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Fine-scale habitat characterization of The Gully, the Flemish Cap, and the Orphan Knoll, Northwest Atlantic, with a focus on cold-water corals


Abstract and Figures

This case study focuses on the characterization of fine-scale habitats associated with cold-water corals in three areas off Eastern Canada. Remotely operated vehicle (ROV)-based video, oceanographic, and bathymetric data were collected in 13 dives ranging from 200 to 3000 m deep at The Gully, the Flemish Cap, and the Orphan Knoll. Maps of potential habitats were produced and spatially compared with different taxa distributions, and species distribution models were computed to quantify the association of different environmental factors with cold-water corals. Results highlighted significant differences in corals' environmental preferences at all taxonomic levels. Results also showed the importance of collecting high-resolution chemical and oceanographic data as their integration with geomorphometric variables (e.g., aspect, rugosity, slope, topographic position) provides a more comprehensive picture of environmental niches. The exploration of cold-water coral habitats helped identify many cases of co-occurrences with a variety of other taxa including many deep-sea sponges.
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Fine-scale habitat characterization
of The Gully, the Flemish Cap, and
the Orphan Knoll, Northwest Atlantic,
with a focus on cold-water corals
Vincent Lecours
, Luka
, Evan Edinger
and Rodolphe Devillers
Fisheries & Aquatic Sciences Program, School of Forest Resources & Conservation, University of
Florida, Gainesville, FL, United States
Geomatics Program, School of Forest Resources &
Conservation, University of Florida, Gainesville, FL, United States
Department of Applied
Geoinformatics & Spatial Planning, Czech University of Life Sciences Prague, Prague, Czech Republic
Department of Geography, Memorial University of Newfoundland, St. John’s, NL, Canada
Department of Biology, Memorial University of Newfoundland, St. John’s, NL, Canada
This case study focuses on the characterization of fine-scale habitats associated with cold-water corals
in three areas off Eastern Canada. Remotely operated vehicle (ROV)-based video, oceanographic, and
bathymetric data were collected in 13 dives ranging from 200 to 3000 m deep at The Gully, the
Flemish Cap, and the Orphan Knoll. Maps of potential habitats were produced and spatially compared
with different taxa distributions, and species distribution models were computed to quantify the
association of different environmental factors with cold-water corals. Results highlighted significant
differences in corals’ environmental preferences at all taxonomic levels. Results also showed the
importance of collecting high-resolution chemical and oceanographic data as their integration with
geomorphometric variables (e.g., aspect, rugosity, slope, topographic position) provides a more
comprehensive picture of environmental niches. The exploration of cold-water coral habitats helped
identify many cases of co-occurrences with a variety of other taxa including many deep-sea sponges.
Keywords: The Gully; Flemish Cap; Orphan Knoll; cold-water corals; deep-sea sponges;
geomorphometry; oceanography; potential habitats; ROV; surrogacy
This study describes fine-scale benthic habitats in three areas off Eastern Canada
(Northwest Atlantic Ocean), with a particular focus on cold-water coral habitats. The Gully,
Seafloor Geomorphology as Benthic Habitat.
©2020 Elsevier Inc. All rights reserved.
the Flemish Cap, and the Orphan Knoll (Fig. 44.1) are known to offer diverse habitats and
to shelter relatively high levels of biodiversity. Ecological marine units (see Sayre et al.,
2017) found in those three areas are generally characterized by cool (,70 m deep) to very
cold waters ( .70 m deep), normal salinity, moderate oxygen levels, medium nitrate levels,
and low phosphate and silicate levels.
Located at the edge of Canada’s continental shelf, The Gully is the largest submarine
canyon in eastern North America and extends from 150 m deep to depths greater than
2000 m. The eastern side of The Gully is characterized by recessional moraines that can
reach several kilometers long and tens of meters in height (Cameron et al., 2016). On the
west side smaller canyons and channels are characterized by ledges, terraces, and steep
slopes of sandstone and semiconsolidated mudstone (Fader and Strang, 2002; Mortensen
and Buhl-Mortensen, 2005). Sandwave fields are also found at the head of The Gully
(Cameron et al., 2016). Mortensen and Buhl-Mortensen (2005) highlighted the geomorphic
and habitat diversity of The Gully.
The Flemish Cap is a continental fragment forming an isolated offshore bank about 600 km
east of the island of Newfoundland, Canada (Piper, 2005). It extends from 126 m deep to
more than 3000 m deep at the bottom of the continental slope (Shaw, 2006). The Flemish
Cap is characterized by steep sides and by narrow and abrupt margins (Funck et al., 2003);
its southern margin has numerous faults and tilted blocks (Welford et al., 2010). The
Flemish Cap has a low sedimentation rate (Huppertz and Piper, 2010), and is one of the
only places at continental shelf depth in Atlantic Canada that does not have extensive
deposits of late Pleistocene glacial till covered with younger proglacial and Holocene
postglacial terrigenous sediment (Piper, 2005; Edinger et al., 2011), allowing continuous
occupation of sponge grounds since the last glacial maximum at least (Murillo et al., 2016).
While the Flemish Cap was apparently not glaciated during the last glacial cycle, it is
strongly influenced by sea ice and icebergs that deliver mixed lithology clasts ranging from
coarse sand through to large boulders, making hard substrates (e.g., boulder-strewn bedrock,
coarse sand with ice-rafted debris) predominant (Shaw, 2006). The hydrodynamic
conditions around the Flemish Cap, influenced by the Labrador Current, the Deep Western
Boundary Current, the Gulf Stream, and the North Atlantic Current, favor a higher level of
primary and secondary production than the rest of the Canadian continental shelf (Maillet
et al., 2005; Stein, 2007).
The last site, the Orphan Knoll, is a foundered fragment of continental crust - a portion of
continental crust that has sunk to depths below the typical depth of the continental shelf -
formerly linked to the Canadian continental margin (Ruffman and van Hinte, 1973;
Enachescu, 2004). While it is less known than the other two areas because of its depth (but
see Ruffman and van Hinte, 1973), recent investigations at the Orphan Knoll established the
presence of a variety of geomorphological features, including seamounts, valleys, terraces,
736 Chapter 44
Figure 44.1
Map of Eastern Canada showing the three study areas, with the location of the different ROV
dives identified by a star. Examples of the distribution of individual organisms along three
transects—those with a red star—are also shown. The pie charts describe the ratio of observations
of corals, sponges, and other taxa for each of the three areas, and the associated absolute
number of observations. For example, 1616 corals were observed during the six dives at the
Orphan Knoll, representing 8% of all observations recorded on the Orphan Knoll.
and steep slopes (Pe-Piper et al., 2013). The top of the Orphan Knoll, at about 1800 m
deep, contains abundant enigmatic mounds, generally 50150 m high, which together form
a plateau (Parson et al., 1984). Several possible origins for those mounds have been
proposed, including block-faulted bedrock (Meredyk, 2017), a karst plateau of Paleozoic
carbonates (Parson et al., 1984), cold-water coral bioherms (van Hinte et al., 1995), or
hydrocarbon-related mud-volcanos (Enachescu, 2004). Like the Flemish Cap, the Orphan
Knoll was likely not covered by ice during the last glacial maximum and is influenced by
the Labrador Current (Shaw, 2006; Shaw et al., 2006).
This case study focuses on smaller areas located on the slopes of The Gully and the
Flemish Cap, and on the top and sides of the Orphan Knoll and Orphan Seamount.
Naturalness, condition, and trend
Table 44.1 summarizes the condition and trend of the three study areas. While those
describe the entire extent of the features, the smaller surveyed areas had different levels of
naturalness than the features they are on. For instance, one of the surveyed areas at the
Flemish Cap had been trawled. However, most other areas were in more rocky habitats and
did not seem to have been impacted, at least by fishing.
