Hidden Habitats of Greenland; Identifying benthic community distribution within Disko Bay
Conor Nicoll 27/08/2020
A thesis submitted in partial ful lment of the requirements for the degree of Master of Research at Imperial College London
Formatted in the journal style of the Marine Ecology Progress Journal. Submitted for Ecology, Evolution and Conservation MRes Declaration
I declare that benthic footage and data for this thesis were provided by Chris Yesson of the Zoological Society of London whilst GINR provided the DTM and backscatter layers, which were both created from multibeam survey data.
Acknowledgements
I would like to thank Chris Yesson for his continued tutelage and patience throughout this project. I would also like to thank the Greenland Insitute of Natural Resources' MAPHAB team for their advice and knowledge regarding Disko Bay. For their support throughout this project, I would like to thank my atmates. Finally, I would like to acknowledge that this project was signi cantly aected by the COVID-19 pandemic which prevented data collection and increased di culty of analysis.
Word Count: 5867
1 Abstract
The sustainable exploitation of marine resources is dependent on our understanding of marine ecosystems. Benthic communities provide a wide range of ecosystems services which both directly, and indirectly, bene t the management of economically vital resources. By documenting and describing the dierent biological communities found within Disko Bay (western Greenland), this thesis aims to produce the rst high-resolution benthic habitat map for the region. I analysed drop camera footage from 55 dierent stations covering 18.6km with a depth range of 607m (34m to 641m). This led to the identi cation of four distinct habitat assemblages formed by dominant taxa: Ascidian, Anemone, Bryozoan and Sponge. The in uence of multiple environmental drivers on the distribution of these communities and their respective organisms were then analysed using a series of generalised linear models. Though signi cant in uencers such as roughness and current speed were identi ed, the dominant drivers for benthic assemblage distribution within Disko Bay were not identi ed. Nevertheless, this thesis has been successful in improving the understanding of the benthic communities within Disko Bay, as well as the creation of a high-resolution sediment map.
Key Terms
Benthic Community Distribution, Benthic Imagery, Benthic Mapping, Disko Bay, Emergent Epifaunal As- semblages, EUNIS, Western Greenland
2 1 Introduction
1 In the 21st century, food security has become a dominant issue due to the unsustainable exploitation
2 of natural resources, climate change, and global population growth (Rosegrant and Cline, 2003; Challinor
3 et al., 2014). Concerns that terrestrial solutions are limited has led to an increased reliance on marine
4 systems (Rice and Garcia, 2011; FAO, 2014). Subsequently, global sh consumption has already increased
5 at an average annual rate of 3.1% over 56 years (1961 2017; FAO, 2020). Whilst the recent growth in
6 aquaculture has been able to compensate for increasing demands, commercial sheries still capture more
7 than 84.4 million tonnes in wild catch annually (FAO, 2020). To achieve such landings, many sheries
8 employ techniques which harm marine ecosystems (Pauly et al., 2005; Amoroso et al., 2018). One of the
9 most damaging techniques used by commercial sheries is demersal trawling (Watling and Norse, 1998;
10 Hiddink et al., 2006; Oberle et al., 2016).
11 De ned by the usage of equipment which makes sustained contact with substrate when towed, demersal
12 trawls are the most extensive anthropogenic threat to global seabed habitats (Watling and Norse, 1998;
13 Oberle et al., 2016). Used to target benthic and demersal organisms, 25% of global seafood landings were
14 captured using demersal trawls in 2018 (Amoroso et al., 2018). Due to the prominence of this technique,
15 up to 18% of continental shelf has been subject to demersal trawling (Amoroso et al., 2018). By coming
16 into contact with bottom sediment, trawling techniques directly lead to epibenthos mortality via physical
17 contact and removal from substrate (Lambert et al., 2011; Bowden et al., 2013). Sediment resuspension
18 and increased bottom water turbidity can also contribute to epibenthos fatalities via sediment smothering
19 (Erftemeijer et al., 2012; Pineda et al., 2017). Outside of the contribution to epibenthic mortality, trawling
20 can directly alter nutrient cycling and trophic interactions (Hinz et al., 2009; Lambert et al., 2011). By
21 aecting benthic communities and interacting with surrounding sediment, trawling contributes to local
22 disturbance regimes (Hinz et al., 2009).
23 Disturbance regimes are the spatiotemporal characteristics of a disturbance agent and its impact on
24 ecological systems (Lundquist et al., 2010; Harris, 2014). These regimes are the culmination of disturbances
25 from both natural and anthropogenic sources (Lundquist et al., 2010; Harris, 2014). The in uence of
26 disturbance regimes on benthic assemblage distribution has been observed in Disko Bay, Greenland's largest
27 open water bay (Meldgaard, 2004; Yesson et al., 2017). Due to input from local glacial activity, icebergs
28 are present within the bay and can scour benthic substrate via the gouging eect of their keels (Gutt et al.,
29 1996; Scheick et al., 2019). Disko Bay is also host to commercial shery activity in the form of demersal
30 trawling which has impacted 82% of western Greenland's continental shelf (Garcia, 2007; Yesson et al.,
31 2017). However, this demersal trawling activity is economically vital, accounting for 8095% of Greenland's
32 export income (The Economic Council, 2017; Long et al., 2020). Yet, if current shing practises are harming
33 emergent epifaunal communities by contributing to local disturbance regimes, the long term sustainability
34 of these sheries may be negatively aected (Collie, 2000).
35 Emergent epifaunal assemblages are comprised of sessile benthic invertebrates, de ned by their devel-
36 opment of vertical structures (Bradshaw et al., 2003; Lambert et al., 2011). Organisms that fall into
37 this category include sponges, corals, zoanthids, bryozoa, hydrozoa and ascidians (Bradshaw et al., 2003;
38 Lambert et al., 2011). These assemblages increase habitat complexity, provide shelter and sustenance for
39 associated organisms, and expand ecological niches, especially for epiphytes (Sebens, 1991; Diehl, 1992;
40 Bradshaw et al., 2003; Taniguchi and Tokeshi, 2004). Their structures can also be used as breeding and
41 nursery grounds for economically harvested species (Collie et al., 2000). Therefore, these assemblages are
42 considered essential for their provision of ecosystem services (Lambert et al., 2011). Currently, demersal
43 trawling threatens the health and stability of emergent epifaunal assemblages within Disko Bay (Lambert
44 et al., 2011; Wood and Probert, 2013; Yesson et al., 2017). If the provision of these ecosystem services is
45 lost, the harvesting of economically vital species within the region may be negatively impacted.
3 46 Many emergent epifaunal assemblages have low resilience to disturbance due to their physiological and life-
47 history traits (Lambert et al., 2011). By forming fragile, sessile structures which protrude from the substrate,
48 emergent epifaunal organisms are vulnerable to physical impact from trawling activity and therefore have
49 a low resistance (Collie et al., 1997). Emergent epifaunal organisms also possess low metabolic rates
50 leading to later maturity, lower annual reproductive output and lower natural mortality (Hiddink et al.,
51 2019). Though these traits bene t the longevity of their respective assemblages, they conversely limit
52 the recovery rate of these communities following disturbances (Hiddink et al., 2019). Furthermore, abiotic
53 factors such as substrate type, bottom water turbidity, light availability, nutrient supply, temperature, salinity,
54 and hydrodynamical conditions can mitigate or exacerbate the impacts of trawls upon these assemblages
55 (McArthur et al., 2010; Yesson et al., 2015; Jansen et al., 2020; Montagna et al., 2020).
56 To conserve the emergent epifaunal assemblages of Disko Bay and reduce the impact of demersal trawling,
57 information is required regarding the identity and distribution of benthic communities within the bay (Diaz
58 et al., 2004). Accessible information on the distribution of vulnerable communities would enable informed
59 decision-making to further protect these vulnerable benthic communities (Brown et al., 2011; Rengstorf
60 et al., 2013). Consequently, international organisations such as European University Information Systems
61 (EUNIS) have been encouraging the development of greater benthic mapping. Whilst broad benthic habitat
62 maps of western Greenland exist, there are currently no such maps for Disko Bay (Boertmann and Mosbech,
63 2011). Due to the high levels of spatial variability in the distribution of biological communities, broader
64 studies cannot be used to produce accurate local maps (Buch et al., 2004). The absence of a benthic
65 habitat map exacerbates the vulnerability of Disko Bay's emergent epifaunal assemblages.
