Tizard et al. BMC Evolutionary Biology
(2019) 19:52
https://doi.org/10.1186/s12862-019-1346-y
RESEARCH ARTICLE
Open Access
DNA barcoding a unique avifauna: an
important tool for evolution, systematics
and conservation
Jacqueline Tizard1†, Selina Patel1†, John Waugh2, Erika Tavares3,4,5, Tjard Bergmann6, Brian Gill7, Janette Norman8,9,
Les Christidis10, Paul Scofield11, Oliver Haddrath3,4, Allan Baker3,4, David Lambert12 and Craig Millar1*
Abstract
Background: DNA barcoding utilises a standardised region of the cytochrome c oxidase I (COI) gene to identify
specimens to the species level. It has proven to be an effective tool for identification of avian samples. The unique
island avifauna of New Zealand is taxonomically and evolutionarily distinct. We analysed COI sequence data in
order to determine if DNA barcoding could accurately identify New Zealand birds.
Results: We sequenced 928 specimens from 180 species. Additional Genbank sequences expanded the dataset to
1416 sequences from 211 of the estimated 236 New Zealand species. Furthermore, to improve the assessment of
genetic variation in non-endemic species, and to assess the overall accuracy of our approach, sequences from 404
specimens collected outside of New Zealand were also included in our analyses. Of the 191 species represented by
multiple sequences, 88.5% could be successfully identified by their DNA barcodes. This is likely a conservative
estimate of the power of DNA barcoding in New Zealand, given our extensive geographic sampling. The majority
of the 13 groups that could not be distinguished contain recently diverged taxa, indicating incomplete lineage
sorting and in some cases hybridisation. In contrast, 16 species showed evidence of distinct intra-species lineages,
some of these corresponding to recognised subspecies. For species identification purposes a character-based
method was more successful than distance and phylogenetic tree-based methods.
Conclusions: DNA barcodes accurately identify most New Zealand bird species. However, low levels of COI
sequence divergence in some recently diverged taxa limit the identification power of DNA barcoding. A small
number of currently recognised species would benefit from further systematic investigations. The reference
database and analysis presented will provide valuable insights into the evolution, systematics and conservation of
New Zealand birds.
Keywords: New Zealand birds, Cytochrome c oxidase subunit I, COI, Specimen identification, Conservation, DNA
barcodes
Background
DNA barcoding sensu Hebert et al. [1] has been suggested as a means of species identification through comparison of a standardised segment of the mitochondrial
genome. In the case of animals, the ‘barcode’ is a 648 bp
region of the 5′ end of the cytochrome c oxidase I (COI)
gene. Since its proposal, DNA barcoding has become a
* Correspondence: cd.millar@auckland.ac.nz
†
Jacqueline Tizard and Selina Patel contributed equally to this work.
1
School of Biological Sciences, University of Auckland, Private Bag 92019,
Auckland 1142, New Zealand
Full list of author information is available at the end of the article
large scale and well-supported global enterprise [2].
DNA barcoding has two distinct goals; species discovery
and specimen identification [1, 3, 4]. The former, which
involves using DNA barcodes to delimit species boundaries or identify novel species has been criticised, for
among other reasons, being a form of DNA taxonomy
and for relying on a single gene to infer species relationships [3, 5, 6]. Although DNA barcoding does not provide a way of defining new species, the results of such
studies can highlight taxa that require further investigation. When applied to the latter problem of identifying
© The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Tizard et al. BMC Evolutionary Biology
(2019) 19:52
specimens within taxonomically well-resolved groups,
DNA barcoding has proven to be a very useful tool [7, 8].
Traditional taxonomic identification requires increasingly rare expert knowledge and is often difficult or impossible for degraded specimens or incomplete remains.
As only a small amount of DNA is required, samples that
would usually be difficult or impossible to identify morphologically such as blood, eggs, embryos, feathers and
faeces can be accurately identified by DNA barcoding.
DNA barcoding has been successfully applied to a variety
of issues, such as the identification of historic specimens
[9, 10], wildlife forensics [11–13], diet analysis [14, 15],
identification of species involved in birdstrike (a collision
between a bird and an aircraft) [16, 17] and conservation
biology (reviewed in Krishnamurthy et al., [18]). In cases
where DNA is highly degraded, a shorter “mini-barcode”
may still enable specimen identification [19]. Furthermore,
where DNA barcodes have highlighted inconsistences
with established taxonomy, more detailed studies using a
range of approaches have been undertaken and in many
cases have been able to inform the processes of molecular
evolution, biogeography and speciation (reviewed in Barreira et al., [20]).
Debate has centred on the best way to use DNA barcodes for species identification. Early studies analysed
barcodes exclusively using distance based methods that
numerically quantify the degree of genetic divergence
between taxa e.g. [1, 21]. However, character-based
methods that rely on the presence or absence of diagnostic characters (in this case nucleotides), are considered more consistent with modern taxonomy [22]. Many
early studies also reported the existence of a global ‘barcode gap’, a discontinuity between intra- and interspecific genetic divergences. However, most of these studies
had limited congeneric and geographic sampling resulting
in underestimation of intraspecific variation and overestimation of interspecific divergence [8]. Subsequent studies
have found that within well-sampled groups, intra- and interspecific distances usually overlap significantly so that
no global barcode gap exists [8]. However, when used in
combination with character-based methods, distance
based analyses can still provide useful insights [4].
Avian taxonomy is relatively well-resolved making it
an ideal group with which to test the efficacy of DNA
barcoding for specimen identification [23]. The All Birds
Barcoding Initiative (http://www.barcodingbirds.org/)
was launched in 2005 and so far the avifauna of a variety
of different geographic regions has been successfully
DNA barcoded including North America, the eastern
Palearctic, the Neotropics, Scandinavia, the Netherlands,
Japan and Turkey [21, 23–30]. While methodology differs between each study, generally they report high success rates for species identification between 93% (520
species) [26] and 96.6% (226 species) [29].
