Documenting Cold Seeps on the West Coast of Greenland Using Videos of the Seabed

Documenting cold seeps on the west coast of Greenland using videos of the seabed

Author: Lauren Cook

Primary Supervisor: Chris Yesson

Secondary Supervisor: Stephen Long

UCL MRes Biodiversity, Evolution and Conservation

BIOS0013_18-19

Abstract

Cold seeps are upwellings of gas, oil or water from the seabed. Secreted hydrocarbons can provide energy for benthic organisms via chemosynthesis and are thought to be the habitat where life first began. Methane secreted at these sites could potentially contribute to the greenhouse gas load and ultimately global warming. Seeps are continuously discovered and have recently been observed in Disko Bay, Greenland. Seeps have never been thoroughly documented in the area or examined by benthic videography. Here we document the distribution of cold seeps in Disko Bay, assess their ecological impact and predict their whereabouts through environmental modelling. Seeps discovered are widespread, occurring in muddy substrate between 221-631m depth. Sediment plumes, assumed to be caused by seeps, are easily observed in the videos emanating from small mounds or flat seabed. No distinguishable chemo-dependant fauna was observed, although notable densities of anemones and polychaete-worms inhabited some seep sites. Substantial local temperature fluctuations around seeps in an otherwise invariable environment provides secondary evidence for identification. Through high-resolution environmental machine modelling, seep distribution was predicted in the MapHab area. Our results demonstrate the use of benthic photography in seabed habitat exploration- adding these seeps to the existing knowledge base. The map produced will be a useful informant for researchers, management and conservationists. We anticipate our study to be a starting point for future excursions where detailed information on topography, temperature, acoustics, biochemistry and video surveys will give the best picture of the important geological and ecological mechanisms associated with cold seeps.

Introduction

Cold seeps The ocean sea floor is a diverse environment. At great depths is a habitat for communities with little or no light from the sun where most rely on photosynthetically produced food falling from the surface. There are however some unique communities that employ chemosynthesis instead – where energy is derived from

hydrocarbons secreted from the seabed. Cold seeps are one example of this phenomenon. Cold seeps are upwellings of water, gas and or oil. Gas is generated both microbially within seabed substrate and thermogenically at great depths below the seafloor (1–4) (Fig 1). Cold seep emissions include methane (CH4), sulphides, carbon dioxide (CO2), oil and brine, all of which have been proven to be highly productive environments in terms of biodiversity and biomass (5, 6). The high chemical gradients of the local environment around seeps provide the conditions that are theorised to be potential locations of the original evolution of complex life; namely, the abiotic synthesis of organic molecules and the release of thermal energy via chemosynthesis. Gas is created within the Earth’s mantle and released into the modern oceans today in the same way as it was for the Archean (7–9).

The release of greenhouse gasses (GHG) such as methane and CO2 into the oceans and ultimately into the atmosphere has the potential to add to the GHG load and impact our changing climate. The importance of cold seeps in terms of the global methane budget is still not definitively accounted for and estimates of seeps’ -1 contributions vary from 0.4 - 65 Tg CH4 yr , with most sources estimating ~40 Tg -1 CH4 yr (4). This constitutes around 7% of the budget, which is a similar amount to that emitted by landfills globally (4, 10). Efforts are underway to link terrestrial and marine carbon emissions (11). With the potency of methane as a greenhouse gas it is increasingly important to document these sites so their value in terms of climate change can be estimated (12).

Both methane and CO2 have the potential to contribute to ocean acidification. For benthic corals and other calcifying organisms this is especially important (13, 14). Along with other anthropogenic threats such as seabed trawling and pollution, this contribution can render these species particularly vulnerable (15). Furthermore, seep sites are commonly associated with the presence of gas hydrates (16, 17). These are ice-like structures of water molecules forming a lattice which encases a molecule of natural gas, mainly methane (18). Methane stored in hydrate form is only stable at low temperatures and high pressures. Oceanic warming will cause dissociation of the hydrates into gas, causing further warming of the atmosphere – a highly significant positive feedback mechanism, given that current estimates suggest that 3000 times the amount of methane in the atmosphere is locked as methane hydrate crystals (14, 19).

Considering their biological and geochemical importance it is noteworthy that seep sites continue to be discovered, suggesting that only a relatively small proportion of seeps in existence are already known to science (20). Their presence has become increasingly recorded over the last four decades. Seeps are distributed worldwide, from depths as shallow as 15 metres to up to 7400 metres below sea level (20–23).

Figure 1. Illustration of cross section of a typical seep site showing common characteristics.

Detection of cold seeps It is possible to identify cold seeps from physical characteristics. High concentrations of methane around seep areas are extensively documented (11, 24, 25). An upwelling column of bubbles or liquid can break through the sedimentary strata creating a vertical disturbance, or flare, that can be detected with an echo- sounder (Fig. 2) (17). As well as such acoustic and seismic profiles, detection can be via the associated presence of gas hydrates and ‘pockmarks’. This has been well

documented at Vestnesa Ridge, Svalbard (17, 26, 27). Pockmarks are craters (>1m) in soft sediment caused by upwelling pressure from gas created below the seabed. Pockmarks are the surface evidence of chimney systems that have developed in the substrate, and notably can sometimes resemble large burrows or mud diapirs (17, 26). They can often provide suitable conditions for gas-hydrates to develop. Methane hydrate environments can indicate a gas reservoir, which could be later explored for the harvesting of gas and oil (27), as is the case with Disko Bay.

Seabed surface

Figure 2. Nielsen et al. (2014) show an example of flares in an echo-sounder photograph indicating the presence of a cold seep. X axis represents time (route of ship). Colours indicate the strength of reflectance of the acoustic signal (red=strong/blue=weak). This is used to determine the depth of the seabed (as labelled).

The documented temperature of seeps varies widely from site to site but is largely un-reported in studies. Seeps are generally believed to be warmer than their surroundings. Some have been found at temperatures up to 50˚C warmer (29, 30) ◦ while others show only a small anomaly of 0.005–0.05 C increase in close vicinity to the seeps (31), or display no temperature signature at all (32). Visual evidence of sediment plumes has been used to identify or describe the sites (6, 21–23, 33), although videography has not been the main form of analysis;

Geilert et al. (36) employed a video-guided multicorer. Videography has been a successful tool in many other instances of benthic and general marine sampling (35, 36). The method allows ecosystem-based management for conservation as large areas can be sampled and revisited with different objectives in mind (35). It provides advantages compared with extractive monitoring techniques such as catch-based sampling which can cause damage to biological systems.

Biological associations with cold seeps Methane seeps in particular provide important fuel for chemotrophic organisms. With this attribute and their ability to create small scale heterogeneous environments through methane-derived authigenic carbon (MDAC) sequestration they can provide an environment which promotes biodiversity and overall biomass (23, 27, 37). The resulting disturbance to an otherwise very stable benthic habitat would increase biodiversity in-keeping with the intermediate disturbance hypothesis (27, 38). The health of benthic communities is of intrinsic, ecological and economic value. Hydrocarbon gas emissions from seeps can promote the establishment of communities. Chemosynthetic bacteria flourish and provide a substantial food source for both colonising and vagrant megafauna, leading to the popular opinion that seeps are ‘biological oases’ in the deep sea (22, 32). Seeps can influence local assemblages by attracting or repelling some species. For example, certain crab species are found to be notably absent, while bacterial mats, assemblages of polychaetes, corals, shrimps and clams that are chemosynthesis-dependent inhabit the immediate surroundings of well-established seep sites (21, 22, 39, 40). The faunal assemblages surrounding seeps are however dependent on their ephemerality. The time scale of seepage activity varies widely and has been recorded from those existing only as a seasonal or tidal phenomenon to others lasting centuries. This can be evaluated from analysis of isotopic signature in microfossils (32). Long established seeps with strong chemical gradients are likely to be dominated by fewer, more specialised taxa (27).