The Gully has long been recognized for its biodiversity, including sensitive benthic fauna
such as corals and sponges, and for its resident population of northern bottlenose whales.
Groundfish fisheries, particularly bottom longline fishing for Atlantic Halibut, have been
long established on its margins. More recently, The Gully is surrounded by extensive
hydrocarbon production activities on the Sable Island Bank, including gas pipelines from a
Table 44.1: Summary of the condition and trend at the three study areas.
score Baseline Trend Confidence Notes
G5 overall
G6 in deeper
2000 Steady High Largely intact, with the exception of some of the
shallower portions of the MPA. The MPA has
been largely monitored since 2004.
G6 in deeper
1900 Steady High Heavily fished in shallow waters, only exploratory
fishing has occurred in deeper waters, which
now has fishery closures.
G7 1900 Steady High Too deep for fishing, no active exploratory oil
and gas drilling. Risk of decline if oil and gas
exploratory drilling expands to the western side
of the Knoll.
738 Chapter 44
number of wells. The Gully was voluntarily closed to oil and gas exploration in the late
1990s to protect the resident whales, and the area was officially protected as a Marine
Protected Area (MPA) in 2004. Bottom longline fishing for Atlantic Halibut is still
permitted in the shallow portions of the MPA, but most of the Gully itself is closed to all
fishing (DFO, 2008).
In international waters managed by the Northwest Atlantic Fisheries Organization (NAFO),
the Flemish Cap has an extensive fishery for a variety of groundfish species. While the
upper parts of the Flemish Cap have been extensively trawled, the deeper portions have had
limited impact from fisheries. European Union fisheries surveys encountered abundant
sponges and cold-water corals in deep portions of the Flemish Cap, which have enjoyed
temporary protection under NAFO fishery closures for Vulnerable Marine Ecosystems since
2012. The Flemish Pass, the deepwater channel between the Flemish Cap and the Grand
Banks, has been an active area for hydrocarbon exploration for more than 10 years, with
several major petroleum discoveries in the last 5 years. None of these have yet been
developed to the production stage.
Unlike the Flemish Cap, the Orphan Knoll is too deep for most commercial fishing. In 2006
NAFO protected the Orphan Knoll from fishing activities, except for experimental trawl
fisheries. While there have been extensive oil industry seismic surveys of the Orphan Knoll,
exploratory drilling has taken place within the Orphan Basin, to the west, but not directly
on the Orphan Knoll.
Data acquisition
High-resolution bathymetric, video, and environmental data were collected in July 2010
using the Remotely Operated Vehicle (ROV) CSSF ROPOS (Canadian Scientific
Submersible Facility Remotely Operated Platform for Ocean Science), at depths ranging
from 200 to 3000 m at The Gully (two overlapping transects), the Flemish Cap (five
distinct transects), and the Orphan Knoll (six distinct transects) (Fig. 44.1). The total length
covered by the transects is about 120 km. The bathymetric data were collected using an
Imagenex 837 A DeltaT3000 multibeam system mounted on the ROV. ROPOS
simultaneously collected downward-looking and forward-looking video observations from
about 2 m above the seafloor, in addition to a variety of high-resolution chemical and
oceanographic data (e.g., conductivity, density, nitrogen saturation, pH, plume anomaly,
pressure temperature, thermosteric anomaly). While those video data have been used before
for surficial geology and coral fauna studies on some of the transects (e.g., Meredyk, 2017;
Miles, 2018), this is the first study to look at all transects and to use the bathymetric and
oceanographic data.
Cold-water Coral Habitats of the NW Atlantic 739
Geomorphic features and habitats
Bathymetric data were processed in Caris HIPS and SIPS 9.1. The level of uncertainty
associated with the data and the prevalence of artifacts in the data prevented the production
of bathymetric surfaces at a very high resolution (Lecours and Devillers, 2015). The
bathymetry was thus generated at 20 m resolution and a low-pass filter was applied to
remove the remaining outliers. A validation analysis was performed against depth values
measured by the ROV navigation system and the ROV-mounted CTD instrument. A suite
of terrain attributes was derived from the bathymetry using the TASSE toolbox for ArcGIS
v1.1 (Lecours, 2017): measures of aspect (i.e., easterness and northerness), relative
deviation from mean value (hereafter referred to as topographic position), slope, and
standard deviation (hereafter referred to as rugosity). The combination of those measures is
considered to optimize the amount of topographic variability that is captured by terrain data
(Lecours et al., 2017).
The oceanographic and chemical data were processed using the SBE Data Processing
software from Sea-Bird Scientific. For instance, hysteresis and tau corrections were
applied to raw oxygen data. Those data were collected every second, enabling a one-to-
one match of environmental data with biological observations, which were extracted from
video data and georeferenced using the ROV navigation data. Biological observations
were logged at sea to the highest possible taxonomic resolution (i.e., lowest taxonomic
level) that could be confidently assigned by experienced observers with taxonomic
expertise; taxonomic resolution varied depending on observations as variations in
illumination, turbidity, and distance and location within the field of view often prevented
a confident identification of observations to the species or genus levels. Observations
were validated in postprocessing.
Differences in mean environmental conditions were statistically evaluated to assess
whether they were similar in the three areas. While The Gully generally presented
some minor differences (e.g., lower oxygen and nitrogen concentrations) when
compared to the other two areas, those differences were not statistically significant.
Following the approach detailed in Lecours et al. (2017), correlation and principal
component analyses were then performed to reduce the number of variables used for
the habitat mapping exercise. For example, those analyses identified relationships
between temperature and nitrogen saturation and between slope and rugosity in this
area. In the end, oxygen concentration, salinity, temperature, depth, rugosity,
topographic position, and aspect (i.e., easterness and northerness) were selected as
uncorrelated variables that were the most representative of this particular environment.
Because fine-scale data collected along transects do not characterize the environment at
a scale suitable for the identification of relevant geomorphic features, habitat maps
were produced using this combination of variables under the assumption that terrain
740 Chapter 44
attributes would serve as proxies for the presence of intermediate-scale (20100 m)
geomorphic features.
Following methods from Brown et al. (2012) and Lecours et al. (2016), an unsupervised
approach to habitat mapping was used to generate a map of potential habitats for each of
the three study areas. The multivariate clustering algorithm in ArcGIS Pro v.2.1.3 was used
to identify potential habitats, that is, classes that maximize similarity within themselves
while maximizing differences among them. A pseudo-F-statistics was used to determine the
optimal number of classes. Boxplots of standardized values for each environmental variable
were then analyzed to characterize each class, that is, to identify which environmental
factors make a class distinct from the others.
Results are summarized in Table 44.2 (The Gully), Table 44.3 (the Flemish Cap), and
Table 44.4 (the Orphan Knoll), and an example of the resulting classified transects is
shown in Fig. 44.2. Despite being the smallest area, the statistically optimal number of
classes for The Gully was the highest, with 14. The classifications for the Flemish Cap
and the Orphan Knoll respectively identified seven and eight statistically different
potential habitats. Some similarities can be found in the three different maps of potential
habitats. For example, flat areas located on topographic lows were identified as distinct
habitats at both The Gully (Habitat G14) and the Flemish Cap (Habitat F7), where they
respectively accounted for 2% and 31% of the surveyed areas. The Orphan Knoll also had
a similar potential habitat (Habitat O6), which covered about 39% of its surveyed area,
although it differed from habitats G14 and F7 by the fact that the topographic position
variable was on average similar to the other classes. In some cases, those results provide
insights into the presence of some specific geomorphic features. For instance, habitat G6,
which is characterized by waters that are shallower than the average for this dataset and a
low complexity seafloor located at topographic highs, could very likely represent
local plateaus.
