Chapter 12
A First Approach to Assess the Impact of Bottom
Trawling Over Vulnerable Marine Ecosystems on the
High Seas of the Southwest Atlantic
J. Portela, J. Cristobo, P. Ríos, J. Acosta, S. Parra,
J.L. del Río, E. Tel, V. Polonio, A. Muñoz,
T. Patrocinio, R. Vilela, M. Barba and P. Marín
Additional information is available at the end of the chapter
http://dx.doi.org/10.5772/59268
1. Introduction
The Southwest Atlantic (SW Atlantic), corresponding to FAO Statistical Area 41, includes a
total continental shelf area of approximately 1.96 million km2 of which a large portion lies off
the Argentine coast (the Patagonian Shelf) and extends beyond Exclusive Economic Zones
(EEZs) in the region [1-3]. This area is therefore integrated in the Southeast South American
Shelf Large Marine Ecosystem (SSASLME) [4,5]. Currently, this region is the only worldwide
significant area for high seas (HS) fisheries not covered by any Regional Fisheries Management
Organisation (RFMO) [3].
The Patagonian Shelf (PS) hosts some of the most important fisheries in the world, targeting
cephalopods (Illex argentinus [Castellanos, 1960] and Doryteuthis gahi [D’Orbigny, 1835]), and
hakes (Merluccius hubbsi [Marini, 1933] Merluccius australis [Hutton, 1872]) [3,6-14]. Most of the
exploited demersal stocks on the HS are straddling stocks, including Argentine shortfin squid
(I. argentinus), Argentine hake (M. hubbsi) and southern blue whiting (Micromesistius australis
[Norman, 1937]) [15].
Several authors [2,3,16-23] have studied the potential disturbance of the seabed by bottom otter
trawls and the possible negative effects on the structure of benthic communities. In recent
years, several resolutions of the United Nations General Assembly [24-28] on sustainable
fisheries made a call to States and RFMOs to identify vulnerable marine ecosystems (VMEs)
© 2015 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons
Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution,
and reproduction in any medium, provided the original work is properly cited.
2 Biodiversity in Ecosystems - Linking Structure and Function
290
and determine whether bottom fishing activities would cause a significant adverse impact on
such ecosystems.
Sensitive species such as deep-water corals and deep-water sponges are found throughout the
world oceans. Thus, the importance of habitat-structuring organisms is not restricted to
shallow water, but also to shelf-break, hydrothermal vents, seamounts, and even the once
considered constant and uniform deep-sea basins. Deep-water corals are vulnerable organisms
occurring in the upper bathyal zones throughout the world and threatened by human
activities, particularly fishing and oil exploration [29-31]. Fishing has a significant adverse
impact (SAI) on deep-water coral communities in all oceans [32-35], particularly in the
Northeast and Northwest Atlantic [36-40], Northeast Pacific [41,42], and Southwest Pacific
[43-46]. In the SW Atlantic, the HS are one of the areas where deep-sea science has, to date, not
been very active.
Protection of VMEs is a significant element of the management framework for bottom fisheries
in high seas areas of the world ocean and its identification for selecting suitable protection
areas is a challenge that conventional fisheries science cannot alone solve satisfactorily. Instead,
it requires a multidisciplinary approach [21,22,47]. From the point of view of management of
bottom fisheries and the governance of high seas areas, the situation in the PS poses an added
problem as there is no any RFMO in force [2]. In its 2014 report [48], the Global Ocean
Commission (GOC) recognises that continued scientific research is necessary to assess the
cumulative impacts of human activities on the high seas so that informed decisions can be
made about reversing the degradation of the global ocean.
Submarine canyons are unique habitats in terms of complexity, instability, material processing,
and hydrodynamics. They may support diverse assemblages of larger epibenthos [49]. Inside
canyons, abundance and diversity of the macrofauna depend, to some extent, on the physical
disturbance regime and on the rate and quantity of organic matter deposited. In the study area,
canyons and submarine mounts were shown to be hot spots of benthic biodiversity of species
and ecosystems.
Benthos refers to the community of organisms which live on, in, or near the seabed, also known
as the benthic zone. Megabenthos or macrobenthos comprises the more visible, benthic
organisms exceeding 1 mm in size and large enough to be determined on photographs [50,51].
Megabenthos is a key issue of environmental studies, as it represents a major fraction of the
deep-sea benthic biomass and plays a key role in deep-sea ecosystems [52]. Tracey et al. (2007)
in [53] reported linear and radial annual growth rates of 20 mm and 0.2 mm, respectively, for
some genera of the ISIDIDAE Family (Lamouroux, 1812), which is presumably evidence of the
high vulnerability of these taxa to direct or indirect mechanical impact produced by the
sediment removal, re-suspension, etc. caused by bottom fishing activities.
Some of these organisms form complex 3D structures protruding from the seabed, allowing
for the settlement of sessile species needing consolidated substrata to settle and develop
(sponges, other cnidarians), and providing shelter and food for a wide range of vagile fauna
(crustaceans, echinoderms, molluscs, and some fish).