Greenland Seeps have recently been discovered in the North Atlantic and Arctic oceans (3). Some seeps have been recorded in Disko Bay, Greenland (11) in order to

document the presence of gas hydrates, a phenomenon commonly associated with cold seeps. Analysis of seismic profiles and evidence of high levels of organic matter in the substrate indicate that methane is the primary emission of the seeps in this area. Methane is created through the biodegradation of organic matter below the seafloor and through serpentinization of ultramafic rocks in the Earth’s mantle (3). These recent discoveries have yet to be mapped or their presence analysed with video footage. Only one other seep system has been recorded in Greenland – the Ikka Fjord in south-western Greenland, consisting of ikaite columns which emit cold, alkaline water. Several unique fauna have been documented here (41, 42). The seabed in Greenland has been commercially exploited for shrimp (Pandalus borealis) since 1950 and is extremely important for Greenland’s economy (15). The fishery entered Marine Stewardship Council (MSC) certification which highlighted the lack of knowledge of seabed communities in Greenland. The increased interest in mapping seabed habitats has lead to several areas being identified as Vulnerable Marine Environments (VMEs) and protected under legislation. There is still however, much to be achieved, considering the large scale of the area, restricted accessibility during sea-ice months and working with the limited resources of a poor country. As the seep sites are often associated with higher diversity and abundance of benthic fauna, they might well serve as indicators of VMEs. The negative impacts of trawling on benthic communities are widespread in West Greenland. Documentation of these seep systems and their associated fauna can provide essential support to marine spatial planning and conservation measures where required (15, 43).

Disko Bay

Map Projection: WGS 84 / EPSG: 3847

Figure 3A. Above left: position of Disko Bay on the west coast of Greenland. Figure. 3B. Above right: map of Disko Bay (Bing maps).

Seep-like characteristics were noticed in Disko Bay, Greenland (Figs. 3A & 3B) during a 2018 cruise onboard the research vessel- The Sanna. This was a multibeam acoustic survey for gathering high resolution bathymetry and backscatter data for the objective of predicting seabed types, focussed in the MapHab area of the bay (-53.16°–52.35° W, 68.91°-69.08° N). Disko Bay (Bugt) is an embayment in the west coast of Greenland, part of eastern Baffin Bay. It lies between 68.30° N - 69.15° N and 50.00° W - 54.00° W, 150km from north to south and 100km from east to west. It has a polar maritime climate and extremely rugged seabed topography. There has been a statistically significant rise in temperature in the area in the last 20 years (44). The seabed depth

in this area at its maximum measures around 990m (43, 45, 46). Recent glacial activity in Illulissat icefjord has resulted in carving, ice-berg scouring and glacial deposits. The complicated topography of Disko Bay distinguishes it from other oceanographic settings along the rest of West Greenland’s coast. Thick sediment in some areas of the bay is a result of high-sediment melt-water from retreating glaciers and melting icebergs (11). The embayment is covered by seasonal sea-ice with average thickness of 70cm which lasts around five months (47). The bay has been trawled extensively for halibut and shrimp since the 1950s (43).

Aims To document the presence of cold seeps in Disko Bay and assess their ecological impact. To improve the habitat map for the area, using video-sled footage taken from five cruises in the area from 2017-2018. To use the analysis of environmental factors to inform a model that can be used to predict the presence or absence of seeps in other areas. The map should be a useful tool for researchers, management and conservationists.

Materials and Methods

Study Site and Data collection

Figure 4. Deployment of benthic sled aboard the R/V Paamiut.

Benthic videos were collected onboard The R/V Paamiut and The Sanna research vessels on five separate cruises from 2017-2018. Stations from these cruises were selected within the Disko Bay area for analysis. A benthic sled was custom made by our collaborators at the Greenland Institute of Natural Resources (GINR) (dimensions 3m x 2m x 1m, weight approximately 1 ton, Fig. 4) with a mounted GoPro camera and two Nautilux torches in GB-PT 1750 group Binc underwater housings. A housed ‘Stamon’ temperature sensor was available for the cruises within Disko bay in May-June 2017 and September 2018. The sensor was set to log 10 second intervals, recording to a hundredth of a degree Celsius. A large area could be surveyed from a fixed distance pointing forward from the sled. The camera angle was set to approximately 31˚ from horizontal to optimize the distance covered with the available torch lighting. In the 2018 cruises, two parallel scaling lasers (Z-BOLT Job-Sight XT Green) were attached to the sled with distance between them measuring 20cm. The sled trawl was deployed for ~15 minutes bottom-contact time. The sled was dragged from a winch wire system from the ship where the optimal speed for

image quality was 0.8 knots. Travelling faster would result in blurry video footage while slower caused the wire to droop in-front of the camera, disturbing the sediment. The standard ratio of wire length used was 1.5 x depth. Areas of the seabed have been classified into substrate types by Gougeon et al. (48). This is important as previous seeps have been found in soft substrate, primarily in mud. Deep channels and basins are dominated by muddy sediments (48) which makes them possible seep habitats.

Video Analysis Seep sites were identified from evidence of a continuous sediment plume (Fig. 5) and were only observed in muddy substrates (as classified in Gougeon et al.(48)). Some seeps did not emit gas continuously, but rather only appeared as the sled drew closer to a point in the soft mud. These were labelled as “uncertain”, in case the plume was caused instead by the heavy sled pushing up sediment from burrows. “Non-seep” sites without visible sediment plumes were chosen for statistical comparison where the main substrate type of the transect was mud. Still images were captured from the appropriate videos at 30 second intervals – or nearest to 30 seconds that a clear image could be taken. A minimum of 20 images were taken at each site. These were subsequently analysed using the software BIIGLE 2.0 (49)– a programme designed for labelling and analysis of benthic imagery. Image labels included substrate type, seep, macrofaunal groups and bioturbations such as burrows and mounds. Dominant fauna were identified to varying levels of taxonomy depending on their distinctiveness. For some fauna, only phylum was necessary (e.g. Porifera, Bryozoa) which were then divided into functional groups based on their morphology (e.g. Large Porifera (>50cm), soft Bryozoa). These methods are further outlined in Yesson (43). For others, identification was possible at a class level (e.g. Polychaeta, Asteroidea, Holothuroidea, Actinaria). Two species of polychaete were seen; these were nominally labelled for analysis as ‘Polychaete tube’ and ‘Polychaete2’. Identification to species level was unnecessary for the purposes of this study and for most of the taxa in question is not possible without direct specimen sampling. Fauna was recorded as present or absent and no attempt was made to account for abundance in each image. The counts for dominant fauna and

bioturbations were summarised using pivot tables (Microsoft Excel) to give an overview for each station.

Figure 5. Image of an active seep in muddy substrate, creating a sediment plume. Burrows and an individual prawn (Pandulus borealis) are also visible. Scaling lasers measure 20cm distance.

Statistical Analyses

To calculate the exact GPS position of each video image, the exact time of each image was determined from the start of the transect. The ship’s position was recorded at the start and end of each transect. The start of the transect was noted as the time when the sled hit the seabed. The position of the camera relative to the ship was estimated based on the depth of the seabed and the length of the wire used for the camera deployment. We estimate a spatial uncertainty of c. 50m for these positions due to the potential affect of current velocity on camera and wire position, as we are assuming the camera was directly behind the ship and the ship was travelling in an exactly straight line (M. Blicher pers. Comm.), method as described in Brodie et al. (50).This was calculated using the ‘sp’, ‘geosphere’ and ‘data.table’ packages of R (RStudio Version 1.1.456) (51–54). Raster grids of topographic variables were obtained through the International Bathymetric Chart of the Arctic Ocean (IBCAO) (55). Two-dimensional variables such as slope and roughness were calculated from the v4 bathymetry grid using the

gdaldem software (56). Climatic variables were sourced from the Copernicus Marine environment monitoring service; TOPAZ4 Arctic Ocean Reanalysis dataset. Methods described in Yesson et al.(15, 43). Environmental factors were calculated for each image position using the ‘raster’ and ‘proj4’ packages of R (58, 59) using the ‘extract’ tool. Data was fetched from each grid for the selected coordinates. For three sites outside the geographical extent of some environmental grids, values were obtained by extrapolation of mean values using a buffer around the edge pixels of 20km. All variables considered are displayed in Table S1 in Supplementary Materials. Correlation testing was used to choose variables for analysis. Variables with high correlation (cor >0.9) were eliminated leaving those with significant uncorrelation and the highest possible resolution (500 x 500m) as explanatory variables. Current magnitude was calculated from layers of velocity towards east (U), and velocity towards north (V) by the square-root of sum of squares. Temperature, salinity, summer and winter current magnitudes, depth, Topographic Position Index (TPI) and slope were chosen for further analysis.