66 Currently, there is limited information regarding the benthic communities of Disko Bay (Boertmann and
67 Mosbech, 2011). However, wider studies investigating Western Greenland can be used to infer the drivers of
68 assemblage distribution. Piepenburg et al. (2011) showed that relative to the Arctic, benthic communities
69 in western Greenland had enhanced diversity with the identi cation of various benthic communities. The
70 distribution of these assemblages has been linked to substrate type, depth, slope, current speeds and salinity
71 can all signi cantly in uence the distribution of benthic communities (Baynes and Szmant, 1989; Etter
72 and Grassle, 1992; Aller, 1997; Wanamaker et al., 2007; Sejr et al., 2010; Yesson et al., 2015; Durden
73 et al., 2020). Trawling and iceberg scouring also exhibited signi cant levels of in uence over benthic
74 assemblage distribution by contributing to local disturbance regimes (Yesson et al., 2017). By examining
75 how environmental drivers in uence the distribution of emergent epifaunal organisms within Disko Bay, a
76 predictive habitat can be formed around their respective optimal zones (Figure 1).
Figure 1: The relationship between the tness of an organism and its position relation relative to its optimal zone (Dolédec et al., 2000; Slatyer et al., 2013; Sexton et al., 2017). Fish within the gure are representative of all organisms.
4 77 1.1 Aims
78 To conserve epifaunal assemblages in Disko Bay, the identity, distribution and features of these benthic
79 communities must be established. I hypothesise that vulnerable emergent epifaunal assemblages are present
80 in Disko Bay and their distributions are signi cantly in uenced by surrounding abiotic factors. If possible,
81 I will produce a high-resolution benthic habitat map by combining a sediment map of Disko Bay with
82 predictive habitat models.
83 2 Methodology
84 2.1 Data
85 Data for this project has been provided by Zoological Society of London (ZSL) and Greenland Institute
86 of Natural Resources (GINR). Drop camera footage was taken at 55 dierent stations between the dates
87 11/09/2019 13/09/2019. Stations for sampling were chosen to provide a variety of oceanographical
88 environments. The drop camera used was a GoPro camera in a groupbinc underwater housing with 2
89 Nautilux torches which recorded continuous footage. When in contact with the sea oor, the GoPro sat
90 85cm above benthos, angled at 31o. Consequently, after accounting for the refractive index of the water
91 and lenses, each image covered approximately 2.07m2 of the benthos. Once sampling was initiated, the drop
92 camera was towed 5m above benthos to be lowered unto the sea oor at 1-minute intervals. This ensured
93 that sites recorded were not replicated. For each of these drops, a still image was extracted from the benthic
94 footage. Together, this produced an average of 14 images per station (Sample size = 742, SD= 9, Range
95 = 5 56; Figure 2).
Figure 2: The method with which benthic imagery and footage was collected during surveying.
96 Alongside drop camera footage, multi-beam acoustic data of the sea oor was collected. Both pulse
97 length and emitted wavelength remained constant throughout the survey to ensure results were standardised.
98 Backscatter was produced at two dierent resolutions, 5m and 25m. To keep a consistent resolution with
99 bathymetric data, backscatter with a resolution of 25m was used in subsequent data analysis. Digital Terrain
100 Model (DTM) and backscatter layers were provided by GINR, based on multibeam surveys conducted at
101 the same time as the camera surveys. Environmental factors such as slope and roughness were extracted
102 from the DTM using QGIS v3.0.2 (QGIS Development Team, 2020).
5 103 2.2 Visual Inspection
104 I visually inspected drop camera footage to identify both sediment and assemblage types within benthic
105 imagery. I analysed drop camera footage 15 seconds before and after image capture to ensure images were
106 representative of the wider area. If needed, to compensate for varying drop and drag velocities, I adjusted the
107 length of this inspection (Range: 10s 30s). I used a dichotomous key to determine sediment type which
108 evaluated and classi ed the dierent substrate types (Supplementary Information 5.1). These sediment
109 types built upon the pre-existing EUNIS classi cations by expanding the categorisations (Figure 3). I chose
110 this method as it allowed higher resolution of information regarding the type of substrate present, whilst
111 still being able to t within EUNIS categories. Where appropriate, I divided stations into substations to
112 ensure the highest level of substrate type homogeneity. Substations had to contain 3 images and cover a ≥ 113 minimum distance of 40m.
Figure 3: Sediment types grouped to t within pre-existing EUNIS classi cation codes (A6.1, A6.2 & A6.5). Sediment types without a code are not o cially recognise by EUNIS guides.
114 For each image, I identi ed key organisms to the highest taxonomic rank possible via visual inspection using
115 Bio-Image Indexing and Graphical Labelling Environment (BIIGLE; Langenkämper et al., 2017). BIIGLE is
116 a web-based application developed to aid the annotation of large image sets, in particular those collected
117 via marine monitoring platforms (Langenkämper et al., 2017). I determined which methodology was used
118 to annotate and quantify the abundance of an organism by how discrete individual organisms were. For
119 organisms with clearly de ned individual units (ascidians, anemones and caridean shrimp), I used Machine
120 Learning Assisted Image Annotation (MAIA) to gather count data (Zurowietz et al., 2018). MAIA is a
121 programme which assists in annotation e ciency by detecting organisms within the imagery (Zurowietz
122 et al., 2018). Due to its integration within BIIGLE, MAIA can use pre-existing annotations from benthic
123 imagery as training data (Zurowietz et al., 2018). Though this can be performed on any annotation, I found
124 MAIA to be ineective on organisms with less discrete individual units. This limitation meant I employed a
125 dierent methodology when assessing colonial organisms. To achieve this, I quanti ed the abundance of all
126 key taxa using a modi ed Dominant, Abundant, Common, Frequent, Occasional, Rare (DACFOR) scaling
127 method (Table 1; Crisp and Southward, 1958; Hiscock, 1998). I removed the Dominant, Abundant and
128 Common categories of the scaling due to the lack of viable examples, whilst adding an absent category to
129 account for samples with no organism coverage. Additionally, binary presence and absence within benthic
130 imagery was noted for anemones, ascidians, bryozoans, soft corals, red algae and maerl beds, hydrozoa,
131 sponges, and zoanthids. At substation resolution, I used the median presence and absence of an organism
132 within the substation to determine whether it was present or absent.
6 Table 1: The modi ed scaling used for DACFOR classi cations.
133 2.3 Assemblage Determination
134 Within the benthic communities of Disko Bay, assemblages of primary taxa were observed forming the
135 foundation of a community's habitat complexity. Thus I used these primary taxa to de ne the identity of
136 biological assemblages. Due to a lack of samples, communities which comprised of two dominant species
137 were considered as two dierent, overlapping assemblages. I determined assemblage type at an image level
138 by considering two aspects: DACFOR Results and Video Footage. Primarily, I used DACFOR results to
139 determine how an assemblage was classi ed. I assumed organisms categorised as "Frequent" had su cient
140 coverage to justify classi cation as a biological assemblage. For organisms with an "Occasional" categori-
141 sation, I applied two steps to determine whether they were considered biological assemblages. Initially, I
142 compared the DACFOR result of the "Occasional" organism with the results of other organisms within the
143 image. If the organism exhibited DACFOR results equivalent to or higher than surrounding organisms, I
144 would then analyse surrounding imagery. If this trend was continuous and not anomalous, I would con-
145 sider the "Occasional" organism to be a biological assemblage. For organisms with a "Rare" classi cation,
146 consideration as a biological assemblage was uncommon though not impossible. If surrounding imagery con-
147 sistently exhibited the presence of the organism's respective biological assemblage, I considered the DACFOR
148 classi cation as potentially inaccurate. I would then analyse footage from the relevant substations to assess
149 the accuracy of this classi cation. If visual inspection suggested the "Rare" abundance scaling result was not
150 representative of the organism's wider assemblages observed, I considered the results "Occasional" instead.