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2. Materials and methods
In accordance with the aforementioned UNGA resolutions [24-28] and the FAO deepwater
guidelines [54], the Spanish Institute of Oceanography (Instituto Español de Oceanografía
[IEO]) conducted from October 2007 to April 2010 a series of 13 multidisciplinary research
cruises on the HS of the SW Atlantic, to identify VMEs and to assess the potential interactions
with fishing activities. This paper presents the results of the five first cruises, consistently with
UNGA resolutions (paragraphs 80 and 83 to 87 of resolution 61/105 (2007) and paragraphs 117
and 119 to 127 of resolution 64/72 (2010) in [27,28], which support making publicly available
information on interactions between bottom fisheries and VMEs in the HS.
The use of spatial management tools to preserve the marine biodiversity of species inhabiting
the HS has been broadly discussed in recent years [55]. To make such spatial management
possible, our immediate objectives are: assessing specific biodiversity (mainly describing new
species to science); describing the different habitats, ecosystems and deep-sea geomorpholog‐
ical features identified; and analysing their interactions and relationships to protect the full
range of potentially different habitats.
The explored area during the five cruises conducted between October 2007 and April 2008
(Table 1) was located on the southern part of the HS of the SW Atlantic, to the east of the
Argentinian EEZ 200 miles limit and between 44° 40’S and 47° 51’S up to the 1500 m depth
contour (Figure 1). The rest of the study area (up to 42°S) was surveyed during the eight
following cruises (October 2008-April 2010), but the analysis of the information concerning
VMEs collected during those last cruises, is still ongoing.
Cruise name
Start
End
Total days
Patagonia 11/07
28/10/2007
20/11/2007
24
Patagonia 12/07
24/11/2007
21/12/2007
28
Patagonia 01/08
08/01/2008
30/01/2008
23
Patagonia 02/08
30/01/2008
11/03/2008
41
Atlantis 2008
12/03/2008
15/04/2008
40
Table 1. Cruises carried out by R/V “Miguel Oliver”.
In the right image of Figure 1 a non coloured area in the shelf can be roughly appreciated
around 45°30’S and between 60°00’W-60°40’W, for which it was not possible to collect
multibeam bathymetry data (no data) due to bad sea state conditions. The exploration of this
area was carried out during one of the cruises conducted in 2009. Nevertheless, this type of
data is not relevant for the present study, for which several trawl and CTD stations allowed
the collection of pertinent information. The blue lines in the left image of Figure 1 correspond‐
ing to the 600, 1000 and 1500 m depth contours.
4 Biodiversity in Ecosystems - Linking Structure and Function
292
Key concepts for definition of VMEs were applied according to the FAO International
Guidelines for the Management of Deep-Sea Fisheries in the High Seas [54]. These guidelines
classify marine ecosystems as vulnerable based on several criteria: (1) uniqueness or rarity; (2)
functional significance of the habitat; (3) fragility; (4) life-history traits of component species
that make recovery difficult; and (5) structural complexity.
Figure 1. Study area and positioning of the stations carried out during the research cruises onboard the R/V “Miguel
Oliver”.
For an adequate identification of VMEs, the two approaches in operation since 2008 by the
NAFO Scientific Committee and the NAFO Working Group on Ecosystem Approach to
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Fisheries Management (WGEAFM) were applied in this study [56,57]: (1) the examination of
cumulative catch data by ranking the biomass of VME taxa in each trawl from lowest to highest
and then plotting the increase in cumulative biomass with each additional trawl; and (2) the
use of Geographical Information System (GIS) to map the density of vulnerable species and
groups’ by-catch [58].
The study area included part of the outer shelf and upper and middle slope of the PS and was
divided into thirteen depth strata in order to obtain a higher resolution in the description of
vulnerable organisms. The research cruises involved five scientific disciplines: cartography,
geology, benthos, fisheries, and hydrography.
This study used data from three main sources: i) Information from the five research cruises
(geological, echosounder and oceanographic data; benthos and fish samples; fishery catch data
[cpue]); ii) Data from commercial fishing activity collected by onboard scientific observers from
1989 to 2007 (fishery footprint); and iii) Commercial information on historical landings and
effort data (provided by the Spanish fishing sector), as well as catch data for the main com‐
mercial species during the period 2000-2007 (logbooks filled in by captains of the fishing
vessels, and provided by the Spanish General Secretariat for Fisheries [SGP]).
Geophysical and geological data were collected following internationally accepted standards
and protocols for habitat mapping [20,59]. Full sea floor coverage using swath bathymetry
provided a very high resolution of sea floor morphology. The backscatter data from multibeam
echosounder together with high resolution seismic reflection profiles made available valuable
data on the seabed sediments types. These data provided the geomorphological and acoustic
basis to design a ground-truth planning strategy allowing for precise habitat mapping.
Navigation during the surveys was via differential GPS Simrad GN33 using satellite correc‐
tions integrated into an inertial-aided Seapath 200 system. Swath-bathymetric data were
acquired using a hull-mounted Kongsberg-Simrad EM 302 multibeam echosounder (288
individual beams, angular coverage up to 150°) operating at a frequency of 30 kHz. To correct
the multibeam bathymetry, we carried out systematic casts of direct sound velocity profiles
on the water column with an Applied Microsystems SV Plus equipment. Data processing
included the removal of anomalies and the necessary sound velocity corrections using the
Kongsberg–Simrad swath bathymetric software package NEPTUNE. Valid data were gridded
at 50×50 m cell size resolution on a SUN workstation. The seismic parametric system Topas 18
produced very high resolution seismic profiles along all ship tracks. Sub-bottom penetration
varied, according to the lithology, between 150 and 250 m. Morphometrical data were obtained
using ArcGis (ESRI) and Fledermaus software (Interactive Visualization Systems [IVS]) to
provide final 3D images of the seafloor morphology.