Multidimensional Scaling (MDS) Analysis The spread of the data was investigated first through MDS analysis using the vegan library of R (60). This used count data of dominant taxa and mean environmental values for each station. The purpose was to highlight which environmental factors had significant directional effect on the model distribution (p<0.05) and to show any clustering of faunal or site groups.

Boxplots Examination of differences between seep groups (‘seep’, ‘non-seep’ and ‘uncertain’) was carried out via boxplots for each group of macrofauna and each environmental variable. Shannon-Weaver diversity index was calculated using the vegan package of R (60). Although this test is designed for species level identification, it serves as a functional metric for diversity for comparison in this case. Histograms plotted for each variable indicated non-parametric data hence Kruskal- Wallis tests for significance were used.

Support Vector Machine (SVM) The environmental variables were used to train an SVM model that could predict seep sites outside of the recorded areas, specifically three classes of area - seep, uncertain and non-seep sites. On first iteration, only mud substrate sites were used. To increase the size of the sample, non-seep sites with hard substrates (‘course rocky’, ‘bedrock with mud’ and ‘gravelly mud’) were added into the analysis. Locations of seep observations and cruises in the area from Mikkelsen et al.(11) and Nielsen et al. (28) were used as independent data for model testing. These were subsequently added in to the model training data to further increase the sample size. Raster layers for temperature, salinity, summer and winter current strength were interpolated into higher resolution by the ‘disaggregate’ function of the R package raster in order to become commensurate with the higher resolution bathymetry layers. Model predictions were assessed by comparing predicted with observed values. Class agreement should be close to 1 for the diagonal parameter of the observed-predicted matrix which shows the proportion of sites correctly predicted. Kappa is a metric that shows the proportion of the correctly predicted values appearing by chance. It can be used as an estimate of error, to which values were expected to be above 0.75 for a reliable model outcome (61). The model parameters gamma and cost were chosen for a robust selection of values [gamma=2^(-13:3), cost=2^(-5:10)], which imposes a penalty on misclassified samples (62). These constraints on the model were chosen using the ‘best.parameter’ function of e1071 R package (63), so that parameter values fell comfortably within the set constraints (62). High resolution bathymetry and backscatter was available from the 2018 MapHab area cruise. A multibeam echo-sounder was used to map seabed types (C. Yesson pers. comm.) where the strength of acoustic reflection is interpreted to gauge substrate type. Hard substrates reflect strongly while reflections from soft substrates (e.g. mud) are more diffuse (64). Standard deviation of backscatter at a 10 x 10m resolution was calculated using R (raster library) as a measure of substrate fluctuation. Using the software LandSerf (65) we were able to use bathymetry data to calculate new variables of slope and fractal dimension as a proxy for seabed roughness. Neighbourhood windows of 1 (fine scale) and 100 (courser scale) pixels were used to test seabed variation for two levels of resolution. The scale at which the seeps may be linked to substrate or topographic features is as yet unknown, thus is

preferable to test two levels of geographic scale which may return different solutions in the model. Data was re-extracted for our chosen stations (method as above). The same values for robust selection of cost and gamma were appropriate for the training data.

Temperature Timeseries Detailed temperature readings for several sites within the 2018 MapHab cruise were available for each video transect. Temperature timeseries were compared for seep and uncertain sites, as well as appropriate non-seep sites with muddy substrates, to observe a standard temperature profile of the water column at a depth similar to those of the seeps in question. Standard deviation was calculated as a measure of temperature fluctuation.

Results

1. Spatial mapping of seeps and seep sites

A total of 64 video stations were reviewed from three cruises from June 2017 to September 2018. The seep sites discovered in Disko Bay are presented in the map in Fig. 6. 34 stations were chosen for analysis; 9 seep sites, 5 uncertain sites and 20 non-seep sites (6 muddy substrate and 14 rocky), between depths of 154- 871m. Seeps were only seen in stations where the main substrate type was ‘mud’. Only sites with mud substrate were used for faunal analysis to be a congruent comparison with seep sites. Sediment plumes observed were approximately 10- 30cm in height and emanate from small mounds or flat seabed. 12 faunal groups were identified from the video images (Table 1).

Map Projection: WGS 84 / EPSG: 3847 Figure 6. Station positions in Disko bay, coloured according to seep presence or absence. (QGIS)

Table 1. Dominant taxa data for comparison stations. Numbers represent number of images where that taxon is dominant. Station groups are labelled by seep class (Y – seep site; U – uncertain; N – non-seep).

Pandul Poly- Ascidia- Aster us Bryozoa Bryozoa Actin- chaete Poly- cea Pori- Holoth- - boreali Brachy Nephtheid Station Seep Soft Erect aria Tube chaete2 Solitary fera uroidea oidea s ura ae 2017-PA- 1_114 Y 0 0 23 18 0 3 0 0 0 0 0 0 2017-PA- 1_125 Y 0 0 14 30 0 0 0 0 1 0 0 0 2017-PA- 1_126 Y 0 0 27 27 0 0 0 0 0 0 0 0 2017-PA- 1_137 N 0 0 0 0 0 0 0 0 0 2 0 0 2018-SA- 08_1 N 1 0 0 0 0 0 0 0 0 20 0 0 2018-SA- 08_11 N 7 2 0 0 0 0 0 0 1 0 0 0 2018-SA- 08_16 N 1 0 0 0 0 0 1 0 0 7 3 0 2018-SA- 08_72 N 0 2 9 0 0 1 3 0 5 0 0 0 2018-SA- 4_106 Y 0 0 0 0 0 0 0 0 0 8 0 0 2018-SA- 4_113 U 2 0 7 0 0 0 0 0 0 1 0 3 2018-SA- 4_63 N 5 0 0 0 0 0 0 0 0 2 4 0 2018-SA- 4_64 Y 0 0 0 0 0 0 0 0 0 0 1 0 2018-SA- 4_80 U 0 0 0 0 0 0 0 0 0 4 0 0 2018-SA- 4_81 U 1 0 0 0 0 0 0 0 0 4 0 0 2018-SA- 8_002 Y 0 0 0 0 3 0 0 0 0 2 0 0 2018-SA- 8_017 U 1 1 0 0 10 0 2 6 0 1 0 0 2018-SA- 8_055 U 0 0 0 0 19 0 0 2 0 0 0 0 2018-SA- 8_061 Y 9 5 0 0 3 0 2 0 0 6 0 0 2018-SA- 8_076 Y 0 0 0 0 0 0 0 0 0 8 0 0 2018-SA- 8_087 Y 0 0 0 0 0 0 0 0 0 6 0 0

2. Multidimensional Scale (MDS) analysis

Figure 7. Plot of first two axes of multidimensional scaling (MDS) analysis, based on taxon dominance count and environmental variables data. Points represent stations which are split into three seep groups (Y – seep site; U – uncertain; N – non-seep). Vectors show directional influence of environmental parameters, significant influence in red and non-significant in grey (p max = 0.2). Two convergent solutions were reached after 20 tries of Wisconsin double standardization. 2D stress value as measure of error (<0.1).

The MDS analysis summarises faunal groupings around sites and shows the directional effect of environmental variables; winter current magnitude, slope and incidence of burrows show significant influence (Fig. 7, Table 2). It displays a correlation between the effects of current strength, depth and TPI. Actinaria and Polychaete tubes are clustered together which was expected from video analysis as these two taxa are often seen in close proximity (Fig. 8). There is no distinct

clustering between seep groups or major association with a certain type of fauna or direction of an environmental variable.

Table 2. MDS variables with associated parameters. NMDS1 and 2 are first two axes of multidimensional scaling (MDS) analysis. Significance codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1. Number of permutations: 999.

Variable NMDS1 NMDS2 r2 Pr(>r)

Depth 0.74962 0.66187 0.0977 0.429 Slope -0.62145 -0.78346 0.4377 0.010 ** Salinity 0.20456 -0.97885 0.0740 0.522 Temperature 0.36691 -0.93026 0.1056 0.379 Burrows 0.95964 -0.28123 0.2491 0.091 . TPI 0.57959 0.81491 0.1120 0.351 WinterCurrentStrength 0.46610 0.88473 0.1877 0.155 SummerCurrentStrength -0.16250 0.98671 0.0324 0.746

Figure 8. Common assemblage of Actinaria and Polychaete worm tubes, only seen in close proximity to seepages.