151 If either of these conditions were not met, the classi cation would remain "Rare". "Absent" classi cations
152 were never noted as assemblages. I noted which stations had their video footage analysed to assist in
153 assemblage classi cation as this could potentially alter future data analysis. At the scale of substation, I
154 used the homogeneity of biological assemblages observed at the image level to determine substation wide
155 assemblages. I considered a biological assemblage to be substation-wide when 50% of images from the ≥ 156 substation exhibited the presence of the relevant biological assemblage. Assemblages identi ed had their
157 relation to abiotic factors analysed visually using Violin plots and Spearman's Rank Correlation tests using
158 the packages 'gridExtra v2.3' (Auguie, 2017), 'corrplot v0.84' (Taiyun and Viliam, 2017) and 'ggcorrplot
159 v0.1.3.' (Kassambara, 2019).
160 2.4 Generalised Linear Models
161 I used binomial, Poisson and negative binomial generalised linear models (GLM) to assess the abiotic
162 factors which in uenced the distribution of emergent epifaunal assemblages and their associated organisms.
163 I produced models using 'R v3.4.4' (R Core Team, 2018) in "Rstudio v1.2.5001" (Team, 2019), alongside
164 the packages 'lme4 v.1.1-20' (Bates et al., 2015) and 'MASS v7.52' (Venables and Ripley, 2002). For
165 binomial GLMs, I used the presence and absence data of target emergent epifaunal assemblages as the
166 response variable. For Poisson and negative binomial models, I used the count data from appropriate
167 organisms. To test for abiotic drivers of distribution, I have used depth, slope, EUNIS category, backscatter,
168 current, roughness, trawl eort and sediment type as explanatory variables in modelling. Data was treated
169 to assume model assumptions (Table 2). I used backwards stepwise elimination to select the minimum
170 adequate models based on AIC values, using the package 'stats v3.6.2' (R Core Team, 2018). I also used
171 the package 'performance' (Lüdecke et al., 2020) to run diagnostic plots on all models to ensure their
172 suitability. All summary statistics for binomial GLMs are in the logit scale whilst all Poisson GLM results
7 173 are in the log scale. I performed post hoc emmeans tests on EUNIS Category or Sediment type using the
174 package 'emmeans v1.50' (Lenth, 2020). I considered p 0.05 to be signi cant for GLMs |Z| 2 for emmeans ≤ ≥ 175 tests. Table 2: Explanatory variables tested in statistical modelling and their respective adjustments.
Explanatory Description Treatment Justi cation Variables Depth Median depth (m) Quadratic To address the non-linear re- sponse of organisms due to depth range exceeding their tol- erable limit.
Slope Median Slope (0o = Horizontal) log To address positive skew and al- low data to better t the as- sumptions of normality. EUNIS category EUNIS sediment categories None
Backscatter Median Backscatter Cube Root To address negative skew and allow data to better t the as- sumptions of normality.
Interaction To address collinearity, allowing backscatter to act as a proxy for substrate hardness (McArthur et al., 2010).
1 Current Median current speed (cms− ) None
Roughness Median rugosity of benthos None
Trawl eort Median trawl eort (min) Cube Root To address negative skew and allow data to better t the as- sumptions of normality.
Sediment type Ground-truthed sediment types None
176 2.5 Map Creation
177 The benthic habitat map of Disko Bay required the creation of two separate components; a sediment
178 distribution map and biological community distribution map. By combining these maps, I could produce a
179 suitable gure to exemplify the habitat distributions within Disko Bay. Following conventional methodology
180 for sediment distribution maps, backscatter values were combined with the sediment types I identi ed in
181 ground-truthing. Unique backscatter patterns were assigned to sediment types, allowing the identi cation
182 in areas which had backscatter recordings but lacked benthic imagery. To create the biological community
183 distribution map, I had to identify su cient indicators for biological assemblage distribution within the bay.
184 If su cient, I could use in uential factors to predict the distribution of taxa and their relative assemblages,
185 producing a predictive habitat model (PHM). To produce both of these maps, I used QGIS. I have projected
186 all maps in World Geodetic System 72, Universal Transverse Mercator zone 22N to avoid inaccurate scaling.
8 187 3 Results
188 3.1 Image Data
189 The average image depth taken from ship readings was 241m (Sample Size = 742, SD = 105, Range =
190 34 641) and 238m when taken from DTM (Sample Size = 731 SD = 100 , Range = 32 617). Average
191 slope within imagery was 15◦ (Sample Size = 726, SD = 15◦, Range = 0.12◦ 64◦) and average current 1 192 speed was 0.03ms− (Sample Size = 738, SD = 0.01 , Range = 0.01 0.05). Images had an average spatial 193 separation of 13.61m and covered a total area of 1535.94m2 (Figure 4). ≈
Figure 4: Stations divided by EUNIS sediment category.
194 3.2 Sediment Types
195 A total of 14 dierent sediment types were identi ed within survey imagery. These were simpli ed into 7
196 categories to fall within previously de ned EUNIS archetypes (Supplementary Information 5.2: Table 14). At
197 a station-wide resolution, 5 sediment types were identi ed (Supplementary Information 5.2: Table 15). Two
198 sediment maps for Disko Bay have been created; One based o sediment types identi ed in ground-truthing
199 and the other upon how these sediments fall into EUNIS classi cation (Figure 5; Figure 6).
200 3.3 Biological Assemblages
201 The benthic footage of 26 substations was analysed to assess the eectiveness of DACFOR results. This
202 led to the alteration of results to better represent biological assemblages, eight of which consisted of an
203 upgraded DACFOR category from 'Rare' to 'Occasional'. Processing DACFOR results identi ed a total of 7
204 dierent biological assemblages: ascidian, anemone, bryozoan, red algae and maerl, hydrozoan, sponge, and
205 zoanthid. A summary table and Spearmans Rank Correlation plot have been produced to show how these
206 assemblages relate to environmental factors (Supplementary Information 5.2: Table 16; Figure 7). Due to
207 a lack of samples, zoanthid, red algae and maerl, and hydrozoan assemblages were removed from further
208 analysis. This left four dominant assemblages: ascidian, anemone, bryozoan and sponge. A summary table
209 and violin plots have been generated to better represent how the four dominant taxa and their assemblages
210 relate to environmental factors (Supplementary Information 5.2; Table 17; Figure 8).
9 Figure 5: Sediment type for Disko Bay, based of backscatter and ground truthing data.
Figure 6: Sediment types for Disko Bay, based of backscatter and ground truthing data placed into the overarching EUNIS classi cation system.
10 211 3.4 Generalised Linear Models
212 GLMs were run on the four main emergent epifaunal taxa and their respective assemblages; ascidians,
213 anemones, bryozoa and sponges.
214 3.4.1 Ascidians
215 Backscatter was the only factor that in uenced the chance of ascidian presence, which decreased as
216 the backscatter increased (p=0.45; Table 3). A negative relationship was also observed between ascidian
217 frequency and roughness (p=0.043; Table 4). In addition, this frequency also exhibited a non-linear relation
218 with depth (p=0.016; Table 4). However, no signi cant in uence was observed amongst abiotic factors in
219 regards to predicting ascidian assemblages presence (Table 5).
Table 3: Results from binomial GLM testing interaction between the median presence and absence of ascidians across substations. Explanatory factor used was Backscatter. * = p 0.05, ** = p 0.01, *** = p 0.001 ≤ ≤ ≤ Predictors Estimate Standard error p Intercept 0.05 0.50 0.093 Backscatter <-0.01 <0.01 0.049* Observations 74 R2 Tjur 0.06
Table 4: Results from negative binomial GLM testing interaction between the median frequency of ascidians across substations. Explanatory factors used were EUNIS category and Roughness. * = p 0.05, ** = p 0.01, *** = P 0.001 ≤ ≤ ≤ Predictors Estimate Standard error p Intercept 0.29 1.03 0.780 Depth <-0.01 <-0.01 0.016* Roughness -0.12 0.06 0.043* Observations 74 R2 Nagelkerke 0.29
Table 5: Results from binomial GLM testing interaction between the median presence and absence of ascidian assemblages across substations. Explanatory factors used were Backscatter and Roughness. * = p 0.05, ** = p 0.01, *** = p 0.001 ≤ ≤ ≤ Predictors Estimate Standard error p Intercept -2.10 1.06 0.048* Roughness -0.11 0.07 0.129 Backscatter <-0.01 <0.01 0.072 Observations 74 R2 Tjur 0.06
11 220 3.4.2 Anemones
221 The chance of anemone presence decreased signi cantly if the substation fell within the EUNIS classi -
222 cation 'mixed sediment' (p=0.041; Table 6). Anemones were also more likely to be present in substations
223 with greater roughness (p=0.021; Table 6). Both of these results are supported by anemone count data
224 which shows the same relationships with mixed sediment (p=0.041) and roughness (p=0.004; Table 7). In
225 addition, count data also shows that greater current speeds signi cantly increased the frequency of observed
226 anemones (p=0.012). However, at the assemblage level, anemones had no signi cant abiotic indicators
227 (Table 8).