Samples of benthic fauna analysed in this study were collected with the Lofoten bottom trawl
gear itself. Benthic fauna samples were sorted on board and preserved (70% ethanol or 4%
buffered formaldehyde-seawater solution) for further identification analysis. Even if the
bottom-trawl by-catch collected information did not allow for a detailed habitat mapping of
VMEs, it provided a valuable indication of VME presence/absence that can be used to propose
conservation measures, such as candidate areas for bottom fishery closures [23].
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294
Sediment samples were collected using net collectors attached to the Lofoten fishing gear
(Atlantis 2008 cruise) and with an USNEL type box-corer (BC) (maximum breakthrough of 60
cm; effective sampling area of 0.25 m2 [50×50 cm]). A few samples were taken using a Bouma
type box-corer (effective sampling area of 0.0175 m2 [10×17.5 cm]). Both gears are designed to
take undisturbed samples from the top of the seabed, and are suitable for almost every type
of sediment. Sediment temperature and redox profiles (Eh) were immediately performed for
the box-corer sample after each station. In the laboratory, the granulometrical analysis of the
sediment was carried out by dry sorting the coarse fraction (>62 µm) and the sedimentation
of the fine fraction (<62 µm). The organic matter content was assessed after calcinating (at
500°C for 24 h) and drying the sediment sample.
The hydrographical conditions in the studied area during the Atlantis 2008 cruise were
characterised by means of a Seabird-25 CTD probe (SBE-25), equipped with oximeter, fluor‐
ometer and PAR detector. The survey schedule was optimized by systematically deploying
the CTD at fishing stations below 500 m, but not always at greater depths. At each cast, the
CTD was deployed to 5 m depth and stabilised for approximately 3 min. Once stable, the CTD
was brought back to the surface and started profiling at a constant speed of 1 m⋅s-1. The SBE-25
worked in auto-contained mode at a frequency of 8 scans⋅s-1 and the downloaded data were
converted into physical units and pre-processed by using the SeaBird software (SeaSave/SBE
DataProcesing-win32) with standard calibration values. Quality control and post-processing
was performed with MATLAB.
Atlantis 2008 stratified bottom trawl survey enabled the assessing of the biomass and bathy‐
metric distribution of the main commercial and most abundant fishery stocks by means of the
swept area method. The survey used a stratified random design with strata boundaries
definedby latitude and depth ranges, depth strata 1-7 located south of parallel 45°S and depth
strata 8-13 sited north of the referred parallel (Table 2). Scheduled fishing stations (hauls of 30
min) were performed using a Lofoten bottom trawl net fitted with a rockhopper mix train with
bobbins and rubber separators, suitable for deep-water fishing over irregular bottoms. Mean
trawl speed was of 3.2 knots and trawl direction followed the bathymetric profile in the upper
slope, but was variable in the outer shelf and middle slope.
Data recorded by scientific onboard observers from 1989 to 2007 between latitude 42°S and
48°S were used for mapping only the Spanish fishery footprint, since fishing data of other fleets
were unavailable to us. The IEO observers’ program placed one observer per selected vessel
to cover 12% to 15% of the whole fleet. Table 3 summarize the activities (number of hauls
year-1) of the IEO observers on the HS of the SW Atlantic, were Divisions 42 and 46 correspond
to the areas roughly around parallels 42°S and 46°S.
Data used for each fishing haul corresponded to the middle tow position, since it offers more
relevant information than the initial or final positions. All middle tow positions were imported
into ArcGIS 9.3 mapping software to plot all trawl tows as straight lines between the reported
start and end positions. They were then exported to a grid of 5’×10’ min blocks, and any block
including at least two tows was retained for mapping the bottom trawl footprint.
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No. of hauls made
Depth range
Surface
No. of grids
No. of scheduled
(m)
(mn2)
(~5 mn2)
hauls
Valid
1
<200
1148
219
12
12
2
201-300
272
51
3
4
3
301-400
381
71
4
3
4
401-500
518
119
7
7
5
501-700
1513
318
18
18
6
701-1000
1952
349
20
20
3
7
1001-1500
2007
435
24
2
5
8
<200
1394
254
14
15
9
201-300
111
24
2
2
10
301-400
121
21
2
2
11
401-500
78
26
2
2
12
501-1000
933
170
10
12
13
1001-1500
Strata
Total
Null
2507
515
29
26
5
12933
2571
147
125
13
Table 2. Scheme of hauls by depth stratum, and main characteristics (ATLANTIS 2008 cruise).