3. A. Boxplots for Environmental Variables

A B

C D

E F

Figure 9. A-G. Summary of environmental variables by seep group (N – non-seep; U – uncertain; Y – seep). Kruskal-Wallis values for significance are as follows: Salinity (A)- p < 2.2e-16; Temperature (B)- p< 2.2e-16; Slope (C)- p< 2.2e-16; Depth (D)- p< 2.2e-16; TPI (E)- p= 0.0007816; Winter current magnitude (F)- p = 0.7403; Summer current magnitude (G)- p = 3.935e-15. See Table S1 in Supplementary Materials for variables used.

A significant difference in mean between seep groups was seen for salinity, temperature, summer current magnitude, slope, depth, and TPI (p<0.05). Current, salinity and temperature are associated with depth as a result of the oceanography of the region (47). Seeps appear to be active in the deepest areas of the seabed in our sample (Fig. 9D). The average TPI for seep sites was negative, which would mean an elevation that is lower than the surroundings, for example in a valley or local depression (Fig. 9E). The range occurred around zero however, which indicates flat areas with a more or less consistent slope angle (66).

3. B. Boxplots for faunal groups

B A

C D

E F

I J

K L

M N

Figure 10. A-N. Boxplots summarising differences in faunal presences between seep groups (N – non-seep; U – uncertain; Y – seep). Numbers based on presence- absence count data per station (Table 1). Kruskal-Wallis values for significance are as follows: Bryozoa soft (A)- p=0.1391 ; Bryozoa erect (B)- p=0.6295; Actinaria (C)- p=0.5768; Polychaete Tube (D)- p=0.1309; Polychaete 2 (E)- p=0.2131; Ascidiacea Solitary (F)- p=0.6813; Porifera (G)- p=0.5942; Holothuroidea (H)- p=0.04252; Asteroidea (I)- p=0.2728; Pandulus Borealis (J)- p=0.9037; Brachyura (K)- p=0.25; Nephtheidae (L)- p=0.2231; Burrows (M)- p= 0.5626; Shannon’s diversity index (N)- p=0.6389.

Although there appear to be stark differences in some taxa between seep groups, there is no significant difference in means as the data set is markedly zero heavy (Kruskal-Wallis p>0.05). Only Holothuroidea gave a significant result, which appeared only in sites where the seep was ‘uncertain’, typically in very soft substrate (Fig. 10H). Although the difference between groups was not significant, it is notable that Bryozoa erect, Porifera, Brachyura and Asteroidea appear only in non-seep sites (Figs. 10B,G,I&K). The same goes for the sole appearance in seep sites of Actinaria and Polychaete tubes. Burrows appear in all seep groups but have the highest prevalence in seep sites. These are often found in soft substrate and are evidence of faunal habitat. There was no significant difference in overall diversity and boxplots show substantial overlap between groups (Fig. 10N).

4. SVM

Map Projection: WGS 84 / EPSG: 3847

Figure 11. Map of Disko bay showing the SVM model output and the position of sites investigated by video sled. Positions of cruises from Nielsen et al.(28) and Mikkelsen et al. (11) are also mapped. Parameter values: gamma = 0.25; cost = 2.

See Table S1. in supplementary materials- All stations used in SVM with descriptions. Table S2 for layers used (1-29)

Table 3. Observed-predicted matrix for seep site class (N – non-seep; U – uncertain; Y – seep). Predicted Observed N U Y N 28 0 0 U 2 6 0 Y 4 0 11

The model predictions were incongruent with observed seep sites. Many areas where seeps have been discovered were predicted to be inappropriate areas for seepage activity and vice versa (Fig. 11). The SVM made 6 errors where two uncertain sites and two seep sites were classified as non-seep (Table 3). When independent sites from Mikkelsen et al. (11) and Nielsen et al. (28) were included in the training data the model performed better, with gamma and cost becoming stable (gamma = 0.25; cost = 2), however the output did not change substantially. The SVM shows mainly deep areas in the ocean to be suitable seep habitats.

Map Projection: WGS 84 / EPSG: 4326

Figure 12. Support Vector Machine (SVM) model output of the MapHab area (-53.1– 52.4 W, 68.9-69.0 N), displaying predictions for distribution of seeps. Sites mapped within the Maphab area show observed seep, non-seep and uncertain sites. Parameter values: gamma = 0.5, cost = 2. Table S2 in Supplementary materials for layers used (30-40).

Table 4. Observed-predicted matrix for seep site class (N – non-seep; U – uncertain; Y – seep). Predicted Observed N U Y N 30 0 0 U 0 3 0 Y 0 0 11

The second iteration of the SVM included high resolution backscatter and bathymetry layers, and calculated variables of slope, fractal dimension and standard deviation of backscatter (Fig.12). This is a detailed view of the seabed in this area

and is a much more viable model for prediction, where all sites were correctly predicted by the model (Table 4).

5. Temperature Profiles

B A

C D

Figure 13 A-G. Temperature timeseries for station transects from MapHab area cruise in September 2018. 10 second interval for temperature readings. Plot lines are coloured by seep group (blue = seep site, yellow = uncertain seep site, red = non-seep site).

Figure 14. A measure of local temperature fluctuation by standard deviation. Sites are coloured by seep group (N – non-seep; U – uncertain; Y – seep).

Figure 15. A measure of local temperature fluctuation by standard deviation. Sites are coloured by seep group (N – non-seep; U – uncertain; Y – seep).

Temperature timeseries were available for the above stations from the September 2018 MapHab cruise, all of which exist at seabed depth between 190- 402m where the main substrate is ‘mud’ or ‘bedrock with mud’. Although the sample size is insufficient to draw major conclusions, it is interesting to note differences inferentially. Seep sites fluctuate in temperature substantially more than non-seep sites of similar topographic profile (Figs.13A-G, 14 & 15). In non-seep sites, some fluctuations can be seen in the temperature as the sled begins its ascent to the cooler surface (Fig. 13E & 10G).

Discussion

Spatial mapping and evidence of seeps Seeps were found uniquely in muddy substrate associated with deep, flat areas (-221-631m). There was no noticeable spatial clustering between seep and non-seep sites which might have indicated a relationship to any particular topographic feature. Instead there seems to be a wide spread of cold-seeps throughout the whole Disko Bay area. However, it is evident that there may not be sufficient evidence to conclude that these are indeed seeps. Some assumptions have been made from previous findings of pockmarks, gas hydrates and acoustic signatures in the area which are evidence of seabed methane emissions (11, 28). Other evidence is needed to verify the seeps. The results of the temperature timeseries could provide promising secondary evidence for seeps. The temperature profile of Disko Bay demonstrates that the shallower stations closer to the surface are colder than deeper stations. The deep sections are warmer and have higher stability due to water advected by the West Greenland Current (WGC)(45). The water is also more saline at the depths where seeps occur. This occurs as a result of the Irminger Sea Water of Atlantic origin being transported by the WGC (67). Temperature at this depth in Disko Bay should provide an extremely stable environment, where fluctuations even a hundredth of a degree would be considered an unusual occurrence (47). These fluxes in temperature are also consistent with the findings of Lizarralde et al. (31), where the seeps were recorded as warmer than their surroundings but by less than 0.1°C. It is however difficult to discern the ambient water temperature in each transect without seeps, as fluctuations in the temperature timeseries are not directly associated with visible sediment plumes. Seeps just outside the visible area of the video might be detected by the temperature sensor. This method could be especially useful for confirmation of ‘uncertain’ sites, as we would not expect to see a temperature change if the sediment plume was in-fact a result of the presence of burrows in soft sediment. The temperature analysis for the seven available sites indicated high fluctuation in the ‘uncertain’ site, perhaps corroborating these sediment plumes as evidence of cold seeps. The available data for detailed temperature analysis was limited to a few appropriate sites (deep and muddy) and was too restricted a sample

size to arrive at firm conclusions. However, as part of a tool kit, in conjunction with echo-sounder profiles and geochemical analysis of the water column as used in Mikkelsen et al. (11) to verify the seeps, it seems that this could be a very useful addition to future cruises. There was no obvious visual evidence of pockmarks as the area encompassed in the video was perhaps insufficient to capture them in an image - some pockmarks have been recorded up to 450m wide (4). However, it may be important to consider the similarity of the look of a small pockmark to a large burrow (4, 26). Burrows were observed predominantly in the sites associated with seeps and a few large (>10cm) elliptical shaped burrows were observed. This may mean that pockmarks were underrepresented in the analysis due to misidentification. Pockmarks have been visualised previously by echo-sounder data, which can identify a large crater in the substrate (1, 26, 68, 69). The size of the pockmarks may make the feature difficult to photograph and thus is perhaps left open to interpretation. Photography at a known pockmark location would be beneficial for future videographic studies. Gas hydrates and MDAC structures were not visible in the videos although the presence of gas hydrates has previously been recorded in the area (11, 28). The active seeps often escape from mounded structures in accordance with Bunz et al. (17) and Hovland et al. (68) but in other instances the topography was very smooth.