Table 6: Results from binomial GLM testing interaction between the median presence and absence of anemones across substations. Explanatory factors used were EUNIS category, Roughness and Current speed. * = p 0.05, ** = p 0.01, *** = p 0.001 ≤ ≤ ≤ Predictors Estimate Standard error p EUNIS Category - Hard (Intercept) -2.78 3.14 0.378 EUNIS Category - Mixed -2.94 1.44 0.041* EUNIS Category - Soft -0.61 1.10 0.581 Roughness 0.13 0.06 0.021* Current 1.53 0.85 0.072 Observations 74 R2 Tjur 0.30
Table 7: Results from Poisson GLM testing interaction between the median frequency of anemones across substations. Explanatory factors used were EUNIS category, Current and Roughness. * = p 0.05, ** = p 0.01, *** = p 0.001 ≤ ≤ ≤ Predictors Estimate Standard error p EUNIS category - Hard (Intercept) -2.55 2.19 0.246 EUNIS Category - Mixed -2.55 1.08 0.018* EUNIS Category - Soft -0.58 0.86 0.505 Roughness 0.12 0.04 0.004** Current 1.23 0.49 0.012* Observations 74 R2 Nagelkerke 0.76
Table 8: Results from binomial GLM testing interaction between the median presence and absence of anemone assemblages across substations. Explanatory factors used was depth. * = p 0.05, ** = p 0.01, *** = p 0.001 ≤ ≤ ≤ Predictors Estimate Standard error p Intercept -1.92 0.96 0.045* Depth <0.01 <0.01 0.232 Observations 74 R2 Tjur 0.02
12 228 3.4.3 Byrozoa
229 Increasing backscatter was observed to negatively in uence the presence of bryozoa signi cantly (p=0.012;
230 Table 9). Despite this, no factors observed signi cantly in uenced bryozoa assemblage presence (Table 10).
231
Table 9: Results from binomial GLM testing interaction between the median presence and absence of bryozoa across substations. Explanatory factors used were EUNIS category, Roughness, Current and Backscatter. * = p 0.05, ** = p 0.01, *** = p 0.001 ≤ ≤ ≤ Predictors Estimate Standard error p EUNIS Category - Hard (Intercept) -2.22 2.26 0.327 EUNIS Category - Mixed 0.56 0.93 0.548 EUNIS Category - Soft -1.43 0.95 0.133 Roughness -0.14 0.10 0.156 Current 0.86 0.53 0.103 Backscatter <-0.01 <0.01 0.012* Observations 74 R2 Tjur 0.43
Table 10: Results from binomial GLM testing interaction between the median presence and absence of bryozoa assemblages across substations. Explanatory factors used were EUNIS category and Backscatter. * = p 0.05, ** = p 0.01, *** = p 0.001 ≤ ≤ ≤ Predictors Estimate Standard error p EUNIS Category - Hard (Intercept) -0.32 1.03 0.758 EUNIS Category - Mixed -0.30 1.05 0.774 EUNIS Category - Soft 1.81 2.59 0.994 Backscatter 1.87 1.29 0.147 Observations 74 R2 Tjur 0.15
13 232 3.4.4 Sponges
233 The EUNIS classi cation of the sediment type was the only factor which signi cantly in uenced both the
234 presence of sponges and their assemblages (Tables 11; Table 12). Soft sediment had a negative in uence
235 on presence(p=0.005) and assemblage presence (p=0.045; Table 11 ; Table 12).
Table 11: Results from binomial GLM testing interaction between the median presence and absence of sponges across substations. Explanatory factor used was EUNIS category. * = p 0.05, ** = p 0.01, *** = p 0.001 ≤ ≤ ≤ Predictors Estimate Standard error p EUNIS Category - Hard (Intercept) 0.18 0.61 0.763 EUNIS Category - Mixed -0.55 0.75 0.460 EUNIS Category - Soft -2.16 0.77 0.005** Observations 74 R2 Tjur 0.15
Table 12: Results from binomial GLM testing interaction between the median presence and absence of sponge assemblage across substations. Explanatory factor used was EUNIS category. * = p 0.05, ** = p 0.01, *** = p 0.001 ≤ ≤ ≤ Predictors Estimate Standard error p EUNIS Category - Hard (Intercept) -0.98 0.68 0.147 EUNIS Category - Mixed 0.22 0.82 0.789 EUNIS Category - Soft -1.99 0.99 0.045* Observations 74 R2 Tjur 0.12
14 236 3.5 Post hoc Tests
237 Emmeans were performed on anemone presence, anemone frequency, byrozoa presence, byrozoa assem-
238 blage presence, sponge presence, and sponge assemblage presence GLMs (Table 6; Table 7; Table 9; Table
239 10; Table 11; Table 12). This enabled analysis on how diering EUNIS sediment types impacted the
240 distribution of key taxa and their assemblages.
Table 13: Results from Emmeans post hoc tests for EUNIS category. Signi cant values (|Z| 2) have been emboldened. ≥
EUNIS Category Pair Estimate Standard error Z Ratio Anemone- Presence Hard - Mixed 2.94 1.44 2.05 Hard - Soft 0.61 1.10 0.55 Mixed - Soft -2.33 1.47 -1.59
Anemone- Frequency Hard - Mixed 2.55 1.077 2.36 Hard - Soft 0.58 0.86 0.67 Mixed - Soft -1.97 1.225 -1.61
Bryozoa- Presence Hard - Mixed -0.56 0.93 -0.60 Hard - Soft 1.43 0.95 1.50 Mixed - Soft 1.99 0.79 2.53
Bryozoa- Community Hard - Mixed 0.30 1.05 0.29 Hard - Soft 18.11 2585.61 0.01 Mixed - Soft -1.97 1.225 -1.61
Sponge- Presence Hard - Mixed 0.55 0.75 0.74 Hard - Soft 2.16 0.77 2.80 Mixed - Soft 1.61 0.645 2.49
Sponge- Community Hard - Mixed -0.22 0.82 -0.27 Hard - Soft 1.99 0.99 2.01 Mixed - Soft 2.21 0.86 2.58
241 3.6 Predictive Habitat Model
242 This thesis was unable to produce an accurate PHM based o GLM and emmean results.
15 243 4 Discussion
244 4.1 Emergent Epifaunal Taxa
245 This thesis identi ed seven emergent epifaunal assemblages within Disko Bay, four of which had su cient
246 sample sizes to support data analysis. By analysing the dierent responses each taxon had to environmental
247 variables, we can better understand the factors driving assemblage distribution.
248 Ascidians are lter feeders from the sub-phylum Tunicata. The most frequent assemblage observed in
249 Disko Bay, ascidians formed ' elds' comprised of high-density patches (Supplementary Information 5.2:
250 Figure 11). Ascidians could also be observed acting as secondary organisms amongst the assemblages of all
251 other taxa. Data analysis showed that the abiotic factors of backscatter and depth had small eect sizes
252 despite of their signi cance. The only factor of signi cance which had a biologically meaningful in uence
253 was roughness which had a negative relationship with ascidian frequency. Benthos with a greater roughness
254 could have smaller but more frequent sediment deposits when compared to lower roughness counterparts
255 (Jumars and Nowell, 1984; McArthur et al., 2010). This is due to the high variability in terrain leading to
256 small deposits of deeper sediment (Jumars and Nowell, 1984; McArthur et al., 2010). If ascidians require
257 shallower substrate to settle, they may be more likely to be found in habitats with low roughness. Previous
258 studies have shown how ascidians exhibit species-speci c preferences in sediment type during settlement
259 (Chase et al., 2016). Disko Bay's ascidian populations might favour atter substrate, ergo habitats with a
260 lower roughness.