Page 2 of 13
The avifauna of New Zealand is evolutionarily and taxonomically distinct. After the continent of Zealandia split
from Gondwana approximately 83 million years ago [31],
it became the largest landmass free from ground-dwelling
mammals allowing the avifauna to flourish [32]. Today,
New Zealand is an archipelago of two main islands and
over 330 smaller ones, with a total land area of approximately 270,000 km2, separated from any other significant
land mass by almost 1500 km [33, 34]. Despite this geographic isolation, the region has not been completely isolated biologically, as demonstrated by the heterogeneous
composition of the modern avifauna which consists of
representatives from globally diverse taxa [35]. Although
there is strong evidence for vicariant speciation in some
groups, other taxa dispersed to New Zealand following
the break-up of Gondwana with the majority arriving from
Australia or the Pacific [35]. There is a high degree of endemism (of 168 contemporary native bird species, 93 are
endemic [36]), which is also indicative of isolation.
Many features of the New Zealand avifauna are reflective of the country being an archipelago. As with other
islands, representation of groups is highly variable and
the overall diversity of some groups is low [35]. The numerous offshore islands have facilitated allopatric divergence, with some island taxa being recognised as
separate species from their mainland New Zealand relatives [37]. These islands provide breeding grounds for
many seabird species, and as a result New Zealand is
often referred to as the ‘Seabird Capital of the World’
[38]. Nearly a quarter of the world’s 359 seabird species
breed in New Zealand and almost 10% breed exclusively
in New Zealand [38]. Unfortunately, 80% of New Zealand’s native birds are now either ‘threatened’ or ‘at risk’,
mostly as a consequence of predation by introduced
mammalian predators [36]. Native birds are a large part
of New Zealand’s national identity and the country’s
strong conservation ethos has established it as a
world-leader in avian conservation [39].
The composition and evolutionary history of the New
Zealand avifauna is very different from that of other regions where DNA barcoding of birds has been successful.
With the exception of Saitoh et al. [29], most studies have
focused on continental regions. New Zealand however, is
a continental island [40], and its avifauna has characteristics of both a continental remnant and an isolated archipelago [32]. Additionally, seabirds which make up a large
portion of native species, have very different life history
traits and population dynamics than land birds [41]. These
features make it difficult to predict the success of DNA
barcodes for species identification in New Zealand. The
present study aims to: 1) develop a working DNA barcoding database for the birds of New Zealand; 2) determine
the percentage of currently recognized species that can be
discriminated by DNA barcoding; 3) test the potential of
Tizard et al. BMC Evolutionary Biology
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DNA barcodes to correctly assign specimens to their
nominal species; 4) identify taxa that could benefit from
further investigation.
Results
COI sequence data was obtained from 1416 specimens
representing 211 avian species found within the New Zealand region. Over 90% of these species were represented
by > 2 specimens (Table 1). Where available, the sister species or a close relative of all New Zealand species were included in the analyses (an additional 404 sequences from
107 species). Data was analysed using three methods. The
first two methods are based on analysis of genetic distances (pairwise distance analysis and neighbour-joining
tree building). The third is a diagnostic character assignment method implemented in the program CAOS [42–
44]. The mean intraspecific uncorrected p-distance was
0.32% (range 0.00–7.94%) and the mean nearest neighbour (i.e. minimum interspecific) p-distance was 4.24%
(range 0.00–13.27%). There was substantial overlap between these values (see Additional file 1). The optimised
threshold was 0.25% with a cumulative error rate of 15.8%
(see Additional file 2).
The local barcode gap (Fig. 1a) reflects whether or not,
within each species, the genetic distance between each
conspecific individual is smaller than to any allospecific
individual [4]. For New Zealand species with > 1 specimen, 17.8% did not have a local barcoding gap meaning
that the difference between the maximum intraspecific
and the minimum interspecific distances for that species
was ≤0 (Table 2). There was no correlation between the
number of specimens per species and the maximum
intraspecific distance (Pearsons correlation coefficient
0.13; p-value = 0.07) (Fig. 1b).
Of the 191 New Zealand species represented by > 1 specimen, 134 (70.2%) formed well supported monophyletic
groups (≥95% bootstrap support) and 29 (15.2%) were
monophyletic but with < 95% support (Additional file 3).
Nine species (4.7%) were paraphyletic and the remaining
19 species (9.9%) were polyphyletic (Table 2 and
Additional file 3). For species represented by only one specimen, no bootstrap support could be calculated. However, with the exception of the fulmar prion (Pachyptila
crassirostris) and the Chatham Island pigeon (Hemiphaga
chathamensis), sequences from these single specimens
formed distinct branches in the tree and did not interfere
with other groupings (Additional file 3). Species with two
or more distinct clusters in the neighbour-joining tree,
supported by high bootstrap values, were identified as
candidates for further investigation. Sixteen species
showed evidence of two or more divergent lineages (>
1.7% divergence with > 82% bootstrap support) (Table 3).
The majority of these groupings corresponded to recognised subspecies and/or populations separated by large
geographic distances.