Biological Associations From initial viewing of the videos, it became evident that these are not the type of seeps which have a strongly established surrounding faunal community - there was no evidence of dense aggregations or bacterial mats as described in other regions (21, 22, 39, 40). Polychaete-worm tubes and anemones (Actinaria) occurred together in reasonably high concentrations only where seeps were also present. Densities of Polychaetes were not as high as has been reported at other sites (5, 27, 70). There is however some suggestion of chemosynthetic dependence occurring (71). The tube-forming Polychaetes under consideration are assumed from previous investigation to be of the family Sabellidae (C. Yesson, pers. comm.) although this identification cannot be ascertained from video images alone. Within the same order are the Siboglinids (72), which are known to inhabit cold seeps utilising chemoautotrophic bacteria as endosymbionts. Chemosynthesis is not otherwise

widely employed by the Polychaetes (71). Physical sampling is required to provide species level identification and would be recommended for future study to resolve any doubt as to whether they are seep-dependant. Crabs (Brachyura) were only found in non-seep sites, a phenomenon previously reported by Martin & Haney (21). The non-seep sites were similar in topography to the seep sites but tended to be shallower, which may have had an influence on the presence or absence of these species. Sea cucumbers (Holothuroidea) were only found in uncertain seep sites which were primarily very soft, muddy sites. These have however also been documented in rocky seabed in Disko Bay (43). Northern prawn (Pandulus borealis), although present in many of the videos, cannot be analysed for any specific association as a result of their diurnal vertical migration- they are only found on the seabed during the day (73, 74). Temporal variation of distribution in these and other taxa is unaccounted for in this study since cruises were conducted throughout the day and night. Crabs, sea stars (Asteroidaea) and prawns are the only ‘vagrant’ taxa reported in this study and could have been expected in the area irrespective of seep presence or absence. When analysing moving, often sediment-obscured images, accuracy in identification to species level remains problematic. Higher-level taxon identification is applied in other cases of marine photography as some species are only discriminable from fine scale morphological analyses (75). Using Shannon-Weaver as a measure of diversity was perhaps not the most applicable as the classification of taxa was at multiple levels. A more relevant method of representing the fauna around each site might have included abundance and community composition. This could have revealed some patterns in megafauna distribution. The fauna in our sample was at low densities in most instances, however there were some notable patterns as discussed herein. Time restrictions have prevented a more in depth degree of processing for the purposes of this study so automated image and video analysis in future surveys will be an important time saver (35). The non-significant differences in dominant taxa between seep and non-seep sites suggest that the seeps could be ephemeral or episodic in their activity, as has been reported at Vestnesa Ridge (17). They could also be explained if the emergent gas lacked high concentrations of hydrocarbons and was thereby not supportive of greater densities of chemoautotrophs. Seep-type has a direct effect on faunal associations so it would be helpful to determine the chemical composition of the

seep emissions. Sampling the water directly above the sediment plumes is a reliable gauge (11), using fluorescence and mass spectrometry for oil and gas components respectively (19). Sclerocronology of calcifying organisms, such as bivalve molluscs – common members of well-established methane seeps, can be used to assess the age of the seep (76), although calcifying megafauna was not present in high densities around these particular seeps. Microfossils of other organisms can also be tested with isotopic signatures (32). In addition, samples of the infauna collected through grab sampling may show evidence of seep-dependence and whether seeps are contributing to seabed complexity through methane accretion (27, 77). This could be a priority for future investigations. Incidence of trawling is an important factor when making assumptions of seep influence on faunal distribution. Sites intersecting with trawling can be negatively affected in terms of faunal biomass and diversity (15). However, soft sediment areas - where seeps occur - have been shown to have the most resilience to trawling. Conversely, soft sediment habitats are known to be less biodiverse than hard substrate habitats in the area (43). These confounding trends could also explain why a higher diversity around seep sites was not observed.

Statistical Limitations The Disko Bay SVM analysis using environmental and topographic factors at a resolution of 500 x 500m, over-predicted the presence of seeps. This is the result of the bathymetry, rather than the oceanographic layers, which are highly interpolated from 12500 x 12500m resolution. It provides a coarse, first level filter of suitable areas, rather than actually pin-pointing potential seep locations. Accurate analysis requires much finer topographical detail as one transect might encompass substantial variations in substrate, slope and depth. Influence that seeps might have on the environment will be limited to their immediate locale and would not necessarily be evident in large area data. The sample size of sites was also inadequate for reliable training of the model and could not reach a stable conclusion within the constraints set by the cost and gamma parameters. Employing high resolution backscatter and bathymetry proved a better way to predict seeps within the MapHab area, as mud substrate is an important distinguishing factor and occurs in flatter areas (48). The resultant model output was more plausible in its extent than the lower resolution SVM. However, the high

resolution data was only available in the MapHab area because of the increase in time and expenditure required in this surveying method– the ideal would be to have this level of information for a wider area.

Conclusion This is the first documentation of wide spread occurrence of cold seeps in Disko Bay, Greenland. No distinct seep-associated megafaunal assemblages have been documented. It is reasonable to conclude that benthic photography is a worthwhile technique for examining seep activity and surrounding fauna, and that fine scale bathymetry and backscatter can predict their distribution. Secondary evidence in the form of local temperature, geochemical analysis and acoustics are advantageous in confirming seep existence.

References 1. M. Hovland, A. G. Judd, R. A. J. Burke, The Global Flux of Methane From Shallow Submarine Sediments. Chemosphere. 26, 559–578 (1993). 2. G. Aloisi et al., CH4-consuming microorganisms and the formation of carbonate crusts at cold seeps. Earth Planet. Sci. Lett. 203, 195–203 (2002). 3. J. E. Johnson et al., Abiotic methane from ultraslow-spreading ridges can charge Arctic gas hydrates. Geology. 43, 371–374 (2015). 4. A. G. Judd, M. Hovland, L. I. Dimitrov, S. García Gil, V. Jukes, The geological methane budget at continental margins and its influence on climate change. Geofluids. 2, 109–126 (2002). 5. D. F. Coleman, R. D. Ballard, A highly concentrated region of cold hydrocarbon seeps in the Southeastern Mediterranean Sea. Geo-Marine Lett. 21, 162–167 (2001). 6. A. Boetius, F. Wenzhöfer, Seafloor oxygen consumption fuelled by methane from cold seeps. Nat. Geosci. 6, 725–734 (2013). 7. J. A. Baross, S. E. Hoffman, Submarine hydrothermal vents and associated gradient environments as sites for the origin and evolution of life. Orig. Life. 15, 327–345 (1985). 8. T. M. McCollom, J. S. Seewald, Serpentinites, hydrogen, and life. Elements. 9, 129–134 (2013).