Year
Division 46
Division 42
Total
1989
756
734
1490
1990
411
222
633
1991
152
28
180
1992
561
9
570
1993
515
0
515
1994
469
0
469
1995
186
0
186
1996
310
21
331
1997
811
35
846
1998
709
0
709
1999
384
4
388
2000
590
44
634
2001
673
111
784
2002
452
142
594
2003
191
0
191
2004
472
0
472
2005
561
1
562
2006
477
0
477
2007
333
0
333
Total
9013
1351
10,364
Table 3. Number of hauls/year and division recorded by scientific observers.
8 Biodiversity in Ecosystems - Linking Structure and Function
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Proper identification of the areas where VMEs are present followed the methodology used by
the NAFO in its Regulatory Area [60]. Threshold catches, defined as catch levels of significant
concentrations of invertebrates to be considered as possible VME areas, were assessed by
analysing the cumulative biomass frequencies. Cumulative catch curve method was chosen to
calculate the threshold catch. The cumulative frequency was plotted for all capture sets where
taxa, considered as vulnerable by the International Guidelines for the Management of Fisheries
[54] and by the Convention for the Protection of the marine Environment of the North-East
Atlantic (OSPAR), were identified. The threshold selection for each taxon was made on the
basis of minimum/maximum catch, density and morphological characteristics. Once a location
of significant concentrations of vulnerable organisms was defined (key location), a 2 nm radius
buffer zone around it was drawn to provide a safe margin of error on site.
The Random Forest algorithm for classification (RF) was used to predict the potential distri‐
bution of vulnerable benthic species by rating environmental conditions on the basis of
previous observations.
RF is a non-parametric statistical method for data analysis that makes no distributional
assumptions about the predictor or response variables [61], showing high prediction accuracy
classifying rocky benthic communities [62] and beating other methods commonly used for
ecological prediction [61,63]; The algorithm calculate the suitability of a given habitat for a
given species based on known affinities with habitat characteristics, stored as raster maps, and
called independent ecogeographical variables (EGV). According to HSI values, a map of
species’ expected distribution is produced, a value ranging from 0 to 1 showing the probability
that the habitat of a given location is suitable for the species occurrence [64]. Thus, for a
particular location, high HSI values mean high chances of the species' occurrence. To perform
this mapping, presence/absence data from different vulnerable benthic organisms found in the
study area were used as dependent variables of the different EGV.
Gathering accurate sampling presence/absence data is a critical part of the study, since the
absence of a species in a given location can be due to several reasons: the species is present but
is not observed, the species is absent even though the habitat is suitable, or the species is absent
because of the unsuitability of the habitat. Only the last reason is considered as a “true absence”
[65,66]. As presence data were aggregated into one single group named “vulnerable organ‐
isms”, the resulting HSI predicted the potential habitat of any of the considered vulnerable
organisms in the HS of the SW Atlantic under study.
The RF method offers the possibility to calculate an accurate unbiased estimator, using OutOf-Bag (OOB) observations as an internal validation data set [67], computed from the resulting
confusion matrix [68]. Accuracy is the proportion of the total number of predictions that were
correct and this accuracy indicator is offered to the user as a measure of the model’s predictive
performance. It is determined using the equation:
ACC OOB =
TS + TU
N
Where TS is the number of truly suitable locations, i.e. suitable locations correctly classified
by the model; TU is the number of truly unsuitable locations, in other words unsuitable
locations that have been correctly classified; and N is the total number of observations.
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Data was analyzed using the R statistical software [69] and the “Random Forest” package [70]
and predictions were exported to a shapefile format using the “maptools” package [71]. GIS
visualization of results was performed using the ESRI ArcMap 10.0 software.
Selected environmental variables involved in the study included depth, slope, sea bottom
temperature, substrate characteristics and topographic position (Table 4). The topographic
position was sorted into six categories: shelf (1), outcrop areas on the shelf (2), high slope (3),
low slope (4), abyssal flats (5), and canyons (6).
Variable
Hydrography
Type of variable
Range (min- max)
Sea bottom temperature
Continuous
1.70ºC – 6.14ºC
Bathymetry
Continuous
110.1m – 1848.6m
Slope
Continuous
0º – 14.792º
Q50
Continuous
2 – 3.59
Coarse sand fraction
Continuous
0.17% – 11.2%
Fine sand fraction
Continuous
57.2% – 97.28%
Mud fraction
Continuous
2.17% – 41.31%
Seabed morphology
Discrete
1–6
Topography
Substrate characteristics
Topographic position
Table 4. Summary of the environmental variables used for the Habitat Suitability Index (HSI) modelling of vulnerable
organisms.
CTD stations’ sea bottom temperature data were interpolated for the whole area using the local
polynomial interpolation function (LPI) implemented in the ArcGIS 10.0 software. Slope was
derived from the bathymetry high resolution data, and after studying the semivariogram,
substrate characteristics were interpolated from granulometrical measures for the whole area
using a universal kriging interpolator (Unpublished).
All the explanatory data were extracted for the presence/absence data locations, subsequently
exported and analysed with the R statistical software using the BIOMOD package [72]. Several
presence/absence models were performed: Generalized Additive Models [73,74], Multivariate
Adaptative Regression Splines [74,75], Generalized Boosting Models [74,76] and Random
Forest model (RF) [67,74].
