a national port survey
program to collect a baseline dataset of the introduced species in Australian ports and the threat
they pose. This program went on for 10 years and was conducted at 39 Australian ports to form
a baseline dataset of the introduced species across Australia including Tasmania. The ports
were selected based on commercial shipping facilities, non-commercial facilities and adjacent
areas outside ports.
The baseline survey aimed to define the distribution of non-natives in Australia (Hewitt
& Martin 1999, 2001). The results of the APS were further entered into a National Port Survey
Database (Hayes et al. 2019). The survey was designed by Hewitt & Martin, 1996, 2001 to
cover most of the habitats within each port with replicated sampling. Even though this survey
was designed to detect non-native species, information of native and cryptogenic species within
ports was also provided. This protocol was later adopted by various other countries such as
GloBallast programme undertaken countries South Africa, Brazil, China, India, Iran, Ukraine
and some to form baseline surveys at a larger scale; Australia, New Zealand, USA, Scotland,
Ireland, Guam and Chile (see Campbell et al. 2007). A complete list of the survey methods
adopted to form ‘CRIMP protocols’ is given in section C (Table 2.1). The species were
identified and categorised as native, (resident species), non-native (non-resident/ invaded
invasive species) and cryptogenic (species whose status is unclear) by various specialised
taxonomic experts and scientists in Australia.
The Australian Port Survey dataset the Australian dataset was provided to me by my
co-supervisors Prof Chad Hewitt and Prof Marnie Campbell. However, due to some
inadequacies in the dataset with regards to presence/ absence data in each port. The ports with
less than 5 species listed in the dataset were eliminated. Analyses were carried out on 27 ports
out of the 39 ports for my study. Individuals with only genus name and missing species name
were excluded to keep the dataset consistent which resulted in 70% deletion of unknown
cryptogenic species.
B. New Zealand Port Biological Baseline Survey (NZPS)
In 2000, the New Zealand Government, encouraged by the International Maritime Organisation
(IMO), funded a comprehensive five-year (2000-2005) biological baseline survey programme
24
called the ‘New Zealand Port Biological Baseline Surveys (NZPS)’ to identify non-native
species in port areas. National Institute of Water and Atmospheric Research Ltd (NIWA) was
commissioned to carry out the baseline surveys (and the resurveys) at commercial shipping
ports (13) and marinas (2) in New Zealand (Table 2.2.2). The principal aim of the NZPS was
to form an inventory of native, introduced and cryptogenic species present in New Zealand
ports. In 2010, the Ministry of Primary Industries (MPI) and NIWA made the port survey
dataset available to the general public. Moreover, this baseline survey provided a preliminary
inventory of native and non-native species in New Zealand ports. The NZPS datasets were
acquired
from
the
Biosecurity
New
Zealand
Technical
Papers
available
at
https://www.marinebiosecurity.org.nz/ (Inglis et al. 2006, 2008).
The sampling methods used were based on the CSIRO Centre for Research on
Introduced Marine Pests (CRIMP) protocols developed by Hewitt & Martin (1996, 2001) for
Australian port surveys. The list of surveys methods is given in section C (Table 2.1). MAF
Biosecurity New Zealand funded NIWA and Marine Invasive Taxonomic Service (MITS) to
provide with species-level identification of collected species and management of the
specimens. Each identified sample was further categorised as native species, non-indigenous
species (NIS), cryptogenic species 1 (previously recorded as cryptogenic in New Zealand),
cryptogenic species 2 (recently reported in New Zealand), and species indeterminate (not
identified to species level). However, in this study, cryptogenic 1, cryptogenic 2 and species
intermediate were grouped as cryptogenic species.
C. Overview of the sampling ‘CRIMP protocols’
The ‘CRIMP protocols’ (Hewitt & Martin, 1996, 2001) were first designed for Australian Port
Surveys which was which were later adopted by more than 15 countries including New Zealand
(Campbell et al. 2007). The surveys were designed to determine distribution and abundance of
target species, baseline assessment of native, introduced and cryptogenic species. The surveys
concentrated on specific sites at port areas (e.g. harbours) and adjacent harbour areas that are
most likely to be invaded. Samplings were carried out by consistent qualitative and quantitative
methods in marina areas, on wharf piles by scraping method (0.10 m2 quadrats were fixed at 0.5 m, -3 m and -7 m below the surface). Visual surveys by divers, sediment corers and cyst
identification, beam trawl/benthic sledge, traps, plankton and drop nets at various habitats as
explained in Table 2.1.1. (see Hewitt & Martin, 1996, 2001 for detailed methodology).
25
However, for this study, samples from ‘Quadrat scrapping’ were analysed to be consistent with
the theme of the thesis, which is dealing with the impacts of man-made hard substrata.
Table 2.1.1. Sampling methods for port surveys as per CRIMP protocols (Hewitt & Martin
2001).
Sampling Technique
Taxa
Sampled habitats
Soft
Hard
substrate substrate
Seagrass/ Plankton/ Beach
algal bed
nekton
Small core
dinoflagellate cysts
X
Large core
benthic infauna
X
20 µm plankton net
dinoflagellates
X
zoo/phytoplankton
X
100 µm drop net
X
Traps
crab/shrimp
X
X
X
Qualitative visual survey
macro biota
X
X
X
Quadrat scraping
sedentary/encrusting
Video/ photo transect
sedentary/encrusting
X
mobile epifauna
X
fish
X
fish/mobile epifauna
X
Beam trawl/benthic sledge
Poison station
Beach seine
wrack
X
X
X
X
X
X
X
X
X
X
X
2.1.5. Limitations of large surveys
Large datasets are formed by passive surveillance strategies, i.e. quantifying single occurrences
of species; however, depending on the environmental conditions and experimental strategy, the
species can settle selectively. Detection, identification of small or initial stages of an organism
and can lead to cryptic invasions. It is also important to consider similar sampling methods at
each sampling areas to have a fair interpretation of the study.
Current surveillance programmes are conducted with preliminary taxonomic
identification based on morphology instead of molecular techniques, very few taxonomic
experts, time-consuming and costly (Bishop & Hutchings 2011). Such inadequacies in
surveillance and monitoring programmes can hinder the processes of well-developed
biosecurity and management approaches to restrict invasions (Peters et al. 2017). Lastly,
carrying out large-scale surveys and monitoring studies are a costly affair. For instance, New
Zealand’s species-specific survey for detection of 7 high-risk species (green alga Caulerpa
taxifolia, northern Pacific seastar Asterias amurensis, Mediterranean fan worm Sabella
26
spallanzanii, European green crab Carcinus maenas, Chinese mitten crab Eriocheir sinensis,
Asian clam Potamocorbula amurensis and clubbed sea squirt Styela clavannually) cost
annually approximately NZ $2 million (Arthur et al. 2015).
This study aimed to examine the community composition and spatial patterns of species with
regard to species status – native, non-native and cryptogenic as a function of the surveyed port,
port type, and latitudinal groups. These variables act as predictor factors to describe the spread
patterns of non-native species among Australia and New Zealand ports, respectively. I
hypothesised that; 1) occurrences of non-native and cryptogenic species will be relatively
greater at major commercial ports than at minor ports due to high levels of international marine
vessels berth at commercial ports may carry invaders, and 2) frequencies of non-native and
cryptogenic species increases with an increase in latitude because there is evidence of high
invasibility at temperate climates compared to tropical climates.
2.2. Methods
2.2.1. Data analysis
A. Australian Port Survey (APS)
A total of 39 ports across Australia were surveyed for the Australian port baseline survey
(Figure 2.2.1) with 4 major and 23 minor shipping ports. The ports used for analyses in this
study are listed in Table 2.2.1. Australian latitudinal scale ranges from 43.0 to 12.46°S, and
longitudinal scale ranges from 13.66 to 153.61°E. The dataset was assessed to observe and
interpret patterns of distribution of species status - native, non-native and cryptogenic. And, to
understand the potential impacts on the marine biodiversity as a correspondence between
surveyed ports, port type (major vs minor ports), and latitudinal groups.
The APS surveyed a total of 39 ports; however, ports with low replicates and ports with
species number less than 5 indicated in the provided dataset were rejected from the analyses to
avoid underlying outliers. The data for the sampling method ‘Quadrat scrapping’ was used to
analyse the hypotheses for this study. The presence/absence data rather than abundance were
used as recommendations of severe transformations are required for species communities
where rare species are present, otherwise may be lost between more common species (Clarke
and Warwick 2001). Also, there were inadequacies in the abundance data of the species
provided.
The data analyses were performed on 27 ports as a function of the surveyed port. The
number of replicate sampling (sampling effort) varied across all surveyed ports. The dataset
27
with replicates (presence/absence) was further averaged for each port to analyse the community
composition and species status (native, non-native, cryptogenic) for the factors; port type
(major vs minor) and longitudinal groups. This standardisation approach was undertaken to
eradicate the bias caused due to the different number of replicate sampling (sampling effort)
(Table 2.2.1) conducted at each port.
Table 2.2.1. Ports sampled for the Australian Port Surveys with latitudinal and longitudinal
groups, port type, and replicates (sampling effort).
Ports
Latitude
Longitude
Port Type
Replicates
Abbot Point
19° 53' S
148° 50' E
Minor
16
Adelaide
34° 47' S
138° 30' E
Major
66
Albany
35° 20' S
117° 54' E
Minor
59
Bunbury
33° 18' S
115° 39' E
Minor
52
Burnie
41° 30' S
145° 54' E
Minor
44
Devonport
41° 11' S
146° 21' E
Minor
22
Eden
37° 94' S
149° 56' E
Minor
31
Esperance
33° 52' S
121° 53' E
Minor
60
Fremantle
32° 30' S
115° 44' E
Major
250
Geelong
38° 70' S
144° 23' E
Minor
41
Geraldton
28° 47' S
114° 36' E
Minor
21
Gladstone
23° 50' S
151° 35' E
Minor
100
Hastings
38° 18' S
145° 13' E
Minor
18
Hay Point
21° 13' S
149° 20' E
Minor
44
Hobart
42° 52' S
147° 20' E
Major
106
Lady Barron
40° 12' S
148° 14' E
Minor
17
Launceston
41° 26′ S
147° 80′ E
Minor
78
Lucinda
18° 31' S
146° 21' E
Minor
18
Mackay
21° 80' S
149° 15' E
Minor
25
Melbourne
37° 49' S
144° 55' E
Major
135
Mourilyan
17° 37' S
146° 70' E
Minor
12
Newcastle
32° 55' S
151° 47' E
Minor
86
Port Hedland
20° 19' S
118° 36' E
Minor
52
Port Lincoln
34° 44' S
135° 56' E
Minor
45
Portland
38° 20' S
141° 36' E
Minor
15
Townsville
19° 15' S
146° 50' E
Minor
45
Weipa
12° 40' S
141° 52' E
Minor
18
28
Figure 2.2.1. Map of Australia with commercial shipping ports surveyed for Australian Port Survey study.
Map sourced from Hewitt & Martin (2001) and Commonwealth of Australia (2011).
B. New Zealand Port Biological Baseline Surveys (NZPS)
The data for ‘pile scrapping method’ was obtained from the Biosecurity New Zealand
Technical papers available at https://www.marinebiosecurity.org.nz/ (Inglis et al. 2006, 2008).
This baseline survey provided a preliminary inventory of native, non-native and cryptogenic
species in New Zealand ports. The baseline surveys were performed at 15 ports (13 shipping
ports and 2 marinas – from here on referred to as ports) in New Zealand (Table 2.2.2). The
dataset assessed: to observe and interpret patterns of distribution of species status- native, nonnative and cryptogenic, and to understand the potential impacts on the marine biodiversity as a
correspondence between ports, port types (major vs minor ports) and latitudinal groups.
The data analyses were performed on 15 ports as a function of the surveyed port. The
number of replicate sampling (sampling effort) varied across all the surveyed ports. The dataset
with replicates was further averaged for each port to analyse the community composition and
species status (native, non-native, cryptogenic) for the factors; port type (major vs minor) and
longitudinal groups. The averaged dataset, i.e., the standardised dataset provided a non-bias
29
approach excluding the effects of variations in the number of replicate sampling (sampling
effort) (Table 2.2.2).
The dataset was transformed into presence/absence rather than abundance because;
recommendations of severe transformations are required for species communities where rare
species are present, otherwise may be lost between more common species (Clarke and Warwick
2001).
Also, not all species were quantified at each sampling time; therefore, the
presence/absence coding approach was used. The presence/absence data of the replicates
(sampling effort) were averaged for each port as the sampling effort was different for each port.
The averaged dataset was further analysed to examine the community composition and species
status (native, non-native, cryptogenic) as a function of the port type and latitudinal groups
Table 2.2.2. Ports sampled for the New Zealand Port Biological Baseline Surveys with
latitudinal and longitudinal groups, port type and replicates (sampling effort).
Ports
Latitude
Longitude
Port type
Replicates
Auckland
36° 84' S
174° 78' E
Major
71
Bluff
46° 37' S
168° 18' E
Minor
53
Dunedin Harbour
45° 81' S
170° 62' E
Minor
47
Gisborne
38° 67' S
178° 02' E
Minor
31
Gulf Harbour Marina
36° 62' S
174° 78' E
Minor
58
Lyttelton
43° 61' S
172° 72' E
Major
50
Napier
39° 47' S
176° 91' E
Major
48
Nelson
41° 25' S
173° 17' E
Minor
94
Opua Marina
35° 31' S
174° 12' E
Minor
30
Picton
41° 28' S
174° 00' E
Minor
35
Taranaki
39° 05' S
174° 03' E
Minor
44
Tauranga
37° 64' S
176° 18' E
Major
68
Timaru
44° 39' S
171° 25' E
Minor
39
Wellington
41° 31' S
174° 81' E
Major
73
Whangarei
35° 75' S
174° 34' E
Minor
47
30
2.2.2.
Statistical analysis
The total number of species community and species status at each surveyed port was divided
with replicate numbers at each port due to varying sampling replicates at each survey port. The
relative number of native, non-native cryptogenic species were regressed to observe the
relationship between the number of native vs non-native species, non-native vs cryptogenic
species, and native vs cryptogenic species. Regressions were plotted with 95% confidence
intervals using the STATISTICA v.7 (Stat Soft Inc.) software. R2 and P values were calculated
for each association. The significance of these tests was set at P < 0.05.
Figure 2.2.2. Map of Australia with commercial shipping ports surveyed for New Zealand Port
Biological Baseline Survey study. Map sourced from Department of Conservation and Ministry of
Fisheries (2011).
An initial, two-dimensional multidimensional scaling (MDS) ordination plot was
performed to visualise the similarity in community composition and species status as a function
of the port type and latitudinal and longitudinal groups. In MDS, similar samples, cluster
together whereas samples which are dissimilar, cluster further apart. MDS plot stress values
were used to interpret the reliability of the relationships; values < 0.15 = good representation
between groups. The stress levels are also affected by the number of samples (Clarke 1993).
31
Data analyses for community composition and species status were performed using the
statistical package Plymouth Routines in Multivariate Ecological Research (PRIMER v.6);
with permutational multivariate analysis of variance (PERMANOVA) as an add-on package
(Clarke & Gorley 2006; Anderson et al. 2008). The resemblance matrix based on Bray-Curtis
similarity matrices was zero-adjusted (dummy variable) for clearer assemblages which were
present across the presence/absence data. The replicates in each port were averaged and square
root transformed to carry out further analyses. When species were tallied as per their status native, non-native and cryptogenic, the dataset was square-root transformed to down-weight
the dominant species (Clarke & Gorley 2006).
PERMANOVA analysis was used to determine any significant differences between
surveyed ports, port type and latitudinal groups with a significance level of P < 0.05.
PERMANOVA based on 9999 permutations with Type III (partial) sums of squares was
performed for each factor. The independent factors were surveyed port, port type and latitudinal
groups. PERMANOVA helps statistically test the differences between two and among multiple
groups, and the effects of factors on the species communities with permutations to avoid
possible biases. PERMANOVA was followed with post-hoc pairwise tests between factors –
surveyed port, port type and latitudinal groups, respectively to indicate significant correlations
between each factor group, within-group average similarity and between-group dissimilarity
results
SIMPER (Similarity percentages) in PRIMER was employed to determine the species
as well as species status, respectively contributing most to the overall patterns in community
composition and species status (native, non-native and cryptogenic). The SIMPER analyses
were restricted to top 5 species describing the location of differences leading to between-group
dissimilarity.
32
2.3. Results (A)
AUSTRALIAN PORT SURVEY
2.3.1. Presence/absence data
A total of 1352 species were sampled across the 27 ports for the Australian port survey study.
The species were grouped in 14 phyla: Chlorophyta, Ochrophyta, Rhodophyta, Annelida,
Arthropoda, Bryozoa, Chordata, Cnidaria, Echinodermata, Mollusca, Platyhelminthes,
Porifera, Sipuncula and Heterokontophyta (see Appendix – Table A2 for the species list). The
most represented phyla in this survey were the Mollusca (272 species) followed by Annelida
(260 species) and Arthropoda (234 species). The species were further grouped according to
their status; native, non-native and cryptogenic. Of the 1352 species, 1181 were native species
(88%), 126 non-native species (9%) and 45 cryptogenic species (3%) (Table 2.3.1).
Table 2.3.1. The total number of species noted in Australian Port Surveys grouped to Phylum
and status (native, non-native and cryptogenic).
Phylum
Total
Native
Non-native
Cryptogenic
Chlorophyta
26
19
4
3
Ochrophyta
32
27
5
0
Rhodophyta
103
88
9
6
Annelida
260
239
14
7
Arthropoda
234
199
27
8
Bryozoa
126
93
24
9
Chordata
113
98
14
1
Cnidaria
126
102
16
8
Echinodermata
41
39
2
0
Mollusca
272
261
10
1
Platyhelminthes
1
1
0
0
Porifera
13
12
1
0
Sipuncula
3
3
0
0
Heterokontophyta
2
0
0
2
1352
1181
126
45
Total
33
2.3.2. Variations in the total presence of species as a function of replicates (sampling
effort) at each surveyed port
The replication of surveys varied at each surveyed port, so to have a fair comparison of the
presence of species between ports, the total number of species at each port was divided by the
number of replicates to obtain the relative number of species (Table 2.3.2). The relative percent
of species was highest at Abbot Point (9.13%) followed by Mourilyan (5.5%), and Lucinda
(5.5%), and lowest relative percent of species were observed at Fremantle (0.54%) and
10
8
6
4
2
0
Abbot Point
Adelaide
Albany
Bunbury
Burnie
Devonport
Eden
Esperance
Fremantle
Geelong
Geraldton
Gladstone
Hastings
Hay Point
Hobart
Lady Barron
Launceston
Lucinda
Mackay
Melbourne
Mourilyan
Newcastle
Port Hedland
Port Lincoln
Portland
Townsville
Weipa
Relative number of species
Melbourne (0.56%) (Figure 2.3.1).
Surveyed port
Figure 2.3.1. The total relative number of species across all 27 surveyed ports.
Table 2.3.2. The total number, sampling effort (replicates) and the relative number of species
as a function of replicates at each surveyed port.
Ports
Total number
Replicates
Relative number
Abbot Point
146
16
9.13
Adelaide
94
66
1.42
Albany
48
59
0.81
Bunbury
52
52
1.00
Burnie
165
44
3.75
Devonport
49
22
2.23
Eden
102
31
3.29
Esperance
121
60
2.02
Fremantle
136
250
0.54
Geelong
68
41
1.66
Geraldton
27
21
1.29
34
Gladstone
68
100
0.68
Hastings
44
18
2.44
Hay Point
140
44
3.18
Hobart
198
106
1.87
Lady Barron
35
17
2.06
Launceston
187
78
2.40
Lucinda
99
18
5.50
Mackay
120
25
4.80
Melbourne
75
135
0.56
Mourilyan
66
12
5.50
Newcastle
119
86
1.38
Port Hedland
99
52
1.90
Port Lincoln
64
45
1.42
Portland
38
15
2.53
Townsville
106
45
2.36
Weipa
57
18
3.17
2.3.3. Variations in the community composition as a function of the surveyed port, port
type and latitudinal groups
a) Surveyed ports
Figure 2.3.2. Multidimensional Scaling (MDS) plot. The proximity of surveyed ports to each
other indicates similarity in species (based on presence/absence data).
35
The graphical representation of the MDS ordination of the presence of species
(presence/absence data) as a function of surveyed port showed some patterns in clustering of
ports with similar species. However, the patterns are not clear enough to state the groupings
(Figure 2.3.2). The 2D stress result of 0.01 indicates that the MDS is an excellent representation
of the data.
Multivariate analyses were carried out to observe patterns of community composition
among/between ports. PERMANOVA based on presence/absence data as a function of
surveyed ports indicates significant differences in community composition among 27 surveyed
ports (P < 0.001; Table 2.3.3).
Table 2.3.3. Results of the PERMANOVA test performed on the presence/absence of species
as a function of the surveyed port. Significance marked in bold (P < 0.05).
df
SS
MS
Pseudo-F
P (perm)
Unique
perms
Surveyed port
Residual
26
1449
2.3875E6
4.3316E6
Total
1475
6.7192E6
91828
2989.4
30.718
9389
0.001
Pairwise comparisons also showed a high significance in community composition
between surveyed ports (P < 0.001). The within-group similarity was relatively more robust at
Port Geelong (40.69%) followed by Hobart (40.16%), and Esperance had the least withingroup similarity (9.23%) (Table 2.3.4). The between-ports dissimilarity ranged from 75.86 100%, indicating high dissimilarity in the community composition between ports. The paired
ports that showed less than 90% dissimilarity are; Geelong vs Melbourne (75.86%), Hastings
vs Portland (85.95%), Fremantle vs Melbourne (86.02%), Eden vs Melbourne (87.21%),
Geelong vs Hobart (88.09%), Geelong vs Portland (88.36%), Adelaide vs Melbourne
(88.73%), Fremantle vs Newcastle (89.20%), Fremantle vs Geelong (89.71%) and Hay point
vs Mackay (89.84%). Further, observing these paired ports, the between-ports dissimilarity is
relatively low for ports at proximity.
Table 2.3.4. The average similarity of the presence/absence of species as a function of the
surveyed port.
Ports
Avg. sim. (%)
Ports
Avg. sim. (%)
Ports
Avg. sim. (%)
Abbot Point
13.07
Devonport
19.68
Geraldton
27.66
Adelaide
28.34
Eden
25.48
Gladstone
28.27
36
Albany
13.81
Esperance
9.23
Hastings
38.67
Bunbury
13.50
Fremantle
24.87
Hay Point
21.26
Burnie
15.77
Geelong
40.69
Hobart
40.16
Lady Barron
11.22
Mackay
19.66
Newcastle
24.25
Launceston
35.17
Melbourne
34.38
Port Hedland
28.91
Lucinda
11.98
Mourilyan
15.15
Port Lincoln
15.68
Portland
32.76
Townsville
11.92
Weipa
9.65
SIMPER (similarity percentage) analysis was applied to identify the top 5 contributing
and discriminating species between surveyed ports. It is important to note that red alga, Jania
adhaerens, contributed 94.68% to the average similarity within Port Geraldton explaining the
port as an outlier (Table 2.3.5). The SIMPER results indicated that the species contributing to
the within-ports similarity also contributed to the between-ports dissimilarity (considering the
top 5 species). There was no specific trend observed in terms of species contribution between
surveyed ports highlighting the dissimilarities. However, the dissimilarity between ports was
dependent on the average abundance of the species at each port.
37
Table 2.3.5. SIMPER analysis: average similarity in the presence/absence of species as a function of the surveyed port.
Abbot Point
Adelaide
Burnie
Average similarity: 13.07%
Average similarity: 28.34%
Average similarity: 15.77%
Species
Avg.
Abund
0.69
C%
Cum.%
15.49
15.49
Sabella spallanzanii
Chama fibula
0.56
11.86
27.35
Pinctada sugillata
0.56
10.85
Dendostrea folium
0.5
Pyura stolonifera
0.5
Ophiactis cf. savignyi
Species
Avg.
Abund
0.67
C%
Cum.%
Species
Avg.
Abund
0.45
C%
Cum.%
12.69
12.69
Xenostrobus pulex
10.83
10.83
Hydroides elegans
0.65
11.24
23.94
Lasaea australis
0.48
9.29
20.12
38.21
Styela plicata
0.56
8.85
32.78
Celleporaria foliata
0.45
7.82
27.94
9.55
47.76
Harmothoe waahli
0.58
8.02
40.8
Hiatella australis
0.45
7.51
35.45
8.83
56.59
Botryllus schlosseri
0.47
7.13
47.94
Paradexamine churinga
0.43
6.99
42.44
Albany
Bunbury
Devonport
Average similarity: 13.81%
Average similarity: 13.50%
Average similarity: 19.68%
Species
Bugula stolonifera
Sabella spallanzanii
Watersipora subtorquata
Bugula neritina
Cryptosula pallasiana
Avg.
Abund
0.32
0.34
0.29
0.27
0.24
C%
Cum.%
15.28
14.21
13.55
8.42
7.73
15.28
29.49
43.03
51.46
59.19
Eden
Average similarity: 25.48%
Species
Balanus trigonus
Podarkeopsis galangaui
Mytilus edulis planulatus
Hiatella australis
Watersipora subtorquata
Species
Avg.
C%
Abund
Paracerceis sculpta
0.46
56.77
Sabella spallanzanii
0.27
18.47
Leptochiton liratus
0.21
9.32
Herpetopoma aspersa
0.17
4.51
Hiatella australis
0.13
3.03
Esperance
Average similarity: 9.23%
Cum.%
Species
Avg.
Abund
0.71
C%
Cum.%
23.01
23.01
Hiatella australis
0.74
0.61
0.48
0.42
21.7
14.29
6.9
6
44.71
58.99
65.89
71.89
Pilumnus acer
Halicarcinus ovatus
Alpheus socialis
Musculus cf. nanus
56.77
75.24
84.56
89.07
92.1
Species
Avg.
C%
Abund
Thelepus extensus
0.59
24.78
Sertularella cf. robusta
0.5
19.15
Tubularia cf. crocea
0.45
15.73
Bimeria australis
0.36
8.81
Sarsia cf. eximia
0.32
6.04
Fremantle
Average similarity: 24.87%
Cum.%
Species
Avg.
Abund
0.45
C%
Cum.%
26.42
26.42
Balanus trigonus
0.27
0.28
0.22
0.23
14.25
11.23
5.83
5.02
40.67
51.91
57.73
62.76
Hiatella australis
Pilumnus fissifrons
Mytilus edulis planulatus
Celleporaria cf. nodulosa
24.78
43.93
59.66
68.47
74.5
Avg.
Abund
0.84
C%
Cum.%
37.13
37.13
0.48
0.45
0.46
0.33
8.91
8.83
8.06
5.86
46.03
54.87
62.93
68.79
38
Geelong
Average similarity: 40.69%
Species
C%
Cum.%
16.44
15.24
14.66
16.44
31.68
46.35
Pisidia gordoni
Sphaeroma sculpta
Hiatella arctica
0.66
9.04
0.66
8.67
Lady Barron
Average similarity: 11.22%
55.39
64.06
Scruparia ambigua
Aora maculata
Mytilus galloprovincialis
Balanus trigonus
Pyura stolonifera
Avg.
Abund
0.85
0.83
0.83
Gladstone
Average similarity: 28.27%
Ascidiella aspersa
Platynereis antipoda
Species
Avg.
Abund
0.41
Species
C%
Cum.%
38.14
21.22
8.03
38.14
59.36
67.39
0.33
6.81
0.36
6.16
Hay Point
Average similarity: 21.26%
74.2
80.36
C%
Cum.%
46.7
46.7
Chama lazarus
0.35
18.34
0.29
11.98
0.18
4.25
0.18
3.82
Launceston
Average similarity: 35.17%
65.04
77.01
81.26
85.08
Bugula dentata
Herdmania momus
Amathia distans
Celleporaria fusca
Stolonica australis
Species
Balanus trigonus
Halicarcinus ovatus
Balanus variegatus
Achelia assimilis
Molgula ficus
Avg.
Abund
0.87
0.76
0.69
0.69
0.64
C%
Cum.%
9.2
6.46
5.47
5.13
4.99
9.2
15.66
21.12
26.25
31.24
Species
Avg.
Abund
0.78
0.56
0.39
Hastings
Average similarity: 38.67%
C%
Cum.%
15.06
15.06
Pilumnus cf. tomentosus
0.64
14.67
Striatobalanus amaryllis
0.64
12
Lumbrineris coccinea
0.45
5.29
Hyastenus cf. convexus
0.43
5.12
Lucinda
Average similarity: 11.98%
29.73
41.73
47.02
52.13
Species
Balanus reticulatus
Isognomon nucleus
Chama fibula
Brachidontes maritimus
Balanus amphitrite
Avg.
Abund
0.64
Avg.
Abund
0.28
0.5
0.44
0.33
0.28
C%
Cum.%
29.07
15.35
9.98
7.48
4.74
29.07
44.42
54.4
61.88
66.61
Species
C%
Cum.%
20.34
16.1
15.5
20.34
36.44
51.95
0.72
15.19
0.56
8.95
Hobart
Average similarity: 40.16%
67.14
76.09
Stolonica australis
Bugula dentata
Triphyllozoon cf.
moniliferum
Amastigia cf. texta
Cryptosula pallasiana
Species
Avg.
Abund
0.83
0.72
0.72
Avg.
Abund
0.92
C%
Monocorophium
9.81
acherusicum
Hiatella australis
0.81
7.36
Watersipora subtorquata
0.79
6.87
Mytilus galloprovincialis
0.79
6.83
Crassostrea gigas
0.79
6.77
Mackay
Average similarity: 19.66%
Species
Amathia distans
Eupolymnia koorangia
Thelepus robustus
Lumbrineris coccinea
Lumbrineris inflata
Avg.
Abund
0.72
0.64
0.56
0.48
0.44
Cum.%
9.81
17.16
24.03
30.86
37.63
C%
Cum.%
16.78
11.57
10.83
6.96
5.59
16.78
28.36
39.19
46.15
51.75
39
Melbourne
Average similarity: 34.38%
Species
C%
Cum.%
28.86
28.86
Sabella spallanzanii
0.59
20.55
Pyura stolonifera
0.52
13.84
Mytilus edulis planulatus
0.48
13.16
Ascidiella aspersa
0.37
6.94
Port Hedland
Average similarity: 28.91%
49.4
63.24
76.4
83.34
Balanus trigonus
Species
Avg.
Abund
0.7
Mourilyan
Average similarity: 15.14%
Avg.
C%
Abund
Lumbrineris inflata
0.73
19.23
Crisia cf. acropora
0.69
17.53
Syllis australiensis
0.52
9.92
Microcosmus helleri
0.54
9.55
Pomatostegus stellatus
0.54
9.49
Townsville
Average similarity: 11.92%
Species
Cum.%
19.23
36.76
46.68
56.22
65.71
Species
C%
Cum.%
15.13
15.13
Balanus variegatus
0.42
10.28
0.42
9.25
0.42
9.14
0.33
7.13
Port Lincoln
Average similarity: 15.68%
25.41
34.66
43.8
50.93
Balanus trigonus
Irus crebrelamellatus
Augeneria verdis
Bugula neritina
Dendostrea
sandvichensis
Balanus amphitrite
Leonnates decipens
Chama fibula
Striostrea mytiloides
Avg.
Abund
0.5
Newcastle
Average similarity: 24.25%
Species
Avg.
C%
Abund
Hiatella australis
0.56
36.63
Musculus impactus
0.36
11.33
Ophiactis resiliens
0.29
8.39
Lasaea australis
0.27
8.29
Trichomusculus barbatus
0.29
5.88
Weipa
Average similarity: 9.65%
Avg.
C%
Cum.% Species
Abund
Branchiomma nigromaculata
0.44
14.45
14.45
Thelepus robustus
Lepidonotus carinulatus
0.4
9.02
23.46
Striostrea mytiloides
Lumbrineris cf. coccinea
0.38
8.28
31.75
Balanus amphitrite
Pseudopotamilla cf. laciniosa
0.36
7.54
39.29
Lysidice collaris
Lysidice collaris
0.33
5.88
45.17
Striatobalanus amaryllis
C% - Percent contribution; Cum.%- cumulative percentage
Avg.
Abund
0.44
0.28
0.22
0.28
0.28
Cum.%
36.63
47.96
56.35
64.64
70.51
C%
Cum.%
28.23
17.04
10.85
8.15
6.9
28.23
45.27
56.12
64.27
71.17
Species
Avg.
Abund
0.67
C%
Cum.%
29.1
29.1
0.62
15.52
0.43
7.91
0.38
5.54
0.34
4.61
Portland
Average similarity: 32.76%
44.62
52.53
58.07
62.68
Species
Avg.
C%
Abund
Bugula dentata
0.8
24.58
Paratanais ignotus
0.53
10.6
Halicarcinus ovatus
0.53
9.38
Eucoelium mariae
0.53
9.01
Watersipora subtorquata
0.53
8.52
Geraldton
Average similarity: 27.66%
Cum.%
Species
C%
Cum.%
94.68
94.68
Jania adhaerens
Avg.
Abund
0.52
40
24.58
35.18
44.56
53.56
62.09
b) Port type (major and minor ports)
The graphical representation of the MDS ordination of the presence/absence data showed no
distinct groupings of similar species composition as a function of port type, 4 major commercial
shipping ports and 23 minor shipping ports (2D stress = 0.11; Figure 2.3.3).
Figure 2.3.3. Multidimensional Scaling (MDS) Map. The proximity of surveyed ports to each other
indicates similarity in community composition as a function of port type.
Further, multivariate analyses, PERMANOVA showed a significant difference in
species community composition as a function of port type (P = 0.047; Table 2.3.6). Pairwise
comparison, however, showed relatively more similar species composition at major ports
(18.42%) than minor ports (6.84%) (Table 2.3.6).
Table 2.3.6. Results of the PERMANOVA and pairwise test performed as a function of port
type (2 levels). Significance marked in bold (P < 0.05).
Source
df
SS
MS
PseudoF
P
(perm)
Port type
1
5666.7
5666.7
1.3364
0.047
Residual
25
1.06E+05
4240.2
Total
26
1.116E+05
Unique
t
perms value
7541
1.156
Avg. similarity
Major port
Minor port
18.42
6.84
41
SIMPER analysis showed 154 species contributing to the 50% dissimilarity between
major and minor ports. The top 5 species contributing to the differences were: cryptogenic
Balanus trigonus (1.44%), native Mytilus edulis planulatus (1.06%), non-native Sabella
spallanzanii (1.05%), non-native Ciona intestinalis (0.89%) and native Pyura stolonifera
(0.85%). The species at major ports were better discriminators (Table 2.3.7).
Table 2.3.7. SIMPER analysis: average similarity in the community composition as a function
of port type. Species status, i.e., native (N), non-native (NN) and cryptogenic (C), is noted.
Minor ports
Average similarity = 6.84%
Species
Major ports
Average similarity = 18.42%
C%
Species
Minor & Major ports
Average dissimilarity = 90.99%
C%
Species
Minor
Major
C%
Hiatella australis (N)
8.07
Balanus trigonus (C)
12.91
Balanus trigonus (C)
0.18
0.75
1.44
Watersipora
subtorquata (NN)
3.33
Ciona intestinalis (NN)
7.25
0.05
0.43
1.06
Bugula neritina (NN)
2.52
5.63
0.1
0.45
1.05
Eupolymnia koorangia
(N)
2.10
0.02
0.42
0.89
Balanus amphitrite (C)
2.05
Mytilus edulis
planulatus (N)
Halicarcinus ovatus
(N)
Sabella spallanzanii
(NN)
Mytilus edulis
planulatus (N)
Sabella spallanzanii
(NN)
Ciona intestinalis
(NN)
Pyura stolonifera (N)
0.14
0.39
0.85
5.07
4.92
C% = percent contribution
c) Latitudinal groups
The surveyed ports were grouped as a function of their latitude forming 6 latitude groups
ranging from 15°S to 40°S. The MDS plot is displayed in Figure 2.3.4; 2D stress = 0.15,
showing the ordination patterns and overlaps between ports as a function of latitudinal groups.
The ports at each latitudinal group clustered together but the ports at high (30°S, 35°S and
40°S) and low (15°S and 20°S) latitudinal groups showed distinct separation. Port Geraldton
at latitude 25°S was an outlier and only port sampled from that latitude.
42
Latitude
Figure 2.3.4. Multidimensional Scaling (MDS) plot. The proximity of surveyed ports to each
other indicates similarity in community composition as a function of latitudinal groups.
The results of the PERMANOVA analysis showed a statistical significance in
community composition as a function of latitude (P < 0.001; Table 2.3.8) indicating variations
in community composition for ports at each latitudinal group. Pairwise tests indicated
significance (P < 0.05) between latitudes (15, 20, 30, 35, 40°S) except for 25°S, i.e. Port
Geraldton, which is an outlier (Table 2.3.9).
Table 2.3.8. Results of the PERMANOVA test performed as a function of latitude (7 levels).
Significance (P < 0.05) marked in bold.
df
SS
MS
Pseudo-F
P (perm)
Unique perms
Latitude
6
42473
7078.8
2.046
0.0001
9661
Residual
20
69201
3460
Total
26
1.1167E+05
43
Table 2.3.9. Results of pairwise PERMANOVA test performed as a function of latitudinal
groups with only significant paired latitudes. Significance (P < 0.05) marked in bold.
Latitude (°S)
t
P (perm)
Unique perms
15 vs 30
1.6903
0.0052
210
15 vs 35
1.7544
0.0049
210
15 vs 40
1.7009
0.0081
126
15 vs 20
1.3609
0.0309
35
30 vs 35
1.2686
0.0179
462
30 vs 40
1.3786
0.0026
461
30 vs 20
1.5535
0.0062
210
35 vs 40
1.3431
0.0065
462
35 vs 20
1.6481
0.0043
210
40 vs 20
1.5486
0.0089
126
SIMPER was performed to identify the species (top 5) that contributed to the significant
dissimilarity between paired latitudes (Table 2.3.11). The species at 15°S were better
discriminators leading to the dissimilarity between latitude 15°S and other latitudes, i.e., 20,
30, 35, 40°S. The top species contributing to the dissimilarity are; Chama fibula (NN),
Ophiactis cf. savignyi (N), Dendostrea folium (N) and Eunice tubifex (N). Species that
explained the dissimilarity between latitude 20°S and latitudes 30, 35 and 40°S are;
Lumbrineris inflata (N), Chama lazarus (N) and Thelepus robustus (N). The species at latitude
20°S were the better discriminators. The dissimilarity for latitudinal groups 30°S vs 35°S was
contributed by species; Balanus trigonus (C), Bugula dentata (N), Cryptosula pallasiana (NN)
and Watersipora subtorquata (NN) with species at 35°S being the better discriminators. Lastly,
the dissimilarity between latitudes 30°S and 40°S were explained by species; Pomatoceros
taeniata (N), Balanus trigonus (C), Crassotrea gigas (NN), Bimeria australis (N) and Bugula
dentata (N). The species at 40°S latitude being the better discriminators (Table 2.3.10).
To summarize, the percentage contribution (C%) of each species to the dissimilarities
between the latitudes was less than 2%. Also, the results indicated no specific trend in species
contribution between latitudinal groups. However, the species at lower latitudes, i.e., 15°S and
20°S were better discriminators compared to higher latitudes, i.e., 30°S, 35°S, 40°S.
44
Table 2.3.10. SIMPER analysis: average dissimilarity for the community composition as a function of latitude with only significant paired latitudes.
Species status, i.e., native (N), non-native (NN) and cryptogenic (C), is noted.
15 & 30°S
Average dissimilarity = 97.48%
15
30
Avg. Abund. Avg. Abund
0.52
0
0.51
0
0.06
0.56
0.49
0.01
0.45
0
15 & 40°S
Average dissimilarity = 98.21%
Species
Chama fibula (NN)
Ophiactis cf. savignyi (N)
Hiatella australis (N)
Dendostrea folium (N)
Eunice tubifex (N)
15
40
Avg. Abund. Avg. Abund
0.52
0
0.51
0
0.49
0
0.45
0
0
0.47
35 & 20°S
Average dissimilarity = 97.79%
Species
Chama fibula (NN)
Ophiactis cf. savignyi (N)
Dendostrea folium (N)
Eunice tubifex (N)
Pomatoceros taeniata (N)
Species
Watersipora subtorquata (NN)
Lumbrineris inflata (N)
Chama lazarus (N)
Cryptosula pallasiana (NN)
Thelepus robustus (N)
35
20
Avg. Abund.
0.52
0
0
0.43
0
Avg. Abund
0
0.53
0.47
0
0.45
15 & 35°S
Average dissimilarity = 98.40%
15
35
C%
0.97
0.96
0.95
0.89
0.87
Cum.%
0.97
1.93
2.88
3.77
4.64
Species
Watersipora subtorquata (NN)
Chama fibula (NN)
Ophiactis cf. savignyi (N)
Dendostrea folium (N)
Eunice tubifex (N)
Avg. Abund. Avg. Abund
0
0.52
0.52
0
0.51
0
0.49
0
0.45
0
15 & 20°S
Average dissimilarity = 92.19%
C%
0.83
0.82
0.78
0.74
0.73
Cum.%
0.83
1.65
2.43
3.16
3.89
Species
Chama fibula (NN)
Ophiactis cf. savignyi (N)
Lumbrineris inflata (N)
Eunice tubifex (N)
Dendostrea folium (N)
C%
1.15
1.07
0.98
0.96
0.92
Cum.%
1.15
2.22
3.19
4.15
5.07
Species
Hiatella australis (N)
Lumbrineris inflata (N)
Chama lazarus (N)
Balanus trigonus (C)
Thelepus robustus (N)
15
20
Avg. Abund. Avg. Abund
0.52
0
0.51
0
0
0.53
0.45
0
0.49
0.04
30 & 20°S
Average dissimilarity = 96.13%
30
20
Avg. Abund.
0.56
0
0
0.45
0
Avg. Abund
0.07
0.53
0.47
0
0.45
C%
1.03
1.02
1
0.95
0.90
Cum.%
1.03
2.04
3.04
3.99
4.89
C%
0.91
0.9
0.88
0.81
0.8
Cum.%
0.91
1.81
2.70
3.51
4.3
C%
1.08
1.04
0.94
0.91
0.89
Cum.%
1.08
2.12
3.06
3.97
4.85
45
40 & 20°S
Average dissimilarity = 96.40%
40
20
Species
Avg. Abund. Avg. Abund
Lumbrineris inflata (N)
0
0.53
Pomatoceros taeniata (N)
0.47
0
Chama lazarus (N)
0
0.47
Thelepus robustus (N)
0
0.45
Hiatella australis (N)
0.53
0.07
30 & 40°S
Average dissimilarity = 89.63%
Species
Pomatoceros taeniata (N)
Balanus trigonus (C)
Crassotrea gigas (NN)
Bimeria australis (N)
Bugula dentata (N)
30
Avg. Abund.
0
0.45
0
0
0
40
Avg. Abund
0.47
0.35
0.43
0.29
0.31
35 & 40°S
Average dissimilarity = 88.37%
C%
0.89
0.81
0.80
0.76
0.75
C%
0.99
0.86
0.78
0.77
0.75
Cum.%
0.89
1.69
2.49
3.25
4
Cum.%
0.99
1.85
2.63
3.40
4.15
35
40
Species
Avg. Abund. Avg. Abund
Balanus trigonus (C)
0.43
0.35
Cryptosula pallasiana (NN)
0.43
0.13
Pomatoceros taeniata (N)
0.07
0.47
Bugula dentata (N)
0.41
0.31
Sabella spallanzanii (NN)
0.36
0
30 & 35°S
Average dissimilarity = 86.99%
Species
Balanus trigonus (C)
Bugula dentata (N)
Cryptosula pallasiana (NN)
Watersipora subtorquata (NN)
Sabella spallanzanii (NN)
30
Avg. Abund.
0.45
0
0.04
0.17
0.31
35
Avg. Abund
0.43
0.41
0.43
0.52
0.36
C%
1.04
0.99
0.97
0.93
0.91
Cum.%
1.04
2.03
2.99
3.92
4.83
C%
1.29
1.20
1.17
1.14
1.10
Cum.%
1.29
2.49
3.67
4.80
5.90
C% = Per cent Contribution, Cum. % = Cumulative percentage
46
In summary, statistically significant differences in the presence of species were
observed as a function of the surveyed port. The average dissimilarities between ports was
more than 75%. The results indicated no specific trend in species contribution to the
dissimilarities between ports; except for, Port Geraldton where the red alga, Jania adhaerens,
contributed 94.68%. The community composition as a function of port type (major vs minor
ports) indicated significant differences. The minor ports had relatively diverse species when
compared to major ports. However, this could be because of 4 major and 23 minor ports in the
dataset. The latitudinal groups indicated significance between latitude groups, except for 25°S,
i.e., Port Geraldton. The species at lower latitudes, i.e., 15°S and 20°S were better
discriminators compared to species at higher latitudes (30, 35, 40°S). The top species are
contributing to the between-group dissimilarities for the factors, port type and latitudinal
groupsare listed below; Table 2.3.11.
Table 2.3.11. The list of species that indicated variations as a function of the port type and
latitudinal groups.
Species
Phyla
Species status
Balanus trigonus
Arthropoda
Cryptogenic
Bugula dentata
Bryozoa
Native
Bugula neritina
Bryozoa
Non-native
Chama fibula
Mollusca
Non-native
Chama lazarus
Mollusca
Native
Ciona intestinalis
Chordata
Non-native
Crassotrea gigas
Mollusca
Non-native
Cryptosula pallasiana
Bryozoa
Non-native
Dendostrea folium
Mollusca
Native
Eunice tubifex
Annelida
Native
Hiatella australis
Mollusca
Native
Jania adhaerens
Rhodophyta
Native
Lumbrineris inflata
Annelida
Native
Mytilus edulis planulatus
Mollusca
Native
Ophiactis cf. savignyi
Echinodermata
Native
Pomatoceros taeniata
Annelida
Native
Sabella spallanzanii
Annelida
Native
Thelepus extensus
Annelida
Native
Watersipora subtorquata
Bryozoa
Non-native
47
2.3.4.
Variations in species status - native, non-native and cryptogenic species as a
function of the surveyed port, port type and latitudinal groups
a) Surveyed ports
To eliminate the bias due to differences in the replicates (sampling effort) at each port; the total
number of species status (native, non-native and cryptogenic) was divided by the number of
replicates at each surveyed port. The total relative number of species (total number/number of
replicates) at each surveyed port showed relatively higher percentages of native species,
followed by non-native and cryptogenic species (Figure 2.3.5). The relative number of native
species was highest at Abbot point (8.44), and lowest at Melbourne (0.33) whereas the nonnative species were highest at Lucinda (0.5%) and lowest at Port Gladstone. The number of
cryptogenic species was relatively lower compared to native and non-native species (Table
Relative number of species status
2.3.12).
10.00
Native
Non-native
8.00
Cryptogenic
6.00
4.00
2.00
0.00
Surveyed port
Figure 2.3.5. The total relative number of species status - native (blue), non-native (orange) and
cryptogenic (grey) species across 27 surveyed ports.
Table 2.3.12. The total number of species, species status, replicates (sampling effort) and the
relative number of native, non-native and cryptogenic species - across 27 surveyed ports.
Surveyed port
Native
Non-native
Cryptogenic
Survey
Relative
Relative
Relative
species
species
species
Replicates
Native
Non-native
Cryptogenic
Abbot Point
135
7
4
16
8.44
0.44
0.25
Adelaide
70
17
7
66
1.06
0.26
0.11
Albany
31
10
7
59
0.54
0.19
0.08
Bunbury
37
11
4
52
0.73
0.21
0.06
48
Burnie
140
19
6
44
3.23
0.39
0.14
Devonport
39
7
3
22
1.77
0.27
0.18
Eden
82
14
6
31
2.65
0.45
0.19
Esperance
103
14
4
60
1.68
0.27
0.07
Fremantle
116
16
4
250
0.46
0.06
0.02
Geelong
49
16
3
41
1.17
0.39
0.10
Geraldton
23
3
1
21
1.10
0.14
0.05
Gladstone
62
2
4
100
0.64
0.02
0.02
Hastings
37
7
0
18
2.00
0.44
0.00
Hay Point
131
6
3
44
2.98
0.09
0.11
Hobart
129
50
19
106
1.21
0.48
0.18
Lady Barron
25
7
3
17
1.41
0.47
0.18
Launceston
153
22
12
78
1.95
0.24
0.21
Lucinda
85
11
3
18
4.78
0.50
0.22
Mackay
109
8
3
25
4.28
0.32
0.20
Melbourne
50
17
8
135
0.33
0.16
0.06
Mourilyan
60
4
2
12
4.92
0.33
0.25
Newcastle
92
22
5
86
1.08
0.22
0.08
Port Hedland
89
7
3
52
1.69
0.13
0.08
Port Lincoln
53
9
2
45
1.18
0.16
0.09
Portland
31
5
2
15
2.00
0.40
0.13
Townsville
96
8
2
45
2.04
0.24
0.07
Weipa
51
4
2
18
2.83
0.22
0.11
Furthermore, the number of species status was correlated against each other to observe
the interaction between the number of native, non-native and cryptogenic species across all
surveyed ports. The results revealed high significance and a strong positive relationship
between species status (Figure 2.3.6). The correlation between the number of native and
cryptogenic (R2 = 0.50; P < 0.0001) (Figure 2.3.6 a) species was relatively stronger compared
to correlation of the number of cryptogenic and non-native (R2 = 0.37; P < 0.0001) (Figure
2.3.6 b) species and number of native and non-native species (R2 = 0.22; P < 0.05) (Figure
2.3.6 c).
49
a)
b)
c)
Figure 2.3.6. Regression between relative native and non-native species across 27 surveyed ports; a)
Native vs Cryptogenic species, b) Cryptogenic vs Non-native species and c) Native vs Non-native
species.
50
Multivariate analysis, PERMANOVA was performed to indicate variations in the
species status (native, non-native and cryptogenic) as a function of the surveyed port. The
species status varied significantly among surveyed ports (PERMANOVA; P < 0.0001; Table
2.3.13). The pairwise tests revealed significance between most of the ports with more than 50%
average similarities.
Table 2.3.13. Results of the PERMANOVA test performed on the species status as a function
of the surveyed port. Significant value in bold (P < 0.05).
df
SS
MS
Pseudo-F
P (perm)
Unique perms
26
5.43E+05
20878
33.983
0.0001
9845
Residuals
1449
8.90E+05
614.36
Total
1475
1.43E+06
Surveyed port
SIMPER results revealed more than 50% average similarity at all ports, with native
species to be the highest contributing species status (Table 2.3.19; Figure 2.3.7). At ports such
as Hobart, Geelong, Albany and Melbourne, the difference in percent contribution of native
and non-native species was significantly similar. To note, native red alga, Jania adherens was
the only contributor to the average similarity at Port Geraldton (Table A1). The between-group
dissimilarities were mostly contributed by native species followed by non-native and
cryptogenic species, except for ports Eden, Fremantle, Launceston and Weipa where
Percent contribution (%)
cryptogenic species contributed more than non-native species (Figure 2.3.7).
Native
Non-native
Cryptogenic
100
90
80
70
60
50
40
30
20
10
0
Surveyed port
Figure 2.3.7. SIMPER analysis: Percent contribution of species status - native, non-native, cryptogenic
to the average similarity as a function of surveyed port.
51
b) Port type
The graphical representation of the MDS ordination of the species status- native, non-native
and cryptogenic show no distinct groupings between major and minor ports (2D stress =
0.17; Figure 2.3.8).
Figure 2.3.8. Multidimensional Scaling (MDS) plot. The proximity of surveyed ports to each other
indicates similarity in species status (native, non-native, cryptogenic) as a function of port type.
The PERMANOVA tests were carried out to reveal significant differences in the
number of species status as a function of port type. The results showed significant differences
(P < 0.05; Table 2.3.14) with relatively higher within-group similarity at major ports (85.11%)
than the major ports (82.87%). However, the high average similarity (80.96%) between
contribution percent of species status at major and minor ports indicates not many variations
in terms of species status as a function of port type (Table 2.3.14).
Table 2.3.14. Results of the PERMANOVA and pairwise test performed on the species status
as a function of port type (2 levels). Significance marked in bold (P < 0.05).
df
SS
MS
Port type
1
647.89
647.89
Residual
25
4413.1
176.52
Total
26
5060.9
Pseudo-
P
Unique
F
(perm)
perms
3.6703
0.044
7637
Average similarity (%)
Major
Minor
Minor x Major
85.114
82.867
80.96
52
SIMPER analyses indicate the native species to be the most contributing species
status within-group similarity (Minor port = 63.31%; Major = 55.59%) followed by non-native
(Minor port = 22.94%; Major = 28.14%) and cryptogenic species (Minor port = 13.35%; Major
= 16.27%). The species at major ports were better discriminators between major and minor
ports (19.04%). The native species contributed the highest (45.13%) followed by non-native
(33.13%) and cryptogenic species (21.74%) to the differences between major and minor ports
(Table 2.3.15).
Table 2.3.15. SIMPER analysis: percent contribution of species status as a function of the port
type.
Port type
Minor ports
Major ports
Average similarity =
82.87%
Average similarity =
85.11%
Minor & Major ports
Average dissimilarity = 19.04%
Species
status
C%
Cum.%
C%
Cum.%
Minor ports
Av. Abund.
Major ports
Av. Abund.
C%
Native
63.71
63.71
55.59
55.59
8.32
9.39
45.13
Non-native
22.94
86.65
28.14
83.73
2.99
4.83
33.13
Cryptogenic
13.35
100
16.27
100
1.8
2.96
21.74
C% = Per cent Contribution.
c) Latitudinal groups
The surveyed ports were grouped as a function of their latitude forming 6 latitude groups
ranging from 15°S to 40°S. The MDS plot is displayed in Figure 2.3.9; 2D stress = 0.07,
showing the ordination patterns and overlaps between ports as a function of latitudinal groups.
Ports at 30°S, 35°S and 40°S latitudinal groups showed patterns of grouping and ports at 15°S
and 20°S formed another group. This indicates variations in a number of species status between
low (15°S and 20°S) and high (30°S, 35°S and 40°S) latitudinal groups. Port Geraldton at
latitude 25°S, the only port sampled from that latitude is an outlier.
53
Latitude
Figure 2.3.9. Multidimensional Scaling (MDS) plot. The proximity of surveyed ports to each other
indicates similarity in species status (native, non-native, cryptogenic) as a function of latitudinal groups.
The multivariate PERMANOVA tests revealed statistical significance (P < 0.05) in
the number of species status as a function of the latitudinal groups (Table 2.3.16). The pairwise
PERMANOVA indicated significance (P < 0.05) between only 15°S vs 35°S and 35°S vs 20°S
latitudinal groups (Table 2.3.17).
Table 2.3.16. Results of the PERMANOVA test performed on the status of the species as a
function of latitude (6 levels), Significant value in bold (P < 0.05).
Latitudinal group
Residuals
Total
df
SS
MS
Pseudo-F
P (perm)
Unique perm
5
21
26
1839.6
3221.4
5060.9
367.92
153.4
2.3984
0.0342
9945
Table 2.3.17. Results of pairwise PERMANOVA test performed on status of the species as a
function of latitude, Significant value in bold (P < 0.05).
Latitude °S
t
P (perm)
Unique perms
Average similarity (%)
15 vs 30
15 vs 35
15 vs 40
15 vs 25
1.4962
1.9003
1.0301
2.6089
0.1139
0.0326
0.331
0.1678
462
462
126
6
86.604
82.375
79.657
73.196
54
15 vs 20
30 vs 35
30 vs 40
30 vs 25
30 vs 20
35 vs 40
35 vs 25
35 vs 20
40 vs 25
40 vs 20
25 vs 20
0.57764
1.7398
0.44573
3.0893
1.5833
1.3764
1.7008
2.2963
1.5328
0.73562
3.1916
0.6894
0.0865
0.7151
0.147
0.0975
0.1796
0.1423
0.0193
0.1654
0.4962
0.195
126
462
462
7
210
462
7
210
6
126
5
89.853
84.548
81.929
68.861
86.519
78.154
76.691
80.101
66.575
80.52
69.76
SIMPER results revealed the native species to be the highest contributor to the withingroup average similarity followed by non-native and cryptogenic species as a function of the
latitudinal group (Table 2.3.18).
The between-group dissimilarity between latitudes 15 vs
35°S and 35 vs 20°S was explained by the high contribution of native species followed by nonnative and cryptogenic species (Table 2.3.19).
Table 2.3.18. SIMPER analysis: percent contribution (C%) of species status to average
similarity as a function of the latitudinal groups.
Latitude
15°S
20°S
30°S
35°S
40°S
89.57%
72.97%
87.98%
84.21%
75.91%
Species status
C%
C%
C%
C%
C%
Native
70.51
71.53
58.95
60.1
57.71
Non-native
15.55
14.62
26.85
28.34
25.73
Cryptogenic
13.95
13.85
14.2
11.56
16.56
Average similarity
55
Table 2.3.19. SIMPER analysis: species status contributing (C%)to the average dissimilarity
as a function of the latitude that indicated significant differences.
Latitude °S
Avg. dissimilarity (%)
15 & 35°S
35 & 20°S
17.62%
19.90%
Avg. Abundance
15
35
C%
35
20
C%
Native
9.1
6.72
58.43
6.72
9.8
62.56
Non-native
2.56
3.32
21.75
3.32
2.33
21.34
Cryptogenic
1.59
1.84
19.82
1.84
1.8
16.1
To summarise, the number of species status, i.e., native, non-native and cryptogenic
species significantly varied across surveyed ports. The native species were the most
contributing species status across all surveyed ports. However, a high percentage of non-native
species was observed at ports; Hobart, Geelong, Albany and Melbourne and cryptogenic
species at ports; Eden, Fremantle, Launceston and Weipa. Native red alga, Jania adherens was
the only contributor at Port Geraldton. The species status as a function of port type
significantly varied between major and minor ports with a relatively high percentage of native
species at minor ports than major ports. But, non-native and cryptogenic species had relatively
higher percentages at major ports than at minor ports. Therefore, accepting the hypothesis that
occurrences of non-native and cryptogenic species will be relatively greater at major
commercial ports than at minor ports because of increased international marine traffic at the
major ports. Lastly, species status significantly varied as a function of latitudinal groups. The
native species had a relatively high percentage across all latitudinal groups. However, when
comparing the non-native and cryptogenic across the latitudinal groups; the results are in
coherence with my hypothesis that frequencies of non-native and cryptogenic species increase
with the increase in latitude (15 - 40°S).
56
2.4. Results (B)
New Zealand Port Biological Baseline survey (NZPS)
2.4.1. Presence/absence data
A total of 585 species were identified across the 15 surveyed ports. The species were grouped
in 11 phyla groups: Chlorophyta, Ochrophyta, Rhodophyta, Annelida, Arthropoda, Bryozoa,
Chordata, Cnidaria, Echinodermata, Mollusca and Porifera (see Appendix - Table A3) for the
species list). The most represented phyla in this survey were Arthropoda (128 species) followed
by Annelida (84 species) and Mollusca (73 species). The species were further grouped as per
their species status; native, non-native and cryptogenic. Of the 585 species, 461 were native
species (78.80%), 65 non-native species (11.11%) and 59 cryptogenic species (10.09%) (Table
2.4.1).
Table 2.4.1. The total number of species noted in New Zealand port surveys grouped as per
their Phyla and species status (native, non-native and cryptogenic).
Phyla
Total
Native
Non-native
Cryptogenic
Chlorophyta
7
7
0
0
Ochrophyta
16
14
2
0
Rhodophyta
65
56
4
5
Annelida
84
74
8
2
Arthropoda
128
100
13
15
Bryozoa
51
34
14
3
Chordata
56
43
3
10
Cnidaria
35
19
9
7
Echinodermata
11
9
1
1
Mollusca
73
68
3
2
Porifera
59
37
8
14
Total
585
461
65
59
57
2.4.2. Variations in total presence of species as a function of replicates (sampling effort)
at each surveyed port
The total number of species at each port was divided with the number of replicates to obtain
the relative number of species (Table 2.4.2). The relative number of species excludes the bias
caused due to the differences in replicates (sampling effort). The relative total number of
species was relatively highest at Picton 2005 (10.76%) followed by Timaru 2005 (8.29%), and
Total relative number of species
lowest at Gulf Harbour Marina (3.73%) (Figure 2.4.1).