In order to assess whether or not those potential habitats were associated with specific
taxa, habitatspecies associations were identified by spatially comparing the biological
observations extracted from the downward-looking camera to the habitat classes. Taxa
with more than 50% of their observations belonging to one specific habitat class were
associated with that class, and confusion matrices were built to evaluate the
classifications. Results are summarized in Tables 44.244.4; four of the potential habitats
at The Gully and at the Flemish Cap were found to be strongly associated with specific
taxa, compared to five at the Orphan Knoll. Those habitats cover 41% of the surveyed
area at The Gully, 67% of the area at the Flemish Cap, and 58% for the Orphan Knoll.
Some of the potential habitats were highly associated with only a few species. For
instance, Habitat F1, which covers about 10% of the surveyed area at the Flemish Cap
Cold-water Coral Habitats of the NW Atlantic 741
and was characterized by topographic highs in deep and cold waters, was only associated
with the presence of one species of bamboo corals (Keratoisis sp.). On the other end,
some potential habitats were associated with many different taxa. While the accuracy of
the three classifications was not very high (ranging between 62% and 70%), those maps
of potential habitats provide insights into some of the taxa’s environmental preferences.
For instance, many coral genera were found to favor areas of topographic highs and
higher rugosity, which suggest a relationship to elevated and complex seafloor topography
and geomorphic features.
Table 44.2: Summary of the characteristics of potential habitats at The Gully as defined by the
unsupervised classifications, and of the specieshabitat spatial relationships.
Main habitat characteristics
(distinctive from other
of area (%)
Habitat G1 Areas of topographic lows GFan sponges GAcesta
Habitat G2 Warmer waters with high
salinity and low oxygen
GPrimnoa sp.
GRadicipes sp.
GBall sponges
Habitat G3 Shallower waters GPolymastia sp. 20.5
Habitat G4 Areas with higher rugosity
Habitat G5 Coldest waters with high
oxygen concentration
Habitat G6 Shallowest waters with low
rugosity seafloor located on
topographic highs
Habitat G7 Warmer waters 1.2
Habitat G8 Areas with the highest
oxygen and lower salinity
Habitat G9 Colder waters 2.0
Habitat G10 Areas of topographic highs 10.2
Habitat G11 Deepest waters with high
rugosity seafloor
Habitat G12 Areas of topographic lows
with lower oxygen
Habitat G13 Deeper waters 13.4
Habitat G14 Flat areas on topographic
742 Chapter 44
Biological communities
Overall, more than 65,600 individual organisms were observed on the video data from the
downward-looking camera, of which 19,969 were corals and 24,468 were sponges. As
highlighted in Tables 44.244.4, other observations include, for example, various bivalves,
Table 44.3: Summary of the characteristics of potential habitats at the Flemish Cap as defined
by the unsupervised classifications, and of the specieshabitat spatial relationships.
Main habitat
(distinctive from other
classes) Associated corals
associated taxa
of area (%)
Habitat F1 Topographic highs in
the deepest, coldest
GKeratoisis sp. 10.1
Habitat F2 Shallower, warmer
GFlabellum sp.
GSea pens
GTube worms
Habitat F3 Higher rugosity areas
with lower oxygen
GAnthomastus sp.
GChrysogorgia sp.
GNephtheidae sp.
GParagorgia sp.
GParamuricea sp.
GStylaster sp.
GSwiftia sp.
GAsconema sp.
GGeodia sp.
GBall sponges
GFan sponges
Habitat F4 Shallower waters with
lower salinity and
higher oxygen
GPolymastia sp.
Habitat F5 Shallowest, warmest
waters with high
rugosity seafloor
Habitat F6 Higher oxygen
concentrations with
lower salinity
Habitat F7 Flat areas on
topographic lows
Cold-water Coral Habitats of the NW Atlantic 743
brittle stars, cephalopods, crustaceans, sea anemones, starfishes, tube-dwelling anemones,
urchins, tube worms, and ribbon worms. Inconsistencies with the reporting of observations
make it challenging to generalize them, particularly at higher taxonomic resolutions (e.g.,
species, genus). In addition to observations of individual organisms (e.g., one coral colony,
one sponge, or one urchin), areas with a concentration of a taxon that was too high to count
(high to full coverage of a video frame, e.g., sponge grounds) were identified. As such, 134
sponge concentrations were recorded at The Gully (including 49 concentrations of
Polymastiidae), 17,502 at the Flemish Cap (of which 910 were identified as encrusting
Table 44.4: Summary of the characteristics of potential habitats at the Orphan Knoll as defined
by the unsupervised classifications, and of the specieshabitat spatial relationships.
Main habitat
(distinctive from
other classes) Associated corals
associated taxa
of area (%)
Habitat O1 Areas of
topographic highs,
oriented Northwest
and other
Habitat O2 Shallowest areas of
topographic highs
with lower oxygen
GBamboo corals
Habitat O3 Deepest, coldest
waters with high
GChrysogorgia sp.
GNephtheidae sp.
GFan sponges
Habitat O4 Shallower waters,
seafloor oriented
Eastward (currents)
GFlabellum sp.
GVaughanella sp.
GTube worms
Habitat O5 Deeper waters with
high rugosity
of Geodiidae
Habitat O6 Flat areas 38.5
Habitat O7 Warmer waters with
low oxygen
Habitat O8 Warmest waters
with low salinity
levels and high
744 Chapter 44
sponges), and 5108 at the Orphan Knoll. Over a thousand concentrations of Ectoprocta
were also found on the Flemish Cap.
In terms of biodiversity, the Flemish Cap had the highest number of records per km
surveyed with almost 1200. With only one transect, The Gully came second with an
average number of observations per km of 549, which is about 250 more than for the
Orphan Knoll. The Orphan Knoll had significantly fewer coral observations per km than the
other areas (25 observations per km compared to 301 for The Gully and 470 for the
Flemish Cap), and The Gully had significantly fewer sponge observations per km than the
other areas (83 compared to 145 for the Orphan Knoll and 408 for the Flemish Cap). About
half of all observations on the Orphan Knoll were sponges and only 8% were corals
(Fig. 44.1). The Flemish Cap had a more balanced ratio of observations with 39% for
corals, 34% for sponges, and 27% for other organisms. Finally, The Gully had a higher
ratio of corals (55%) than sponges (15%) and other taxa (30%) combined.
The Paragorgia genus was relatively rare, with 16 observations of P. arborea at The
Gully, and only six observations at the Flemish Cap, including three of P.johnsonii.
Anthomastus grandiflorus were only identified to the species level at The Gully, but
Figure 44.2
Examples of habitat maps produced along three transects, one for each study area. The scale and
north arrow apply to the three examples.
Cold-water Coral Habitats of the NW Atlantic 745
Anthomastus sp. were abundant at the Flemish Cap with over 12,700 observations.
Acanella arbuscula were also more common at the Flemish Cap and to a lesser extent at
The Gully, but only two observations were reported at the Orphan Knoll. The Gully was
rich in Keratoisis sp.; all but one observation of K. ornata were found there.
Observations of Stylaster sp. (3) and Swiftia sp. (91) were only reported at the Flemish
Cap, and Radicipes sp. (3) were only found at The Gully. About 78% of the records of
Vaughanella sp. were from the Flemish Cap, with the others being from the Orphan
Knoll. The Orphan Knoll was mostly characterized by the presence of Chrysogorgia sp.