3. Results
3.1. Geomorphology
Geomorphological and geophysical data from the five research cruises revealed that the outer
shelf was mantled by 15 m high sand ridges, and was 60 to 67 m deeper than the maximum
120 m lowering of sea-level during the last glacially induced regression. This difference in
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depth indicates that the PS had experienced subsidence in the Holocene. These ridges are relict
and were probably constructed during the post-glacial transgression by the north flowing
Falkland (Malvinas) Current, since they are resting on shell layers of <35,000 to 11,000 years
old [77].
The upper continental slope descends from the shelf break, located at depths from 200 to 750
m, and is scarred by iceberg plough marks whose orientation and morphology suggest that
icebergs carried northwards by the Falkland (Malvinas) Current were probably responsible
for this erosion during the last glaciations [78].
Scattered over the study area (south of 45˚S) we found pockmarks, carbonate mounds formed
by deep-water corals, northwards furrows, areas of smooth topography and sediment waves
indicating that deposition on this part of the middle slope is controlled by bottom currents [79].
Seven submarine canyons were identified on the middle slope surveyed (Figure 2). Canyons
1 to 6 were cut by turbidity currents, whereas canyon 0 resulted from the combined effect of
turbidity currents and coalescence of pockmarks (formed by the expulsion of thermogenic
gas). These gas and fluid seepages contributed to the formation of canyons and to the partial
detachment of blocks from the canyon walls. Thus, the thermogenic gas responsible for the
formation of the identified pockmarks on the middle slope could be deep-seated, probably
related to the Falkland Rift Basin, north of the Falkland (Malvinas) Islands [80,81].
Figure 2. Colour shaded 3D bathymetric map of a segment of the Patagonian Argentinian margin compiled from mul‐
tibeam backscatter data. Arabic numbers identify submarine canyons discussed in text. CS=Continental shelf; US=Up‐
per continental slope; MS=Middle continental slope; P=Pockmark; PL=Iceberg plough marks.
The association of gas seepage with deep-water corals has been reported by [82] in pockmarks
off Brazil. If such association also occurs on the Patagonian margin, those communities may
be quite widespread in our study area.
3.2. Benthic communities
Bathelia candida (Moseley, 1881) was found to be one of the main reef builder species in the
study area, providing habitat for diverse associated fauna of sponges, crustaceans, echino‐
derms, molluscs, and other cnidarians. The benthic megafauna caught during the cruises
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included invertebrates as well as Phyla Chordata and Hemichordata. Phyla Cnidaria and
Porifera were dominant in terms of biomass (46% and 30%, respectively [Figure 3A]). The high
abundance of Cnidaria is remarkable, since 33.7% of the biomass of this phylum corresponded
to the Class Octocorallia, including significant groups such as gorgonians (sea fans), alcyona‐
ceans (leather corals) and pennatulaceans (sea pens). In addition, the VMEs dominated by
suspensivore and/or filter feeding organisms are habitats with high biodiversity and many
resources.
Figure 3. Biomass per Phyla in total strata (A) and by stratum < 200 (B), 201-300 (C), 301-400 (D), 401-1000 (E),
1001-1500 m depth (F).
A large part of the benthic samples contained erect sponges, octocorals, colonial scleractini‐
ans, calcified antipatharians, and hydrozoans (Family STYLASTERIDAE), all of them slowgrowing organisms considered as vulnerable by the UN and the OSPAR standards (see Table
7).
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Bathymetric strata differences clearly arise by comparing the composition of the sampled
benthic megafauna (Figures 3B-F):
Strata 1 and 8 (<200 m) showed a low catch of benthos (17,209 and 41,202 g. respectively), both
in number and diversity. We observed a strong dominance of pectinid molluscs of the Genus
Zygochlamys (Ihering, 1907) (60.39% of the biomass [Figure 3B]), mainly Z. patagonica (King &
Broderip, 1832), followed by those of the Genus Chlamys (Röding, 1798). Vulnerable organisms
were practically unrepresented in these shallower strata, probably due to the bottom trawling
activities for years by bottom trawlers from international fleets.
Strata 2 and 9 (201-300 m) recorded the lowest catch in terms of biomass (2121 and 1576 g,
respectively). In these strata, detritivorous and opportunistic species were predominant, and
the presence of vulnerable organisms was negligible again. Compared to strata 1 and 8, we
observed an increase of the benthic cnidarians’ biomass values, dominated by gorgonians from
Family PRIMNOIDAE (Milne Edwards, 1857) (Octocorallia; Gorgonacea) (80.32% in biomass,
[Figure 3C]).
Strata 3 and 10 (301-400 m) were hardly sampled due to the reduced number of valid hauls (3
in stratum 3 and 2 in stratum 10). The low benthic biomass and the negligible presence of
vulnerable organisms (Figure 3D) could be attributed to bottom fishing activities, as above‐
mentioned for strata 1 and 8.
Strata 4, 11, 5, 6, and 12 of intermediate depths (401-1000 m, [Figure 3E]) recorded high biomass
and numbers of octocorals, sponges, colonial scleractinians (Bathelia candida), and large
hydrocorals. Octocorals included colonies of various genera belonging to families PRIMNOI‐
DAE and ISIDIDAE. As aforementioned, the increase and proliferation of these species create
complex 3D structures providing the ideal habitat for a wide range of organisms. In those
strata, the large amount of filter feeders and suspensivore sessile organisms is an indication
of the presence of unaltered, complex and structured ecosystems. In the future, ROV and other
submersible camera systems could confirm these assumptions.