Of the 25 groups that were problematic to distinguish
using neighbour-joining trees and/ or distance methods,
species within 13 groups could be correctly identified
using diagnostic characters in CAOS (Table 2). Although
CAOS distinguished the pacific black duck (Anas superciliosa) from the mallard (A. platyrhynchos) based on
two nucleotides at positions 315 (A/G) and 402 (C/T),
two mallard sequences had ambiguous calls at these positions (R and Y respectively) indicating the occurrence
of heteroplasmy in these individuals. As such, these
Table 1 Summary of species used in this study, including sequences obtained from Genbank (New Zealand endemic species are
also by definition New Zealand native species)
New Zealand species
Closest related species
Combined
Orders represented
19
17
19
Families represented
51
35
51
Genera represented
124
70
130
Species represented
211
107
318
New Zealand endemic species represented
75
n/a
75
New Zealand native species represented
180
n/a
180
New Zealand introduced species represented
31
n/a
31
New Zealand species not included in study
14
n/a
14
Species with 1 sequence
20
14
34
Species with 2–4 sequences
53
44
97
Species with 5+ sequences
138
49
187
Sequences generated in this study
928
n/a
928
Sequences obtained from Genbank
488
404
892
Total sequences
1416
404
1820
Tizard et al. BMC Evolutionary Biology
a
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(2019) 19:52
b
Fig. 1 Distance analysis of COI data. a Comparison of nearest neighbour (minimum interspecific distance) and maximum intraspecific distances of
the COI sequences from each New Zealand species represented by > 1 specimen (n = 191). Equal intra– and inter–specific variation is marked by
the black line. Points above the black line indicate species with ‘local barcode gaps’. b Comparison of maximum intraspecific distance and
sampling effort (number of specimens) for each species. There is no observable sampling bias in levels of intraspecific variation. In both
scatterplots, green points represent species with a local barcode gap, while red points represent those with no barcode gap
characters were not truly diagnostic and the species were
considered to be indistinguishable. In total 169 out of
191 species with > 1 specimen (88.5%) could be successfully identified from their COI barcodes. Fifteen species
had COI sequences that were difficult or impossible to
distinguish from their respective closest relatives which
do not occur in New Zealand highlighting the importance of thorough within genera sampling (Table 2).
Discussion
DNA barcoding using the COI region has proven to be an
effective tool for identifying New Zealand birds to species
level, correctly identifying 88.5% of species represented by
multiple specimens. Our success rate is slightly lower than
other avian DNA barcoding studies which have reported
upwards of a 93% success rate [23, 24, 26–29]. This is
likely a reflection of our comprehensive dataset in which
intraspecific variation was determined through the inclusion of conspecific individuals from throughout their
world-wide distribution and through the inclusion of
other closely related species that do not occur in New
Zealand. The average intraspecific distance of 0.32% was
slightly larger than the values reported for the avifauna of
Scandinavia (0.24%), North America (0.23%), Argentina
(0.24%) and the Netherlands (0.29%) [23, 25, 27, 28]
though smaller than for the Japanese (0.46%) [29] and
Turkish (0.62%) [30] avifaunas. This result is also likely a
reflection of intraspecific sampling from a wide geographic distribution. While these earlier studies have used
Kimura-2-Parameter (K2P) genetic distances, this does
not affect comparisons as the average intraspecific K2P
distance for this study is only 0.01% higher (0.33%) than
the uncorrected p-distance. Nearest neighbour divergence
varied from 0 to 13.27%, similar to the range found in
eastern Palearctic birds by Kerr et al. [24]. Despite our
best efforts, this is likely an inflated estimate due to the
under sampling of some groups.
Evolutionary and systematic applications
Low levels of genetic divergence, particularly at just one
locus, do not invalidate established taxonomy [45]. In
cases of recent divergence, phenotypic differentiation
can occur more rapidly than the complete sorting of
mtDNA [45] while hybridisation and back-crossing can
result in genetic introgression from one species to another [46]. Similar genetic patterns can also result from
misidentification of specimens, although we made all efforts to minimise this issue. In this study, the majority of
the 13 species pairs and triads that could not be distinguished by their COI barcodes, represent well-studied,
valid species. For example, the species status of the extant New Zealand snipes (Coenocorypha spp.) are supported by reciprocal monophyly in both nuclear and
mitochondrial markers as well as morphometric and
plumage data [47]. Divergence is estimated to have occurred only about 96,000 years ago [47] suggesting incomplete lineage sorting as the most likely explanation
for COI similarity. This is likely also the case for the
masked gulls (Chroicocephalus scopulinus and C. bulleri)
which diverged about 240,000 years ago [48]. Occasional
hybridisation between these species has also been observed [49] and slow mutation rates have also been implicated [50]. Many gull species within the closely
related genus Larus have indistinguishable COI barcodes
Common name
Scientific name
No.