9. W. L. Davis, C. P. McKay, Origins of life: A comparison of theories and application to mars. Orig. Life Evol. Biosph. 26, 61–73 (1996). 10. K. A. Kvenvolden, B. W. Rogers, Gaia’s breath - Global methane exhalations. Mar. Pet. Geol. 22, 579–590 (2005). 11. N. Mikkelsen, T. Laier, T. Nielsen, A. Kuijpers, N. Norgaard-Pedersen, Methane and possible gas hydrates in the Disko Bugt region, central West Greenland. Geol. Surv. Denmark Greenl. Bull., 69–72 (2012). 12. Y. Zhang, Methane escape from gas hydrate systems in marine environment, and methane-driven oceanic eruptions. Geophys. Res. Lett. 30, 1–4 (2003). 13. C. T. Hasler et al., Biological consequences of weak acidification caused by elevated carbon dioxide in freshwater ecosystems. Hydrobiologia. 806, 1–12 (2018). 14. C. Roth et al., Geophys. Res. Lett., in press, doi:10.1029/2011gl047222. 15. C. Yesson et al., The impact of trawling on the epibenthic megafauna of the west Greenland shelf. ICES J. Mar. Sci. 74, 866–876 (2017). 16. A. Sen et al., Geophysical and geochemical controls on the megafaunal community of a high Arctic cold seep. Biogeosciences. 15, 4533–4559 (2018). 17. S. Bünz, S. Polyanov, S. Vadakkepuliyambatta, C. Consolaro, J. Mienert, Active gas venting through hydrate-bearing sediments on the Vestnesa Ridge, offshore W-Svalbard. Mar. Geol. 332–334, 189–197 (2012). 18. E. D. Sloan, Gas Hydrates: Review of Physical/Chemical Properties. Energy & Fuels. 12, 191–196 (1998). 19. I. Leifer, J. F. Clark, R. F. Chen, Modifications of the Local Environment by Natural Marine Hydrocarbon Seeps. Geophys. Res. Lett. 27, 3711–3714 (2000). 20. P. P. Ristova, F. Wenzhöfer, A. Ramette, J. Felden, A. Boetius, Spatial scales of bacterial community diversity at cold seeps (Eastern Mediterranean Sea). ISME J. 9, 1306–1318 (2015). 21. J. W. Martin, T. A. Haney, Decapod crustaceans from hydrothermal vents and cold seeps: A review through 2005. Zool. J. Linn. Soc. 145, 445–522 (2005). 22. M. Sibuet, K. Olu, Biogeography, biodiversity and fluid dependence of deep- sea cold-seep communities at active and passive margins. Deep. Res. Part II Top. Stud. Oceanogr. 45, 517–567 (1998). 23. L. A. Levin, Ecology of cold seep sediments: Interactions of fauna with flow,

chemistry and microbes. Oceanogr. Mar. Biol. An Annu. Rev. 43, 1–46 (2005). 24. J. S. Hornafius, D. Quigley, B. P. Luyendyk-, J. Geophys. Res., in press. 25. A. Plaza-Faverola et al., Role of tectonic stress in seepage evolution along the gas hydrate-charged Vestnesa Ridge, Fram Strait. Geophys. Res. Lett., 733– 742 (2015). 26. P. R. Vogt, K. Crane, E. Sundvor, M. D. Max, S. L. Pfirman, Methane- generated (?) pockmarks on young , thickly sedimented oceanic crust in the Arctic : Vestnesa ridge , Fram strait. Geology. 22, 255–258 (1994). 27. E. K. L. Åström, M. L. Carroll, W. G. Ambrose, J. Carroll, Arctic cold seeps in marine methane hydrate environments: Impacts on shelf macrobenthic community structure offshore Svalbard. Mar. Ecol. Prog. Ser. 552, 1–18 (2016). 28. T. Nielsen, T. Laier, A. Kuijpers, N. Nørgård-pedersen, Fluid flow and methane occurrences in the Disko Bugt area offshore West Greenland : indications for gas hydrates? Geophys. Mar. Lett., 511–523 (2014). 29. S. J. Loyd et al., Methane seep carbonates yield clumped isotope signatures out of equilibrium with formation temperatures. Nat. Commun. 7, 1–12 (2016). 30. H. Watanabe, K. Fujikura, S. Kojima, J.-I. Miyazaki, Y. Fujiwara, Japan: Vents and Seeps in Close Proximity, 379–401 (2010).. 31. D. Lizarralde, S. A. Soule, J. S. Seewald, G. Proskurowski, Carbon release by off-axis magmatism in a young sedimented spreading centre. Nat. Geosci. 4, 50–54 (2011). 32. E. K. L. Åström et al., Methane cold seeps as biological oases in the high- Arctic deep sea. Limnol. Oceanogr. 63, S209–S231 (2018). 33. T. Sasaki, T. Okutani, K. Fujikura, Molluscs from Hydrothermal Vents and Cold Seeps in Japan: A Review of Taxa Recorded in Twenty Recent Years (1984- 2004). Venus. 64, 87–133 (2005). 34. S. Geilert et al., Transition from hydrothermal vents to cold seeps records timing of carbon release in the Guaymas Basin, Gulf of California. Biogeosciences Discuss., 1–34 (2018). 35. D. Mallet, D. Pelletier, Underwater video techniques for observing coastal marine biodiversity: A review of sixty years of publications (1952-2012). Fish. Res. 154, 44–62 (2014). 36. O. B. Humborstad, L. Nøttestad, S. Løkkeborg, H. T. Rapp, RoxAnn bottom

classification system, sidescan sonar and video-sledge: Spatial resolution and their use in assessing trawling impacts. ICES J. Mar. Sci. 61, 53–63 (2004). 37. M. Elvert, E. Suess, J. Greinert, M. J. Whiticar, Archaea mediating anaerobic methane oxidation in deep-sea sediments at cold seeps of the eastern Aleutian subduction zone. Org. Geochem. 31, 1175–1187 (2000). 38. A. Randal Hughes, J. E. Byrnes, D. L. Kimbro, J. Stachowicz, Reciprocal relationships and potential feedbacks between biodiversity and disturbance. Ecol. Lett. 10, 849–864 (2007). 39. A. Vanreusel, A. de Groote, S. Gollner, M. Bright, Ecology and biogeography of free-living nematodes associated with chemosynthetic environments in the deep sea: A review. PLoS One. 5, e12449 (2010). 40. M. Hovland, E. Thomsen, Cold-water corals-are they hydrocarbon seep related? Mar. Geol. 137, 159–164 (1997). 41. J. Kristiansen, A. Kristiansen, A new species of Chroomonas (Cryptophyceae) living inside the submarine ikaite columns in the Ikkafjord, Southwest Greenland, with remarks on its ultrastructure and ecology. Nord. J. Bot. 19, 747–758 (1999). 42. M. Schmidt, A. Priemé, P. Stougaard, Bacterial diversity in permanently cold and alkaline ikaite columns from Greenland. Extremophiles. 10, 551–562 (2006). 43. C. Yesson et al., Community composition of epibenthic megafauna on the West Greenland Shelf. Polar Biol. 38, 2085–2096 (2015). 44. M. M. Zweng, A. Münchow, Warming and freshening of Baffin Bay, 1916-2003. J. Geophys. Res. Ocean. 111, 1–13 (2006). 45. M. Moros, K. Jensen, A. Kuijpers, Mid- to late-Holocene hydrological and climatic variability in Disko Bugt, central West Greenland. The Holocene. 16, 357–367 (2006). 46. M. Melgaard, Ancient Harp Seal Hunters in Disko Bay (Meddelelser om Grønland: Man and Society, Copenhagen, 2004). 47. E. Buch, M. H. Nielsen, Oceanographic investigations off West Greenland 2007. NAFO Sci. Counc. Res. Doc. 02/17, N46, 1–14 (2008). 48. S. Gougeon, K. M. Kemp, M. E. Blicher, C. Yesson, Mapping and classifying the seabed of the West Greenland continental shelf. Estuar. Coast. Shelf Sci. 187, 231–240 (2017).

49. T. W. Nattkemper, BIIGLE, (available at https://biigle.de). 50. J. Brodie, L. V. Ash, I. Tittley, C. Yesson, A comparison of multispectral aerial and satellite imagery for mapping intertidal seaweed communities. Aquat. Conserv. Mar. Freshw. Ecosyst. 28, 872–881 (2018). 51. E. Pebesma, R. Bivand, B. Rowlingson, V. Gomez-Rubio, R Package:sp (2018), (available at https://cran.r-project.org/web/packages/sp/index.html). 52. R. Hijmans, E. Williams, C. Vennes, R Package:Geosphere (2017). 53. M. Dowle et al., R Package:data.table (2019) (available at https://cran.r- project.org/web/packages/data.table/index.html). 54. RStudio Team, RStudio (2016), (available at http://www.rstudio.com/). 55. M. Jakobsson et al., The International Bathymetric Chart of the Arctic Ocean (IBCAO) Version 3.0. Geophys. Res. Lett. (2012), :doi: 10.1029/2012GL052219. 56. M. Perry, E. Rouault, H. Butler, C. Yesson, GDALDEM (2018), (available at http://gdal.org). 57. Copernicus Marine environment monitoring service, (available at http://marine.copernicus.eu/about-us/about-eu-copernicus/). 58. R. J. Hijmans, R Package: Raster (2019), (available at https://cran.r- project.org/web/packages/raster/raster.pdf). 59. S. Urbanek, R Package: proj4 (2012), (available at https://cran.r- project.org/web/packages/proj4/index.html). 60. W. H. Oksanen J, Blanchet FG, Kindt R, Legendre P, Minchin PR, O’Hara RB, Simpson GL, Solymos P, Stevens MHH, R Package:Vegan (2013), (available at http://cran.r-project.org/package=vegan). 61. J. L. Fleiss, B. Levin, M. Cho Paik, Statistical Methods for Rates and Proportions (A John Wiley & Sons, Inc., Publication, New York, Third Edit., 2003), vol. 25. 62. M. Boardman, T. Trappenberg, in 2006 International Joint Conference on Neural Networks (Vancouver, 2006), pp. 610–617. 63. E. Dimitriadou et al., Package ‘e1071’ (2009). 64. A. Bale, A. Kenny, in Eleftheriou A, McIntyre A (eds) Methods for the study of marine benthos (Blackwell Publishing, Oxford, 2005), pp. 43–87. 65. J. Wood, LandSerf (2009), (available at www.landserf.org/%0D). 66. A. D. Weiss, in The Nature Conservancy, Seattle, p.200 (2000).