12.00
10.00
8.00
6.00
4.00
2.00
0.00
Surveyed port
Figure 2.4.1. The total relative number of species across 15 surveyed ports.
Table 2.4.2. The total relative number of species as a function of replicates (sampling effort)
at each surveyed port.
Surveyed ports
Auckland
Bluff
Dunedin Harbour
Gisborne
Gulf Harbour Marina
Lyttelton
Napier
Nelson
Opua Marina
Picton
Taranaki
Tauranga
Timaru
Wellington
Whangarei
Total number
Replicates
Relative Total
Total percent (%)
96
146
110
79
73
121
83
194
51
127
102
151
109
155
116
71
53
47
31
58
50
48
94
30
35
44
68
39
73
47
1.35
2.75
2.34
2.55
1.26
2.42
1.73
2.06
1.70
3.63
2.32
2.22
2.79
2.12
2.47
4.01
8.17
6.94
7.56
3.73
7.18
5.13
6.12
5.04
10.76
6.87
6.59
8.29
6.30
7.32
58
2.4.3. Occurrences of species
The presence of each species was calculated across all surveyed ports to observe the high
occurring species in the New Zealand port survey (NZPS). The cryptogenic tunicates,
Asterocarpa cerea and Corella eumyota occurred at all surveyed ports. The top 10 species
occurring in most of the surveyed ports are listed in Table 2.4.3.
Table 2.4.3. Total occurrences of top 10 species across 15 surveyed ports.
Species
Phyla
Species status
Total port
occurrences
Asterocarpa cerea
Chordata
Cryptogenic
15
Corella eumyota
Chordata
Cryptogenic
15
Notomithrax minor
Arthropoda
Native
14
Austrominius modestus
Arthropoda
Native
13
Bugula flabellata
Bryozoa
Non-native
13
Cnemidocarpa bicornuta
Chordata
Native
13
Cnemidocarpa nisiotus
Chordata
Native
13
Molgula mortenseni
Chordata
Native
13
Lepidonotus polychromus
Annelida
Native
12
Modiolarca impacta
Mollusca
Native
12
59
2.4.4. Variations in the community composition as a function of the surveyed port, port
type and latitudinal groups
a) Surveyed ports
The multivariate PERMANOVA was performed to indicate patterns in species composition as
a function of the surveyed port. The results revealed high significance (P < 0.0001; Table 2.4.4)
as a function of the surveyed port. The pairwise PERMANOVA also revealed high significance
between the ports (P < 0.0001). The ports; Picton (26.45%) had the most similar species
followed by Opua Marina (26.11%) and least within-group similarity at Port Nelson (8.88%)
(Table 2.4.5). The between-group dissimilarity in species composition between surveyed ports
ranged from 80.05% (Picton vs Wellington) to 98.37% (Dunedin vs Gisborne).
Table 2.4.4. Results of the PERMANOVA test performed on the presence/absence of species
as a function of the surveyed port. Significance marked in bold (P < 0.05).
df
SS
MS
Pseudo-F
P (perm)
Unique
perms
Surveyed port
Residual
14
773
6.49E+05
2.78E+06
Total
787
3.43E+06
46329
3602
12.862
0.0001
9606
SIMPER results revealed the top 5 species to contribute to more than 50% average
within-group similarity as a function of surveyed ports (Table 2.4.5). Native barnacle,
Austrominius modestus was observed to be the highest contributing species at ports, Auckland
(21.57%), Bluff (22.59%), Dunedin (19.54%), Nelson (27.54%), Opua Marina (58.6%) and
Tauranga (20.48%). The non-native bryozoan, Bugula neritina contributed relatively highest
at ports Gisborne (25.11%), Napier (19.04%) and Whangarei (17.70%). The non-native
bryozoan, Watersipora subtorquata contributed highest at ports, Timaru (24.79%) and
Lyttelton (22.04%). The average between-group dissimilarities ranged from 80.05% (Picton vs
Wellington) to 98.37% (Dunedin vs Gisborne). SIMPER results revealed that the species that
contributed to the within-group similarity also contributed to the between-group similarity, but
the average abundance contribution from each port varied, leading to the dissimilarities in
community composition between surveyed ports.
60
Table 2.4.5. SIMPER analysis: average similarity in the presence/absence of species as a function of the surveyed port.
Auckland
Bluff
Dunedin Habour
Average similarity: 17.69
Average similarity: 11.84
Average similarity: 14.95
Species
Avg.
Abund
0.42
C%
Cum.%
21.57
21.57
Austrominius modestus
Caberea rostrata
0.52
15.58
37.15
Crassostrea gigas
0.46
13.72
50.87
Ostrea chilensis
0.37
6.5
Pyura picta
0.34
5.39
Austrominius modestus
Species
Avg.
Abund
0.28
C%
Cum.%
22.59
22.59
Corella eumyota
0.47
15.21
Phycodrys quercifolia
0.36
7.72
57.38
Galeolaria hystrix
0.32
6.22
62.77
Nereis falcaria
0.3
5.3
Cum.%
19.54
19.54
37.8
Haplocheira barbimana
0.53
16.21
35.75
45.52
Nereis falcaria
0.4
9.06
44.81
51.74
Asterocarpa cerea
0.34
5.92
50.74
57.04
Aplidium adamsi
0.3
4.55
55.28
Gulf Harbour Marina
Lyttelton
Average similarity: 13.59
Average similarity: 16.16
Average similarity: 23.89
Avg.
Abund
0.55
C%
Cum.%
25.11
25.11
Styela plicata
Scrupocellaria ornithorhyncus
0.45
14.41
39.51
Leptograpsus variegatus
0.26
5.85
Cnemidocarpa nisiotus
0.29
5.43
Paguristes setosus
0.29
5.34
Bugula neritina
Species
Avg.
Abund
0.4
C%
Cum.%
12.48
12.48
Watersipora subtorquata
Balanus trigonus
0.36
11.31
23.79
45.36
Schizoporella errata
0.29
9.07
50.8
Dictyota dichotoma
0.36
8.1
56.14
Asterocarpa cerea
0.34
7.78
Napier
Average similarity: 13.15
Species
Avg.
Abund
0.76
C%
Cum.%
22.04
22.04
Branchiomma curta
0.6
10.62
32.66
32.86
Austrominius modestus
0.44
9.03
41.69
40.97
Asterocarpa cerea
0.52
7.83
49.52
48.75
Leucothoe trailli
0.46
5.65
55.18
Nelson
Average similarity: 8.92
Avg.
Abund
0.44
C%
Cum.%
19.04
19.04
Austrominius modestus
Bugula flabellata
0.4
12.89
31.93
Proscoloplos bondi
0.31
7.39
39.32
Austrominius modestus
0.25
7.23
Pterocirrus brevicornis
0.29
6.73
Bugula neritina
C%
Austrominius modestus
Avg.
Abund
0.38
Gisborne
Species
Species
Species
Species
Opua Marina
Average similarity: 26.11
Avg.
Abund
0.38
C%
Cum.%
Species
Avg.
Abund
0.53
C%
Cum.%
27.54
27.54
Austrominius modestus
58.6
58.6
Asterocarpa cerea
0.35
10.8
38.35
Asterocarpa cerea
0.43
6.41
65
0.3
9.07
46.55
Petrolisthes
novaezealandiae
Haplocheira barbimana
47.42
Balanus trigonus
0.4
6.2
71.2
0.24
5.93
53.35
Neanthes kerguelensis
0.4
6.18
77.38
53.27
Xenostrobus pulex
0.15
3.69
57.04
Halicarcinus varius
0.33
3.68
81.06
61
Picton
Average similarity: 26.45
Species
Taranaki
Average similarity: 19.71
Avg.
Abund
0.86
C%
Cum.%
15.27
15.27
Chaemosipho columna
Ostrea chilensis
0.71
10.38
25.66
Xenostrobus pulex
Petrolisthes elongatus
0.66
8.16
33.82
Perna canaliculus
0.57
6.01
39.83
Nicolea armilla
0.54
5.14
44.97
Aulacomya atra maoriana
Species
Avg.
Abund
0.66
C%
Cum.%
31.49
31.49
Molgula mortenseni
0.57
23.27
54.76
Austrominius modestus
0.3
7.34
Watersipora subtorquata
0.36
7.01
Lumbrineris
sphaerocephala
0.25
2.15
71.26
Timaru
Average similarity: 22.15
Species
Species
Avg.
Abund
0.59
C%
Cum.%
10.5
10.5
Austrominius modestus
0.29
9.99
20.48
62.1
Balanus trigonus
0.51
8.18
28.66
69.11
Lepidonotus polychromus
0.46
6.49
35.15
Maoricrypta costata
0.47
6.45
41.6
Wellington
Average similarity: 21.72
Avg.
Abund
0.74
C%
Cum.%
24.79
24.79
Petrolisthes elongatus
Branchiomma curta
0.59
11.54
36.32
Austrominius modestus
0.44
9.94
Cryptosula pallasiana
0.41
Pseudosphaeroma
campbellense
0.33
Watersipora subtorquata
Tauranga
Average similarity: 18.95
Species
Whangarei
Average similarity: 12.91
Avg.
Abund
0.63
C%
Cum.%
Species
Avg.
Abund
0.45
C%
Cum.%
9.18
9.18
Bugula neritina
17.7
17.7
Mytilus galloprovincialis
0.6
8.34
17.53
Austrominius modestus
0.28
12.31
30.01
46.27
Aulacomya atra maoriana
0.6
7.56
25.09
Ostrea aupouria
0.45
12.03
42.03
7.58
53.85
Ostrea chilensis
0.53
6.88
60.73
Petrolisthes
novaezealandiae
0.49
5.75
30.84
Balanus trigonus
0.43
11.36
53.39
5.14
35.99
Bugula flabellata
0.3
4.83
58.22
62
b) Port type (major and minor ports)
The graphical representation of the MDS ordination of the presence/absence data showed no
groupings of similar species composition as a function of port type, 5 major commercial
shipping ports and 10 minor shipping ports (2D stress = 0.13; Figure 2.4.2). The
PERMANOVA tests results were in coherence with the MDS ordination and indicated no
significant (P = 0.96) variations in community composition as a function of port type (Table
2.4.6).
Figure 2.4.2 Multidimensional Scaling (MDS) plot. The proximity of surveyed ports to each
other indicates similarity in community composition as a function of port type.
Table 2.4.6. Results of the PERMANOVA analysis and pairwise test performed on the
presence of species as a function of port type (2 levels). Significance marked in bold (P < 0.05).
Source
df
SS
MS
Pseudo-F
P (perm)
Port type
1
1500.1
1500.1
0.63315
0.967
Residual
13
30799
2369.2
Total
14
32300
Unique
perms
2889
Average similarity (%)
Major
Minor
Minor x Major
35.53
29.84
34.47
63
c) Latitudinal groups
The surveyed ports at each latitude were grouped, forming 3 latitudinal groups; 35°S, 40°S and
45°S. The MDS plot showed the ordination patterns as a function of the latitudinal groups (35,
40, 45°S) (Figure 2.4.3; 2D stress = 0.13).
Latitude°S
Figure 2.4.3. Multidimensional Scaling (MDS) plot. The proximity of latitudes to each
other indicates similarity in the community composition.
The PERMANOVA tests, as a function of latitude, revealed high significance (P <
0.0001) (Table 2.4.7). The pairwise PERMANOVA test revealed significant differences
between 35 vs 45°S (P = 0.02) and 35 vs 40°S (P = 0.002) but not between 40 vs 45°S (P =
0.14) (Table 2.4.8).
Table 2.4.7. Results of the PERMANOVA test performed as a function of latitude (3 levels).
Significance (P < 0.05) marked in bold.
df
SS
MS
Pseudo-F
P (perm)
Unique perms
Latitude
2
8279.5
4139.7
2.0681
0.0001
9499
Residual
12
24020
2001.7
Total
14
32300
64
Table 2.4.8. Results of the PERMANOVA and pairwise test as a function of port type (2 levels).
Significance marked in bold (P < 0.05).
Latitude
t
P (perm)
Unique perms
35 vs 45 °S
1.48
0.022
45
35 vs 40 °S
1.45
0.003
1282
45 vs 40 °S
1.32
0.139
21
SIMPER analysis for latitudinal groups indicated latitudes 35 vs 45°S to have the
highest average dissimilarity (76.09%). The species that indicated the dissimilarities are;
Bugula flabellata (NN), Forsterygion lapillum (N), Nereiphylla castanea (N), Halisarca
dujardini (NN) and Leuconopsis obsoleta (N). The species at latitude 45°S were better
discriminators. The species indicating dissimilarities between latitudes 35 vs 40°S (71.28%)
are; Petrolisthes elongatus (N), Asterocarpa coerulea (N), Watersipora subtorquata (NN),
Undaria pinnatifida (NN) and Bougainvillia muscus (C) (Table 2.4.9).
Table 2.4.9. SIMPER analysis: average dissimilarity of community composition as a function
of latitude. Species status – native (N), non-native (NN) and cryptogenic (C).
35 & 45 °S
Average dissimilarity = 76.09%
35 & 40 °S
Average dissimilarity = 71.28%
Species
35
45
C%
Species
35
40
C%
Bugula flabellata (NN)
0.47
0
1.14
Petrolisthes elongatus (N)
0.07
0.5
0.99
Forsterygion lapillum (N)
0.06
0.5
1.06
Asterocarpa coerulea (N)
0.03
0.43
0.94
Nereiphylla castanea (N)
0.2
0.59
0.98
Watersipora subtorquata (NN)
0.24
0.52
0.94
Halisarca dujardini (NN)
0.2
0.55
0.97
Undaria pinnatifida (NN)
0
0.35
0.81
Leuconopsis obsoleta (N)
0
0.41
0.97
Bougainvillia muscus (C)
0.1
0.31
0.80
C% = Per cent Contribution.
In summary, the presence of species as a function of surveyed ports showed
significance; however, the community composition as a function of port type did not show the
significance. The latitudinal groups indicated significant differences for groups; 35 vs 40°S and
35 vs 45°S. The species at higher latitudes (40, 45°S) being better discriminators explaining
the differences. A strong association in the community composition between ports; i)
Auckland, Gulf Harbour and Opua Marina (native barnacle Austrominius modestus,
cryptogenic tunicate Asterocarpa cerea, cryptogenic barnacle Balanus trigonus), ii) Tauranga,
65
Whangarei, Taranaki and Napier (native barnacle Austrominius modestus, cryptogenic
barnacle Balanus trigonus), iii) Wellington, Picton and Nelson (native mollusc Aulacomya atra
maoriana, native false crab Petrolisthes elongatus and native molluscs Ostrea chilensis) iv)
Lyttelton and Timaru (non-native bryozoan Watersipora subtorquata, native polychaete
Branchiomma curta and native barnacle Austrominius modestus, v) Bluff and Dunedin (native
barnacle Austrominius modestus and cryptogenic tunicate Corella eumyota), was observed.
The results indicated similar high contributing species at proximity ports. The species showing
variations as a function of surveyed ports and latitudinal groups are stated in Table 2.4.10.
Table 2.4.10. The list of species that indicated variations as a function of surveyed
ports and latitudinal groups.
Species
Phyla
Species status
Asterocarpa cerea
Chordata
Cryptogenic
Aulacomya atra maoriana
Mollusca
Native
Austrominius modestus
Arthropoda
Native
Balanus trigonus
Arthropoda
Native
Bougainvillia muscus
Cnidaria
Cryptogenic
Bugula flabellata
Bryozoa
Non-native
Bugula neritina
Bryozoa
Non-native
Chaemosipho columna
Arthropoda
Native
Corella eumyota
Chordata
Cryptogenic
Halisarca dujardini
Porifera
Non-native
Molgula mortenseni
Chordata
Native
Mytilus galloprovincialis
Mollusca
Cryptogenic
Nereiphylla castanea
Annelida
Native
Ostrea chilensis
Mollusca
Native
Petrolisthes elongatus
Arthropoda
Native
Petrolisthes novaezealandiae
Arthropoda
Native
Styela plicata
Chordata
Cryptogenic
Watersipora subtorquata
Bryozoa
Non-native
Xenostrobus pulex
Mollusca
Native
66
2.4.5. Variations in the species status - native, non-native and cryptogenic species as a
function of surveyed ports, port type and latitudinal groups
a) Surveyed ports
The total number of species at each port was divided with the number of replicates to obtain
the relative number of species (Table 2.4.11). The native species were relatively observed in
higher percentages, followed by non-native and cryptogenic species (Figure 2.4.4). The
relatively high levels of native species were highest at Port Picton (15.20) and lowest at
Melbourne (0.33%). In contrast, the non-native species were highest at Lyttelton (2.62%),
Timaru (2.51%) and lowest at Port Picton (0.51). The cryptogenic species were relatively in
Relative number of species status
high levels at Wellington (2.11) and lowest at Port Taranaki (0.45) (Table 2.4.11).
Relative native
Relative non-native
Relative cryptogenic
16.00
12.00
8.00
4.00
0.00
Surveyed port
Figure 2.4.4. The total relative number of species status - native (blue), non-native (orange)
and cryptogenic (grey) species across 15 surveyed ports.
Table 2.4.11. The total number of species as a function of status - native, non-native and
cryptogenic species - across 15 surveyed ports.
Total
Total
Total
Relative
Relative
Relative
Native
Non-native
Cryptogenic
native
non-native
cryptogenic
Auckland
423
53
81
71
5.96
0.75
1.14
Bluff
426
46
57
53
8.04
0.87
1.08
Dunedin Harbour
325
48
46
47
6.91
1.02
0.98
Gisborne
165
33
34
31
5.32
1.06
1.10
Gulf Harbour Marina
219
82
108
58
3.78
1.41
1.86
Lyttelton
412
131
73
50
8.24
2.62
1.46
Napier
247
53
40
48
5.15
1.10
0.83
Nelson
625
58
123
94
6.65
0.62
1.31
Surveyed port
Replicates
67
Opua Marina
119
32
46
30
3.97
1.07
1.53
Picton
532
18
45
35
15.20
0.51
1.29
Taranaki
331
50
20
44
7.52
1.14
0.45
Tauranga
725
9
119
68
10.66
0.13
1.75
Timaru
318
98
37
39
8.15
2.51
0.95
Wellington
987
138
154
73
13.52
1.89
2.11
Whangarei
278
62
85
47
5.91
1.32
1.81
Furthermore, the species status, i.e., native, non-native and cryptogenic, were
regressed to observe the relationship between species status across all surveyed ports.
However, no significant relationship was observed between native vs non-native (P = 0.08),
cryptogenic vs non-native (P = 0.47), and cryptogenic vs non-native (P = 0.79) (Table 2.4.12).
Table 2.4.12. Results for regression for species status - native, non-native and cryptogenic
species (significance = P < 0.05, marked in bold).
Species status
R2
R
P
y
Native vs Non-native
0.002
-0.046
0.0868
7.9319 + 0.2216*x
Cryptogenic vs Non-native
0.042
0.204
0.466
5.7133 + 1.4906*x
Cryptogenic vs Non-native
0.006
0.076
0.788
1.2501 + 0.0497*x
Multivariate analysis, PERMANOVA was performed to indicate variations in the
number of species status as a function of the surveyed port. The number of species (native,
non-native and cryptogenic) was found to vary significantly among surveyed ports
(PERMANOVA; P < 0.0001; Table 2.4.13).
Table 2.4.13. Results of the PERMANOVA test performed on the species status as a function
of surveyed port (15 levels). Significance marked in bold (P < 0.05).
Source
df
SS
MS
Pseudo-F
P (perm)
Unique perms
Port
14
89881
6420.1
5.6423
0.0001
9839
Residual
773
8.80E+05
1137.8
Total
787
9.69E+05
The pairwise tests revealed significance (P < 0.05) between most of the ports with more
than 50% average similarities. The native species contributed the most to the within-port
similarity and between-port dissimilarity followed by non-native species (Table 2.4.14).
68
However, a relatively high percent of cryptogenic species was observed at ports; Nelson,
Wellington, Tauranga and Picton.
Table 2.4.14. SIMPER analysis: percent contribution of species status - native, non-native and
cryptogenic species, as a function of the surveyed port.
Surveyed port
Avg. similarity
Native
Non-native
Cryptogenic
(%)
(%)
(%)
(%)
56.95
61.52
65.63
62.96
51.35
76.17
53.49
56.71
55.85
72.3
70.74
65.61
72.09
58.92
55.43
72.56
76.97
71.3
61.76
50.34
58.18
71.45
78.98
77.91
75.9
87.1
80.36
65.45
57.29
57.5
16.3
12.19
14.98
21.54
26.98
30.59
17.2
4.76
12.98
8.84
12.14
3.11
27.87
19.22
22.88
11.13
10.83
13.72
16.7
22.68
11.23
11.35
16.26
9.11
15.27
0.76
16.53
6.69
23.49
19.62
Auckland
Bluff
Dunedin Harbour
Gisborne
Gulf Harbour Marina
Lyttelton
Napier
Nelson
Opua Marina
Picton
Taranaki
Tauranga
Timaru
Wellington
Whangarei
b) Port type
The PERMANOVA results for species status revealed no statistical significance (P = 0.09) as
a function of port type (Table 2.4.15). Therefore, rejecting my hypothesis that occurrences of
non-native and cryptogenic species will be relatively greater at major commercial ports than at
minor ports.
Table 2.4.15. The PERMANOVA analysis used to determine differences in the species status
as a function of port type (2 levels), Significant value in bold (P < 0.05).
Source
df
SS
MS
Pseudo-F
P (perm)
Unique perms
Port type
1
76.872
76.872
2.514
0.091
2881
Residual
13
397.51
30.578
Total
14
474.38
69
c) Latitudinal groups
PERMANOVA results revealed no significance (P = 0.37) between the number of the status
of the species as a function of latitude (Table 2.4.16). Therefore, rejecting my hypothesis that
frequencies of non-native and cryptogenic species increase with increase in latitude.
Table 2.4.16. The PERMANOVA analysis used to determine differences in the species status
as a function of latitude (3 levels), Significant value in bold (P < 0.05).
Source
df
SS
MS
Pseudo-F
P (perm)
Unique perm
Latitude
2
74.818
37.409
1.1235
0.3681
9593
Residuals
12
399.56
33.297
Total
14
474.38
To summarise, the species status – native, non-native and cryptogenic species showed
significance as a function of surveyed ports. The relatively high percentages of native species
compared to non-native and cryptogenic species were observed at all ports. However, no
significance was observed as a function of the port type and latitudinal groups. For NZPS, I
reject both my hypotheses based on the factors; port type and latitudinal groups.
2.5. Discussion
2.5.1. Background
Marine organisms have spread from their native range to another through human transport such
as shipping (ballast water exchange, hull fouling, dry ballast, etc.), aquaculture (intentional or
unintentional), trade escape and man-made canals where species move from one place to
another (Ruiz et al. 2000; Hewitt & Hayes 2002). The rate of bioinvasions has immensely
increased during the last decade and most likely be increasing due to accelerated global trade,
transport and tourism. Marine traffic plays a key role as transport hubs facilitating the spread
of species through hitchhiking on cargo ships (Hulme 2009; Seebens et al. 2013). Although not
all species survive the range expansion, some die during transit whilst some cannot withstand
the new environmental conditions. However, the species that survive and establish can cause
immense impacts on ecological and socio-economic ecosystems. Therefore, understanding the
pathway and predicting potential invasive species’ entry point is likely to be the first step
towards strategising any rapid management and eradication plans. This chapter, for this reason,
analysed two large national-scale baseline port surveys (the Australian Port Survey dataset
[APS] and the New Zealand Port Biological Baseline Survey dataset [NZPS]) to determine the
70
community structure and the species status – native, non-native and cryptogenic – at the
surveyed ports and tested factors of port type (major vs minor ports) and latitudinal groups explain the occurrence of non-native species. The results of this study highlighted the major
commercial shipping ports being the hotspots for non-native species and latitudinal separation
with regard to occurrences of non-native species.
2.5.2. Australian Port Surveys (APS)
The extensive sampling of Australian port surveys indicated 88% native species, 9%
non-native and 3% cryptogenic species. The species composition amongst surveyed ports
significantly differed, with relatively stronger species composition (within-group similarity) at
Port Geelong (40.69%) followed by Hobart (40.16%) whereas weak species composition
among Port Esperance (9.23%). Port Geraldton sat as an outlier as a native red alga, Jania
adherens was the only species contributing to its within-group similarity. This led to high
dissimilarities between Port Geraldton and other surveyed ports. Port Geelong and Port
Melbourne however, had the average lead dissimilarity indicating a similar set of species
contributing at each port.
It is interesting to observe that ports in the southern region of Australia such as Geelong,
Melbourne, Hobart, Portland, Fremantle, Newcastle, Eden, Adelaide and two ports in the north
region of Australia, Port Hay point and Mackay had relatively low between ports dissimilarity.
These ports are located within close proximity to each other and experience immense domestic
traffic which may likely be the reason for the sharing of species. These results for the factor
latitudinal groups supported these assumptions. The ports in the north of Australia at 15°S and
20°S formed one group whereas ports at 30°S, 35°S and 40°S formed another (MDS plot). A
strong separation between northern (low latitudes) and southern (high latitudes) of Australia
was observed with regards to species composition. Similarly, the species status- native, nonnative and cryptogenic significantly varied among latitude groups with a strong association
between species status at high latitudes and low latitudes. Although native species were the
main species status explaining similarity and dissimilarity among and between latitude groups,
the non-native species were relatively abundant at higher latitudes. These results were
explained by the top 5 species explaining similarity for low latitudes are cryptogenic barnacle
Balanus amphitrite, native barnacle Striatobalanus amaryllis, native polychaete Thelepus
robustus, cryptogenic polychaete Lysidice collaris and native oyster Dendostrea folium. In
contrast, species such as native clam Hiatella australis, non-native bryozoan Watersipora
71
subtorquata, native sea spider Halicarcinus ovatus, non-native bryozoan Bugula neritina and
cryptogenic barnacle Balanus trigonus at higher latitudes.
The species, however, did not show significance, but pairwise tests showed a difference
in species composition between major and minor ports. The species at major ports had stronger
within-group similarity than at minor ports (SIMPER). Similarly, the species status
significantly differed between port types with native species as the main species contributing
to within-group and between-group similarity. For non-native species, the major ports had high
levels of non-native species compared to minor ports. The species (top 5) explaining the
similarities within minor ports are native clam Hiatella australis, non-native bryozoan
Watersipora subtorquata, cryptogenic barnacle Balanus amphitrite, non-native bryozoan
Bugula neritina and native tunicate Pyura stolonifera whereas species such as cryptogenic
barnacle Balanus trigonus, non-native tunicate Ciona intestinalis, native sea spider
Halicarcinus ovatus, native brittle star Amphipholis squamata and non-native mussel
Musculista senhousia at major ports.
Given these results, the major ports analysed - Adelaide, Fremantle, Hobart and
Melbourne are in the south of Australia explaining a high number of non-native species at high
latitudes. These results highlight several on-going challenges such as major commercial ports
being hotspots for marine invasions and the role of shipping as the main pathway for
introductions and thereby spread of species (especially non-native species) through regional
transport connecting ports for domestic trade or recreational activities.
2.5.3. New Zealand Port Biological Baseline survey (NZPS)
The analysis of the NZPS dataset identified 78.80% native species, 11.11% non-native
species and 10.09% cryptogenic species. The species composition significantly differed
amongst 15 surveyed ports. The MDS plots, in this study, indicated close species interaction
between ports forming groups such as; i) Nelson, Wellington and Picton, ii) Bluff and Dunedin
Harbour, iii) Lyttelton and Timaru, iv) Whangarei, Tauranga and Taranaki, v) Auckland, Gulf
Harbour Marina and Opua Marina. The major ports Auckland, Lyttelton, Napier, Tauranga and
Wellington grouped with minor ports. The multivariate analyses supported the MDS patterns,
the species composition and species status did not vary as a function of major and minor port
types. The port groups observed are located at proximity distance. These results indicate the
domestic transfer of species presumably due to local or regional marine traffic with hull fouling
72
being the important pathway for the spread of species at regional scales (Coutts & Taylor 2004;
Floerl et al. 2004; Floerl & Inglis 2005).
Considering the factor; latitudinal groups (35°S, 40°S and 45°S) of New Zealand, the
species composition significantly differed amongst latitudinal groups with high similarity in
species composition between higher latitudes (33.39%) (40°S vs 45°S) than between low
latitudes (35°S vs 40°S = 31.51%; 35°S vs 45°S = 25.58%). Considering the species status
analyses, species status did not show a significant difference between latitudinal groups. As
seen above, with the species contributing to the similarity among each latitudinal group, the
native species were the main species representing latitude groups followed by cryptogenic and
lastly the non-native species. These results were explained by the top 5 species explaining
similarity with 35°S latitudes were cryptogenic tunicate Asterocarpa cerea, non-native
bryozoan Bugula neritina, cryptogenic tunicate Corella eumyota, native sea spider
Halicarcinus cookii and native sea spider Notomithrax minor. At 40°S latitude, species
contributing to the within-group similarity are cryptogenic tunicate Asterocarpa cerea, native
barnacle Austrominius modestus, non-native bryozoan Bugula flabellata, native tunicate
Cnemidocarpa bicornuta and native tunicate Cnemidocarpa nisiotus. In contrast, species such
as native red algae Adamsiella chauvinii, native amphipod Amaryllis macrophthalma, native
tunicate Aplidium adamsi, cryptogenic tunicate Aplidium phortax and cryptogenic tunicate
Asterocarpa cerea explained similarity at 45°S latitude. These species consist of biofouling
species which commonly encrust on the hull of the ships, engine or port equipment that may
result in the domestic transfer of species. These results highlight the complexity of humanmediated transfer of species from one port to another or even port to marinas (Floerl et al.
2004).
2.5.4. Defining pathways
Marine traffic has been highlighted as the potential vector to transfer non-native marine
species across the world (Hewitt et al. 2009; Seebens et al. 2016; Ziako et al. 2016; O’Brien et
al. 2017). Marine traffic arriving at major international shipping ports are the main access
points (Campbell & Hewitt 2007). The marine vessels dock at ports and harbours for extended
periods and are a substantial time for fouling to occur (Hewitt et al. 2009). Regarding invasion
success at major ports, the commercial ports receiving large volumes of international trade as
well as propagules of non-native species. The release of a large number of propagules will
increase the chance of non-native species to survive and reproduction at new environments, i.e.
ports. Therefore, the heavily invaded areas may serve as hubs for the transfer of species from
73
major ports to nearby minor ports (Lockwood et al. 2009; Firth et al. 2016; Olenin et al. 2016;
Johnston et al. 2017). This is in consistence with the results of this study indicating similar
species between major ports and nearby minor ports where the marine vessels frequently travel
to and from. With an increase in human activities over the past 50 years, maritime traffic has
increased risks of transfer of species from one region to another.
Recent changes to the climatic conditions with increasing global warming, the species
from warm regions (low latitudes) spread to colder regions (high latitudes) consequently
changing their geographical ranges thereby affecting the ecological ecosystems (Walther et al.
2002, 2009). There a climate-mediated invasion process follows a classic invasion process
(Walther et al. 2009). Human mediated vectors and climate change are the two most prevalent
problems impacting marine biodiversity (Rahel & Olden 2008). The altered habitats put stress
on the native species to adapt new conditions; in contrast, the non-native species are excellent
in adapting, establishing and spreading. Combination of the number of major ports at high
latitudes increases invasion pressure.
The ports and marinas receiving a high frequency of marine traffic are the focal point
of entry for marine bioinvasions (Firth et al. 2016; Olenin et al. 2016; Johnston et al. 2017).
Commercial shipping vessels have also been associated with non-native species in other studies
(Hopkins and Forrest 2010; Lo et al. 2012). As such, major shipping ports supporting a high
number of non-native species was not surprising, especially in the south of Australia where the
shipping traffic is relatively high (Commonwealth of Australia 2015). Domestic marine traffic
poses a threat to the intra-regional transfer of non-native species from port to port within a
region (Forrest et al. 2009; Clarke Murray 2011; Hänfling et al. 2011). Domestic trade vessels,
fishing vessels, pleasure boats and tour boats move extensively among harbours connecting the
major port to other harbours or marinas. The southern regions of Australia are home to some
of Australia’s busiest shipping routes including Bass Strait, east-west and west-east
international shipping routes (Commonwealth of Australia 2015). The marine traffic comprises
of international and coastal cargo trading ships, passenger shipping, and ferry services across
the Bass Strait. These regions are also productive for fisheries, and commercial fishing is
concentrated in inshore coastal waters, and most of the recreational fishing occurs inland, near
the coasts and bays. Major ports and adjacent marinas in these areas have numerous marinebased industries and are connected to various minor ports through commercial and recreational
fishing, yachts or pleasure crafts.
74
The main shipping routes for international and domestic marine traffic in New Zealand
and the frequency of marine vessels increase the risk of inter-port transfer of non-native
species. However, the frequency of vessels is not the only factor facilitating invasions; the
marine vessels carrying high volumes of ballast water - non-native discharge species. For
instance, Auckland is the busiest port in New Zealand; however, Port Taranaki receives high
volumes of bulk carriers and tankers carrying petroleum. Coutts & Taylor (2004) examined 30
merchant ships arriving New Zealand and concluded that hulls of the ships are more susceptible
to fouling and spread of species on vessels. Biosecurity New Zealand and Cawthron Institute
have developed a database to record international cargo vessels and ballast water operations
(Dodgshun et al. 2007). However, domestic vessels operating exclusively between NZ
domestic ports are not required to report the ballast water discharge, the introduction of nonnative species via ballast water is most likely.
Domestic vessels and local crafts such as fishing vessels and pleasure crafts put a
significant pressure of transfer of non-native species through hull fouling (Forrest et al. 2009;
Clarke & Johnston 2011; Hänfling et al. 2011). A report on eradication measures of Undaria
pinnatifida, spread from Wellington to South Island revealed fishing vessels to be the primary
vector for the transfer of the seaweed (Department of Conservation 2005). Facilities such as
marinas, harbours and berths are exposed to frequent movements of recreational and fishing
vessels along the coastline. Additionally, these pleasure boats dock in marinas and harbours
for a long period – become heavily exposed to hull fouling and being potential vectors for nonnative species. For example, about 47% yachts and 30% of launches in ports between Timaru
and Bluff, New Zealand were heavily fouled by non-native seaweed, Undaria pinnatifida
(Department of Conservation 2005). New Zealand in recent years has implemented regulations
for recreational vessels posing risks on intra-coastal species transfer are required to follow Craft
Risk Management Standard (CRMS) and IMO guidelines (i.e. Biofouling Management Plan
and BioFouling Record Book), provide evidence of records of non-permanent ballast water,
vessel hull cleaning and use of appropriate antifouling treatment (MPI 2014; IMO 2015).
2.5.5. Management implications
As outlined in this study, human-mediated pathways at a regional scale also indicate as highrisk pathways linking major and minor ports (Coutts & Forrest 2007). Connectivity between ports
has increased in recent years – increasing the possibility of introduction of non-native species.
Progress has recently been made by Australia and New Zealand to develop region-based
surveillance programmes for the spread of non-native species. Examples include Marine High75
Risk Site Surveillance (MHRSS, NZ) (Woods et al. 2015), Australian State government and
Commonwealth government department led regional surveillance and legislation with regard
to non-native species (DAFF 2015).
With changing environments, significant new path risks may emerge for specific nonnative species even where the introduction of species is managed. The propagule pressure
primarily correlate to invasion success (Ruiz et al. 2000; Floerl & Inglis 2005; Lockwood et al.
2005). Many scientists have explained the importance of propagule pressure with regard to
non-native species and its management measures; however, it is hard to predict parameter
(Johnston et al. 2009). For example, evaluation in Port Phillip Bay, Australia indicates a new
introduction every 41.5 weeks (Thresher et al. 1999). The issues of non-native species have
grown in recent years – controlling and eradication are quite complicated. Government around
the world have taken up national priorities to establish programs and management protocols
for prevention (pre and post borders), early detection and management of non-native species.
Examples include EU Biodiversity Strategy and the Marine Strategy Framework Directive
(Lehtiniemi et al. 2015), Australian National System for the Prevention and Management of
Marine Pest Incursions (DAFF 2015), Marine Biosecurity Programme of New Zealand,
Aquatic Nuisance Species Task Force (ANSTF) in the United States.
Prevention, early detection and rapid management response reduce the potential
introduction of non-native species and their impacts on ecosystems. Australia and New Zealand
have established some of the world’s strongest biosecurity and management measures, i.e. a
comprehensive pre-border, at-border and post-border management responses (Hewitt &
Campbell 2007; Commonwealth of Australia 2013; Ojaveer et al. 2015). Both countries have
adopted and implemented international pre-border measures for ballast water management, i.e.
the International Maritime Organization, the Ballast Water Management Convention, to control
and minimise the spread of non-native species. The Biosecurity Act, NZ (1993) and the
Biosecurity Act, AUS (2015), are oriented towards the management of the unintentional
introduction of non-native species and post-border incursion measures and long-term
management. Ballast water management following the International Maritime Organisation
(IMO) protocols and ballast water treatment in the mid-ocean measures are undertaken to
reduce the risks of invasions (Hewitt and Campbell 2007; Tamelander et al. 2010; IMO 2017).
Recent measures have been focussed on biofouling management; IMO proposed protocols to
clean marine vessels, using appropriate anti-fouling agents, evidence of biofouling
maintenance and record books (Ministry of Primary Industries 2018; Australian Government
76
Department of Agriculture and Water Resources 2019). These countries have also undertaken
long term management plans of non-native species conducted by regional councils for baseline
evaluations. Australia and New Zealand led many countries across the world in establishing
management strategies for non-native species (Hayes et al. 2005; Hewitt and Campbell 2007).
2.5.6. Pros and cons of large-scale studies
The passive sampling devices used in APS and NZPS includes dive surveys, fouling panels
traps settlement trays for sediment infauna (Hewitt & Martin 2001). Such sampling designs
provide the opportunity to sample biofouling species at large scale locations; however, there
are still drawbacks for such sampling designs. The ‘snapshot’ data capture only provides realtime information; organisms may settle selectively depending on design flaws or
environmental conditions such as temperature or wave exposure. Advantage of such passive
sampling is that it can acquire samples of species at larval/juvenile stages which thereby
impacts the development of species structure. However, the success of such samples depends
on taxonomic expertise; the identification of species plays an important role for such large
datasets with numerous species collection. Poor identification of species may lead to confusion
between congeners or identifying non-native species as native species (Ponchon et al. 2013).
Lastly, such large datasets (APS and NZPS) are time consuming and expensive task. Resurveys may be an option to observe the effectiveness of management plans or identify new
introductions, but it highly depends on the funding on such large-scale projects.
While the usefulness of large-scale datasets for a broad sense of potential vectors of the
introduction of non-native species to aid with rapid management measures is undeniable, this
study provides an overall perspective of major commercial shipping ports being the hotspots
for non-native species. Ports as entry points for invaders is established, but the second key
result indicates the regional transfer of non-native species through domestic vectors.
Nonetheless, marine traffic being transport hubs of non-native species is evident. Increased
maritime traffic leads to continuous transfer of non-native species. Another factor considered
for this study was latitude groups. Relatively abundant non-native species were observed at
high latitudes (35°S, 40°S, 45°S) but these results are in an argument with the locations of
major ports at high latitudes. Most of the species observed were biofouling species which
further indicate strict management plans towards the eradication of non-native biofouling
species. Such species easily attach on vessels’ hulls, engines or crevices of the ship which is
not easily visible. Australia and New Zealand have developed several guidelines for biofouling
77
considering IMO Biofouling Management Plan 2011 (MEPC.207[62]) and are in the process
of considering biofouling regulations.
The Australian Port surveys and New Zealand Port Biological Baseline Surveys offered
as good datasets to identify the potential factors, i.e. major ports and high latitudes facilitating
occurrences of non-native species. While I encourage in using this general applicability of this
observed pattern; these results also suggest that port characteristics such as disturbed
physicochemical environments, new structures, structure maintenance should be prioritised
whilst monitoring for non-native species which will aid with early detection.
78
CHAPTER 3
COMMUNITY DEVELOPMENT AND STRUCTURE ON NATURAL
AND MAN-MADE SUBSTRATA IN NATURAL AND MAN-MADE
ENVIRONMENTS
3.1. Background
Natural coasts around the world have been heavily degraded and replaced by man-made
structures such as seawalls, groynes, marinas, jetties and so on (Lam et al. 2009; Airoldi &
Bulleri 2011; Scyphers et al. 2015; Cacabelos et al. 2016). Although these man-made structures
are mostly built to protect the coastlines from erosion and act as defence structures, such manmade structures do provide novel habitats for marine communities (Burt et al. 2009, 2011;
Cacabelos et al. 2016; Heery et al. 2017). These man-made structures are expected to
proliferate in the future owing to the migration of humans to the coasts and alteration of
coastlines as per human needs (Hinkel et al. 2014; Bulleri & Chapman 2015; Dafforn et al.
2015; Neumann et al. 2015; Dangendorf et al. 2017). The building of continuous coastal
defences on the natural coastline can cause habitat loss and habitat fragmentation of natural
coastlines having local and regional impacts on marine biodiversity (Airoldi et al. 2008, 2009;
Chapman 2012; Scyphers et al. 2015; Perkol-Finkel et al. 2018; Macura et al., 2019). Measures
to rectify the negative impacts of the man-made structures by coming up with conservation
strategies have only begun relatively recently (Thompson et al. 2002; Airoldi et al. 2005;
Chapman & Underwood 2011; Kueffer & Kaiser-Bunbury 2014).
Natural coastal shores are heterogeneous environments with a range of microhabitats
providing refuge to many intertidal species from predation and desiccation (Thompson et al.,
2002; Waltham & Dafforn, 2018; Bulger et al. 2019). The correlation between heterogeneous
habitats and biodiversity has been observed across all ecosystems, be it land such as rainforests
or sea such as coral reefs (Simberloff & Von Holle 1999; Tews et al. 2004; Davies et al. 2005;
Hansen & Clevenger 2005). This can even be observed at small scales with increased
microbiota communities on rough surfaces rather than on smooth surfaces (Lam et al. 2009;
(Pister 2009; Cacabelos et al. 2016). Unlike natural habitats, the man-made structures have
smooth surfaces, i.e. low structural complexity, with no rockpools or crevices, making it
difficult for the species to colonise or find refuge from desiccation, predation or wave action
(Chapman & Clynick, 2006; Von Holle, 2011). Therefore, the building of man-made structures
79
tends to homogenise natural coastlines, having a negative impact on the native marine
biodiversity (Mack et al. 2000; Braby & Somero 2006; Brandl et al. 2017; Pastro et al. 2017;
Perkol-Finkel et al. 2018). Once coastlines are modified, it is difficult to re-establish diverse
communities as there would be competition over limited resources such as food and space
(Simberloff & Von Holle 1999; Levine 2000; Bruno et al. 2003; Rius & McQuaid 2009; Branch
et al. 2010).
Man-made coastal structures are built using materials such as granite, concrete, plastic,
or wood, which are usually not observed in natural coastal habitats (Bulleri & Chapman 2010;
Loke & Todd 2016). There has been evidence that some marine organisms are selective of
chemical cues provided by substrata (Pawlik 1992; Dobretsov & Wahl 2001; Tamburri et al.
2008). Therefore, species-specific preference for settle on certain substrata (material types) is
possible. Experiments using different materials to test for species community comparisons
have yielded contradictory results. Some studies indicated similar species richness, but low
abundances of species on artificial materials such as ceramic, glass, granite, concrete, steel,
aluminium, wood, brick, rubber compared to natural reefs (Anderson & Underwood 1994;
Creed & De Paula 2007; Field et al. 2007; Tyrrell & Byers 2007; Loke & Todd 2016; Kennedy
et al. 2017; Mallela et al. 2017). Whilst some studies in estuaries showed increased species
abundances on artificial structures (tyres, wood, metal) due to additional substrata for the
organisms to settlement on (Chapman & Bulleri 2003; Chapman & Clynick 2006; Smith et al.
2014), and still, other studies showed differences in species diversity and abundances as an
effect of different habitat type rather than the material type (Burt et al. 2009; Cacabelos et al.
2016). For instance, even when the seawalls along the Sydney coast were built with sandstone
to mimic natural reefs, the structures did not support similar communities (Chapman & Bulleri
2003). Therefore, man-made structures on its own cannot replace natural habitats (Bulleri &
Chapman 2004; Bulleri & Chapman 2010; Carvalho et al. 2013; Cacabelos et al. 2016).
However, eco-engineered man-made structures can act as surrogates for natural habitats.
The orientation of man-made structures is usually vertical and steep, providing reduced
settlement opportunity, whilst most natural habitats are horizontal or gently sloping, supporting
relatively more species (Chapman & Clynick 2006; Spagnolo et al. 2014; Kennedy et al. 2017).
The steep man-made structures act as a barrier for larval dispersal, movement of mobile
species, alter recruitment patterns and reduce water flow causing impacts on ecological
connectivity (Bulleri & Chapman 2004; Moreira et al. 2006; Rivero et al. 2013 Bishop et al.
2017). This further impacts the gene flow and trophic transfer along coastlines (Branch &
80
Steffani 2004; Trussell et al. 2004; Burlakova et al. 2012; Inglis & Seaward 2016; Bishop et
al. 2017). Thus, low genetic diversity is one of the factors that may be observed amongst
species on man-made structures (Fauvelot et al. 2009; Sammarco et al. 2012). Subsequently,
some species may develop interspecific and intraspecific relationships amongst the community
which are not seen at natural habitats ( Chapman 2006, 2013; Tyrrell & Byers 2007; Chapman
& Underwood 2009; Quinn et al. 2012). For instance, some invertebrates had a smaller sized
body and less reproductive output as a response to increased community density (Moreira et
al. 2006).
Recent re-assessments of man-made coastal structures have concluded that it is
important to redefine the design features of the structures. Engineering structures with
multifunctional use, i.e. for coastal defence as well as providing habitat for marine organisms,
have been considered. Addition of rockpools, crevices forming tide pools or even addition of
overhangs can promote biodiversity (Bulleri & Chapman, 2010; Chapman & Underwood 2011;
Loke & Todd, 2016). For instance, the addition of flowerpots over the vertical seawalls led to
an increase in species assemblage diversity by 62%, with 25 species which were not previously
found on the seawall (Browne & Chapman 2011). Cost-effective hybrid structures such as
revetments, tetra-pods and geo-tubes can be multifunctional, especially in areas where the
natural habitats are lost (Moschella et al. 2005; Chapman & Underwood 2011; Browne &
Chapman 2014; Firth et al. 2014; Loke et al. 2014). Additionally, precautionary policies
regarding the placement of coastal structures had been implemented more towards the beach
to protect intertidal marine communities (Dethier et al. 2017).
Man-made habitats such as marinas and ports are usually enclosed areas with limited
water/wave action, as well as increased sedimentation rates leading to high turbidity and
reduced photosynthesis, thereby degrading the natural habitat and reducing the resident
biodiversity (Guerra-García & García-Gómez 2004; Perkol-Finkel & Benayahu 2007; Rivero
et al. 2013; Pastro et al. 2017). Levels of disturbance are also thought to vary between manmade and natural habitats (e.g., via strong waves or predation) (Moschella et al. 2005).
However, anthropogenic disturbances such as maintenance of man-made structures or overharvesting of resident species tend to dislodge competitively dominant and settled species,
thereby providing bare space for colonisation of opportunistic species (Airoldi et al. 2005;
Airoldi & Bulleri 2011; Bracewell et al. 2013; Oricchio et al. 2016). The community structure
often varies between different habitats, for example, sessile species composition greatly
differed among complex habitats compared to the bare substrate (Burlakova et al. 2012) and
81
even the microbial communities differed between man-made structures and natural rocky reefs
(Tan et al. 2015).
Studies comparing the community composition on natural and man-made structures
have observed similar species richness but different relative abundances on both structures
(Connell & Glasby 1999; Thompson et al. 2002; Chapman 2003; Bulleri & Chapman 2004;
Pister 2009; Carvalho et al. 2013). However, many recent studies have highlighted the two
habitats, natural and man-made to support different species assemblages (Moschella et al.
2005; Perkol-Finkel et al. 2006; Clynick et al. 2007; Lam et al. 2009; Bulleri & Chapman 2015;
Lai et al. 2018). Whilst some studies have observed the man-made structures to support species
that are not generally observed in natural habitats (Goodsell et al. 2007; Browne & Chapman
2011). Man-made structures may act as novel habitat for various benthic communities,
especially in sedimentary habitats, providing additional habitat where they support greater
species diversity compared to natural habitats (Bulleri & Chapman 2010; Airoldi et al. 2015;
Heery et al. 2017). In some instances, species richness was relatively lower at man-made
structures than natural habitats (Connell & Glasby 1999; Moschella et al. 2005; Pister 2009;
Firth et al. 2013; Aguilera et al. 2014; Munsch et al. 2014). Species richness and effects on
ecosystem functioning are dependent on specific functions of species (Chapman 2003; Rius &
McQuaid 2006; Stachowicz et al. 2008; Pister 2009; Mineur et al. 2012; Albano & Obenat
2019). Besides, the composition of assemblages is also an important factor as the changes in
the diversity of the species (Creed & De Paula 2007; Field et al. 2007).
Ecosystem functions are influenced by ‘keystone species’ as they strongly affect the
energy pathways, and by ‘ecosystem engineers’ which create or modify habitats. Additionally,
species such as sessile organisms that usually facilitate a positive relationship with other
species by providing refuge, especially for mobile species and larger predators; are
economically significant (Borthagaray & Carranza 2007; Sousa et al. 2009; Green et al. 2013;
Martins et al. 2016; O’Shaughnessy et al. 2019). The habitat-forming species or the early
recruiters help with the development of a community by providing refuge or acting as a food
source. Overgrowth of these initial recruits due to the absence of grazers and plenty of space
can have adverse effects on late settlers by blocking the surfaces (Connell 1961; Sousa 1979;
Bracewell et al. 2013; Aguilera et al. 2014). However, recognising which species can withstand
anthropogenic disturbances and how such disturbance can impact the overall ecosystem
structure is essential (Liversage et al. 2014). For instance, habitat-forming sessile species such
as ascidians, bivalves, bryozoans, cnidarians, corals, and sponges that are permanently fixed
82
on substrata may be majorly affected by anthropogenic disturbances that lead to their
dislodgment, thereby disrupting the ecosystem structure (Lockwood et al. 2007).
An important change to the community composition is conferred by invasive species
(Bulleri et al., 2016). It is universally accepted that bioinvasions are a major threat to marine
biodiversity as well as the global economy (Ojaveer et al. 2015; Gestoso et al. 2017; Olenin et
al. 2017; Simpson et al. 2017). Invasive species are ubiquitous and can degrade habitats in
many ecosystems (Wonham 1999; Pauchard & Shea 2006; Bellard et al. 2016; Bulleri et al.
2016). Consequently, there has been a surge in studies of invasive species all around the world.
Frequently, the invasive species have been seen to establish in regions with heavy
anthropogenic activities (Thompson et al. 2002; Ruiz et al. 2009; Johnston et al. 2017).
Disturbance and maintenance along the coastlines for urbanisation and coastal development
are ubiquitous, and provision of non-native species are such sites is typical (Clark & Johnston
2009; Piola et al. 2009; Bulleri & Chapman 2010; Dumont et al. 2011; Rivero et al. 2013). For
example, maintenance of breakwaters leads to displacement of dominant space occupiers,
mussels and oysters, leading to the growth of opportunistic and invasive biofilms and
macroalgae (Airoldi & Bulleri 2011; Ceccherelli et al. 2014).
Ports and bays are sites where ships’ ballast water discharge and hull fouling
communities may contribute to the introductions of non-native species (Ruiz et al. 1997; Hewitt
et al. 2004; Chapman & Underwood 2011; Mineur et al. 2012; Choi et al. 2016; Foster et al.
2016; Olenin et al. 2017). Coastal habitats are vulnerable to invasions due to the changing of
natural habitats, local biodiversity and propagule pressure from non-native species (Johnston
et al. 2009; Simberloff 2009; Simpson et al. 2017; Epstein & Smale 2018; Riera et al. 2018).
Many studies suggest that non-native species are less successful at natural habitats as compared
to man-made structures (Chapman & Carlton 1991; Dafforn et al. 2012). There is evidence of
non-native species being strongly related to disturbed areas with high turbidity and wave
exposure (James & Shears 2016). Man-made structures act as ‘stepping stones’ for the spread
of non-native species and support a high number of non-native species (Dumont et al. 2011;
Mineur et al. 2012; Saura et al. 2014; Dong et al. 2016).
Not all non-native species can successfully establish due to unsuitable climatic
conditions, diseases, predation and competition by native species through species-specific
interactions or invasion resistance by the diverse native community (Elton 1958; Tilman et al.
1994; Mack et al. 2000; Stachowicz et al. 2002; Dumont et al. 2011; Firth et al. 2013;
83
Henriksson et al. 2016; Leclerc & Viard 2018). The successful non-native species may be
highly competitive in terms of their high reproductive/growth rates and phenotypic plasticity,
which aids their introduction and establishment success in a new environment (Petes et al.
2008; Piola et al. 2009; MacKie et al. 2012; Fava et al. 2016; Purroy et al. 2019). Once
established, the non-native species disperse further from the introductory areas (Tyrrell &
Byers 2007; Forrest et al. 2009).
Invasive species can cause changes to community structure and impact ecosystem
functioning (Firth et al. 2016), including the loss of native species and a decrease in diversity,
thereby leading to a homogenous community (Browne & Chapman 2014; Airoldi et al. 2015;
Ferrario et al. 2017). Invasive species, when established in an environment, can co-exist with
native species if they have similar ecosystem functions: however, if their functioning is
different, it may lead to modified habitats and cascading effects on the other species (Crooks
2002; Trussell et al. 2004; Babarro & Abad 2013; Saura et al. 2014). It is therefore essential to
understand the ecological functioning of an invasive species in its new habitat and its
relationships with other species (Airoldi & Bulleri 2011; Perkol-Finkel et al. 2012; Zwerschke
et al. 2016; Leclerc & Viard 2018; Mayer-pinto et al. 2018). For example, the predatory
European green crab (Carcinus maenas) in California, established and altered the community
structure and reduced the number of native species through predation (Grosholz et al. 2000).
Predation by the invasive crab, Carcinus maenas, led to the decrease of a grazer, Littorina
littorea, which in turn resulted in the proliferation of ephemeral algae in rocky reefs (Trussell
et al. 2004). Some studies have also highlighted the replacement of native species by nonnative species (Geller 1999, Byers 2000; Ojaveer et al. 2002; Rodriguez 2006; Tan et al. 2015).
Spatial, temporal and environmental heterogeneity determine ecological diversityfunction relationships (Stachowicz et al. 2008). Ecological succession in community structure
helps identify patterns or interactions in abundances and diversity over time (Connell & Slatyer
1977; Loke et al. 2016; Johnston et al. 2017; Chang & Turner 2019). Ecological succession is
the study of patterns of colonisation and resilience of species after a disturbance (Connell &
Slatyer 1977). In recent times, the two main factors that disturb ecological succession are
human activities and climate change (Bishop et al. 2017; Firth et al. 2016). Anthropogenic
disturbances causing newly exposed habitats have a significant effect on ecological succession,
especially the first succession being highly driven by stochastic processes and colonisation is
variable and patchy (Chapman & Underwood 2009; Clark & Johnston 2009; Chapman 2012;
Spagnolo et al. 2014). Pioneer species first colonise new barren habitats as they can withstand
84
extreme conditions - ‘primary autogenic succession’ but are short-lived. Bacteria and biofilms,
and other settlers such as diatoms, form an initial layer over the bare substrata. Chemical cues
emitted by the initial larval colonists and even the associated substratum are known to influence
changes in the recruitment of other species (Pawlik 1992; Dobretsov & Wahl 2001; Tamburri
et al. 2008). Over time, new species colonise - ‘secondary succession’ - and may tend to
stabilise - ‘climax community’ (Connell & Slatyer 1977; Chang & Turner 2019). The ‘climax
community’ is mature and is dominated by a few species (Berlow 1997). In the marine
environment, the sequence of recruitment and settlement of species have a strong influence on
the diversity as well as the structure of the climax community (Bulleri 2005; Lu & Wu 2007;
Becker et al. 2018; Gouezo et al. 2019). Various factors, such as the level of disturbances or
prey-predator interaction, can lead to alternative stable states with different community
structure (Petraitis & Latham, 1999).
Nevertheless, we cannot assume that similar community structure and sequence of
succession observed at natural habitats will be seen on man-made habitats. Another question
arises in terms of species status (native vs non-native species), the competitive traits of nonnative species could displace native species. This displacement of species can produce changes
in community structure found in natural successions (Pandolfi 2008). Thus, comparative
studies across a variety of ecosystems can help us to understand the main factors facilitating
the succession of both native and non-native species (Chang & Turner 2019). Whilst there is
some research done on the succession of species, but no similar studies have been conducted
comparing natural and artificial habitats/ substrata in the context of species status.
Studies investigating the community structure, species abundances and diversity as well
as the status of the species – native and non-native – with a focus on natural vs man-made
structures have been performed all over the world, especially in temperate regions (Connell &
Glasby 1999; Connell 2001; Hewitt 2002; Airoldi 2003; Bacchiocchi & Airoldi 2003;
Chapman & Bulleri 2003; Airoldi et al. 2005; Bulleri & Airoldi 2005; Wyatt et al. 2005; Airoldi
& Beck 2007; Parsons et al. 2016; Mayer-Pinto et al. 2018). The proliferation of built structures
along with New Zealand’s (NZ) coastline and an increase of 10% non-native species since
2009 has been outlined by New Zealand's Environmental Reporting Series (Ministry for the
Environment & Stats NZ, 2016). However, to the best of my knowledge, no research has been
carried out in New Zealand to examine the effects of man-made built structures and natural
rocky reefs on marine community composition and species’ abundances over a period of time,
85
and that also examines preference of native and non-native species in terms of habitat type
(Natural reef vs Man-made habitat).
Many previous studies have acknowledged the impacts of man-made structures on
community composition and species’ abundances, as well as the facilitation of non-native
species.
Therefore, this chapter attempts to quantify species diversity and community
composition at the rocky reef (natural) and marina habitats (built) using slate (natural) and PVC
(polyvinylchloride) (man-made) tiles in Wellington Harbour. The successional pathways from
the initial bare surface to final community composition have been examined to observe the
effects of man-made habitats/substratum for 2 years. This study also analysed the impact of
habitat type and substratum type on the recruitment of fouling species with respect to their
status; native, non-native and cryptogenic (species which could not be identified or the status
could not be determined) and tested if there is a preference by non-native species for a particular
habitat or substratum. The variability in the association of native and non-native (positive, i.e.
increase in the number of native and non-native species; negative, i.e. increase in non-native
abundance with a decrease in the number of native species) may help determine a positive or
negative relationship between native and non-native species. The hypotheses investigated in
this chapter are: 1) community composition at man-made habitat (3 replicated marinas) is less
diverse than at neighbouring natural rocky reefs; 2) community composition on the man-made
substratum (PVC) is less diverse than that on a natural substratum (slate), and 3) non-native
species are more abundant at the artificial habitats and on artificial substratum relative to
natural habitats and substratum.
86
3.2. Methods
3.2.1. Study sites
Six study sites (3 paired sites) were selected in Wellington Harbour to examine if the marinas
(man-made habitats) support similar or different communities to the adjacent natural rocky
reefs. The three marina sites were; Chaffers Marina (CM), Evans Bay Marina (EB) and
Seaview Marina (SM) and three natural reef sites were; Oriental Bay (OB), Shelly Bay (SB)
and Sorrento Bay (SR) (Figure 3.2.1; Table 3.2.1). The three paired sites were; Chaffers Marina
– Oriental Bay; Evans Bay Marina – Shell Bay; Seaview Marina – Sorrento Bay which was at
̴ 200 m distance between each marina and reef site.
Figure 3.2.1. Map of Wellington Harbour, New Zealand, indicating natural reef and man-made
habitats (marinas) as sampling sites for the study.