Spheniscidae
Royal penguin
Macaroni penguin
Eudyptes schlegeli
E. chrysolophus
Procellariidae
Southern giant petrel
Northern giant petrel
Macronectes giganteus
M. halli
Anatidae
Mallard1
Pacific black duck2
Laysan duck3
Anas platyrhynchos1
A. superciliosa2
A. laysanensis (related)
Anatidae
Australasian shoveler
Northern shoveler
Anas rhynchotis
A. clypeata (related)
Haematopodidae
Variable oystercatcher
Chatham Island oystercatcher
South Island pied oystercatcher
Haematopus unicolor
H. chathamensis 2
H. finschi 3
Snares Island snipe
Subantarctic snipe
Chatham Island snipe
Coenocorypha huegeli
C. aucklandica 2
C. pusilla 3
Stercorariidae
Brown skua
South polar skua
Stercorarius antarcticus
S. maccormicki
12
11
poly
Laridae
Red-billed gull
Black-billed gull
Chroicocephalus scopulinus
C. bulleri
10
3
Columbidae
New Zealand pigeon
Chatham Island pigeon
Hemiphaga novaeseelandiae
H. chathamensis
Psittaculidae
Red-crowned parakeet
Reischek’s parakeet
Antipodes Island parakeet
Cyanoramphus novaezelandiae
C. hochstetteri 2
C. unicolor 3
Alaudidae
Eurasian skylark
Oriental skylark
Emberizidae
Fringillidae
Scolopacidae
Neighbourjoining treea
Local barcode
gap
CAOS
Max Intra
Min Inter
Mean Inter
Potential issue
Ref
4
3
poly
N
N
0.15
0.00
0.00
0.04
Further studies
required
[60–62, 98, 99]
5
6
poly
N
N
0.31
0.31
0.00
0.23
Phylogenetic /
Introgression
[54, 55]
10
2
2
para
< 95
para
N
N
[57, 100, 101]
Y
–
0.00
0.62
Phylogenetic /
Introgression
Y
0.31
0
0
2
6
para
< 95
N
N
0.15
0.80
0.00
Phylogenetic /
Introgression
[101]
4
2
2
N
N
0.16
0.49
0.00
–
0.15
0.00
Further studies
required
[64, 65]
poly
para
N
N
Y
N
0.16
0.31
0.00
–
0.31
0.00
Phylogenetic
[47]
N
N
0.32
0.33
0.00
0.23
Phylogenetic /
Introgression
[51, 52]
poly
N
N
0.31
0.31
0.00
0.31
Phylogenetic
[48–50]
9
1
poly
n/a
N
n/a
N
n/a
0.62
n/a
0.15
0.21
Insufficient data
[67, 68]
3
6
5
poly
N
N
[56]
Y
Y
–
0.15
0.31
Phylogenetic
< 95
0.15
0.46
0.00
Alauda arvensis
A. gulgula (related)
9
1
poly
n/a
N
n/a
N
n/a
7.94
n/a
0.16
5.98
Insufficient data /
High intraspecific
variation
[102]
Yellowhammer
Pine bunting
Emberiza citrinella
E. leucocephalos (related)
9
5
para
< 95
N
N
1.54
0.00
0.00
0.25
Phylogenetic /
Introgression
[103]
Common redpoll
Lesser redpoll
Acanthis flammea
A. cabaret (related)
8
5
poly
N
N
0.46
0.18
0.00
0.12
Further studies
required
[104]
3
1
1
10
14
11
1
< 95
(1–2)
(1–3)
–
0.29
0.91
(1–2)
(1–3)
0.31
(1–2)
(1–3)
(1–2)
(1–3)
(1–2)
(1–3)
–
0.36
0.04
–
0.42
0.06
–
0.24
0.36
(1–2)
(1–3)
(1–2)
(2019) 19:52
Family
Tizard et al. BMC Evolutionary Biology
Table 2 Groups of New Zealand bird species with limited COI divergence. For each species the number of specimens analysed is indicated, as is the neighbour-joining tree
profile (≥ 95 = monophyletic with greater than or equal to 95% bootstrap support, < 95 = monophyletic with less than 95% bootstrap support). Whether a species had a local
barcode gap and could be reliably identified by CAOS is indicated. Maximum intraspecific as well as both minimum and mean distances between the species are given in
percentages. The potential reason(s) for the observed similarity in barcodes is provided along with supporting references
(1–3)
(1–2)
(1–3)
a
Page 5 of 13
poly = polyphyletic, para = paraphyletic, n/a = not applicable
Superscript numbers denote the species within each family that are used to calculate the minimum and mean interspecific distances
Common name Scientific name
New Zealand
Statusa
No. of specimens Bootstrapb Mean distance Collection areasc
in each cluster
Spheniscidae
Gentoo
penguin
Pygoscelis papua
N
5/6
100/100
2.37
MQI/FI
Spheniscidae
Blue penguin
Eudyptula minor
N
8/4
100/100
3.63
NI, SI/AUS
Y
[70,
105]
Procellariidae
South Georgia
diving petrel
Pelecanoides
georgicus
N
2/1/2
100/−/100 7.42
CI/HI/SG
Y
[75,
106]
Procellariidae
Little shearwater Puffinus assimilis
N
6/2
100/100
1.90
KI/MI, AKL
Hydrobatidae
Wilson’s storm
petrel
Oceanites oceanicus
N
1/2
−/100
2.43
AUS/CHL
Hydrobatidae
White-faced
storm petrel
Pelagodroma
marina
N
7/2/1
99/92/−
4.58
MI, NI/KI/AUS
Ardeidae
Great egret
Ardea alba
N
4/1/1/6
99/−/−/78 4.85
IND, KOR/AUS/JPN/
NA, SA
Y
Phasianidae
Ring-necked
pheasant
Phasianus colchicus
I
4/2
100/100
1.78
USA, NZL, RUS/NOR,
SWE
Y
Charadriidae
Spur-winged
plover
Vanellus miles
N
1/1
−/−
2.47
Nth-AUS/NZ
Scolopacidae
Whimbrel
Numenius phaeopus N
3/2
100/100
3.28
RUS, AUS/CAN, BRA
Strigidae
Little owl
Athene noctua
I
3/2
100/100
5.54
?/UK,?