(579 out of 581 total observations). Overall, The Gully was the richest area in sea pens,
while the Flemish Cap was richer in soft and stony corals. On average, those two areas
had similar density of gorgonian corals. Compared to those regions, the Orphan Knoll
only displayed a higher density of black corals. Finally, The Gully presented the highest
rate of dead colonies, with an average of 8.5 observations per km (total of 34). A total
of 48 dead coral observations were made on the Flemish Cap, including 36 of
Paramuricea sp.,and45countswerereportedontheOrphanKnoll,sixofwhichwere
Desmophyllum sp.
In terms of individual sponges identified at the genus level, The Gully had the highest
density of Asconema sp. and Polymastia sp. While the Flemish Cap had higher densities of
Geodia sp., the Orphan Knoll had the highest abundance of Geodiidae F. Rare observations
of Stylochordyla sp. were only reported on the Flemish Cap. Glass sponges were not
abundant at The Gully compared to the other areas. Overall most observations of ball
sponges, encrusting sponges, and fan sponges were reported at the Flemish Cap, and most
of those of vase sponges came from the Orphan Knoll. Funnel sponges, finger sponges, and
branching sponges were only reported as such for the Orphan Knoll.
The most abundant observations for taxa other than sponges and corals were ophiuroids, sea
stars, crinoids, echinoids, and anemones (both Actiniaria and Ceriantharia). Individuals of
the Aphroditidae family were only recorded at the Orphan Knoll and the presence of Acesta
sp. was only noted at The Gully.
Species distribution models (SDMs) were used to measure the statistical relationships
between cold-water corals distribution and potential environmental surrogates. Models
were produced using biological data described at different taxonomic resolutions, based
on whether or not there were enough samples to produce valid models. For instance,
SDMs were not computed for Radicipes sp. and Stylaster sp. given that they only had
three observations each. However, those observations were integrated into models
at the family and order levels (e.g., Radicipes sp. observations in the model for
746 Chapter 44
Chrysogorgiidae). Two factors influenced the number of samples: the actual number of
observations and the availability of adequate environmental data at the location of the
Observations were separated in training and test subsets (70/30) to compute a suite of
validation measures, and replications of the models with different subsets were computed
to ensure consistency and provide an average contribution of each environmental variable
to the different taxa distributions.
Overall results confirm the trade-off involved between the number of samples and thematic
scale (i.e., taxonomic resolution) when modeling species distribution (Jansen et al., 2018).
The average accuracy of the models generally increases with higher taxonomic resolution
(e.g., genus at 0.93 compared to order at 0.90), except when the number of samples is too
low (e.g., at the species level). Generally, models built using a finer taxonomic resolution
(e.g., species level) included more explanatory variables than models produced at a broader
thematic scale (e.g., family level). This may relate to the spatial scale at which habitats are
described, and whether species are generalists or specialists. At finer taxonomic resolutions,
the different variables contributing to explaining the distribution of coral species and genera
had generally a more balanced contribution among them, as opposed to broader taxonomic
resolutions for which the distribution of families or orders were generally explained by two
or three variables with a higher mean percentage of contribution.
At the species level, only four series of SDMs could be produced due to the limited number
of observations for other species (Table 44.5). Of those, models computed for Paragorgia
arborea, which linked their distribution to temperature and salinity, were not considered
accurate enough by validation statistics. The models for the three other species all identified
different environmental variables as drivers of their respective distribution. Results indicate
that the distribution of Acanella arbuscula is driven by a range of factors, including oxygen
concentration, temperature, depth, northerness, and rugosity. The picture is simpler for
Table 44.5: Percentage contribution of environmental variables for species distribution models.
Variable Acanella arbuscula Anthomastus grandiflorus Keratoisis ornata Paragorgia arborea
Depth 17.1 9.1 65.9 7.3
Easterness 8.6 13.8 0.3 0.7
Northerness 15.7 68.6 18.3 2.9
Oxygen concentration 27.4 8.5 10.9 3.1
Rugosity 13.1 0.0 1.4 0.0
Salinity 0.1 0.0 1.5 9.4
Temperature 18.1 0.0 1.5 76.4
Topographic position 0.0 0.0 0.3 0.0
Mean AUC 0.92 0.84 0.94 0.61*
The top two contributors are indicated in bold font. The asterisk indicates absence of statistical significance.
Cold-water Coral Habitats of the NW Atlantic 747
Table 44.6: Percentage contribution of environmental variables for genus distribution models.
Variable Acanella Acanthogorgia Anthomastus Chrysogorgia Desmophyllum Flabellum Keratoisis Paragorgia Paramuricea Swiftia Vaughanella
Depth 52.4 0.8 20.8 32.8 1.3 5.2 2.5 28.3 49.6 44.1 41.8
Easterness 1.4 47.7 2.3 0.7 6.2 0.8 1.2 17.1 27.1 2.7 8.9
Northerness 10.4 0.4 23.5 2.8 47.8 38.0 2.0 1.4 10.5 20.8 9.0
30.7 43.7 0.9 5.4 0.5 8.1 52.5 2.4 0.3 4.1 5.4
Rugosity 0.4 0.4 4.7 8.4 43.0 29.8 8.4 27.0 9.7 23.3 18.0
Salinity 1.3 0.2 1.8 1.8 0.1 1.0 22.7 0.0 0.0 0.7 4.5
Temperature 1.2 4.5 21.6 32.8 0.4 9.0 10.4 11.4 2.5 0.9 8.6
2.2 2.3 24.3 15.3 0.6 8.1 0.4 12.5 0.2 3.4 3.8
Mean AUC 0.88 0.87 0.85 0.99 0.90 0.99 0.93 0.92 0.89 0.99 0.97
The top two contributors are indicated in bold font.
Table 44.7: Percentage contribution of environmental variables for family distribution models.
Variable Acanthogorigiidae Alcyoniidae Caryophylliidae Chrysogorgiidae Flabellidae Isididae Nephtheidae Paragorgiidae Plexauridae
Depth 1.8 56.3 2.4 46.6 4.8 8.7 2.5 17.1 77.2
Easterness 55.2 5.1 1.0 0.3 0.5 1.1 0.1 25.5 2.1
Northerness 0.2 13.1 29.4 6.7 43.0 3.9 1.4 2.6 8.5
36.0 2.6 0.3 1.6 7.3 84.1 94.6 1.8 3.3
Rugosity 1.8 3.5 64.3 1.0 27.8 0.6 0.0 18.7 4.2
Salinity 0.0 0.7 0.1 0.7 0.7 0.5 0.2 0.0 2.1
Temperature 2.3 13.3 1.8 20.9 8.6 0.9 0.5 21.5 1.4
2.7 5.4 0.6 22.2 7.2 0.2 0.6 12.7 1.0
Mean AUC 0.86 0.82 0.95 0.97 0.99 0.88 0.89 0.92 0.94
The top two contributors are indicated in bold font.
Anthomastus grandiflorus: northerness and easterness, which are known to be surrogates of
currents that bring food to corals and clear them of sediments (Tong et al., 2016), were
identified as the main drivers of this species’ distribution. Finally, depth, northerness, and
oxygen were identified as surrogates of Keratoisis ornata’s presence.
At the genus level (Table 44.6), the drivers of Acanella sp. are simplified from those
specific to A. arbuscula: depth, oxygen, and northerness are the main variables that were
found to explain this genus’ distribution. Depth was also one of the main factors explaining
the presence of about half of the genera. Many of the genera were impacted by one of the
components of aspect (i.e., easterness or northerness), and oxygen concentration was
important for Acanthogorgia sp. and Keratoisis sp. Scleractinian corals (e.g., Desmophyllum
sp., Flabellum sp.) were found in areas of higher rugosity. Salinity was only found to be
significant for Keratoisis sp., perhaps indicating a preference for a particular water mass,
while temperature and topographic position were relevant mainly for Anthomastus sp. and
Chrysogorgia sp.