Table 3.2.1. List of sampling sites with habitat types, codes, latitude and longitudes.
Site
Site code
Habitat
Latitude
Longitude
Chaffers Marina
CM
Marina
S 41°17.194’
E 174°48.165’
Oriental Bay
OB
Reef
S 41°17.377’
E 174°47.340’
Evans Bay Marina
EB
Marina
S 41° 17.983’
E 174°49.028’
Shelly Bay
SB
Reef
S 41°18.100’
E 174°49.049’
Seaview Marina
SM
Marina
S 41°24.765’
E 174°90.299’
Sorrento Bay
SR
Reef
S 41°15.278’
E 174°54.188’
87
3.2.2. Substratum
To study the fouling community composition on tiles of different substrata (material), PVC as
man-made and slate as natural substrata were used. PVC as a man-made material has been
previously used in many settlement arrays studies whilst, slate is a natural material and is not
chemically treated. Each tile was cut into 0.0225 m2 (0.15 x 0.15 m) and was 10 mm in
thickness. Four holes were drilled at 2 cm from the edge of the tiles in the four corners. The
tiles were secured back to back (PVC to slate) with cable ties through the four holes exposing
only one side of each tile for settlement, i.e. front view of the tiles (Figure 3.2.2 c & d). The
surface of the PVC tiles was lightly scraped with sandpaper to remove the industrial
smoothness, to help with species attachment. The slate tile was left untreated because it is
naturally slightly rough.
3.2.3. Experimental design
In marinas, wooden wharf pilings were a perfect set-up for the experimental design from which
to suspend the settlement tiles, whilst natural reefs had no such set-up. Therefore, different
experimental set-ups were designed for the marina (man-made habitat) and reef sites (natural
habitat).
a) Marina (Man-made habitat)
At each of the three marina sites, the settlement tiles were hung between two wharf pilings with
ropes at ̴ 2 m below the sea surface estimated at low tide considering the Mean Low Water
Springs (MLWS). Eight sets of PVC and slate paired tiles were tied to a rope with cable ties at
equal intervals (Figure 3.2.2 a). In total, 5 such rope set-ups were deployed on 5 different sets
of wharf pilings. Eight paired tiles were attached equidistant from each other and were
constantly submerged. Hence, for the 2-year study period, 1 marina site had 5 replicate set-ups
with 8 paired tiles (1 marina site × 5 replicates × 8 paired tiles = 40 paired tiles at 1 marina site
[40 PVC + 40 slate]). At 3 marina sites × 5 replicates × 8 paired tiles = 120 paired tiles [120
PVC + 120 slate].
b) Reef (Natural habitat)
Galvanised steel frames were constructed (1.6 x 0.25 m), from which the tiles were hung across
the frames with ropes. The frames were maintained in position underwater with the help of
cement-filled tyres used as weights and subsurface buoys to help the frame stay vertical (Figure
3.2.2 b). The frames were placed perpendicular to the coast so that the surfaces of both the tiles
are exposed to the waves. Similar to the tile set-up at marina sites, 5 replicates of the frame set-
88
up were placed at each natural reef site. For the 2-year study period, 1 reef site had 5 replicate
set-ups with 8 paired tiles (1 reef site × 5 replicates × 8 paired tiles = 40 paired tiles at 1 reef
site [40 PVC + 40 slate]). At 3 reef sites × 5 replicates × 8 paired tiles = 120 paired tiles [120
PVC + 120 slate].
3.2.4. Field sampling and sampling intervals
The placement of the set-ups and the sampling (retrieval) of the tiles was carried out by SCUBA
divers. Whilst field sampling, the cable ties were cut, and pairs of tiles (PVC and slate) were
placed in pre-labelled plastic bags. Once the tiles were retrieved from the water, they were
placed in an icebox and then transferred to a freezer (-18 o C) in the laboratory until they were
processed. At every sampling interval (see below), 30 paired tiles (PVC and slate) were
collected from the six study sites (marina and reef): in total, 60 tiles (30 PVC + 30 slate) were
processed at anyone sampling period. Hence, for 8 sampling intervals, a total of 480 tiles were
collected by the end of the 2-year study. The tiles were sampled randomly for every sample
interval, for example, at time 1 (Nov 2017) number 6 tiles from all the experimental set-ups
were retrieved.
This study was designed to compare the fouling community on PVC and slate substrata
from the first time they were deployed in the water (August 2017 - austral winter) to the end
of the 2 years (August 2019). Sampling interval after the first deployment in August 2017 was
every three months until the final retrieval in August 2019 (Table 3.2.2).
Table 3.2.2. List of sampling intervals, months, seasons and sampling year examined in this
study, following set-up and deployment in August 2017.
Sampling
Month
interval
Season
Sampling year
(Austral)
1
November 2017
Spring
2
February 2018
Summer
3
May 2018
Autumn
4
August 2018
Winter
5
November 2018
Spring
6
February 2019
Summer
7
May 2019
Autumn
8
August 2019
Winter
Year 1
Year 2
89
a) Set-up in marinas
SetSssss
c) Front view
b) Set-up at natural reef sites
d) Side view
Figure 3.2.2. Set-up of settlement tiles deployed in the a) marinas and at b) natural reef sites; c) front view and d) side view of the PVC and slate
substrata.
90
3.2.5. Tile processing
The fouling community on each tile was thawed and gently patted dry before processing.
Subsequently, to have a 2D view of the community on the tiles, high-resolution digital images
were taken of the front of the tiles (the side exposed for settlement). At each sampling interval,
these images were then analysed using Coral Point Count with Excel extensions (CPCe) to
record the community composition over a 2-year study period (Kohler & Gill 2006). A 100point random grid within 14 × 14 cm was generated (for each tile, a 1 cm border was not
included to avoid edge effects). The number of points (100) that overlaid on the image was
determined using Lenth’s power test as suggested in CPCe manual (Power = 0.998 for 100
points). The species cover at each point was determined for each plate at each time interval for
all sites. Similarly, points on the bare space on the tiles were also noted to assess the availability
of bare space for settlement through the study period.
Species were identified using field guides for common intertidal and shallow subtidal
species of New Zealand, including known non-native species. Species were identified to the
lowest possible taxonomic level (most usually to species) and then placed in major systematic
groups. The organisms which could not be identified to species were identified to genus or
family level and coded as sp. 1 or sp. 2, for example, Ulva sp. 1 (see Table 3.3.1).
‘Biofilm’, herein coded as Biofilm type 1, was classified as a single major group as it
is biologically important as an early coloniser, is highly variable in time and space in its exact
content and could not be accurately identified to any meaningful taxonomic level. An unknown
black-dotted biofilm that was visually different from the clear biofilm (Biofilm type 1) was
observed in the initial stages of sampling herein and was labelled ‘Biofilm type 2’. An
unidentified green moss-like structure (Chlorophyta) was labelled as Green sp. 1; the sheetlike green alga was labelled as ‘Ulva lactuca’, the green ribbon-like alga was labelled as ‘Ulva
sp. 1’, the beige sheet-like tunicate as ‘Tunicate sp. 1’ and the mustard sheet-like tunicate as
‘Tunicate sp. 2’.
3.2.6. Data analyses
a) Preliminary analyses
Data were analysed using the statistical package Plymouth Routines in Multivariate Ecological
Research (PRIMER v.6) (Clarke & Gorley 2006). The DIVERSE routine was used to calculate
the total number of ‘species’ occurrences (total species richness) and the total number of
‘individuals’ as represented by point counts (total individuals); at marina sites (man-made
91
habitat) on PVC and slate settlement tiles (man-made and natural substrata) as well as for
natural reef sites (natural habitat) on PVC and slate settlement tiles. Species accumulation
curves were plotted across all 480 samples to determine if the sampling effort was sufficient to
discover all likely (expected) taxonomic diversity in Wellington Harbour over 2 years. The
metric Sobs showed the observed total number of species with the increase in the number of
samples (the asymptotic value of the species accumulation curve). The non-parametric
estimators used for plots were; Chao1, Jacknife1 and Bootstrap. Chao1 and Jacknife1 estimate
richness from single samples (abundance-based)
b) Bare space availability
Multivariate analyses were performed using PRIMER v.6 with permutational multivariate
analysis of variance (PERMANOVA) as an add-on package (Anderson et al. 2008).
PERMANOVA helps statistically test the differences between two groups and among a group,
and the effects of factors on communities using a permutation approach to avoid possible biases
and problems associated with regular parametric testing.
Availability of bare space (expressed as a percentage) as a function of habitat and
substratum through the tri-monthly sampling periods was tested using permutational
multivariate analysis of variance (PERMANOVA). As the units for bare space and species
community composition were similar, the bare space data were square-root transformed. The
data were square-root transformed based on Bray-Curtis similarity matrices with 9999
permutations of the raw data. PERMANOVA was run with Type III (partial) sums of squares
with relevant factors. The main independent factors were substratum (fixed), sample interval
(fixed), habitat (fixed) and sites nested within habitat (fixed) and available bare space (point
count) was the dependent variable. Factor, sites nested within the habitat, in the PERMANOVA
model resulted in no tests. Therefore, a PERMANOVA model with factors habitat, substratum
and sample interval were considered. CPCe points for bare space at each sampling time were
also plotted on a graph to observe the variations in availability of bare space over time.
c) Fouling community composition
The multivariate analyses based on Bray-Curtis similarity matrices were run after the data were
square-root transformed to reduce the effects of abundant species, with 9999 unrestricted
permutations of the raw data with Type III sum of squares. A two-dimensional
multidimensional scaling (MDS) ordination plot was performed to visualise the similarity in
samples for 3 factors; habitat type, substratum and sample intervals (12 months). Additionally,
92
MDS with the temporal variations displayed as an overlay connecting each sampling interval
to visualise the trajectory of change across all 6 sites, respectively. MDS helps visualise the
multivariate patterns in fouling community change over time between the habitat types and the
substrata. In MDS, samples that are similar cluster together whereas samples which are
dissimilar cluster further apart. MDS plot stress values were used to interpret the reliability of
the relationships; values < 0.15 = good representation between groups. The stress levels are
also affected by the number of samples (Clarke 1993).
The variations in fouling community composition as a function of; habitat type, site
(habitat), substratum type and sample interval (given as sampling sequence in the order from
the first to the eighth) with their relevant interaction terms were analysed using multi-factorial
PERMANOVA. Pairwise PERMANOVA tests were employed to test for the location of the
differences (equivalent to standard post-hoc tests).
Species contributing to the similarities and dissimilarities between the two groups as a
function of Habitat, Substratum and Time were identified using the routine similarity
percentages (SIMPER) at a 50% contribution cut-off. The PERMANOVA results were mostly
significant (P < 0.001), the SIMPER analyses were performed only for major factors (Habitat,
Sites, Substratum and Time).
d) Species status (native, non-native and cryptogenic) within the fouling community
The species within the fouling community sampled on the tiles were classified with respect to
their status, i.e., native, non-native and cryptogenic species with the help of Word Register of
Marine Species (WoRMS). The species whose status could not be identified were classified as
a cryptogenic status group. A multivariate PERMANOVA was performed with the species
status as the dependent factor whilst habitat, site (habitat), substratum and sample interval as
the independent variables. This test helped indicate if the factors of; habitat, site (habitat),
substratum and sample interval had any effects on the abundance of native, non-native or
cryptogenic species. SIMPER indicated similarity of the presence of species with respect to
their status within each factor as well as the dissimilarity between the groups.
93
Natural Habitat
Man-made Habitat
Substratum type
PVC
PVC
Slate
1st sampling interval (Nov 2017)
2 years
Slate
8th sampling interval (Aug 2019)
Figure 3.2.3. Representative PVC and slate tiles collected in the study, showing the community growth at first sampling interval (November
2017) and last sampling interval (August 2019)
94
3.3. Results
3.3.1. Diversity of the fouling community
In total, 47 putative species were identified from 480 experimental tiles. The species were
pooled to form 12 major groups; Annelida, Arthropoda, Biofilm, Bryozoa, Chlorophyta,
Chordata, Echinodermata, Mollusca, Nemertea, Phaeophyta, Porifera and Rhodophyta (Table
3.3.1).
Table 3.3.1. List of species and major groups recorded at the 6 sampling sites for all 8-time
periods in Wellington Harbour, with S = total number of species richness on 480 settlement
tiles and N = total number of individuals as represented by point counts.
Habitat type
Species
Reef
Marina
Status
Substratum
PVC
Species / Major group
Slate
PVC
Slate
S
N
S
N
S
N
S
N
Native
7
14
11
14
7
16
6
20
Cryptogenic
3
3
10
15
3
5
0
0
Serpula vermicularis
Native
0
0
0
0
5
10
41
90
Spirobranchus cariniferus
Native
67
358
79
475
31
77
4
23
Spirorbis spirorbis
Native
28
52
38
80
33
72
20
164
Amphibalanus amphitrite
Non-native
1
1
6
7
2
2
42
92
Amphipod sp. 1
Cryptogenic
3
5
3
3
0
0
0
0
Crab sp. 1
Cryptogenic
1
2
2
3
0
0
0
0
Elminius modestus
Non-native
16
60
23
64
3
5
1
1
Isopod sp. 1
Cryptogenic
0
0
0
0
1
1
8
91
Biofilm type 1
Cryptogenic
96
2608
83
1543
99
2952
3
75
Biofilm type 2
Cryptogenic
6
263
12
517
9
102
0
0
Bugula flabellata
Non-native
17
247
13
126
27
132
0
0
Bugula neritina
Non-native
0
0
0
0
4
205
30
466
Bugula stolonifera
Non-native
14
164
9
85
21
171
46
239
Membranipora membranacea
Native
60
803
57
718
33
153
17
111
Rhynchozoon larreyi
Native
4
25
3
23
6
58
23
157
Schizoporella errata
Non-native
17
58
15
65
12
84
44
183
Watersipora subtorquata
Non-native
70
945
59
742
43
230
41
291
Annelida
Galeolaria hystrix
Nereid sp. 1
Arthropoda
Biofilm
Bryozoa
95
Chlorophyta
Codium fragile
Non-native
3
47
1
7
0
0
4
16
Green sp. 1
Cryptogenic
68
1826
62
1426
61
1662
36
413
Ulva lactuca
Native
11
119
8
53
16
111
2
61
Cladophora
Native
26
196
22
129
43
613
9
17
Cryptogenic
4
7
10
58
10
94
28
138
Aplidium stellatum
Native
0
0
2
8
4
31
19
181
Asterocarpa humilis
Native
31
313
45
1108
27
425
60
613
Botrylloides leachii
Non-native
1
2
2
5
16
194
4
59
Native
6
13
8
18
7
31
16
128
Tunicate sp. 1
Cryptogenic
20
195
20
255
37
340
3
3
Tunicate sp. 2
Cryptogenic
7
145
8
236
6
67
0
0
Tunicate sp. 3
Cryptogenic
0
0
1
7
0
0
0
0
Native
0
0
1
1
0
0
35
391
Native
0
0
0
0
4
50
3
5
Non-native
5
15
3
19
1
2
0
0
Ostrea chilensis
Native
12
83
25
166
40
429
11
25
Perna canaliculus
Native
4
25
3
24
1
1
64
946
Cryptogenic
3
3
10
20
2
12
9
15
Non-native
7
16
7
87
2
6
66
1400
Native
91
1444
66
508
60
560
1
1
Non-native
1
6
6
118
3
46
56
800
Clathrina sp. 1
Cryptogenic
24
382
29
355
21
197
0
0
Cliona sp. 1
Cryptogenic
1
1
2
10
1
1
67
799
Native
1
9
0
0
1
2
7
18
Non-native
39
345
34
315
73
1094
9
60
Coralline algae
Native
13
41
11
24
21
138
0
0
Gigartina circumcincta
Native
4
9
10
80
8
57
2
14
Rhodymenia dichotoma
Non-native
0
0
2
7
0
0
0
0
Ulva sp. 1
Chordata
Corella eumyota
Echinodermata
Patiriella regularis
Mollusca
Anomia trigonopsis
Mytilus galloprovincialis
Nemertea
Nemertine sp. 1
Phaeophyta
Colpomenia peregrina
Ralfsia verrucosa
Undaria pinnatifida
Porifera
Rhodophyta
Apophlaea lyallii
Bangia atropurpurea
96
3.3.2. Species accumulation
Species accumulation curves across all 480 samples for the two-year study period indicated a
rapid initial increase in species count and then stabilised (Figure 3.3.1). Species count
estimators (Chao1, Jacknife1, Bootstrap) did not vary much from the observed species count
(Sobs). Within the first 100 samples, the accumulation of new species had flattened off, such
that ≥90% of all species had been recorded. However, the results indicated that approximately
400 samples were required to observe all the species sampled (i.e., to reach the asymptotic
value of each curve at approx. 50 species). Overall, these results support the contention that the
sampling effort was adequate and sufficient to reasonably represent the diversity of the fouling
community in Wellington Harbour.
Figure 3.3.1. Species accumulation curves for observed species (Sobs) and for nonparametric estimators of species count richness (Chao1, Jacknife1, Bootstrap).
3.3.3. Bare space availability
The multivariate PERMANOVA showed no significant differences in the availability of bare
space considering the interaction of all factors; Habitat, Substratum and Time (Habitat ×
Substratum × Time; P = 0.48). However, the bare space availability significantly differed with
the Habitat × Time interaction (P < 0.05). The availability of bare space differed as a function
of Habitat, Substratum and Time, respectively (P < 0.001) (Table 3.3.2).
Pairwise tests for main factors indicated significantly (P < 0.001) more available bare
space at the marina (73.78%) than reef habitats (72.48%). In terms of substratum, Slate vs PVC
indicated a significant difference with significantly (P < 0.001) more available bare space on
the slate (78.49%) than PVC (72.15%) (Table 3.3.3). The factor, habitat × sample interval
97
indicated significant results (P < 0.05) for sample interval 2, 3 and 7 (Table 3.3.3), with
relatively higher bare space availability at reef sites for time 2 and 7, whilst the bare space
availability was higher at marina sites for time 3.
Table 3.3.2. Results for PERMANOVA to determine the availability of bare space as a function
of habitat, substratum and sample interval with their interactions.
Source
df
SS
MS
Pseudo-F
P (perm)
HABITAT × SUBSTRATUM × SAMPLE INTERVAL
Habitat
1
8403.9
8403.9
20.168
0.0001
Substratum
1
34121
34121
81.885
0.0001
Time
7
9759.7
1394.2
3.3459
0.0007
Habitat × Substratum
1
312.97
312.97
0.75108
0.4094
Habitat × Sample interval
7
8135.1
1162.2
2.789
0.0037
Substratum × Sample interval
7
2871.4
410.2
0.98441
0.4456
Habitat × Substratum × Sample interval
7
2768.4
395.48
0.94909
0.4778
Residuals
448
1.8668E5
416.7
Total
479
2.6641E5
Table 3.3.3. Results of pairwise PERMANOVA test for bare space as a function of habitat
type, substratum type and habitat × sample interval. Significance marked in bold (P < 0.05).
t
P (perm)
Habitat (Reef vs Marina)
4.49
0.0001
Substratum (PVC vs Slate)
9.05
0.0001
Groups
Habitat × Sample interval
Avg. similarities
Reef
Marina
Reef × Marina
72.48
73.78
72.10
PVC
Slate
PVC × Slate
72.15
78.49
69.91
Reef
Marina
Reef ×Marina
Time 1
1.666
0.084
69.55
72.43
67.99
Time 2
2.557
0.011
73.42
73.20
73.75
Time 3
4.058
0.001
72.54
74.03
58.99
Time 4
0.889
0.423
75.12
72.83
74.51
Time 5
0.924
0.385
80.88
73.11
76.70
Time 6
1.258
0.162
66.24
80.81
70.91
Time 7
2.602
0.007
78.10
77.39
73.30
Time 8
0.793
0.483
79.72
74.78
77.23
98
Figure 3.3.2 indicates the temporal variation in available bare space observed in this
study. The tiles at the time of deployment (Time 0) were barren, i.e. 100% bare space
availability with nearly 64% bare space availability within 3 months. Settlement of species led
to variations in bare space availability over time, indicating rapid settlement within 3 months
(Time 1). Throughout the study, there was always ̴ 20% available bare space for settlement and
species cover did not reach its limit (0% bare space). However, a clear trend of an increasing
number of species indicated the variations in recruitment and settlement of fouling community
over time, with rapid settlement within the first 3 months (Time 1) of deployment of the bare
tiles (PVC and slate).
Bare space availability
100
100
90
90
80
80
70
70
60
60
50
50
40
40
30
30
20
20
10
10
Species richness (%)
Bare space availability (%)
Species richness
0
0
0
1
2
3
4
5
Sample Time
6
7
8
Figure 3.3.2. Temporal variation of average bare space availability (%) and species richness (%) by
point counts (100-point grid) for the 2-year study (irrespective to habitat type and substratum type).
99
3.3.4. Fouling community ordination
Two-dimensional MDS ordination plots to see if the communities varied as a function of
habitat type, substratum and time showed a cluster of points in the centre with no distinct
separation between habitat type, substratum and sampling time. However, the high-stress level
of the plot (2D Stress: 0.28) indicated that the 2-D plot could not represent well the
multidimensional nature of the data set (Figure 3.3.3 a, b & c). Examination of the 3-D plot
revealed a stress level of 0.20, which is the generally accepted upper limit for such a plot
(Clarke 1993). The 3-D plots based on habitat type (marina vs reef) and substratum type (PVC
vs slate), showed evidence of segregation in the communities for each group, but both the
groups clustered in the centre. In terms of sampling intervals, the communities showed some
evidence of distinct clusters for each sampling interval, although there was still a level of cluster
overlap (Figure 3.3.3 c).
a)
b)
c)
Figure 3.3.3. Two-dimensional MDS plot based on community composition between a) habitat
type (marina and reef) b) substratum type (PVC and slate) and c) sampling interval (1-8).
100
The dataset was averaged for each sampling interval (1-8), and substratum type (PVC
and slate) for each study site. MDS plots were generated for each site (6 sites) to examine the
change in community composition over time. The paired tiles (PVC and slate) for each
sampling time tended to group (Figure 3.3.4) with the clear transitions between sampling times
(time intervals 1-8), indicating that the fouling communities changed continuously between
consecutive sampling times. As expected, all six site-specific plots showed a trajectory of
change, from the initial colonisation community (sample interval 1) through to the final
community (sample interval 8).
CM
OB
EB
SB
SM
SR
Figure 3.3.4. MDS ordination of temporal variation of the fouling community on three marina sites
(CM= Chaffers marina, EB= Evans Bay marina, SM= Seaview marina) and three reef sites (OB=
Oriental Bay, SB= Shelly Bay, SR= Sorrento Bay). Sample labelling: P = PVC substratum and S =
Slate substratum; numbers denoted sampling intervals: 1= November 2017, 2= February 2018, 3= May
2018, 4= August 2018, 5= November 2018, 6= February 2018, 7= May 2018 and 8= August 2018.
101
3.3.5. Variations in the fouling community composition as a function of habitat, site
(habitat), substratum and sample intervals
Multivariate PERMANOVA of the fouling community composition revealed that all terms
were statistically significant (P < 0.05) and that most were highly significant (P < 0.001) (Table
3.3.4).
Table 3.3.4. Permutational ANOVA (PERMANOVA) analysis used to determine differences
in community composition between factors: habitat type (2 levels), site (habitat) (6 levels),
substratum type (2 levels) and sampling intervals (8 levels), Significant value in bold (P <
0.05).
Source of variation
df
SS
MS
Pseudo-F
P (perm)
Habitat
1
53993
53993
37.112
0.0001
Substratum
1
25917
25917
17.814
0.0001
Sample interval
7
2.69E+05
38385
26.384
0.0001
Sites (Habitat)
4
1.15E+05
28668
19.705
0.0001
Habitat × Substratum
1
6990.7
6990.7
4.805
0.0001
Habitat × Sample interval
7
56417
8059.6
5.5397
0.0001
Substratum × Sample interval
7
22258
3179.7
2.1856
0.0001
Sites (Habitat) × Substratum
4
8925.2
2231.3
1.5337
0.0173
Sites (Habitat) × Sample interval
28
1.77E+05
6331.1
4.3517
0.0001
Habitat × Substratum × Sample interval
7
17342
2477.4
1.7028
0.0005
Sites (Habitat) × Substratum × Sample
28
57669
2059.6
1.4157
0.0001
Residuals
384
5.59E+05
1454.9
Total
479
1.37E+06
interval
i.
Habitat type
The pairwise PERMANOVA revealed that the reef sites exhibited slightly more within-group
community similarity (29.35%) than the marina sites (26.12%) and that this difference was
significant (P < 0.001) (Table 3.3.5). At both the habitat types, the same suite of species was
observed. However, the difference in abundances of species explained the dissimilarity
between habitat type (Table 3.3.6 & Table 3.3.7). For instance, overall, Biofilm type 1 and
Green sp. 1 were the dominant contributors for average similarity and dissimilarity between
habitat type. A subset of 7 species for marina and reef habitats explained most patterns of
102
similarity and dissimilarity; Biofilm type 1, Green sp. 1, Ralfsia verrucosa, Bangia
atropurpurea,
Watersipora
subtorquata,
Asterocarpa
humilis
and
Membranipora
membranacea (Table 3.3.7).
ii.
Substratum type
The pairwise PERMANOVA revealed that the PVC substratum exhibited more within-group
community similarity (29.89%) than the slate substratum (23.80%) and that this difference was
significant (P < 0.001) (Table 3.3.5). Similar to habitats, a similar suite of species was
observed. However, the differences in abundances of species explained the dissimilarity
between substratum type (Table 3.3.6 & Table 3.3.7). Biofilm type 1 and Green sp. 1 were the
main contributors among PVC vs slate substratum similarity and between PVC vs slate
substratum dissimilarity. A subset of 7 species for PVC and slate substratum explained the
most patterns of similarity and dissimilarity; Biofilm type 1, Green sp. 1, Ralfsia verrucosa,
Bangia atropurpurea, Watersipora subtorquata, Asterocarpa humilis and Membranipora
membranacea (Table 3.3.7).
Table 3.3.5. Results of pairwise PERMANOVA test performed on fouling communities as a
function of habitat and substratum with the average similarities between groups.
Groups
t
P (perm)
Unique perms
Habitats
6.09
0.0001
9940
Marina vs Reef
Substratum
PVC vs Slate
4.22
0.0001
9921
Average similarity (%)
Marina
Reef
Marina x Reef
(26.12)
(29.35)
(24.55)
PVC
Slate
PVC x Slate
(29.89)
(23.80)
(25.43)
103
Table 3.3.6. SIMPER results for major species contributing to the average similarity between habitat type and substratum type.
Marina = Average similarity: 26.12
Reef = Average similarity: 29.35
Species
Avg. Abund.
Contrib %
Cum. %
Species
Avg. Abund
Contrib%
Cum.%
Biofilm type 1
2.93
27.33
27.33
Biofilm type 1
3.11
27.49
27.49
Green sp. 1
2.27
17.55
44.88
Ralfsia verrucosa
2.04
16.23
43.71
Bangia atropurpurea
1.88
14.19
59.07
Green sp. 1
2.35
15.58
59.29
Ostrea chilensis
1.27
6.63
65.7
Watersipora subtorquata
1.69
9.95
69.24
Ralfsia verrucosa
1.01
5.11
70.81
Spirobranchus cariniferus
1.22
8.62
77.86
Tunicate sp. 1
1.05
4.97
75.78
Membranipora membranacea
1.39
6.79
84.65
Cladophora
1
4.42
80.2
Asterocarpa humilis
1.16
4.11
88.77
Watersipora subtorquata
0.68
3.2
83.41
Bangia atropurpurea
0.76
2.5
91.27
Membranipora membranacea
0.64
2.77
86.17
Asterocarpa humilis
0.8
2.45
88.63
Spirorbis spirorbis
0.44
2.23
90.86
PVC = Average similarity: 29.89
Slate = Average similarity: 25.43
Species
Avg. Abund.
Contrib %
Cum. %
Species
Avg. Abund
Contrib%
Cum.%
Biofilm type 1
3.87
37.78
37.78
Biofilm type 1
2.17
20.02
20.02
2.19
Green sp. 1
2.44
15.63
53.41
Green sp. 1
18.91
38.93
Ralfsia verrucosa
1.98
13.58
66.99
Ralfsia verrucosa
1.07
7.32
46.25
Bangia atropurpurea
1.44
7.19
74.18
Bangia atropurpurea
1.2
7.16
53.41
Watersipora subtorquata
1.29
6.13
80.31
Spirobranchus cariniferus
0.91
6.9
60.31
Membranipora membranacea
0.99
3.67
83.98
Watersipora subtorquata
1.09
6.32
66.63
Spirobranchus cariniferus
0.73
3.25
87.23
Membranipora membranacea
1.05
6.01
72.65
Cladophora
0.84
0.75
2.76
1.91
89.99
91.90
Asterocarpa humilis
1.21
5.52
78.16
Ostrea chilensis
1.05
5.27
83.44
Tunicate sp. 1
0.94
4.26
87.69
Spirorbis spirorbis
0.46
2.97
90.66
Asterocarpa humilis
104
Table 3.3.7. Results for SIMPER analysis. Average similarities within groups and average dissimilarities in fouling communities between habitat
type and substratum type.
Marina & Reef = Average dissimilarity = 74.45
Marina
Reef
Avg. Abund.
Avg. Abund.
Biofilm type 1
2.93
Green sp. 1
Ralfsia verrucosa
Bangia atropurpurea
Watersipora subtorquata
Asterocarpa humilis
Membranipora membranacea
Ostrea chilensis
Cladophora
Tunicate sp. 1
Spirobranchus cariniferus
Clathrina sp.
Bugula stolonifera
Bugula flabellata
Spirorbis spirorbis
Biofilm type 2
Coralline algae
Ulva lactuca
Schizoporella errata
Tunicate sp. 2
Botrylloides leachii
2.27
1.01
1.88
0.68
0.8
0.64
1.27
1
1.05
0.42
0.45
0.62
0.44
0.44
0.15
0.41
0.35
0.27
0.18
0.35
Species
PVC & Slate = Average dissimilarity = 74.57
PVC
Slate
Avg. Abund.
Avg. Abund.
11.16
3.87
21.84
29.09
35.92
41.93
47.54
52.93
57.64
62.03
66.37
70.55
73.97
76.72
79.07
81.23
83.37
85.11
86.8
88.29
89.71
90.86
2.44
1.98
1.44
1.29
0.75
0.99
0.59
0.84
0.61
0.73
0.59
0.38
0.43
0.34
0.26
0.28
0.25
0.24
0.19
0.2
Contrib %
Cum. %
3.11
11.16
2.35
2.04
0.76
1.69
1.16
1.39
0.37
0.46
0.5
1.22
0.69
0.25
0.35
0.36
0.47
0.15
0.19
0.24
0.28
0.02
10.68
7.25
6.83
6.01
5.61
5.38
4.72
4.39
4.33
4.19
3.42
2.75
2.35
2.16
2.14
1.74
1.69
1.49
1.42
1.15
Contrib %
Cum. %
2.17
11.96
11.96
2.19
1.07
1.2
1.09
1.21
1.05
1.05
0.62
0.94
0.91
0.56
0.49
0.36
0.46
0.36
0.27
0.29
0.27
0.26
0.17
10.82
7.15
6.57
5.77
5.65
5.28
4.59
4.4
4.29
3.93
3.44
2.74
2.37
2.19
2.13
1.74
1.7
1.5
1.43
1.14
22.79
29.94
36.51
42.28
47.93
53.21
57.8
62.2
66.49
70.42
73.85
76.59
78.95
81.15
83.28
85.02
86.72
88.22
89.65
90.79
105
iii.
Habitat × Substratum
Fouling community composition as a function of habitat and substratum (P < 0.001) exhibited
relatively more within-group similarity on PVC than on slate at both marina and reef habitats
(pairwise PERMANOVA tests; Table 3.3.8).
Table 3.3.8. Results of pairwise PERMANOVA test performed on fouling communities as a
function of the interaction between habitat × substratum with the average similarities between
groups.
Groups
t
P (perm)
Unique perms
3.65
0.0001
9930
Average similarity (%)
Habitat x Substratum
Marina
PVC vs Slate
2.97
Reef
PVC vs Slate
0.0001
9953
PVC
Slate
PVC x Slate
(30.04)
(24.49)
(24.99)
PVC
Slate
PVC x Slate
(33.18)
(26.66)
(28.79)
In summary, the fouling community composition as a function of habitat and
substratum and their interactions indicated statistically significant results (PERMANOVA; P
< 0.001). Pairwise PERMANOVA indicated relatively more within-group community
similarity at reef sites than at marina sites and on PVC substratum than on slate substratum.
However, when comparing the fouling community composition between substrata at each
habitat, PVC substratum had more within-group similarity than that on slate substratum at both
reef and marina habitats. SIMPER results also revealed a similar suite of 2 species contributing
to > 50% within-group similarity and 7 species > 50% between-group dissimilarity [Habitat
(Marina vs Reef); Substratum (PVC vs Slate)] in the relative abundance of the community.
iv.
Sample interval
The fouling community composition differed significantly between sample intervals (P <
0.001). As expected, Time 1 (41.19%) had relatively fewer species contributing to its withingroup similarity, with an increase in fouling community species diversity over time resulting
in a decrease in within-group similarity over time (Table 3.3.9). However, most of the betweengroup community dissimilarity was explained by the slight differences in abundances of the
observed species.
SIMPER analysis showed that Biofilm type 1 and Green sp. 1 were the first recruits
that appeared on the tiles (they were present within 3 months, by Time 1) suggesting these two
106
species to be opportunistic species which rapidly dominated much of the available bare space.
Biofilm type 1 and Green sp. 1 also consistently contributed the most to the within-group
community similarity and between-group community dissimilarities for all sample intervals.
Biofilm type 1 increased with time (Time 1; Biofilm type 1 = 29.97%) and was the highest at
Time 4 (60.08%) and decreased thereafter, suggesting Biofilm type 1 may be an ephemeral
species, i.e. short-lived species. Additionally, Green sp. 1 showed a bimodal trend, with a
decrease at Time 4, which marks the austral winter, suggesting that abundance of Green sp. 1
may be seasonally dependent (Figure 3.3.5). As observed, the dominance of Biofilm type 1 and
Green sp. 1 changed over time and was replaced by the dominance of the red alga, Bangia
atropurpurea at Time 8, i.e. the end of the 2-year study. Other contributors to top-ranked
abundance included Asterocarpa humilis, Membranipora membranacea, Ostrea chilensis,
Ralfsia verrucosa and Watersipora subtorquata. Increase in the abundance of these
aforementioned species was gradual with no interaction observed between these species,
suggesting varied patterns of succession for each species (Table 3.3.9).
The interaction factor, Habitat × Sample interval, the community composition indicated
significant (P < 0.005) results between habitat type (marina vs reef) for all sampling intervals.
The community composition for the interaction factor Substratum × Sample interval showed
significant results (P < 0.05) each sample interval except for time 1 (P = 0.54) (Table A4).
When comparing the temporal variation of the top 8 species between habitat type and
substratum type (SIMPER, Figure 3.3.5), the species showed a similar overall trend of
percentage cover. The species between habitat type (marina vs reef) indicated a relatively high
abundance of; native tunicate Asterocarpa humilis, cryptogenic Biofilm type 1, native
bryozoan Membranipora membranacea, native crustose brown seaweed Ralfsia verrucosa and
non-native bryozoa Watersipora subtorquata in reef habitat than at marina habitat. Whilst, nonnative red alga Bangia atropurpurea, native oyster Ostrea chilensis, cryptogenic Tunicate sp.
1 and cryptogenic Ulva sp. 1 had relatively high abundances at marina habitat than at reef
habitat.
107
Table 3.3.9. SIMPER results for 10 major consistent species contributing to the average similarity within each sampling interval. A = Avg. Abundance, C% =
Per cent Contribution.
Avg. similarity
Species
Time 1
Time 2
Time 3
Time 4
Time 5
Time 6
Time 7
Time 8
41.19%
37.81%
32.49%
39.48%
36.47%
31.69%
26.17%
28.28%
A
C%
A
C%
A
C%
A
C%
A
C%
A
C%
A
C%
A
C%
Asterocarpa humilis
0.23
0.49
0.52
0.74
1.26
4.21
1.4
3.78
1.25
4.3
0.91
2.77
0.77
2.14
1.51
5.57
Bangia atropurpurea
0.1
0.06
0.45
0.54
1.00
4.82
1.53
7.11
0.83
2.29
1.42
7.19
2.22
18.42
3.02
29.9
Biofilm type 1
3.34
29.97
2.04
13.3
3.77
35.47
6.13
60.08
4.09
41.11
1.97
14.63
1.68
7.54
1.15
4.37
Green sp. 1
3.84
44.23
4.76
43.96
1.86
8.96
0.37
0.24
3.55
27.14
2.83
21.64
0.72
1.28
0.59
0.79
Membranipora membranacea
0.34
0.78
0.59
1.23
1.37
4.37
1.12
5.23
1.1
2.87
0.84
3.27
1.59
11.95
1.21
5.92
-
-
0.12
0.11
0.77
2.4
0.83
2.73
1.07
3.36
0.9
2.26
1.32
7.39
1.52
7.89
Ralfsia verrucosa
0.77
2.41
2.62
22.37
1.65
11.66
0.69
1.75
0.68
2.14
1.88
10.8
1.96
13.92
1.94
13.65
Watersipora subtorquata
0.09
0.11
0.99
4.08
1.75
8.92
1.09
3.67
0.92
3.3
1.49
8.46
1.7
11.91
1.46
7.47
Ostrea chilensis
108
a) Man-made habitat
b) Natural habitat
50
50
Percent cover (%)
Percent cover (%)
40
30
20
10
40
30
20
10
0
1
2
3
4
5
6
7
0
8
1
2
3
4
Sample interval
6
7
8
Sample interval
d) Natural substratum
c) Man-made substratum
70
70
60
60
Percent cover (%)
Percent cover (%)
5
50
40
30
20
10
50
40
30
20
10
0
0
1
2
3
4
5
Sample interval
6
7
8
1
2
3
4
5
6
7
8
Sample interval
Figure 3.3.5. The percent cover (SIMPER) of top 8 major consistent species contributing to within-group similarity for each sample interval
indicating variations over time at a) man-made habitat b) natural habitat c) man-made substratum and d) natural substratum
109
3.3.6. Species status (native, non-native and cryptogenic) within the fouling community
a) Species status and their occurrences
The 47 putative species were classed according to their status as native, non-native and
cryptogenic. There were 19 native species, 12 non-natives and 16 cryptogenic species (Table
3.3.1).
b) Species status as a function of habitat, site, substratum and sample interval
Multivariate PERMANOVA for the species status revealed that all main factors were
statistically significant (P < 0.001), with significant interaction between Habitat × Sample
interval and Site (Habitat) × Sample interval (P < 0.001), indicating variations in the species
status between habitat type/site (habitat) over time. Other interaction terms were not significant
(Table 3.3.10).
Table 3.3.10. Permutational ANOVA (PERMANOVA) analysis used to determine differences
in the status of the species between factors: habitat type (2 levels), site (habitat) (6 levels),
substratum type (2 levels) and sampling time (8 levels), Significant value in bold (P < 0.05).
Source
df
SS
MS
Pseudo-
P
Unique
F
(perm)
perms
Habitat
1
2759.6
2759.6
8.1738
0.0003
9966
Substratum
1
4920
4920
14.573
0.0001
9961
Time
7
43896
6270.9
18.574
0.0001
9914
Site (Habitat)
4
20889
5222.2
15.468
0.0001
9942
Habitat × Substratum
1
807.32
807.32
2.3913
0.0911
9952
Habitat × Time
7
19211
2744.5
8.1291
0.0001
9913
Substratum × Time
7
4158.6
594.08
1.7597
0.04
9918
Site (Habitat) × Substratum
4
2302.6
575.65
1.7051
0.089
9931
Site (Habitat) × Time
28
53135
1897.7
5.6208
0.0001
9871
Habitat × Substratum × Time
7
2238.2
319.74
0.94705
0.5121
9925
Site (Habitat) × Substratum × Time
28
11276
402.72
1.1928
0.164
9828
Residuals
384
1.30E+05
337.61
Total
479
2.95E+05
110
i.
Habitat type
Numbers of occurrences as a function of the status of the species - native, non-native and
cryptogenic- were similar as a function of time at reef and marina habitats (Figure 3.3.6).
However, PERMANOVA revealed a significant difference (P < 0.001) between the reef and
marina habitats, but the within-group similarity was very similar (Reef = 70.09%; Marina =
69.01%; Table 3.3.11). SIMPER analysis further revealed that the cryptogenic species
contributed most to similarity at both reef (47.44%) and marina sites (47.33%) followed by
native species (Reef = 37.15%; Marina = 31.70%). The non-native species were abundant
significantly (P < 0.05; Table 3.3.12) at marina sites (20.97%) than reef sites (15.41%). The
cryptogenic species contributed most to the between-group dissimilarity (Table 3.3.13 a).
a) Reef habitat
b) Marina habitat
Figure 3.3.6. Temporal change in the number of species with respect to species status (native, nonnative and cryptogenic) as a function of habitat type (Reef vs Marina)
Table 3.3.11. Results of pairwise PERMANOVA test performed on species status as a function
of habitat type and substratum type with the average similarities between groups.
Groups
Habitat
t
P (perm)
Avg. similarities
Reef
Marina
Reef × Marina
2.88
0.0004
70.09
69.01
69.29
3.83
0.0001
PVC
Slate
PVC × Slate
71.53
67.86
69.14
(Reef vs Marina)
Substratum
(PVC vs Slate)
111
Table 3.3.12. Results of pairwise PERMANOVA test performed for non-native species as a
function of habitat type and substratum type.
Groups
Habitat
(Reef vs Marina)
Substratum
(PVC vs Slate)
ii.
t
P (perm)
Unique perms
1.53
0.03
9923
0.88
0.56
9924
Substratum type
The number of native, non-native and cryptogenic species as a function of time on PVC and
slate substrata were similar (Figure 3.3.7). However, PERMANOVA revealed a significant
difference (P < 0.001) between PVC and slate substrata, with PVC exhibiting greater withingroup similarity (71.53%) than the slate substratum (67.86%) (Table 3.3.11). SIMPER analyses
revealed that cryptogenic species followed by native species contributed most to the withingroup similarities and between-group dissimilarities for both substrata. (Table 3.3.13 b). The
non-native species observed to contribute more on slate substratum than PVC; however, their
difference was not significant (Table 3.3.12).
a) Slate substratum
b) PVC substratum
Figure 3.3.7. Temporal change in the number of species with respect to species status (native, nonnative and cryptogenic) as a function of substratum type (PVC vs Slate)
112
Table 3.3.13. SIMPER results for species status contributing to the average within-group similarity and dissimilarity between a) habitat type (Reef
vs Marina and b) substratum type (PVC vs Slate).
a)
Marina: Average similarity = 69.01%
Species
Status
Avg.
Contrib Cum.
Abund.
%
%
Habitat (Marina vs Reef)
Reef: Average similarity = 70.09%
Species
Status
Avg.
Contrib
Cum.%
Abund.
%
Average dissimilarity = 30.71%
Avg.
Avg.
Contrib Cum.
Abund. Abund.
%
%
Marina
Reef
Species
Status
Cryptogenic
5.69
62.09
62.09
Cryptogenic
5.96
76.27
76.27
Cryptogenic
5.69
5.96
34.50
34.50
Native
4.27
29.64
91.73
Native
4.87
20.65
96.92
Native
4.27
4.87
32.91
67.40
Non-native
3.28
8.27
100
Non-native
2.95
3.08
100
Non-native
3.28
2.95
32.60
100
b)
PVC: Average similarity = 71.53%
Species
Substratum (PVC vs Slate)
Slate: Average similarity = 67.86%
Average dissimilarity = 30.86%
Contrib
%
Cum.
%
Species
Status
Avg.
Abund.
50.80
Cryptogenic
5.23
44.28
44.28
31.57
82.37
Native
4.55
37.24
17.63
100
Non-native
3.00
18.47
Contrib
%
Cum.
%
Species
Status
Avg.
Abund.
Cryptogenic
6.40
5.80
Native
4.59
Non-native
3.23
Status
Avg.
Avg.
Abund. Abund.
Contrib
%
Cum.
%
Slate
PVC
Cryptogenic
5.25
6.40
35.31
35.31
81.53
Native
4.55
4.59
32.41
67.72
100
Non-native
3.00
3.23
32.28
100
113
iii.
Sample interval
The PERMANOVA tests revealed that species status - native, non-native and cryptogenic
species - exhibited significant variation (P < 0.001) between sample intervals irrespective of
the effects of habitats or substratum, with >50% within-group species status similarity for all
sample intervals. SIMPER analysis of species status at each sample interval revealed that
cryptogenic species were the most abundant group contributing to within-group similarity,
followed by native species (Table 3.3.14). However, with time, the cryptogenic species
decreased with an increase in the abundance of native and non-native species. This trend was
also observed for species status as a function of sample interval at both habitat type and
substratum type (Table 3.3.15).
Table 3.3.14. Major species status contributing to the average similarity within each sampling
interval. A = Avg. Abundance, C% = Per cent Contribution.
Average similarity
Species Status
Time 1
Time 2
Time 3
Time 4
73.57%
70.56%
69.60%
70.86%
A
C%
A
C%
A
C%
A
C%
Cryptogenic
6.16
66.45
5.82
46.60
5.44
40.13
6.91
52.66
Native
3.71
31.75
4.50
36.43
4.86
34.37
4.30
28.03
Non-native
0.58
1.80
2.98
16.47
3.93
25.50
3.39
19.31
Average similarity
Species Status
Time 5
Time 6
Time 7
Time 8
71.04%
62.62%
67.33%
67.31%
A
C%
A
C%
A
C%
A
C%
Cryptogenic
6.47
56.32
6.66
51.39
5.06
36.40
4.11
26.97
Native
4.11
29.21
4.37
29.02
4.97
37.29
5.72
43.15
Non-native
2.40
14.47
3.21
19.59
3.99
26.31
4.45
29.88
114
Table 3.3.15. The temporal variation of species status (percent contribution) as per SIMPER
results for habitat type (reef vs marina) and substratum type (slate vs PVC).
Habitat type × Sample interval
Reef habitat
Sampling
interval
Cryptogenic
Native
Non-native
1
2
3
4
5
6
7
8
66.73
32.63
0.63
51.13
40.42
8.46
31.05
44.3
24.65
53.82
30.7
15.47
49.12
38.8
12.08
59.29
25.11
15.6
31.66
44.25
24.1
28.22
33.89
37.89
Marina habitat
Sampling
interval
Cryptogenic
Native
Non-native
1
2
3
4
5
6
7
8
65.22
31.25
3.54
39.25
30.77
29.97
48.57
26.25
25.17
51.19
25.64
23.18
62.49
21.19
16.31
43.67
32.88
23.45
40.82
31.17
28.01
24.99
52.6
22.41
Substratum type× Sample interval
Slate substratum
Sampling
interval
Cryptogenic
Native
Non-native
1
2
3
4
5
6
7
8
65.81
32.34
1.85
41.17
44.1
14.73
34.78
35.76
29.46
46.53
31.66
21.81
48.45
36.09
15.46
54.95
28.31
16.74
35.21
36.8
27.99
25.18
46.03
28.79
PVC substratum
Sampling
interval
Cryptogenic
Native
Non-native
1
2
3
4
5
6
7
8
67.48
30.88
1.65
52.09
29.33
18.58
46.04
32.57
21.39
58.81
24.47
16.73
64.52
22.4
13.09
47.65
29.63
22.72
37.58
37.93
24.5
28.97
40.24
30.79
115
3.3.7. Regression between the native and non-native species as a function of the sample
interval
The relationships of native and non-native species number were strongly positive, with
significant positive relationships for all 8 sample intervals at both habitat type (r2 = 0.58, r =
0.76, P < 0.001) and substratum type (r2 = 0.58, r = 0.76, P < 0.001) (Figure 3.3.8). These
results suggest that; an increase in the number of native species led to an increase in the number
of non-native species over time.
Number of non-native species
a) Habitat
r2 = 0.2965;
r = 0.5445,
P = 0.0292;
y = 2.5152 + 0.3723*x
Number of native species
b) Substratum
r2 = 0.3725;
r = 0.6103,
P = 0.0121;
y = 3.2474 + 0.3196 *x
Number of native species
Figure 3.3.8. Correlation of number of native and non-native species as a function of time at a) habitat
type (Reef vs Marina); b) substratum type (Slate vs PVC).
116
3.4. Discussion
Humans have contributed significantly to the modification of the marine environment and the
corresponding impacts of these modifications on the associated marine assemblages (Airoldi
& Beck 2007; Firth et al. 2016; Ruiz et al. 2009). This study aimed to examine the effect of
natural and man-made built habitats (Reef vs Marina) and substrata (Slate vs PVC) on a fouling
community. The settlement tiles (Slate & PVC) were deployed at 3 reef sites and 3 marina sites
over two years, with tri-monthly sampling. To my knowledge, this study is the first to examine
and compare fouling community and species status (native, non-native, cryptogenic) using an
experimental setup of slate and PVC tiles at reef and marina habitats which are analogous to
natural and man-made habitats or structures in New Zealand. I observed a total of 47 species
on 480 tiles, consistent with species number observed in other studies concerning artificial
structures (Glasby 1999; Connell & Glasby 1999; Bacchiocchi & Airoldi 2003; Firth et al.
2013). The majority of the species identified were observed to have settled by ̴ 6 months of
the study period.
3.4.1. Availability of bare space
In this study, the bare space availability varied significantly between habitat type; however, the
differences were too small to be of ecological importance. Considering the substratum type,
the bare space availability significantly varied between PVC and slate tiles with relatively more
available bare space on a slate tile. However, the temporal variations in the availability of bare
space between substratum type were similar. The temporal and spatial changes indicate the rate
of change of bare space availability varied with temporal changes in the development of species
community. The rapid settlement of species covered ̴ 64% of the tile within 3 months of
immersion of the bare tiles (100% bare space). However, the percentage cover of species did
not reach 100%, indicating the availability of bare space for future settlements.
3.4.2. Fouling community composition as a function of habitat type and substratum type
Species number was observed to be similar between habitats (Reef vs Marina) and substrata
(Slate vs PVC). Upon further analysis, the species abundances (i.e., not presence/absence) and
per cent contributions to the group similarity differed between both habitat type and substratum
type resulting in significant differences. These results are consistent with studies comparing
natural rocky reefs and seawalls as artificial structures that observed similar species taxa
between both habitats, even if community structure and abundances were different (Chapman
2003; Bulleri et al. 2005; Albano & Obenat 2019). These authors speculated the differences in
117
assemblages to intrinsic features of the substrates at both habitats and physical attributes such
as wave-exposure. Previous studies using settlement plates have shown differences in
community composition at the initial stages, but over time the community became more similar
(Anderson & Underwood 1994; Andersson et al. 2009). This highly depends on the seasons of
submersion of settlement tiles and the recruitment or settlement time of the species (Anderson
& Underwood 1994). Submersion time is an essential factor in any community structure
comparison study. Submersion time coinciding with reproductive periods of species can lead
to high larval settlement due to the availability of free space (Anderson & Underwood 1994;
Andersson et al. 2009; Smith et al. 2014).
Upon further analysing the species composition between substratum-type and habitattype, respectively, at each sample time, a similar suite of species colonised the Slate and PVC
tiles at Reef and Marina habitats but with slightly differing abundances. These results suggest
that differences between Reef vs Marina habitat and Slate vs PVC substratum, although
statistically significant, maybe due to small-scale temporal and spatial differences in the
recruitment patterns of each species (Chang & Turner, 2019). Ultimately, in this study, these
small-scale differences that most likely reflect stochastic processes, give rise to similar patterns
of ecological succession between both habitat type and substrata type in Wellington Harbour.
Whilst the statistical analyses revealed highly significant differences in community
composition (based on differences in abundance, not in species presence) between habitats and
substrata, the importance of these highly significant differences from an ecological point of
view is still not clear. The man-made habitat (marina) and substratum (PVC) do not promote
or degrade species richness and community composition, although many other studies have
shown this to be the case. Therefore, indicating that relative to adjacent natural reefs (~200 m),
i.e. local spatial scale, marinas do not degrade species richness. However, this might not be the
case in terms of broader scale effect.
This study also revealed similar multi-species succession trajectories between habitat
type and substratum type. For example, the transition of Biofilm type 1, which was the
dominant species led to the dominance of red algae, Bangia atropurpurea and other encrusting
species by the end of the 2-year study. Within 3 months of immersion, the initially bare
settlement tiles (PVC vs Slate) ̴ 64% of all observed species were found amongst the initial
colonisers (Biofilm type 1 and Green sp. 1). Early settlers are also known to modify a habitat,
which may make it habitable for later recruiting species (Connell 1972; Morand & Briand
1996; Dang & Lovell 2000; Lewis et al. 2003; Salta et al. 2013; Lotze et al. 2020). This finding
118
is consistent with earlier observations that new, barren, surfaces are more prone to be colonised
by rapidly growing opportunistic species (Sousa 1979; Fletcher & Callow 1992; Dafforn et al.
2012; Tan et al. 2015). In this study, Biofilm type 1 readily settled on both PVC and slate
substratum; however, the percent contribution to the within-group similarity by Biofilm type 1
was 38% on PVC substratum whereas 20% on the slate substratum. This difference, however,
did not impact the community composition or succession patterns.
Biofilm type 1 was the major space occupant, at least initially until it reached a peak of
abundance (cover on the tiles) at about 1 year into the 2-year study, before declining
dramatically in abundance after that. The other colonists observed in the study were encrusting
species such as Ostrea chilensis, Asterocarpa humilis, Ralfsia verrucosa, Watersipora
subtorquata and Membranipora membranacea which have slow growth rates and occupancy
of the available space (Chapman 2012). However, it is not clear in this study if the Biofilm
Type 1 facilitated the establishment of other species. Besides, the recruitment and community
development may be largely dependent on local recruitment, reproduction and growth rates
which are likely to vary with time, available space and nutrients (Smith et al. 2014).
Presumably, the reproduction time of a given species with available bare space for settlement
led to the settlement of the other species. Whether the species composition would change given
a longer time for colonisation or whether the red algae, Bangia atropurpurea observed
represents as ‘climax state’ remains unknown.
Grazers are known to influence the species distribution, abundance and diversity of
algal species (Williams et al. 2013). Grazers are dependent on space availability and predation
intensity (Williams et al. 2000). The grazers were not quantified in this study which may have
provided more information about the algal succession and similarities in other species
succession patterns. The other factor impacting patterns of colonisation are physical factors
such as environmental stress, disturbance, availability of resources, space and functional
characteristics like recruitment, competition, growth, dispersal and reproduction rate of a
species (Sousa 1985; Menge et al. 1986; Benedetti-Cecchi 2000; Petes et al. 2007). A study
conducted on a larger scale comparing intertidal species composition and biogeographic
patterns between New Zealand and New South Wales, Australia, found relatively similar
species composition and contribution of major taxa due to similarity in various other patterns
such as temperature and latitude that regulate the communities in these areas (Schiel et al.
2019). Hence, determining the changes in physical and biological processes at specific habitats
at a given time may also explain the variations in species diversity and species composition
119
(Tilman et al. 1994). Unfortunately, environmental data for this study could not be processed
due to failure in calibrations; however, this is one factor to be considered for future research.
Wellington Harbour is one of the largest natural harbours in the Southern Hemisphere and is
heavily modified by port development, indicating that it is a ‘disturbed’ environment.
Therefore, dispersal of species from artificial structures to nearby habitats, thereby altering the
developing community may be a cause-effect of disturbance (Sousa 1979; Chapman 2012).
3.4.3. Species status – native, non-native and cryptogenic species
Analyses of multi-species community composition (described above) provide a powerful
approach by which to identify how and perhaps why built environment when compared to the
natural environment influences biological diversity. Another approach, using the species status,
i.e., native, non-native, cryptogenic allows for a more definitive test of if and how native and
non-native biodiversity make use of natural (reef) and man-made environments in coastal
regions.
Artificial structures may provide habitats for certain species that are not found on
natural rocky reefs because of their low predation rates and increased availability of bare space
(Bulleri & Airoldi 2005). The native, non-native and cryptogenic species significantly differed
between Reef vs Marina habitats and between PVC vs Slate substrata. The cryptogenic species
were the dominant species status between habitat type and substrata type. For non-native
species, the species were relatively abundant at marina sites. This confirms my hypothesis that
non-native species are more abundant than native species at the man-made habitats (marina)
relative to natural habitats. These results coincide with many other studies where a high number
of non-native species are observed at artificial habitats (Connell 2001; Bacchiocchi & Airoldi
2003; Airoldi et al. 2005; Bulleri & Airoldi 2005; Airoldi & Beck 2007; Glasby et al. 2007;
Perkol-Finkel et al. 2012). However, the non-native species were abundant on PVC substratum;
however, the difference of non-native species between PVC vs Slate substrata was not
significant.
The number of native and non-native species were positive and strong correlated for
each time at both habitat type and substratum type. However, a previous comparative study of
different substratum types indicated that the non-native tunicates increased in abundance on
artificial substrates, with an associated decline in other native species (Tyrrell & Byers, 2007).
Non-native species are generally seen as a threat to the native species richness. However, if a
habitat is conducive to native species with sufficient resources and space, it also provides an
120
opportunity for non-native species (Davis 2003). Therefore, the quality of a habitat determines
the settlement of species irrespective to the species status (Sax 2002; Stohlgren et al. 2003).
These results are in accordance with the ecological theory stating effective ‘biotic acceptance
hypothesis’ (Stohlgren 2003, 2006). The ‘biotic acceptance hypothesis’ defined as the
establishment and coexistence of introduced species despite the presence and abundance of
native species (Stohlgren et al. 2006). Another ecological theory to consider for this study is
the ‘empty niche’ hypothesis where the ecosystem is not saturated with native species, and the
non-native species occupy the vacant niches and available resources (Elton 1958; Stohlgren et
al. 2003). However, co-existence of native and non-native species in a habitat further raises
need to analyse for ‘invasional meltdown’. Invasion meltdown’ hypothesis is the presence of
non-native species in a habitat facilitates the invasion of other species, increasing their
likelihood of survival and ecological impact (Simberloff & Von Holle 1999). That being said,
there is a need for a comprehensive study to observe if we see the same positive relationship
between native and non-native species at a large spatial and temporal scale.
In summary, these results suggest that the differences between Reef vs Marina habitat
and Slate vs PVC substratum, although statistically significant, maybe due to small-scale
temporal and spatial differences in the recruitment patterns of each species (Chang & Turner
2019). These small-scale differences that most likely reflect stochastic processes give rise to
similar patterns of ecological succession between both habitat type and substratum type in
Wellington Harbour. The present study also highlighted the importance of species statusnative, non-native and cryptogenic with different habitat types and substratum types. Although
the native species were predominant, the non-native species were relatively abundant on PVC
tiles and marina sites. These results indicate relatively more preference of non-native species
towards man-made substratum and habitats. Besides, the native and non-native species
positively correlated, indicating co-existence of the same. Given these observations, future
studies should focus more on basic knowledge of the life-history traits and functioning of the
species at various trophic levels, which will give a better understanding of species-specific
interactions. Longer duration of experimental study will aid with acquiring complex
community to have an overall knowledge of the species composition and interactions.
This study from a management point of view informs that anthropogenic alterations of
natural habitats can lead to the destruction of natural habitats. Empty niches and disturbed
habitats are a haven for non-native species (Airoldi et al. 2000; Guerra-García et al. 2004;
Erlandsson et al. 2006; Oricchio et al. 2016; Pastro et al. 2017). It is evident from this study
121
that non-native species prefer man-made habitats and substratum. Maintenance of structures or
dredge along the coast can lead to the removal of intertidal species exposing it to the settlement
of non-native species (Airoldi et al. 2005; Airoldi & Bulleri 2011; Bracewell et al. 2013;
Oricchio et al. 2016). Building new port infrastructures along the coast also provides additional
habitats which are relatively more preferred by non-native species than native species, i.e. nonnative more in abundance (Bulleri & Chapman 2010; Dumont et al. 2011; Rivero et al. 2013).