Strigidae
Morepork
Ninox
novaeseelandiae
N
2/7
100/100
2.84
AUS/NZL
Acanthisitta chloris
E
4/5
82/90
1.86
MBH/HKB
Acanthisittidae Rifleman
Alaudidae
Eurasian skylark
Alauda arvensis
I
2/7
100/100
7.83
JPN/NZL, USA, NOR
Motacillidae
Australasian
pipit
Anthus
novaeseelandiae
N
5/1
100/−
4.07
NZL/AUS
Petroicidae
South Island
robin
Petroica australis
E
10/6
100/100
4.12
NI/SI
New Zealand Split within
Subspecies Ref
specific
New Zealand
Y
Y
Y
[89]
[107]
Y
Y
Y
Y
Y
(2019) 19:52
Family
Tizard et al. BMC Evolutionary Biology
Table 3 List of New Zealand species that show geographically structured populations or divergent lineages. Indicated are New Zealand specific lineages, splits within New
Zealand and the presence of subspecies recognised by Clements [87]. Individual clusters are separated by the symbol /
[77]
Y
Y
Y
Y
[108]
Y
Y
Y
Y
Y
[109]
Y
[29]
Y
[110]
Y
[78]
a
E endemic, N native and I introduced
Bootstrap support (%) for each cluster and the mean distance (%) between all clusters
NZL New Zealand, NI North Island, SI South Island, AKL Auckland, HKB Hawke’s Bay, MBH Marlborough, KI Kermadec Islands, MI Mokohinau Island, AI Antipodes Island, CI Codfish Island. Collection areas outside of New
Zealand: HI Heard Island, MQI Macquarie Island, FI Falkland Islands, SG South Georgia, JPN Japan, USA United States, NOR Norway, IND India, KOR South Korea, NLD Netherlands, SWE Sweden, AUS Australia, CHL Chile,
RUS Russia, NA North America, SA South America,? = unknown
b
c
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Tizard et al. BMC Evolutionary Biology
(2019) 19:52
e.g. [24, 27–29] which is attributed to recent speciation
and hybridisation. The brown and south polar skua
(Stercorarius antarcticus and S. maccormicki respectively) diverged only about 200,000 years ago and speciation is considered incomplete with hybridisation
common [51, 52]. Originally considered a single species,
the northern and southern giant petrels (Macronectes
halli and M. giganteus) were split on the basis of morphological and behavioural differences [53]. This taxonomy is supported by nuclear and mitochondrial
markers though genetic divergence levels are low [54], a
reflection of recent divergence (about 200,000 years ago
[54]) and hybridisation [55]. The low divergence between parakeets (Cyanoramphus spp.) is also likely a reflection of recent speciation [56]. Mitochondrial control
region sequences were used to measure the divergence
of the Antipodes parakeet (C. unicolor) which was estimated to have occurred ~ 270,000 years ago [56]. The
sympatric Reischek’s parakeet (C. hochstetteri) colonised
the Antipodes Islands much more recently, diverging
from the red-crowned parakeet (C. novaezelandiae) ~
100,000 years ago [56]. This is consistent with our finding that the Antipodes parakeet could be distinguished
from the other two species by two diagnostic nucleotides. The introduced mallard and native pacific black
duck are known to hybridise extensively with mtDNA
introgression being bidirectional [57]. In two mallards,
there was evidence of heteroplasmy at two nucleotide
sites. Mitochondrial heteroplasmy, the occurrence of
more than one haplotype within an individual, can occur
as a result of mutation, recombination or paternal leakage [58]. The general assumption that mtDNA is uniparentally inherited and homoplasmic is being questioned
by the accumulating evidence of paternal leakage in a
variety of taxa (reviewed in Barr et al., [59]).
For other species that could not be identified by their
COI barcodes, in-depth studies are lacking and further
investigation is required. For example, the royal penguin
(Eudyptes schlegeli) and macaroni penguin (E. chrysolophus) are considered conspecific by some [60, 61] and
the mitochondrial hypervariable control region and the
COI barcoding region show very low levels of divergence
[62, 63]. Here we show that the COI sequences of specimens from royal and macaroni penguins generated by
Baker et al. [63] could not be distinguished by CAOS.
Taxonomic uncertainty also surrounds the New Zealand
oystercatchers. A preliminary genetic study using
mtDNA found no differences between the mainland species the South Island pied oystercatcher (Haematopus
finschi) and the variable oystercatcher (H. unicolor) [64]
which occasionally hybridise [65], but vary substantially
in morphology. However, the Chatham Island species
(H. chathamensis), which is considered by some to be a
subspecies of the variable oystercatcher [66], was found
Page 7 of 13
to be distinct from the mainland species [64]. While
none of the three species could be distinguished by COI
barcodes, the Chatham Island oystercatcher showed the
highest genetic divergence. Consistent plumage differences are currently the only basis for the separation of
the Australasian bittern (Botaurus poiciloptilus) and the
great bittern (B. stellaris) [61]. Our results suggest limited genetic divergence at the COI locus between the
two species (mean distance of 0.12%) indicating their
taxonomy may require further investigation.
Since we only have one Chatham Island pigeon (Hemiphaga chathamensis) specimen we can conclude little
from the similarity between it and its sister taxa the
New Zealand pigeon (H. novaeseelandiae). The Chatham
Island pigeon was only recently elevated to species status
on the basis of morphometric differences [67]. In a larger study, Goldberg et al. [68] found no differences in
the COI region, and low divergence in cytochrome b and
D-loop sequences (1.2 and 2.8% respectively) which was
attributed to recent widespread dispersal. In the present
study, the fulmar prion (Pachyptila crassirostris) was represented by a single sequence which showed low divergence when compared to the fairy prion (P. turtur). These
species are sometimes considered conspecific [69].
Divergent COI lineages were evident within 16 species
(Table 3). There is no level of genetic distance that can
be used as a cut-off for species status, as speciation results in genetic divergence but is not caused by it [70].