67. E. Buch, M. H. Nielsen, Oceanographic investigations off West Greenland 2011. NAFO Sci. Counc. Res. Doc. 02/17, N46, 1–14 (2012). 68. M. Hovland et al., Complex pockmarks with carbonate-ridges off mid-Norway: Products of sediment degassing. Mar. Geol. 218, 191–206 (2005). 69. R. Ramasamy, S. Subramanian, R. Sundaravadivelu, Formation of Gas Hydrates at Deep Interior of the Earth and Their Dissipation to Near Surface Horizon. J. Geol. Res. 2013, 1–11 (2013). 70. E. E. Cordes, D. C. Bergquist, C. R. Fisher, Macro-Ecology of Gulf of Mexico Cold Seeps. Ann. Rev. Mar. Sci. 1, 143–168 (2009). 71. M. T. Aguado, M. Capa, G. W. Rouse, in Worms (Sinauer Associates, Inc., 2014; https://www.researchgate.net/publication/264277019), pp. 256–270. 72. G. W. Rouse, F. Pleijel, Polychaetes (Oxford University Press, New York, 2001). 73. Y. Simard, L. Savard, Variability, Spatial Patterns and Scales of Similarity in Size–Frequency Distributions of the Northern Shrimp (Pandalus borealis) and its Migrations in the Gulf of St. Lawrence. Can. J. Fish. Aquat. Sci. 47, 794– 804 (1990). 74. S. A. Pedersen, L. Storm, Northern Shrimp ( Pandalus borealis ) Recruitment in West Greenland Waters Part I . Distribution of Pandalus Shrimp Larvae in Relation to Hydrography and Plankton. J. Northwest Atl. Fish. Sci. 30, 19–46 (2002). 75. J. S. Collie, G. A. Escanero, P. C. Valentine, Photographic evaluation of the impacts of bottom fishing on benthic epifauna. ICES J. Mar. Sci. 57, 987–1001 (2000). 76. M. Carroll et al., Shell growth and environmental control of methanophyllic Thyasirid bivalves from Svalbard cold seeps. Geophys. Res. Abstr. 18, 12108 (2016). 77. M. K. Sejr, M. Włodarska-Kowalczuk, J. Legezynska, M. E. Blicher, Macrobenthic species composition and diversity in the Godthaabsfjord system, SW Greenland. Polar Biol. 33, 421–431 (2010).

Supplementary Materials

Table S1. Complete environmental data for all sites used in SVM analysis. Sites PG2011-PG2012 taken from Neilsen et al. (1). Mikkelsen et al. (2) seeps labelled A- E. Seeps labelled by group where (N- non-seep, mud; NR- non-seep, rocky; U- uncertain; Y- seep). (Lat. – latitude, Long. – longitude). Sites sampled in videos excluding those labelled NR were used in the MDS and boxplots.

Winter Summer Temper- Current Current Salinity ature Depth Slope Strength Strength Station Seep Lat. Long. (PSU) (°C) (m) (°) TPI (m/s) (m/s) - - - 11. 2017-PA- 68.9 53.37 506.7 8.248 060 1_114 Y 421 61 34.4426 1.7555 130 5 2 0.0052 0.0018 - - 2017-PA- 68.9 53.21 430.0 7.006 4.9 1_125 Y 115 14 34.4396 1.7696 066 2 210 0.0084 0.0028 - - - 2017-PA- 68.8 53.35 613.2 5.375 8.1 1_126 Y 608 07 34.5606 1.8523 838 8 880 0.0074 0.0034 - - - 2017-PA- 68.8 52.97 192.6 0.284 1.1 1_137 N 969 03 34.4329 1.7977 222 8 710 0.0111 0.0032 - - 2018-SA- 69.0 52.74 475.3 3.888 2.3 08_1 N 618 10 34.4115 1.7343 112 8 066 0.0067 0.0015 - - - 2018-SA- 68.7 52.87 9.975 0.394 3.0 08_11 N 266 95 33.9893 0.4711 7 9 299 0.0082 0.0112 - 14. 2018-SA- 68.7 52.72 22.89 1.725 191 08_16 N 587 14 33.8724 0.1594 30 1 3 0.0057 0.0096 - - - 2018-SA- 68.9 52.41 218.4 1.902 0.5 08_72 N 552 73 34.3928 1.6431 929 9 119 0.0065 0.0020

- - - 2018-SA- 69.4 51.20 221.7 2.494 4.7 4_106 Y 959 87 34.4555 1.9549 961 5 183 0.0074 0.0053 - - 2018-SA- 69.3 51.06 81.78 2.874 0.1 4_113 U 340 89 34.4262 1.8428 75 1 553 0.0039 0.0019 - - - 2018-SA- 69.5 51.33 216.0 4.181 6.0 4_63 N 710 72 34.3557 1.6181 554 5 715 0.0051 0.0040 - - - 10. 2018-SA- 69.7 51.44 422.6 7.425 161 4_64 Y 206 61 34.4582 1.9738 905 1 0 0.0031 0.0019 - - - 2018-SA- 69.9 52.10 88.92 4.242 0.3 4_80 U 960 45 34.7196 2.9092 32 4 638 0.0081 0.0035 - - - 2018-SA- 70.0 52.06 29.62 4.405 0.0 4_81 U 106 95 34.7196 2.9092 90 8 250 0.0081 0.0035 - - 2018-SA- 69.0 52.53 379.0 2.640 1.3 8_002 Y 465 55 34.4130 1.7458 680 2 689 0.0075 0.0016 - - 2018-SA- 68.7 52.70 0.769 1.444 0.6 8_017 U 577 49 33.8724 0.1594 3 3 041 0.0057 0.0096 - - 2018-SA- 69.0 52.93 323.4 2.136 0.4 8_055 U 047 27 34.4074 1.7299 967 1 577 0.0079 0.0027 - - 2018-SA- 68.9 52.63 271.4 0.695 1.7 8_061 Y 710 54 34.4291 1.7779 807 2 867 0.0099 0.0027 - - - 2018-SA- 68.9 52.45 365.1 1.441 4.3 8_076 Y 256 62 34.4133 1.6800 325 9 141 0.0074 0.0023 - - 2018-SA- 69.0 52.38 386.5 2.001 2.4 8_087 Y 440 31 34.4188 1.7621 215 8 286 0.0066 0.0008 - - - 2017-PA- 68.9 52.98 178.0 0.879 0.2 1_113 NR 739 29 34.4095 1.7378 155 0 603 0.0086 0.0030 - - - 2017-PA- 68.9 53.36 578.1 6.125 11. 1_124 NR 314 23 34.4567 1.7677 854 6 237 0.0057 0.0024