261 Anemones are predatory lter feeders from the phylum Cnidaria. Within Disko Bay, anemones were
262 frequently observed as secondary organisms within sponge and bryozoa communities. The assemblages
263 formed by anemones were found in a range of dierent conditions, though shared a low density of individuals
264 (Supplementary Information 5.3: Figure 9; Figure 10). Anemones were signi cantly in uenced by both
265 roughness and sediment type, with both a positive and negative in uence observed, respectively. Both
266 of these factors may relate to the larval settlement of the anemone, where it's planktonic larval stage
267 (planula) attaches to the substratum (Harrison and Booth, 2007; Scott and Harrison, 2008). Settlement
268 is not a random process and, by actively choosing the substrate it settles upon, anemones can increase
269 their chances of survival post-settlement (Scott and Harrison, 2008). By actively avoiding habitats which
270 promote ascidian settlement, anemones can reduce the interspeci c competition during larval settlement,
271 improving their chances of survival (Buhl-Mortensen et al., 2010). In choosing substrates exhibiting high
272 levels of roughness, anemones can reduce the chance of interspeci c competition with ascidians due to their
273 aforementioned relation with sediment. In addition, though not a signi cant indicator for ascidian presence,
274 a majority of ascidian populations within Disko Bay were found upon mixed substrata. It is possible that
275 anemones are not averse to mixed sediment habitats, rather those within the sample areas coincidentally
276 supported ascidian populations. Additionally, the frequency of anemones was in uenced by currents due to
277 their reliance on the ow of water to transport prey into their oral tentacles. Greater current speeds may
278 be able to compensate for intraspeci c competition for nutrition amongst anemone populations and allow
279 for greater densities of individuals to develop.
280 Byrozoa are a phylum of colonial organisms which predominantly form calciferous skeletal structures. The
281 bryozoa of Disko Bay were observed in high densities alongside and within sponge assemblages. This was
282 exempli ed by bryozoa assemblages which covered high proportions of the available substrate surrounding
283 sponges (Supplementary Information 5.3: Figure 12). Whilst backscatter had a positive signi cant in uence
284 on bryozoa presence, its biological eect size was small. The main signi cant indicator regarding bryozoa
285 presence was sediment type, in particular the dierence between mixed and soft sediment. Soft sediment
286 habitats were signi cantly less likely to have bryozoa present when compared to mixed sediment habitats.
287 However, neither the dierence between hard and soft sediment or hard and mixed was signi cant. Mixed
288 substrate may be the favoured habitat for bryozoa populations. However, previous studies have suggested
289 that harder substrate is more suitable for bryozoa settlement (McKinney and McKinney, 1993). Instead, I
290 propose that the elevated presence of bryozoa in mixed sediment habitats is due to the interactions between
291 bryozoa and sedimentation. As previously mentioned, Disko Bay is home to both iceberg and trawling activity
16 292 (Gutt et al., 1996; Yesson et al., 2017). This means regular physical disturbance to the skeletal structures
293 of bryozoa is plausible given the localised disturbance regime. If su cient in frequency, the remains of
294 damaged bryozoa could slow localised currents, leading to the deposition of suspended sediment (Bone and
295 James, 1993; McKinney and Jaklin, 2001). Combined with the ba ing eects of the branched structures
296 of bryozoa, a mixed substrate layer could be formed (Bone and James, 1993; McKinney and Jaklin, 2001).
297 Though studies such as Bone and James (1993) have shown this to occur in benthic habitats, a more
298 in-depth analysis of Disko Bay's sediment would be needed before any conclusions can be drawn.
299 Sponges are primitive lter feeders which form the phylum Porifera. Within Disko Bay, sponges were
300 observed within a wide range of environments and assemblages. Sponge assemblages themselves were
301 normally comprised of multiple large individuals (Supplementary Information 5.3: Figure 13 ; Figure 14).
302 Data analysis showed that sediment type signi cantly in uenced the presence of sponges and their respective
303 assemblages. Sponges were less likely to be found upon soft-sediment habitats when opposed to mixed and
304 hard substratum. One possibility is that, like a majority of sessile organisms, sponges prefer hard surfaces to
305 settle e ciently (Davis, 1988; Maldonado, 2006). However, due to the limited information available, further
306 research into the factors aecting the larval settlement of sponges is needed to con rm this (Maldonado,
307 2006).
308 Analysis of benthic imagery also identi ed the presence of hydrozoa, zoanthid, and red algae and maerl
309 assemblages within Disko Bay, though their frequency was limited. Hydrozoa is a class of non-skeletal colonial
310 organisms which are predatory in nature. Though present within benthic imagery, hydrozoa frequency was
311 relatively minimal when compared with other taxa. Observations of their presence suggest that hydrozoa
312 prefer muddy habitats, an idea supported by their assemblage being formed upon coarse rocky ground with a
313 thin layer of mud. Small, branching hydrozoa formed the canopy of this assemblage whilst colonial ascidians
314 could be observed in between fronds Supplementary Information 5.3: Figure 15). Zoanthids are an order
315 of cnidaria which form individual polyps. Within Disko Bay, zoanthids were only observed in one station
316 where they formed an assemblage alongside sponges (Supplementary Information 5.3: Figure 16). Within
317 the station, zoanthids were found in high densities upon steep rock faces. Settling on this rock face may
318 suggest an aversion to sedimentation. Red algae, known scienti cally as Rhodophtya, was found to form
319 two communities within Disko Bay. Though technically not epifaunal organisms, red algae within Disko Bay
320 formed 'maerl beds' of coralline red algae layers (Supplementary Information 5.3: Figure 17). 'Maerl beds'
321 increase habitat complexity and increase local biodiversity but are threatened by trawling activity (Bernard
322 et al., 2019). Both 'maerl beds' were found in relatively shallow stations, presumably due to the reliance of
323 sunlight for photosynthesis. Though identi ed, further sampling would be needed to assess the in uence of
324 abiotic drivers on the distributions of these three communities.
325 4.2 Overview
326 Though abiotic in uences were identi ed, the creation of an accurate predictive habitat map (PHM) was
327 not feasible. There are multiple reasons which may explain this. One explanation is the fact in uential abiotic
328 drivers for assemblage distribution may have been unassessed in data analysis. Though eorts were made to
329 test as many relevant abiotic factors as possible, some factors were unable to be tested. For example, Gutt
330 et al. (1996) showed that the benthic habitats of Disko Bay experience regular iceberg scouring and therefore
331 iceberg activity can in uence assemblage distribution. However, there is currently no high resolution metric
332 publicly available to assess the rate of scouring due to iceberg activity within Disko Bay. Other important
333 factors unavailable for analyses were temperature, nutrients, and salinity (Aller, 1997; Wanamaker et al.,
334 2007; Sejr et al., 2010). These factors may be in uential in benthic assemblage distribution, though further
335 research is required to determine this.
336 Alternatively, the lack of abiotic in uencers could be because biological factors are a major determining
337 factor in the distribution of benthic communities within Disko Bay. Though previous studies have shown
338 how abiotic factors in uence benthic assemblage distribution across Western Greenland, the distribution of
339 communities in Disko Bay due to biological drivers is limited (Yesson et al., 2015). The niche breadth of an
17 340 organism consists of both biological and abiotic factors (Slatyer et al., 2013). One example of a biological
341 driver for benthic assemblage distribution could be intra- and interspeci c competition (Aller, 1997; Buhl-
342 Mortensen et al., 2010). By forcing organisms to avoid habitats where resources are shared, competition
343 can shape assemblage distribution (Buhl-Mortensen et al., 2010). If competition is a signi cant in uencer
344 in benthic assemblage distribution, metrics such as distance from neighbouring communities, assemblage
345 density and assemblage fragmentation may be important indicators for predicting assemblage distributions
346 (Buhl-Mortensen et al., 2010). Similarly, predation, invasive species, co-adaptation, and pathogen activity
347 have all exhibited signi cant in uence over benthic organism distribution (Homan, 1978; Micheli et al.,
348 2002; Danovaro et al., 2008; Galil et al., 2019).
349 Whilst this project does identify the in uence of abiotic drivers in Disko Bay, the results of this thesis
350 suggest these are insu cient in predicting the distribution of emergent epifaunal assemblages. These may
351 indicate that factors unassessed by this thesis are the primary determinates in benthic assemblage distribu-
352 tion, a contrast to previous studies into Western Greenland's benthic communities. This may, however, be
353 a consequence of the limitations of this paper.
354 4.3 Critique
355 One limitation of this thesis was the initial collection of benthic imagery. Limited survey time and
356 resources meant slopes were targeted to obtain the greatest breadth of biological information possible.