At the family level (Table 44.7), bamboo corals (i.e., Isididae) and Nephtheidae were highly
influenced by oxygen concentration. Depth was among the most influential variables once
again with a contribution greater than 10% for four families. Northerness, oxygen
concentration, rugosity, and temperature contributed to explaining the distribution of a third
of the families. Similarly to some of their corresponding genera, topographic position
remained a predictor of the Chrysogorgiidae and Paragorgiidae families.
While remaining satisfactory, the accuracy of the different models at the order level was
lower than at the other taxonomic level (Table 44.8). Depth was identified as one of the
main drivers of coral distribution at this thematic resolution. Easterness and northerness,
and thus likely currents were found to explain soft and black corals’ distribution. Rugosity
was identified as a surrogate for soft and stony corals, while temperature would be a
surrogate of the presence of black corals, gorgonians, and sea pens.
Table 44.8: Percentage contribution of environmental variables for order distribution models.
Variable Alcyonacea Antipatharia Gorgonacea Pennatulacea Scleractinia
Depth 14.4 24.2 78.1 11.3 22.8
Easterness 36.3 20.1 0.7 0.4 0.7
Northerness 6.6 11.8 1.2 0.9 9.2
Oxygen concentration 3.7 1.4 2.7 2.2 0.4
Rugosity 33.7 3.1 2.0 0.7 63.6
Salinity 1.6 3.2 1.2 0.6 0.2
Temperature 1.9 33.9 12.8 83.8 3.0
Topographic position 2.0 2.3 1.2 0.1 0.1
Mean AUC 0.88 0.90 0.86 0.89 0.95
The top two contributors are indicated in bold font.
Cold-water Coral Habitats of the NW Atlantic 749
Many thanks are due to Arnaud Vandecasteele for his assistance with the processing of the oceanographic data,
to Vonda Wareham and Shawn Meredyk for helping with the identification of taxa from the video data, and to
everyone involved in data collection. This work was supported by a Natural Sciences and Engineering Council
of Canada (NSERC) Alexander Graham Bell Scholarship (VL), NSERC Discovery Grants (EE, RD), an NSERC
ship time grant, and Fisheries & Oceans Canada International Governance Strategy.
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Cold-water Coral Habitats of the NW Atlantic 751
... Orphan Knoll is relatively well described in terms of its topography, surficial geology, oceanographic setting (see Enachescu, 2004;Greenan et al., 2010;Edinger et al., 2011;Pe-Piper et al., 2013;Meredyk et al., 2020), and more recently, its benthic communities (Meredyk, 2017;Lecours et al., 2019). Observations from benthic video transects revealed a highly varied surficial geology, ranging from relatively flat surfaces draped with fine-grained sediments at non-mound locations, to highly sloped regions with bedrock outcroppings covered by talus and ice-drafted debris at mound locations (Meredyk et al., 2020). ...
... Observations from benthic video transects revealed a highly varied surficial geology, ranging from relatively flat surfaces draped with fine-grained sediments at non-mound locations, to highly sloped regions with bedrock outcroppings covered by talus and ice-drafted debris at mound locations (Meredyk et al., 2020). These diverse geomorphic features support its rich epibenthic megafaunal communities (Meredyk, 2017;Lecours et al., 2019). While a previous study on the oceanography and lower trophic level biology of Orphan Knoll (see Greenan et al., 2010) gave little evidence to support an enhanced lower trophic level biology in the water column above the knoll, near-bottom current measurements revealed a mean anticyclonic (clockwise) circulation at and above the knoll known as a Taylor column, which may have important implications for the benthic community. ...
... A total of six ROPOS benthic imagery (continuous video and highresolution digital still photographs) transects were collected on Orphan Knoll and Orphan Seamount, with the aim of describing the benthic biodiversity and geological landscape of the flanks and plateau of the knoll and its associated seamount. Although these transects have been the subject of subsequent analyses to describe the benthic communities of Orphan Knoll (see Meredyk, 2017;Lecours et al., 2019), a detailed inventory of the observed taxa has not yet been published. ...
... Mapped features of the open ocean are often areas of high productivity, diversity or significant ecological processes, and need to be protected in their entirety to allow for the full range of ecological processes to take place. Hyrenbach et al., 2000;Sibert et al., 2000;Sibert and Hampton, 2002;Alpine and Hobday, 2007;Grober-Dunsmore et al., 2008;Sutton et al., 2008;Long et al., 2013;Garrigue et al., 2015;Rigby et al., 2019;Lecours et al., 2020 7a. Have at least three replicate no-take MPAs: within bioregions; of very large features (e.g., topographic or hydrodynamic features); and of known habitats and ecological processes. ...
... The functioning of a habitat or feature depends on linked processes that may occur in different areas (e.g., the seamount summit vs. the slope), but are connected across the entire habitat or feature. It is therefore important to represent entire habitats or features within the same level of protection and avoid "split zoning" (Day et al., 2012;Fernandes et al., 2012;Rigby et al., 2019;Lecours et al., 2020). The concept of split zoning is not often encountered in the coastal MPA literature, as it is likely to reduce the ecological integrity of an MPA and lead to problems of public understanding and compliance (Day, 2002). ...
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Networks of no-take marine protected areas (MPAs), where all extractive activities are prohibited, are the most effective tool to directly protect marine ecosystems from destructive and unsustainable human activities. No-take MPAs and MPA networks have been globally implemented in coastal seas, and their success has been significantly enhanced where science-based biophysical guidelines have informed their design. Increasingly, as human pressure on marine ecosystems is expanding further offshore, governments are establishing offshore MPAs—some very large—or MPA networks. Globally, there are growing calls from scientists, non-government organisations, and national governments to set global conservation targets upwards of 30%. Given that most of the ocean is found either in the high seas or offshore within national Exclusive Economic Zones, large offshore MPAs or networks of MPAs must be a major component of these global targets for ocean protection. However, without adequate design, these offshore MPAs risk being placed to minimise conflict with economic interests, rather than to maximise biodiversity protection. This paper describes detailed biophysical guidelines that managers can use to design effective networks of no-take MPAs in offshore environments. We conducted a systematic review of existing biophysical design guidelines for networks of MPAs in coastal seas, and found consistent elements relating to size, shape, connectivity, timeframes, and representation of biophysical features. However, few of the guidelines are tailored to offshore environments, and few of the large offshore MPAs currently in place were designed systematically. We discuss how the common inshore design guidelines should be revised to be responsive to the characteristics of offshore ecosystems, including giving consideration of issues of scale, data availability, and uncertainty. We propose 10 biophysical guidelines that can be used to systematically design offshore networks of MPAs which will also contribute to the global goal of at least 30% protection globally. Finally, we offer three priority guidelines that reflect the unique conservation needs of offshore ecosystems: emphasising the need for larger MPAs; maximising the inclusion of special features that are known and mapped; and representing minimum percentages of habitats, or, where mapped, bioregions. Ultimately, MPA guidelines need to be embedded within an adaptive management framework, and have the flexibility to respond to emerging knowledge and new challenges.
... Modelling the geographic distribution of species and the composition of ecological communities is key to preserve biodiversity and support a proper management of the habitats in which species live and have adapted over their evolutionary history [4][5][6][7] . From this point of view, predicting the distributions of species and communities in space and time provides a powerful tool for conservation planning [8][9][10][11][12] . Hence, studying species distribution changes might represent an effective approach to understand the complex interplay between the current biodiversity crisis and anthropogenic climate change [13][14][15] . ...