Hardening the coastlines will degrade the quality of the habitat by obstructing the water flow
bringing in nutrients thereby causing competition between native and non-native species for
resources (Clark & Johnston 2009; Piola et al. 2009).
Additionally, the vertical orientation of substrata provides relatively less surface area
to colonise compared to natural reefs. Even though this study showed co-existence of native
and non-native species, there is a chance of provision of other non-native species by existing
non-native species. This study highlights the need for a conservation strategy to manage natural
habitats along the Wellington Harbour coastline. With the increasing modification of natural
habitats to artificial habitats, there is a need to have a comprehensive understanding of the
novel design of structures that can facilitate more native/local species and maintain native
biodiversity (Chapman 2012; Dafforn et al. 2015a, 2015b; O’Shaughnessy et al. 2019). Ecoengineering of artificial structures is still at an experimental stage and may vary at different
habitats. Future studies considering this study as a baseline work in Wellington Harbour should
focus on different potential designs that can enhance the native biodiversity, reduces the nonnative abundance and provide beneficial ecosystem services.
122
CHAPTER 4
DO NATIVE M. GALLOPROVINCIALIS LINEAGE, AND ITS NONNATIVE M. GALLOPROVINCIALIS LINEAGE CONGENER DIFFER IN
REPRODUCTIVE RESPONSE TO MAN-MADE HABITATS?
4.1. Introduction
4.1.1. Importance of habitat type
Modified coastal environments have recently raised many ecological concerns, new challenges
and opportunities for an improved understanding of the management of marine biodiversity in
the modern world (Firth et al. 2016). The heterogeneous natural habitats inhabit more diverse
and complex species, providing refuge from biotic and abiotic stressors (Moschella et al. 2005;
Moreira et al. 2006; Chapman & Underwood 2011; Firth et al. 2013; Firth et al. 2014; Loke et
al. 2014; Aguilera et al. 2014; Oliver et al. 2015; Firth et al. 2016; Loke & Todd 2016). Unlike
natural habitats, the man-made habitats have very different physical characteristics such as the
smooth surface textures, artificial materials (e.g. cement, plastic, etc.) and vertical orientation
(Perkol-Finkel et al. 2006; Dafforn et al. 2012; Perkol-Finkel et al. 2012; Spagnolo et al. 2014;
Cacabelos et al. 2016; Brzozowska et al. 2017; Johnston et al. 2017). The different habitat
structures have a significant impact on the environment as well as the ecological functioning
of the species (Glasby & Connell 2001; Bulleri & Chapman 2004, 2010; Bulleri 2005; Tyrrell
& Byers 2007; Chapman 2012; Tan et al. 2015; Megina et al. 2016).
Numerous studies have investigated the impacts of man-made habitats and substrata on
the population and community structure (Bulleri 2005; Airoldi et al. 2005; Bulleri & Airoldi
2005; Wyatt et al. 2005; Airoldi & Beck 2007; Parsons et al. 2016; Mayer-Pinto et al. 2018).
However, we still know very little about the effects of the man-made structures on the energy
output, i.e., fitness of a species. For instance, Moreira et al. (2006) reported smaller sized limpet
Siphonaria denticulata on seawalls compared to natural rocky reefs leading to reduced
reproductive output. Similarly, Martins et al. (2016) reported smaller sized and low densities
of barnacle Chthamalus stellatus on man-made structures compared to natural rocky reefs.
At a local scale, the physiological processes of a species are directly or indirectly
affected by environment factors temperature, food quality/quantity, wave exposure and water
quality (Green et al. 2011; Rivero et al. 2013; Bagley et al. 2015; Bishop et al. 2017; Heery et
123
al. 2017). In case of port and marinas as man-made habitats, semi-enclosed habitats have
restricted water flow, reduced nutrient availability, increase water temperatures, contaminated,
turbid waters which may have an adverse effect on the physiological processes of a species
(Johnston & Keough 2002; Piola & Johnston 2008; Vaselli et al. 2008; Piola et al. 2009). The
differences in environmental conditions also influence the growth, survival and reproduction
of a species. Furthermore, the inter and intraspecific interactions in a community also influence
the larval dispersal, recruitment, reproduction, growth, predation, competition and co-existence
(Airoldi et al. 2005; Bulleri 2006; Perkol-Finkel et al. 2006; Burt et al. 2009; Quinn et al. 2012;
Munari 2013; Firth et al. 2014; Bishop et al. 2017; Mayer-Pinto et al. 2018); and are therefore
important factors to study the performance of a species on the man-made structures.
4.1.2.
Impacts of man-made structures on bioinvasions
Bioinvasions are an increasing threat to biodiversity worldwide, especially to native
biodiversity (Ruiz et al. 1999; Glasby et al. 2007; Hewitt & Campbell 2007; MacKie et al.
2012; Thomsen et al. 2014; Ojaveer et al. 2015; Cook et al. 2016; Gestoso et al. 2017; Olenin
et al. 2017; Wells 2018; Albano & Obenat 2019). There have been previous studies indicating
the man-made habitats to favour low density of native species and the successful invasion of
non-native species (Ruiz et al. 2000; Byers 2002; Glasby et al. 2007; Bulleri & Chapman 2010;
Airoldi & Bulleri 2011; Airoldi et al. 2015; Marraffini & Geller 2015; Johnston et al. 2017).
Several studies have also indicated that the invasive species have a competitive edge over the
native species as they have relatively high reproductive rate, high survival rate, fast growth and
phenotypic plasticity (Dafforn 2017; Johnston et al. 2017; Simpson et al. 2017; Epstein &
Smale 2018; Riera et al. 2018). Ultimately, non-native species may perform better at an invaded
area than at their native regions (e.g., Parker et al. 2013). Several other studies have raised
concerns regarding hybridisation and introgression between native and non-native species
(Seehausen 2004; Wonham 2004; Roman & Darling 2007; Fauvelot et al. 2009; Pickett &
David 2018); leading to genetic homogeneity in a community.
However, not all non-native species cause negative impacts, and some non-native
species tend to naturalise, i.e. they become established without causing any major impact on
the environment (Simberloff & Von Holle 1999; Mack et al. 2000; Branch & Nina Steffani
2004). Such naturalised species tend to co-exist with the native species in an environment with
plenty of food and space without replacing them (Rius & McQuaid 2006; Zardi et al. 2008;
Nicastro et al. 2010; Dafforn et al. 2012; Ojaveer & Kotta 2015; Zwerschke et al. 2016; Reise
et al. 2017). For example, Ruiz et al. (1999) showed that of the 196-non-native species studied
124
in the Chesapeake Bay (Atlantic coast of the USA), 6% of the species had some measurable
negative impact. Therefore, indicating that invasive species may promote biodiversity. Another
example is that of a study in Chile which reported competition between the non-native tunicate
Pyura praeputialis and native primary space occupiers. These tunicates formed massive mats
along the coast, creating a new habitat for 116 invertebrates and algae, whereas the adjacent
natural (non-invaded) coast had just 66 species (Castilla et al. 2004). A recent meta-analysis
by Katsanevakis et al. (2014) reported that out of 63 non-native species, about 35% species had
a positive impact by increasing the diversity of other species. Therefore, non-native species do
not always have a negative effect on an invaded system, and understanding why this is the case
may be challenging. It is harder still to predict a priori what the outcome of an invasion maybe.
4.1.3. Study species
Mussels are dominant space occupiers on hard substrata, colonising the entire middle region
of the intertidal zone (Paine 1966; Capelle et al. 2016; Hetherington et al. 2019). Mussels are
also ecosystem engineers and play an important role in promoting other species by forming
biogenic reefs and because they are a food source for predators (Crooks 2002; Borthagaray &
Carranza 2007; Sousa et al. 2009; Bertolini et al. 2017). The genus Mytilus (common blue
mussels) is widely distributed, having an anti-tropical distribution around the world (Hilbish et
al. 2000). The genus contains numerous species, but its taxonomy and systematics are still
unresolved in many parts of the world. The widely distributed smooth-shelled Mytilus edulis
species complex has three distinct lineages, Mytilus edulis (Linnaeus 1758), Mytilus
galloprovincialis (Lamarck 1819) and Mytilus trossulus (Gould 1850), all of which are of
Northern hemisphere origin.
Evolutionary evidence suggests allopatric speciation of Mytilus spp., with M. trossulus
being the oldest of the three Northern hemisphere origin Mytilus spp. which first originated in
North Pacific (Vermeij 1991). According to genomic and mitochondrial DNA analysis, M.
edulis and M. galloprovincialis are closely related whilst M. trossulus is most distinct (Geller
1999; Hilbish et al. 2000). About 3.5 M ybp (years before present), first range expansion of M.
trossulus into the North Atlantic via the Bering Strait gave rise to M. edulis (Cunningham &
Collins, 1994; Vermeij, 1991). By ̴ 2 M ybp, sea-level changes led to spread of north Atlantic
M. edulis to Mediterranean Sea (Vermeij 1991). However, persistent sea-level fluctuations led
to the separation between the North Atlantic M. edulis hindering the gene flow, which led to
Mediterranean Sea M. galloprovincialis (Barsotti & Meluzzi 1968; Riginos & Cunningham
2005). These different lines of evidence show how natural range expansion (and associated
125
invasion pressure) has led to hybridisation and introgression between the genetically similar
Mytilus spp. (Seehausen 2004; Wonham 2004; Pickett & David 2018; Gardner et al. 2020).
The second wave of range expansion of North Pacific M. trossulus during the Pleistocene or
Holocene period gave rise to M. trossulus on the Atlantic coast of North America. These two
lineages varied genetically and showed a difference in physiological tolerance (e.g. salinity)
(Gardner & Thompson 2001; Braby & Somero, 2006). Further, RFLP assays and DNA
sequencing by various researchers confirmed M. trossulus, M. edulis and M. galloprovincialis
of the Northern hemisphere origins (Levinton & Koehn 1976; Gardner & Skibinski 1991; Toro
1998; Hilbish et al. 2000; Gérard et al. 2008).
Grant & Cherry (1985) were the first to point out that the blue mussels in South Africa
are not native and are the invasive Northern hemisphere origin M. galloprovincialis by
examining the shell morphometric and genetic variations. This study was of much importance
regarding the taxonomy of blue mussels in the Southern hemisphere. Following that,
McDonald et al. (1991) identified two groups of Southern hemisphere mussels, a South
America groups (Chile, Argentina, the Falkland Islands and the Kerguelen Islands) which had
more M. edulis-like alleles and, an Australasian group including Australia and New Zealand
that had more M. galloprovincialis - like alleles. This has been further confirmed by Borsa et
al. (2007), Pickett & David (2018) and Malachowicz & Wenne (2019). Additionally, Hilbish
et al. (2000) and Gérard et al. (2008), both suggested two Southern hemisphere invasion events
via the North Atlantic Ocean, with first expansion ̴ 1.2 M ybp in South America, i.e. Chile
(Mytilus chilensis), Argentina (Mytilus platensis), the Falkland Islands (Mytilus platensis) and
Kerguelen Islands (Mytilus desolationis) and a second recent expansion ̴ 0.7 M ybp in the
Australasian group i.e. Australia (Mytilus planulatus) and New Zealand (Mytilus aoteanus)
(Gardner et al. 2020) . The nomenclature for these blue mussel sub-species in the Southern
hemisphere has been controversial, with historical samples (i.e., pre-human arrival) being
classified and described with the help of fossil and morphological data (e.g. Gardner 2004).
Recent evidence regarding the ‘cryptic dispersal’ of M. galloprovincialis has suggested
both natural, and human mediation range expansion; together with adaptation and hybridisation
events has led to the absence of distinct differences between Southern and Northern hemisphere
haplotypes (Hilbish et al. 2000; Gérard et al. 2008; Westfall & Gardner 2010; Pickett & David
2018). Considering all these evolutionary evidences of Mytilus spp. range expansion and
molecular studies relating to taxonomy, it is evident that Australasian mussels are most similar
to Northern hemisphere M. galloprovincialis but are, however, native to Southern hemisphere.
126
Fossil evidence indicates the presence of some form of M. galloprovincialis in the Southern
hemisphere before human arrival, but which may have been displaced by, or are co-occurring
with, accidentally introduced Northern hemisphere M. galloprovincialis in some regions
(McDonald et al. 1991; Westfall & Gardner 2013). In New Zealand, concerns about
hybridisation and introgression of Southern and Northern hemisphere lineages were expressed
because the invading mussel may displace the Southern hemisphere lineage M. aoteanus
(Gardner et al. 2016; Zbawicka et al. 2019). There are still many ongoing studies (e.g. SNPsbased work) relating to the taxonomy of the aforementioned species especially the Northern vs
Southern hemisphere M. galloprovincialis lineages and insights into hybridisation and
introgression between the invasive and native mussel (Gardner et al., 2016; Gardner et al.
2020).
Mytilus galloprovincialis is one of the world’s most successful invading species, being
ranked in the Top 100 (Lowe et al. 2000). Mytilus galloprovincialis, the Mediterranean mussel,
has spread from its native Mediterranean coastline to numerous locations in the world except
for Antarctica, including but not limited to the Pacific coast of Canada, the California coast
(Wonham 1999; Anderson & Thompson 2002), Hong Kong (Lee & Morton 1985), Japan
(Wilkins et al. 1983), Australia and New Zealand (reviewed by Daguin & Borsa 2000; Pickett
& David 2018), and Chile (Borsa et al. 2012; Oyarzún et al. 2016). M. galloprovincialis is
cultivated as one of the most important commercial species all over the world; however, in
New Zealand, this species is a pest on native green shell mussel, Perna perna, aquaculture
farms (Forrest & Atalah 2017).
The impacts of M. galloprovincialis on various native species and its congeners have
been studied extensively throughout the years. Previous studies in South Africa have reported
displacement of native species due to the dominance of invasive M. galloprovincialis (Griffiths
et al. 1992; Branch & Steffani 2004). Whilst, Bownes & McQuaid 2006, reported co-existence
but with niche separation for the native Perna perna and non-native M. galloprovincialis in the
south coast of South Africa. Competition for habitat and hybridisation between M.
galloprovincialis and resident dominant M. trossulus were observed on the Pacific coast of
North America and Canada (Wonham 2004; Dutton & Hofmann 2008; Shields et al. 2008,
2010). The M. galloprovincialis is a strong competitor, possess traits which help them
acclimatise to fluctuating water temperatures due to metabolic adjustments, heat shock protein
expression, high fecundity, rapid growth rate and resistance to desiccation (Hockey & Van
Erkom Schurink 1992; Erlandsson et al. 2006; Anestis et al. 2007; Shinen & Morgan 2009).
127
Subsequently, mussels observed at such varied environments experience differences in
physiological processes (Bayne & Thompson 1970). The mussels have strategies to overcome
the variable environments, by maximizing the growth, reproduction and survival processes
(Bayne & Thompson 1970; Hockey & Van Erkom Schurink 1992). Therefore, there might be
differences in life-history traits such as reproduction timing (lifecycles), feeding or growth in
different habitats (for example, natural vs man-made habitats).
In New Zealand, the impacts of the non-native Northern hemisphere M.
galloprovincialis lineage on the native Southern hemisphere M. galloprovincialis lineage is not
yet known. This is especially the case in the context of how man-made habitats influence the
invasion success of Northern hemisphere lineage (henceforth, NHMg) vs Southern hemisphere
lineage (henceforth, SHMg) mussels. For co-occurring mussel lineages (native vs non-native)
where interspecific competition is taking place, differences in the energy utilised for
reproduction, growth and byssus thread production for attachment may explain the outcome of
competitive interactions (e.g., Gosling 1992; Steffani & Branch 2003). Therefore, examining
the energy invested in reproductive output (e.g. gonadosomatic index - GSI) at natural vs manmade habitats may be a pivotal way to understand the mussel’s comparative physiological
processes. GSI is measured by quantifying the gonad weight with respect to body/soma weight
(Hickman 1979; Devlaming et al. 1982; Santos et al. 2011). The gonadosomatic index (GSI) is
an important metric to study the magnitude of gametogenic events and seasonal patterns in
gonad development. It has been used by Gardner & Skibinski (1990) to study the fecundity and
temporal variations in Mytilus spp. (M. edulis, M. galloprovincialis and their hybrids in
southwest England). Their results showed M. galloprovincialis to have greater mean fecundity
compared to congener M. edulis. Therefore, in New Zealand, it is interesting to observe two
congener species that co-occur, one of which is the non-native NHMg lineage, and the closely
related, native SHMg lineage.
The objective of the present study is to measure and compare the reproductive output
(GSI) of the two lineages to observe if reproductive output contributes to their differences in
performance (presumptive fitness) at reef vs marina sites, which are analogous to natural
habitats and man-made habitats. Firstly, I hypothesised that both the blue mussel lineages
(NHMg and SHMg) would have bigger shells on natural reefs compared to marinas. Due to the
close evolutionary affinities between the non-native, NHMg and native, SHMg (McDonald et
al. 1991; Hilbish et al. 2000; Gérard et al. 2008; Westfall & Gardner 2010; Pickett & David
2018); I hypothesised that both the blue mussel lineages would have similar reproductive
128
patterns (i.e., timing and magnitude of spawning events). I further hypothesised that the
mussels at the natural reef sites have a relatively higher reproductive output (GSI) than those
at marina sites. Lastly, hypothesised that the non-native NHMg would have higher reproductive
output at marina sites (man-made) compared to rocky reefs (natural).
4.2. Methods
4.2.1. Study site
The study was carried out in Wellington Harbour (41°16'45.5"S, 174°52'02.3"E) which is an
enclosed bay on the southernmost tip of New Zealand’s North Island (Figure 4.2.1). The
Harbour is one of the largest natural harbours in the Southern hemisphere, whilst most of it is
heavily modified for urban and port development, some of it is still intact. Hutt River on the
eastern side of the Harbour is the only source of freshwater into the catchment. Wellington
Harbour serves as a significant international commercial shipping port and is one of the busiest
ports in New Zealand. Hence, sites around Wellington harbour were selected to compare
gonadosomatic index (GSI) as an indicator for the reproductive effort by SHMg (native) and
NHMg (non-native) blue mussels on the reef (natural) and marina (man-made) habitats.
Westfall (2011) indicated the presence of SHMg, NHMg and M. edulis/M. galloprovincialis
(NHMg/Me) hybrids at different proportions at different sites in Wellington Harbour.
Figure 4.2.1. Map of Wellington Harbour, New Zealand. Points denoting marinas as man-made
structure sites (black) and natural rocky reefs as natural sites (green) for this study.
129
In my study, three paired sites (natural vs man-made) were chosen around the harbour:
Oriental Bay (OB; natural) and Chaffers Marina (CM; man-made), Shelly Bay (SB; natural)
and Evans Bay Marina (EB; man-made) and Sorrento Bay (SR; natural) and Seaview Marina
(SM; man-made). Paired sites (natural vs man-made) were selected at a distance of ~ 200 m to
examine the monthly GSI of NHMg and SHMg in the marinas and at adjacent reef habitats. All
sites had extensive coverage of blue mussels present through the sampling period.
4.2.2. Field sampling
Westfall (2011) identified 8 SHMg, 1 NHMg and 1 NHMg/Me (hybrid) with 20
unknowns out of the 30 blue mussels collected in Wellington Harbour from her study.
Therefore, at each site, random collections of 40 blue mussels were carried out (irrespective of
their sex) to provide what was expected to be large enough lineage-specific sample sizes per
site. Mussel size was > 2 cm shell length to ensure that only sexually mature individuals, for
which a GSI could be calculated, were sampled. The mussels were collected from the intertidal
zone at each site during low tide (similar tidal elevations) every month for a year from June
2017 to May 2018 (40 mussels × 12 months × 6 sites; Ntotal = 2880). The mussels on natural
sites were collected from rocky reefs and for marina sites, the mussels were collected from
wharves, artificial structures or ripraps. Immediately after collection, the mussels were placed
in pre-labelled bags and frozen (-18 oC) until processing. Environmental variables such as pH,
dissolved oxygen (DO, mg.l-1), salinity (PSU), turbidity (NTU) and chlorophyll (µg.l-1) were
measured at each site for each month. The pH, DO, and salinity measurements were taken with
a handheld YSI Meter (PRO-Series, YSI) and turbidity as well as chlorophyll with a handheld
fluorometer/turbidimeter (Aquafluor™, Turner Designs). Unfortunately, the data for turbidity
and chlorophyll was not accurate even after cross-referencing with CTD data for the same.
Therefore, environmental data were not included in this study.
4.2.3. Shell length
The weight of soma and gonad tissue increases as a function of shell length (Suchanek 1981).
Therefore, after removal of all the flesh, each mussel was numbered. The shell length (distance
from the anterior to the posterior side of the shell), height (distance from dorsal to ventral side)
130
and width (maximum distance between closed valves) of the mussel shell (Gardner 2004) were
measured using a vernier calliper (± 0.01 cm) (Figure 4.2.2).
Figure 4.2.2. Illustration representing the measurements taken for shell length, height
and width; a) frontal view; b) lateral view. Image sourced and modified from Gardner
(2004).
4.2.4. Laboratory analysis
After defrosting, ̴ 30 mg of mantle tissue was extracted from each mussel for DNA extraction
(details below). Subsequently, the soma and gonad of each mussel were dissected and weighed
separately (0.001 g) after 24 h at 60 oC to determine their dry weights. The gonadosomatic
index (GSI = gonad mass/body mass × 100%) was calculated for each mussel to qualitatively
determine the energy investment in gametogenesis as a function of total body weight, and also
the timing of spawning events as indicated by a decrease in GSI values from one month to the
next (Seed & Suchanek 1992).
4.2.5. Lineage identification
a) DNA extraction
The DNA extraction was performed from the mantle tissue using Tissue Genomic DNA kits
(Geneaid) following the manufacturer’s recommendations. DNA concentrations were
measured using a Nano-Photometer™ NP 80 (Implen, Germany) before the concentrations
were standardised to a 50 ng/µl stock concentration with double distilled water to prepare a
final volume of 20 µl.
b) PCR
The 16s rRNA RFLP assay (Westfall et al., 2010) was used to distinguish the lineage of SHMg
(native), NHMg (non-native) and NHMg/Me (hybrids). The primers 16sAR/16sBR (1 µl each),
12.5 µl MyTaq Remix, 8.5 µl double distilled water and 2 µl template DNA (100 ng) were
used to amplify the 16sRNA gene in a 25 µl total solution. PCR was carried out in an Eppendorf
131
Thermocycler (Master cycler ep groups S). Amplification conditions applied were 3 min at 95
o
C, 30 cycles at 95 oC for 30 s, 30 cycles at 52 oC for 30 s, 30 cycles at 72 oC for 45 s and final
extension of 3 min at 72 oC.
c) DNA Digest
A double restriction endonuclease digest was performed on the PCR product using 1 µl each
of the EcoRV and NheI enzymes, 1 µl loading buffer, 2 µl red buffer, 5 µl distilled water and
10 µl PCR product that was incubated in the Thermocycler at 37 oC for 15 min. The enzymes
EcoRV and NheI distinguish SHMg (native) and NHMg (non-native) (Westfall et al. 2010).
The enzyme SpeI enzyme used by Westfall & Gardner (2010) to distinguish the blue mussels
was not used in this study because after testing a few mussels with EcoRV and NheI enzymes,
SHMg and NHMg were successfully determined.
d) Gel Electrophoresis
DNA concentrations (5 µl) were run on a 2% agarose gel with an Easy Ladder 1 (band size
range: 100 to 2000 bp) to help with fragment sizing. The gel was run at 100 volts for 30 to 40
mins. The gel was viewed under ultraviolet, and pictures were recorded using an imaging
system (UVITEC, Essential V6, Cambridge, UK). The resulting bands were observed for each
sample and referenced against the Easy Ladder 1 and fragment profiles of NHMg at 537 bp
whilst SHMg at 370 and 167 bp and NHMe/Mg (hybrid) at 370, 85 and 82 bp (Westfall et al.
2010).
4.2.6. Statistical analyses
Lineage distributions between reef and marina habitats were analysed using a contingency test
(R×C). Statistical tests were performed using the STATISTICA v.7 (Stat Soft Inc.) software
package. Normality testing using the Kolmogorov-Smirnov (K-S) test (shell length: d = 0.05,
P < 0.01; GSI: d = 0.04, P < 0.01) revealed that both variables were not normally distributed,
whilst examination of quantile-quantile plots (Q-Q plots) showed that the data were
approximately normally distributed, although some heteroscedasticity was observed. Because
the violations of assumptions of normality were small in both cases, and because the parametric
analysis is generally robust to such small-medium deviations, the data were not transformed.
a) Shell length
Variation in shell length was analysed using a two-way ANOVA (analysis of variance) as a
function of habitat type and lineage and their interactions (P < 0.05). Paired t-tests were
132
employed to test the hypothesis that shell length differs as a function of habitat type irrespective
of lineage and with regard to the lineage regardless of habitat type, respectively. Cohen’s ‘d’
effect size ‘r’ of shell length was tested for lineages (NHMg vs SHMg) and habitat type (reef
vs marina). Cohen’s ‘d’ evaluates the size of an effect of the test statistic (observed P-value)
because a significant effect does not necessarily mean a large effect. The evaluation relies on
standard deviations instead of standard errors. Cohen’s ‘d’ measures the size of the mean
difference in terms of the standard deviation. The magnitude interpretations of the Cohen’s d
value are; < 0.30 is small effect size, 0.50 is moderate effect size, and > 0.80 is a large effect
size (Cohen 1992).
Cohen's d = M1 - M2 / spooled
where spooled =√[(s 12+ s 22) / 2]
rYl = d / √ (d2 + 4)
where; d = Cohen value, r = effect-size, M1 = Mean of group 1, M2 = Mean of group 2, 1 =
Standard deviation of group 1, 2 = Standard deviation of group 2.
b) Gonadosomatic index (GSI)
Correlation and regression analyses were conducted to determine the relationship
between GSI (%) and shell length (cm) of SHMg and NHMg between the reef and marina
habitats. Because larger (presumptive older) individuals have greater gamete production than
smaller mussels (Rodhouse et al. 1986), it is important to establish that any difference in GSI
between the lineages results from the genotypic background rather than a bias in the collection
of larger versus smaller mussels. The regressions between GSI vs shell length were plotted
with 95% confidence intervals; R2 and P values were calculated for each association. The
significance of these tests was set at P < 0.05.
Analysis of covariance (ANCOVA) was performed, with shell length as co-variate,
habitat-type, lineage and month as the independent variables and GSI as the dependent variable.
GSI were analysed using a three-way factorial ANCOVA as a function of; the effects of the
interaction of the factors; month, habitat type and lineage were analysed. The significance level
was set at P < 0.05 with P < 0.001, indicating high significance Paired t-tests were also
employed to test the hypothesis that GSI values differ as a function of habitat type irrespective
of lineage and for lineage regardless of habitat type, respectively. Lineage-specific GSI was
also tested in each habitat using a paired t-test. Cohen’s ‘d’ effect-size ‘r’ was calculated for
GSI for the habitat type (reef vs marina) to test the effect size of the significant results.
133
4.3. Results
Of the 2880 mussels, the 16s mitochondrial rDNA RFLP assay was able to identify a total of
1884 SHMg (native), 656 NHMg (non-native) and 273 NHMe/Mg hybrids from the monthly
reef (natural) and marina (man-made) habitat collections (i.e., 2813 of 2880 mussels or 97.7%
of all individuals). However, the RFLP assay failed to identify the lineage for 67 mussels which
were removed from further analyses. As M. edulis/M. galloprovincialis hybrids are of Northern
hemisphere origin (i.e., they are invasive in New Zealand), they were assigned as Northern
hemisphere non-native lineage (NHMg). Subsequently, 1884 SHMg and 929 NHMg (2:1,
native: non-native) individuals were tested in this study.
Based on the RFLP assay results, testing revealed a non-significant difference in NHMg
and SHMg distributions between the reef and marina habitats (χ2 = 0.003, df = 1, P = 0.96).
Both the habitats were dominated by native SHMg compared to NHMg (Reef: SHMg =
66.74%, NHMg = 33.26%; Marina: SHMg = 66.57%, NHMg = 33.43%; Figure 4.3.1).
Total number of
individuals
1000
800
600
400
NHMg
200
SHMg
0
Marina
Habitat
Reef
Figure 4.3.1. Total number of Northern hemisphere (NHMg- non-native) and Southern
hemisphere (SHMg- native) Mytilus galloprovincialis at the marina and reef habitats.
4.3.1.
Shell length
The shell length of the mussels ranged from 2.5 - 8.30 cm with a mean shell length of 5.23 cm
± 0.85 irrespective of habitat type and lineage. Two-way ANOVA revealed that shell length
differed significantly between habitat type (P = 0.008), but not between lineage (P = 0.19) and
no interaction effect between Habitat × Lineage was detected (P = 0.26) (Table 4.3.1). Paired
t-tests of shell length as a function of habitat type revealed a significant difference (t-value = 2.40, df = 2812, P < 0.05; Table 4.3.2) with larger mean shells occurring at marina (5.27 cm ±
0.9) than at reef (5.20 cm ± 0.8) habitats. However, such small differences in shell length (to
the level of tenths of a millimetre) are not likely to have any biological significance. These
results were supported by Cohen’s d value (habitat type: d = 0.09, r = 0.046) which states that
134
the effect size of shell length between habitat type is trivial (Table 4.3.2). A paired t-test of
shell length as a function of lineage indicated no significant difference in shell length between
NHMg and SHMg lineage (t-value = 1.30, df = 2812, P = 0.19; Table 4.3.2).
Table 4.3.1. Two-way ANOVA for shell length (cm) as a function of habitat type and lineage
(significance = P < 0.05, marked in bold).
Source of variation
df
MS
F
P
Habitat type
1
5.00
6.94
0.008
Lineage
1
1.19
1.65
0.19
Habitat type x Lineage
1
0.90
1.25
0.26
2810
0.72
Error
(df = degree of freedom, MS = mean square, F = F-statistic, P = P-value)
Table 4.3.2. Paired t-tests for shell length (SL) as a function of habitat type and lineage
(significance = P < 0.05, marked in bold). Cohen’s d effect size ‘r’ of shell length for habitat
type (reef vs marina) and lineage (NHMg vs SHMg).
Mean SL (cm) ± SD
t- value
P
Cohen’s d
Effect-size ‘r’
Marina
5.28 ± 0.90
-2.40
0.02
0.093
0.046
Reef
5.20 ± 0.80
NHMg
5.20 ± 0.87
1.30
0.19
0.058
0.029
SHMg
5.25 ± 0.84
Variable
Habitat type
Lineage
SD = Standard deviation, t-value = t-statistic
4.3.2. Regression - GSI as a function of shell length for the NHMg and SHMg lineages
At reef sites, no significant effect (R2 = 0.0009, P = 0.35) was observed between shell length
and GSI of NHMg and SHMg, but there was a significant weak relation (R2 = 0.01, P < 0.0001)
at marina sites for both lineages where shell length coincided with GSI (Table 4.3.3).
Table 4.3.3. Results for regression for Gonadosomatic index (GSI %) as a function of shell
length for NHMg and SHMg at the marina and reef habitats (significance = P < 0.05, marked
in bold).
Habitat type
R2
R
P
y
Reef
< 0.001
0.03
0.35
13.9451 + 1.1855*x
Marina
0.011
0.12
< 0.0001
13.9451 + 1.1855*x
135
4.3.3.
GSI as a function of habitat type and lineage
GSI values of SHMg (native) at reef sites ranged from 0 - 51.3% and at the marina sites from
0 - 60.6%. GSI values of NHMg (non-native) ranged from 0 - 50.8% and 0 - 57.6% at marina
sites. Three-way ANCOVA testing for GSI as a function of the month (sample intervals),
lineage and habitat type with shell length as co-variate showed no significant interaction (Table
4.3.4). However, significant results were observed for factors; month, habitat type and their
interaction (Month × Habitat type; P < 0.001). Furthermore, Tukey HSD post hoc test indicated
statistically higher GSI values of mussels at reef than at marina sites (Marina: 20.20% ± 10.03;
Reef: 22.80% ± 8.84).
Cohen’s d test (d = 0.275, r = 136) showed a trivial effect of the significance of GSI
between habitat type (Table 4.3.5). The GSI values as a function of Habitat type × Month
indicated significant results only for the months, July (d = 0.426, moderate effect), November
(d = 809, large effect) and December (d = 667, large effect) (Table 4.3.5). The GSI values of
mussels were relatively higher at reef (natural) habitat than at marina (man-made) habitat
(Table 4.3.5). Therefore, indicating relatively larger spawning activity in marinas, especially
during November followed by quick gametogenesis in December compared to reproductive
activity in reef habitat (Figure 4.3.3).
Table 4.3.4. Results of ANCOVA for GSI values and shell length (cm; Co-variate) as a function
of Month × Lineage × Habitat type (significance = P < 0.05; marked in bold).
Source of variation
df
MS
F
P
Shell length
1
6492.501
85.5688
< 0.0001
Month
11
2809.963
37.0343
< 0.0001
Lineage
1
13.699
0.1805
0.67
Habitat type
1
4228.210
55.7263
< 0.0001
Month × Lineage
11
92.633
1.2209
0.27
Month × Habitat type
11
339.841
4.4790
< 0.0001
Lineage × Habitat type
1
11.594
0.1528
0.69
Month × Lineage × Habitat type
11
44.818
0.5907
0.84
2765
75.875
Month × Lineage × Habitat type
Error
(df = degree of freedom, MS = mean square, F = F-statistic, P = P-value)
136
Table 4.3.5. Tukey HSD post hoc tests for GSI values as a function of significant factors as
per ANCOVA, i.e. habitat type and month (significance P < 0.05, marked in bold). Cohen’s d
effect size of GSI for habitat type (reef vs marina) for significant effects.
Variable
GSI values
MS
df
P
(%) ± SD
Habitat type
Marina
20.20 ± 10.03
Reef
22.80 ± 8.84
78.194
2766
< 0.0001
78.194
2766
0.926
Cohen’s
Effect-
d
size ‘r’
0.275
0.136
0.426
0.208
Month
x
June
Habitat
type
July
0.011
August
0.136
September
1.00
October
0.617
November
0.00001
0.809
0.375
December
0.00007
0.667
0.316
January
0.467
February
0.99
March
1.00
April
0.99
May
0.54
(Natural
Man-made)
vs
SD = Standard deviation, t-value = t-statistic
Overall, the GSI cycles of both mussel lineages at the reef and marina sites were
qualitatively very similar (Figure 4.3.2). At reef habitats, the GSI values were highest during
the start of the austral winter (June) for both species (SHMg = 28.82% ± 7.28, NHMg = 29.08%
± 6.32) indicating gametogenesis had occurred earlier. GSI values decreased dramatically in
August (end of winter) reaching their lowest values (SHMg = 18.76% ± 6.93, NHMg = 18.18%
± 7.04) (Figure 4.3.2 a), suggesting spawning activity. This was followed by an increase in GSI
values during September-October (gametogenesis), and again a drop in GSI values (spawning)
during austral spring (October-November), especially in November (SHMg = 19.39% ± 10.27,
NHMg = 20.55% ± 8.72). A gradual recovery period (increase in GSI values – gametogenesis)
from December to May (Summer-Autumn) then followed (Table 4.3.6).
At the marina sites, the highest GSI values (SHMg = 27.33% ± 11.47, NHMg = 25.40%
± 11.56) were observed in June (Figure 4.3.2 b), suggesting that gametogenesis occurred from
May-June (austral autumn and early winter), or perhaps earlier. A sudden drop in GSI values
137
in August (SHMg = 14.51% ± 8.54, NHMg = 15.51% ± 6.88) indicated a spawning period. The
GSI values were lowest in November (SHMg = 12.88% ± 8.27, NHMg = 11.24% ± 8.98), and
the gametes seemed to be spawned out (Table 4.3.6). Two spawning events were observed
from June-August (austral winter) and October-November (spring) for NHMg and SHMg on
both the habitats.
b) Marina habitat
a) Reef habitat
Lineage
Figure 4.3.2. Monthly variations of the GSI values of SHMg (blue) and NHMg (red) lineages
at a) marina and b) reef habitats in Wellington Harbour from June 2017-May 2018.
Table 4.3.6. Monthly variations in Gonadosomatic Index (%) ± standard deviation (SD) of
SHMg and NHMg at reef and marina habitats during June 2017-May 2018.
Reef habitat
Marina habitat
Month
NHMg ± SD
SHMg ± SD
NHMg ± SD
SHMg ± SD
June
29.08 ± 6.32
28.82 ± 7.28
25.40 ± 11.56
27.33 ± 11.47
July
28.98 ± 10.64
26.48 ± 10.74
24.22 ± 11.29
22.05 ± 10.88
August
18.18 ± 7.04
18.76 ± 6.93
15.51 ± 6.88
14.51 ± 8.54
September
21.33 ± 6.15
20.69 ± 5.96
20.80 ± 8.67
20.64 ± 9.87
October
20.87 ± 7.69
23.29 ± 7.37
17.84 ± 7.94
20.50 ± 9.45
November
20.55 ± 8.72
19.39 ± 10.27
11.24 ± 8.98
12.88 ± 8.27
December
22.17 ± 9.84
22.87 ± 8.48
17.21 ± 9.08
16.66 ± 9.03
138
January
20.90 ± 6.88
23.37 ± 7.69
20.18 ± 7.41
19.18 ± 7.87
February
22.56 ± 11.57
21.21 ± 10.50
21.68 ± 7.58
19.77 ± 8.47
March
22.42 ± 5.58
21.12 ± 6.29
22.45 ± 8.71
21.36 ± 7.93
April
23.22 ± 6.95
24.46 ± 6.06
21.58 ± 8.99
22.69 ± 9.07
May
24.99 ± 10.39
22.26 ± 11.31
25.64 ± 11.78
26.15 ± 10.59
4.4. Discussion
4.4.1. Background
With the proliferation of man-made structures along the world’s coast, there are many chances
for a non-native species to establish and spread (Simberloff & Von Holle 1999; Bulleri &
Airoldi 2005; Bulleri et al. 2006; Tyrrell & Byers 2007; Dafforn et al. 2009, 2012; Airoldi &
Bulleri 2011; Perkol-Finkel et al. 2012; Theuerkauf et al. 2018). This is especially the case for
Northern hemisphere M. galloprovincialis, which is known as one of the aggressive invaders,
with competitive traits such as high growth, high reproduction rate and phenotypic plasticity
that help them acclimatise to many environments (Dafforn 2017; Johnston et al. 2017; Simpson
et al. 2017; Epstein & Smale 2018; Riera et al. 2018). Having said that, M. galloprovincialis is
highly used in shellfish industries for its economic value all around the world as it has
successfully invaded most of the regions worldwide (Wonham 1999; Daguin & Borsa, 2000;
Hilbish et al. 2000; Anderson & Thompson 2002; Borsa et al. 2012; Oyarzún et al. 2016;
Pickett & David 2018). Northern hemisphere M. galloprovincialis lineage has invaded ̴ 0.7 M
ybp in NZ and has observed to have spread in most of the regions, marinas, ports or natural
rocky reefs (Gardner et al. 2020). These blue mussels form biogenic reefs providing refuge to
various other species but competing for food and space could be a negative impact on the native
habitat-forming species (Crooks 2002; Castilla et al. 2004; Borthagaray & Carranza 2007;
Sousa et al. 2009; Bertolini et al. 2017). The congeneric comparisons help to determine the
attributes of invasions, to examine the performance of non-native species compared to native
species and to determine if they outcompete native congeners. This chapter aimed to investigate
the reproductive output (measured as GSI) of NHMg and SHMg on natural rocky reefs and
marina sites as analogous to natural and man-made habitats. Patterns of abundances, shell
length and GSI of the two lineages were tested for a year at natural reef and marina sites.
4.4.2. Abundances
In this study, the NHMg and SHMg co-occur on both natural and man-made habitats as also
previously seen in Wellington Harbour (Westfall 2011; Gardner & Westfall 2012). The two
139
lineages, NHMg and SHMg, were distinguished using 16s RNA RFLP assay developed by
Westfall & Gardner (2010). When considering the reproduction rate in displacing native
lineage with non-native lineage, competitive traits and abundances of native species also play
an important role to decide the success of non-native species. The native SHMg lineage was
most abundant at both natural and man-made habitats throughout the one year of the study
period. It has been widely reported that non-native species prefer man-made structures/ habitats
and native habitats to promote more native species and local communities (Chapman & Bulleri
2003; Glasby et al. 2007; Airoldi et al. 2015; Marraffini & Geller 2015; Gestoso et al. 2017;
Johnston et al. 2017; Simpson et al. 2017). However, in this case, there was no preference for
habitats observed by NHMg, but it is observed to be very well established. As seen in NW
Spain, the larvae of M. galloprovincialis settled on various kinds of available substrata (Caceres
Martinez et al. 1994). Native lineage SHMg was still the dominant space occupier at both
habitat types and is not yet seen to be displaced by non-native NHMg lineage.
4.4.3. Shell length as a function of habitat type and lineage
The shell length of the blue mussels was examined in this study to observe if any differences
exist in the shell lengths of NHMg and SHMg at natural reef sites or marina sites. Previous
studies have reported smaller sized limpets (Moreira et al. 2006) and barnacles (Martins et al.
2016) on man-made structures compared to natural reefs. The shell length of the mussels
collected in this study ranged from 25 to 83 mm. The M. galloprovincialis observed in eastern
Pacific, California was an average shell length of 60.7 mm (Dutton & Hofmann 2008) whilst
in Chile ranged from 32 to 70 mm (Díaz et al. 2019). In this study, the shell length of the two
lineages collected at random from reef and marina sites were relatively similar with an average
length of 52.0 mm ± 8.7 for NHMg, and 52.5 mm ± 8.4 for SHMg. Therefore, the average shell
length of the mussels collected was within a standard shell length range. The shell length
showed statistically significant differences between habitats (Reef vs Marina) irrespective of
the lineage; however, the difference was small and probably without any ecological
significance.
Generally, an increase in reproductive output, i.e. gamete production in a mussel is
directly comparable to the age or size of the mussels. Reproductive output is calculated by
gonad weight divided by total body weight (i.e., gonad + soma). Older or larger mussels are
expected to have higher gamete production (Gardner & Skibinski 1990; Seed & Suchanek
1992). In this study, NHMg and SHMg had a mean shell length of 52.0 mm ± 8.7 and 52.5 mm
± 8.4, respectively, and a mean GSI value of 22.07% ± 9.61 and 21.24% ± 9.5, respectively.
140
M. galloprovincialis studied in Chile had an average shell length ̴ 55.0 mm with GSI of ̴ 28.9%
(Díaz et al. 2019) therefore, a common range of size and reproductive output range was seen
in this study for both M. galloprovincialis lineages. Furthermore, there was no relationship
observed between shell length and GSI for both NHMg and SHMg at reef sites whilst, a
significant but very weak relation was observed between shell length and GSI at marina sites.
It is important to note that the different habitat types (Reef vs Marina) did not have any distinct
impact on the relationship of shell length to GSI for NHMg and SHMg. Therefore, I reject the
hypothesis that the shell lengths of both lineages are greater at natural reef sites than at marina
sites. This suggests but does not prove that growth rates of the two lineages are not significantly
different at both habitat types. However, a study in Chile, comparing gonad weight in similar
sized (̴ 70 mm) native Mytilus chilensis and non-native M. galloprovincialis showed relatively
higher gonad weight for native M. chilensis due to the physical characteristics of the two blue
mussels (Díaz et al. 2019).
4.4.4. GSI as a function of habitat type and lineage
Comparing the reproductive effort between two congeneric species helps to determine their
response to different habitat types (Thompson 1984). At exposed sites (natural rocky reefs),
where there is plenty of food supply, M. galloprovincialis is observed to grow faster with
increased soma and gonad production than at sheltered sites (marinas), although, stronger
waves at exposed areas lead to energy investment for production of byssus threads for
attachment (Steffani & Branch 2003). There is evidence of different reproductive effort
between different populations, even at a local scale (Bayne et al. 1983). For instance, food
quality, food availability, tidal level and sediment type influence growth and reproduction
(Honkoop and Beukema 1997; Beukema et al. 2002). Elsewhere, a study comparing the
reproductive output of limpets on seawalls vs natural reefs observed relatively small-sized
limpets leading to low reproductive output by limpets on seawalls (Moreira et al. 2006).
However, in my study, the reproductive output (GSI) as a function of habitat type showed
significant differences (Marina = 20.20% ± 10.03, Reef = 22.80% ± 8.84), therefore accepting
the hypothesis that reproductive output of both lineages is relatively higher at natural reef than
at marina sites, but the differences in GSI values are very small. The ecological significance of
such small differences in GSI values is yet to be determined. This study observed no significant
effect of GSI values between lineages
GSI values as a function of the interaction effect of habitat type ×lineage did not show
significant differences. Therefore, the lineages (NHMg and SHMg) did not show differences
141
in reproductive output at each habitat type, i.e. reef (natural) and marina (man-made) habitats,
respectively. Thus, I reject my hypothesis that NHMg has a greater reproductive output at
marina sites than at natural reef sites, as both the lineages had relatively higher GSI values at
reef sites than marina sites. The reproductive output of the mussels is a function of the
partitioning of energy between somatic growth and reproduction and also acts as a
physiological stress response (Cáceres-Martínez & Figueras 1998; Seed & Suchanek 1992;
Okaniwa et al. 2010). In the case of environmental stress like temperature variation, low food
availability, desiccation, predation, and wave exposure, mussels may invest relatively more
energy in defence against the stress than in reproduction (e.g., Gosling 1992; Steffani & Branch
2003). In this study, the co-occurring NHMg and SHMg were sampled within environments
with not very distinct variations in temperature and food availability (Gardner, pers. comm.,
unpublished data), which might be the reason for the similar reproductive output by the two
lineages at both habitats (Reef vs Marina).
The GSI values as a function of the interaction of factors, month × habitat type indicated
significance during the months – July, November and December. The GSI values were
relatively higher at reef sites than at marina sites. Subsequently, these results indicate that the
mussels irrespective to the lineages had a quicker first spawning response in July and a larger
spawning activity during November was observed in the marina sites compared to reef sites.
However, the reason for larger spawning activity by the mussels in the marina could be
speculated because of the slightly warmer temperatures in enclosed marina conditions.
However, there are no environmental data to support this speculation.
4.4.5. Reproductive cycle
Temporal variations in reproductive output is a likely result of gamete production
(gametogenesis) and gamete loss (mostly spawning, but may also be resorption) (Seed &
Suchanek 1992; Cáceres-Martínez & Figueras 1998). In this study, temporal variations
(monthly) in GSI values showed significant difference as a function of habitat type (Reef vs
Marina) but not as a function of lineage (NHMg vs SHMg). However, the reproductive patterns
for both lineages (NHMg vs SHMg) showed a similar timing of gametogenesis and spawning
at both Reef vs Marina habitats. Previous studies of mussel reproductive output have reported
differences in spawning periods between other Mytilus spp. (Gardner & Skibinski 1990; Secor
et al. 2001; Toro et al. 2002). In this study, two spawning events were observed with two
periods of gonad build-up (gametogenesis) observed during summer and spring. In this study,
gametogenesis took place in early winter (June) and with spawning during late winter (August),
142
followed by a quick gonad condition recovery in spring (October), and a second spawning
during late spring (November). These events were recorded by observing the conspicuous
peaks and drops in GSI values. These observations also indicate the influence of temperature
on the gonad cycle of the mussels (Seed & Suchanek 1992; Cáceres-Martínez & Figueras 1998)
as well as food availability during May leading to gametogenesis (Lachowicz 2005). For
instance, Okaniwa et al. (2010) reported that the gametogenesis in M. galloprovincialis in
Japan coincided with an increase in chlorophyll-a concentration as well as low sea and air
temperature.
The spawning events of NHMg and SHMg lineages coincided with rising water
temperatures as has been previously reported for other Mytilus spp. (Seed & Suchanek 1992;
Carrington 2002; Okaniwa et al. 2010). Warmer temperatures were observed to influence
gametogenesis, but lower temperatures stimulated spawning (Ceccherelli & Rossi 1984;
Oyarzún et al. 2011). Such spawning periods were also observed in M. galloprovincialis in
South Africa (Zardi et al. 2007) and NW Spain (Cáceres-Martínez & Figueras 1998) during
late spring and summer. However, M. galloprovincialis in Pacific coast (Curiel-Ramirez &
Caceres-Martinez 2004) and M. edulis and M. galloprovincialis in southwestern England
(Secor et al. 2001) has previously shown a single spawning event from autumn to early spring.
M. galloprovincialis in South Africa showed two spawning periods, during summer and winter
(Schurink & Griffiths 1991). Gardner & Skibinski (1990) reported prolonged reproductive
cycles and multiple spawning events in M. edulis/ M. galloprovincialis hybrids.
The NHMg and SHMg lineages show similar responses in terms of reproduction timing
and output in Wellington Harbour, during this study period. Even though, in this study,
difference by sex of the mussels in the GSI was not addressed. A study in southern New
Zealand on gonad indices of blues mussels, irrespective to their lineages, i.e. NHMg and SHMg
indicated synchronous trend between male and female blue mussels (Smart et al 2020). Similar
spawning, i.e. release gametes at the same time may facilitate hybridisation between the two
lineages, and further studies should concentrate on observing if there are chances of
backcrossing and future impacts on the native SHMg lineage (Gardner & Skibinski 1990; Secor
et al. 2001). Many studies have referred to the importance of spawning periods of closely
related species to examine the scope of hybridisation (Gardner & Skibinski 1990, Seed 1992;
Seed & Suchanek 1992; Wonham 2004; Westfall & Gardner 2013; Oyarzún et al. 2016),
especially where they co-exist (McDonald et al. 1991; Seed 1992; Elliott et al. 2008; Brannock
et al. 2009).
143
Hybridisation and repeated backcrossing with the parental species can risk the
extinction of the native species (Fitzpatrick et al. 2010; Harrison 2012). Subsequently, the
concern of hybridisation was also reported by Westfall & Gardner (2013), they reported that
continuous invasion, hybridisation and introgression preferring the non-native NHMg lineage
could extirpate the native SHMg lineage. There is evidence of high rates of hybridisation
between native and non-native Mytilus trossulus and Mytilus galloprovincialis, respectively,
with low introgression in Japan (Brannock et al. 2009) and California, USA (Saarman &
Pogson 2015), whilst a higher rate of introgression in Australia and NZ (Westfall & Gardner
2013). In the North Atlantic, hybridisation between M. edulis and M. galloprovincialis had led
to asymmetric introgression favouring M. galloprovincialis alleles (Gardner & Skibinski 1988;
Bierne et al. 2002). In the north-eastern Pacific, low rates of hybridisation and limited
introgression were observed between M. galloprovincialis and M. trossulus favouring larger
sized M. galloprovincialis alleles (Anderson et al. 2002; Wonham 2004).
In summary, this study concluded that the co-occurring non-native, NHMg and native,
SHMg lineages have no preference towards natural reefs or man-made habitats for their
ecological functioning. This is because the reproductive output (GSI) and shell length were
nearly similar between the two lineages at both the habitats. NHMg and SHMg lineages also
showed similar reproductive patterns at both habitats for the 1-year study period. Two
spawning events (late winter and late spring) with a major and a quick gametogenesis period
observed. Future work comparing the performance of congeners should focus on the effects of
highly varying environmental conditions on natural and man-made structures.
4.4.6. Management implications
It is challenging to predict invasion occurrences and success through accidental human
mediation and to avoid the continuous invasions of NHMg lineage. Therefore, it is crucial to
undertake management applications and strategies focussing on hull cleaning and ballast water
exchange (Schwindt et al. 2014). A baseline study is required to identify the non-native M.
galloprovincialis lineage and then to examine what impacts they have on the native
communities, especially the co-existence of NHMg and SHMg over time (e.g., Gardner et al.
2016). However, with the ongoing addition of coastal structures, stepping stone invasions are
ubiquitous (e.g., Apte et al. 2000) and once established, eradication is nearly impossible (Mack
et al. 2000). M. galloprovincialis is an aggressive invader, with external fertilisation and the
production of millions of larvae with a long-lived pelagic state it is impossible to stop its spread.
144
The competitive traits of M. galloprovincialis, i.e. fast growth, tolerance to stress, high
reproduction and immunity to disease is essential in terms of aquaculture/ shellfish farming
(Branch & Steffani 2004) and ironically is also important in terms of invasions.
In this study, it is evident that NHMg and SHMg respond similarly, in terms of their
reproductive patterns. Furthermore, the lack of ecological differences – GSI, shell length, etc.
– reflect the very close evolutionary history of NHMg and SHMg and raises a question about
how important it is to invest in the management of NHMg. From a manager’s perspective, it
may not be important to invest in the management of NHMg instead focus on different
problems such as the high-risk invasive species. This study has highlighted that native, and
non-native M. galloprovincialis have similar biological characteristics; therefore, eradication
of non-native Northern M. galloprovincialis lineage may not be necessary.
145
CHAPTER 5
GENERAL DISCUSSION
5.1. Background
The number of biological invasions has increased over the decades, and invasions are now
ubiquitous (Firth et al. 2016; Olenin et al. 2016; Johnston et al. 2017). There is evidence of
ports and harbours, increasing opportunities for non-native species to settle and proliferate
(Zbawicka et al. 2019). This thesis aimed to investigate the marine man-made environments,
their impacts on the marine biodiversity regarding species status and the factors facilitating the
non-native species. In this thesis, the community composition and species status (native, nonnative and cryptogenic) between natural and man-made habitats were analysed. Chapter 1
presented basic information on habitat type and effects of man-made structures in the coastal
environment and in relation to bioinvasions. Data Chapter 2 focussed on the national scale
baseline port surveys (Australian and New Zealand port surveys) and examined the factors port
type and latitudinal groups for the community composition and species status. The local-scale
study (Wellington Harbour) was conducted using settlement tile arrays (PVC and slate tiles) in
the reef and marina habitats (Chapter 3). The data was applied for comparative assessment of
the community composition, ecological succession of species and frequencies of species status
between the habitat types and substratum types. Lastly, in Chapter 4, the reproductive output
(GSI, presumptive fitness) of the congener blue mussel lineages (non-native, NHMg and native
SHMg) was analysed at the natural reef and man-made marina habitats. By comparing the GSI
results between the habitat type, the reproductive cycles (spawning and gametogenesis), and
the reproductive output, between the lineages, were constructed. In the present chapter, the
main conclusions from the previous chapters are summarised. Additionally, the limitations,
future research and management suggestions are discussed.
5.2. Chapter synthesis
In chapter 2, the two national-scale baseline port surveys; Australian port survey (APS) and
New Zealand port surveys (NZPS) assessed the community composition and species status as
a function of surveyed ports, port type (major vs minor) and latitudinal groups. Results for APS
showed that the community composition significantly varied as a function of the surveyed port.
However, despite the higher frequencies of native species across all surveyed ports, there was
146
no specific pattern in surveyed ports observed for community composition. The community
composition and species status significant results for the factor port type with relatively
abundant non-native species observed at major ports compared to minor ports. For latitudinal
groups, the community structure and species status showed a significant relationship with an
increase in non-native species with the increase in latitudes. For NZPS, results indicated
significance for community composition and species status as a function of surveyed ports.
There was a significant relationship observed for community composition as a function of
latitudes with high significance observed between low (35oS) and high latitudes (40, 45 oS).
Hence, the results for both the dataset indicated grouping of ports located at proximity, i.e.,
having common species. The natural dispersal of species or domestic marine traffic may be the
pathways for the spread of species at regional scales (Coutts & Taylor 2004; Floerl et al. 2004;
Floerl & Inglis 2005).
The findings of the dataset analyses highlight several on-going challenges such as major
commercial ports being hotspots for marine invasions and the role of shipping as the main
pathway for introductions at multiple spatial scales (Bishop et al. 2017). Thereby spread of
species through regional transport connecting ports for domestic trade or recreational activities.
However, the influence of human-mediated transfer of species may be of less importance for
marine species which have typically 3-5 weeks of larval stage (for example, molluscs,
polychaetes) (Shanks 2009). Therefore, the dispersal rates of the species might be an important
factor to consider the propagule pressure at regional and local scales. The species observed at
both APS and NZPS are benthic species such as tunicates, barnacles, mussels, crabs, bryozoans
and polychaetes, which typically need a hard substratum to adhere. Nevertheless, for species
such as bryozoans and ascidians, with 2-10 hours of larval could disperse long distances;
however, it can reduce their chances of survival, reduced growth rates or smaller sized adults
(Marshall et al. 2003; Burgess et al. 2012). The influence of anthropogenic activities results
in alteration of connectivity in marine systems (Bishop et al. 2017) and a major challenge to
identify the factors characterizing the bioinvasions at multiple spatial scales. This study,
however, highlights that the responses in Australia are very different from those in New
Zealand, which suggests that responses are regional or country-specific and not global.
In Chapter 3, the marine biological community composition and species’ abundances
regarding species status were compared between man-made, and natural habitats/substrata,
respectively. The field tests were undertaken in Wellington Harbour using settlement tile arrays
(PVC vs slate). The aim was to determine differences in the overall species assemblage and
147
species status between habitat type (natural reef vs man-made habitat) and substratum type
(PVC vs slate tile). Rapid colonisation of species was observed within 3 months of submersion,
with nearly 64% cover of the bare tiles. The two-year study was not enough to observe the
climax community. A total of 47 putative species were observed on the tiles in this two-year
study; which is nearly 1/4th of the number of species seen in Wellington Harbour sampled for
New Zealand port survey (Chapter 2 B).
The ecological succession observed on both habitat type and substratum type showed a
similar pattern of recruitment and post-settlement processes. The community structure may be
influenced to a greater extent by stochastic recruitment (Chang & Turner 2019). The species
explaining most of the variation among and between habitat types and substratum types were;
cryptogenic Biofilm type 1, Green sp. 1, native crustose brown seaweed, Ralfsia verrucosa,
non-native red alga Bangia atropurpurea, non-native bryozoan Watersipora subtorquata,
native tunicate Asterocarpa humilis and native bryozoan Membranipora membranacea. These
species are typically seen fouling on ships hulls or marina infrastructures (Connell 2001; Wood
& Probert 2013). For example, the of the larval life span of bryozoan species may range from
one to a few weeks (Gordon 1977); however, in the case of sea squirts Asterocarpa humilis,
their natural dispersal is very limited. The marina and reef sites were at ~ 200 m distance from
each other; therefore, their natural dispersal is possible. The community composition showed
significant results as a function of habitat type and substratum type. The differences in
biological communities between man-made (marina; PVC) and natural (reef; slate)
habitats/tiles are explained by abundance differences, not by species differences. The
cryptogenic species were abundant at both habitat type and substratum type. The non-native
species were relatively abundant at marina sites; however, did not show preference to the two
substratum types. This study acknowledges the on-going problem of the role of man-made
structures as more suitable habitats for non-native species. The species dispersal rate plays an
important role in the spread of species from marina sites to neighbouring natural habitats. These
results suggest that Wellington Harbour is a well-mixed site, and control of invasive species
cannot be easily achieved.
The aim of chapter 5 was to examine whether the reproductive out (GSI) of the native
(SHMg) and non-native (NHMg) lineages of the blue mussel, Mytilus galloprovincialis,
differed between natural (reef) and man-made (marina) habitats. To analyse the reproductive
performance, the temporal variation in the reproductive output (GSI) of the NHMg and SHMg
were measured for 1 year in Wellington Harbour. The shell length of the two mussel lineages,
148
when compared between natural and man-made habitats, showed a significant difference, but
the difference was trivial (~ 0.5 mm). The GSI of the mussels indicated significant between
habitat type and sample intervals and not for lineage × sample interval, indicating similar GSI
values between native SHMg and non-native NHMg. The GSI values were relatively greater at
reef sites, thus, indicating increased spawning activity in the marina sites. These results were
portrayed in the reproduction patterns of both lineages, with NHMg and SHMg showing similar
timing of gametogenesis and spawning at both the reef and marina sites. The temporal variation
of GSI showed with two spawning events (August and November). The mussels (NHMg and
SHMg) had a large spawning event in November in the marina sites. This presumably could be
because of warmer temperatures in the semi-enclosed marina conditions. The results suggest
that the ecologically similar response of NHMg and SHMg (GSI: NHMg = SHMg) could be
due to their close evolutionary affinities.