0 - - - 2018-SA- 68.7 52.89 10.22 0.310 2.2 08_10 NR 247 23 34.0043 0.5114 89 6 804 0.0086 0.0115 - 13. 2018-SA- 68.7 52.85 11.84 1.437 459 08_12 NR 234 97 33.9689 0.4170 82 1 9 0.0078 0.0109 - - - 2018-SA- 68.7 52.83 8.675 0.175 0.6 08_13 NR 442 63 33.9326 0.3196 2 7 175 0.0071 0.0107 - - - 2018-SA- 68.7 52.85 10.91 0.069 0.4 08_14 NR 328 12 33.9551 0.3798 54 1 168 0.0075 0.0109 - - 2018-SA- 69.0 52.93 256.4 3.002 6.1 08_3 NR 413 44 34.4099 1.7295 640 6 384 0.0067 0.0021 - - - 2018-SA- 69.0 52.62 394.7 1.826 1.4 08_6 NR 258 69 34.4103 1.7405 814 8 318 0.0088 0.0023 - - 2018-SA- 69.0 52.41 308.2 1.579 4.1 08_8 NR 303 24 34.4183 1.7618 452 4 890 0.0078 0.0015 - - 2018-SA- 68.9 52.86 268.2 2.519 4.0 08_52 NR 909 86 34.4062 1.7307 083 7 929 0.0090 0.0031 - - - 2018-SA- 68.9 52.98 181.8 0.893 0.4 08_57 NR 758 07 34.4090 1.7365 661 2 775 0.0086 0.0030 - - 2018-SA- 68.9 52.38 184.4 0.277 0.6 08_58 NR 758 62 34.3912 1.6538 772 2 185 0.0073 0.0020 - - 2018-SA- 68.9 53.02 151.0 1.140 0.1 08_68 NR 526 12 34.4146 1.7508 817 3 753 0.0090 0.0031 - - 2018-SA- 68.9 52.99 117.1 0.888 0.9 08_82 NR 276 89 34.4224 1.7710 711 7 421 0.0100 0.0034 Mikkelsen - - - et al. 69.0 53.11 626.4 6.104 9.9 (2012)A Y 070 31 34.4080 1.7295 840 5 659 0.0066 0.0025 Mikkelsen - - - et al. 68.8 53.32 661.2 0.660 8.8 (2012)B Y 608 23 34.5448 1.8443 793 7 466 0.0080 0.0043

Mikkelsen - - et al. 68.3 56.03 481.9 0.560 0.1 (2012)C Y 497 19 34.4833 1.5853 900 7 760 0.0081 0.0018 Mikkelsen - - et al. 69.8 52.10 279.1 5.143 4.5 (2012)D Y 521 26 34.5891 2.4352 466 3 176 0.0051 0.0008 Mikkelsen - - et al. 69.2 51.43 388.7 1.089 1.3 (2012)E Y 058 78 34.4227 1.8011 765 5 107 0.0013 0.0004 - - - PG2011- 68.8 53.32 348.4 3.427 0.0 05 N 662 43 34.4181 1.6373 120 1 166 0.0072 0.0052 - - - PG2011- 69.2 51.45 267.5 2.074 0.0 10 N 068 65 34.4259 1.8074 197 9 700 0.0027 0.0014 - - - PG2011- 69.2 51.45 269.3 2.059 0.0 12 N 127 90 34.4255 1.8072 368 9 744 0.0027 0.0014 - - - PG2012- 68.4 55.78 429.3 0.689 0.0 01 U 638 17 34.5202 1.6484 966 0 713 0.0054 0.0029 - - - PG2012- 68.3 55.82 418.3 0.802 0.0 02 U 780 52 34.4844 1.5747 392 1 615 0.0066 0.0031 - - - PG2012- 68.4 55.72 427.0 0.814 0.0 03 U 023 30 34.5013 1.6258 849 5 709 0.0063 0.0029

Table S2. Environmental variables considered and used in this study. Copernicus marine environment monitoring service for climatic variables (3), taken from the TOPAZ4 Arctic Ocean - Reanalysis dataset (variables 1-12). International Bathymetric Chart of the Arctic Ocean (IBCAO) used for topographic variables (variables 13-29) (4). Methods described in Yesson et al. (5). Greenland Institute of Natural Resources (GINR) and C. Yesson provided multi-beam bathymetry and backscatter layers (variables 30-40). *’ - chosen for final analysis.

Variable Source Units Resolution 1 Annual salinity * Copernicus Practical 12500 x Salinity Units 12500 (PSU)

2 Summer salinity Copernicus Practical 12500 x Salinity Units 12500 (PSU) 3 Winter salinity Copernicus Practical 12500 x Salinity Units 12500 (PSU) 4 Annual seabed temperature * Copernicus Degrees 12500 x Celsius (˚C) 12500 5 Summer seabed temperature Copernicus Degrees 12500 x Celsius (˚C) 12500 6 Winter seabed temperature Copernicus Degrees 12500 x Celsius (˚C) 12500 7 Current velocity West-East Copernicus metres per 12500 x (U) (annual) second (m/s) 12500 8 Current velocity West-East (U) Copernicus metres per 12500 x (summer) * second (m/s) 12500 9 Current velocity West-East (U) Copernicus metres per 12500 x (winter) * second (m/s) 12500 10 Current velocity South-North Copernicus metres per 12500 x (V) (annual) second (m/s) 12500 11 Current velocity South-North Copernicus metres per 12500 x (V) (summer) * second (m/s) 12500 12 Current velocity South-North Copernicus metres per 12500 x (V) (winter) * second (m/s) 12500 13 Bathymetry course scale IBCAO metres (m) 12500 x 12500 14 Bathymetry fine scale * IBCAO metres (m) 500 x 500

15 Fractal Dimension IBCAO Df (index) 12500 x Bathymetry course scale 12500 16 Maximum Bathymetry IBCAO m (index) 500 x 500 17 Minimum Bathymetry IBCAO metres (m) 12500 x 12500 18 Range of Bathymetry IBCAO metres (m) 12500 x 12500 19 Range of Slope IBCAO degrees 12500 x 12500 20 Roughness IBCAO (index) 12500 x 12500 21 Roughness IBCAO (index) 500 x 500

22 Standard Deviation IBCAO metres (m) 12500 x Bathymetry 12500 23 Standard Deviation Slope IBCAO metres (m) 12500 x 12500 24 Slope course scale IBCAO degrees ( ˚ ) 12500 x 12500 25 Slope fine scale * IBCAO degrees ( ˚ ) 500 x 500 26 Topographic Position Index IBCAO (index) 12500 x (TPI) (course scale) 12500 27 Topographic Position Index IBCAO (index) 12500 x (TPI) (fine scale) * 12500 28 Terrain Ruggedness Index IBCAO (index) 12500 x (TRI) (course scale) 12500 29 Terrain Ruggedness Index IBCAO (index) 500 x 500 (TRI) (fine scale) 30 Annual current Magnitude Copernicus metres per 12500 x second (m/s) 12500 31 Winter Current Magnitude * Copernicus metres per 12500 x second (m/s) 12500 32 Summer Current Magnitude * Copernicus metres per 12500 x second (m/s) 12500 33 Backscatter (multi-beam) GINR index 10 x 10 34 Bathymetry (multi-beam) GINR metres 10 x 10 35 Fractal Dimension GINR + fractal (index) 10 x 10 Bathymetry fine scale dimension (multibeam) package R 36 Fractal Dimension Fine scale GINR + fractal (index) 10 x 10 Bathymetry course scale dimension (multibeam) package R 37 Fine Scale Slope (multibeam) GINR + degrees ( ˚ ) 10 x 10 LandSerf 38 Course Scale GINR + degrees ( ˚ ) 10 x 10 Slope (multibeam) LandSerf 39 Standard deviation GINR + fractal metres (m) 10 x 10 backscatter fine dimension scale (multibeam) package R 40 Standard deviation GINR + fractal metres (m) 10 x 10 backscatter fine dimension scale (multibeam) package R

References 1. T. Nielsen, T. Laier, A. Kuijpers, N. Nørgård-pedersen, Fluid flow and methane occurrences in the Disko Bugt area offshore West Greenland : indications for gas hydrates? Geophys. Mar. Lett., 511–523 (2014). 2. N. Mikkelsen, T. Laier, T. Nielsen, A. Kuijpers, N. Norgaard-Pedersen, Methane and possible gas hydrates in the Disko Bugt region, central West Greenland. Geol. Surv. Denmark Greenl. Bull., 69–72 (2012). 3. Copernicus Marine environment monitoring service, (available at http://marine.copernicus.eu/about-us/about-eu-copernicus/) accessed: 25/04/19. 4. M. Jakobsson et al., The International Bathymetric Chart of the Arctic Ocean (IBCAO) Version 3.0. Geophys. Res. Lett. (2012), doi:doi: 10.1029/2012GL052219. 5. C. Yesson et al., The impact of trawling on the epibenthic megafauna of the west Greenland shelf. ICES J. Mar. Sci. 74, 866–876 (2017).