However, divergence in COI barcodes can identify taxa
in which further scrutiny may be required [24]. In seven
globally distributed species, divergent lineages corresponded to recognised subspecies separated by large
geographic distances; gentoo penguin (Pygoscelis papua),
Wilson’s storm petrel (Oceanites oceanicus), great egret
(Ardea alba), common pheasant (Phasianus colchicus),
whimbrel (Numenius phaeopus), little owl (Athene noctua) and Eurasian skylark (Alauda arvensis). Within five
of the six species that showed divergent lineages between New Zealand and Australia there are recognised
subspecies; spur-winged plover (Vanellus miles), morepork (Ninox novaeseelandiae), little penguin (Eudyptula
minor), New Zealand pipit (Anthus novaeseelandiae) and
white-faced storm petrel (Pelagodroma marina). However, detailed geographic sampling would be required for
each species to determine if COI barcodes could distinguish subspecies. Furthermore, it has been suggested
that some of these currently recognised subspecies warrant separate species status. For example, there is evidence in the form of nuclear and mitochondrial markers
[70, 71], behavioural [72] and plumage differences [71]
that the New Zealand and Australian populations of little penguins (E. minor) should be recognised as separate
species. Additionally, a recent study of the genus Ninox
recommended that the mainland Australian population
Tizard et al. BMC Evolutionary Biology
Page 8 of 13
(2019) 19:52
be treated as a separate species from Tasmanian and
New Zealand populations [73].
The taxonomy of diving petrels remains unresolved
and is the subject of debate [74, 75]. Within New Zealand, there was evidence of divergent lineages corresponding to recognised subspecies within four species.
Populations of both little shearwater (Puffinus assimilis)
and white-faced storm petrel (Pelagodroma marina)
were divergent between the Mokohinau and Kermadec
Islands. The white-faced storm petrels collected from
the Mokohinau Islands and a beach wrecked individual
found in the North Island (presumably from the Mokohinau Islands population) were over 5% divergent from
the specimens collected in Australia and the Kermadec
Islands. The Kermadec Island population is regarded as
a distinct species (P. albiclunis) by Birds New Zealand
[76]. Though we cannot be certain of which breeding
population the Australian sample originated from, given
its collection location it was probably a member of the
Australian subspecies P. m. dulciae. Genetic comparisons of these populations are lacking, however, Silva et
al. [77] found that the Mokohinau Island population was
highly differentiated from North and South Atlantic
populations using mitochondrial and nuclear markers.
Rifleman (Acanthisitta chloris) and New Zealand robin
(Petroica australis) showed divergent lineages between the
North and South Islands. New Zealand robin lineages
showed a divergence of 4.12%, similar to control region
sequences which showed 5.9% divergence suggesting
long-term isolation [78]. Indeed, Birds New Zealand recognises the North Island robin as a separate species P.
longipes [76].
There was no clear pattern in the success rates of
DNA barcoding despite the unique composition of the
New Zealand avifauna. High levels of endemism had no
obvious effect on success rates. For New Zealand species
represented by > 1 specimen, 85.5% of endemic, 88.75%
of native and 87.1% of introduced species could be identified by DNA barcodes. Divergent lineages were evident
in a similar proportion of native and introduced species
(6.9 and 9.7% respectively). The high prevalence of seabirds did not appear to influence success rates with
90.4% of seabirds successfully identified compared to
88% of land birds and 7.2% of seabirds showing evidence
of divergent lineages compared to 9.2% of land birds.
Importantly, this demonstrates that DNA barcoding can
be successfully applied to species discrimination in fauna
with a wide range of evolutionary patterns and life history traits.
Conservation management applications
The 928 COI sequences from 180 New Zealand bird species
generated from this study form a substantial reference database that will be a valuable tool for specimen identification
and the conservation of New Zealand birds. DNA barcoding
has many advantages over morphological identification
when applied to conservation management [14]. DNA barcoding can utilise non-invasive samples such as feathers or
faeces [11] which is beneficial when dealing with rare and
endangered birds or elusive predators [79]. Invasive mammalian predators are the biggest threat to the survival of
New Zealand birds, responsible for the majority of the 26.6
million chick and egg losses of native bird species each year
[80]. Diet studies using DNA barcodes can be used to assess
predator impact on prey populations and provide superior
detection and identification of prey species when compared
to morphological analysis [14].
Performance of different methods of analysis
CAOS was found to be the most successful method for
identifying specimens to the species level. All species that
were distinguishable using neighbour-joining tree or other
distance-based methods were also successfully identified by
CAOS and an additional 14 species could only be identified using CAOS. While previously the application of
CAOS has been limited by scalability issues [24], this has
now been overcome and large datasets such as ours can be
successfully analysed. We found that there were no differences in output when CAOS was run using smaller datasets consisting of species from one order. When sequences
from species not found in our database were queried,
CAOS correctly identified these individuals to the genus
level. While this issue highlights the importance of thorough sampling in the reference database, genus level identification is more useful than no identification at all.
It is well-established that phylogenetic trees may perform
poorly for the purpose of specimen identification [81, 82].
It is not possible to determine if a query sequence belongs
to the species which it is topologically closest to unless it is
nested within a monophyletic cluster [83]. Additionally,
when either speciation is recent and individual genes are
still incompletely sorted, or when introgressive hybridisation is occurring, non-monophyly is to be expected [84, 85]. Despite these limitations, quantifying
the level of monophyly is still a useful descriptor of the data
[4]. In this study, non-monophyly was observed in 14.7% of
species, that is similar to values reported in other studies of
Aves, between 10.4% [86] and 16.7% [46]. While distance
and phylogenetic tree-based methods do not have the same
level of success as CAOS, they reveal interesting features of
the data which character-based methods do not. For example, evidence of divergent lineages can be quickly observed in a phylogenetic tree while large intraspecific
variation may also indicate divergence. For species that
show small interspecific distances and/or non-monophyly,
we should be more cautious about identifications provided
by CAOS as discussed above.
Tizard et al. BMC Evolutionary Biology
Page 9 of 13
(2019) 19:52
Conclusions
This study demonstrates that DNA barcoding can identify the majority of New Zealand birds to the species
level. DNA barcoding has proved effective in ‘the Seabird Capital of the world’, a region where the unique avifauna has characteristics of both a continent and an
island and is of mixed evolutionary origin. COI barcodes
have highlighted species groups with limited divergence
and other species that show evidence of divergent lineages that require further taxonomic scrutiny. Widespread geographic sampling means that the reported
success rates are more conservative than they would
have been had we only included specimens from the
New Zealand region. The reference database generated
by this study will provide a powerful tool for the conservation management of New Zealand birds.
range in order to determine levels of geographic variation. Additional Genbank sequences were also included
in the analysis (see Additional file 4). Taxonomy was
based upon Clements [87], including corrections and
updates up to 7 March 2017.