Strata 7 and 13 (1001-1500 m, [Figure 3F]) were the most difficult ones for trawling. Numerous
tows failed to produce valid results. In these strata, the highest proportion of animals was of
benthopelagic crustaceans, usually making diel migrations, even though they were normally
present on the seafloor. Benthic cnidarians were dominated by octocorals of the Order
PENNATULACEA (Verrill, 1865), with a wide bathymetric distribution, adapted to live on
soft substrates.
3.3. Sediments
Sediment data obtained during Patagonia 1207, Patagonia 0108, and Atlantis 2008 cruises
showed that fine sands were generally predominant throughout the study area, with low
contents of organic matter and sediment sorting varying from poor to moderately good. In
more detail, the bathymetric sedimentary classification would be as follows:
Depths <200 m: fine sand (mean diameter=210 µm) with low organic matter content (mean
value=1.14 %), moderately sorted.
Depths from 201 to 400 m: fine sand (mean diameter ranging from 150 to 189 µm) with low
organic matter content (mean value=1.06%), moderately well sorted.
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Depths from 401 to 700 m: very fine sand (mean diameter from 110 to 120 µm) recording the
highest organic matter content (mean value ranging from 2.23% to 2.35%) and also the highest
percentage (up to 44.50%) of silt and clay (<62 µm). Sorting was poor to moderate.
Depths from 701 to 1500 m: fine sand sediments similar to those of the shallowest stratum
(mean diameter 160 to 190 µm), with low organic matter contents (mean value ranging from
1.43% to 1.68%). Moderately sorted.
Depths >1501 m: the deepest stratum, located in the bottom of submarine channels and
canyons, was characterised by the presence of heterogeneous sediments mainly composed of
fine sand (mean diameter=200 µm), with low organic content (mean value=1.68%) and poor
sorting. This stratum showed the highest percentage (up to 39.5%) of coarse particles (>500
µm).
3.4. Fishery footprint
The statistical analysis of the bottom trawl footprint plot generated with the georeferenced
fishery data obtained by the IEO scientific observers (between 1989 and 2007, 9013 fishing
operations) showed that most of the commercial hauls of the Spanish fishing fleet in the study
area (99.85%) took place at depths below 300 m (Figure 4).
Figure 4. Location of commercial hauls and fishery footprint (5’×10’) of the Spanish bottom trawl fleet on the HS of the
SW Atlantic (1989-2007).
14 Biodiversity in Ecosystems - Linking Structure and Function
302
4. Multivariate analysis
4.1. Model selection
Predictive accuracy of the models was evaluated through multiple cross-validation proce‐
dures, splitting the original data three times into two random subsets for calibration (80% data)
and evaluation (20% data). The mean area under the receiver operating characteristic (ROC)
curve (AUC) obtained from the three repetitions served to assess the predictive performance
index of the model. AUC ranks from 0.5 to 1, null accuracy or perfect accuracy of the model,
respectively [83]. Table 5 shows the best predictive performance score of the RF model, which
was subsequently chosen for vulnerable species modelling.
Model
Mean cross validation score
RF
0.876
GBM
0.825
MARS
0.804
GAM
0.778
Table 5. Validation of the predictive performance of the four candidate presence/absence models tested (RF: Random
Forest; GBM: Generalized Boosting Model; MARS: Multivariate Adaptative Regression Splines; GAM: Generalized
Additive Model).
4.2. Variable influence
The Mean Decrease Gini method, implemented in the BIOMOD package, was used to measure
the importance of the dependent variable. This exploration tool shows graphically the total
decrease in node impurities from splitting on the variable, averaged over all trees. Thus, for
classification, the node impurity is measured by the Gini index [72]. The higher the value in
the X axis, the higher importance the indicated variable will have on the classification of the
dependent variable. Figure 5 show that the topographic position is the main variable affecting
the distribution of vulnerable organisms in the HS of the PS, followed by the slope and the sea
bottom temperature. Comparatively, the sea floor granulometry has a negligible effect on the
distribution of the vulnerable organisms.
In addition to this, it is possible to visualize how each environmental variable, independently
from any other, influences the response variable using partial dependence plots [73], which
graphically represents the relationships between each predictor variable and the predicted
occurrence probabilities of the vulnerable organisms obtained from the RF model. Figure 6
show that bathymetry has a positive effect between 500 and 1000 m depth. Regarding the
topographic position, the highest interactions with the presence of vulnerable organisms were
observed in canyons (6), followed by abyssal flats (5) and the slope (3 and 4). On the shelf, only
outcrop areas (2) were positively correlated with the dependent variable.
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Figure 5. Mean Decrease Gini for each explanatory variable in the RF model. Higher values in the X axis indicate high‐
er influence of the environmental variable on the occurrence of benthic vulnerable organisms.
Figure 6. Partial dependence plots showing quantitative influence from each environmental variable on the occurrence
of benthic vulnerable organisms predicted probability.