357 By sampling across substantial depth gradients, high levels of heterogeneity in community and substrate
358 type was observed. Consequently, there were high levels of benthic habitat variability between and within
359 benthic imagery. Due to the low resolution of information available for abiotic factors, substation analysis
360 was required to test the response of key taxa and their biological communities. In doing so, the high
361 levels of variability within substations was not expressed at the substation resolution. The discrepancy in
362 the resolutions of abiotic factors and biological variability could explain why there was little signi cance
363 amongst abiotic factors tested. It is possible that until the resolution of abiotic factors within Disko Bay
364 matches that of benthic imagery, the in uence of abiotic factors cannot be eectively assessed. Additionally,
365 by selecting transects with high habitat heterogeneity, it is possible that the communities observed did not
366 occur in more homogeneous environments. The consequence of this it the fact that biological communities
367 observed during surveying may not be fully representative of the wider communities of Disko Bay due to
368 their inherent relation with sloped habitats.
369 To allow for the development of a standardised methodology, benthic imagery was used to determine
370 assemblage identity. However, there are limitations associated with the usage of benthic imagery in assem-
371 blage identi cation. As previously mentioned, observed biological communities varied drastically over small
372 spatial scales. Consequently, the limited spatial area captured by benthic imagery may not be representative
373 of the wider biological community. Though addressed with the visual inspection of benthic footage, this po-
374 tentially reduced the reproducibility of results. Benthic imagery also restricted the taxonomic resolution that
375 could be ascertained during annotation (Long et al., 2020). To account for this, organisms were grouped
376 by their most easily recognisable taxonomic rank. By aggregating benthic organisms into broad taxonomic
377 groupings, intra-taxa dierences in the response to environmental drivers were unassessed.
378 Additionally, the usage of the DACFOR scaling system to determine biological assemblages potentially
379 limits the comparison of results with similar research. DACFOR was chosen to assess the benthic communities
380 of Disko Bay due to the high proportion of colonial organisms observed within benthic imagery, a restriction
381 faced by similar studies (Long et al., 2020). By opting to utilise percentage cover, thresholds could be
382 set to determine the transition between the presence of an organism and the presence of its respective
383 assemblages. However previous methods have utilised density-based measurements to determine biological
384 communities such as Long et al. (2020) and Clark et al. (2016). By using a separate metric to determine
385 biological communities, the results of this project should not be compared with surrounding literature which
386 utilises density.
18 387 4.4 Applications and further research
388 Despite limitations, the presence of various emergent epifaunal assemblages and signi cant abiotic drivers
389 within Disko Bay were con rmed by this thesis. Crucial to the provision of ecosystem services, the existence
390 of these assemblages may be used to justify further exploratory surveys into the region. Additionally, this
391 thesis produced the rst high-resolution benthic sediment map for Disko Bay. By utilising the EUNIS
392 standardised categories, incorporating the results of this thesis into the pan-European map will be possible
393 in the future, contributing to the knowledge of oceanographical processes on the continental shelves of
394 Europe.
395 To directly develop upon this thesis, more information regarding the benthic communities of Disko Bay
396 should be collected. In doing so, one could potentially determine whether biotic or abiotic factors primarily
397 drive benthic assemblage distribution in Disko Bay. If biological factors are identi ed as the predominant
398 driver in assemblage distribution, subsequent research could focus upon elucidating the interactions between
399 key taxa. If abiotic factors are the predominant driver of assemblage distribution, further research into
400 more speci c abiotic factors should be considered including the quanti cation of iceberg activity or a more
401 in-depth analysis of sediment.
402 As the demand for food increases, so will the pressure upon marine ecosystems. Understanding the drivers
403 of assemblage distribution is essential if vulnerable emergent epifaunal assemblages are to be conserved.
404 This study elucidates the role of abiotic factors in the distribution of these communities within Disko Bay,
405 information that will be crucial for future investigations. The identi cation of signi cantly in uential factors
406 in the distribution of benthic assemblages, alongside the creation of the rst high-resolution benthic sediment
407 map for Disko Bay, highlights the importance of this thesis as a foundation for future research into the region.
19 408 5 Supplementary Information
409 5.1 Dichotomous Key for sediment identi cation
410 1. What is the predominant sediment type visible in imagery?
411 (a) If mud, go to question 2
412 (b) If rock, go to question 4
413 (c) If neither of the above, go to question
414 2. Inspect benthic footage, is the later of mud thick enough that underlying substrate is not visible
415 throughout?
416 (a) If yes, go to question 3
417 (b) If no, the substrate type is Coarse rocky ground with thin layer of mud (A6.2)
418 3. Are there multiple rocks upon the substrate?
419 (a) If yes, the substrate type is Mud with Dropstones (A6.5)
420 (b) If no, the substrate type is Mud (A6.5)
421 4. Is the rock continuous or fragmented?
422 (a) If continuous, go to question 5
423 (b) If fragmented, go to question 9
424 5. Does the rock have an assortment of sediments upon it?
425 (a) If yes, go to question 6
426 (b) If no, the substrate type is Coarse rocky ground (A6.1)
427 6. Is the layer of substrate upon the rock consistent in size and type?
428 (a) If yes, go to question 7
429 (b) If no, the substrate type is Bedrock with mud, boulders and pebbles (A6.1)
430 7. Is the sediment type on the rock mud or other?
431 (a) If mud, the substrate type is Coarse rocky ground with thin layer of mud (A6.2)
432 (b) If other, go to question 8
433 8. Is the sediment primarily comprised of biological components?
434 (a) If yes, the substrate type is Coarse rocky ground with thin layer of biogenic gravel (A6.2)
435 (b) If no, the substrate type is Coarse rocky ground with thin layer of gravel (A6.2)
436 9. Would you consider the sediment on substrate is large?
437 (a) If yes, the substrate type is Coarse rocky ground (A6.1)
438 (b) If no, the substrate type is Boulders on the sea-bed (A6.1)
439 10. Is the sediment primarily comprised of biological components?
440 (a) If yes, the substrate type is Biogenic Gravels (A6.2)
441 (b) If no, the substrate type is Muddy Gravels (A6.2)
20 442 5.2 Summary Tables and plots
Table 14: A summary for the depth, current speed and slope for sediment types at the resolution of benthic imagery. Averages taken were means. CRG = Coarse rocky ground. Bedrock with m, p & b = Bedrock with mud, pebbles and boulders.
1 Depth (m) Current Speed (ms− ) Slope (◦) Sediment Type Sample Size Average SD Range Average SD Range Average SD Range
A6.5 Soft Sediment Mud 353 272 96 55 629 0.031 0.009 0.007 0.048 4 11 1 59 Mud with Dropstones 63 244 95 86 496 0.031 0.005 0.008 0.047 8 13 1 59
21 A6.2 Mixed Sediment Biogenic Gravel 13 155 36 100 253 0.031 0.005 0.021 0.044 11 18 1 58 CRG with various substrata 154 205 80 42 340 0.032 0.100 0.008 0.047 16 15 1 59 Gravelly Mud 28 105 67 42 301 0.029 0.006 0.021 0.045 6 13 2 59
A6.1 Hard Sediment Boulders on Seabed 12 219 84 125 352 0.026 0.011 0.008 0.041 58 17 18 64 Bedrock with m, p & b 75 185 83 33 313 0.032 0.007 0.021 0.045 32 17 1 58 CRG 9 158 17 134 190 0.027 0.003 0.023 0.032 9 14 2 35 Table 15: A summary of depth, current speed and slope for sediment types at the resolution of sampling station. Averages taken were means. CRG = Coarse rocky ground. Bedrock with m, p & b = Bedrock with mud, pebbles and boulders.
1 Depth (m) Current Speed (ms− ) Slope (◦) Sediment Type Sample Size Average SD Range Average SD Range Average SD Range
A6.5 Soft Sediment Mud 38 273 87 153 628 0.028 0.008 0.007 0.048 7 7 1 28 22
A6.2 Mixed Sediment Mixed Substrata 15 226 100 121 495 0.033 0.006 0.025 0.047 16 15 2 58 CRG with various substrata 14 196 86 44 309 0.029 0.010 0.008 0.044 21 13 1 45 Gravelly Mud 1 88 0.024 2
A6.1 Hard Sediment Bedrock with m,p & b 7 182 82 86 302 0.032 0.009 0.021 0.044 33 14 9 46 CRG 2 167 110 57 276 0.034 0.011 0.024 0.045 12 4 8 16 Table 16: A summary of depth, current speed and slope for assemblage types at the resolution of Benthic Imagery. Averages taken were means.