DNA sequencing
For the majority of samples, the DNA extraction protocol, PCR conditions, sequencing methodology and primer details were as previously described by Patel et al.
[88]. For the remaining samples the methodology is outlined in Tavares and Baker [89]. Sequences shorter than
519 bp or which contained ten or more ambiguous base
calls were excluded from analysis. Specimen information,
sequences and trace files can be accessed on the Barcode
of Life Data Systems website (BOLD, GenBank accession
numbers MK261779 - MK262706) [90].
Methods
Sampling
Additional data
We generated COI sequences from 928 specimens
representing 180 species from the New Zealand region
(Fig. 2a and b). Samples included voucher specimens
from the Auckland War Memorial Museum, Museum
Victoria, Museum of Natural Sciences at Louisiana State
University and the Royal Ontario Museum. Other specimens were collected in the field by a large number of
people over the last 35 years. Where possible, individual
birds were sampled from across the species’ geographic
The data gathered in this study were supplemented by
892 sequences from GenBank that fell into two categories. Firstly, 488 sequences from species that occur in the
New Zealand region. For non-endemic species, specimens from across their geographic distribution were
preferentially included, to capture the most geographic
variation. Secondly, in instances where a species’ closest
relative did not occur in New Zealand, sequences from
the most closely related species available were included
b
a
Norfolk Is.
Macquarie Is.
Lord Howe Is.
180°
Kermadec Is.
160°E
Three Kings Is.
60°S
35°
Mokohinau Is.
40°
Balleny Is.
New Zealand
Scott Is.
70°S
Ross Sea
Chatham Is.
Is
45°
Ross Is.
Roosevelt Is.
Stewart Is.
Snares Is.
Bounty Is.
Antipodes Is.
50°
Ross Ice Shelf
Auckland Islands
Campbell Is.
Macquarie Is.
165°
180°
0
200 kms
0
600 kms
Fig. 2 Map of New Zealand region as defined by this study including (a) New Zealand and its outlying islands and (b) the Ross Dependency, Antarctica
Tizard et al. BMC Evolutionary Biology
Page 10 of 13
(2019) 19:52
to increase within genera sampling. These additional 404
sequences are referred to as related species and while
they were included in all analyses, only the success rates
and divergence levels of New Zealand species are reported. A number of GenBank sequences followed outdated taxonomic classifications and were renamed to
follow Clements [87] (see Additional file 4).
In total 1820 sequences were included in the analysis,
of which 1416 were from 211 species that occur in the
New Zealand region. A complete list of GenBank accession numbers of the sequences used in this study is
available in Additional file 4.
Analysis
Three DNA barcoding analysis methods were used; tree
building, distance analysis and diagnostic character assignment. Tree building was conducted in MEGA version 7 [91]. Sequence alignment was performed with
MUSCLE [92] and a neighbour-joining tree was produced based on uncorrected p-distances. P-distances
have been shown to produce higher or similar levels of
correct identification than Kimura-2-Parameter (K2P)
distances which are commonly employed in barcoding
studies [93, 94]. Support for monophyletic clades was
measured using bootstrap values with 1000 replicates.
Patterns of divergence were classified as either monophyletic with either greater than or less than 95% bootstrap support, paraphyletic or polyphyletic.
Local barcode gap analysis was conducted by calculating maximum intraspecific and minimum interspecific
(nearest neighbour) genetic distances for each species
using the Spider package [95] for RStudio [96]. For each
species, these values were plotted against each other to
visualise ‘local’ barcoding gaps; discontinuity between
levels of intraspecific and interspecific distances [4].
Nearest neighbour distances were used in preference to
average interspecific distances because species identification is ultimately dependent upon how different a sequence is from its closest allospecific sequence, as
opposed to the distance to the “average” sequence [97].
An optimised global distance threshold was also calculated from the data, minimising the cumulative error
rate [8].
Character-based identification was implemented in
CAOS [42–44]. CAOS identifies diagnostic characters,
termed ‘character attributes’ (CA’s) from a tree of
pre-defined species. Single CA’s may be either pure
(sPu’s) if they are shared by all members of a clade and
are absent from the other clades or private (sPr’s) if they
are shared only by some members of a clade [7]. Detailed methodology can be found in the Additional file 5.
In brief, CAOS barcoding is comprised of three steps,
each performed by a separate program. Firstly, the
CAOS-Analyzer extracts CA’s from the input nexus file
that consists of the sequence alignment and tree file fused
together. Next, the outputs of the CAOS-Analyzer are
converted into an easily interpretable character-based barcode matrix using the CAOS-Barcoder. Finally, the
CAOS-Classifier tests the efficacy of this matrix by
attempting to assign a new query specimen to the correct
species in the reference dataset. For species with multiple
representatives, the shortest sequence was excluded from
the reference database and used as a query sequence.
Additional files
Additional file 1: Frequency distribution of maximum intraspecific and
minimum interspecific genetic distances measured using a standardised
648 bp region of the cytochrome c oxidase gene for all New Zealand
bird species with > 1 specimen obtained during the study. The dashed
line indicates the calculated optimised distance threshold (0.025%).
(DOCX 96 kb)
Additional file 2: Cumulative error plot of type I (false positive) and
type II (false negative) errors for different divergence thresholds of
maximum intraspecific and minimum interspecific genetic distances
measured using a standardised 648 bp region of the cytochrome c
oxidase gene for all New Zealand bird species with > 1 specimen
obtained during the study. The optimal threshold occurs at 0.25%.