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Ecological processes are often spatially and temporally structured, potentially leading to autocorrelation either in environmental variables or species distribution data. Because of that, spatially-biased in-situ samples or predictors might affect the outcomes of ecological models used to infer the geographic distribution of species and diversity. There is a vast heterogeneity of methods and approaches to assess and measure spatial bias; this paper aims at addressing the spatial component of data-driven biases in species distribution modelling, and to propose potential solutions to explicitly test and account for them. Our major goal is not to propose methods to remove spatial bias from the modelling procedure, which would be impossible without proper knowledge of all the processes generating it, but rather to propose alternatives to explore and handle it. In particular, we propose and describe three main strategies that may provide a fair account of spatial bias, namely: (i) how to represent spatial bias; (ii) how to simulate null models based on virtual species for testing biogeographical and species distribution hypotheses; and (iii) how to make use of spatial bias - in particular related to sampling effort - as a leverage instead of a hindrance in species distribution modelling. We link these strategies with good practice in accounting for spatial bias in species distribution modelling.
... Therefore, they implicitly assume the existence of a common ecological scale for all predictor variables. However, it has been shown that the ecological scale is variable-specific since species often respond to different environmental variables at different spatial scales and sometimes even respond differently to a single environmental variable at multiple grains (Leitão et al., 2010;Lecours et al., 2020;Miguet et al., 2016;Roilo et al., 2022). However, despite theoretical concepts and extensive empirical evidence that species respond to their environment at different spatial grains (e.g. ...
There is a lack of guidance on the choice of the spatial grain of predictor and response variables in species distribution models (SDM). This review summarizes the current state of the art with regard to the following points: (i) the effects of changing the resolution of predictor and response variables on model performance; (ii) the effect of conducting multi-grain versus single-grain analysis on model performance; and (iii) the role of land cover type and spatial autocorrelation in selecting the appropriate grain size. In the reviewed literature, we found that coarsening the resolution of the response variable typically leads to declining model performance. Therefore, we recommend aiming for finer resolutions unless there is a reason to do otherwise (e.g. expert knowledge of the ecological scale). We also found that so far, the improvements in model performance reported for multi-grain models have been relatively low and that useful predictions can be generated even from single-scale models. In addition, the use of high-resolution predictors improves model performance; however, there is only limited evidence on whether this applies to models with coarser-resolution response variables (e.g. 100 km2 and coarser). Low-resolution predictors are usually sufficient for species associated with fairly common environmental conditions but not for species associated with less common ones (e.g. common vs rare land cover category). This is because coarsening the resolution reduces variability within heterogeneous predictors and leads to underrepresentation of rare environments, which can lead to a decrease in model performance. Thus, assessing the spatial autocorrelation of the predictors at multiple grains can provide insights into the impacts of coarsening their resolution on model performance. Overall, we observed a lack of studies examining the simultaneous manipulation of the resolution of predictor and response variables. We stress the need to explicitly report the resolution of all predictor and response variables.
... Notably, with the increasing use of fine-scale resolution data in SDM, such as variables derived from LiDAR with a resolution of a few meters (e.g. Lecours et al., 2020;Moudrý et al., 2021;Pradervand et al., 2014;Sillero & Goncalves-Seco, 2014;Simonson et al., 2014;Wüest et al., 2020), the negative effects of positional error in species occurrence data are no longer associated only with relatively old datasets (e.g. from herbarium or museum collections), but it is also necessary to consider positional errors inherent to data georeferenced using global navigation satellite systems. Indeed, used a 5 × 5 m analysis grain and reported that the largest drop in model performance was observed at the smallest simulated positional error of 5-10 m (they simulated errors up to 500 m). 4. We recommend fitting models with the finest possible analysis grain and as close to the response grain as possible even when available species occurrences suffer from positional errors. ...
The performance of species distribution models (SDMs) is known to be affected by analysis grain and positional error of species occurrences. Coarsening of the analysis grain has been suggested to compensate for positional errors. Nevertheless, this way of dealing with positional errors has never been thoroughly tested. With increasing use of fine‐scale environmental data in SDMs, it is important to test this assumption. Models using fine‐scale environmental data are more likely to be negatively affected by positional error as the inaccurate occurrences might easier end up in unsuitable environment. This can result in inappropriate conservation actions. Here, we examined the trade‐offs between positional error and analysis grain and provide recommendations for best practice. We generated narrow niche virtual species using environmental variables derived from LiDAR point clouds at 5 × 5 m fine‐scale. We simulated the positional error in the range of 5 m to 99 m and evaluated the effects of several spatial grains in the range of 5 m to 500 m. In total, we assessed 49 combinations of positional accuracy and analysis grain. We used three modelling techniques (MaxEnt, BRT and GLM) and evaluated their discrimination ability, niche overlap with virtual species and change in realized niche. We found that model performance decreased with increasing positional error in species occurrences and coarsening of the analysis grain. Most importantly, we showed that coarsening the analysis grain to compensate for positional error did not improve model performance. Our results reject coarsening of the analysis grain as a solution to address the negative effects of positional error on model performance. We recommend fitting models with the finest possible analysis grain and as close to the response grain as possible even when available species occurrences suffer from positional errors. If there are significant positional errors in species occurrences, users are unlikely to benefit from making additional efforts to obtain higher resolution environmental data unless they also minimize the positional errors of species occurrences. Our findings are also applicable to coarse analysis grain, especially for fragmented habitats, and for species with narrow niche breadth.
... These environmental predictors, such as landcover or habitat type, are most often included in SDMs as the area or percentage of a particular land cover type within the individual sites (e.g. grid cells or atlas mapping squares; Milanesi et al. 2017, Halstead et al. 2019, Lecours et al. 2020, Tessarolo et al. 2021. ...
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The representation of a land cover type (i.e. habitat) within an area is often used as an explanatory variable in species distribution models. However, it is possible that a simple binary presence/absence of the suitable habitat might be the most important determinant of the presence/absence of some species and, thus, be a better predictor of species occurrence than the continuous parameter (area). We hypothesize that the binary predictor is more suitable for relatively rare habitats (e.g. wetlands) while for common habitats (e.g. forests) the amount of the focal habitat is a better predictor. We used the Third Atlas of Breeding Birds in the Czech Republic as the source of species distribution data and CORINE Land Cover inventory as the source of the landcover information. To test our hypothesis, we fitted generalized linear models of 32 water and 32 forest bird species. Our results show that for water bird species, models using binary predictors (presence/absence of the habitat) performed better than models with continuous predictors (i.e. the amount of the habitat); for forest species, however, we observed the opposite. Thus, future studies using habitats as predictors of species occurrences should consider the prevalence of the habitat in the landscape, and the biological role of the habitat type in the particular species' life history. In addition, performing a preliminary comparison of the performance of the binary and continuous versions of habitat predictors (e.g. using information criteria) prior to modelling, during variable selection, can be beneficial. These are simple steps that will improve explanatory and predictive performance of models of species distributions in biogeography, community ecology, macroecology and ecological conservation.
... (e.g., 2 mm res. at 1 m altitude to 30 mm res. at 15 m altitude) was forward mounted on the ROPOS ROV and calibrated by the Canadian Hydrographic Service (CHS) and the ROPOS engineering team. In addition to measuring small-scale bathymetry along the path of video transects (reported in Lecours et al., 2019), high-resolution maps of two mound features were recorded using the Imagenex Delta T with the ROV at 20 m off bottom. These features were recorded on the second mound observed on dive R1341, and on the principal mound targeted by dive R1343 (see Table 1). ...