1 Depth (m) Current Speed (ms− ) Slope (◦) Assemblage Type Sample Size Average SD Range Average SD Range Average SD Range
Anemone 22 230 58 152 311 0.037 0.005 0.028 0.041 4 14 2 34 Ascidian 122 215 56 88 310 0.032 0.008 0.008 0.045 12 14 1 59 Bryozoa 59 133 41 68 352 0.031 0.007 0.021 0.044 10 17 1 59 Red Algae 20 45 8 33 56 0.033 0.006 0.032 0.045 11 6 5 24 Hydroid 46 243 53 33 352 0.027 0.007 0.008 - 0.041 13 1 15 - 59 Sponge 121 178 95 55 496 0.032 0.008 0.008 0.045 21 15 3 59 Zoanthid 14 302 8 289 313 0.041 40 5 34 - 47 23
Table 17: A summary of depth, current speed and slope for assemblage types at the resolution of substation. Averages taken were means.
1 Depth (m) Current Speed (ms− ) Slope (◦) Assemblage Type Sample Size Average SD Range Average SD Range Average SD Range
Anemone 4 199 40 153 262 0.031 0.006 0.027 0.041 6 3 4 10 Ascidian 16 212 57 88 305 0.029 0.080 0.080 0.044 14 14 1 45 Bryozoa 4 125 27 86 159 0.035 0.007 0.025 0.044 16 16 3 44 Sponge 13 199 84 57 309 0.030 0.011 0.008 0.045 16 12 1 40 Figure 7: A Spearman's Rank Correlation of the observed emergent epifauana with environmental factors
Figure 8: Split violin plots comparing the distribution of organism presence and organism assemblage presence across dierent environmental factors. The greater the horizontal amplitude of the plot, the greater the number of observations.
24 443 5.3 Assemblage Images
Figure 9: Benthic imagery for the start of an anemone assemblage formed on a mud at
Figure 10: Benthic imagery of an anemone assemblage formed on bedrock and boulders
Figure 11: Benthic imagery of an ascidian assemblage formed on gravelly mud
25 Figure 12: Benthic imagery of a Bryozoa assemblage formed on bedrock
Figure 13: Benthic imagery of a sponge assemblage formed on bedrock
Figure 14: Benthic imagery of a sponge assemblage formed on coarse rock with thin layer of mud
26 Figure 15: Benthic imagery of a hydrozoa assemblage formed on coarse rock with thin layer of mud
Figure 16: Benthic imagery of a zoanthid assemblage formed on a bedrock face
Figure 17: Benthic imagery of a maerl bed
27 444 Data Availability
445 Due to restricted access to benthic imagery/footage, please contact Chris Yesson of the ZSL or GINR's
446 MAPHAB team if you wish to recreate this analysis. Partial data and full code for this project can be found
447 in the following imperial box account:
448
449 https://imperialcollegelondon.box.com/s/7yfuhafbxl2hfrs1oxi3xl5qatgz1f5w
450 References
451 Aller, J. Y. (1997). Benthic community response to temporal and spatial gradients in physical disturbance
452 within a deep-sea western boundary region. Deep-Sea Research Part I: Oceanographic Research Papers,
453 44(1):3969.
454 Amoroso, R. O., Pitcher, C. R., Rijnsdorp, A. D., McConnaughey, R. A., Parma, A. M., Suuronen, P.,
455 Eigaard, O. R., Bastardie, F., Hintzen, N. T., Althaus, F., Baird, S. J., Black, J., Buhl-Mortensen, L.,
456 Campbell, A. B., Catarino, R., Collie, J., Cowan, J. H., Durholtz, D., Engstrom, N., Fairweather, T. P.,
457 Fock, H. O., Ford, R., Gálvez, P. A., Gerritsen, H., Góngora, M. E., González, J. A., Hiddink, J. G.,
458 Hughes, K. M., Intelmann, S. S., Jenkins, C., Jonsson, P., Kainge, P., Kangas, M., Kathena, J. N.,
459 Kavadas, S., Leslie, R. W., Lewise, S. G., Lundy, M., Makin, D., Martin, J., Mazor, T., Gonzalez-Mirelis,
460 G., Newman, S. J., Papadopoulou, N., Posen, P. E., Rochester, W., Russok, T., Salal, A., Semmens,
461 J. M., Silvan, C., Tsoloso, A., Vanelslander, B., Wake eld, C. B., Wood, B. A., Hilborn, R., Kaiser, M. J.,
462 and Jennings, S. (2018). Bottom trawl shing footprints on the world's continental shelves. Proceedings
463 of the National Academy of Sciences of the United States of America, 115(43):E10275-E10282.
464 Auguie, B. (2017). gridExtra: Miscellaneous Functions for "Grid" Graphics.
465 Bates, D., Machler, M., Bolker, B., and Walker, S. (2015). Fitting Linear Mixed-Eects Models Using lme4.
466 Journal of Statistical Software, 67(1):1 48.
467 Baynes, T. W. and Szmant, A. M. (1989). Eect of current on the sessile benthic community structure of
468 an arti cial reef. Bulletin of Marine Science, 44(2):545566.
469 Bernard, G., Romero-Ramirez, A., Tauran, A., Pantalos, M., De andre, B., Grall, J., and Grémare, A.
470 (2019). Declining maerl vitality and habitat complexity across a dredging gradient: Insights from in situ
471 sediment pro le imagery (SPI). Scienti c Reports, 9(1):112.
472 Boertmann, D. and Mosbech, A. (2011). Eastern Ba n Bay. A strategic environmental impact assessment
473 of hydrocarbon activities. Technical report, DCE Danish Centre for Environment and Energy,Aarhus
474 University, Aarhus.
475 Bone, Y. and James, N. P. (1993). Bryozoans as carbonate sediment producers on the cool-water Lacepede
476 Shelf, southern Australia. Sedimentary Geology, 86(3-4):247271.
477 Bowden, D. A., Rowden, A. A., Thurber, A. R., Baco, A. R., Levin, L. A., and Smith, C. R. (2013).
478 Cold Seep Epifaunal Communities on the Hikurangi Margin, New Zealand: Composition, Succession, and
479 Vulnerability to Human Activities. PLoS ONE, 8(10).
480 Bradshaw, C., Collins, P., and Brand, A. R. (2003). To what extent does upright sessile epifauna aect
481 benthic biodiversity and community composition? Marine Biology, 143(4):783791.
482 Brown, C. J., Smith, S. J., Lawton, P., and Anderson, J. T. (2011). Benthic habitat mapping: A review of
483 progress towards improved understanding of the spatial ecology of the sea oor using acoustic techniques.
484 Estuarine, Coastal and Shelf Science, 92(3):502520.
485 Buch, E., Pedersen, S. A., and Ribergaard, M. H. (2004). Ecosystem variability in West Greenland Waters.
486 Journal of Northwest Atlantic Fishery Science, 34:1328.
28 487 Buhl-Mortensen, L., Vanreusel, A., Gooday, A. J., Levin, L. A., Priede, I. G., Buhl-Mortensen, P., Gheerar-
488 dyn, H., King, N. J., and Raes, M. (2010). Biological structures as a source of habitat heterogeneity and
489 biodiversity on the deep ocean margins. Marine Ecology, 31(1):2150.
490 Challinor, A. J., Chhetri USA, N., Garrett, K., Aggarwal, P., Hakala, K., Jordan, J., Barros, V. R., Dokken,
491 D. J., Mach, K. J., Bilir, T., Chatterjee, M., Ebi, K. L., Estrada, Y., Genova, R. C., Girma, B., Kissel,
492 E., Levy, A., and MacCracken, S. (2014). 7 Food Security and Food Production Systems Coordinating
493 Lead Authors: Lead Authors: Contributing Authors: Review Editors. In Food Security and Production
494 Systems, pages 485533.
495 Chase, A. L., Dijkstra, J. A., and Harris, L. G. (2016). The in uence of substrate material on ascidian larval
496 settlement. Marine Pollution Bulletin, 106(1-2):3542.