(DOCX 171 kb)
Additional file 3: Neighbour Joining tree of sequences of a
standardised 648 bp region of the cytochrome c oxidase gene obtained
from New Zealand and closely related bird species in this study.
Bootstrap support values ≥0.5 are indicated. Monophyletic clades have
been collapsed. Branches are coloured by Order. (PDF 6097 kb)
Additional file 4: List of all sequences of a standardised 648 bp region
of the cytochrome c oxidase gene obtained from New Zealand and
closely related bird species used in this study including Genbank
accession numbers. (DOCX 63 kb)
Additional file 5: Detailed methodology of CAOS analysis. (DOCX 29 kb)
Abbreviations
CAOS: Characteristic Attribute Organization System; COI: Cytochrome c
oxidase subunit I; mtDNA: mitochondrial DNA
Acknowledgements
This project would not have been possible without the support of a large
number of people who very generously provided samples. We wish to thank
the following people and institutions for providing samples: Murray Potter
(Massey University), Bruce Robertson (University of Otago), Todd Landers
(Auckland Council), James Russell (University of Auckland), Matt Rayner
(Auckland War Memorial Museum), Gerry Kooyman (Scripps Institution of
Oceanography), Emma Marks (University of Auckland), Graeme Taylor (New
Zealand Department of Conservation), Kate McInnes (New Zealand Department
of Conservation), Mark Hauber (University of Illinois), Steffanie Ismar (Helmholtz
Centre for Ocean Research), Murray Williams (Victoria University of Wellington),
Steven Lawrence, Colin Miskelly (Museum of New Zealand Te Papa Tongarewa),
Sara Treadgold (New Zealand Department of Conservation), Mick Clout
(University of Auckland), Bill Peacock (Northcote College), Shaun O’Connor
(New Zealand Department of Conservation) Lawson Davey (Nelson
Marlborough Fish and Game), Sandra Anderson (University of Auckland), Daryl
Eason (New Zealand Department of Conservation), Andrew Fidler (Cawthron
Institute), Rosemary Barraclough (University of Auckland), Shinichi Nakagawa
(University of New South Wales), Michael Anderson (Massey University), Erica
Sommer (New Zealand Department of Conservation), Jo Hiscock (New Zealand
Department of Conservation), Stuart Cockburn (New Zealand Department of
Conservation), Peter Moore (New Zealand Department of Conservation), David
Thompson (National Institute of Water and Atmospheric Research), the late Ian
Jamieson (University of Otago), Sylvia Durant (Bird Rescue) and Rosemary Tulley
(Bird Rescue).
Tizard et al. BMC Evolutionary Biology
Page 11 of 13
(2019) 19:52
Funding
This research was funded by the University of Auckland’s Vice-Chancellor’s
University Development Fund; the University of Auckland Faculty Research
Development Funds; the Allan Wilson Centre for Molecular Ecology and
Evolution; the Canadian Barcode of Life Network from Genome Canada
through the Ontario Genomics Institute, the Natural Sciences and
Engineering Research Council of Canada; and the Royal Ontario Museum
Governors’ Fund. Tjard Bergmann was financed by the H. Wilhelm
Schaumann Stiftung.
Availability of data and materials
Specimen information, sequences and trace files can be accessed on the
Barcode of Life Data Systems website (BOLD, GenBank accession numbers
MK261779 - MK262706).
Authors’ contributions
CM, DL, AB, LC, JN and BG conceived the study and participated in its design
and coordination. SP, JW, ET, JT and OH generated the sequence data and
curated the database. JT and TB conducted the analysis. JT, SP, CD, JW, PS
and JN interpreted the data. JT, SP and CM drafted the manuscript. All
authors contributed to revisions and approved the final manuscript.
5.
6.
7.
8.
9.
10.
11.
12.
13.
Ethics approval and consent to participate
Many of the samples in this study were obtained from dead specimens held
in museums and other institutions for which no ethics approval is required.
The remainder of the samples were collected by the New Zealand
Department of Conservation under their animal ethics committee approval.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
14.
15.
16.
17.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
18.
19.
Author details
1
School of Biological Sciences, University of Auckland, Private Bag 92019,
Auckland 1142, New Zealand. 2Unitec Institute of Technology, Auckland, New
Zealand. 3Department of Natural History, Royal Ontario Museum, 100
Queen’s Park, Toronto, Ontario M5S 2C6, Canada. 4Department of Ecology
and Evolutionary Biology, University of Toronto, 25 Willcox Street, Toronto,
Ontario M5S 3B2, Canada. 5Present address: Laboratory Research Project
Manager, The Hospital for Sick Children, Toronto, Ontario, Canada. 6Institute
for Animal Ecology and Cell Biology, University of Veterinary Medicine
Hannover Foundation, Bünteweg 17d, D-30559 Hannover, Germany.
7
Associate Emeritus, Auckland War Memorial Museum, Private Bag 92018,
Auckland 1142, New Zealand. 8Molecular Biology Sciences Department,
Museum Victoria, GPO Box 666, Melbourne, Victoria 3001, Australia. 9Present
address: Graduate School, Southern Cross University, Lismore, New South
Wales, Australia. 10National Marine Science Centre, Southern Cross University,
Coffs Harbour, New South Wales, Australia. 11Canterbury Museum, Rolleston
Ave, Christchurch 8001, New Zealand. 12Environmental Futures Research
Institute, Griffith University, 170 Kessels Road, Brisbane, Queensland 4111,
Australia.
20.
21.
22.
23.
24.
25.
26.
27.
Received: 18 June 2018 Accepted: 2 January 2019
28.
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