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Deep-sea mounds can have a variety of origins and may provide hard-substrate features in depths that are normally dominated by mud. Orphan Knoll, a 2 km high bedrock horst off northeast Newfoundland, hosts more than 200 mounds, or mound complexes, of unknown composition, in water depths of 1720–2500 m. Most mounds are 10–600 m high, with average mound height 187 m, and 1–3 km wide. The study objective was to characterize the size, shape, orientation, and composition of the enigmatic Orphan Knoll mounds, in order to determine their age and origin. Archival ship-based side-scan sonar, multibeam sonar, airgun, high-resolution sparker and 3.5 kHz acoustic sub-bottom profiling, and newly acquired ship-based multibeam sonar, video transects by remotely operated vehicle (ROV), rock samples, and near-bottom multibeam sonar data were analyzed. Four mounds were studied during two ROV dives. Archival sidescan sonar data show > 200 mounds. Sparker profiles show that the mound crests are covered by condensed stratified Quaternary sediment and airgun seismic data show faults reaching near the seafloor. New multibeam sonar data show mounds are dominantly conical to elliptical in shape, but without preferred orientation or alignment. Remotely operated vehicle (ROV) transects and near-bottom multibeam showed that three mounds were rounded and symmetrically arranged, while a fourth was more asymmetrical, with steep faces on the southwestern and southeastern flanks, where finely bedded to massive sedimentary bedrock outcropped dipping 15–45°SW. Rock samples from the mounds include Eocene calcareous ooze and mid-Miocene bedded pelagic limestone. Thick ferromanganese crusts were found on many surfaces, obscuring possible outcrops from physical sampling. Polymetallic nodules were found on the slope of one mound. Ice-rafted detritus, including igneous and metamorphic rocks and Paleozoic limestone and dolostone, was common in the sediments immediately surrounding the mounds. Quaternary sub-fossil solitary scleractinian corals accumulated over a span of at least 0.18 Ma at the base of one mound. The presence of uplifted condensed Eocene-Miocene rocks on the mounds and faulting in seismic profiles suggest uplift during reactivation of old rift-related faults during the Neogene, with seabed mass wasting creating residual mounds, which were then draped by Quaternary proglacial muds. Sculpting of hemipelagic Quaternary sediment by bottom currents probably contributed to mound morphology.
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Species distribution models (SDMs) have become a common tool in studies of species–environment relationships but can be negatively affected by positional uncertainty of underlying species occurrence data. Previous work has documented the effect of positional uncertainty on model predictive performance, but its consequences for inference about species–environment relationships remain largely unknown. Here we use over 12 000 combinations of virtual and real environmental variables and virtual species, as well as a real case study, to investigate how accurately SDMs can recover species–environment relationships after applying known positional errors to species occurrence data. We explored a range of environmental predictors with various spatial heterogeneity, species' niche widths, sample sizes and magnitudes of positional error. Positional uncertainty decreased predictive model performance for all modeled scenarios. The absolute and relative importance of environmental predictors and the shape of species–environmental relationships co-varied with a level of positional uncertainty. These differences were much weaker than those observed for overall model performance, especially for homogenous predictor variables. This suggests that, at least for the example species and conditions analyzed, the negative consequences of positional uncertainty on model performance did not extend as strongly to the ecological interpretability of the models. Although the findings are encouraging for practitioners using SDMs to reveal generative mechanisms based on spatially uncertain data, they suggest greater consequences for applications utilizing distributions predicted from SDMs using positionally uncertain data, such as conservation prioritization and biodiversity monitoring.
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Species distribution models (SDMs) are powerful tools in ecology and conservation. Choosing the right environmental drivers and filtering species' occurrences taking their biases into account are key factors to consider before modeling. In this case study, we address five common problems arising during the selection of input data for presence-only SDMs on an example of a general-ist species: the endangered Cantabrian brown bear. First, we focus on the selection of environmental variables that may drive its distribution, testing if climatic variables should be considered at a 1-km analysis grain. Second, we investigate how filtering the species' data in view of (1) their collection procedures , (2) different time frames, (3) dispersal areas, and (4) subpopulations affects the performance and outputs of the models at three different spatial analysis grains (500 m, 1 km, and 5 km). Our results show that models with different input data yielded only minor differences in performance and behaved properly in terms of model validation, although coarsening the analysis grain deteriorated model performance. Still, the contribution of individual variables and the habitat suitability predictions differed among models. We show that a combination of limited data availability and poor selection of environmental variables can lead to inaccurate predictions. Specifically for the brown bear, we conclude that climatic variables should not be considered for exploring habitat suitability and that the best input data for modeling habitat suitability in the study area originate from (1) observations and traces from the (2) most recent period (2006-2019) in which the population is expanding, (3) not considering cells of dispersing bear occurrences and (4) modeling sub-populations independently (as they show distinct habitat preferences). In conclusion , SDMs can serve as a useful tool for generalist species including all available data; still, expert evaluation from the perspective of data suitability for the purpose of modeling and possible biases is recommended. This is especially important when the results are intended for management and conservation purposes at the local level, and for species that respond to the environment at coarse analysis grains.
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Benthic marine biodiversity on the Antarctic continental shelf is high and unique, yet its distributional patterns are still relatively poorly understood. Some of the main issues are that biological data are sparse, and that many species are rare and seem only weakly related to environmental conditions. Grouping species by taxonomic or functional similarity has historically been used to compensate for missing species identification, to generate a more widespread distribution of data-points, and this practice can help to gain a better understanding of the distribution of biodiversity. However, there are few guidelines on how to group species, the implicit assumptions about species associations in the groups are difficult to validate, and the information loss associated with grouping species is unknown. Here, we analyse whether grouping benthic macrofaunal species by taxonomic or functional similarity preserves distributional patterns seen in species distributions, using a model-based approach called “species archetype model” that groups species or other units based on the similarity in their responses to environmental factors. Using presence-absence data, the species archetype models identify twice as many assemblages when used on the highest taxonomic resolution data, than when applied to taxonomic data at lower resolution (e.g., class) or functional groups based on mobility, feeding type, and body shape. Further, confidence in the predictions of either taxonomic or functional groups is far less than for predictions based on the highest taxonomic resolution data. Although using functional groups is often thought to accumulate species with similar environmental responses, our analysis shows that functional groups may insufficiently resolve assemblage structure for presence-absence data. Model-based approaches provide key information to understanding the regional distribution of Antarctic marine biodiversity, and care needs to be taken when using a-priori groupings of species to make statements about the distribution of biodiversity.
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Author Posting. © The Oceanography Society, 2017. This article is posted here by permission of The Oceanography Society for personal use, not for redistribution. The definitive version was published in Oceanography 30, no. 1 (2017): 90–103, doi:10.5670/oceanog.2017.116.