Overall, this study highlights that the most species observed were native species
suggesting that the local species pools are still important for overall diversity than the
introductions, even in the highly modified habitats. Additionally, many cryptogenic species
were observed across all the chapters; molecular approach to taxonomy and frequent
surveillance programmes - to detect the cryptic introductions should be considered. At the local
scale (Chapter 3 and 4), the reef sites are performing differently from the marina sites, with
relatively higher non-native species in the marinas and better spawning activity by native and
non-native species in the marinas.
5.3. Statistical vs Biological significance
The results of my study raise several questions with regards to statistical significance and
biological relevance. Under a 95% confidence interval, the denoted P-values exhibit statistical
significance; however, under stricter parameters (i.e. 99% CI), statistical significance is
refuted. This calls into question the biological impacts being described in these tests and the
magnitude of these impacts with regards to the animal’s physiology.
To process large sample size data, statistical software is convenient to conclude results.
Statistical significance is important to detect differences between treatments; however,
assessing biological relevance should be of primary importance. Hence, both the statistical
significance and biological relevance are important to evaluate a dataset. That is why, even if
the results are statistically significant, the differences are small enough to consider them as not
biologically important (Lovell 2013). Cohen’s d test is an effect size index that evaluates the
149
size of an effect in a study. It is a standardised measure of the impact of a statistically significant
intervention (independent variable). Cohen’s d equals the difference in the means divided by
the average of the standard deviations (Cohen 1992). This test can be used to accompany t-test
or ANOVA to examine the strength of the significant results. Cohen (1992) indicated an effect
size of about 0.25 was “small”, about 0.5 was “medium”, and about 0.8 was “large”.
5.4. Limitations of the monitoring study
Large scale monitoring datasets (APS and NZPS) are formed by quantifying single occurrences
of species on a single date; however, factors at local scales such as environmental conditions,
nutrient supply, pollution, habitat complexity and physical disturbances can lead to variations
in species assemblages. For instance, Underwood & Murphy (2008) found a seasonal
latitudinal increase in species richness from north to south in winter but not in summer.
Therefore, a complete census of available species in an area is impossible due to seasonal
variations. Surveying and re-surveying sites to have better knowledge are not cost-efficient and
are very timing consuming (Bishop & Hutchings 2011).
Current surveillance programmes are conducted with preliminary taxonomic
identification based on morphology instead of molecular techniques. Very few taxonomic
experts and scientists have the training to identify congeneric native and non-native species,
which results in the number of cryptic invasions not being identified (Ponchon et al. 2013).
Consequently, the accuracy of identification of the native and non-native species is
questionable. Such inadequacies in surveillance and monitoring programmes can hinder the
process of well-developed biosecurity and management approaches to restrict invasions (Peters
et al. 2017). In recent years, a variety of DNA based (molecular) techniques have been
developed to derive information about organisms (Pochon et al. 2017). These molecular
techniques can be used to identify species, especially non-native species (Zaiko et al. 2018).
Several countries – USA, Canada, Australia and New Zealand have already adopted molecular
genetic techniques to survey and early detection of non-native species and support management
decisions.
For settlement tile study (Chapter 3), the settlement tile arrays justified the community
composition and identifying impacts of habitats for 2 years. However, it should be kept in mind
that the growth on PVC and slate tiles might be a representation of natural reef or marina
habitats but not an exact copy. The study period of 2 years was not enough to observe a stable
or climax state of the community, as there was always 20% bare space observed on the tiles.
150
With respect to environmental factors, it is important to have accurate measurements in the
study areas. The variability of growth between habitats and substrata could have explained with
the environmental data. Unfortunately, due to calibration difficulties, environmental data could
not be included in my study. Similarly, for Chapter 4, the fact that NHMg and SHMg
reproductive activity – gametogenesis and spawning could have been influenced by
environmental conditions. The environmental data could have explained the differences in GSI
values between reef and marina sites.
5.5. Management implications
Australia and New Zealand have established some of the world’s strongest biosecurity
and management measures, i.e. comprehensive pre-border, at-border and post-border
management responses (Hewitt & Campbell 2007; Commonwealth of Australia 2013; Ojaveer
et al. 2015). However, this study has highlighted the need for regulations that address the
problem of regional or local spread of non-native species. Ports, harbours and other coastal
structures play a role in facilitating non-native species by providing suitable habitats and
substratum, which is evident in this study. The macrofouling species observed in the study such
as the bryozoans, ascidians, arthropods, settle on readily available substrata irrespective to the
substratum type. Non-native species are often r-selected strategists, i.e., fast-growing
opportunist species, and the availability of bare space can be enough to facilitate their
settlement and establishment. Maintenance of coastal structures, construction of new structures
and marine traffic close to intertidal habitats should be managed appropriately as displacement
of native assemblages due to these activities may provide opportunities for the invaders to settle
on vacant surfaces (Clark and Johnston 2009; Airoldi and Bulleri 2011; Hedge and Johnston
2012). Maintenance of structures can be employed at suitable timings considering the spawning
periods of species. For instance, many species do no show reproductive activities in winter,
thereby encouraging the timing of a controlled maintenance approach (Hopkins and Forrest
2010).
Many eco-engineering approaches related to multifunctional designs of coastal
structures have now been investigated such as the cost-effective approaches - designing coastal
structures more similar to natural rocky reefs by building structures on a gentler slope
(Department of Environment and Climate Change 2009). Also, increasing the surface area for
the settlement of native species or restoring local biodiversity to restrict the establishment of
non-native species (Sella and Perkol-Finkel 2015). However, there is a need to have a full
understanding of the functional properties of the man-made structures. Different characteristics
151
of man-made structures such as size, substratum material, construction design and orientation
may alter the fouling community structure. The community structure in this study did not differ
between natural and man-made substratum materials, though the size of the substrata and
orientation was similar between habitat type. Eco-engineering and it is potential to reduce
invasions are still at an experimental stage, and the designing of appropriate structures will
largely depend on coastal attributes. Therefore, coastal structures need to be monitored for
longer timescales and at different habitats since varying physicochemical conditions may have
different impacts on species and their functioning.
The number of bioinvasions has increased over the decades, and invasions are now
ubiquitous. Eradicating every invaded species will be an expensive and more so impossible
task (Ruiz et al. 2000, 2009; Hewitt et al. 2004; Forrest & Hopkins 2013). Much of the
conservation management work to date has focussed on prevention of new invasions followed
by eradication of non-native species (Melbourne et al. 2007; Hewitt et al. 2009; Cook et al.
2016). However, there is still no stopping the invasion pressure. Some researchers think that
the introduction of non-native species can improve the stocks of declining native species, also
the economically valuable species and benefit the economy, e.g. shellfish production industries
(Leppäkoski et al. 2002; Schlaepfer et al. 2011; Cook et al. 2016). It is evident from the decade
worth of studies that it is difficult to get to grips with invasions and their unpredictable
consequences to the environment. The importance of bioinvasions varies on a case by case
basis, as in some instances they can improve the functional diversity and can also pose a threat
to the native species and their environment (Leppäkoski 2002; Glasby et al. 2007; Gallardo &
Aldridge 2013; Thomsen et al. 2014; Corriero et al. 2016; Gestoso et al. 2017; Riera et al.
2018). An ongoing increase in human population and demand for resources will put pressure
on the marine environment, thereby altering ecosystem services. Better integrative ecological
theories providing new knowledge to stakeholders and managers can help to deliver effective
management approaches.
5.6. Future work
Based on my results, it is clear that non-native species are abundant in major
commercial shipping ports with a high frequency of international marine traffic. This study
also supports the growing evidence that non-native species are generally more abundant in
man-made habitats (e.g. ports, harbours, marinas) compared to natural reefs. It is undisputed
that large-scale surveys and monitoring can be costly. Still, frequent monitoring of major ports
receiving high volumes of marine traffic can aid with early detection and eradication measures.
152
The next step is to examine the environmental conditions (biotic and abiotic) that determine
the invasions and proliferation of non-native species (not included in my study). The settlement
tile array study indicated ecological succession patterns for 2 years; however, if the community
reached its climax state (stable state) is still not known. Therefore, future studies with relatively
long immersion periods for settlement tiles may be an effective monitoring approach and could
detect the climax state of a community on both habitat/substratum types. It is important to
improve the knowledge about life-history traits of a species - to understand the propagule
lifespan, dispersal rate and recruitment timing. Lastly, manual identification of species has its
major drawback, as stated in the ‘Limitation section’ above. It is advisable to find cost-effective
ways such as molecular tools to identify species which are already providing accurate detection
of non-native species (Westfall and Gardner 2010).
5.7. Conclusion
This study found that the fouling community composition on the natural reef and at man-made
marina habitats was similar, although relative abundances of species differed. The results
suggest that Wellington Harbour is well-mixed site and with natural sites being adjacent (~ 200
m) to marina sites; the dispersal of species from one site to another is highly possible. However,
the differences in abundances at each habitat could probably be due to environmental
conditions and community dynamics in the habitat. This study forms a baseline of the
community composition of the modified habitats in an already modified busy harbour
(Wellington harbour). Further, the congener M. galloprovincialis native and non-native
lineages lack ecological differences – GSI, shell length, etc. at reef and marina sites may be
due to the very close evolutionary history of NHMg and SHMg. Additionally, it would be
worthwhile to assess other biotic and abiotic factors such as grazing, predation, salinity,
temperature, water flow and light to explain the differences in community composition between
habitat type. Analyses of baseline surveys examined potential predictors for the distribution of
non-native species and indicated major ports as hotpots for invaders as well as point of transfer.
Combination of the number of major ports at high latitudes increases invasion pressure.
Consequently, the transfer of marine traffic from major to minor ports risks domestic transfers.
Globalisation and urban sprawl are expected to increase in future with requirements for more
man-made coastal infrastructure such as ports, harbours and marinas. These findings suggest
that most vulnerable habitats, such as the major ports should be prioritised and frequently
monitored for non-native species for early detection.
153
References
Ab Rahim, E. S., Nguyen, T. T. T., Ingram, B., Riginos, C., Weston, K. J., & Sherman, C. D.
H. 2016. Species composition and hybridisation of mussel species (Bivalvia: Mytilidae)
in Australia. Marine and Freshwater Research, 67, 1955-1963.
Adams, T. P., Miller, R. G., Aleynik, D., & Burrows, M. T. 2014. Offshore marine renewable
energy devices as stepping stones across biogeographical boundaries. Journal of Applied
Ecology, 51(2), 330-338.
Aguilera, M. A., Broitman, B. R., & Thiel, M. 2014. Spatial variability in community
composition on a granite breakwater versus natural rocky shores: Lack of microhabitats
suppresses intertidal biodiversity. Marine Pollution Bulletin, 87(1), 257–268.
Airoldi, L. 2003. The effects of sedimentation on rocky coast assemblages. Oceanography and
Marine Biology, 41, 161-236.
Airoldi, L., & Beck, M. W. 2007. Loss, status and trends for coastal marine habitats of Europe.
Oceanography and Marine Biology, 45, 345-405.
Airoldi, L., & Bulleri, F. 2011. Anthropogenic disturbance can determine the magnitude of
opportunistic species responses on marine urban infrastructures. PLoS ONE, 6(8),
e22985.
Airoldi, L., Abbiati, M., Beck, M. W., Hawkins, S. J., Jonsson, P. R., Martin, D., Moschella,
P. S., Sundelöf, A., Thompson, R. C., & Åberg, P. 2005. An ecological perspective on the
deployment and design of low-crested and other hard coastal defence structures. Coastal
Engineering, 52(10-11), 1073-1087.
Airoldi, L., Bacchiocchi, F., Cagliola, C., Bulleri, F., & Abbiati, M. 2005. Impact of
recreational harvesting on assemblages in artificial rocky habitats. Marine Ecology
Progress Series, 299, 55–66.
Airoldi, L., Balata, D., & Beck, M. W. 2008. The Gray Zone: Relationships between habitat
loss and marine diversity and their applications in conservation. Journal of Experimental
Marine Biology and Ecology, 366(1-2), 8-15.
Airoldi, L., Connell, S. D., & Beck, M. W. 2009. The Loss of Natural Habitats and the Addition
of Artificial Substrata. Marine Hard Bottom Communities, 206, 269-280.
Airoldi, L., Fontana G., Ferrario F., Franzitta G., Perkol-Finkel S., Magnani A., Bianchelli S.,
Pusceddu A., Colangelo M. A., Thrush S. 2010. Detrital enrichment form urban structures
and its far-field effects on soft-bottom assemblages. Rapp. Comm. Int. Mer
Méditerranean, 39, 712.
Airoldi, L., Turon, X., Perkol-Finkel, S., & Rius, M. 2015. Corridors for aliens but not for
natives: Effects of marine urban sprawl at a regional scale. Diversity and Distributions,
21(7), 755-768.
Albano, M. J., & Obenat, S. M. 2019. Fouling assemblages of native, non-indigenous and
cryptogenic species on artificial structures, depths and temporal variation. Journal of Sea
Research, 144, 1–15.
Altieri, A. H., & Irving, A. D. 2017. Species coexistence and the superior ability of an invasive
species to exploit a facilitation cascade habitat. PeerJ, 5(2), e2848.
154
Andersen, M. C., Adams, H., Hope, B., & Powell, M. 2004. Risk assessment for invasive
species. In Risk Analysis, 24(4), 787-793.
Anderson, A. S., Bilodeau, A. L., Gilg, M. R., & Hilbish, T. J. 2002. Routes of introduction of
the Mediterranean mussel (Mytilus galloprovincialis) to Puget Sound and Hood Canal.
Journal of Shellfish Research, 21(1):75-79.
Anderson, E. C., & Thompson, E. A. 2002. A model-based method for identifying species
hybrids using multilocus genetic data. Genetics, 160(3), 1217-1229.
Anderson, M. J., & Underwood, A. J. 1994. Effects of substratum on the recruitment and
development of an intertidal estuarine fouling assemblage. Journal of Experimental
Marine Biology and Ecology, 184(2), 217–236.
Anderson, M. J., Crist, T. O., Chase, J. M., Vellend, M., Inouye, B. D., Freestone, A. L.,
Sanders, N. J., Cornell, H. V., Comita, L. S., Davies, K. F., Harrison, S. P., Kraft, N. J.
B., Stegen, J. C., & Swenson, N. G. 2011. Navigating the multiple meanings of β diversity:
A roadmap for the practicing ecologist. Ecology Letters, 14, 19–28.
Anderson, M. J., Gorley, R. N., & Clarke, K. R. 2008. PERMANOVA+ for PRIMER: Guide
to Software and Statistical Methods. Plymouth, UK.
Andersson, M. H., Berggren, M., Wilhelmsson, D., & Öhman, M. C. 2009. Epibenthic
colonization of concrete and steel pilings in a cold-temperate embayment: A field
experiment. Helgoland Marine Research, 63(3), 249-260.
Anestis, A., Lazou, A., Pörtner, H. O., & Michaelidis, B. 2007. Behavioral, metabolic, and
molecular stress responses of marine bivalve Mytilus galloprovincialis during long-term
acclimation at increasing ambient temperature. American Journal of Physiology Regulatory Integrative and Comparative Physiology, 293(2), 911-921.
Apte S., Holland B. S., Godwin L. S., Gardner J. P. A. 2000. Jumping ship: a stepping stone
event mediating transfer of a non-indigenous species via a potentially unsuitable
environment. Biological Invasions, 2, 75-79.
Arthur, T., Arrowsmith, L., Parsons, S., and Summerson, R. 2015. Monitoring for Marine
Pests: A Review of the Design and Use of Australia’s National Monitoring Strategy and
Identification of Possible Improvements. Canberra, ACT: Australian Government
Department of Agriculture, Water Resources.
Australian Government Department of Agriculture and Water Resources. 2019. Australian
biofouling management requirements for international vessel arrivals. Consultation
Regulation Impact Statement. Canberra, ACT: Australian Government Department of
Agriculture, Water Resources, Biosecurity Animal Division.
Bacchiocchi, F., & Airoldi, L. 2003. Distribution and dynamics of epibiota on hard structures
for coastal protection. Estuarine, Coastal and Shelf Science, 56(5–6), 1157–1166.
Bagley, P. M., Smith, K. L., Bett, B. J., Priede, I. G., Rowe, G., Clarke, J., Wall, A., Ruh, H.
A., Bailey, D. M., & Bazica, B. 2015. The DELOS project: Development of a long-term
observatory in an oil field environment in the Tropical Atlantic Ocean. In Seafloor
Observatories: A New Vision of the Earth from the Abyss. Springer Praxis Books, 325344.
Bayne, B. L., Salkeld, P. N., & Worrall, C. M. 1983. Reproductive effort and value in different
populations of the marine mussel, Mytilus edulis L. Oecologia, 59(1), 18–26.
155
Bayne, B. L., & Thompson, R. J. 1970. Some physiological consequences of keeping Mytilus
edulis in the laboratory. Helgo. wiss. Meeresunters, 20, 526-552.
Beca-Carretero, P., Rotini, A., Mejia, A., Migliore, L., Vizzini, S., & Winters, G. 2019.
Halophila stipulacea descriptors in the native area (Red Sea): A baseline for future
comparisons with native and non-native populations. In Marine Environmental Research,
104828.
Becker, A., Taylor, M. D., Folpp, H., & Lowry, M. B. 2018. Managing the development of
artificial reef systems: The need for quantitative goals. Fish and Fisheries, 19(4), 740-752.
Bellard, C., Cassey, P., & Blackburn, T. M. 2016. Alien species as a driver of recent
extinctions. Biology Letters, 12(2), 20150623.
Berlow, E. L. 1997. From canalization to contingency: Historical effects in a successional
rocky intertidal community. Ecological Monographs, 67(4), 435-460.
Beukema, J.J., Cadée, G.C., Dekker, R., 2002. Zoobenthic biomass limited by phytoplankton
abundance: evidence from parallel changes in two long-term data series in the Wadden
Sea. Journal of Sea Research, 48, 111-125.
Biosecurity New Zealand. 2010. Managing and controlling the risk posed to the marine
environment from biofouling on arriving vessels. MAF Biosecurity New Zealand
consultation paper 10/04. Wellington, MAF. 15 p.
Biosecurity New Zealand. 2020. Import health standard for ships ballast water from all
countries. Retrieved from https://www.biosecurity.govt.nz/dmsdocument/1167/direct
Bishop, M. J., & Hutchings, P. A. 2011. How useful are port surveys focused on target pest
identification for exotic species management? Marine Pollution Bulletin, 62(1), 36-42.
Bishop, M. J., Mayer-Pinto, M., Airoldi, L., Firth, L. B., Morris, R. L., Loke, L. H. L., Hawkins,
S. J., Naylor, L. A., Coleman, R. A., Chee, S. Y., & Dafforn, K. A. 2017. Effects of ocean
sprawl on ecological connectivity: impacts and solutions. Journal of Experimental Marine
Biology and Ecology, 492, 7–30.
Borsa, P., Daguin, C., & Bierne, N. 2007. Genomic reticulation indicates mixed ancestry in
Southern hemisphere Mytilus spp. mussels. Biological Journal of the Linnean Society,
92(4), 747-754.
Borsa, P., Rolland, V., & Daguin-Thiébaut, C. 2012. Genetics and taxonomy of Chilean
smooth-shelled mussels, Mytilus spp. (Bivalvia: Mytilidae). Comptes Rendus - Biologies,
335(1), 51-61.
Borthagaray, A. I., & Carranza, A. 2007. Mussels as ecosystem engineers: Their contribution
to species richness in a rocky littoral community. Acta Oecologica, 31(3), 243-250.
Boudouresque, C. F., & Verlaque, M. 2012. An overview of species introduction and invasion
processes in marine and coastal lagoon habitats. Cahiers de Biologie Marine, 53(3), 309317.
Bownes, S. J., & McQuaid, C. D. 2006. Will the invasive mussel Mytilus galloprovincialis
Lamarck replace the indigenous Perna perna L. on the south coast of South Africa?
Journal of Experimental Marine Biology and Ecology, 338(1), 140-151.
Bownes, S. J., & McQuaid, C. D. 2010. Mechanisms of habitat segregation between an invasive
156
(Mytilus galloprovincialis) and an indigenous (Perna perna) mussel: Adult growth and
mortality. Marine Biology, 157(8), 1799–1810.
Braby, C. E., & Somero, G. N. 2006. Following the heart: Temperature and salinity effects on
heart rate in native and invasive species of blue mussels (genus Mytilus). Journal of
Experimental Biology, 209(13), 2554-2566.
Bracewell, S. A., Robinson, L. A., Firth, L. B., & Knights, A. M. 2013. Predicting free-space
occupancy on novel artificial structures by an invasive intertidal barnacle using a removal
experiment. PLoS ONE, 8(9), e74457.
Branch, G. M., & Nina Steffani, C. 2004. Can we predict the effects of alien species? A casehistory of the invasion of South Africa by Mytilus galloprovincialis (Lamarck). Journal
of Experimental Marine Biology and Ecology, 300(1–2), 189-215.
Branch, G. M., Odendaal, F., & Robinson, T. B. 2008. Long-term monitoring of the arrival,
expansion and effects of the alien mussel Mytilus galloprovincialis relative to wave action.
Marine Ecology Progress Series, 370, 171–183.
Branch, G. M., Odendaal, F., & Robinson, T. B. 2010. Competition and facilitation between
the alien mussel Mytilus galloprovincialis and indigenous species: Moderation by wave
action. Journal of Experimental Marine Biology and Ecology, 383(1), 65-78.
Brannock, P. M., Wethey, D. S., & Hilbish, T. J. 2009. Extensive hybridization with minimal
introgression in Mytilus galloprovincialis and M. trossulus in Hokkaido, Japan. Marine
Ecology Progress Series, 383, 161–171.
Brockerhoff E. G., Liebhold A. M., Richardson B., Suckling D. M. 2010. Eradication of
invasive forest insects: concept, methods, costs and benefits. New Zealand Journal of
Forestry Science, 40, 117–135.
Browne, M. A., & Chapman, M. G. 2011. Ecologically informed engineering reduces loss of
intertidal biodiversity on artificial shorelines. Environmental Science and Technology, 45,
8204–8207.
Browne, M. A., & Chapman, M. G. 2014. Mitigating against the loss of species by adding
artificial intertidal pools to existing seawalls. Marine Ecology Progress Series, 497, 119129.
Browne, Mark A., & Chapman, M. G. 2011. Ecologically informed engineering reduces the
loss of intertidal biodiversity on artificial shorelines. Environmental Science and
Technology, 45(19), 8204-8207.
Bulleri, F. 2005. Role of recruitment in causing differences between intertidal assemblages on
seawalls and rocky shores. Marine Ecology Progress Series, 32(2), 101-102.
Bulleri, F. 2006. Is it time for urban ecology to include the marine realm? Trends in Ecology
and Evolution, 21, 656-658.
Bulleri, F., & Airoldi, L. 2005. Artificial marine structures facilitate the spread of a nonindigenous green alga, Codium fragile ssp. tomentosoides, in the north Adriatic Sea.
Journal of Applied Ecology, 42(6), 1063–1072.
Bulleri, F., & Chapman, M. G. 2004. Intertidal assemblages on artificial and natural habitats in
marinas on the north-west coast of Italy. Marine Biology, 145(2), 381-391.
157
Bulleri, F., & Chapman, M. G. 2010. The introduction of coastal infrastructure as a driver of
change in marine environments. Journal of Applied Ecology, 47(1), 26–35.
Bulleri, F. & Chapman, M. G. 2015. Artificial physical structures. In book: Marine
Ecosystems: Human Impacts on Biodiversity, Functioning and Services, Chapter 7, 167201.
Bulleri, F., Abbiati, M., & Airoldi, L. 2006. The colonisation of human-made structures by the
invasive alga Codium fragile ssp. tomentosoides in the north Adriatic Sea (NE
Mediterranean). Marine Biodiversity,183, 263-269.
Bulleri, F., Benedetti-Cecchi, L., Jaklin, A., & Iveša, L. 2016. Linking disturbance and
resistance to invasion via changes in biodiversity: A conceptual model and an
experimental test on rocky reefs. Ecology and Evolution, 6(7), 2010-2021.
Burgess S. C., Treml E. A., & Marshall, D. J. 2012. How do dispersal costs and habitat selection
influence realized population connectivity? Ecology, 93, 1378−1387.
Burlakova, L. E., Karatayev, A. Y., & Karatayev, V. A. 2012. Invasive mussels induce
community changes by increasing habitat complexity. Hydrobiologia, 685(1), 121–134.
Burt, J., Bartholomew, A., & Sale, P. F. 2011. Benthic development on large-scale engineered
reefs: A comparison of communities among breakwaters of different age and natural reefs.
Ecological Engineering, 37(2), 191–198.
Burt, J., Bartholomew, A., Bauman, A., Saif, A., & Sale, P. F. 2009. Coral recruitment and
early benthic community development on several materials used in the construction of
artificial reefs and breakwaters. Journal of Experimental Marine Biology and Ecology,
373(1), 72-78.
Byers, J. E. 2002. Impact of non-indigenous species on natives enhanced by anthropogenic
alteration of selection regimes. Oikos, 97, 449-458.
Cacabelos, E., Martins, G. M., Thompson, R., Prestes, A. C. L., Azevedo, J. M. N., & Neto, A.
I. 2016. Material type and roughness influence structure of intertidal communities on
coastal defences. Marine Ecology, 37(4), 801-812.
Caceres Martinez, J., Robledo, J. A. F., & Figueras, A. 1994. Settlement and post-larvae
behaviour of Mytilus galloprovincialis: Field and laboratory experiments. Marine Ecology
Progress Series, 112(1–2), 107–118.
Cáceres-Martínez, J., & Figueras, A. 1998. Long-term survey on wild and cultured mussels
(Mytilus galloprovincialis Lmk) reproductive cycles in the Ria de Vigo (NW Spain).
Aquaculture, 162(1–2), 141–156.
Callaway, R. M. & Ridenour, W. M. 2004. Novel weapons: Invasive success and the evolution
of increased competitive ability. Frontiers in Ecology and the Environment, 2, 436–443.
Campbell, M. L., Gould, B., & Hewitt, C. L. 2007. Survey evaluations to assess marine
bioinvasions. Marine Pollution Bulletin, 55(7–9), 360–378.
Carlton, J. T. & Ruiz, G. M. 2005. Vector science and integrated vector management in
bioinvasion ecology: conceptual frameworks. In Invasive Alien Species: A New Synthesis
(ed. H. A. Mooney, R. N. Mack, J. A. McNeely, L.E. Neville, P. J. Schei and J. K. Waage),
pp. 36 – 58. Island Press, Washington, D C.
158
Carvalho, S., Moura, A., Cúrdia, J., Cancela da Fonseca, L., & Santos, M. N. 2013. How
complementary are epibenthic assemblages in artificial and nearby natural rocky reefs?
Marine Environmental Research, 92, 170–177.
Castilla, J. C., Guiñez, R., Caro, A. U., & Ortiz, V. 2004. Invasion of a rocky intertidal shore
by the tunicate Pyura praeputialis in the Bay of Antofagasta, Chile. Proceedings of the
National Academy of Sciences of the United States of America, 101, 8517-8524.
Ceccherelli, G., Pinna, S., Cusseddu, V., & Bulleri, F. 2014. The role of disturbance in
promoting the spread of the invasive seaweed Caulerpa racemosa in seagrass meadows.
Biological Invasions, 16, 2737–2745.
Ceccherelli, V., & Rossi, R. 1984. Settlement, growth and production of the mussel Mytilus
galloprovincialis. Marine Ecology Progress Series, 16, 173–184.
Chan, F. T., & Briski, E. 2017. An overview of recent research in marine biological invasions.
Marine Biology, 164, 121.
Chang, C. C., & Turner, B. L. 2019. Ecological succession in a changing world. Journal of
Ecology, 107(2), 503–509.
Chapman, J. W., & Carlton, J. T. 1991. A Test of Criteria for Introduced Species: the Global
Invasion by the Isopod Synidotea laevidorsalis (Miers, 1881). Journal of Crustacean
Biology, 11(3), 386-400.
Chapman, M. G. 2003. Paucity of mobile species on constructed seawalls: Effects of
urbanization on biodiversity. Marine Ecology Progress Series, 264, 21–29.
Chapman, M. G. 2006. Intertidal seawalls as habitats for molluscs. Journal of Molluscan
Studies, 72(3), 247-257.
Chapman, M. G. 2012. Restoring Intertidal Boulder-Fields as Habitat for “Specialist” and
“Generalist” Animals. Restoration Ecology, 20(2), 277–285.
Chapman, M. G. 2013. Constructing replacement habitat for specialist and generalist molluscsthe effect of patch size. Marine Ecology Progress Series, 473, 201-214.
Chapman, M. G., & Blockley, D. J. 2009. Engineering novel habitats on urban infrastructure
to increase intertidal biodiversity. Oecologia, 161(3), 625-635.
Chapman, M. G., & Bulleri, F. 2003. Intertidal seawalls- New features of landscape in intertidal
environments. Landscape and Urban Planning. 62(3),159-172.
Chapman, M. G., & Clynick, B. G. 2006. Experiments testing the use of waste material in
estuaries as habitat for subtidal organisms. Journal of Experimental Marine Biology and
Ecology, 338(2), 164–178.
Chapman, M. G., & Underwood, A. J. 2009. Evaluating the accuracy and precision of species
- Area relationships for multiple estimators and different marine assemblages. Ecology,
90(3), 754-766.
Chapman, M. G., & Underwood, A. J. 2011. Evaluation of ecological engineering of
“armoured” shorelines to improve their value as habitat. Journal of Experimental Marine
Biology and Ecology, 400(1-2), 302-313.
Chee S. Y., Othman A. G., Sim Y. K., Adam A. N. M., Firth L. B. 2017. Land reclamation and
artificial islands: walking the tightrope between development and conservation. Global
159
Ecology Conservation, 12, 80–95.
Cheung, W. W. L., Lam, V. W. Y., Sarmiento, J. L. 2009. Projecting global marine biodiversity
impacts under climate change scenarios. Fish and Fisheries, 10, 235–251.
Choi, F. M. P., Murray, C. C., Therriault, T. W., & Pakhomov, E. A. 2016. Intertidal invasion
patterns in Canadian ports. Marine Biology, 163(9), 1–12.
Clark, G. F., & Johnston, E. L. 2011. Temporal change in the diversity-invasibility relationship
in the presence of a disturbance regime. Ecology Letters, 14(1), 52-57.
Clarke K.R., Warwick R.M. 2001. Change in Marine Communities: An Approach to Statistical
Analysis and Interpretation (second ed.), PRIMER-E, Plymouth, UK.
Clarke, K. R. 1993. Non‐parametric multivariate analyses of changes in community structure.
Australian Journal of Ecology, 18(1), 117-143.
Clarke, K. R., & Gorley, R. N. 2006. Plymouth routines in multivariate ecological research.
PRIMER v6: Use manual/Tutorial. PRIMER-E: Plymouth, UK.
Clarke Murray C., Pakhomov E. A., Therriault T. W. 2011. Recreational boating: a large
unregulated vector transporting marine invasive species. Diversity and Distributions, 17,
1161–1172.
Clynick, B. G., Chapman, M. G., & Underwood, A. J. 2007. Effects of epibiota on assemblages
of fish associated with urban structures. Marine Ecology Progress Series, 332, 201–210.
Cohen, J. 1992. A power primer. Psychological Bulletin, 112(1), 155-159.
Commonwealth of Australia. 2011. Marine Bioregional planning in the EEZ. Australian
Department of the Environment. Retrieved from http://msp.ioc-unesco.org.
Commonwealth of Australia. 2013. Anti-fouling and in-water cleaning guidelines. CC BY 3.0,
26
Commonwealth of Australia. 2015. South-east marine region profile: A description of the
ecosystems, conservation values and uses of the South-east Marine Region, 94.
Connell, J. H. 1961. The Influence of Interspecific Competition and Other Factors on the
Distribution of the Barnacle Chthamalus stellatus. Ecology, 42(4), 710–723.
Connell, J. H. 1972. Community interactions on marine rocky intertidal shores. Annual Review
of Ecology and Systematics, 3, 169–192.
Connell, J. H., & Slatyer, R. O. 1977. Mechanisms of succession in natural communities and
their role in community stability and organization. The American Naturalist, 111(982),
1119–1144.
Connell, S. D. 2001. Urban structures as marine habitats: An experimental comparison of the
composition and abundance of subtidal epibiota among pilings, pontoons and rocky reefs.
Marine Environmental Research, 52(2), 115-125.
Connell, S. D., & Glasby, T. M. 1999. Do urban structures influence local abundance and
diversity of subtidal epibiota? A case study from Sydney Harbour, Australia. Marine
Environmental Research, 47(4), 373–387.
Cook, E., Brown, S., Payne, R., & Macleod, A. 2016. Marine biosecurity: protecting
indigenous marine species. Research and Reports in Biodiversity Studies, 5, 1-14.
160
Coombes M. A., La Marca E. C., Naylor L. A., Thompson R. C. 2015. Getting into the groove:
opportunities to enhance the ecological value of hard coastal infrastructure using finescale surface textures. Ecological Engineering, 77, 314–323.
Coombes M. A., Naylor L. A., Viles H. A., Thompson R. C. 2013. Bioprotection and
disturbance: seaweed, microclimatic stability and conditions for mechanical weathering
in the intertidal zone. Geomorphology, 202, 4–14.
Cope, R. C., Prowse, T. A. A., Ross, J. V., Wittmann, T. A., & Cassey, P. 2015. Temporal
modelling of ballast water discharge and ship-mediated invasion risk to Australia. Royal
Society Open Science, 2(4), e150039.
Correia, M., Palma, J., Koldewey, H., & Andrade, J. P. 2013. Can artificial holdfast units work
as a habitat restoration tool for long-snouted seahorse (Hippocampus guttulatus Cuvier)?
Journal of Experimental Marine Biology and Ecology, 448, 258-264.
Corriero, G., Pierri, C., Accoroni, S., Alabiso, G., Bavestrello, G., Barbone, E., Bastianini, M.,
Bazzoni, A. M., Bernardi Aubry, F., Boero, F., Buia, M. C., Cabrini, M., Camatti, E.,
Cardone, F., Cataletto, B., Cattaneo Vietti, R., Cecere, E., Cibic, T., Colangelo, P., Basset,
A. 2016. Ecosystem vulnerability to alien and invasive species: A case study on marine
habitats along the Italian coast. Aquatic Conservation: Marine and Freshwater
Ecosystems, 26(2), 392-409.
Costanza, R., dArge, R., de Groot, R., Farber, S., Grasso, M., Hannon, B., Limburg, K., Naeem,
S., Oneill, R.V., Paruelo, J., Raskin, R.G., Sutton, P., van den Belt, M., 1997. The value
of the world’s ecosystem services and natural capital. Nature, 387, 253–260.
Costanza, R., de Groot, R., Sutton, P., van der Ploeg, S., Anderson, S. J., Kubiszewski, I.,
Farber, S., & Turner, R. K. 2014. Changes in the global value of ecosystem services.
Global Environmental Change, 26, 152-158.
Coutts A. D. M., Piola R. F., Hewitt C. L., Connell S. D., Gardner J. P. A. 2010a. Effect of
vessel voyage speed on survival of biofouling organisms: implications for translocation
of non-indigenous marine species. Biofouling, 26, 1-13.
Coutts A. D. M., Piola R. F., Taylor M. D., Hewitt C. L., Gardner J. P. A., 2010. The Effect Of
Vessel Speed On The Survivorship Of Biofouling Organisms At Different Hull Locations.
Biofouling, 26, 539-553.
Coutts, A. D. M., & Forrest, B. M. 2007. Development and application of tools for incursion
response: Lessons learned from the management of the fouling pest Didemnum vexillum.
Journal of Experimental Marine Biology and Ecology, 342 (1), 154-162.
Coutts, A. D. M., Moore, K. M., & Hewitt, C. L. 2003. Ships’ sea-chests: An overlooked
transfer mechanism for non-indigenous marine species? Marine Pollution Bulletin,
46(11), 1510-1513.
Coutts, A.D.M.; Taylor, M.D. 2004. A preliminary investigation of biosecurity risks associated
with biofouling on merchant vessels in New Zealand. New Zealand Journal of Marine and
Freshwater Research, 38, 215–229.
Creed, J. C., & De Paula, A. F. 2007. Substratum preference during recruitment of two invasive
alien corals onto shallow-subtidal tropical rocky shores. Marine Ecology Progress Series,
330, 101-111.
Crooks A, J., & Khim S, H. 1999. Architectural vs. biological effects of a habitat-altering
161
exotic mussel, Musculista senhousia. Journal of Experimental Marine Biology and
Ecology, 240(1), 53-75.
Crooks, J. A. 2002. Characterizing ecosystem-level consequences of biological invasions: The
role of ecosystem engineers. Oikos, 97(2), 153-166.
Crooks, J. A., Chang, A. L., & Ruiz, G. M. 2011. Aquatic pollution increases the relative
success of invasive species. Biological Invasions, 13, 165–176.
Cunningham, C. W., & Collins, T. M. 1994. Developing model systems for molecular
biogeography: Vicariance and interchange in marine invertebrates. Molecular Ecology
and Evolution: Approaches and Applications, 69, 405-433.
Curiel-Ramirez, S., & Caceres-Martinez, J. 2004. Reproductive cycle of coexisting mussels,
Mytilus californianus and Mytilus galloprovincialis, in Baja California, New Mexico.
Journal of Shellfish Research, 23(2), 515-520.
Currie, D. J., Mittelbach, G. G., Cornell, H. V., Kaufman, D. M., Kerr, J. T., Oberdorff, T.
(2004). Predictions and tests of climate-based hypotheses of broad-scale variation in
taxonomic richness. Ecology Letters, 7 (12): 1121–1134.
Dafforn, K. A. 2017. Eco-engineering and management strategies for marine infrastructure to
reduce establishment and dispersal of non-indigenous species. Management of Biological
Invasions, 8(2), 153-161.
Dafforn, K. A., Glasby, T. M., & Johnston, E. L. 2012. Comparing the invasibility of
experimental “reefs” with field observations of natural reefs and artificial structures. PLoS
ONE, 7(5), 1–16.
Dafforn, K. A., Glasby, T. M., Airoldi, L., Rivero, N. K., Mayer-Pinto, M., & Johnston, E. L.
2015 a. Marine urbanization: An ecological framework for designing multifunctional
artificial structures. Frontiers in Ecology and the Environment, 13(2), 82–90.
Dafforn, K. A., Johnston, E. L., & Glasby, T. M. 2009. Shallow moving structures promote
marine invader dominance. Biofouling, 25(3), 277–287.
Dafforn, K. A., Mayer-Pinto, M., Bugnot, A. B., Coleman, R. A., Morris, R. L., & Johnston,
E. L. 2016. Guiding principles for marine foreshore developments. Report Prepared for
UrbanGrowth NSW. University of New South Wales, 1-53.
Dafforn, K. A., Glasby, T. M., Airoldi, L., Rivero, N. K., Mayer-Pinto, M., & Johnston, E. L.
2015a. Marine urbanization: An ecological framework for designing multifunctional
artificial structures. Frontiers in Ecology and the Environment, 13(2), 82–90.
Dafforn, K. A., Mayer-Pinto, M., Morris, R. L., & Waltham, N. J. 2015b. Application of
management tools to integrate ecological principles with the design of marine
infrastructure. Journal of Environmental Management, 158(1), 61-73.
Daguin, C., & Borsa, P. 2000. Genetic relationships of Mytilus galloprovincialis Lamarck
populations worldwide: Evidence from nuclear-DNA markers. Geological Society
Special Publication, 177, 389–397.
Dang, H., & Lovell, C. R. 2000. Bacterial primary colonization and early succession on
surfaces in marine waters as determined by amplified rRNA gene restriction analysis and
sequence analysis of 16S rRNA genes. Applied and Environmental Microbiology, 66(2),
467–475.
162
Dangendorf, S., Marcos, M., Wöppelmann, G., Conrad, C. P., Frederikse, T., & Riva, R. 2017.
Reassessment of 20th-century global mean sea level rise. Proceedings of the National
Academy of Sciences of the United States of America, 114(23), 5946-5951.
Darwin C. 1860. The Voyage of the Beagle. Doubleday, New Jersey.
Davis M. A., 2003. Biotic globalization: Does competition from introduced species threaten
biodiversity? Bioscience, 53, 481-489.
DAWR. 2017. Australian Ballast Water Management Requirements Version 7. Department of
Agriculture and Water Resources. Retrieved from: http://www.agriculture.gov.au
/biosecurity/avm/vessels/ballast/australian-ballast-water-management-requirements.
DAWR. 2019. Australian biofouling management requirements for international vessel
arrivals—Consultation Regulation Impact Statement. Canberra: Department of
Agriculture
and
Water
Resources.
Retrieved
from
https://haveyoursay.agriculture.gov.au/445
Dearborn, D. C., & Kark, S. 2010. Motivations for conserving urban biodiversity. Conservation
Biology, 24, 432–440.
Department of Conservation. 2005. Stop the spread of aquatic pests. Retrieved from
www.doc.govt.nz/ Conservation/Freshwater/Aquatic-pests/index.asp.
Department for Transport Statistics United Kingdom. 2016. Port Freight Statistics 2016: Notes
and definitions.
Department of Agriculture, Fisheries and Forestry (DAFF), Biosecurity Act 2015 No. 24
(NSW).
Department of Conservation and Ministry of Fisheries. 2011. Coastal marine habitats and
marine protected areas in the New Zealand Territorial Sea: a broad scale gap analysis.
Wellington, New Zealand.
Department of Environment, New Zealand Ministry for Primary Industries and Department of
Agriculture. 2015. Anti‐fouling and in‐water cleaning guidelines.
Department of Environment and Climate Change. 2009. Environmentally Friendly Seawalls:
A Guide to Improving the Environmental Value of Seawalls and Seawall-lined Foreshores
in Estuaries. NSW Department of Environment and Climate Change, Sydney, Australia,
1–34.
deRivera, C., Ruiz, G., Crooks, J., Wasson, K., Lonhart, S., Fofonoff, P., Steves, B., Rumrill,
S., Brancato, M., Pegau, S., Bulthuis, D., Preisler, R., Schoch, C., Bowlby, E.,
DeVogelaere, A., Crawford, M., Gittings, S., Hines, A., Takata, L., Powell, S. 2005.
Broad-scale Non-indigenous Species Monitoring Along the West Coast in National
Marine Sanctuaries and National Estuarine Research Reserves: Report to National Fish &
Wildlife Foundation. Otro, 1–24.
Dethier, M. N., Toft, J. D., & Shipman, H. 2017. Shoreline Armoring in an Inland Sea: ScienceBased Recommendations for Policy Implementation. Conservation Letters, 10(5), 626633.
Devlaming, V., Grossman, G., & Chapman, F. 1982. On the use of the gonosomatic index.
Comparative Biochemistry and Physiology - Part A: Physiology, 73(1), 31-39.
163
Dias, P. J., Fotedar, S., & Snow, M. 2014. Characterisation of mussel (Mytilus spp.) populations
in Western Australia and evaluation of potential genetic impacts of mussel spat
translocation from interstate. Marine and Freshwater Research, 65, 486-496.
Díaz, C., Sobenes, C., & Machino, S. 2019. Comparative growth of Mytilus chilensis (Hupé
1854) and Mytilus galloprovincialis (Lamarck 1819) in aquaculture longline system in
Chile. Aquaculture, 507, 21–27.
Dobretsov, S., & Wahl, M. 2001. Recruitment preferences of blue mussel spat (Mytilus edulis)
for different substrata and microhabitats in the White Sea (Russia). Hydrobiologia, 445,
27–35.
Dodgshun T.J., Taylor M. D. & Forrest B. M. 2007. Human-mediated pathways of spread for
non-indigenous marine species in New Zealand, DOC Research & Development series
Technical Report. 266.
Dong, Y. wei, Huang, X. wei, Wang, W., Li, Y., & Wang, J. 2016. The marine “great wall” of
China: Local- and broad-scale ecological impacts of coastal infrastructure on intertidal
macrobenthic communities. Diversity and Distributions, 22(7), 731–744.
Duarte, C. M., Pitt, K. A., Lucas, C. H., Purcell, J. E., Uye, S. I., Robinson, K., Brotz, L.,
Decker, M. B., Sutherland, K. R., Malej, A., Madin, L., Mianzan, H., Gili, J. M., Fuentes,
V., Atienza, D., Pages, F., Breitburg, D., Malek, J., Graham, W. M., & Condon, R. H.
2013. Is global ocean sprawl a cause of jellyfish blooms? Frontiers in Ecology and the
Environment, 11(2), 91-97.
Dugan, J. E., Airoldi, L., Chapman, M. G., Walker, S. J., & Schlacher, T. 2012. Estuarine and
Coastal Structures: Environmental Effects, A Focus on Shore and Nearshore Structures.
Treatise on Estuarine and Coastal Science, 8, 17-41.
Dumont, C. P., Gaymer, C. F., & Thiel, M. 2011. Predation contributes to invasion resistance
of benthic communities against the non-indigenous tunicate Ciona intestinalis. Biological
Invasions, 13, 2023-2034.
Dutton, J. M., & Hofmann, G. E. 2008. Spatial and temporal variation in distribution and
protein ubiquitination for Mytilus congeners in the California hybrid zone. Marine
Biology, 154(6), 1067-1075.
Ehrenfeld. 2003. Globalisation: Effects on Biodiversity, Environment and Society.
Conservation and Society, 1(1), 99-111.
Ehrlich, P. R. 1986. Which animal will invade? In: Mooney H. A. and Drake J. A. (eds)
Ecology of Biological Invasions of North American and Hawaii, pp 79–95. SpringerVerlag, New York.
Elton, C. S. 1958. The Ecology of Invasions by Animals and Plants. The Ecology of Invasions
by Animals and Plants, 15-32.
Elton, C. S. 1958. The Ecology of Invasions by Animals and Plants. London: Chapman and
Hall.
Epstein, G., & Smale, D. A. 2018. Environmental and ecological factors influencing the
spillover of the non-native kelp, Undaria pinnatifida, from marinas into natural rocky reef
communities. Biological Invasions, 20, 1049-1072
Erlandsson, J., Pal, P., & McQuaid, C. D. 2006. Re-colonisation rate differs between co164
existing indigenous and invasive intertidal mussels following major disturbance. Marine
Ecology Progress Series, 320, 169–176
Evans, A. J., Firth, L. B., Hawkins, S. J., Morris, E. S., Goudge, H., & Moore, P. J. 2016. Drillcored rock pools: An effective method of ecological enhancement on artificial structures.
Marine and Freshwater Research, 67(1), 123-130.
FAO 2018, Food and Agriculture Organisation of the United Nations. World aquaculture
production by species groups. Retrieved from http://www.fao.org.
Fauvelot, C., Bertozzi, F., Costantini, F., Airoldi, L., & Abbiati, M. 2009. Lower genetic
diversity in the limpet Patella caerulea on urban coastal structures compared to natural
rocky habitats. Marine Biology, 156(11), 2313-2323.
Fava, F., Ponti, M., & Abbiati, M. 2016. Role of recruitment processes in structuring
coralligenous benthic assemblages in the northern Adriatic continental shelf. PLoS ONE,
11(10), e0163494.
Ferrario, F., Iveša, L., Jaklin, A., Perkol-Finkel, S., & Airoldi, L. 2016. The overlooked role of
biotic factors in controlling the ecological performance of artificial marine habitats.
Journal of Applied Ecology, 53(1), 16-24.
Ferrario, J., Caronni, S., Occhipinti-Ambrogi, A., & Marchini, A. 2017. Role of commercial
harbours and recreational marinas in the spread of non-indigenous fouling species.
Biofouling, 30(8), 651-660.
Firth, L. B., Browne, K. A., Knights, A. M., Hawkins, S. J., & Nash, R. 2016. Eco-engineered
rock pools: A concrete solution to biodiversity loss and urban sprawl in the marine
environment. Environmental Research Letters, 11(9), 094015-094015.
Firth, L. B., Grant, L. M., Crowe, T. P., Ellis, J. S., Wiler, C., Convery, C., & O’Connor, N. E.
2017. Factors affecting the prevalence of the trematode parasite Echinostephilla patellae
(Lebour, 1911) in the limpet Patella vulgata (L.). Journal of Experimental Marine Biology
and Ecology, 492, 99-104.
Firth, L. B., Knights, A., Bridger, D., Evans, A., Mieszkowska, N., Moore, P., O’connor, N.,
Sheehan, E., Thompson, R., & Hawkins, S. 2016. Ocean Sprawl: Challenges and
Opportunities for Biodiversity Management In A Changing World. In, Hughes, R. N.,
Hughes, D. J., Smith, I. P. and Dale, A. C. (eds.) Oceanography and Marine Biology: an
annual review. Taylor & Francis, 54, 193-269.
Firth, L. B., Mieszkowska, N., Grant, L. M., Bush, L. E., Davies, A. J., Frost, M. T., Moschella,
P. S., Burrows, M. T., Cunningham, P. N., Dye, S. R., & Hawkins, S. J. 2015. Historical
comparisons reveal multiple drivers of decadal change of an ecosystem engineer at the
range edge. Ecology and Evolution, 5(15), 1-13.
Firth, L. B., Mieszkowska, N., Thompson, R. C., & Hawkins, S. J. 2013. Climate change and
adaptational impacts in coastal systems: The case of sea defences. Environmental
Sciences: Processes and Impacts, 15, 1665-1670
Firth, L. B., Schofield, M., White, F. J., Skov, M. W., & Hawkins, S. J. 2014. Biodiversity in
intertidal rock pools: Informing engineering criteria for artificial habitat enhancement in
the built environment. Marine Environmental Research, 102, 122-130.
Firth, L. B., Thompson, R. C., Bohn, K., Abbiati, M., Airoldi, L., Bouma, T. J., Bozzeda, F.,
Ceccherelli, V. U., Colangelo, M. A., Evans, A., Ferrario, F., Hanley, M. E., Hinz, H.,
165
Hoggart, S. P. G., Jackson, J. E., Moore, P., Morgan, E. H., Perkol-Finkel, S., Skov, M.
W., Hawkins, S. J. 2014. Between a rock and a hard place: Environmental and engineering
considerations when designing coastal defence structures. Coastal Engineering, 87, 122135.
Firth, L. B., Thompson, R. C., White, F. J., Schofield, M., Skov, M. W., Hoggart, S. P. G.,
Jackson, J., Knights, A. M., & Hawkins, S. J. 2013. The importance of water-retaining
features for biodiversity on artificial intertidal coastal defence structures. Diversity and
Distributions, 19(10), 1275-1283.
Firth, L., Knights, A., Bridger, D., Evans, A., Mieszkowska, N., Moore, P., O’connor, N.,
Sheehan, E., Thompson, R., & Hawkins, S. 2016. Ocean Sprawl: Challenges and
Opportunities for Biodiversity Management In A Changing World. In, Hughes, R. N.,
Hughes, D. J., Smith, I. P. and Dale, A. C. (eds.) Oceanography and Marine Biology: an
annual review. Taylor & Francis, 54, 193-269.
Firth, L.B., Browne, K. A., Knights, A. M., Hawkins, S. J., & Nash, R. 2016. Eco-engineered
rock pools: A concrete solution to biodiversity loss and urban sprawl in the marine
environment. Environmental Research Letters, 11(9), 094015-094015.
Fitzpatrick, B. M., Johnson, J. R., Kump, D. K., Smith, J. J., Voss, S. R., & Shaffer, H. B. 2010.
Rapid spread of invasive genes into a threatened native species. Proceedings of the
National Academy of Sciences of the United States of America, 107(8), 3606-3610.
Fletcher, R. L., & Callow, M. E. 1992. The settlement, attachment and establishment of marine
algal spores. British Phycological Journal, 27(3), 303-329.
Floerl, O., Inglis, G.J. 2005, Starting the invasion pathway: the interaction between source
populations and human transport vectors. Biological Invasions, 7, 589–606.
Floerl, O., Inglis G. J., Dey K., Smith A. 2009. The importance of transport hubs in stepping‐
stone invasions. Journal of Applied Ecology, 46(1): 37-45.
Floerl, O., Inglis, G. J., Peacock, L., & Plew, D. 2012. The Efficacy of Settlement Plate Arrays
for Marine Surveillance, MPI Technical Paper, 2012/16, 91.
Floerl, O., Pool, T.K.; Inglis, G.J. 2004, Positive interactions between non-indigenous species
facilitate transport by human vectors. Ecological Applications, 14, 1724–1736.
Forrest, B. M., & Atalah, J. 2017. Significant impact from blue mussel Mytilus
galloprovincialis biofouling on aquaculture production of green-lipped mussels in New
Zealand. Aquaculture Environment Interactions, 9(1), 115-126.
Forrest, B. M., & Hopkins, G. A. 2013. Population control to mitigate the spread of marine
pests: Insights from management of the asian kelp Undaria pinnatifida and colonial
ascidian Didemnum vexillum. Management of Biological Invasions, 4(4), 317-326.
Forrest, B. M., Gardner, J. P. A., & Taylor, M. D. 2009. Internal borders for managing invasive
marine species. Journal of Applied Ecology, 46(1), 46–54.
Foss, S. F., Ode, P. R., Sowby, M., & Ashe, M. 2007. Non-indigenous aquatic organisms in
the coastal waters of California. California Fish and Game, 93(3), 111–129.
Foster, V., Giesler, R. J., Wilson, A. M. W., Nall, C. R., & Cook, E. J. 2016. Identifying the
physical features of marina infrastructure associated with the presence of non-native
species in the UK. Marine Biology, 163(8), 173.
166
Francisco, P. M., Mori, G. M., Alves, F. M., Tambarussi, E. V., & de Souza, A. P. 2018.
Population genetic structure, introgression, and hybridization in the genus Rhizophora
along the Brazilian coast. Ecology and Evolution, 8(6), 3491-3504.
Freestone, A. L., Ruiz, G. M., & Torchin, M. E. 2013. Stronger biotic resistance in tropics
relative to temperate zone: Effects of predation on marine invasion dynamics. Ecology,
94(6), 1370–1377.
Gallardo, B., & Aldridge, D. C. 2013. Evaluating the combined threat of climate change and
biological invasions on endangered species. Biological Conservation, 160, 225-233.
Garciá Molinos, J., Halpern, B. S., Schoeman, D. S., Brown, C. J., Kiessling, W., Moore, P. J.,
Pandolfi, J. M., Poloczanska, E. S., Richardson, A. J., & Burrows, M. T. 2016. Climate
velocity and the future global redistribution of marine biodiversity. Nature Climate
Change, 6, 83–88.
Gardner, J. P. A. 2004. A historical perspective of the genus Mytilus (Bivalvia: Mollusca) in
New Zealand: Multivariate morphometric analyses of fossil, midden and contemporary
blue mussels. Biological Journal of the Linnean Society, 82(3), 329–344.
Gardner J. P. A., Oyarzún P., Toro J., Wenne R., Zbawicka M. 2020. Phylogeography of
Southern Hemisphere blue mussels of the genus Mytilus: evolution, biosecurity,
aquaculture and food labeling. Oceanography and Marine Biology: Annual Reviews, in
submission.
Gardner J. P. A., Wenne R., Westfall K. R. Zbawicka M. 2016. Invasive mussels threaten
regional scale genetic diversity in mainland and remote offshore locations: the need for
baseline data and enhanced protection in the Southern Ocean. Global Change Biology, 22,
3182–3195.
Gardner, J. P. A., & Skibinski, D. O. F. 1988. Historical and size-dependent genetic variation
in hybrid mussel populations. Heredity, 61(1), 93–105.
Gardner, J. P. A., & Skibinski, D. O. F. 1990. Genotype-dependent fecundity and temporal
variation of spawning in hybrid mussel (Mytilus) populations. Marine Biology, 105(1),
153–162.
Gardner, J. P. A., & Skibinski, D. O. F. 1991. Mitochondrial DNA and allozyme covariation
in a hybrid mussel population. Journal of Experimental Marine Biology and Ecology,
149(1), 45-54.
Gardner, J. P. A., & Thompson, R. J. 2001. The effects of coastal and estuarine conditions on
the physiology and survivorship of the mussels Mytilus edulis, M. trossulus and their
hybrids. Journal of Experimental Marine Biology and Ecology, 265(2), 119-140.
Gardner, J. P. A., Zbawicka, M., Westfall, K. M., & Wenne, R. 2016. Invasive blue mussels
threaten regional scale genetic diversity in mainland and remote offshore locations: the
need for baseline data and enhanced protection in the Southern Ocean. Global Change
Biology, 22(9), 3182-3195.
Geller, J. B. 1999. Decline of a native mussel masked by sibling species invasion. Conservation
Biology, 13(3), 661-664.
Geller, J. B., Darling, J. A., & Carlton, J. T. 2010. Genetic Perspectives on Marine Biological
Invasions. Annual Review of Marine Science, 2, 367-393.
167
Geller, J. B., Darling, J. A., Carlton, J. T., 2010. Genetic perspectives on marine biological
invasions. Annual Review of Marine Science, 2, 367-393.
Gérard, K., Bierne, N., Borsa, P., Chenuil, A., & Féral, J. P. 2008. Pleistocene separation of
mitochondrial lineages of Mytilus spp. mussels from Northern and Southern Hemispheres
and strong genetic differentiation among southern populations. Molecular Phylogenetics
and Evolution, 49(1), 84-91.
Gestoso, I., Ramalhosa, P., Oliveira, P., & Canning-Clode, J. 2017. Marine protected
communities against biological invasions: A case study from an offshore island. Marine
Pollution Bulletin, 119(1), 72–80.
Glasby, T. M. 2001. Development of sessile marine assemblages on fixed versus moving
substrata. Marine Ecology Progress Series, 215, 37-47.
Glasby, T. M., Connell, S. D., Holloway, M. G., & Hewitt, C. L. 2007. Nonindigenous biota
on artificial structures: Could habitat creation facilitate biological invasions? Marine
Biology, 151, 887-895.
GloBallast. 2014. The IMO Ballast Water Management Convention.www.globallast.imo.org.
GloFouling. 2017. The IMO Biofouling Management Convention. www.glofouling.imo.org.
Goldsmit, J., Nudds, S. H., Stewart, D. B., Higdon, J. W., Hannah, C. G., & Howland, K. L.
2019. Where else? Assessing zones of alternate ballast water exchange in the Canadian
eastern Arctic. Marine Pollution Bulletin, 139, 74-90.
Goldstien, S. J., Inglis, G. J., Schiel, D. R., & Gemmell, N. J. 2013. Using Temporal Sampling
to Improve Attribution of Source Populations for Invasive Species. PLoS ONE. 8(6),
e65656.
Gollasch, S., David, M., Voigt, M., Dragsund, E., Hewitt, C., & Fukuyo, Y. 2007. Critical
review of the IMO international convention on the management of ships’ ballast water
and sediments. Harmful Algae, 6(4), 585-600.
Goodsell, P. J., Chapman, M. G., & Underwood, A. J. 2007. Differences between biota in
anthropogenically fragmented habitats and in naturally patchy habitats. Marine Ecology
Progress Series, 351, 15-23.
Gordon, D. P. 1977. The Aging Process in Bryozoans. In Biology of Bryozoans; Woollacott,
R. M., Zimmer, R. L., Eds.; Academic Press: New York, NY, USA, 335–376.
Gosling, E. M. 1992. Genetics of Mytilus. In the mussel Mytilus: ecology, physiology, genetics
and culture, 309-382.
Grant, W. S., & Cherry, M. I. 1985. Mytilus galloprovincialis Lmk. in Southern Africa. Journal
of Experimental Marine Biology and Ecology, 90(2), 179-191.
Green, D. S., Rocha, C., & Crowe, T. P. 2013. Effects of non-indigenous oysters on ecosystem
processes vary with abundance and context. Ecosystems, 16, 881–893.
Green, P. T., O’Dowd, D. J., Abbott, K. L., Jeffery, M., Retallick, K., & Mac Nally, R. 2011.
Invasional meltdown: Invader-invader mutualism facilitates a secondary invasion.
Ecology, 92(9), 1758-1768.
Gribben, P. E., I’Ons, S., Phillips, N. E., Geange, S. W., Wright, J. T., & Murray, B. R. 2013.