16 Biodiversity in Ecosystems - Linking Structure and Function
304
4.3. HSI mapping
Table 6 shows the presence data of benthic vulnerable organisms from the 169 sampled
locations. Predicted values were plotted to produce a habitat suitability map showing survey
sampling stations with presence/absence data and the vulnerable organisms’ probability of
occurrence (Figure 7).
Organism
Presence
Alcyonacea
15
Bathelia candida
23
Demospongiae
22
Gorgonacea
24
Hexactinellidae
14
Hydrozoa
41
Pennatulacea
7
Rhodalidae
9
Stylasteridae
25
Total VO
76
Table 6. Summary of the presence sampling data of vulnerable organisms (VO).
Figure 7. HSI map of benthic vulnerable organisms. Higher probability of occurrence is shown in darker tones. Survey
sampling stations are overlapped, showing presence (black dot) or absence (circle) of such organisms.
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5. Conclusions
Multibeam acoustic data showed that the upper slope and uppermost middle slope were
scarred by iceberg plough marks. The middle slope surveyed was entrenched by seven
submarine canyons [78]. Pockmarks and other seismic and morphologic evidence of gas/fluids
seepage were pervasive throughout the entire survey area and more intense in the southern
middle part [80]. Water coral communities associated with those pockmarks could be quite
extensive in the study area.
The highest benthic biodiversity was found between 800 and 1500 m depth. Biodiversity was
higher along the continental margin (per an equal number of individuals, and in terms of
abundance) than biodiversity found along the continental shelf. Our results have confirmed
the existence of close ecological relationships between Patagonian deep-sea fauna and
Antarctic fauna of shallow waters. Benthic megafauna collected included invertebrates,
chordates, and hemichordates. There was a clear dominance in biomass and diversity of the
Phyla Porifera and Cnidaria. Most species of these groups are considered as vulnerable
according to UN and OSPAR criteria: sponges, octocorals, colony scleractinians, anthipatari‐
ans, calcified hydrozoans (Family STYLASTERIDAE), and erect bryozoans (Table 7).
Shallow waters (<400 m) are the strata having sustained most of the fishing pressure for almost
50 years. Below 400 m we recorded the lowest biomass, abundance and diversity values, most
likely due to this fishing pressure. In these strata we noted the presence of sparse organisms
with erected growth, a high dominance of pectinid mollusks (Zygochlamys patagonica), and
minor presence of species considered as indicators of VMEs.
Intermediate depths (401-1000 m) showed an important increase in number and biomass of
vulnerable organisms, with outstanding numbers, densities and biomasses of octocorals,
sponges, colony scleractinians (Bathelia candida) and big hydrocorals (Errina spp., Cheiloporidion
pulvinatum [Cairns, 1983], Sporadopora sp., and Stylaster densicaulis [Moseley, 1879]). Also
remarkable was the presence of sponges of the Family CLADORHIZIDAE (Dendy, 1922), a
group of a great zoological importance because they are carnivorous and have developed a
trophic adaptation to live in the ocean’s depths.
In deeper strata (1001-1500 m) we found more anomuran crustaceans of the Family LITHO‐
DIDAE (Samouelle, 1819) (mainly Paralomis formosa [Henderson, 1888]). Amongst benthic
cnidarians, the pennatulid octocorals (Order PENNATULACEA) were the most abundant.
The model accuracy is acceptable (0.876). Although the modelling’ accuracy values were
higher when considering each organism, this was an expected fact due to the different
environmental preferences of the studied organisms. However, HSI mapping is a useful
conservation management tool enabling an initial observation of how environmental condi‐
tions control the spatial distribution of vulnerable organisms in the study area. The research
will proceed further when data from all 13 survey cruises undertaken in the area are analysed.
The main environmental conditions affecting presence of vulnerable organisms seems to be
connected to the topographic position, slope and bathymetry. Sea bed granulometry appeared
18 Biodiversity in Ecosystems - Linking Structure and Function
306
Porifera Grant, 1836
Chondrocladia sp.
Class Hexactinellida Schmidt, 1870
Euchelipluma sp.
Rossella antarctica Carter, 1872
Mycale (Oxymycale) acerata Kirkpatrick, 1907
Class Demospongiae Sollas, 1885
Mycale (Carmia) gaussiana Hentschel, 1914
Tetilla leptoderma Sollas, 1886
Isodictya kerguelenensis Ridley & Dendy, 1886
Cynachyra sp
Latrunculia sp.
Geodia sp.
Axinellidae indet.
Polymastia sp.
Haliclona (Haliclona) sp.
Radiella sp.
Haliclona (Gellius) sp.
Tentorium sp.
Dictyoceratida indet.
Stylocordyla cf. stipitata
Cnidaria Hatschek, 1888
Timea sp.
Class Hydrozoa Owen, 1843
Lithistidindet.
Errina sp.
Iophon sp.
Stylaster cf. densicaulis
Clathria sp.
Class Anthozoa Ehrenberg, 1831
Raspailia sp.
Alcyonium sp.
Inflatella sp.
Anthomastus sp.
Pyloderma latrunculioides Ridley & Dendy, 1886
Paragorgia sp.
Desmacidon, sp.
Primnoella sp.
Hymedesmia (Hymedesmia) sp.
Isididae indet.
Hymedesmia (Stylopus) sp.
Anthoptilum sp.