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Toolbox for ArcGIS built from results of Lecours et al. (2017) and validated in Lecours et al. (2016). This toolbox generates six independent terrain attributes that together summarize topographic or bathymetric variability. References: Lecours, V., Devillers, R., Simms, A.E., Lucieer, V.L., and Brown, C.J. (2017) Towards a framework for terrain attribute selection in environmental studies. Environmental Modelling & Software, 89:19-30. Lecours, V., Brown, C.J., Devillers, R., Lucieer, V.L., and Edinger, E.N. (2016) Comparing selections of environmental variables for ecological studies: a focus on terrain attributes. PLoS ONE, 11:e0167128. TO CITE: v. 1.1: Lecours, V. (2017) Terrain Attribute Selection for Spatial Ecology (TASSE) v. 1.0: Lecours, V. (2015) Terrain Attribute Selection for Spatial Ecology (TASSE)
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Selecting appropriate environmental variables is a key step in ecology. Terrain attributes (e.g. slope, rugosity) are routinely used as abiotic surrogates of species distribution and to produce habitat maps that can be used in decision-making for conservation or management. Selecting appropriate terrain attributes for ecological studies may be a challenging process that can lead users to select a subjective, potentially sub-optimal combination of attributes for their applications. The objective of this paper is to assess the impacts of subjectively selecting terrain attributes for ecological applications by comparing the performance of different combinations of terrain attributes in the production of habitat maps and species distribution models. Seven different selections of terrain attributes, alone or in combination with other environmental variables, were used to map benthic habitats of German Bank (off Nova Scotia, Canada). 29 maps of potential habitats based on unsupervised classifications of biophysical characteristics of German Bank were produced, and 29 species distribution models of sea scallops were generated using MaxEnt. The performances of the 58 maps were quantified and compared to evaluate the effectiveness of the various combinations of environmental variables. One of the combinations of terrain attributes–recommended in a related study and that includes a measure of relative position, slope, two measures of orientation , topographic mean and a measure of rugosity–yielded better results than the other selections for both methodologies, confirming that they together best describe terrain properties. Important differences in performance (up to 47% in accuracy measurement) and spatial outputs (up to 58% in spatial distribution of habitats) highlighted the importance of carefully selecting variables for ecological applications. This paper demonstrates that making a subjective choice of variables may reduce map accuracy and produce maps that do not adequately represent habitats and species distributions, thus having important implications when these maps are used for decision-making.
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Located on the Scotian Shelf, The Gully is the largest submarine canyon on the outer southeastern Canadian continental margin. It indents the continental shelf much deeper than other canyons on the Scotian margin, connecting the middle shelf to the continental slope. It is generally recognized that The Gully formed by fluvial, glacial and meltwater erosion that cut deeply into Cenozoic mudstone and sandstone units. It was excavated over the past million years as successive continental glaciations lowered sea level, exposing the shelf and allowing rivers to erode and deposit sediments near the top of the continental slope. The Gully was also partially eroded by turbidity currents that flowed down the canyon at times of glacial maxima.
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Recent studies on deep-sea sponges have focused on mapping contemporary distributions while little work has been done to map historical distributions; historical distributions can provide valuable information on the time frame over which species have co-evolved and may provide insight into the reasons for their persistence or decline. Members of the sponge family Geodiidae are dominant members of deep-sea sponge assemblages in the northwestern Atlantic. They possess unique spicules called sterrasters, which undergo little transport in sediment and can therefore indicate the Geodiidae sponge historical presence when found in sediment cores. This study focuses on the slopes of Flemish Cap and Grand Bank, important fishing grounds off the coast of Newfoundland, Canada, in international waters. Sediment cores collected in 2009 and 2010 were visually inspected for sponge spicules. Cores containing spicules were sub-sampled and examined under a light microscope for the presence of sterrasters. These cores were also dated using X-radiographs and grouped into five time categories based on known sediment horizons, ranging from 17,000 years BP to the present. Chronological groupings identified Geodiidae sponges in four persistent sponge grounds. The oldest sterrasters were concentrated in the eastern region of the Flemish Cap and on the southeastern slope of the Grand Bank. Opportunistic sampling of a long core in the southeastern region of the Flemish Cap showed the continuous presence of sponge spicules to more than 130 ka BP. Our results indicate that the geodiids underwent a significant range expansion following deglaciation, and support a contemporary distribution that is not shaped by recent fishing activity.
Conference Paper
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Knowledge of deep-sea environments is limited by the difficulties of using traditional sampling methods in such remote areas. Sampling the deep-sea from the ocean surface rarely yields data at a spatial scale that is helpful in understanding ecological processes or meaningful for management and conservation. One way to collect better information about the seafloor is to reduce the distance between the instruments and the seafloor. This is now possible using submersible platforms. A challenge with the use of these underwater systems is the inaccuracies associated with data positioning. Positioning high-resolution datasets accurately in an underwater geospatial context is complicated by the fact that many sources of uncertainty exist, contributing to a total propagated uncertainty (TPU) on the position. These complications are acute for acoustic remote sensing systems, in which the footprint and the resulting spatial resolution of data are a direct function of depth. While quality of deep-sea data is highly variable, it is rarely assessed or explicitly considered in marine ecological studies. In this contribution, we measured the mean TPU of bathymetric data collected during surveys performed in 2010 with a remotely operated vehicle in the Northwest Atlantic, to depths down to 3,000m. We found that TPU increases with depth, leading to a " paradox of data quality " : sensors' resolution increase with depth (i.e. when reducing the distance between the sensor and the seafloor) while sensors' positional accuracy decrease with depth. We conclude that in order to be able to accurately position high-resolution datasets in the deep-sea within the same absolute reference system, the spatial resolution of the data should be larger than the TPU. Spatial data quality of underwater datasets should always be assessed, as often only the spatial resolution side of this paradox is explicitly addressed in the literature.
Terrain attributes (e.g. slope, rugosity) derived from digital terrain models are commonly used in environmental studies. The increasing availability of GIS tools that generate those attributes can lead users to select a sub-optimal combination of terrain attributes for their applications. Our objectives were to identify sets of terrain attributes that best capture terrain properties and to assess how they vary with surface complexity. 230 tools from 11 software packages were used to derive terrain attributes from nine surfaces of different topographic complexity levels. Covariation and independence of terrain attributes were explored using three multivariate statistical methods. Distinct groups of correlated terrain attributes were identified, and their importance in describing a surface varied with surface complexity. Terrain attributes were highly covarying and sometimes ambiguously defined within software documentation. We found that a combination of six to seven particular terrain attributes always captures more than 70% of the topographic structure of surfaces.
From 2000-2003, over 200 km of modern 2D data were collected in the Orphan Basin, offshore Newfoundland. In spite of being specifically acquired for hydrocarbon exploration with a conventional air-gun array, the data have excellent resolution in the shallow part where reef-like mounds are visible at the water bottom, in 1800-2300 m below sea level. Seismic lines illustrate the location, configuration and seismic character of the identified geomounds.
An understanding of how terrain features influence abundance of a particular species greatly aids in the development of accurate predictive habitat suitability models. In this study, we investigated the observed seafloor coverage of cold-water coral Lophelia pertusa in relation to seabed topography at the Sotbakken and Røst Reefs on the Norwegian margin. The primary terrain features at the study sites are a SW-NE stretching mound at Sotbakken Reef and SW-NE running ridges at Røst Reef, located at depths of ~300–400 m and ~250–320 m respectively. Ship-borne multibeam bathymetry data, JAGO dive video data and JAGO positioning data were used in this study. Terrain variables were calculated at scales of 30 m, 90 m and 170 m based on the bathymetry data. Additionally, we investigated the relationships between the terrain variables at multiple scales using the Unweighted Pair Group Method. The observed L. pertusa coverage at both reefs was found to be significantly correlated with most investigated terrain variables, with correlations increasing in strength with increase in analysis scale, suggesting that large scale terrain features likely play an important role in influencing L. pertusa distribution. Small scale terrain variations appear less important in determining the suitability of a region of seafloor for L. pertusa colonization. We conclude that bathymetric position index and curvature, as well as seabed aspect, most strongly correlate with coral coverage, indicating that local topographic highs, with an orientation into inflowing bottom currents, are most suitable for L. pertusa habitation. These results indicate that developing habitat suitability models for L. pertusa will benefit from inclusion of particular key terrain variables (e.g. aspect, plan curvature, mean curvature and slope) and that these should ideally be computed at multiple spatial scales with a greater gap in scales than we used in this study, to maximize the inclusion of the key variables in the model whilst minimizing redundancy.