Biogeographic comparisons of the traits and abundance of an invasive crab throughout its
168
native and invasive ranges. Biological Invasions. 15, 1877–1885.
Griffen, B. D., Altman, I., Hurley, J., & Mosblack, H. 2011. Reduced fecundity by one invader
in the presence of another: A potential mechanism leading to species replacement. Journal
of Experimental Marine Biology and Ecology, 406(1-2), 6-13.
Griffiths, C. L., Hockey, P. A., Van Erkom Schurink, C., & Le Roux, P. J. 1992. Marine
invasive aliens on South African shores: Implications for community structure and tropillc
functioning. South African Journal of Marine Science, 12(1), 713-722.
Grosholz, E. D. 2005. Recent biological invasion may hasten invasional meltdown by
accelerating historical introductions. Proceedings of the National Academy of Sciences of
the United States of America, 102(4), 1088–1091.
Grosholz, E., Olin, P., Williams, B., Man, R. T., Grant, S., Avenue, V., Rosa, S., Box, P. O.,
& Bay, B. 2000. Reducing predation on manila clams by nonindigenous European green
crabs. Journal of Shellfish Research, 20(3), 913-919.
Guerra-García, J. M., & García-Gómez, J. C. 2004. Crustacean assemblages and sediment
pollution in an exceptional case study: A harbour with two opposing entrances.
Crustaceana, 77(3), 353-370.
Guerra-García, J. M., Corzo, J., Espinosa, F., & García-Gómez, J. C. 2004. Assessing habitat
use of the endangered marine mollusc Patella ferruginea (Gastropoda, Patellidae) in
northern Africa: Preliminary results and implications for conservation. Biological
Conservation, 116(3), 319-326.
Hänfling, B. 2007. Understanding the establishment success of non-indigenous fishes: Lessons
from population genetics. Journal of Fish Biology, 71, 115 - 135.
Hänfling, B., Edwards, F., & Gherardi, F. 2011. Invasive alien Crustacea: Dispersal,
establishment, impact and control. BioControl, 56(4), 573-595.
Hansen, M. J., & Clevenger, A. P. 2005. The influence of disturbance and habitat on the
presence of non-native plant species along transport corridors. Biological Conservation,
125(2), 249–259.
Harrison, R. G. 2012. The language of speciation. Evolution, 66(12), 3643-3657.
Hayes, K.R., Cannon, R., Neil , K., Inglis, G., 2005. Sensitivity and cost considerations for the
detection and eradication of marine pests in ports. Marine Pollution Bulletin, 50, 823-834.
Hayes, K. R., Inglis, G., & Barry, S. C. 2019. The assessment and management of marine pest
risks posed by shipping: The Australian and New Zealand experience. Frontiers in Marine
Science, 22.
Hedge L. H., Johnston E. L. 2012. Propagule pressure determines recruitment from a
commercial shipping pier. Biofouling, 28, 73– 85.
Heery, E. C., Bishop, M. J., Critchley, L. P., Bugnot, A. B., Airoldi, L., Mayer-Pinto, M.,
Sheehan, E. V., Coleman, R. A., Loke, L. H. L., Johnston, E. L., Komyakova, V., Morris,
R. L., Strain, E. M. A., Naylor, L. A., & Dafforn, K. A. 2017. Identifying the consequences
of ocean sprawl for sedimentary habitats. Journal of Experimental Marine Biology and
Ecology, 492, 31-48.
Heery, E. C., Dafforn, K. A., Smith, J. A., Ushiama, S., & Mayer-Pinto, M. 2018. Not all
169
artificial structures are created equal: Pilings linked to greater ecological and
environmental change in sediment communities than seawalls. Marine Environmental
Research, 142, 286-294.
Herbert, R. J. H., Southward, A. J., Sheader, M., & Hawkins, S. J. 2007. Influence of
recruitment and temperature on distribution of intertidal barnacles in the English Channel.
Journal of the Marine Biological Association of the United Kingdom, 87(2), 487-499.
Hewitt, C. L. & Campbell, M. L. 2007. Mechanisms for the prevention of marine bioinvasions
for better biosecurity. Marine Pollution Bulletin, 55, 395–401.
Hewitt, C. L. 2002. Distribution and Biodiversity of Australian Tropical Marine Bioinvasions.
Pacific Science, 56(2), 213–222.
Hewitt, C. L., & Hayes, K. R. 2002. Risk assessment of marine biological invasions. In
Invasive aquatic species of Europe. Distribution, impacts and management, Springer
Netherlands, 456-466.
Hewitt, C. L., & Martin, R. B. 2001. Centre for Research on Introduced Marine Pests Technical
Report No . 22. Revised Protocols for Baseline Port Surveys for Introduced Marine
Species : Survey Design Sampling Protocols and Specimen Handling, 46.
Hewitt, C. L., Campbell, M. L., 2007. Mechanisms for the prevention of marine bioinvasions
for better biosecurity. Marine Pollution Bulletin, 55, 395-401.
Hewitt, C. L., Campbell, M. L., Thresher, R. E., Martin, R. B., Boyd, S., Cohen, B. F., Currie,
D. R., Gomon, M. F., Keough, M. J., Lewis, J. A., Lockett, M. M., Mays, N., McArthur,
M. A., O’Hara, T. D., Poore, G. C. B., Ross, D. J., Storey, M. J., Watson, J. E., & Wilson,
R. S. 2004. Introduced and cryptogenic species in Port Phillip Bay, Victoria, Australia.
Marine Biology, 144, 183–202.
Hewitt, C. L., Gollasch, S., & Minchin, D. 2009. The Vessel as a Vector – Biofouling, Ballast
Water and Sediments. Biological Invasions in Marine Ecosystems, 204, 117-131.
Hewitt, C. L., Willing, J., Bauckham, A., Cassidy, A. M., Cox, C. M. S., Jones, L., & Wotton,
D. M. 2004. New Zealand marine biosecurity: Delivering outcomes in a fluid
environment. New Zealand Journal of Marine and Freshwater Research. 38(3), 429-438.
Hewitt, C., Campbell, M., Thresher, R., & Martin, R. 1999. Marine Biological Invasions of
Port Phillip Bay, Victoria. CSIRO CRIMP Technical Report No. 20.
Hickman, R. W. 1979. Allometry and growth of the green- lipped mussel Perna canaliculus in
New Zealand. Marine Biology, 51, 311-327.
Hilbish, T. J., Mullinax, A., Dolven, S. I., Meyer, A., Koehn, R. K., & Rawson, P. D. 2000.
Origin of the anti-tropical distribution pattern in marine mussels (Mytilus spp.): Routes
and timing of transequatorial migration. Marine Biology, 136(1), 69–77.
Hinkel, J., Lincke, D., Vafeidis, A. T., Perrette, M., Nicholls, R. J., Tol, R. S. J., Marzeion, B.,
Fettweis, X., Ionescu, C., & Levermann, A. 2014. Coastal flood damage and adaptation
costs under 21st century sea-level rise. Proceedings of the National Academy of Sciences
of the United States of America, 111(9), 3292-3297.
Hobday, A., Hewitt, C., Condie, S., Cahill, M., Mansbridge, M., Ling, S. D. 2002. Mapping
the Australian ballast water uptake and deballasting contingency zones, CSIRO Marine
Research, MR-OEEZ 02-06.
170
Hockey, P. A. R., & Van Erkom Schurink, C. 1992. The invasive biology of the mussel Mytilus
galloprovincialis on the southern african coast. Transactions of the Royal Society of South
Africa, 48(1), 123-139.
Holland B. S, Dawson M. N, Crow G. L, Hofmann D. K. 2004. Global phylogeography of
Cassiopea (Scyphozoa: Rhizostomeae): molecular evidence for cryptic species and
multiple invasions of the Hawaiian Islands. Marine Biology 145, 1119–1128.
Holland B. S. 2000. Genetics of marine bioinvasions. Hydrobiologia, 420, 63–71.
Holloway, M. G., & Connell, S. D. 2002. Why do floating structures create novel habitats for
subtidal epibiota? Marine Ecology Progress Series, 235, 43-52.
Honkoop, P.J.C., Beukema, J.J., 1997. Loss of body mass in winter in three intertidal bivalve
species: An experimental and observational study of the interacting effects between water
temperature, feeding time and feeding behaviour. Journal of Experimental Marine
Biology and Ecology, 212, 277-297.
Hopkins G. A., Forrest B. M. 2010. A preliminary assessment of biofouling and nonindigenous marine species associated with commercial slow-moving vessels arriving in
New Zealand. Biofouling, 26, 613–621.
Hopkins G. A., Forrest B. M., Jiang W., Gardner J. P. A. 2011a. Successful eradication of a
non-indigenous marine bivalve from a subtidal soft sediment environment. Journal of
Applied Ecology, 48, 424-431.
Hopkins G. A., Forrest B. M., Piola R. F., Gardner J. P. A. 2011b. Factors affecting
survivorship of defouled communities and the effect of fragmentation on establishment
success. Journal of Experimental Marine Biology and Ecology, 396, 233-243.
Huang, X. W., Wang, W., & Dong, Y. W. 2015. Complex ecology of China’s seawall. Science,
347 (6226), 1079.
Hulme, P. E. 2006. Beyond control: Wider implications for the management of biological
invasions. Journal of Applied Ecology, 43, 835-84.
Hulme, P. E. 2009. Trade, transport and trouble: managing invasive species pathways in an era
of globalization. Journal of Applied Ecology, 46(1), 10-18.
Inglis, G., Gust, N., Fitridge, I., Floerl, O., Woods, C., Kospartov, M., Hayden, B., & Fenwick,
G. 2005. Gulf Harbour Marina: Baseline survey for non-indigenous marine species
(Research Project ZBS2000-04). In Biosecurity New Zealand. Technical Paper No:
2005/12.
Inglis, G., Gust, N., Fitridge, I., Floerl, O., Woods, C., Kospartov, M., Hayden, B., & Fenwick,
G. 2005. Opua Marina: Baseline survey for non-indigenous marine species (Research
Project ZBS2000-04). In Biosecurity New Zealand. Technical Paper No: 2005/15.
Inglis, G., Gust, N., Fitridge, I., Floerl, O., Woods, C., Kospartov, M., Hayden, B., & Fenwick,
G. (2005). Port of Bluff: Baseline survey for non-indigenous marine species (Research
Project ZBS2000-04). In Biosecurity New Zealand. Technical Paper No: 2005/09.
Inglis, G., Gust, N., Fitridge, I., Floerl, O., Woods, C., Kospartov, M., Hayden, B., & Fenwick,
G. 2006. Dunedin Harbour: Baseline survey for non-indigenous marine species (Research
Project ZBS2000-04). In Biosecurity New Zealand. Technical Paper No: 2005/10.
171
Inglis, G., Gust, N., Fitridge, I., Floerl, O., Woods, C., Kospartov, M., Hayden, B., & Fenwick,
G. 2006. Port of Auckland: Baseline survey for non-indigenous marine species (Research
Project ZBS2000-04). In Biosecurity New Zealand. Technical Paper No: 2005/08.
Inglis, G., Gust, N., Fitridge, I., Floerl, O., Woods, C., Kospartov, M., Hayden, B., & Fenwick,
G. 2006. Port of Gisborne: Baseline survey for non-indigenous marine species (Research
Project ZBS2000-04). In Biosecurity New Zealand. Technical Paper No: 2005/11.
Inglis, G., Gust, N., Fitridge, I., Floerl, O., Woods, C., Kospartov, M., Hayden, B., & Fenwick,
G. 2006. Port of Lyttelton: Baseline survey for non-indigenous marine species (Research
Project ZBS2000-04). In Biosecurity New Zealand. Technical Paper No: 2005/01.
Inglis, G., Gust, N., Fitridge, I., Floerl, O., Woods, C., Kospartov, M., Hayden, B., & Fenwick,
G. 2006. Port of Napier: Baseline survey for non-indigenous marine species (Research
Project ZBS2000-04). In Biosecurity New Zealand. Technical Paper No: 2005/13.
Inglis, G., Gust, N., Fitridge, I., Floerl, O., Woods, C., Kospartov, M., Hayden, B., & Fenwick,
G. 2006. Port of Picton: Baseline survey for non-indigenous marine species (Research
Project ZBS2000-04). In Biosecurity New Zealand. Technical Paper No: 2005/03.
Inglis, G., Gust, N., Fitridge, I., Floerl, O., Woods, C., Kospartov, M., Hayden, B., & Fenwick,
G. 2006. Port of Taranaki: Baseline survey for non-indigenous marine species (Research
Project ZBS2000-04). In Biosecurity New Zealand. Technical Paper No: 2005/04.
Inglis, G., Gust, N., Fitridge, I., Floerl, O., Woods, C., Kospartov, M., Hayden, B., & Fenwick,
G. 2006. Port of Tauranga: Baseline survey for non-indigenous marine species (Research
Project ZBS2000-04). In Biosecurity New Zealand. Technical Paper No: 2005/05.
Inglis, G., Gust, N., Fitridge, I., Floerl, O., Woods, C., Kospartov, M., Hayden, B., & Fenwick,
G. 2006. Port of Timaru: Baseline survey for non-indigenous marine species (Research
Project ZBS2000-04). In Biosecurity New Zealand. Technical Paper No: 2005/06.
Inglis, G., Gust, N., Fitridge, I., Floerl, O., Woods, C., Kospartov, M., Hayden, B., & Fenwick,
G. 2006. Port of Wellington: Baseline survey for non-indigenous marine species
(Research Project ZBS2000-04). In Biosecurity New Zealand. Technical Paper No:
2005/09.
Inglis, G., Gust, N., Fitridge, I., Floerl, O., Woods, C., Kospartov, M., Hayden, B., & Fenwick,
G. 2006. Whangarei Harbour: Baseline survey for non-indigenous marine species
(Research Project ZBS2000-04). In Biosecurity New Zealand. Technical Paper No:
2005/16.
Inglis, G., Gust, N., Fitridge, I., Floerl, O., Woods, C., Kospartov, M., Hayden, B., & Fenwick,
G. 2008. Port of Lyttelton: Second baseline survey for non-indigenous marine species
(Research Project ZBS2000-04). In Biosecurity New Zealand. Technical Paper No:
2008/02.
Inglis, G., Gust, N., Fitridge, I., Floerl, O., Woods, C., Kospartov, M., Hayden, B., & Fenwick,
G. 2008. Port of Nelson: Baseline survey for non-indigenous marine species (Research
Project ZBS2000-04). In Biosecurity New Zealand. Technical Paper No: 2008/05.
Inglis, G., Gust, N., Fitridge, I., Floerl, O., Woods, C., Kospartov, M., Hayden, B., & Fenwick,
G. 2008. Port of Taranaki: Second baseline survey for non-indigenous marine species
(Research Project ZBS2000-04). In Biosecurity New Zealand. Technical Paper No:
2008/03.
172
Inglis, G., Gust, N., Fitridge, I., Floerl, O., Woods, C., Kospartov, M., Hayden, B., & Fenwick,
G. 2008. Port of Tauranga: Second baseline survey for non-indigenous marine species
(Research Project ZBS2000-04). In Biosecurity New Zealand. Technical Paper No:
2008/08.
Inglis, G., Gust, N., Fitridge, I., Floerl, O., Woods, C., Kospartov, M., Hayden, B., & Fenwick,
G. 2008. Port of Timaru: Second baseline survey for non-indigenous marine species
(Research Project ZBS2000-04). In Biosecurity New Zealand. Technical Paper No:
2008/03.
Inglis, G., Gust, N., Fitridge, I., Floerl, O., Woods, C., Kospartov, M., Hayden, B., & Fenwick,
G. 2008. Port of Wellington: Second baseline survey for non-indigenous marine species
(Research Project ZBS2000-04). In Biosecurity New Zealand. Technical Paper No:
2008/06.
Inglis, G. J., & Seaward, K. 2016. Indicators of non-indigenous species in marine systems.
NIWA Client Report No. CHC2016-024, 74.
Inglis, G., Morrisey, D., Woods, C., Sinner, J. & Newton, M. 2014. Managing the domestic
spread of harmful marine organisms. Part A – Operational tools for management. Report
prepared for New Zealand Ministry for Primary Industries. National Institute of Water
and Atmospheric Research. Christchurch, 166.
International Maritime Organization (IMO). 2011. Report of the Marine Environment
Protection Committee on the sixty-second session. MEPC\62\24.
International Maritime Organization (IMO). 2015. Guidelines and Guidance Documents
related to the Implementation of the International Convention for the Control and
Management of Ships’ Ballast Water and Sediments, In UN - International Maritime
Organization, www.imo.org.
International Maritime Organization (IMO). 2017. Guidelines and Guidance Documents
related to the Implementation of the International Convention for the Control and
Management of Ships’ Ballast Water and Sediments, In UN - International Maritime
Organization, www.imo.org.
Jackson, C. R., Churchill, P. F., & Roden, E. E. 2001. Successional changes in bacterial
assemblage structure during epilithic biofilm development. Ecology, 82(2), 555-566.
James, K., & Shears, N. T. 2016. Proliferation of the invasive kelp Undaria pinnatifida at
aquaculture sites promotes spread to coastal reefs. Marine Biology, 163(2), 34.
Jeschke J. M., Gómez Aparicio L., Haider S. 2012. Support for major hypotheses in invasion
biology is uneven and declining. NeoBiota, 14, 1–20.
Johnston, E. L., & Keough, M. J. 2002. Direct and indirect effects of repeated pollution events
on marine hard-substrate assemblages. Ecological Applications, 12(4), 1212-1228.
Johnston, E. L., Dafforn, K. A., Clark, G. F., Rius, M., & Floerl, O. 2017. How anthropogenic
activities affect the establishment and spread of non-indigenous species post-arrival. In
Oceanography and Marine Biology: An Annual Review, 55, 389-419.
Johnston, E. L., Marzinelli, E. M., Wood, C. A., Speranza, D. & Bishop, J. D. D. 2011. Bearing
the burden of boat harbours: heavy contaminant and fouling loads in a native habitatforming alga. Marine Pollution Bulletin, 62, 2137–2144.
173
Johnston, E. L., Mayer-Pinto, M., Hutchings, P. A., Marzinelli, E. M., Ahyong, S. T., Birch,
G., Booth, D. J., Creese, R. G., Doblin, M. A., Figueira, W., Gribben, P. E., Pritchard, T.,
Roughan, M., Steinberg, P. D., & Hedge, L. H. 2015. Sydney Harbour: What we do and
do not know about a highly diverse estuary. Marine and Freshwater Research, 66(12),
1073–1087.
Johnston, E. L., Piola, R. F., & Clark, G. F. 2009. The Role of Propagule Pressure in Invasion
Success. Biological Invasions in Marine Ecosystems, 204, 133-151.
Johnston, Emma L., Dafforn, K. A., Clark, G. F., Rius, M., & Floerl, O. 2017. How
anthropogenic activities affect the establishment and spread of non-indigenous species
post-arrival. In Oceanography and Marine Biology: An Annual Review, 55, 389-419.
Jones, E., & Thornber, C. S. 2010. Effects of habitat-modifying invasive macroalgae on
epiphytic algal communities. Marine Ecology Progress Series, 400, 87-100.
Kaiser, B., & Burnett, K. 2010. Spatial economic analysis of early detection and rapid response
strategies for an invasive species. Resource and Energy Economics, 32, 566–585.
Kaluza, P., Kölzsch, A., Gastner, M. T., & Blasius, B. 2010. The complex network of global
cargo ship movements. Journal of the Royal Society Interface, 7(48), 1093–1103.
Katsanevakis, S., Wallentinus, I., Zenetos, A., Leppäkoski, E., Çinar, M. E., Oztürk, B.,
Grabowski, M., Golani, D., & Cardoso, A. C. 2014. Impacts of invasive alien marine
species on ecosystem services and biodiversity: A pan-European review. Aquatic
Invasions, 9(4), 391-423.
Keith, S. A., Herbert, R. J. H., Norton, P. A., Hawkins, S. J., & Newton, A. C. 2011.
Individualistic species limitations of climate-induced range expansions generated by
meso-scale dispersal barriers. Diversity and Distributions, 17(2), 275-286.
Kennedy, E. V., Ordoñez, A., Lewis, B. E., & Diaz-Pulido, G. 2017. Comparison of
recruitment tile materials for monitoring coralline algae responses to a changing climate.
Marine Ecology Progress Series, 569(1), 129–144.
Kimbro, D. L., Grosholz, E. D., Baukus, A. J., Nesbitt, N. J., Travis, N. M., Attoe, S., &
Coleman-Hulbert, C. 2009. Invasive species cause large-scale loss of native California
oyster habitat by disrupting trophic cascades. Oecologia, 160, 563–575.
Klein J. C., Underwood A. J., Chapman M. G. 2011. Urban structures provide new insights
into interactions among grazers and habitat. Ecological Application, 21(2), 427-438.
Knights, A. M., Firth, L. B., & Walters, K. 2012. Interactions between multiple recruitment
drivers: Post-settlement predation mortality and flow-mediated recruitment. PLoS ONE
7(4), e35096.
Kohler, K. E., & Gill, S. M. 2006. Coral Point Count with Excel extensions (CPCe): A Visual
Basic program for the determination of coral and substrate coverage using random point
count methodology. Computers and Geosciences, 32(9), 1259–1269.
Kueffer, C., & Kaiser-Bunbury, C. N. 2014. Reconciling conflicting perspectives for
biodiversity conservation in the Anthropocene. Frontiers in Ecology and the Environment,
21(2), 131-137.
Lachowicz, L. S. 2005. Population biology of mussels (Aulacomya maoriana, Mytilus
galloprovincialis and Perna canaliculus) from rocky intertidal shores in Wellington
174
Harbour, New Zealand. (Doctoral thesis, Victoria University of Wellington,
NewZealand).Retrieved from https://researcharchive.vuw.ac.nz/xmlui/handle10063/164.
Lai, S., Loke, L. H. L., Bouma, T. J., & Todd, P. A. 2018. Biodiversity surveys and stable
isotope analyses reveal key differences in intertidal assemblages between tropical
seawalls and rocky shores. Marine Ecology Progress Series, 587, 41-53.
Lam, N. W. Y., Huang, R., & Chan, B. K. K. 2009. Variations in intertidal assemblages and
zonation patterns between vertical artificial seawalls and natural rocky shores: A case
study from Victoria Harbour, Hong Kong. Zoological Studies, 48(2), 184–195.
Larraín M. A., Zbawicka M., Araneda C., Gardner J. P. A., Wenne R. 2018. Native and invasive
taxa on the Pacific coast of South America: Impacts on aquaculture, traceability and
biodiversity of blue mussels (Mytilus spp.). Evolutionary Applications, 11, 298-311.
Leclerc, J. C., & Viard, F. 2018. Habitat formation prevails over predation in influencing
fouling communities. Ecology and Evolution, 8(1), 477–492.
Lee, S. Y., & Morton, B. 1985. The introduction of the Mediterranean mussel Mytilus
galloprovincialis into Hong Kong. Malacological Review, 18(1-2), 107-109.
Lehtiniemi, M., Ojaveer, H., David, M., Galil, B., Gollasch, S., McKenzie, C., Minchin, D.,
Occhipinti-Ambrogi, A., Olenin, S., Pederson, J., 2015. Dose of truth-Monitoring marine
non-indigenous species to serve legislative requirements. Marine Policy, 54, 26-35.
Leppäkoski, E. 2002. Harmful Non-Native Species in the Baltic Sea - An Ignored Problem. In
Baltic Coastal Ecosystems, 253-275.
Leppäkoski, E., Gollasch, S., & Olenin, S. 2002. Invasive Aquatic Species of Europe.
Distribution, Impacts and Management. Invasive Aquatic Species of Europe. Distribution,
Impacts and Management, 53.
Levine, J. M. 2000. Species diversity and biological invasions: Relating local process to
community pattern. Science, 288(5467), 852-854.
Lewis, P. N., Hewitt, C. L., Riddle, M., & McMinn, A. 2003. Marine introductions in the
Southern Ocean: An unrecognised hazard to biodiversity. Marine Pollution Bulletin,
46(2), 213-223.
Liversage, K., & Chapman, M. G. 2018. Coastal ecological engineering and habitat restoration:
Incorporating biologically diverse boulder habitat. Marine Ecology Progress Series, 593,
173-185.
Liversage, K., Janetzki, N., & Benkendorff, K. 2014. Associations of benthic fauna with
different rock types, and evidence of changing effects during succession. Marine Ecology
Progress Series, 505, 131–143.
Lo, V. B., Levings, C. D., & Chan, K. M. A. 2012. Quantifying potential propagule pressure
of aquatic invasive species from the commercial shipping industry in Canada. Marine
Pollution Bulletin, 64(2), 295-302.
Lockwood, J. L., Cassey, P., & Blackburn, T. M. 2005. The role of propagule pressure in
explaining species invasions. Trends in Ecology and Evolution, 20(5), 223-228.
Lockwood, J. L., Cassey, P. & Blackburn, T. M. 2009. The more you introduce the more you
get: the role of colonization and propagule pressure in invasion ecology. Diversity and
175
Distributions, 15, 904 – 910.
Lockwood, J. L., Hoopes, M. F., Marchetti, M. P. 2007. Invasion Ecology, 2nd Edition, WileyBlackwell, Malden, USA, 304.
Loke, L. H. L., & Todd, P. A. 2016. Structural Complexity and component type increase
intertidal biodiversity independently of area. Ecology, 97(2), 383-393.
Loke, L. H. L., Jachowski, N. R., Bouma, T. J., Ladle, R. J., & Todd, P. A. 2014. Complexity
for artificial substrates (CASU): Software for creating and visualising habitat complexity.
PLoS ONE, 9(2), e87990.
Loke, L. H. L., Liao, L. M., Bouma, T. J., & Todd, P. A. 2016. The succession of seawall algal
communities on artificial substrates. Raffles Bulletin of Zoology, 32, 1-10.
Lotze, H. K., Worm, B., & Sommer, U. 2020. Nordic Society Oikos Propagule Banks,
Herbivory and Nutrient Supply Control Population Development and Dominance Patterns
in Macroalgal Blooms Author ( s ): Heike K . Lotze , Boris Worm and Ulrich Sommer
Published by : Wiley on behalf of Nordic Society O. 89(1), 46–58.
Lovell, D. P. 2013. Biological importance and statistical significance. Journal of Agricultural
and Food Chemistry, 61(35), 8340–8348.
Lowe S., Browne M., Boudjelas S. 2004. 100 of the world’s worst invasive species: a selection
from the global invasive species database. Invasive Species Specialist Group, Auckland.
Lowe, S., Browne, M., Boudjelas, S., & Poorter, M. De. 2000. 100 of the World’s Worst
Invasive Alien Species A selection from the Global Invasive Species Database. The
Invasive Species Specialist Group (ISSG) a specialist group of the Species Survival
Commission (SSC) of the World Conservation Union (IUCN), 12.
Ma, Z., Melville, D. S., Lui, J., Chen, Y., Yang, H., Ren, W., Zhang, Z., Peirsma, T. & Li, B.
2014. Rethinking China’s new great wall. Science, 346 (6212), 912–914.
Macdonald, E., & King, E. G. 2018. Novel ecosystems: A bridging concept for the consilience
of cultural landscape conservation and ecological restoration. Landscape and Urban
Planning, 177, 148-159.
Mack, R. N., Simberloff, D., Lonsdale, W. M., Evans, H., Clout, M., & Bazzaz, F. A. 2000.
Biotic invasions: Causes, epidemiology, global consequences, and control. Ecological
Applications, 10 (3), 689-710.
MacKie, J. A., Darling, J. A., & Geller, J. B. 2012. Ecology of cryptic invasions: Latitudinal
segregation among Watersipora (Bryozoa) species. Scientific Reports, 2, 1-10.
MAF, 1993. Biosecurity Act 1993. Public Act 1993 No 95.
Malachowicz, M., & Wenne, R. 2019. Mantle transcriptome sequencing of Mytilus spp. and
identification of putative biomineralization genes. Peer Journal 6(9), e6245.
Mallela, J., Milne, B. C., & Martinez-Escobar, D. 2017. A comparison of epibenthic reef
communities settling on commonly used experimental substrates: PVC versus ceramic
tiles. Journal of Experimental Marine Biology and Ecology, 486, 290–295.
Marraffini, M. L., & Geller, J. B. 2015. Species richness and interacting factors control
invasibility of a marine community. Proceedings of the Royal Society B: Biological
Sciences, 282(1812).
176
Marshall, D. J., Pechenik, J. A., & Keough M. J. 2003. Larval activity levels and delayed
metamorphosis affect post-larval performance in the colonial ascidian Diplosoma
listerianum. Marine Ecological Progress Series, 246, 153−162.
Martin, D., Bertasi, F., Colangelo, M. A., de Vries, M., Frost, M., Hawkins, S. J., Macpherson,
E., Moschella, P. S., Satta, M. P., Thompson, R. C. & Ceccherelli, V. U. 2005. Ecological
impact of coastal defence structures on sediment and mobile fauna: evaluating and
forecasting consequences of unavoidable modifications of native habitats. Coastal
Engineering, 52, 1027–1051.
Martins, G. M., Neto, A. I., & Cacabelos, E. 2016. 7 Marine Environmental Research, 113, 8894.
Martins, G. M., Thompson, R. C., Neto, A. I., Hawkins, S. J., & Jenkins, S. R. 2010. Enhancing
stocks of the exploited limpet Patella candei d’Orbigny via modifications in coastal
engineering. Biological Conservation, 143(1), 203-211.
Mayer-Pinto, M., Cole, V. J., Johnston, E. L., Bugnot, A., Hurst, H., Airoldi, L., Glasby, T. M.,
& Dafforn, K. A. 2018. Functional and structural responses to marine urbanisation.
Environmental Research Letters, 13(1), 1-17.
Mayer-Pinto, M., Dafforn, K. A., Bugnot, A. B., Glasby, T. M., & Johnston, E. L. 2018.
Artificial structures alter kelp functioning across an urbanised estuary. Marine
Environmental Research, 139, 136–143.
McDonald, J. H., Seed, R., & Koehn, R. K. 1991. Allozymes and morphometric characters of
three species of Mytilus in the Northern and Southern Hemispheres. Marine Biology,
111(3), 323-333.
McKenzie, C. H., Reid V., Lambert, G., Matheson, K., Minchin, D., Brown, L., Gollasch, S.,
Simard, N., Therriault, T., and Pederson, J. 2017. Alien Species Alert: Didemnum
vexillum Kott 2002, Current invasion status, ecosystem impact and mitigation options for
an invasive colonial tunicate. ICES Cooperative Research Report No. 335, 33.
McKinney, M. L. & Lockwood, J. L. 1999. Biotic homogenization: a few winners replacing
many losers in the next mass extinction. Trends in Ecology & Evolution, 14, 450 – 453.
Mead, A., Carlton, J. T., Griffiths, C. L., & Rius, M. 2011. Revealing the scale of marine
bioinvasions in developing regions: A South African re-assessment. Biological Invasions,
13(9), 1991-2008.
Megina, C., González-Duarte, M. M., & López-González, P. J. 2016. Benthic assemblages,
biodiversity and invasiveness in marinas and commercial harbours: an investigation using
a bioindicator group. Biofouling, 32(4), 465-475.
Melbourne, B. A., & Hastings, A. 2009. Highly variable spread rates in replicated biological
invasions: Fundamental limits to predictability. Science, 325(5947), 1536-1539.
Melbourne, B. A., Cornell, H. V., Davies, K. F., Dugaw, C. J., Elmendorf, S., Freestone, A. L.,
Hall, R. J., Harrison, S., Hastings, A., Holland, M., Holyoak, M., Lambrinos, J., Moore,
K., & Yokomizo, H. 2007. Invasion in a heterogeneous world: Resistance, coexistence or
hostile takeover? Ecology Letters, 10(1), 77–94.
Miller, D. A. W., Pacifici, K., Sanderlin, J. S. & Reich, B. J. The recent past and promising
future for data integration methods to estimate species’ distributions. Methods in Ecology
and Evolution, 10, 22–37,
177
Mills, K. A., Hamer, P. A., & Quinn, G. P. 2017. Artificial reefs create distinct fish
assemblages: a study of fish assemblage response to the deployment of artificial patch
reefs. Marine Ecology Progress Series, 585, 155-173.
Mineur, F., Cook, E. J., Minchin, D., Bohn, K., Macleod, A., & Maggs, C. A. 2012. Changing
coasts: Marine aliens and artificial structures. In Oceanography and Marine Biology: An
Annual Review, 50, 189-234.
Ministry for the Environment, & Stats NZ. 2016. Our marine environment 2016. In New
Zealand’s Environmental Reporting Series.
Ministry of Primary Industries (MPI). 2014. Craft risk management standard: biofouling on
vessels arriving to New Zealand. Ministry for Primary Industries, Wellington, New
Zealand.
Ministry of Primary Industries (MPI). 2018. Technical Guidance on Biofouling Management
for Vessels Arriving to New Zealand. MPI Technical Paper No: 2018/07.
Molnar, J. L., Gamboa, R. L., Revenga, C., & Spalding, M. D. 2008. Assessing the global threat
of invasive species to marine biodiversity. Frontiers in Ecology and the Environment,
6(9), 485-492.
Morand, P., & Briand, X. 1996. Excessive growth of macroalgae: A symptom of environmental
disturbance. Botanica Marina, 39(6), 491–516.
Moreira, J., Chapman, M. G., & Underwood, A. J. 2006. Seawalls do not sustain viable
populations of limpets. Marine Ecology Progress Series, 322, 179–188.
Moreira, J., Chapman, M. G., & Underwood, A. J. 2007. Maintenance of chitons on seawalls
using crevices on sandstone blocks as habitat in Sydney Harbour, Australia. Journal of
Experimental Marine Biology and Ecology, 347(1-2), 134-143.
Morris, R. L., Chapman, M. G., Firth, L. B., & Coleman, R. A. 2017. Increasing habitat
complexity on seawalls: Investigating large- and small-scale effects on fish assemblages.
Ecology and Evolution, 7(22), 9567-9579.
Morris R. L., Konlechner T. M., Ghisalberti M., Swearer S. E. 2018a. From grey to green:
efficacy of eco-engineering solutions for nature-based coastal defence. Global Change
Biology, 24(5), 1827-1842.
Morris, R. L., Golding, S., Dafforn, K. A., & Coleman, R. A. 2018b. Can coir increase native
biodiversity and reduce colonisation of non-indigenous species in eco-engineered rock
pools? Ecological Engineering, 120, 622-630.
Morris, R. L., Heery, E. C., Loke, L. H. L., Lau, E., Strain, E. M. A., Airoldi, L., Alexander,
K. A., Bishop, M. J., Coleman, R. A., Cordell, J. R., Dong, Y. W., Firth, L. B., Hawkins,
S. J., Heath, T., Kokora, M., Lee, S. Y., Miller, J. K., Perkol-Finkel, S., Rella, A., Leung,
K. M. Y. 2019. Design options, implementation issues and evaluating success of
ecologically engineered shorelines. In Oceanography and Marine Biology, 57, 169-228.
Morris, R. L., Porter, A. G., Figueira, W. F., Coleman, R. A., Fobert, E. K., & Ferrari, R. 2018b.
Fish-smart seawalls: a decision tool for adaptive management of marine infrastructure.
Frontiers in Ecology and the Environment, 16(5), 278-287.
Moschella, P. S., Abbiati, M., Åberg, P., Airoldi, L., Anderson, J. M., Bacchiocchi, F., Bulleri,
F., Dinesen, G. E., Frost, M., Gacia, E., Granhag, L., Jonsson, P. R., Satta, M. P., Sundelöf,
178
A., Thompson, R. C., & Hawkins, S. J. 2005. Low-crested coastal defence structures as
artificial habitats for marine life: Using ecological criteria in design. Coastal Engineering,
52(10-11), 1053-1071.
Moss, B. 2017. Marine reptiles, birds and mammals and nutrient transfers among the seas and
the land: An appraisal of current knowledge. Journal of Experimental Marine Biology and
Ecology, 492, 63-80.
Munari, C. 2013. Benthic community and biological trait composition in respect to artificial
coastal defence structures: A study case in the northern adriatic sea. Marine
Environmental Research, 90, 47-54.
Munari, C., Corbau, C., Simeoni, U. & Mistri, M. 2011. Coastal defence through low crested
breakwater structures: Jumping out of the frying pan into the fire? Marine Pollution
Bulletin, 62, 1641–1651.
Munsch, S. H., Cordell, J. R., Toft, J. D., & Morgan, E. E. 2014. Effects of seawalls and piers
on fish assemblages and juvenile salmon feeding behaviour. North American Journal of
Fisheries Management, 32(4), 814-827.
Naylor, L. A., Coombes, M. A., Venn, O., Roast, S. D., & Thompson, R. C. 2012. Facilitating
ecological enhancement of coastal infrastructure: The role of policy, people and planning.
Environmental Science and Policy, 22, 36-46.
Neumann, B., Vafeidis, A. T., Zimmermann, J., & Nicholls, R. J. 2015. Future coastal
population growth and exposure to sea-level rise and coastal flooding - A global
assessment. PLoS ONE, 10(3), e0118571.
Nicastro, K. R., Zardi, G. I., & McQuaid, C. D. 2010. Differential reproductive investment,
attachment strength and mortality of invasive and indigenous mussels across
heterogeneous environments. Biological Invasions, 12(7), 2165-2177.
O’Brien, C. E., Johnston, M. W., & Kerstetter, D. W. 2017. Ports and pests: Assessing the
threat of aquatic invasive species introduced by maritime shipping activity in Cuba.
Marine Pollution Bulletin, 125(1–2), 92–102.
O’Shaughnessy, K. A., Hawkins, S. J., Evans, A. J., Hanley, M. E., Lunt, P., Thompson, R. C.,
Francis, R. A., Hoggart, S. P. G., Moore, P. J., Iglesias, G., Simmonds, D., Ducker, J., &
Firth, L. B. 2019. Design catalogue for eco-engineering of coastal artificial structures: a
multifunctional approach for stakeholders and end-users. Urban Ecosystems, 23, 431-443.
Occhipinti-Ambrogi A. 2007. Global change and marine communities: alien species and
climate change. Marine Pollution Bulletin, 55, 342–352.
Ojaveer, H., Galil, B. S., Campbell, M. L., Carlton, J. T., Canning-Clode, J., Cook, E. J.,
Davidson, A. D., Hewitt, C. L., Jelmert, A., Marchini, A., McKenzie, C. H., Minchin, D.,
Occhipinti-Ambrogi, A., Olenin, S., & Ruiz, G. 2015. Classification of Non-Indigenous
Species Based on Their Impacts: Considerations for Application in Marine Management.
PLoS Biology, 13(4), 1-13.
Ojaveer, H., Galil, B. S., Carlton, J. T., Alleway, H., Goulletquer, P., Lehtiniemi, M., Marchini,
A., Miller, W., Occhipinti-Ambrogi, A., Peharda, M., Ruiz, G. M., Williams, S.L., Zaiko,
A., 2018. Historical baselines in marine bioinvasions: Implications for policy and
management. PLoS One, 13, e0202383.
Okaniwa, N., Miyaji, T., Sasaki, T., & Tanabe, K. 2010. Shell growth and reproductive cycle
179
of the Mediterranean mussel Mytilus galloprovincialis in Tokyo Bay, Japan: Relationship
with environmental conditions. Plankton and Benthos Research, 5, 214–220.
Olenin, S., & Leppäkoski, E. 1999. Non-native animals in the Baltic Sea: Alteration of benthic
habitats in coastal inlets and lagoons. Hydrobiologia, 393, 233-243.
Olenin, S., Gollasch, S., Lehtiniemi, M., Sapota, M., & Zaiko, A. 2017. Biological invasions.
In: Snoeijs-Leijonmalm P., Schubert H., Radziejewska T. ed. Biological Oceanography
of the Baltic Sea, Springer, 193-232.
Olyarnik, S. V., Bracken, M. E. S., Byrnes, J. E., Hughes, A. R., Hultgren, K. M., &
Stachowicz, J. J. 2009. Ecological Factors Affecting Community Invasibility. Biological
Invasions in Marine Ecosystems, 204, 215-238.
Oricchio, F. T., Pastro, G., Vieira, E. A., Flores, A. A. V., Gibran, F. Z., & Dias, G. M. 2016.
Distinct community dynamics at two artificial habitats in a recreational marina. Marine
Environmental Research, 122, 85-92.
Oyarzún, P. A., Toro, J. E., Cañete, J. I., & Gardner, J. P. A. 2016. Bioinvasion threatens the
genetic integrity of native diversity and a natural hybrid zone: Smooth-shelled blue
mussels (Mytilus spp.) in the Strait of Magellan. Biological Journal of the Linnean
Society, 117(3), 574-585.
Oyarzún, P. A., Toro, J. E., Jaramillo, R., Guiñez, R., Briones, C., & Astorga, M. 2011. Ciclo
gonadal del chorito Mytilus chilensis (Bivalvia: Mytilidae) en dos localidades del sur de
Chile. Latin American Journal of Aquatic Research, 39(3), 512–525.
Padilla, D. K., & Williams, S. L. 2004. Beyond ballast water: Aquarium and ornamental trades
as sources of invasive species in aquatic ecosystems. Frontiers in Ecology and the
Environment, 2(3), 131-138.
Paine, R. T. 1966. Food Web Complexity and Species Diversity. The American Society of
Naturalists, 100, 65-75.
Paine, R. T., & Levin, S. A. 1981. Intertidal Landscapes : Disturbance and the Dynamics of
Pattern Author (s): R . T . Paine and Simon A . Levin Published by : Wiley on behalf of
the Ecological Society of America Stable, Ecological Monographs, 51(2), 145–178.
Pandolfi, J. M. 2008. Succession. In General Ecology, 3416-3424.
Papacostas K. J., Rielly-Carroll E. W., Georgian S. E. 2017. Biological mechanisms of marine
invasions. Marine Ecology Progress Series, 565, 251–268.
Parker, J. D., Torchin, M. E., Hufbauer, R. A., Lemoine, N. P., Alba, C., Blumenthal, D. M.,
Bossdorf, O., Byers, J. E., Dunn, A. M., Heckman, R. W., Hejda, M., Jarošík, V., Kanarek,
A. R., Martin, L. B., Perkins, S. E., Pyšek, P., Schierenbeck, K., Schlöder, C., Van
Klinken, R., Wolfe, L. M. 2013. Do invasive species perform better in their new ranges?
Ecology, 94(5), 985-994,
Pastro, G., Dias, G. M., Pereira-Filho, G. H., & Gibran, F. Z. 2017. The consequences of smallscale variations in habitat conditions driven by a floating marina on reef fish assemblages
of S.E. Brazil. Ocean and Coastal Management, 141, 98-106.
Pawlik, J. R. 1992. Chemical ecology of the settlement of benthic marine invertebrates. In
Oceanography and marine biology: an annual review, 30, 273–335.
180
People, J. 2006. Mussel beds on different types of structures support different
macroinvertebrate assemblages. Austral Ecology, 31(2), 271-281.
Perkol-Finkel, S., Ferrario, F., Nicotera, V., & Airoldi, L. 2012. Conservation challenges in
urban seascapes: Promoting the growth of threatened species on coastal infrastructures.
Journal of Applied Ecology, 49(6), 1457–1466.
Perkol-Finkel, S., Hadary, T., Rella, A., Shirazi, R., & Sella, I. 2018. Seascape architecture –
incorporating ecological considerations in the design of coastal and marine infrastructure.
Ecological Engineering, 120, 645-654.
Perkol-Finkel, S., Shashar, N., & Benayahu, Y. 2006. Can artificial reefs mimic natural reef
communities? The roles of structural features and age. Marine Environmental Research,
61(2), 121–135.
Peters, K., Sink, K., & Robinson, T. B. 2017. Raising the flag on marine alien fouling species.
Management of Biological Invasions, 8(1), 1-11.
Petes, L. E., Menge, B. A., & Harris, A. L. 2008. Intertidal mussels exhibit energetic trade-offs
between reproduction and stress resistance. Ecological Monographs, 78(3), 387–402.
Petraitis, P. S., & Latham R. E. 1999. The importance of scale in testing the origins of
alternative community states. Ecology, 80(2), 429−442.
Pettersen, A. K., Turchini, G. M., Jahangard, S., Ingram, B. A., & Sherman, C. D. H. 2010.
Effects of different dietary microalgae on survival, growth, settlement and fatty acid
composition of blue mussel (Mytilus galloprovincialis) larvae. Aquaculture, 309(1-4),
115-124.
Pickett, T., & David, A. A. 2018. Global connectivity patterns of the notoriously invasive
mussel, Mytilus galloprovincialis Lmk using archived CO1 sequence data. BMC Research
Notes, 11(1), 1-7.
Piola, R. F., & Johnston, E. L. 2008. Pollution reduces native diversity and increases invader
dominance in marine hard-substrate communities. Diversity and Distributions, 14(2),
329–342.
Piola, R. F., Dafforn, K. A., & Johnston, E. L. 2009. The influence of antifouling practices on
marine invasions. Biofouling, 25(7), 633–644.
Pister, B. 2009. Urban marine ecology in southern California: The ability of riprap structures
to serve as rocky intertidal habitat. Marine Biology, 156, 861-873.
Pochon, X., Bott, N. J., Smith, K. F., Wood, S. A., 2013. Evaluating detection limits of
nextgeneration sequencing for the surveillance and monitoring of international marine
pests. PLoS One 8, e73935.
Pochon, X., Zaiko, A., Fletcher, L.M., Laroche, O., Wood, S.A., 2017. Wanted dead or alive?
Using metabarcoding of environmental DNA and RNA to distinguish living assemblages
for biosecurity applications. PLoS One, 12, e0187636.
Pollard, D. A., & Pethebridge, R. L. 2002. Report on Port Kembla Introduced Marine Pest
Species Survey. NSW Fisheries Final Report Series, 41.
Pörtner, H. O., & Peck, M. A. 2010. Climate change effects on fishes and fisheries: Towards a
cause-and-effect understanding. Journal of Fish Biology, 77(8), 1745-1779.
181
Purroy, A., Bukša, F., Puljas, S., & Peharda, M. 2019. Variations in the reproductive
investment of a venerid bivalve, Callista chione. Journal of the Marine Biological
Association of the United Kingdom, 99(7), 1579-1589.
Pyšek, P., & Richardson, D. M. 2010. Invasive Species, Environmental Change and
Management, and Health. Annual Review of Environment and Resources, 35(1), 25-55.
Rahel, F. J., & Olden, J. D. 2008. Assessing the effects of climate change on aquatic invasive
species. Conservation Biology, 22(3), 521-533.
Read, G. B., Inglis, G., Stratford, P., & Ahyong, S. T. 2011. Arrival of the alien fanworm
Sabella spallanzanii (Gmelin, 1791) (Polychaeta: Sabellidae) in two New Zealand
harbours. Aquatic Invasions, 6(3), 273-279.
Reise, K., Buschbaum, C., Buttger, H., & Wegner, M. K. 2017. Invading oysters and native
mussels: From hostile takeover to compatible bedfellows. Ecosphere, 8(9), e01949.
Ricciardi, A., 2007. Are modern biological invasions an unprecedented form of global change?
Conservation Biology, 21(2), 329–336
Ricciardi, A., Blackburn, T. M., Carlton, J. T., Dick, J. T. A., Hulme, P. E., Iacarella, J. C.,
Jeschke, J. M., Liebhold, A. M., Lockwood, J. L., MacIsaac, H. J., Pysek, P., Richardson,
D. M., Ruiz, G. M., Simberloff, D., Sutherland, W. J., Wardle, D. A., Aldridge, D. C.,
2017. Invasion Science: A Horizon Scan of Emerging Challenges and Opportunities.
Trends in Ecology & Evolution, 32, 464-474.
Riera, L., Ramalhosa, P., Canning-Clode, J., & Gestoso, I. 2018. Variability in the settlement
of non-indigenous species in benthic communities from an oceanic island. Helgoland
Marine Research, 72(1), 1-15.
Riginos, C., & Cunningham, C. W. 2005. Local adaptation and species segregation in two
mussel (Mytilus edulis × Mytilus trossulus) hybrid zones. Molecular Ecology, 14(2), 381400.
Rilov, G., Crooks, J. A., 2009. Biological Invasions in Marine Ecosystems: Ecological,
Management, and Geographic Perspectives. Springer.
Rius, M., & McQuaid, C. D. 2006. Wave action and competitive interaction between the
invasive mussel Mytilus galloprovincialis and the indigenous Perna perna in South
Africa. Marine Biology, 150(1), 69-78.
Rius, M., Turon, X., Bernardi, G., Volckaert, F. A. M., Viard, F., 2015. Marine invasion
genetics: from spatio-temporal patterns to evolutionary outcomes. Biological Invasions
17, 869-885.
Rivero, N. K., Dafforn, K. A., Coleman, M. A., & Johnston, E. L. 2013. Environmental and
ecological changes associated with a marina. Biofouling, 29(7), 803-815.
Rodhouse, P. G., McDonald, J. H., Newell, R. I. E., & Koehn, R. K. 1986. Gamete production,
somatic growth and multiple-locus enzyme heterozygosity in Mytilus edulis. Marine
Biology, 90(2), 209–214.
Rodriguez, L. F. 2006. Can invasive species facilitate native species? Evidence of how, when,
and why these impacts occur. Biological Invasions, 8(4), 927–939.
Roman, J., & Darling, J. A. 2007. Paradox lost: genetic diversity and the success of aquatic
182
invasions. In Trends in Ecology and Evolution, 22(9), 454-64.
Ruiz, G. M., Fofonoff, P. W., Carlton, J. T., Wonham, M. J., & Hines, A. H. 2000. Invasion of
Coastal Marine Communities in North America: Apparent Patterns, Processes, and
Biases. Annual Review of Ecology and Systematics, 31, 481-531.
Ruiz, G. M., Fofonoff, P. W., Steves, B., Foss, S. F., Shiba, S. N. 2011. Marine invasion history
and vector analysis of California: a hotspot for Western North America. Diversity and
Distributions, 17, 362-373.
Ruiz, G. M., Fofonoff, P., Hines, A. H., & Grosholz, E. D. 1999. Non-indigenous species as
stressors in estuarine and marine communities: Assessing invasion impacts and
interactions. Limnology and Oceanography, 44(3), 950–972.
Ruiz, G. M., Freestone, A. L., Fofonoff, P. W., & Simkanin, C. 2009. Habitat Distribution and
Heterogeneity in Marine Invasion Dynamics: the Importance of Hard Substrate and
Artificial Structure. M. Wahl (ed.), Marine Hard Bottom Communities, Ecological
Studies, 206.
Russell, L. K., Hepburn, C. D., Hurd, C. L., & Stuart, M. D. 2008. The expanding range of
Undaria pinnatifida in southern New Zealand: Distribution, dispersal mechanisms and the
invasion of wave-exposed environments. Biological Invasions, 10, 103–115.
Saarman, N. P., & Pogson, G. H. 2015. Introgression between invasive and native blue mussels
(genus Mytilus) in the central California hybrid zone. Molecular Ecology, 24(18), 47234738.
Sakai, A. K., Allendorf, F. W., Holt, J. S., Lodge, M., Molofsky, J. 2001. The Population
Biology of Invasive Species. Annual Review of Ecology and Systematics, 32, 305–332.
Sala, O. E., Chapin, F. S., Armesto, J. J., Berlow, E. & Bloomfield, J. 2000. Global Biodiversity
Scenarios for the Year 2100. Science, 287, 1770–1774.
Salta, M., Wharton, J. A., Blache, Y., Stokes, K. R., & Briand, J. F. 2013. Marine biofilms on
artificial surfaces: Structure and dynamics. Environmental Microbiology, 15(11), 2879–
2893.
Salvaterra, T., Green, D. S., Crowe, T. P., & O’Gorman, E. J. 2013. Impacts of the invasive
alga Sargassum muticum on ecosystem functioning and food web structure. Biological
Invasions, 15, 2563-3576.
Sammarco, P. W., Atchison, A. D., & Boland, G. S. 2004. Expansion of coral communities
within the Northern Gulf of Mexico via offshore oil and gas platforms. Marine Ecology
Progress Series, 280, 129-143.
Sammarco, P. W., Brazeau, D. A., & Sinclair, J. 2012. Genetic connectivity in scleractinian
corals across the northern Gulf of Mexico: Oil/gas platforms, and relationship to the
Flower Garden Banks. PLoS ONE 7(4), e30144.
Santos, S., Cardoso, J. F. M. F., Carvalho, C., Luttikhuizen, P. C., & van der Veer, H. W. 2011.
Seasonal variability in somatic and reproductive investment of the bivalve Scrobicularia
plana (da Costa, 1778) along a latitudinal groups. Estuarine, Coastal and Shelf Science,
92(1), 19-26.
Saura, S., Bodin, Ö., & Fortin, M. J. 2014. Stepping stones are crucial for species’ long-distance
dispersal and range expansion through habitat networks. Journal of Applied Ecology,
183
51(1), 171-182.
Sax, D. F. 2001. Latitudinal groupss and geographic ranges of exotic species: Implications for
biogeography. Journal of Biogeography, 28(1), e0118571.
Sax, D. F. 2002. Native and naturalized plant diversity are positively correlated in scrub
communities of California and Chile. Diversity and Distributions, 8, 193–210.
Schiel, D. R. 2011. Biogeographic patterns and long-term changes on New Zealand coastal
reefs: Non-trophic cascades from diffuse and local impacts. Journal of Experimental
Marine Biology and Ecology. 400(1-2), 33-51.
Schiel, D. R., Underwood, A. J., & Chapman, M. G. 2019. Biogeographic Comparisons of
Pattern and Process on Intertidal Rocky Reefs of New Zealand and South-Eastern
Australia. Interactions in the Marine Benthos, Cambridge University Press, 391–413.
Schwindt, E., López Gappa, J., Raffo, M. P., Tatián, M., Bortolus, A., Orensanz, J. M., Alonso,
G., Diez, M. E., Doti, B., Genzano, G., Lagger, C., Lovrich, G., Piriz, M. L., Mendez, M.
M., Savoya, V., & Sueiro, M. C. 2014. Marine fouling invasions in ports of Patagonia
(Argentina) with implications for legislation and monitoring programs. Marine
Environmental Research, 99, 60-68.
Scyphers, S. B., Gouhier, T. C., Grabowski, J. H., Beck, M. W., Mareska, J., & Powers, S. P.
2015. Natural shorelines promote the stability of fish communities in an urbanized coastal
system. PLoS ONE, 10(6), e0118580.
Seaward, K., Acosta, H., Inglis, G. J., Wood, B., Riding, T. A. C., Wilkens, S., & Gould, B.
2015. The marine biosecurity porthole – A web-based information system on nonindigenous marine species in New Zealand. Management of Biological Invasions, 6(2),
177–184.
Secor, C. L., Day, A. J., & Hilbish, T. J. 2001. Factors influencing differential mortality within
a marine mussel (Mytilus spp.) hybrid population in southwestern England: Reproductive
effort and parasitism. Marine Biology, 138, 731–739.
Secord D. 2003. Biological control of marine invasive species: cautionary tales and land-based
lessons. Biological Invasions, 5,117–131.
Seebens, H., Gastner, M. & Blasius, B. 2013. The risk of marine bioinvasion caused by global
shipping. Ecology Letters, 16(6), 782–790.
Seebens, H., Schwartz, N., Schupp, P. J., & Blasius, B. 2016. Predicting the spread of marine
species introduced by global shipping. Proceedings of the National Academy of Sciences
of the United States of America, 113 (20), 5646-5651.
Seed, R., & Suchanek, T. H. 1992. Population and community ecology of Mytilus. (E. Gosling
(ed.); 25th ed.). The mussel Mytilus: ecology, physiology, genetics and culture.
Developments in Aquaculture and Fisheries Science, 25, 589.
Seehausen, O. 2004. Hybridization and adaptive radiation. In Trends in Ecology and Evolution,
19(4), 198-207.
Sella I., Perkol-Finkel S. 2015. Blue is the new green–Ecological enhancement of concrete
based coastal and marine infrastructure. Ecological Engineering, 84, 260–272.
Serrano, E., Coma, R., Ribes, M., Weitzmann, B., Garcia, M. & Ballesteros, E. 2013. Rapid
184
northward spread of a zooxanthellate coral enhanced by artificial structures and sea
warming in the western Mediterranean. PLoS One, 8, e52739.
Shanks, A. L. 2009. Pelagic larval duration and dispersal distance revisited. The Biological
Bulletin, 216, 373–385.
Shea, K., & Chesson, P. 2002. Community ecology theory as a framework for biological
invasions. In Trends in Ecology and Evolution, 17(4), 170-176.
Sheehan, E. V, Cartwright, A. Y., Witt, M. J., Attrill, M. J., Vural, M., & Holmes, L. A. 2018.
Development of epibenthic assemblages on artificial habitat associated with marine
renewable infrastructure. ICES Journal of Marine Science, 1-12.
Shields, J. L., Heath, J. W., & Heath, D. D. 2010. Marine landscape shapes hybrid zone in a
broadcast spawning bivalve: Introgression and genetic structure in Canadian west coast
Mytilus. Marine Ecology Progress Series, 399, 211-223
Shields, J.L., Barnes, P., & Heath, D. D. 2008. Growth and survival differences among native,
introduced and hybrid blue mussels (Mytilus spp.): Genotype, environment and interaction
effects. Marine Biology, 154, 919–928.
Shinen, J. S., & Morgan, S. G. 2009. Mechanisms of invasion resistance: competition among
intertidal mussels promotes establishment of invasive species and displacement of native
species. Marine Ecology Progress Series, 383, 187-197.
Simberloff, D. 2006. Invasional meltdown 6 years later: important phenomenon, unfortunate
metaphor, or both? Ecology Letters, 9, 912 – 919.
Simberloff, D. 2009. The role of propagule pressure in biological invasions. Annual Review of
Ecology, Evolution, and Systematics, 40, 81 – 102.
Simberloff, D., & Von Holle, B. 1999. Positive interactions of nonindigenous species:
Invasional meltdown? Biological Invasions, 1(1), 21–32.
Simberloff, D., Martin, J. L., Genovesi, P., Maris, V., Wardle, D. A., Aronson, J., Courchamp,
F., Galil, B., García-Berthou, E., Pascal, M., Pyšek, P., Sousa, R., Tabacchi, E., & Vilà,
M. 2013. Impacts of biological invasions: What’s what and the way forward. In Trends in
Ecology and Evolution, 28(1), 58-66.
Simpson, M., Coleman, R. A., Morris, R. L., & Harasti, D. 2019. Seahorse Hotels: Use of
artificial habitats to support populations of the endangered White’s seahorse
Hippocampus whitei. Marine Environmental Research, 157, e104861.
Simpson, T. J. S., Smale, D. A., McDonald, J. I., & Wernberg, T. 2017. Large scale variability
in the structure of sessile invertebrate assemblages in artificial habitats reveals the
importance of local-scale processes. Journal of Experimental Marine Biology and
Ecology, 494, 10-19.
Sinner, J., Forrest, B.M., Newton, M., Hopkins, G.A., Inglis, G., Woods, C. & Morrisey, D.
2013. Managing the domestic spread of harmful marine organisms, Part B: statutory
framework and analysis of options. Report prepared for New Zealand Ministry for
Primary Industries. Cawthron Institute report No. 2442. Nelson, 73.
Skein, L., Alexander, M. E., & Robinson, T. B. 2020. Co-occurring predators increase biotic
resistance against an invasive prey. Marine Environmental Research,157, e104929.
185
Skibinski, D. O. F., Ahmad, M., & Beardmore, J. A. 1978. Genetic Evidence for Naturally
Occurring Hybrids Between Mytilus edulis and Mytilus galloprovincialis. Society for the
Study of Evolution, 32(2), 354–364.
Smart, D. C., Heenan, S., Lokman, P. M., & Lamare, M. D. (2020). Seasonal reproduction of
the blue mussel (Mytilus galloprovincialis) from two locations in southern New Zealand.
New Zealand Journal
of Marine and Freshwater Research,
DOI:
10.1080/00288330.2020.1757472.
Smith, R. S., Johnston, E. L., & Clark, G. F. 2014. The role of habitat complexity in community
development is mediated by resource availability. PLoS ONE, 9(7), 1–13.
Snyder, W. E. and Evans, E. W. 2006. Ecological effects of invasive arthropod generalist
predators. Annual Review of Ecology and Systematics, 37, 95–122.