Phorbas sp.
Halipteris sp.
Myxilla (Myxilla) mollis Ridley & Dendy, 1886
Epizoanthus sp.
Myxilla (Burtonanchora) lissostyla Burton, 1938
Actinostola crassicornis Hertwig, 1882
Tedania (Tedaniopsis) charcoti Topsent, 1907
Bathelia candida Moseley, 1881
Tedania (Tedaniopsis) oxeata Topsent, 1916
Caryophyllia sp.
Tedania (Tedaniopsis) massa Ridley & Dendy, 1886
Desmophyllum sp.
Tedania (Trachytedania) sp.
Flabellum sp.
Asbestopluma sp.
Table 7. Cold-water corals and deep-water sponges concentrations: list of most common species collected in the
campaigns of the Atlantis Project in 2007 and 2008.
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to have a negligible effect on the presence of vulnerable organisms, contradicting published
research results on this subject in other geographical areas, where substrate characteristics
determine to a large extent the presence or absence of a particular benthic species [84-86].
Our study only calculated the general trends of the granulometrical parameters, while
bathymetry, slope and topographic position were variables derived from high resolution data,
strongly correlated with the response variable. Therefore, local conditions are the main factors
ruling the potentiality of a habitat to host benthic vulnerable organisms in the HS of the PS.
The use of the Random Forest model offers both higher classification accuracy and determi‐
nation of variable importance, and more stability where small perturbations of the data exist
[76]. RF is a predictive classification and algorithm that does not make any distributional
assumptions about the predictor or the response variables. It also handles situations in which
the number of predictor variables exceeds the number of observations, offering a powerful
non parametrical alternative for ecological modelling [64].
The vulnerable species groups, communities and habitats described here are mainly distrib‐
uted beyond the 500 m depth contour. The presence of organisms considered as vulnerable is
almost negligible in the fishing area. This fact is almost certainly due to bottom trawling
operations of international fleets taking place in the study area for nearly 50 years. Also, the
fishing grounds are far away from the geographical location of the main geomorphological
features such as canyons, trenches, gas and fluid seepages observed in the middle slope, and
identified as potential sites for VMEs.
The fishery footprint plot shows that the historical activity of the Spanish bottom trawler fleet
has been located in the shallowest depth strata, at depths not generally exceeding 300 m. On
this basis we think that the adverse impacts of current bottom fishing activities on VMEs are
negligible or small. However, the displacement of the fishing fleet to target deep sea species
at greater depths (were the existence of VMEs has been observed) could have a negative impact
on those ecosystems. With this in mind and following the FAO deep-water guidelines, the
potential threat of such a fishing strategy should be assessed.
Apart from Spanish fishing fleet, other bottom trawling fleets from different nations (former
Soviet Union, Poland, GDR, Bulgaria, etc) have been operating intensively in the SW Atlantic
(including our study area) from mid 60’s until mid 80’s, both over the continental shelf and
slope [87-92]. Even if no data were made available to us for assessing the eventual negative
impact of these fleets on VMEs, some experiences in other geographical areas such as the North
Atlantic, Southwest and East Pacific, seamounts off Tasmania, and waters off New Zealand
[31,45,92], have shown that high fishing pressure exerted by a large number of bottom trawlers
over a long period of time could relevantly affect these VMEs. We therefore think that probably
the almost 50 years of intensive bottom trawling in this SW Atlantic area by the abovemen‐
tioned fleets could have contributed to the low presence of VMEs in the study area at depths
lower than 500 m.
20 Biodiversity in Ecosystems - Linking Structure and Function
308
Acknowledgements
We wish to thank the crew of the R/V “Miguel Oliver” (owned by the Spanish General
Secretariat for Fisheries [SGP]) and her captain, for the professionalism and the courtesy
extended towards us during the research cruises. We are also very grateful to all those involved
in the five research campaigns, namely the scientific and technical personnel who made this
work possible, known as the Atlantis Group: J. M. Cabanas, J. Gago, B. Almón, E. Elvira, P.
Jiménez, A. Fontán, C. Alcalá, and V. López, among others. Our thanks also to the Spanish
Secretaría General del Mar (SGM, General Secretariat for the Sea), owner of the research ship,
for giving us the opportunity to conduct this research.
Author details
J. Portela1*, J. Cristobo2, P. Ríos2, J. Acosta3, S. Parra4, J.L. del Río1, E. Tel3, V. Polonio2, A. Muñoz5,
T. Patrocinio1, R. Vilela1, M. Barba1 and P. Marín5
*Address all correspondence to: julio.portela@vi.ieo.es
1 Spanish Institute of Oceanography (IEO), Vigo Oceanographic Center. Fisheries Dept.
Vigo, Spain
2 Spanish Institute of Oceanography, Gijón Oceanographic Center. Benthos Dept. Gijón,
Spain
3 Spanish Institute of Oceanography, Headquarters Madrid. Geology Dept. Madrid, Spain
4 Spanish Institute of Oceanography, A Coruña Oceanographic Center. Epibenthos Dept. A
Coruña, Spain
5 Multidisciplinary Mapping Group (TRAGSATEC-SGP), Spanish General Secretariat for
Fisheries (SGP). Cartography Dept. Madrid, Spain
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