Sorte, C. J. B., Williams, S. L. and Carlton, J. T. 2010. Marine range shifts and species
introductions: comparative spread rates and community impacts. Global Ecology and
Biogeography 19, 303 – 316.
Sousa, W. P. 1979. Experimental Investigations of Disturbance and Ecological Succession in
a Rocky Intertidal Algal Community. Ecological Monographs, 49(3), 227-254.
Sousa, W. P. 1985. Disturbance and Patch Dynamics on Rocky Intertidal Shores. The Ecology
of Natural Disturbance and Patch Dynamics, 472.
Spagnolo, A., Cuicchi, C., Punzo, E., Santelli, A., Scarcella, G., & Fabi, G. 2014. Patterns of
colonization and succession of benthic assemblages in two artificial substrates. Journal of
Sea Research, 88, 78-86.
Spalding, M. D., Fox, H. E., Allen, G. R., Davidson, N., Ferdaña, Z. A., Finlayson, M., Halpern,
B. S., Jorge, M. A., Lombana, A., Lourie, S. A., Martin, K. D., McManus, E., Molnar, J.,
Recchia, C. A., & Robertson, J. 2007. Marine Ecoregions of the World: A
Bioregionalization of Coastal and Shelf Areas. BioScience, 57(7), 573–583.
Spinuzzi, K. R. Schneider, L. J. Walters, W. S. Yuan, E. A. 2013. Tracking the distribution of
non-native marine invertebrates (Mytella charruana, Perna viridis and Megabalanus
coccopoma) along the South-eastern USA, Marine Biodiversity Records, 6, e55.
Stachowicz, J. J., Best, R. J., Bracken, M. E. S., & Graham, M. H. 2008. Complementarity in
marine biodiversity manipulations: Reconciling divergent evidence from field and
mesocosm experiments. Proceedings of the National Academy of Sciences of the United
States of America, 105(48), 18842-18847.
Stachowicz, J. J., Fried, H., Osman, R. W., & Whitlatch, R. B. 2002. Biodiversity, invasion
resistance, and marine ecosystem function: Reconciling pattern and process. Ecology,
83(9), 2575-2590.
Steffani, C. N., & Branch, G. M. 2003. Growth rate, condition, and shell shape of Mytilus
galloprovincialis: Responses to wave exposure. Marine Ecology Progress Series, 246,
197–209.
Steffani, C. N., & Branch, G. M. 2005. Mechanisms and consequences of competition between
an alien mussel, Mytilus galloprovincialis, and an indigenous limpet, Scutellastra
argenvillei. Journal of Experimental Marine Biology and Ecology, 317(2), 127-142.
Stohlgren, T. J., Barnett D. T. & Kartesz J. T. 2003. The rich get richer: patterns of plant
186
invasions in the United States. Frontiers in Ecology and the Environment, 1, 11–14.
Stohlgren, T. J., C. Jarnevich, G. W. Chong & P. H. Evangelista, 2006. Scale and plant
invasions: a theory of biotic acceptance. Preslia, 78, 405–426.
Strain, E. M. A., Morris, R. L., Coleman, R. A., Figueira, W. F., Steinberg, P. D., Johnston, E.
L., & Bishop, M. J. 2018a. Increasing microhabitat complexity on seawalls can reduce
fish predation on native oysters. Ecological Engineering, 120, 637-644.
Strain, E. M. A., Olabarria, C., Mayer-Pinto, M., Cumbo, V., Morris, R. L., Bugnot, A. B.,
Dafforn, K. A., Heery, E., Firth, L. B., Brooks, P. R., & Bishop, M. J. 2018b. Ecoengineering urban infrastructure for marine and coastal biodiversity: Which interventions
have the greatest ecological benefit? Journal of Applied Ecology, 55(1), 426-441.
Suchanek, T. H. 1981. The role of disturbance in the evolution of life-history strategies in the
intertidal mussels Mytilus edulis and Mytilus californianus. Oecologia, 50(2), 143-152.
Suchanek, T. H., Geller, J. B., Kreiser, B. R., & Mitton, J. B. 1997. Zoogeographic distributions
of the sibling species Mytilus galloprovincialis and M. trossulus (Bivalvia: Mytilidae) and
their hybrids in the North Pacific. Biological Bulletin, 193(2), 187–194.
Tamburri, M. N., Luckenbach, M. W., Breitburg, D. L., & Bonniwell, S. M. 2008. Settlement
of Crassostrea ariakensis Larvae: Effects of Substrate, Biofilms, Sediment and Adult
Chemical Cues. Journal of Shellfish Research, 27(3), 601–608.
Tamelander, J., Riddering, L., Haag, F., Matheickal, J. & No, G.M.S. 2010. Guidelines for
development of a national ballast water management strategy, GloBallast Partnerships
Project Coordination Unit, International Maritime Organization.
Tan, E. L. Y., Mayer-Pinto, M., Johnston, E. L., & Dafforn, K. A. 2015. Differences in
intertidal microbial assemblages on urban structures and natural rocky reef. Frontiers in
Microbiology, 6, 1–13.
Tay, Y. C., Noreen, A. M. E., Suharsono, Chou, L. M., & Todd, P. A. 2015. Genetic
connectivity of the broadcast spawning reef coral Platygyra sinensis on impacted reefs,
and the description of new microsatellite markers. Coral Reefs, 34, 301–311.
Theuerkauf, K. W., Eggleston, D. B., & Theuerkauf, S. J. 2018. An exotic species alters
patterns of marine community development. Ecological Monographs, 88(1), 92–108.
Thompson, R. 1984. Production, reproductive effort, reproductive value and reproductive cost
in a population of the blue mussel Mytilus edulis from a subarctic environment. Marine
Ecology Progress Series, 16, 249–257.
Thompson, R. C., Crowe, T. P., & Hawkins, S. J. 2002. Rocky intertidal communities: past
environmental changes, present status and predictions for the next 25 years.
Environmental Conservation, 29(2), 168-191.
Thomsen, M. S., Wernberg, T., South, P. M., & Schiel, D. R. 2016. Non-native seaweeds drive
changes in marine coastal communities around the world. Seaweed Phylogeography:
Adaptation and Evolution of Seaweeds under Environmental Change, 147-185.
Thomsen, M., Wernberg, T., Olden, J., Byers, J. E., Bruno, J., Silliman, B., & Schiel, D. 2014.
Forty years of experiments on aquatic invasive species: are study biases limiting our
understanding of impacts? NeoBiota, 22, 1-22.
187
Thresher, R. E., C. L. Hewitt and M. L. Campbell. 1999. Synthesis: introduced and cryptogenic
species in Port Phillip Bay. pp 283–295. In: C. L. Hewitt, M. L. Campbell, R. E. Thresher
and R. B. Martin, (eds). Marine Biological Invasions of Port Phillip Bay, Victoria. CRIMP
Technical Report No. 20. CSIRO Marine Research, Hobart.
Tilman, D., May, R. M., Lehman, C. L., & Nowak, M. A. 1994. Habitat destruction and the
extinction debt. Nature, 371, 65–66.
Tobin P. C., Kean J. M., Suckling D. M., Mccullough D. G., Herms D. A., Stringer L. D. 2014.
Determinants of successful arthropod eradication programs. Biological Invasions, 16,
401–414.
Toro, J. E. 1998. Molecular identification of four species of mussels from Southern Chile by
PCR-based nuclear markers: the potential use in studies involving planktonic surveys.
Journal of Shellfish Research, 17(4-5), 1203-1205.
Toro, J. E., Thompson, R. J., & Innes, D. J. 2002. Reproductive isolation and reproductive
output in two sympatric mussel species (Mytilus edulis, M. trossulus) and their hybrids
from Newfoundland. Marine Biology, 141(5), 897–909.
Trussell, G. C., Ewanchuk, P. J., Bertness, M. D., & Silliman, B. R. 2004. Trophic cascades in
rocky shore tide pools: Distinguishing lethal and nonlethal effects. Oecologia, 139(3),
427-432.
Tyrrell, M. C., & Byers, J. E. 2007. Do artificial substrates favour nonindigenous fouling
species over native species? Journal of Experimental Marine Biology and Ecology,
342(1), 54-60.
Underwood, A. J., & Murphy, R. J. 2008. Unexpected patterns of facilitatory grazing revealed
by quantitative imaging. Marine Ecology Progress Series, 358, 85-94.
Valentine, J. P., & Johnson, C. R. 2003. Establishment of the introduced kelp Undaria
pinnatifida in Tasmania depends on disturbance to native algal assemblages. Journal of
Experimental Marine Biology and Ecology, 295(1), 63-90.
Van de Koppel, J., van der Heide, T., Altieri, A. H., Eriksson, B. K., Bouma, T. J., Olff, H., &
Silliman, B. R. 2015. Long-Distance Interactions Regulate the Structure and Resilience
of Coastal Ecosystems. Annual Review of Marine Science, 7, 139-158.
Van Erkom Schurink, C., & Griffiths, C. L. 1991. A comparison of reproductive cycles and
reproductive output in four southern African mussel species. Marine Ecology Progress
Series, 76(2), 123–134.
Vaselli, S., Bulleri, F., & Benedetti-Cecchi, L. 2008. Hard coastal-defence structures as habitats
for native and exotic rocky-bottom species. Marine Environmental Research, 66(4), 395403.
Vermeij, G. J. 1991. Anatomy of an invasion: The trans-Arctic interchange. Paleobiology,
17(3), 281-307.
Von Holle, B. 2011. Invasional Meltdown. In book: Encyclopedia of Biological Invasions,
360-364.
Walles, B., Troost, K., van den Ende, D., Nieuwhof, S., Smaal, A. C., & Ysebaert, T. 2016.
From artificial structures to self-sustaining oyster reefs. Journal of Sea Research, 108, 19.
188
Waltham, N. J., & Dafforn, K. A. 2018. Ecological engineering in the coastal seascape. In
Ecological Engineering, 120, 554-559.
Walther, G. R., Post, E., Convey, P., Menzel, A., Parmesan, C., Beebee, T. J., Fromentin, J.
M., Hoegh-Guldberg, O. & Bairlein, F. 2002. Ecological responses to recent climate
change. Nature, 416(6879), 389-395.
Walther, G. R., Roques, A., Hulme, P. E., Sykes, M. T., Pyšek, P., Kuhn, I., Zobel, M., Bacher,
S., Botta-Dukat, Z., Bugmann, H., Czucz, B., Dauber, J., Hickler, T., Jarošik, V., Kenis,
M., Klotz, S., Minchin, D., Moora, M., Netwig, W., Ott, J., Panov, V. E., Reineking, B.,
Robinet, C., Semenchenko, V., Solarz, W., Thuiller, W., Vila, M., Vohland K., & Settele,
J. 2009. Alien species in a warmer world: risks and opportunities. Trends in Ecology &
Evolution, 24(12), 686-693.
Wells, F. E. 2018. A low number of invasive marine species in the tropics: A case study from
Pilbara (Western Australia). Management of Biological Invasions, 9(3), 227-237.
Westfall, K. M. 2011. Molecular ecology and systematics of blue mussels (genus Mytilus)
(Mytilidae; Bivalvia; Mollusca) in the Southern hemisphere (Doctoral thesis, Victoria
University of Wellington, Wellington, New Zealand). Retrieved from
http://hdl.handle.net/10063/1815.
Westfall, K. M., & Gardner, J. P. A. 2010. Genetic diversity of Southern hemisphere blue
mussels (Bivalvia: Mytilidae) and the identification of non-indigenous taxa. Biological
Journal of the Linnean Society, 101(4), 898-909.
Westfall, K. M., & Gardner, J. P. A. 2013. Interlineage Mytilus galloprovincialis Lmk. 1819
hybridization yields inconsistent genetic outcomes in the Southern hemisphere. Biological
Invasions, 15(7), 1493–1506.
Westfall, K. M., Wimberger, P. H., & Gardner, J. P. A. 2010. An RFLP assay to determine if
Mytilus galloprovincialis Lmk. (Mytilidae; Bivalvia) is of Northern or Southern
hemisphere origin. Molecular Ecology Resources, 10(3), 573-575.
Wilkins, N. P., Fujino, K., & Gosling, E. M. 1983. The Mediterranean mussel Mytilus
galloprovincialis Lmk. in Japan. Biological Journal of the Linnean Society, 20(4), 365374.
Willan R. C., Russell B. C., Murfet N. B., Moore K. L., McEnnulty F. R., Horner S. K., Hewitt
C. L., Dally G. M., Campbell M. L., Bourke S. T. 2000. Outbreak of Mytilopsis sallei
(Recluz, 1849) (Bivalvia: Dreissenidae) in Australia. Molluscan Research, 20(2), 25-30.
Williams G. A., Davies M. S. & Nagarkar S. 2000. Primary succession on a seasonal tropical
rocky shore: the relative roles of spatial heterogeneity and herbivory. Marine Ecology
Progress Series, 203, 81−94.
Williams S. L, Bracken M. E. & Jones E. 2013. Additive effects of physical stress and
herbivores on intertidal seaweed biodiversity. Ecology, 94(5): 1089−1101.
Williamson, M. H. 1996. Biological invasions. Population and community biology series, Vol.
15. Chapman and Hall, London. 244 p.
Wonham, M. J. 1999. Factors limiting the spread of the introduced Mediterranean mussel
Mytilus galloprovincialis on Washington’s outer coast. In Peterson, J. (Ed.), Marine
Bioinvasions. Proceedings of the First National Conference. Massachusetts Institute of
Technology, 1-423.
189
Wonham, M. J. 2004. Mini-review: Distribution of the Mediterranean mussel Mytilus
galloprovincialis (Bivalvia: Mytilidae) and hybrids in the northeast Pacific. Journal of
Shellfish Research, 23(2), 535–543.
Wood, A. C. L., & Probert, P. K. 2013. Bryozoan-dominated benthos of Otago shelf, New
Zealand: Its associated fauna, environmental setting and anthropogenic threats. Journal of
the Royal Society of New Zealand, 43(4), 231-249.
Woods, C., Seaward, K., Inglis, G., 2015. Marine High-Risk Site Surveillance Annual report
for all ports and marinas 2014–15, MPI Technical Paper (Project 12099).
Woods, C., Seaward, K., Pryor Rodgers, L., & Inglis, G. J. 2018. Marine High-Risk Site
Surveillance Programme. MPI Technical Paper 2018/45, 209.
Wotton, D. M., & Hewitt, C. L. 2004. Marine biosecurity post-border management:
Developing incursion response systems for New Zealand. Journal of Marine and
Freshwater Research, 38, 553-559
Wyatt, A. S. J., Hewitt, C. L., Walker, D. I., & Ward, T. J. 2005. Marine introductions in the
Shark Bay World Heritage Property, Western Australia: A preliminary assessment.
Diversity and Distributions, 11(1), 33–44.
Zaiko, A., Martinez, J. L., Ardura, A., Clusa, L., Borrell, Y. J., Samuiloviene, A., Roca, A.,
Garcia-Vazquez, E., 2015. Detecting nuisance species using NGST: Methodology
shortcomings and possible application in ballast water monitoring. Marine Environmental
Research, 112, 64-72.
Zaiko, A., Pochon, X., Garcia-Vazquez, E., Olenin, S., Wood, S.A., 2018. Advantages and
Limitations of Environmental DNA/RNA Tools for Marine Biosecurity: Management and
Surveillance of Non-indigenous Species. Frontiers in Marine Science, 5.
Zaiko, A., Schimanski, K., Pochon, X., Hopkins, G.A., Goldstien, S., Floerl, O. & Wood, S.A.
2016. Metabarcoding improves detection of eukaryotes from early biofouling
communities: implications for pest monitoring and pathway management. Biofouling, 32,
671–684.
Zardi, G. I., McQuaid, C. D., & Nicastro, K. R. 2007. Balancing survival and reproduction:
Seasonality of wave action, attachment strength and reproductive output in indigenous
Perna perna and invasive Mytilus galloprovincialis mussels. Marine Ecology Progress
Series, 334, 155–163.
Zardi, G. I., Nicastro, K. R., McQuaid, C. D., & Erlandsson, J. 2008. Sand and wave induced
mortality in invasive (Mytilus galloprovincialis) and indigenous (Perna perna) mussels.
Marine Biology, 153(5), 853-858.
Zazzaro, D. 2016. The biology of the triangle barnacle, Balanus trigonus, fouling green-lipped
mussel farms in the Hauraki Gulf (Doctoral thesis, The University of Auckland, New
Zealand). Retrieved from http://hdl.handle.net/2292/31904.
Zbawicka, M., Gardner, J. P. A., & Wenne, R. 2019. Cryptic diversity in smooth-shelled
mussels on Southern Ocean islands: Connectivity, hybridisation and a marine invasion.
Frontiers in Zoology, 16(1), 1-12.
Zwerschke, N., Emmerson, M. C., Roberts, D., & O’Connor, N. E. 2016. Benthic assemblages
associated with native and non-native oysters are similar. Marine Pollution Bulletin,
111(1-2), 305-310.
190
Appendix
Table A1. SIMPER analysis: average similarity in the status of species as a function of the surveyed port.
Abbot Point
Average similarity: 65.04
Adelaide
Average similarity: 74.02
Albany
Average similarity: 64.69
Bunbury
Average similarity: 56.51
Burnie
Average similarity: 68.56
Species
Abund
C%
Abund
C%
Abund
C%
Abund
C%
Abund
C%
Native
3.7
85.65
2.47
46.7
1.26
47.98
1.03
54.03
3.33
84.72
Non-native
0.86
13.76
1.78
36.84
1.18
47.62
0.84
45.3
0.82
13.7
Cryptogenic
0.21
0.58
1.02
16.46
0.37
4.41
0.12
0.66
0.33
1.59
Devonport
Average similarity: 68.00
Geelong
Average similarity: 85.75
Esperance
Average similarity: 64.91
Geraldton
Average similarity: 82.48
Gladstone
Average similarity: 77.62
Species
Abund
C%
Abund
C%
Abund
C%
Abund
C%
Abund
C%
Native
2.12
81.69
2.17
43.31
2.17
94.46
1.28
99.87
2.17
83.11
Non-native
0.73
17.28
2.23
40.86
0.41
5.16
0.11
0.13
0.68
16.89
Cryptogenic
0.18
1.03
0.92
15.83
0.12
0.38
0
0
0
0
Eden
Average similarity: 71.56
Fremantle
Average similarity: 69.84
Hay Point
Average similarity: 71.34
Launceston
Average similarity: 84.50
Mourilyan
Average similarity: 67.12
Species
Abund
C%
Abund
C%
Abund
C%
Abund
C%
Abund
C%
Native
2.44
52.79
2.26
59.34
3.38
89.98
4.3
61.97
2.87
79.12
Non-native
1.09
20.55
0.79
13.74
0.3
1.95
1.41
18.13
0.57
6.94
Cryptogenic
1.12
26.66
0.96
26.91
0.64
8.07
1.47
19.9
0.7
13.93
Hastings
Average similarity: 85.03
Hobart
Average similarity: 87.28
Lady Barron
Average similarity: 59.92
Lucinda
Average similarity: 52.43
Mackay
Average similarity: 72.99
191
Species
Abund
C%
Abund
C%
Abund
C%
Abund
C%
Abund
C%
Native
2.64
76.82
3.05
41.82
1.39
78.67
2.4
65.92
3.44
82
Non-native
1.12
23.18
3.11
40.39
0.85
20.47
0.96
29.58
0.97
15.83
Cryptogenic
0
0
1.54
17.8
0.2
0.86
0.43
4.49
0.35
2.17
Melbourne
Average similarity: 67.94
Newcastle
Average similarity: 68.68
Port Hedland
Average similarity: 73.23
Port Lincoln
Average similarity: 66.55
Portland
Average similarity: 79.11
Species
Abund
C%
Abund
C%
Abund
C%
Abund
C%
Abund
C%
Native
1.38
41.23
2.35
63.92
2.8
92.67
1.94
93.43
2.49
70.35
Non-native
1.27
37.88
1.06
18.82
0.43
4.85
0.41
6.42
1.23
28.96
Cryptogenic
0.74
20.89
0.81
17.26
0.3
2.48
0.07
0.14
0.23
0.69
Townsville
Average similarity: 57.72
Weipa
Average similarity: 61.01
Species
Abund
C%
Abund
C%
Native
2.64
82.38
2.02
84.65
Non-native
0.79
11.84
0.36
3.98
Cryptogenic
0.51
5.78
0.5
11.36
C% = percent contribution
192
Table A2. Species identified in the 27 port surveys around Australia and species status – native,
non-native and cryptogenic species.
Species
Phyla
Species status
Acanthochitona bednalli
Mollusca
Native
Acanthochitona granostriata
Mollusca
Native
Acanthochitona kimberi
Mollusca
Native
Acanthochitona pilsbryi
Mollusca
Native
Acanthochitona retrojecta
Mollusca
Native
Acanthochitona sueurii
Mollusca
Native
Acanthodesia cf. savartii
Bryozoa
Cryptogenic
Acanthophora cf. muscoides
Rhodophyta
Native
Acanthophora dendroides
Rhodophyta
Native
Acanthophora muscoides
Rhodophyta
Native
Acanthophora spicifera
Rhodophyta
Native
Acanthopleura gaimardi
Mollusca
Native
Acasta cf. dofleini
Arthropoda
Native
Acasta dofleini
Arthropoda
Native
Acasta pectinipes
Arthropoda
Native
Achaeus lacertosus
Arthropoda
Native
Achelia assimilis
Arthropoda
Native
Achelia shepherdi
Arthropoda
Native
Aclophoropsis festiva
Mollusca
Native
Acrocarpia paniculata
Ochrophyta
Native
Acrosorium uncinatum
Rhodophyta
Native
Acrosorium venulosum
Rhodophyta
Native
Actaea cf. ruppelli
Arthropoda
Native
Actaea peronii
Arthropoda
Native
Actaeodes hirsutissimus
Arthropoda
Native
Cnidaria
Native
Actinia cf. tenebrosa
Cnidaria
Native
Echinodermata
Native
Aetea anguina
Bryozoa
Cryptogenic
Aglaophenia cf. parvula
Hydroid
Native
Aglaophenia cf. plumosa
Hydroid
Native
Aglaophenia delicatula
Hydroid
Native
Aglaophenia parvula
Hydroid
Native
Aglaophenia plumosa
Hydroid
Native
Agnewia tritoniformis
Mollusca
Native
Akera soluta
Mollusca
Native
Alabes dorsalis
Chordata
Native
Aliaporcellana cf. pygmaea
Arthropoda
Native
Aliaporcellana suluensis
Arthropoda
Native
Actinia tenebrosa
Actinocucumis cf. typica
193
Annelida
Non-native
Allorchestes compressus
Arthropoda
Native
Alope orientalis
Arthropoda
Native
Alpheus australiensis
Arthropoda
Native
Alpheus cf. australiensis
Arthropoda
Native
Alpheus cf. eulimene
Arthropoda
Native
Alpheus cf. facetus
Arthropoda
Native
Alpheus cf. paracrinitus
Arthropoda
Native
Alpheus cf. parasocialis
Arthropoda
Native
Alpheus cf. spongiarum
Arthropoda
Native
Alpheus cf. villosus
Arthropoda
Native
Alpheus chiragricus
Arthropoda
Native
Alpheus cristatus
Arthropoda
Native
Alpheus facetus
Arthropoda
Native
Alpheus gracilis
Arthropoda
Native
Alpheus hippothoe
Arthropoda
Native
Alpheus novaezelandiae
Arthropoda
Native
Alpheus richardsoni
Arthropoda
Native
Alpheus socialis
Arthropoda
Native
Alpheus villosus
Arthropoda
Native
Amarinus laevis
Arthropoda
Native
Amaryllis macrophthalmus
Arthropoda
Native
Amastigia cf. texta
Bryozoa
Native
Amathia biseriata
Bryozoa
Native
Amathia brongniartii
Bryozoa
Native
Amathia cf. connexa
Bryozoa
Native
Amathia cf. distans
Bryozoa
Non-native
Amathia cf. semiconvoluta
Bryozoa
Native
Amathia connexa
Bryozoa
Native
Amathia distans
Bryozoa
Non-native
Amathia tortuosa
Bryozoa
Cryptogenic
Amathia vidovici
Bryozoa
Native
Amblychilepas oblonga
Mollusca
Native
Ammothea ovatoides
Arthropoda
Native
Ammothella stocki
Arthropoda
Native
Echinodermata
Native
Amphisbetia minima
Cnidaria
Native
Amphitrite pachyderma
Annelida
Native
Amphitritides ithya
Annelida
Native
Amphiura (Amphiura) constricta
Echinodermata
Native
Amphiura (Amphiura) poecila
Echinodermata
Native
Amphiura (Amphiura) tenuis
Echinodermata
Native
Amphiura (Amphiura) trisacantha
Echinodermata
Native
Amphiura (Ophiopeltis) parviscutata
Echinodermata
Native
Alitta succinea
Amphipholis squamata
194
Anachis atkinsoni
Mollusca
Native
Anachis troglodytes
Mollusca
Native
Anadara granosa
Mollusca
Native
Anapella amygdala
Mollusca
Native
Ancorina robusta
Porifera
Native
Ancorina suina
Porifera
Native
Anisodonta subalata
Mollusca
Native
Anodontia omissa
Mollusca
Native
Anomia trigonopsis
Mollusca
Native
Anoplodactylus digitatus
Arthropoda
Native
Anoplodactylus evansi
Arthropoda
Native
Anoplodactylus glandulifer
Arthropoda
Native
Anotrichium elongatum
Rhodophyta
Native
Anotrichium subtile
Rhodophyta
Native
Antedon incommoda
Echinodermata
Native
Antennella secundaria
Cnidaria
Non-native
Anthohebella parasitica
Cnidaria
Native
Anthothoe albocincta
Cnidaria
Native
Anthothoe cf. albocincta
Cnidaria
Native
Antithamnion pinnafolium
Rhodophyta
Native
Antithamnionella glandifera
Rhodophyta
Native
Antithamnionella ternifolia
Rhodophyta
Cryptogenic
Aora maculata
Arthropoda
Native
Aplidium cf. lenticulum
Chordata
Native
Aplysilla cf. rosea
Porifera
Non-native
Apocorophium acutum
Arthropoda
Non-native
Arachnopusia unicornis
Bryozoa
Non-native
Arca avellana
Mollusca
Native
Arca navicularis
Mollusca
Native
Armandia intermedia
Annelida
Native
Artacamella dibranchiata
Annelida
Native
Rhodophyta
Native
Ascidia cf. latesiphonica
Chordata
Native
Ascidia cf. liberata
Chordata
Native
Ascidia cf. munda
Chordata
Native
Ascidia cf. sydneiensis
Chordata
Non-native
Ascidia cf. thompsoni
Chordata
Native
Ascidia challengeri
Chordata
Native
Ascidia decepta
Chordata
Native
Ascidia empheres
Chordata
Native
Ascidia latesiphonica
Chordata
Native
Ascidia munda
Chordata
Native
Ascidia sydneiensis
Chordata
Non-native
Ascidiella aspersa
Chordata
Non-native
Arthrocardia wardii
195
Ascorhynchus tenuirostris
Arthropoda
Native
Asparagopsis taxiformis
Rhodophyta
Native
Asterias amurensis
Chordata
Non-native
Asterocarpa humilis
Chordata
Native
Astralium pileolum
Mollusca
Native
Astralium tentoriformis
Mollusca
Native
Astrangia woodsi
Cnidaria
Native
Athanas dimorphus
Arthropoda
Native
Athanas parvus
Arthropoda
Native
Atys cylindrica
Mollusca
Native
Rhodophyta
Native
Augeneria verdis
Annelida
Native
Augenerilepidonotus dictyolepis
Annelida
Native
Austraeolis cacaotica
Mollusca
Native
Austrobalanus imperator
Arthropoda
Native
Austrodecus tubiferum
Arthropoda
Native
Austromegabalanus nigrescens
Arthropoda
Native
Austrophyllis alcicornis
Rhodophyta
Native
Balanus amphitrite
Arthropoda
Cryptogenic
Balanus cf. amphitrite
Arthropoda
Cryptogenic
Balanus cf. reticulatus
Arthropoda
Native
Balanus cf. variegatus
Arthropoda
Native
Balanus cf. vestitus
Arthropoda
Native
Balanus improvisus
Arthropoda
Non-native
Balanus reticulatus
Arthropoda
Non-native
Balanus trigonus
Arthropoda
Cryptogenic
Balanus variegatus
Arthropoda
Native
Barbatia bistrigata
Mollusca
Native
Barbatia cf. helblingi
Mollusca
Native
Barbatia cf. pistachia
Mollusca
Native
Barbatia foliata
Mollusca
Native
Barbatia helblingi
Mollusca
Native
Barbatia pistachia
Mollusca
Native
Barbatia riculata
Mollusca
Native
Barbatia wendti
Mollusca
Native
Beania magellanica
Bryozoa
Native
Beania mirabilis
Bryozoa
Native
Bedeva hanleyi
Mollusca
Native
Bedeva paivae
Mollusca
Native
Bembicium auratum
Mollusca
Native
Bembicium nanum
Mollusca
Native
Bhawania amboinensis
Annelida
Native
Bhawania cf. amboinensis
Annelida
Native
Bicellariella ciliata
Bryozoa
Native
Audouinella caespitosum
196
Bicellariella gracilis
Bryozoa
Native
Bicrisia edwardsiana
Bryozoa
Native
Arthropoda
Native
Biflustra perfragilis
Bryozoa
Native
Bimeria australis
Cnidaria
Native
Bimeria currumbinensis
Cnidaria
Native
Bispira cf. porifera
Annelida
Native
Bittium granarium
Mollusca
Native
Boccardia cf. chilensis
Annelida
Non-native
Boccardia chilensis
Annelida
Cryptogenic
Boccardia proboscidea
Annelida
Non-native
Boccardiella cf. bihamata
Annelida
Native
Botrylloides leachi
Chordata
Non-native
Botrylloides magnicoecum
Chordata
Cryptogenic
Botrylloides perspicuus
Chordata
Native
Botryllus cf. tuberatus
Chordata
Native
Botryllus schlosseri
Chordata
Non-native
Botryllus tuberatus
Chordata
Native
Botryocladia obovata
Rhodophyta
Native
Botryocladia sonderi
Rhodophyta
Native
Bougainvillia cf. balei
Cnidaria
Native
Bougainvillia muscus
Cnidaria
Non-native
Bowerbankia gracilis
Bryozoa
Non-native
Bowerbankia imbricata
Bryozoa
Non-native
Brachidontes cf. rostratus
Mollusca
Native
Brachidontes erosa
Mollusca
Native
Brachidontes maritimus
Mollusca
Native
Brachidontes rostratus
Mollusca
Native
Branchiomma nigromaculata
Annelida
Native
Branchiosyllis australis
Annelida
Native
Branchiosyllis exilis
Annelida
Native
Biffarius arenosus
Chlorophyta
Non-native
Bugula cf. avicularia
Bryozoa
Non-native
Bugula cf. flabellata
Bryozoa
Non-native
Bugula cf. robusta
Bryozoa
Native
Bugula cf. serrata
Bryozoa
Native
Bugula cf. stolonifera
Bryozoa
Non-native
Bugula dentata
Bryozoa
Native
Bugula flabellata
Bryozoa
Non-native
Bugula neritina
Bryozoa
Non-native
Bugula phillipinata
Bryozoa
Native
Bugula robusta
Bryozoa
Native
Bugula serrata
Bryozoa
Native
Bugula stolonifera
Bryozoa
Non-native
Bryopsis plumosa
197
Bugula vectifera
Bryozoa
Native
Bulla punctulata
Mollusca
Native
Caberea boryi
Bryozoa
Native
Caberea dichotoma
Bryozoa
Native
Caberea dolabrata
Bryozoa
Native
Caberea helicina
Bryozoa
Native
Caberea lata
Bryozoa
Native
Caberea rostrata
Bryozoa
Native
Cabestana tabulata
Mollusca
Native
Arthropoda
Native
CallistoMollusca antiquus
Mollusca
Native
Callithamnion violaceum
Rhodophyta
Native
Callogobius mucosus
Chordata
Native
Callophyllis lambertii
Rhodophyta
Native
Callucina lacteola
Mollusca
Native
Calthalotia mundula
Mollusca
Native
Calyptotheca cf. wasinensis
Bryozoa
Native
Calyptotheca triangula
Bryozoa
Native
Canda cf arachnoides
Bryozoa
Native
Caprella acanthogaster
Arthropoda
Non-native
Caprella cf. danilevskii
Arthropoda
Non-native
Caprella cf. equilibra
Arthropoda
Non-native
Caprella equilibra
Arthropoda
Cryptogenic
Caprella penantis
Arthropoda
Cryptogenic
Caprella scaura
Arthropoda
Non-native
Cardita cf. crassicosta
Mollusca
Native
Cardita excavata
Mollusca
Native
Cardita muricata
Mollusca
Native
Cardita preissii
Mollusca
Native
Cardita variegata
Mollusca
Native
Carijoa cf. multiflora
Cnidaria
Native
Catenicella buskii
Bryozoa
Native
Catenicella cf. uberrima
Bryozoa
Native
Catenicella elegans
Bryozoa
Native
Caulerpa brownii
Chlorophyta
Native
Caulerpa cactoides
Chlorophyta
Native
Caulerpa cf. brachypus
Chlorophyta
Native
Caulerpa longifolia
Chlorophyta
Native
Caulerpa nummularia
Chlorophyta
Native
Caulerpa obscura
Chlorophyta
Native
Caulerpa peltata
Chlorophyta
Native
Caulerpa racemosa
Chlorophyta
Native
Caulerpa racemosa var. laetivirens
Chlorophyta
Native
Caulerpa racemosa var. turbinata
Chlorophyta
Native
Callipallene emaciata micracantha
198
Caulerpa sedoides f. geminata
Chlorophyta
Native
Caulerpa taxifolia
Chlorophyta
Native
Caulibugula dendrograpta
Bryozoa
Non-native
Caulibugula haddoni
Bryozoa
Native
Cellana conciliata
Mollusca
Native
Cellaria pilosa
Bryozoa
Native
Cellaria punctata
Bryozoa
Native
Cellaria tenuirostris
Bryozoa
Native
Celleporaria bispinata
Bryozoa
Native
Celleporaria cf. columnaris
Bryozoa
Native
Celleporaria cf. fusca
Bryozoa
Native
Celleporaria cf. mamillata
Bryozoa
Native
Celleporaria cf. nodulosa
Bryozoa
Native
Celleporaria cf. oculata
Bryozoa
Native
Celleporaria columnaris
Bryozoa
Native
Celleporaria foliata
Bryozoa
Native
Celleporaria fusca
Bryozoa
Native
Celleporaria nodulosa
Bryozoa
Native
Echinodermata
Native
Centroceras clavulatum
Rhodophyta
Non-native
Ceramium cliftonianum
Rhodophyta
Native
Ceramium filiculum
Rhodophyta
Native
Ceramium flaccidum
Rhodophyta
Cryptogenic
Ceramium isogonum
Rhodophyta
Native
Ceramium macilentum
Rhodophyta
Native
Ceramium pusillum
Rhodophyta
Native
Ceramium sympodiale
Rhodophyta
Native
Ceramium tasmanicum
Rhodophyta
Native
Ceramium virgatum
Rhodophyta
Cryptogenic
Ceratonereis amphidonta
Annelida
Native
Ceratonereis cf. costae
Annelida
Native
Ceratonereis cf. mirabilis
Annelida
Native
Ceratonereis mirabilis
Annelida
Native
Ceratonereis perkinsi
Annelida
Native
Cerceis tridentata
Arthropoda
Native
Chaetozone setosa
Annelida
Native
Chama asperella
Mollusca
Native
Chama cf. fibula
Mollusca
Native
Chama cf. ruderalis
Mollusca
Native
Chama fibula
Mollusca
Non-native
Chama lazarus
Mollusca
Native
Chama limbula
Mollusca
Native
Chama pacifica
Mollusca
Native
Chama ruderalis
Mollusca
Native
Cenolia trichoptera
199
Chamaesipho tasmanica
Arthropoda
Native
Champia parvula
Rhodophyta
Cryptogenic
Champia viridis
Rhodophyta
Native
Bryozoa
Native
Charybdis cf. anisodon
Arthropoda
Native
Charybdis cf. hellerii
Arthropoda
Native
Charybdis hellerii
Arthropoda
Non-native
Chelonaplysilla cf. violacea
Porifera
Native
Chelonaplysilla violacea
Porifera
Native
Chlamys aktinos
Mollusca
Native
Chlorodiella laevissima
Arthropoda
Native
Chlorotocella gibber
Arthropoda
Native
Chondria fusifolia
Rhodophyta
Native
Chondria simpliciuscula
Rhodophyta
Native
Chondria succulenta
Rhodophyta
Native
Chordaria cladosiphon
Chaperiopsis cervicornis
Ochrophyta
Native
Chorizocarpa cf. michaelseni
Chordata
Native
Chorizocarpa cf. sydneyensis
Chordata
Native
Chorizocarpa sydneyensis
Chordata
Native
Chromodoris cf. epicuria
Mollusca
Native
Chthamalus antennatus
Arthropodas
Native
Chthamalus malayensis
Arthropodas
Native
Cilicaea crassicaudata
Arthropoda
Native
Cilicaea latreillei
Arthropoda
Native
Ciona intestinalis
Chordata
Non-native
Cirolana erodiae
Arthropoda
Native
Cirolana harfordi
Ispods
Non-native
Cirriformia cf. capensis
Annelida
Native
Cirriformia cf. filigera
Annelida
Native
Cirriformia filigera
Annelida
Native
Cirriformia tentaculata
Annelida
Native
Cladophora albida
Chlorophyta
Native
Cladophora feredayi
Chlorophyta
Native
Cladophora lehmanniana
Chlorophyta
Non-native
Cladophora subsimplex
Chlorophyta
Native
Mollusca
Native
Echinodermata
Native
Clathrina adusta
Porifera
Native
Clava cf. simplex
Cnidaria
Native
Cleidothaerus cf. plicifera
Mollusca
Native
Cleotrivia globosa
Mollusca
Native
Clytia cf. gracilis
Cnidaria
Native
Clytia cf. hemisphaerica
Cnidaria
Non-native
Clytia cf. paulensis
Cnidaria
Non-native
Clanculus undatus
Clarkcoma canaliculata
200
Clytia gravieri
Cnidaria
Native
Clytia hemisphaerica
Cnidaria
Cryptogenic
Clytia johnstoni
Cnidaria
Native
Clytia paulensis
Cnidaria
Cryptogenic
Cnemidocarpa areolata
Chordata
Native
Cnemidocarpa cf. barbata
Chordata
Native
Cnemidocarpa completa
Chordata
Native
Cnemidocarpa fissa
Chordata
Native
Cnemidocarpa floccosa
Chordata
Native
Cnemidocarpa lobata
Chordata
Native
Cnemidocarpa radicosa
Chordata
Native
Cnemidocarpa stolonifera
Chordata
Native
Codium fragile ssp tomentosoides
Chlorophyta
Non-native
Coeloclonium cf. umbellulum
Rhodophyta
Native
Coeloclonium umbellulum
Rhodophyta
Native
Colpomenia sinuosa
Ochrophyta
Cryptogenic
Conopeum cf. seurati
Bryozoa
Native
Conopeum reticulum
Bryozoa
Non-native
Corallina officinalis
Rhodophyta
Cryptogenic
Coralliophila mira
Mollusca
Native
Cordylophora caspia
Cnidaria
Non-native
Corella eumyota
Chordata
Native
Corydendrium parasiticum
Cnidaria
Native
Coscinasterias muricata
Chordata
Native
Cosmetalepas concatenatus
Mollusca
Native
Craspedoplax variabilis
Mollusca
Native
Crassimarginatella cf. papulifera
Bryozoa
Native
Crassimarginatella papulifera
Bryozoa
Native
Crassostrea gigas
Mollusca
Non-native
Cricophorus nutrix
Cnidaria
Native
Crisia acropora
Bryozoa
Native
Crisia cf. acropora
Bryozoa
Native
Crisia margaritacea
Bryozoa
Native
Cronia avellana
Mollusca
Native
Crucigera cf. inconstans
Annelida
Native
Crucigera inconstans
Annelida
Native
Arthropoda
Native
Cryptoplax striata
Mollusca
Native
Cryptosula pallasiana
Bryozoa
Non-native
Culicia australiensis
Cnidaria
Native
Culicia cf. tenella
Cnidaria
Native
Culicia tenella
Cnidaria
Cryptogenic
Ochrophyta
Non-native
Mollusca
Native
Cryptodromia cf. tumida
Cutleria multifida
Cyamiomactra cf. problematica
201
Cyclicopora longipora
Bryozoa
Native
Cymatium exaratum
Mollusca
Native
Cymatium labiosum
Mollusca
Native
Cypraea helvola
Mollusca
Native
Cypraea subviridis
Mollusca
Native
Cystiscus angasi
Mollusca
Native
Dasya capillaris
Rhodophyta
Native
Dasya cf. caraibica
Rhodophyta
Native
Dasya crescens
Rhodophyta
Native
Dasya extensa
Rhodophyta
Native
Dasya hookeri
Rhodophyta
Native
Dasya iyengarii
Rhodophyta
Native
Dasya villosa
Rhodophyta
Native
Demonax leucaspis
Annelida
Native
Dendostrea cf. folium
Mollusca
Native
Dendostrea cf. sandvichensis
Mollusca
Native
Dendostrea folium
Mollusca
Native
Dendostrea sandvichensis
Mollusca
Native
Dendrilla cactos
Porifera
Native
Dendrilla cf. rosea
Porifera
Native
Densipora corrugata
Bryozoa
Native
Dictyopteris muelleri
Ochrophyta
Native
Dictyopteris nigricans
Ochrophyta
Native
Dictyopteris plagiogramma
Ochrophyta
Native
Dictyota bartayresiana
Ochrophyta
Native
Dictyota cervicornis
Ochrophyta
Native
Dictyota dichotoma
Ochrophyta
Cryptogenic
Dictyota divaricata
Ochrophyta
Native
Dictyota furcellata
Ochrophyta
Native
Dilophus marginatus
Ochrophyta
Native
Dimorphostylis colefaxi
Arthropoda
Native
Diodora cf. jukesii
Mollusca
Native
Diodora cf. lincolnensis
Mollusca
Native
Diodora jukesii
Mollusca
Native
Diopatra dentata
Annelida
Native
Diphasia digitalis
Cnidaria
Native
Diphasia subcarinata
Cnidaria
Native
Diplosoma ferrugem
Chordata
Native
Diplosoma listerianum
Chordata
Non-native
Diplosoma velatum
Chordata
Native
Dipolydora flava
Annelida
Cryptogenic
Dipolydora giardi
Annelida
Cryptogenic
Dipolydora socialis
Annelida
Non-native
Distaplia cf. australensis
Chordata
Native
202
Distaplia stylifera
Chordata
Native
Distaplia violetta
Chordata
Native
Distaplia viridis
Chordata
Native
Dofleinia armata
Cnidaria
Native
Doriopsilla peculiaris
Mollusca
Native
Doris cameroni
Mollusca
Native
Dorvillea australiensis
Annelida
Native
Dulichiella australis
Arthropoda
Native
Dumea latipes
Arthropoda
Native
Durvillaea potatorum
Ochrophyta
Native
Dynamena crisoides
Cnidaria
Native
Dynamena mertoni
Cnidaria
Native
Echinothamnion hookeri
Rhodophyta
Native
Ecklonia radiata
Ochrophyta
Native
Chordata
Native
Ecteinascidia cf. diaphanis
Chordata
Native
Ochrophyta
Non-native
Ehlersia ferrugina
Annelida
Native
Elasmopus rapax
Arthropoda
Non-native
Electra tenella
Bryozoa
Cryptogenic
Electroma georgiana
Mollusca
Native
Electroma physoides
Mollusca
Native
Elminius covertus s
Arthropodas
Native
Elminius modestus s
Ecteinascidia rubricollis
Ectocarpus siliculosus
Arthropodas
Native
Elysia ornata
Mollusca
Native
Elzerina blainvillii
Bryozoa
Native
Emarginula devota
Mollusca
Native
Emarginula patula
Mollusca
Native
Endeis straughani
Arthropoda
Native
Engina armillata
Mollusca
Native
Ensiculus cultellus
Mollusca
Native
Chlorophyta
Cryptogenic
Mollusca
Native
Epopella simplex s
Arthropodas
Native
Ericthonius pugnax
Arthropoda
Native
Euchelus cf. ampullus
Mollusca
Native
Euchone limnicola
Annelida
Non-native
Euclavella claviformis
Chordata
Native
Eucoelium mariae
Chordata
Native
Eudendrium aylingae
Cnidaria
Native
Eudendrium capillare
Cnidaria
Native
Eudendrium cf. capillare
Cnidaria
Native
Eudendrium cf. generale
Cnidaria
Native
Eudendrium cf. kirkpatricki
Cnidaria
Native
Enteromorpha intestinalis
Epitonium cf. perplexum
203
Eudendrium glomeratum
Cnidaria
Native
Eudendrium pennycuikae
Cnidaria
Native
Eudistoma laysani
Chordata
Native
Eumarcia fumigata
Mollusca
Native
Eumida cf. sanguinea
Annelida
Native
Eumida fuscolutata
Annelida
Native
Eumida sanguinea
Annelida
Native
Eunice afra punctuata
Annelida
Native
Eunice antennata
Annelida
Native
Eunice australis
Annelida
Native
Eunice bassensis
Annelida
Native
Eunice cf. afra punctuata
Annelida
Native
Eunice cf. australis
Annelida
Native
Eunice cf. bowerbanki
Annelida
Native
Eunice cf. complanata
Annelida
Native
Eunice cf. hirschi
Annelida
Native
Eunice cf. ornata
Annelida
Native
Eunice cf. plicata
Annelida
Native
Eunice complanata
Annelida
Native
Eunice hirschi
Annelida
Native
Eunice laticeps
Annelida
Native
Eunice siciliensis
Annelida
Native
Eunice torresiensis
Annelida
Native
Eunice tubifex
Annelida
Native
Eunice vittata
Annelida
Native
Eupolymnia koorangia
Annelida
Native
Euptilota articulata
Rhodophyta
Native
Euraphia withersi
Arthropoda
Native
Euthelepus cf. marchinbar
Annelida
Native
Fenestrulina mutabilis
Bryozoa
Native
Ficopomatus enigmaticus
Annelida
Non-native
Filellum serratum
Cnidaria
Cryptogenic
Fragum retusum
Mollusca
Native
Arthropoda
Native
Mollusca
Native
Galathea australiensis
Arthropoda
Native
Galeolaria caespitosa
Annelida
Native
Gastrochaena cuneiformis
Mollusca
Native
Gelidium pusillum
Rhodophyta
Non-native
Gnathia biorbis
Fultodromia cf. nodipes
Fulvia tenuicostata
Arthropoda
Native
Gonothyraea loveni
Cnidaria
Non-native
Gracilaria arcuata
Rhodophyta
Native
Gracilaria cf. secundata
Rhodophyta
Native
Chordata
Non-native
Grahamina gymnota
204
Mollusca
Native
Rhodophyta
Native
Bryozoa
Native
Griffithsia monilis
Rhodophyta
Native
Griffithsia subcylindrica
Rhodophyta
Native
Griffithsia teges
Rhodophyta
Native
Gymnangium gracilicaule
Cnidaria
Native
Gymnangium hians
Cnidaria
Native
Gymnangium longirostre
Cnidaria
Native
Halecium cf. lighti
Cnidaria
Native
Halecium cf. tenellum
Cnidaria
Native
Halecium cf. undulatum
Cnidaria
Native
Halecium delicatulum
Cnidaria
Cryptogenic
Halecium fragile
Cnidaria
Native
Halecium sessile
Cnidaria
Native
Halicarcinus innominatus
Arthropoda
Non-native
Halicarcinus ovatus
Arthropoda
Native
Halicarcinus rostratus
Arthropoda
Native
Halimeda cuneata N_Chlorophyta
Chlorophyta
Native
Haliotis cf. conicopora
Mollusca
Native
Haliplanella lineata
Cnidaria
Non-native
Halocynthia dumosa
Chordata
Native
Halopteris buskii
Cnidaria
Native
Granata imbricata
Grateloupia filicina luxurians
Gregarinidra serrata
Cnidaria
Native
Ochrophyta
Native
Cnidaria
Native
Ochrophyta
Native
Mollusca
Native
Rhodophyta
Native
Harmothoe cf. praeclara
Annelida
Native
Harmothoe cf. waahli
Annelida
Native
Harmothoe charlottae
Annelida
Native
Harmothoe dictyophora
Annelida
Native
Harmothoe phillipensis
Annelida
Native
Harmothoe praeclara
Annelida
Native
Harmothoe waahli
Annelida
Native
Hartmeyeria formosa
Chordata
Native
Hebella costata
Cnidaria
Native
Hebellopsis scandens
Cnidaria
Native
Halopteris campanula
Halopteris novaezelandiae
Halopteris plagiocampa
Halopteris ramulosa
Hapalochlaena maculosa
Haraldiophyllum sinuosum
Echinodermata
Native
Helograpsus haswellianus
Arthropoda
Native
Hemiaegina minuta
Arthropoda
Native
Hemitoma subemarginata
Mollusca
Native
Herdmania momus
Chordata
Non-native
Heliocidaris erythrogramma
205
Hermaea evelinemarcusae
Mollusca
Native
Herpetopoma aspersa
Mollusca
Native
Herpetopoma atrata
Mollusca
Native
Herpetopoma rubra
Mollusca
Native
Rhodophyta
Native
Chordata
Native
Arthropoda
Native
Herposiphonia rostrata
Heteroclinus perspicillatus
Heteropanope cf. changensis
Arthropoda
Native
Tracheophyta
Native
Arthropoda
Native
Hiatella arctica
Mollusca
Non-native
Hiatella australis
Mollusca
Native
Hincksia granulosa
Ochrophyta
Non-native
Hincksia sandriana
Ochrophyta
Non-native
Hincksia sordida
Ochrophyta
Native
Heteropanope cf. longipedes
Heterozostera tasmanica
Hexaminius popeiana
Bryozoa
Native
Arthropoda
Native
Hippopetraliella magna
Bryozoa
Native
Hippothoa distans
Bryozoa
Non-native
Echinodermata
Native
Huenia bifurcata
Arthropoda
Native
Hyastenus auctus
Arthropoda
Native
Hyastenus cf. convexus
Arthropoda
Native
Hyastenus convexus
Arthropoda
Native
Hyastenus elatus
Arthropoda
Native
Hyastenus sebae
Arthropoda
Native
Hyatella intestinalis
Porifera
Native
Hyboscolex dicranochaetus
Annelida
Native
Hydrococcus brazieri
Mollusca
Native
Hydroides cf. brachyacanthus
Annelida
Native
Hydroides cf. ezoensis
Annelida
Non-native
Hydroides diramphus
Annelida
Non-native
Hydroides elegans
Annelida
Cryptogenic
Hydroides ezoensis
Annelida
Non-native
Hydroides lunulifera
Annelida
Native
Hydroides minax
Annelida
Native
Hydroides recta
Annelida
Native
Hydroides tambalagamensis
Annelida
Native
Hydroides trivesiculosus
Annelida
Native
Hydroides tuberculatus
Annelida
Native
Hydroides uncinata
Annelida
Native
Rhodophyta
Native
Hyotissa cf. hyotis
Mollusca
Native
Hyotissa hyotis
Mollusca
Native
Hincksinoflustra denticulata
Hippolyte caradina
Holothuria cf. fuscocinerea
Hymenena curdieana
206
Hypnea cervicornis
Rhodophyta
Native
Hypnea cf. spinella
Rhodophyta
Native
Hypnea charoides
Rhodophyta
Native
Hypnea musciformis
Rhodophyta
Non-native
Hypnea ramentacea
Rhodophyta
Native
Hypnea valentiae
Rhodophyta
Native
Mollusca
Native
Iais californica
Arthropoda
Native
Ibla cumingi
Arthropoda
Native
Ibla quadrivalvis
Arthropoda
Native
Idanthyrsus armatus
Annelida
Native
Idanthyrsus australiensis
Annelida
Native
Idiellana pristis
Cnidaria
Native
Inermonephtys cf. palpata
Annelida
Native
Iphione muricata
Annelida
Native
Irus carditoides
Mollusca
Native
Irus crebrelamellatus
Mollusca
Native
Irus crenatus
Mollusca
Native
Irus cumingii
Mollusca
Native
Irus griseus
Mollusca
Native
Irus irus
Mollusca
Native
Isanemonia australis
Cnidaria
Native
IschnoMollusca cf. arbutus
Mollusca
Native
IschnoMollusca virgatus
Mollusca
Native
Isognomon albisoror
Mollusca
Native
Isognomon cf. ephippium
Mollusca
Native
Isognomon cf. isognomon
Mollusca
Native
Isognomon cf. nucleus
Mollusca
Native
Isognomon cf. perna
Mollusca
Native
Isognomon ephippium
Mollusca
Native
Isognomon isognomon
Mollusca
Native
Isognomon nucleus
Mollusca
Native
Isognomon perna
Mollusca
Native
Isolda pulchella
Annelida
Native
Istiblennius meleagris
Chordata
Native
Jania adhaerens
Rhodophyta
Native
Jania verrucosa
Rhodophyta
Native
Annelida
Native
Jassa marmorata
Arthropoda
Non-native
Jassa slatteryi
Hypselodoris obscura
Jasmineira elegans
Arthropoda
Cryptogenic
Jellyella tuberculata
Bryozoa
Cryptogenic
Jujubinus lepidus
Mollusca
Native
Kellia adamsi
Mollusca
Native
Kellia cf. adamsi
Mollusca
Native
207
Kellia cf. yorkensis
Mollusca
Native
Kellia physema
Mollusca
Native
Kellia rotunda
Mollusca
Native
Kellia tumida
Mollusca
Native
Lafoeina amirantensis
Cnidaria
Native
Langerhansia cervantensis
Annelida
Native
Lanice bidewa
Annelida
Native
Lanice cf. bidewa
Annelida
Native
Lanicides attenuata
Annelida
Native
Lanicides cf. fascia
Annelida
Native
Lanicola lobata
Annelida
Native
Lasaea australis
Mollusca
Native
Laurencia arbuscula
Rhodophyta
Native
Laurencia filiformis
Rhodophyta
Native
Laurencia majuscula
Rhodophyta
Native
Lauridromia dehaani
Arthropoda
Native
Leitoscoloplos bifurcatus
Annelida
Native
Lenormandia marginata
Rhodophyta
Native
Leonnates cf. decipens
Annelida
Native
Leonnates cf. stephensoni
Annelida
Native
Leonnates decipens
Annelida
Native
Leonnates jousseaumei
Annelida
Native
Lepidonotus carinulatus
Annelida
Native
Lepidonotus cf. carinulatus
Annelida
Native
Lepidonotus cf. glaucus
Annelida
Native
Lepidonotus glaucus
Annelida
Native
Lepidonotus purpureus
Annelida
Native
Lepidonotus yorkianus
Annelida
Native
Leptochelia cf. dubia
Arthropoda
Native
Leptochelia dubia
Arthropoda
Cryptogenic
Leptograpsus variegatus
Arthropoda
Native
Leptomithrax gaimardii
Arthropoda
Native
Leptomithrax sternocostulatus
Arthropoda
Native
LeptoMollusca badius
Mollusca
Native
LeptoMollusca liratus
Mollusca
Native
LeptoMollusca mathewsianus
Mollusca
Native
Echinodermata
Native
Leucothoe commensalis
Arthropoda
Native
Leucothoe goowera
Arthropoda
Native
Ligia australiensis
Arthropoda
Native
Limaria cf. fragilis
Mollusca
Native
Limaria fragilis
Mollusca
Native
Limaria orientalis
Mollusca
Non-native
Arthropoda
Non-native
Leptosynapta dolabrifera
Limnoria quadripunctata
208
Lissoclinum cf. roseum
Chordata
Native
Lissodendoryx cf. isodictyalis
Porifera
Native
Arthropoda
Native
Lithophaga malaccana
Mollusca
Native
Lithophaga teres
Mollusca
Native
Arthropoda
Native
Litozamia peterdi
Mollusca
Native
Littoraria articulata
Mollusca
Native
Littorina acutispira
Mollusca
Native
Lobopelma microscala
Annelida
Native
Lobophora variegata
Ochrophyta
Native
Lobospira bicuspidata
Ochrophyta
Native
Loimia cf. ingens
Annelida
Native
Loimia ingens
Annelida
Native
Lomentaria monochlamydea
Rhodophyta
Native
Lomis hirta
Lissoporcellana cf. spinuligera
Litocheira bispinosa
Arthropoda
Native
Longicarpus modestus
Annelida
Native
Lopha cristagalli
Mollusca
Native
Rhodophyta
Native
Lovenella chiquitita
Cnidaria
Native
Lumbrineris cf. coccinea
Annelida
Native
Lumbrineris cf. latreilli
Annelida
Native
Lumbrineris cf. tetraura
Annelida
Native
Lumbrineris coccinea
Annelida
Native
Lumbrineris inflata
Annelida
Native
Lumbrineris latreilli
Annelida
Native
Lumbrineris setosa
Annelida
Native
Lysidice cf. natalensis
Annelida
Native
Lysidice collaris
Annelida
Cryptogenic
Lysidice ninetta
Annelida
Native
Lysilla cf. laciniata
Annelida
Native
Lysilla jennacubinae
Annelida
Native
Lysilla laciniata
Annelida
Native
Macrobrachium intermedium
Arthropoda
Native
Macrocystis angustifolia
Ochrophyta
Native
Macromedaeus cf. distinguendus
Arthropoda
Native
Macrophiothrix cf. variabilis
Echinodermata
Native
Macrorhynchia cf. philippina
Cnidaria
Native
Macrorhynchia philippina
Cnidaria
Native
Lophurella periclados
Cnidaria
Native
Macrothamnion cf. secundum
Rhodophyta
Native
Macrothamnion pellucidum
Rhodophyta
Native
Malleus decurtatus
Mollusca
Native
Malleus meridianus
Mollusca
Native
Macrorhynchia phoenicia
209
Maoricolpus roseus
Mollusca
Non-native
Margaretta barbata
Bryozoa
Native
Marphysa sanguinea species complex
Annelida
Native
Megabalanus occator
Arthropoda
Non-native
Megabalanus rosa
Arthropoda
Non-native
Megabalanus tintinnabulum
Arthropoda
Non-native
Melita matilda
Arthropoda
Native
Bryozoa
Cryptogenic
Arthropoda
Native
Bryozoa
Native
Metagoniolithon radiatum
Rhodophyta
Native
Metaprotella cf. haswelliana
Arthropoda
Native
Metavermilia acanthophora
Annelida
Native
Metavermilia cf. acanthophora
Annelida
Native
Microcosmus australis
Chordata
Native
Microcosmus cf. australis
Chordata
Native
Microcosmus cf. squamiger
Chordata
Native
Microcosmus cf. stoloniferus
Chordata
Native
Microcosmus exasperatus
Chordata
Native
Microcosmus helleri
Chordata
Native
Microcosmus pupa
Chordata
Native
Microcosmus squamiger
Chordata
Native
Microcosmus stoloniferus
Chordata
Native
Microporella lunifera
Bryozoa
Native
Mimachlamys asperrima
Mollusca
Native
Mimachlamys australis
Mollusca
Native
Mimachlamys famigerator
Mollusca
Native
Minuspio cirrifera
Annelida
Native
Mitrella cf. eximia
Mollusca
Native
Mitrella cf. lincolnensis
Mollusca
Native
Mitrella lincolnensis
Mollusca
Native
Mitrella semiconvexa
Mollusca
Native
Mitrella tayloriana
Mollusca
Native
Mitrella venulata
Mollusca
Native
Modiolus albicostatus
Mollusca
Native
Modiolus areolatus
Mollusca
Native
Modiolus auriculatus
Mollusca
Native
Modiolus cf. albicostatus
Mollusca
Native
Modiolus cf. areolatus
Mollusca
Native
Modiolus victoriae
Mollusca
Native
Molgula ficus
Chordata
Native
Monia zelandica
Mollusca
Native
Monocorophium acherusicum
Arthropoda
Non-native
Monocorophium insidiosum
Arthropoda
Non-native
Membranipora membranacea
Menaethius monoceros
Menipea roborata
210
Monomyces radiatus
Cnidaria
Native
Monophorus angasi
Mollusca
Native
Monostaechas quadridens
Cnidaria
Native
Monotheca cf. obliqua
Cnidaria
Native
Monotheca compressa
Cnidaria
Native
Monotheca flexuosa
Cnidaria
Native
Monotheca pulchella
Cnidaria
Native
Montfortula rugosa
Mollusca
Native
Mopsella klunzingeri
Cnidaria
Native
Mopsella zimmeri
Cnidaria
Native
Mucropetraliella cf. vultur
Bryozoa
Native
Mucropetraliella ellerii
Bryozoa
Native
Mucropetraliella nodulosa
Bryozoa
Native
Mucropetraliella vultur
Bryozoa
Native
Musculista senhousia
Mollusca
Non-native
Musculus cf. imus
Mollusca
Native
Musculus cf. miranda
Mollusca
Native
Musculus cf. nanus
Mollusca
Native
Musculus chinensis
Mollusca
Native
Musculus cumingianus
Mollusca
Native
Musculus impactus
Mollusca
Native
Musculus nanus
Mollusca
Native
Myriogramme gunniana
Rhodophyta
Native
Myriogramme pulchella
Rhodophyta
Native
Mytilus edulis
Mollusca
Cryptogenic
Mytilus edulis planulatus
Mollusca
Native
Mytilus galloprovincialis
Mollusca
Non-native
Mytilus planulatus
Mollusca
Native
Myxicola infundibulum
Annelida
Non-native
Naineris australis
Annelida
Native
Naineris cf. australis
Annelida
Native
Nannastacus inflatus
Arthropoda
Native
Nassarius burchardi
Mollusca
Native
Nassarius nigellus
Mollusca
Native
Nassarius pauperatus
Mollusca
Native
Nassarius pyrrhus
Mollusca
Native
Arthropoda
Native
Neanthes cf. flindersi
Annelida
Native
Neanthes cf. kerguelensis
Annelida
Native
Neanthes cricognatha
Annelida
Native
Neanthes kerguelensis
Annelida
Native
Neanthes uniseriata
Annelida
Native
Neanthes vaalii
Annelida
Native
Arthropoda
Native
Naxia tumida
Nectocarcinus integrifrons
211
Arthropoda
Native
Nellia oculata
Bryozoa
Native
Nellia tenella
Bryozoa
Native
Nematonereis unicornis
Annelida
Native
Nematonereis unicornis species complex
Annelida
Native
Neoleprea booligal
Annelida
Native
Neorhynchoplax cf. minima
Arthropoda
Native
Neorhynchoplax octagonalis
Nectocarcinus tuberculosus
Arthropoda
Native
Neovermilia cf. globula
Annelida
Native
Neovermilia globula
Annelida
Native
Nephtys australiensis
Annelida
Native
Nephtys longipes
Annelida
Native
Nereis bifida
Annelida
Native
Nereis cf. denhamensis
Annelida
Native
Nereis cockburnensis
Annelida
Native
Nereis denhamensis
Annelida
Native
Nicolea amnis
Annelida
Native
Nicolea cf. amnis
Annelida
Native
Rhodophyta
Native
Nodilittorina praetermissa
Mollusca
Native
Nolella alta
Bryozoa
Native
Notoacmea flammea
Mollusca
Native
Notoacmea petterdi
Mollusca
Native
Notomastus estuarius
Annelida
Native
Notomastus latericeus
Annelida
Native
Notomithrax cf. ursus
Arthropoda
Native
Notomithrax minor
Arthropoda
Native
Notomithrax ursus
Arthropoda
Native
Notophyllum splendens
Annelida
Native
Notoplax addenda
Mollusca
Native
Notopontonia cf. platycheles
Arthropoda
Native
Nymphon molleri
Arthropoda
Native
Obelia angulosa
Cnidaria
Native
Obelia bicuspidata
Cnidaria
Native
Obelia bispinosa
Cnidaria
Native
Obelia cf. dichotoma
Cnidaria
Native
Obelia cf. geniculata
Cnidaria
Native
Obelia cf. longissima
Cnidaria
Native
Obelia dichotoma
Cnidaria
Non-native
Obelia longissima
Cnidaria
Cryptogenic
Odontosyllis australiensis
Annelida
Native
Odostomia occultidens
Mollusca
Native
Oenone fulgida
Annelida
Non-native
Onchidella cf. patelloides
Mollusca
Native
Nizymenia australis
212
Onchidella patelloides
Mollusca
Native
Opercularella humilis
Cnidaria
Native
Opheliidae Travisia
Annelida
Native
Ophiacantha heterotyla
Echinodermata
Native
Ophiacantha pica
Echinodermata
Native
Ophiactis cf. resiliens
Echinodermata
Native
Ophiactis cf. savignyi
Echinodermata
Native
Ophiactis macrolepidota
Echinodermata
Native
Ophiactis resiliens
Echinodermata
Native
Ophiactis savignyi
Echinodermata
Native
Ophiocentrus pilosa
Echinodermata
Native
Ophiocentrus verticillata
Echinodermata
Native
Cnidaria
Native
Ophiomyxa australis
Echinodermata
Native
Ophiothrix caespitosa
Echinodermata
Native
Ophiothrix martensi
Echinodermata
Native
Ophiothrix spongicola
Echinodermata
Native
Ophiothrix vicina
Echinodermata
Native
Orthopyxis caliculata
Cnidaria
Native
Orthopyxis integra
Cnidaria
Native
Orthopyxis mollis
Cnidaria
Native
Ostrea angasi
Mollusca
Native
Oulactis muscosa
Cnidaria
Non-native
Ophiodissa carchesium
Mollusca
Native
Pachycheles sculptus
Arthropoda
Native
Padina elegans
Ochrophyta
Native
Padina sanctae-crucis
Ochrophyta
Native
Pagurus cf. hirtimanus
Arthropoda
Native
Palaemon cf. serenus
Arthropoda
Native
Palaemon serenus
Arthropoda
Native
Palaemonella cf. rotumana
Arthropoda
Native
Palaemonella rotumana
Arthropoda
Native
Paleanotus chrysolepis
Annelida
Native
Palola cf. siciliensis
Annelida
Native
Palola siciliensis
Annelida
Native
Paphies striata
Mollusca
Native
Parablennius tasmanianus
Chordata
Native
Paracalix ambiplica
Cnidaria
Native
Paracerceis sculpta
Arthropoda
Non-native
Paracilicaea gigas
Arthropoda
Native
Paracilicaea septemdentata
Arthropoda
Native
Paradella dianae
Arthropoda
Non-native
Paradella octaphymata
Arthropoda
Native
Paradexamine churinga
Arthropoda
Native
Oxynoe viridis
213
Paradexamine moorhousei
Arthropoda
Native
Paragrapsus gaimardii
Arthropoda
Native
Paragrapsus quadridentatus
Arthropoda
Native
Parahyotissa imbricata
Mollusca
Native
Parahyotissa numisma
Mollusca
Native
Paralepidonotus ampulliferus
Annelida
Native
Paraleucothoe novaehollandiae
Arthropoda
Native
Paranchialina angusta
Arthropoda
Native
Parascyphus simplex
Cnidaria
Native
Parasmittina cf. cheilodon
Bryozoa
Native
Parasmittina cf. delicatula
Bryozoa
Native
Paratanais ignotus
Arthropoda
Native
Parawaldeckia dilkera
Arthropoda
Native
Parawaldeckia yamba
Arthropoda
Native
Paridotea ungulata
Arthropoda
Native
Parthenope cf. longispinus
Arthropoda
Native
Patelloida insignis
Mollusca
Native
Patelloida mufria
Mollusca
Native
Patiriella brevispina
Chordata
Native
Patiriella regularis
Chordata
Non-native
Pecten fumatus
Mollusca
Native
Pennaria disticha
Cnidaria
Non-native
Pennaria wilsoni
Cnidaria
Native
Periclimenes andamanensis
Arthropoda
Native
Periclimenes cf. andamanensis
Arthropoda
Native
Periclimenes cf. elegans
Arthropoda
Native
Periclimenes grandis
Arthropoda
Native
Periclimenes obscurus
Arthropoda
Native
Perinereis amblyodonta
Annelida
Native
Perinereis nigropunctata
Annelida
Native
Perinereis variodentata
Annelida
Native
Perithallia caudata
Ochrophyta
Native
Petrolisthes elongatus
Arthropoda
Non-native
Petrolisthes militaris
Arthropoda
Native
Peyssonnelia capensis
Rhodophyta
Native
Peyssonnelia cf. capensis
Rhodophyta
Native
Phallusia arabica
Chordata
Native
Phallusia barbarica
Chordata
Native
Phallusia julinea
Chordata
Native
Phallusia obesa
Chordata
Native
Phascolosoma annulatum
Sipuncula
Native
Phascolosoma stephensoni
Sipuncula
Native
Phasianella variegata
Mollusca
Native
Phasianotrochus irisodontes
Mollusca
Native
214
Pherusa cf. parmata
Annelida
Native
Pherusa parmata
Annelida
Native
Phialella quadrata
Cnidaria
Non-native
Philippia lutea
Mollusca
Native
Phlyctenanthus australis
Cnidaria
Native
Phycodrys australasica
Rhodophyta
Native
Phyllamphicteis cf. foliata
Annelida
Native
Phyllodoce novaehollandiae
Annelida
Native
Pileolaria cf. roseopigmentata
Annelida
Native
Pilodius miersi
Arthropoda
Native
Pilumnopeus cf. serratifrons
Arthropoda
Native
Pilumnopeus serratifrons
Arthropoda
Non-native
Pilumnus acer
Arthropoda
Native
Pilumnus cf. australis
Arthropoda
Native
Pilumnus cf. longicornis
Arthropoda
Native
Pilumnus cf. minutus
Arthropoda
Non-native
Pilumnus cf. rufopunctatus
Arthropoda
Native
Pilumnus cf. tomentosus
Arthropoda
Native
Pilumnus etheridgei
Arthropoda
Native
Pilumnus fissifrons
Arthropoda
Native
Pilumnus longicornis
Arthropoda
Native
Pilumnus minutus
Arthropoda
Non-native
Pilumnus monilifer
Arthropoda
Native
Pilumnus semilanatus
Arthropoda
Native
Pilumnus terraereginae
Arthropoda
Native
Pilumnus tomentosus
Arthropoda
Native
Pinctada albina
Mollusca
Native
Pinctada cf. sugillata
Mollusca
Native
Pinctada maculata
Mollusca
Native
Pinctada margaritifera
Mollusca
Native
Pinctada sugillata
Mollusca
Native
Mollusca
Native
Pinnotheres hickmani
Arthropoda
Native
Pisidia dispar
Arthropoda
Native
Pisidia gordoni
Arthropoda
Native
Pista australis
Annelida
Native
Pista cf. brevibranchia
Annelida
Native
Pista trunca
Annelida
Native
Pista typha
Annelida
Native
Pinna bicolor
Pistorius bidens
Arthropoda
Native
Plagusia chabrus
Arthropoda
Non-native
Plagusia glabra
Arthropoda
Native
Planopilumnus penicillatus
Arthropoda
Native
Mollusca
Native
Planostrea pestigris
215
Platynereis antipoda
Annelida
Native
Platynereis polyscalma
Annelida
Native
Platysiphonia delicata
Rhodophyta
Non-native
Platythalia quercifolia
Ochrophyta
Native
Plaxiphora albida
Mollusca
Native
Plaxiphora matthewsi
Mollusca
Native
Echinodermata
Native
Plesiocolochirus ignava
Mollusca
Native
Plocamium angustum
Rhodophyta
Native
Plumularia branchiata
Cnidaria
Native
Plumularia caliculata
Cnidaria
Native
Plumularia cf. setacea
Cnidaria
Non-native
Plumularia setacea
Cnidaria
Non-native
Plumularia setaceoides
Cnidaria
Native
Podarke angustifrons
Annelida
Native
Podarkeopsis galangaui
Annelida
Native
Polyandrocarpa australiensis
Chordata
Native
Polyandrocarpa sagamiensis
Chordata
Non-native
Polycarpa aurita
Chordata
Native
Polycarpa biforis
Chordata
Native
Polycarpa cf. obscura
Chordata
Native
Polycarpa cf. olitoria
Chordata
Native
Polycarpa cf. pedunculata
Chordata
Native
Polycarpa contecta
Chordata
Native
Polycarpa nigricans
Chordata
Native
Polycarpa obscura
Chordata
Native
Polycarpa olitoria
Chordata
Native
Polycarpa papillata
Chordata
Native
Polycarpa pedunculata
Chordata
Native
Polycarpa pigmentata
Chordata
Native
Polycarpa stirpes
Chordata
Native
Polycarpa viridis
Chordata
Native
Polycirrus boholensis
Annelida
Native
Polydora cornuta
Annelida
Non-native
Polydora hoplura
Annelida
Cryptogenic
Polydora protuberata
Annelida
Native
Polyonyx cf. obesulus
Arthropoda
Native
Polyonyx obesulus
Arthropoda
Native
Annelida
Native
Pleurobranchaea maculata
Polyophthalmus cf. pictus
Annelida
Native
Polysiphonia blandii
Rhodophyta
Non-native
Polysiphonia brodiei
Rhodophyta
Non-native
Polysiphonia cf. infestans
Rhodophyta
Native
Polysiphonia cf. subtilissima
Rhodophyta
Native
Polyophthalmus pictus
216
Polysiphonia crassiuscula
Rhodophyta
Native
Polysiphonia decipiens
Rhodophyta
Native
Polysiphonia ferulacea
Rhodophyta
Native
Polysiphonia infestans
Rhodophyta
Cryptogenic
Polysiphonia scopulorum
Rhodophyta
Native
Polysiphonia senticulosa
Rhodophyta
Non-native
Polysiphonia subtilissima
Rhodophyta
Non-native
Polysyncraton cf. millepore
Chordata
Native
Polysyncraton rugosum
Chordata
Native
Pomatoceros taeniata
Annelida
Native
Pomatoleios kraussii
Annelida
Native
Pomatostegus stellatus
Annelida
Native
Poricellaria ratoniensis
Bryozoa
Native
Porina tubulifera
Bryozoa
Native
Arthropoda
Native
Potamilla cf. laciniosa
Annelida
Native
Potamilla laciniosa
Annelida
Native
Potamilla neglecta
Annelida
Native
Pretostrea rosacea
Mollusca
Native
Priolepis nuchifasciata
Chordata
Native
Prionospio multipinnulata
Annelida
Native
Proceraea filiformis
Annelida
Native
Proterato lachryma
Mollusca
Native
Protocirrineris chrysoderma
Annelida
Native
Protula cf. palliata
Annelida
Native
Pseudoamphicteis papillosa
Annelida
Native
Pseudobranchiomma cf. orientalis
Annelida
Native
Pseudobranchiomma orientalis
Annelida
Native
Pseudocerceis furculata
Arthropoda
Native
Pseudocerceis trilobata
Arthropoda
Native
Pseudoceros reticularis
Platyhelminthes
Native
Pseudonereis anomala
Annelida
Native
Pseudopolydora cf. kempi
Annelida
Native
Pseudopolydora kempi
Annelida
Cryptogenic
Pseudopotamilla cf. laciniosa
Annelida
Native
Pseudopotamilla laciniosa
Annelida
Native
Pseudopotamilla reniformis
Annelida
Native
Pseudoproclea australis
Annelida
Native
Pteria cf. coturnix
Mollusca
Native
Portunus pelagicus
Annelida
Native
Pterocladia rectangularis
Rhodophyta
Native
Pterosiphonia pennata
Rhodophyta
Native
Pustulostrea cf. tuberculata
Mollusca
Native
Pustulostrea tuberculata
Mollusca
Native
Pterocirrus cf. magalhaensis
217
Pyrene bidentata
Mollusca
Native
Pyrene scripta
Mollusca
Native
Pyrene testudinaria
Mollusca
Native
Pyura australis
Chordata
Native
Pyura cf. robusta
Chordata
Native
Pyura cf. stolonifera
Chordata
Native
Pyura confragosa
Chordata
Native
Pyura elongata
Chordata
Native
Pyura fissa
Chordata
Native
Pyura gibbosa draschii
Chordata
Native
Pyura gibbosa
Chordata
Native
Pyura irregularis
Chordata
Native
Pyura molguloides
Chordata
Native
Pyura robusta
Chordata
Native
Pyura sacciformis
Chordata
Native
Pyura stolonifera
Chordata
Native
Pyura tasmanensis
Chordata
Native
Redigobius macrostoma
Chordata
Native
Reteporella fissa
Bryozoa
Native
Reteporella subimmersa "Fossil"
Bryozoa
Native
Rhabdozoum wilsoni
Bryozoa
Native
Rhinothelepus lobatus
Annelida
Native
Rhodophyta
Native
Rhodoglossum gigartinoides
Chordata
Native
Rhodymenia cf. sonderi
Rhodophyta
Native
Rhodymenia leptophylla
Rhodophyta
Native
Rhodymenia sonderi
Rhodophyta
Native
Rhynchozoon cf. splendens
Bryozoa
Native
Ruditapes largillierti
Mollusca
Non-native
Sabella cf. spallanzanii
Annelida
Native
Sabella spallanzanii
Annelida
Non-native
Sabellate australiensis
Annelida
Native
Sabellate cf. indica
Annelida
Native
Sabellate cf. spectabilis
Annelida
Native
Sabellate indica
Annelida
Native
Saccostrea cucullata
Mollusca
Native
Saccostrea echinata
Mollusca
Native
Saccostrea glomerata
Mollusca
Native
Salmacina australis
Annelida
Native
Salmacis belli
Echinodermata
Native
Salmacis cf. belli
Echinodermata
Native
Sarsia cf. eximia
Cnidaria
Native
Sarsia cf. radiata
Cnidaria
Native
Sarsia eximia
Cnidaria
Cryptogenic
Rhodosoma turcicum
218
Sarsia radiata
Cnidaria
Native
Sassia subdistorta
Mollusca
Native
Savignyella lafontii
Bryozoa
Cryptogenic
Scaeochlamys livida
Mollusca
Native
Schistomeringos loveni
Annelida
Native
Schizophrys aspera
Arthropoda
Native
Schizoporella errata
Bryozoa
Non-native
Bryozoa
Non-native
Rhodophyta
Non-native
Scoloplos cf. novaehollandiae
Annelida
Native
Scoloplos cylindrifer
Annelida
Native
Scoloplos normalis
Annelida
Native
Scoloplos simplex
Annelida
Native
Scruparia ambigua
Bryozoa
Non-native
Scrupocellaria cf. diadema
Bryozoa
Native
Scrupocellaria cf. maderensis
Bryozoa
Native
Scrupocellaria cf. spatulata
Bryozoa
Native
Scrupocellaria ornithorhynchus
Bryozoa
Native
Scyllarides haanii
Arthropoda
Native
Septifer bilocularis
Mollusca
Native
Septifer cf. bilocularis
Mollusca
Native
Serpula cf. jukesii
Annelida
Native
Serpula cf. rubens
Annelida
Native
Serpula cf. vittata
Annelida
Native
Serpula cf. watsoni
Annelida
Native
Serpula jukesii
Annelida
Native
Serpula rubens
Annelida
Native
Sertularella cf. robusta
Cnidaria
Native
Sertularella diaphana
Cnidaria
Native
Sertularella robusta
Cnidaria
Native
Sertularella simplex
Cnidaria
Native
Sertularella tricuspidata
Cnidaria
Native
Sertularia cf. longa
Cnidaria
Native
Sertularia ligulata
Cnidaria
Native
Sertularia orthogonalis
Cnidaria
Cryptogenic
Sertularia stechowi
Cnidaria
Native
Sertularia tenuis
Cnidaria
Native
Sidneioides cf. tamaramae
Chordata
Native
Sinum cf. zonale
Mollusca
Native
Siphonaria diemenensis
Mollusca
Native
Siphonaria funiculata
Mollusca
Native
Siphonaria zelandica
Mollusca
Native
Smittoidea maunganuiensis
Bryozoa
Native
Ochrophyta
Native
Schizoporella unicornis
Schottera nicaeensis
Sphacelaria cf. cirrosa
219
Sphaeroma quoyanum
Arthropoda
Native
Sphaeroma sculpta
Arthropoda
Native
Sphaeroma walkeri
Arthropoda
Non-native
Spirobranchus cf. polytrema
Annelida
Native
Spirobranchus cf. tetraceros
Annelida
Native
Spirobranchus coronatus
Annelida
Native
Spirobranchus tetraceros
Annelida
Native
Spondylus nicobaricus
Mollusca
Native
Spondylus violascens
Mollusca
Native
Sporochnus comosus
Ochrophyta
Native
Spyridia dasyoides
Rhodophyta
Native
Spyridia filamentosa
Rhodophyta
Native
Stavelia cf. subdistorta
Mollusca
Native
Stavelia subdistorta
Mollusca
Native
Stelletta cf. clavosa
Porifera
Native
Stenothoe cf. marina
Arthropoda
Native
Stenothoe miersi
Arthropoda
Native
Stenothoe valida
Arthropoda
Cryptogenic
Stephanollona orbicularis
Bryozoa
Native
Stereotheca elongata
Cnidaria
Native
Sthenelais pettiboneae
Annelida
Native
Echinodermata
Native
Stictosiphonia intricata
Rhodophyta
Native
Stimdromia lateralis
Stichopus mollis
Arthropoda
Native
Stolonica australis
Chordata
Native
Stomatella impertusa
Mollusca
Native
Streblosoma acymatum
Annelida
Native
Streblosoma cf. atos
Annelida
Native
Streblosoma cf. latitudinum
Annelida
Native
Streblosoma latitudinum
Annelida
Native
Striatobalanus amaryllis
Arthropoda
Native
Striatobalanus cf. amaryllis
Arthropoda
Native
Striostrea cf. mytiloides
Mollusca
Native
Striostrea mytiloides
Mollusca
Native
Strombiformis topaziaca
Mollusca
Native
Styela canopus
Chordata
Non-native
Styela clava
Chordata
Non-native
Styela plicata
Chordata
Non-native
Stylactis betkensis
Cnidaria
Native
Annelida
Native
Arthropoda
Native
Subadyte pellucida
Annelida
Native
Suberites cupuloides
Porifera
Native
Sycozoa brevicauda
Chordata
Native
Stylomma palmatum
Stylopallene cheilorhynchus
220
Sycozoa cerebriformis
Chordata
Native
Syllidia armata
Annelida
Native
Syllis australiensis
Annelida
Native
Syllis gracilis australiensis
Annelida
Native
Syllis gracilis
Annelida
Non-native
Symplectoscyphus indivisus
Cnidaria
Native
Synalpheus cf. bituberculatus
Arthropoda
Native
Synalpheus cf. neptunus
Arthropoda
Native
Synalpheus cf. streptodactylus
Arthropoda
Native
Synalpheus cf. tumidomanus
Arthropoda
Native
Synalpheus hastilicrassus
Arthropoda
Native
Synalpheus neomeris
Arthropoda
Native
Synalpheus neptunus
Arthropoda
Native
Synalpheus streptodactylus
Arthropoda
Native
Synalpheus tumidomanus
Arthropoda
Native
Synnotum aegyptiacum
Bryozoa
Native
Synnotum cf. aegyptiacum
Bryozoa
Native
Synnotum cf. pembaense
Bryozoa
Native
SypharoMollusca pellisserpentis
Mollusca
Native
Arthropoda
Native
Tellina albinella
Mollusca
Native
Tellina botanica
Mollusca
Native
Tellina cf. tenuilamellata
Mollusca
Native
Tanais cf. dulongi
Mollusca
Native
Echinodermata
Native
Terebella cf. ehrenbergi
Annelida
Native
Terebella tantabiddycreekensis
Annelida
Native
Tesseropora cf. wireni
Arthropoda
Non-native
Tesseropora rosea
Arthropoda
Native
Tethya communis
Porifera
Native
Tetraclita coerulescens
Arthropoda
Native
Tetraclita squamosa
Arthropoda
Native
Tetraclitella purpurascens
Arthropoda
Native
Thais echinata
Mollusca
Native
Thais orbita
Mollusca
Native
Thalamita cf. spinimana
Arthropoda
Native
Thalamita danae
Arthropoda
Native
Bryozoa
Non-native
Rhodophyta
Native
Thelepus alatus
Annelida
Native
Thelepus australiensis
Annelida
Native
Thelepus cf. alatus
Annelida
Native
Thelepus cf. boja
Annelida
Native
Thelepus cf. extensus
Annelida
Native
Tellina parvitas
Temnopleurus michaelseni
Thalamoporella gothica
Thamnoclonium dichotomum
221
Thelepus extensus
Annelida
Native
Thelepus robustus
Annelida
Native
Themiste cf. fusca
Sipuncula
Native
Theora cf. lubrica I_Mollusca
Mollusca
Non-native
Theora lubrica I_Mollusca
Mollusca
Non-native
Thor amboinensis
Arthropoda
Native
Thor paschalis
Arthropoda
Native
Thormora argus
Annelida
Native
Thormora jukesii
Annelida
Native
Thusaenys irami
Arthropoda
Native
Timoclea cardioides
Mollusca
Native
Tosia australis
Chordata
Native
Trachinops caudimaculatus
Chordata
Native
Tricellaria aculeata
Bryozoa
Native
Tricellaria cf. inopinata
Bryozoa
Non-native
Tricellaria inopinata
Bryozoa
Cryptogenic
Tricellaria occidentalis
Bryozoa
Non-native
Tricellaria porteri
Bryozoa
Cryptogenic
Trichomusculus barbatus
Mollusca
Native
Trichomusculus cf. barbatus
Mollusca
Native
Trichomya hirsutus
Mollusca
Native
Tricolia tomlini
Mollusca
Native
Tridentiger trigonocephalus
Chordata
Non-native
Trinorfolkia clarkei N_bony Chordata
Chordata
Native
Triphyllozoon cf. moniliferum
Bryozoa
Native
Triphyllozoon moniliferum
Bryozoa
Native
Triphyllozoon munitum
Bryozoa
Native
Trypanosyllis gigantea
Annelida
Native
Trypanosyllis taeniformis
Annelida
Native
Trypostega venusta
Bryozoa
Native
Tubastrea coccinea
Stony Cnidaria
Native
Tubastrea diaphana
Stony Cnidaria
Native
Tubastrea micranthus
Stony Cnidaria
Native
Tubularia cf. crocea
Cnidaria
Non-native
Tubularia crocea
Cnidaria
Non-native
Tubulipora cf. maragitacea "Fossil"
Bryozoa
Native
Tugali cicatricosa
Mollusca
Native
Tugali parmophoidea
Mollusca
Native
Turritopsis cf. nutricula
Cnidaria
Native
Turritopsis nutricula
Cnidaria
Non-native
Typosyllis armillaris
Annelida
Native
Typosyllis cervantensis
Annelida
Native
Typosyllis cf. armillaris
Annelida
Native
Typosyllis cf. cervantensis
Annelida
Native
222
Typosyllis cf. crassicirrata
Annelida
Native
Typosyllis cf. gerhardi
Annelida
Native
Typosyllis hyalina
Annelida
Native
Typosyllis lutea
Annelida
Native
Typosyllis pseudopapillata
Annelida
Native
Typosyllis raygeorgei
Annelida
Native
Ulva australis
Chlorophyta
Native
Ulva lactuca
Chlorophyta
Cryptogenic
Ulva laetevirens
Chlorophyta
Native
Ulva rigida
Chlorophyta
Cryptogenic
Ulva stenophylla
Chlorophyta
Non-native
Undaria pinnatifida
Ochrophyta
Non-native
Uniophora dyscrita
Chordata
Native
Uniophora granifera
Chordata
Native
Venerupis anomala
Mollusca
Native
Venerupis cf. anomala
Mollusca
Native
Venerupis galactites
Mollusca
Native
Venerupis iridescens
Mollusca
Native
Vermiliopsis cf. infundibilum
Annelida
Native
Vulsella spongiarum
Mollusca
Native
Vulsella vulsella
Mollusca
Native
Wallucina assimilis
Mollusca
Native
Watersipora arcuata
Bryozoa
Non-native
Watersipora subtorquata
Bryozoa
Non-native
Xenostrobus cf. inconstans
Mollusca
Native
Xenostrobus cf. pulex
Mollusca
Native
Xenostrobus inconstans
Mollusca
Native
Xenostrobus pulex
Mollusca
Native
Xenostrobus securis
Mollusca
Native
Yoldia lata
Mollusca
Native
Zonaria crenata
Ochrophyta
Native
Zonaria turneriana
Ochrophyta
Native
Bryozoa
Non-native
Arthropoda
Native
Echinodermata
Native
Zoobotryon verticillatum
Zuzara venosa
Zygometra cf. microdiscus
223
Table A3. Species identified in the 15 port surveys around New Zealand and species status –
native, non-native and cryptogenic species.
Species
Phyla
Species status
Acanthochitona violacea
Mollusca
Native
Acanthochitona zelandica
Mollusca
Native
Acanthoclinus fuscus
Chordata
Native
Acanthoclinus littoreus
Chordata
Native
Achelia assimilis
Arthropoda
Native
Acontiostoma tuberculata
Arthropoda
Native
Acraspedanthus elongatus
Echinodermata
Native
Annelida
Native
Acrosorium decumbens
Rhodophyta
Native
Adamsiella chauvinii
Acrocirrus trisectus
Rhodophyta
Native
Adocia cf.parietalioides
Porifera
Native
Adocia cf.venustina
Porifera
Native
Aetea australis
Bryozoa
Cryptogenic
Aetea truncata
Bryozoa
Native
Echinodermata
Native
Alloiodoris lanuginata
Mollusca
Native
Allostichaster insignis
Chordata
Native
Aiptasiomorpha minima
Chordata
Native
Alpheus novaezealandiae
Arthropoda
Native
Alpheus socialis
Arthropoda
Native
Amaryllis macrophthalma
Arthropoda
Native
Amphilochus filidactylus
Arthropoda
Native
Amphipholis squamata
Echinodermata
Native
Amphisbetia bispinosa
Hydroid
Native
Amphisbetia fasciculata
Hydroid
Native
Allostichaster polyplax
Amphisbetia minima
Hydroid
Native
Anguinella palmata
Bryozoa
Non-native
Anisoiphimedia haurakiensis
Arthropoda
Native
Anotrichium crinitum
Rhodophyta
Native
Antithamnion applicitum
Rhodophyta
Native
Antithamnion pectinatum
Rhodophyta
Native
Antithamnionella adnata
Rhodophyta
Native
Aora maculata
Arthropoda
Native
Aora typica
Arthropoda
Native
Aplidium adamsi
Chordata
Native
Aplidium benhami
Chordata
Native
Aplidium knoxi
Chordata
Native
Aplidium phortax
Chordata
Cryptogenic
Apocorophium acutum
Arthropoda
Non-native
Apoglossum montagneanum
Rhodophyta
Native
224
Rhodophyta
Native
Archidoris nanula
Mollusca
Native
Archidoris wellingtonensis
Mollusca
Native
Armandia maculata
Annelida
Native
Ascidiella aspersa
Chordata
Non-native
Asteracmea suteri
Mollusca
Native
Asterocarpa cerea
Chordata
Cryptogenic
Asterocarpa coerulea
Chordata
Native
Aulacomya atra maoriana
Mollusca
Native
Austrolittorina antipodum
Mollusca
Native
Austrominius modestus
Arthropoda
Native
Balanus trigonus
Apoglossum oppositifolium
Arthropoda
Cryptogenic
Beania discodermiae
Bryozoa
Native
Beania magellanica
Bryozoa
Native
Beania plurispinosa
Bryozoa
Native
Arthropoda
Native
Bicellariella ciliata
Bryozoa
Native
Biemna rhabderemioides
Porifera
Native
Bitectipora mucronifera
Bryozoa
Native
Bitectipora rostrata
Bryozoa
Native
Boccardia acus
Annelida
Native
Boccardia chilensis
Annelida
Native
Boccardia knoxi
Annelida
Native
Boccardia lamellata
Annelida
Native
Boccardia otakouica
Annelida
Native
Boccardia syrtis
Annelida
Native
Borniola reniformis
Mollusca
Native
Bostrychia harveyi
Rhodophyta
Native
Bostrychia moritziana
Rhodophyta
Native
Bostrychia tenuissima
Rhodophyta
Native
Botrylliodes leachii
Chordata
Cryptogenic
Botryllus stewartensis
Chordata
Native
Bougainvillia muscus
Hydroid
Cryptogenic
Branchiomma curta
Annelida
Native
Brongniartella australis
Rhodophyta
Native
Bryopsis vestita
Betaeopsis aequimanus
Chlorophyta
Native
Buccinulum linea
Mollusca
Native
Buccinulum vittatum
Mollusca
Native
Bugula dentata
Bryozoa
Native
Bugula flabellata
Bryozoa
Non-native
Bugula neritina
Bryozoa
Non-native
Bugula stolonifera
Bryozoa
Non-native
Caberea rostrata
Bryozoa
Native
Caberea zelandica
Bryozoa
Native
225
Cabestana spengleri
Mollusca
Native
Cadlina willani
Mollusca
Native
Calliostoma tigris
Mollusca
Native
Callipallene novaezealandiae
Arthropoda
Native
Callophyllis calliblepharoides
Rhodophyta
Native
Callophyllis variegata
Rhodophyta
Native
Callyspongia cf. bathami
Porifera
Native
Callyspongia cf.irregularis
Porifera
Native
Callyspongia diffusa
Porifera
Cryptogenic
Callyspongia ramosa
Porifera
Cryptogenic
Callyspongia stellata
Porifera
Native
Caloglossa leprieurii
Rhodophyta
Native
Cancer amphioetus
Arthropoda
Non-native
Cancer gibbosulus
Arthropoda
Non-native
Cancer novaezelandiae
Arthropoda
Native
Caprella equilibra
Arthropoda
Native
Caprella mutica
Arthropoda
Non-native
Caprellina longicollis
Arthropoda
Native
Capreolia implexa
Rhodophyta
Native
Carazziella quadricirrata
Annelida
Native
Carpophyllum flexuosum
Ochrophyta
Native
Caulerpa brownii
Chlorophyta
Native
Cellana ornata
Mollusca
Native
Cellaria tenuirostris
Bryozoa
Native
Celleporaria nodulosa
Bryozoa
Non-native
Celleporella delta
Bryozoa
Native
Celleporella tongima
Bryozoa
Native
Celleporina proximalis
Bryozoa
Native
Ceradocopsis carnleyi
Arthropoda
Native
Ceramium aff. Apiculatum
Rhodophyta
Native
Ceramium apiculatum
Rhodophyta
Native
Ceramium flaccidum
Rhodophyta
Native
Ceramium rubrum
Rhodophyta
Native
Ceramium vestitum
Rhodophyta
Native
Chaemosipho columna
Arthropoda
Native
Chaperia granulosa
Bryozoa
Native
Chaperiopsis cervicornis
Bryozoa
Native
Chelonaplysilla cf.violacea
Porifera
Cryptogenic
Chiastosella watersi
Bryozoa
Native
Rhodophyta
Native
Chondropsis topsentii
Porifera
Non-native
Chromodoris aureomarginata
Mollusca
Native
Arthropoda
Native
Chordata
Non-native
Chondracanthus chapmanii
Cilicaea canaliculata
Ciona intestinalis
226
Cirolana kokoru
Arthropoda
Native
Cirolana quechso
Arthropoda
Native
Cladophora feredayi
Chlorophyta
Native
Cladophoropsis herpestica
Chlorophyta
Native
Cladostephus spongiosus
Ochrophyta
Native
Clathria (Isociella) cf. incrustans
Porifera
Native
Clathria (Microciona) dendyi
Porifera
Native
Clathria (Microciona)coccinea
Porifera
Native
Clathria cf.lissosclera
Porifera
Native
Clathria cf.terraenovae
Porifera
Native
Clavisyllis alternata
Annelida
Native
Arthropoda
Native
Cliona celata
Porifera
Non-native
Clytia elongata
Hydroid
Native
Clytia hemisphaerica
Hydroid
Cryptogenic
Cnemidocarpa bicornuta
Chordata
Native
Cnemidocarpa nisiotus
Chordata
Native
Cnemidocarpa otagoensis
Chordata
Native
Cnemidocarpa regalis
Chordata
Native
Colomastix magnirama
Arthropoda
Native
Colomastix subcastellata
Arthropoda
Native
Cominella glandiformis
Mollusca
Native
Cominella quoyana
Mollusca
Native
Conopeum seurati
Bryozoa
Non-native
Cookia sulcata
Mollusca
Native
Rhodophyta
Cryptogenic
Chordata
Cryptogenic
Echinodermata
Cryptogenic
Chordata
Native
Arthropoda
Non-native
Crassimarginatella fossa
Bryozoa
Native
Crassostrea gigas
Mollusca
Non-native
Crella (Pytheas) incrustans
Porifera
Cryptogenic
Crella (Pytheas)affinis
Porifera
Native
Crepidacantha crinispina
Bryozoa
Native
Crisia tenuis
Bryozoa
Native
Cryptogenicconchus porosus
Mollusca
Native
Cryptogenicsula pallasiana
Bryozoa
Non-native
Ochrophyta
Non-native
Bryozoa
Non-native
Dasya collabens
Rhodophyta
Native
Dasya subtilis
Rhodophyta
Native
Delesserian epiphytes
Rhodophyta
Native
Dellichthys morelandi
Chordata
Native
Cleantis tubicola
Coralline (Melobesia)
Corella eumyota
Corynactis australis
Coscinasterias muricata
Crassicorophium bonnellii
Cutleria multifida
Cyclicopora longipora
227
Demonax aberrans
Annelida
Native
Dendrodoris citrina
Mollusca
Native
Desmacella ambigua
Porifera
Native
Ochrophyta
Native
Echinodermata
Native
Dicithais orbita
Mollusca
Native
Dictyociona cf.atoxa C2_Porifera
Porifera
Cryptogenic
Dictyodendrilla dendyi
Porifera
Native
Dictyota dichotoma
Ochrophyta
Native
Didemnum incanum
Chordata
Cryptogenic
Didemnum vexillum
Chordata
Cryptogenic
Diplosoma listerianum
Chordata
Cryptogenic
Dipolydora armata
Annelida
Non-native
Dipolydora flava
Annelida
Non-native
Dodecaceria berkeleyi
Annelida
Native
Dorvillea australiensis
Annelida
Native
Dromia wilsoni
Arthropoda
Cryptogenic
Ecklonia radiata
Ochrophyta
Native
Bryozoa
Non-native
Endarachne binghamiae
Ochrophyta
Native
Epopella plicata
Arthropoda
Native
Ericthonius pugnax
Arthropoda
Non-native
Erythroglossum undulatissimum
Rhodophyta
Native
Escharoides angela
Bryozoa
Native
Escharoides excavata
Bryozoa
Native
Eudendrium capillare
Hydroid
Non-native
Eudendrium generale
Hydroid
Non-native
Eulalia bilineata
Annelida
Cryptogenic
Eulalia capensis
Annelida
Native
Eulalia microphylla
Annelida
Native
Eunice australis
Annelida
Native
Euplacella communis
Porifera
Cryptogenic
Euryspongia cf. arenaria
Porifera
Native
Eurystomella foraminigera
Bryozoa
Native
Eusiroides monoculoides
Arthropoda
Native
Ficopomatus enigmaticus
Annelida
Non-native
Filellum serpens
Hydroid
Non-native
Filograna implexa
Annelida
Native
Flabelligera affinis
Annelida
Native
Forsterygion lapillum
Chordata
Native
Galeolaria hystrix
Annelida
Native
Galeopsis porcellanicus
Bryozoa
Native
Gammaropsis chiltoni
Arthropoda
Native
Gammaropsis dentifera
Arthropoda
Native
Desmarestia ligulata
Diadumene neozelandica
Electra tenella
228
Gammaropsis haswelli
Arthropoda
Native
Gammaropsis longimana
Arthropoda
Native
Gammaropsis typica
Arthropoda
Native
Gigartina atropurpurea
Rhodophyta
Native
Gloiocladia saccata
Rhodophyta
Native
Glossophora kunthii
Ochrophyta
Native
Annelida
Native
Gondogeneia danai
Arthropoda
Native
Gracilaria truncata
Rhodophyta
Native
Grahamina capito
Chordata
Native
Grahamina gymnota
Chordata
Native
Glycera benhami
Porifera
Non-native
Griffithsia antarctica
Rhodophyta
Native
Griffithsia crassiuscula
Rhodophyta
Non-native
Griffithsia teges
Rhodophyta
Cryptogenic
Halecium corrrugatissimum
Hydroid
Native
Halecium sessile
Hydroid
Cryptogenic
Halicarcinus cookii
Arthropoda
Native
Halicarcinus innominatus
Arthropoda
Native
Halicarcinus tongi
Arthropoda
Native
Halicarcinus varius
Arthropoda
Native
Halicarcinus whitei
Arthropoda
Native
Halichondria panicea
Porifera
Cryptogenic
Haliclona cf.isodictyale
Porifera
Native
Haliclona cf.punctata
Porifera
Native
Haliclona cf.tenacior
Porifera
Native
Haliclona glabra
Porifera
Native
Haliclona heterofibrosa
Porifera
Cryptogenic
Haliclona maxima
Porifera
Native
Haliclona stelliderma
Porifera
Native
Halimena aoteoroa
Arthropoda
Native
Haliplanella lineata
Echinodermata
Non-native
Halisarca dujardini
Porifera
Non-native
Haplocheira barbimana
Arthropoda
Native
Haplosyllis spongicola
Annelida
Native
Harmothoe macrolepidota
Annelida
Native
Hebellopsis scandens
Hydroid
Native
Helice crassa
Arthropoda
Native
Heterosiphonia concinna
Rhodophyta
Native
Heterosiphonia squarrosa
Rhodophyta
Native
Mollusca
Native
Hincksia mitchelliae
Ochrophyta
Native
Hippolyte bifidirostris
Arthropoda
Native
Hippolyte multicolorata
Arthropoda
Native
Grantessa intusarticulata
Hiatella arctica
229
Porifera
Native
Arthropoda
Native
Hyboscolex longiseta
Annelida
Native
Hydroides elegans
Annelida
Non-native
Hydroides ezoensis
Annelida
Non-native
Hymenena curdieana
Rhodophyta
Cryptogenic
Hymenena variolosa
Rhodophyta
Native
Porifera
Cryptogenic
Hymenosoma depressum
Arthropoda
Native
Hypsistozoa fasmeriana
Chordata
Native
Iophon proximum
Porifera
Cryptogenic
Ircinia akaroa
Porifera
Native
Irus reflexus
Mollusca
Native
Ischyrocerus longimanus
Arthropoda
Native
Ischyromene cordiforaminalis
Arthropoda
Native
Jassa marmorata
Arthropoda
Non-native
Jassa slatteryi
Arthropoda
Non-native
Jassa staudei
Arthropoda
Non-native
Jasus edwardsi
Arthropoda
Native
Joeropsis neozelandica
Homaxinella erecta
Hyale rubra
Hymeniacidon perleve
Arthropoda
Cryptogenic
Kellia cycladiformis
Mollusca
Native
Lafoeina amirantensis
Hydroid
Non-native
Lamellaria cerebroides
Mollusca
Native
Lamellaria ophione
Mollusca
Native
Lasaea hinemoa
Mollusca
Native
Lepidastheniella comma
Annelida
Native
Lepidonotus banksi
Annelida
Native
Lepidonotus fiordlandica
Annelida
Native
Lepidonotus jacksoni
Annelida
Native
Lepidonotus polychromus
Annelida
Native
Leptograpsus variegatus
Arthropoda
Native
Leptomya retiaria
Mollusca
Native
Leuconopsis obsoleta
Mollusca
Native
Leucosolenia cf. discoveryi
Porifera
Non-native
Leucothoe trailli
Arthropoda
Native
Liljeborgia akaroica
Arthropoda
Native
Liljeborgia barhami
Arthropoda
Native
Liljeborgia hansoni
Arthropoda
Native
Limaria orientalis
Mollusca
Non-native
Lissoclinum notti
Chordata
Native
Lissodendoryx isodictyalis
Porifera
Cryptogenic
Lomentaria umbellata
Rhodophyta
Native
Lophopagurus (L.)thompsoni
Arthropoda
Native
Lophopagurus (Lophopagurus)pumilus
Arthropoda
Native
230
Lophothamnion hirtum
Rhodophyta
Native
Lumbricalus aotearoae
Annelida
Native
Lumbrineris sphaerocephala
Annelida
Native
Lysidice ninetta
Annelida
Native
Macroclymenella stewartensis
Annelida
Native
Arthropoda
Native
Maoricrypta costata
Mollusca
Native
Marphysa capensis
Annelida
Native
Marphysa unibranchiata
Annelida
Native
Arthropoda
Native
Annelida
Native
Mallacoota subcarinata
Megabalanus tintinnabulum linzei
Megalomma kaikourense
Annelida
Native
Melita festiva
Arthropoda
Native
Melita inaequistylis
Arthropoda
Native
Chordata
Native
Arthropoda
Native
Mollusca
Native
Rhodophyta
Native
Microcosmus australis
Chordata
Native
Microcosmus hirsutus
Chordata
Native
Microcosmus squamiger
Chordata
Cryptogenic
Microporella agonistes
Bryozoa
Native
Microporella speculum
Bryozoa
Native
Microzonia velutina
Ochrophyta
Native
Modiolarca impacta
Mollusca
Native
Modiolus areolatus
Mollusca
Native
Molgula amokurae
Chordata
Native
Megalomma suspiciens
Meridiastra mortenseni
Mesanthura affinis
Micrelenchus tenebrosus
Microcladia novae-zelandiae
Chordata
Native
Monocorophium acherusicum
Arthropoda
Non-native
Monocorophium sextonae
Arthropoda
Non-native
Monocorophium sp. aff. M. insidiosum
Arthropoda
Cryptogenic
Monotheca flexuosa
Hydroid
Native
Musculista senhousia
Mollusca
Non-native
Mycale (Carmia) hentscheli
Porifera
Native
Mycale (Carmia) tasmani
Porifera
Native
Myriogramme denticulata
Rhodophyta
Native
Myrionema strangulans
Ochrophyta
Native
Mytilus galloprovincialis
Mollusca
Cryptogenic
Natatolana rossi
Arthropoda
Native
Nauticaris marionis
Arthropoda
Native
Neanthes cricognatha
Annelida
Native
Neanthes kerguelensis
Annelida
Native
Neastacilla aff. Tuberculata
Arthropoda
Native
Neohymenicus pubescens
Arthropoda
Native
Molgula mortenseni
231
Neoleprea papilla
Annelida
Native
Neosabellaria kaiparaensis
Annelida
Native
Neovermilia sphaeropomatus
Annelida
Native
Nereiphylla castanea
Annelida
Native
Nereis falcaria
Annelida
Native
Nicolea armilla
Annelida
Native
Nicolea maxima
Annelida
Native
Notoacmea helmsi
Mollusca
Native
Notoacmea parviconoidea
Mollusca
Native
Notobalanus vestitus
Arthropoda
Native
Notomegabalanus decorus
Arthropoda
Native
Notomithrax minor
Arthropoda
Native
Notomithrax peronii
Arthropoda
Native
Notomithrax ursus
Arthropoda
Native
Obelia bidentata
Hydroid
Cryptogenic
Obelia dichotoma
Hydroid
Cryptogenic
Obelia geniculata
Hydroid
Native
Obelia longissima
Hydroid
Non-native
Onchidella nigricans
Mollusca
Native
Onithochiton neglectus
Mollusca
Native
Opercularella humilis
Hydroid
Native
Ophiactis resiliens
Echinodermata
Native
Ophiocentrus novaezealandiae
Echinodermata
Native
Annelida
Native
Echinodermata
Native
Porifera
Native
Orchomene aahu
Arthropoda
Native
Orchomene sp. Aff. O. aahu
Ophiodromus angustifrons
Ophionereis fasciata
Ophlitospongia reticulata
Arthropoda
Cryptogenic
Ostrea aupouria
Mollusca
Native
Ostrea chilensis
Mollusca
Native
Paguristes setosus
Arthropoda
Native
Pagurus novizealandiae
Arthropoda
Native
Pagurus traversi
Arthropoda
Native
Palaemon affinis
Arthropoda
Native
Pallenopsis obliqua
Arthropoda
Native
Paradexamine pacifica
Arthropoda
Native
Annelida
Native
Arthropoda
Native
Hydroid
Native
Parawaldeckia angusta
Arthropoda
Native
Parawaldeckia sp. aff. Angusta
Arthropoda
Cryptogenic
Parawaldeckia sp. aff. P. karaka
Arthropoda
Cryptogenic
Parawaldeckia sp. aff. P. stephenseni
Arthropoda
Cryptogenic
Parawaldeckia stephenseni
Arthropoda
Native
Paraidanthyrsus quadricornis
Paranthura cf. flagellata
Parascyphus simplex
232
Parawaldeckia vesca
Arthropoda
Native
Parorchestia tenuis
Arthropoda
Native
Patelloida corticata
Mollusca
Native
Patiriella mortenseni
Chordata
Native
Patiriella oliveri
Chordata
Cryptogenic
Patiriella regularis
Chordata
Native
Pectinaria australis
Annelida
Native
Peltopes peninsulae
Arthropoda
Native
Pennaria disticha
Hydroid
Non-native
Pentagonaster pulchellus
Chordata
Native
Periclimenes yaldwyni
Arthropoda
Native
Perinereis amblyodonta
Annelida
Native
Perinereis camiguinoides
Annelida
Native
Perinereis pseudocamiguina
Annelida
Native
Perinereis vallata
Annelida
Native
Perna canaliculus
Mollusca
Native
Petrocheles spinosus
Arthropoda
Native
Petrolisthes elongatus
Arthropoda
Native
Petrolisthes novaezelandiae
Arthropoda
Native
Pherusa parmata
Annelida
Native
Phialella quadrata
Hydroid
Cryptogenic
Phorbas cf.anchorata
Porifera
Native
Phorbas fulva
Porifera
Native
Phycodrys quercifolia
Rhodophyta
Native
Pilumnopeus serratifrons
Arthropoda
Cryptogenic
Pilumnus lumpinus
Arthropoda
Native
Pilumnus novaezealandiae
Arthropoda
Native
Pinnotheres atrinocola
Arthropoda
Native
Pinnotheres novaezelandiae
Arthropoda
Native
Annelida
Native
Arthropoda
Cryptogenic
Plakina monolopha
Porifera
Cryptogenic
Plakina trilopha
Porifera
Cryptogenic
Plaxiphora caelata
Mollusca
Native
Plaxiphora obtecta
Mollusca
Native
Pleurobranchaea maculata
Mollusca
Native
Plocamia novizelanicum
Porifera
Native
Plocamium angustum
Rhodophyta
Native
Plocamium cartilagineum
Rhodophyta
Native
Plocamium cirrhosum
Rhodophyta
Native
Plocamium leptophyllum
Rhodophyta
Native
Plocamium microcladioides
Rhodophyta
Native
Plumularia brachiata
Hydroid
Native
Plumularia setacea
Hydroid
Cryptogenic
Pista pegma
Plagusia chabrus
233
Plumularia setaceoides
Hydroid
Native
Plumularia spirocladia
Hydroid
Native
Podocerus cristatus
Arthropoda
Native
Podocerus karu
Arthropoda
Native
Podocerus manawatu
Arthropoda
Native
Podocerus wanganui
Arthropoda
Native
Pododesmus zelandicus
Mollusca
Native
Polycarpa pegasus
Chordata
Native
Polycera hedgpathi
Mollusca
Cryptogenic
Polycheria obtusa
Arthropoda
Native
Polycheria sp. aff. P. obtusa
Arthropoda
Cryptogenic
Polyclinum sluteri
Chordata
Native
Polydora hoplura
Annelida
Non-native
Polysiphonia abscissoides
Rhodophyta
Native
Polysiphonia brodiaei
Rhodophyta
Non-native
Polysiphonia sertularioides
Rhodophyta
Non-native
Polysiphonia subtilissima
Rhodophyta
Non-native
Chordata
Native
Pontophilus australis
Arthropoda
Native
Pratulum pulchellum
Mollusca
Native
Proscoloplos bondi
Annelida
Native
Protocirrineris nuchalis
Annelida
Native
Psammoclema cf. crassum
Porifera
Non-native
Pseudaxinella australis
Porifera
Native
Pseudophycis breviuscula
Chordata
Native
Pseudopista rostrata
Annelida
Native
Pseudopolydora paucibranchiata
Annelida
Non-native
Pseudopotamilla alba
Annelida
Native
Pseudopotamilla laciniosa
Annelida
Native
Arthropoda
Native
Pseudosuberites sulcatus
Porifera
Cryptogenic
Pterocirrus brevicornis
Annelida
Native
Pterothamnion simile
Rhodophyta
Native
Pyromaia tuberculata
Arthropoda
Non-native
Pyura cancellata
Chordata
Native
Pyura carnea
Chordata
Native
Pyura lutea
Chordata
Native
Pyura pachydermatina
Chordata
Native
Pyura picta
Chordata
Native
Pyura pulla
Chordata
Native
Pyura rugata
Chordata
Native
Pyura spinosissima
Chordata
Native
Pyura subuculata
Chordata
Native
Pyura suteri
Chordata
Native
Polyzoa reticulata
Pseudosphaeroma campbellense
234
Pyura trita
Chordata
Native
Ranella australasia
Mollusca
Native
Rhizoclonium implexum
Chlorophyta
Native
Rhodophyllis centrocarpa
Rhodophyta
Native
Rhodophyllis lacerata
Rhodophyta
Cryptogenic
Rhodymenia aff.dichotoma
Rhodophyta
Cryptogenic
Rhodymenia foliifera
Rhodophyta
Native
Rhodymenia leptophylla
Rhodophyta
Native
Rhodymenia linearis
Rhodophyta
Native
Rhodymenia obtusa
Rhodophyta
Native
Rhynchozoon larreyi
Bryozoa
Cryptogenic
Rhyssoplax aerea
Mollusca
Native
Risellopsis varia
Mollusca
Native
Romanchella perrieri
Annelida
Native
Ruditapes largillierti
Mollusca
Native
Rynkatorpa uncinata
Echinodermata
Native
Sabellidae Indet
Annelida
Cryptogenic
Salacia bicalycula
Hydroid
Native
Sarcothalia livida
Rhodophyta
Native
Sargassum scabridum
Ochrophyta
Native
Sargassum sinclairii
Ochrophyta
Native
Schistomeringos loveni
Annelida
Native
Schizoporella errata
Bryozoa
Non-native
Schizoseris dichotoma
Rhodophyta
Native
Schizoseris griffithsia
Rhodophyta
Native
Bryozoa
Native
Schottea cf.taupoensis
Arthropoda
Native
Schottea sp.
Schizosmittina cinctipora
Arthropoda
Native
Scoloplos cylindrifer
Annelida
Native
Scoloplos simplex
Annelida
Native
Scruparia ambigua
Bryozoa
Cryptogenic
Scrupocellaria ornithorhyncus
Bryozoa
Native
Scutus breviculus
Mollusca
Native
Scytosiphon lomentaria
Ochrophyta
Native
Seba typica
Arthropoda
Native
sedis Adenocystis utricularis
Ochrophyta
Native
Sertularella robusta
Hydroid
Native
Sertularia marginata
Hydroid
Non-native
Sigapatella novaezelandiae
Mollusca
Native
Sigapatella tenuis
Mollusca
Native
Siphonaria australis
Mollusca
Native
Smittina rosacea
Bryozoa
Native
Smittina torques
Bryozoa
Native
Smittoidea maunganuiensis
Bryozoa
Native
235
Spirobranchus cariniferus
Annelida
Native
Spirobranchus polytrema
Annelida
Non-native
Steginoporella magnifica
Bryozoa
Native
Stenothoe miersii
Arthropoda
Cryptogenic
Stenothoe moe
Arthropoda
Native
Stenothoe sp. aff. S. gallensis
Arthropoda
Non-native
Stenothoe valida
Arthropoda
Cryptogenic
Hydroid
Native
Echinodermata
Native
Stictosiphonia hookeri
Rhodophyta
Native
Stictosiphonia vaga
Rhodophyta
Native
Stomacontion sp. aff. S. pungpunga
Stereotheca elongata
Stichopus mollis
Arthropoda
Cryptogenic
Streblosoma toddae
Annelida
Native
Styela clava
Chordata
Non-native
Styela plicata
Chordata
Cryptogenic
Stylotella agminata
Porifera
Non-native
Suberites cf. affinis
Porifera
Native
Sycon cf. ornatum
Porifera
Native
Symplectoscyphus johnstoni
Hydroid
Native
Symplectoscyphus subarticulatus
Hydroid
Native
Synthecium campylocarpum
Hydroid
Non-native
Synthecium elegans
Hydroid
Native
Synthecium subventricosum
Hydroid
Non-native
Sypharochiton pelliserpentis
Mollusca
Native
Sypharochiton sinclairi
Mollusca
Native
Talochlamys zelandiae
Mollusca
Native
Tedania battershilli
Porifera
Native
Tedania diversiraphidiophora
Porifera
Native
Tedania spinostylota
Porifera
Native
Terebella plagiostoma
Annelida
Native
Terebellides narribri
Annelida
Native
Tethya burtoni
Porifera
Native
Thelepus extensus
Annelida
Native
Timarete anchylochaetus
Annelida
Native
Trematocarpus aciculare
Rhodophyta
Native
Tricellaria inopinata
Bryozoa
Non-native
Trichomusculus barbatus
Mollusca
Native
Trochus tiaratus
Mollusca
Native
Trochus viridus
Mollusca
Native
Trypanosyllis gigantea
Annelida
Native
Trypanosyllis zebra
Annelida
Native
Tubulipora cf.connata
Bryozoa
Native
Tugali suteri
Mollusca
Native
Turbo smaragdus
Mollusca
Native
236
Annelida
Native
Ulva spathulata
Chlorophyta
Native
Undaria pinnatifida
Ochrophyta
Non-native
Typosyllis prolifera
Bryozoa
Native
Ventojassa frequens
Arthropoda
Native
Vosmaeria torquata
Porifera
Native
Vosmaeropsis cf macera
Porifera
Non-native
Watersipora arcuata
Bryozoa
Non-native
Watersipora subtorquata
Bryozoa
Non-native
Wittrockiella salina
Chlorophyta
Native
Xanthidae sexlobata
Arthropoda
Cryptogenic
Xenostrobus pulex
Mollusca
Native
Xenostrobus securis
Mollusca
Native
Xymene huttoni
Mollusca
Native
Xymene plebeius
Mollusca
Native
Xymene traversi
Mollusca
Native
Zoobotryon verticillatum
Bryozoa
Non-native
Valdemunitella valdemunitella
Table A4. Pairwise PERMANOVA test for the community composition for the interaction
factors; Habitat × Sample interval and Substratum × Sample interval. Significance marked in
bold (P < 0.05).
t
P (perm)
Habitat × Sample interval
Avg. similarity
Reef
Marina
Reef × Marina
Time 1
2.3252
0.0001
53.26
34.635
38.531
Time 2
2.5417
0.0001
53.506
28.553
34.692
Time 3
2.9758
0.0001
38.784
34.948
28.262
Time 4
2.7269
0.0001
46.081
39.424
36.314
Time 5
2.7292
0.0001
33.818
46.235
33.038
Time 6
2.8138
0.0001
39.637
31.627
27.887
Time 7
2.6967
0.0001
31.772
27.918
22.611
Time 8
1.9043
0.0003
28.754
31.092
26.693
Slate
PVC
PVC × Slate
Substratum × Sample interval
Time 1
0.918
0.5365
38.265
44.499
41.011
Time 2
1.7555
0.0043
35.717
41.666
36.952
Time 3
1.4254
0.0299
28.808
37.251
31.971
Time 4
2.1249
0.0003
34.432
47.581
38.002
Time 5
1.8585
0.0013
33.124
42.273
35.289
Time 6
1.5002
0.0221
32.321
32.372
31.063
Time 7
1.8748
0.0003
26.386
28.844
24.767
Time 8
1.9284
0.0005
27.166
32.606
26.729
237
238
Sonali Pawaskar