497 Clark, M. R., Consalvey, M., and Rowden, A. A. (2016). Biological Sampling in the Deep Sea. Wiley.
498 Collie, J. (2000). Photographic evaluation of the impacts of bottom shing on benthic epifauna. ICES
499 Journal of Marine Science, 57(4):9871001.
500 Collie, J. S., Escanero, G. A., and Valentine, P. C. (1997). Eects of bottom shing on the benthic
501 megafauna of Georges Bank. Marine Ecology Progress Series, 155:159172.
502 Collie, J. S., Escanero, G. A., and Valentine, P. C. (2000). Photographic evaluation of the impacts of
503 bottom shing on benthic epifauna. ICES Journal of Marine Science, 57(4):9871001.
504 Crisp, D. J. and Southward, A. J. (1958). The distribution of intertidal organisms along the coasts of the
505 english channel. Journal of the Marine Biological Association of the United Kingdom, 37(1):157203.
506 Danovaro, R., Gambi, C., Dell'Anno, A., Corinaldesi, C., Fraschetti, S., Vanreusel, A., Vincx, M., and Goo-
507 day, A. J. (2008). Exponential Decline of Deep-Sea Ecosystem Functioning Linked to Benthic Biodiversity
508 Loss. Current Biology, 18(1):18.
509 Davis, A. R. (1988). Eects of variation in initial settlement on distribution and abundance of Podoclavella
510 moluccensis Sluiter. Journal of Experimental Marine Biology and Ecology, 117(2):157167.
511 Diaz, R. J., Solan, M., and Valente, R. M. (2004). A review of approaches for classifying benthic habitats
512 and evaluating habitat quality. Journal of Environmental Management, 73(3):165181.
513 Diehl, S. (1992). Fish predation and benthic community structure: the role of omnivory and habitat
514 complexity. Ecology, 73(5):16461661.
515 Dolédec, S., Chessel, D., and Gimaret-Carpentier, C. (2000). Niche separation in community analysis: A
516 new method. Ecology, 81(10):29142927.
517 Durden, J. M., Bett, B. J., and Ruhl, H. A. (2020). Subtle variation in abyssal terrain induces signi cant
518 change in benthic megafaunal abundance, diversity, and community structure. Progress in Oceanography,
519 186:102395.
520 Erftemeijer, P. L. A., Riegl, B., Hoeksema, B. W., and Todd, P. A. (2012). Environmental impacts of
521 dredging and other sediment disturbances on corals: A review. Marine Pollution Bulletin.
522 Etter, R. J. and Grassle, J. F. (1992). Patterns of species diversity in the deep sea as a function of sediment
523 particle size diversity. Nature, 360(6404):576578.
524 FAO (2014). The state of world sheries and aquaculture. Technical Report disponível em
525 http://www.fao.org/docrep/013/i1820e/i1820e.pdf.
526 FAO (2020). The State of World Fisheries and Aquaculture 2020.
527 Galil, B. S., Danovaro, R., Rothman, S. B., Gevili, R., and Goren, M. (2019). Invasive biota in the deep-
528 sea Mediterranean: an emerging issue in marine conservation and management. Biological Invasions,
529 21(2):281288.
29 530 Garcia, E. G. (2007). The Northern Shrimp (Pandalus borealis) Oshore Fishery in the Northeast Atlantic.
531 Gutt, J., Starmans, A., and Dieckmann, G. (1996). Impact of iceberg scouring on polar benthic habitats.
532 Marine Ecology Progress Series, 137(1-3):311316.
533 Harris, P. T. (2014). Shelf and deep-sea sedimentary environments and physical benthic disturbance regimes:
534 A review and synthesis.
535 Harrison, P. L. and Booth, D. J. (2007). Coral reefs: naturally dynamic and increasingly disturbed ecosys-
536 tems. Marine Ecology, pages 316377.
537 Hiddink, J. G., Hutton, T., Jennings, S., and Kaiser, M. J. (2006). Predicting the eects of area closures
538 and shing eort restrictions on the production, biomass, and species richness of benthic invertebrate
539 communities. ICES Journal of Marine Science, 63(5):822830.
540 Hiddink, J. G., Jennings, S., Sciberras, M., Bolam, S. G., Cambiè, G., McConnaughey, R. A., Mazor, T.,
541 Hilborn, R., Collie, J. S., Pitcher, C. R., Parma, A. M., Suuronen, P., Kaiser, M. J., and Rijnsdorp, A. D.
542 (2019). Assessing bottom trawling impacts based on the longevity of benthic invertebrates. Journal of
543 Applied Ecology, 56(5):10751084.
544 Hinz, H., Prieto, V., and Kaiser, M. J. (2009). Trawl disturbance on benthic communities: Chronic eects
545 and experimental predictions. Ecological Applications, 19(3):761773.
546 Hiscock, K. (1998). Biological monitoring of Marine Special Areas of Conservation: a review of methods
547 for detecting change. Peterborough, Joint Nature Conservation Committee, (JNCC Repo(284):1516.
548 Homan, A. (1978). System concepts and the evolution of benthic communities. Lethaia, 11(2):179183.
549 Jansen, J., Dunstan, P. K., Hill, N. A., Koubbi, P., Melbourne-Thomas, J., Causse, R., and Johnson, C. R.
550 (2020). Integrated assessment of the spatial distribution and structural dynamics of deep benthic marine
551 communities. Ecological Applications, 30(3).
552 Jumars, P. A. and Nowell, A. R. (1984). Eects of benthos on sediment transport: di culties with functional
553 grouping. Continental Shelf Research, 3(2):115130.
554 Kassambara, A. (2019). ggcorrplot: Visualization of a Correlation Matrix using 'ggplot2'.
555 Lambert, G. I., Jennings, S., Kaiser, M. J., Hinz, H., and Hiddink, J. G. (2011). Quanti cation and prediction
556 of the impact of shing on epifaunal communities. Marine Ecology Progress Series, 430:7186.
557 Langenkämper, D., Zurowietz, M., Schoening, T., and Nattkemper, T. W. (2017). BIIGLE 2.0 - Browsing
558 and Annotating Large Marine Image Collections. Frontiers in Marine Science, 4(MAR):83.
559 Lenth, R. (2020). emmeans: Estimated Marginal Means, aka Least-Squares Means.
560 Long, S., Sparrow-Scinocca, B., Blicher, M. E., Hammeken Arboe, N., Fuhrmann, M., Kemp, K. M.,
561 Nygaard, R., Zinglersen, K., and Yesson, C. (2020). Identi cation of a Soft Coral Garden Candidate
562 Vulnerable Marine Ecosystem (VME) Using Video Imagery, Davis Strait, West Greenland. Frontiers in
563 Marine Science, 7:460.
564 Lüdecke, D., Makowski, D., and Waggoner, P. (2020). Assessment of Regression Models Performance.
565 Lundquist, C. J., Thrush, S. F., Coco, G., and Hewitt, J. E. (2010). Interactions between disturbance
566 and dispersal reduce persistence thresholds in a benthic community. Marine Ecology Progress Series,
567 413:217228.
568 Maldonado, M. (2006). The ecology of the sponge larva.
569 McArthur, M. A., Brooke, B. P., Przeslawski, R., Ryan, D. A., Lucieer, V. L., Nichol, S., McCallum, A. W.,
570 Mellin, C., Cresswell, I. D., and Radke, L. C. (2010). On the use of abiotic surrogates to describe marine
571 benthic biodiversity. Estuarine, Coastal and Shelf Science, 88(1):2132.
30 572 McKinney, F. K. and Jaklin, A. (2001). Sediment accumulation in a shallow-water meadow carpeted by a
573 small erect bryozoan. Sedimentary Geology, 145(3-4):397410.
574 McKinney, F. K. and McKinney, M. J. (1993). Larval behaviour and choice of settlement site: Correlation
575 with environmental distribution pattern in an erect bryozoan. Facies, 29(1):119131.
576 Meldgaard, M. (2004). Ancient Harp Seal Hunters of Disko Bay.
577 Micheli, F., Peterson, C. H., Mullineaux, L. S., Fisher, C. R., Mills, S. W., Sancho, G., Johnson, G. A.,
578 and Lenihan, H. S. (2002). Predation structures communities at deep-sea hydrothermal vents. Ecological
579 Monographs, 72(3):365382.
580 Montagna, P. A., Baguley, J. G., Reuscher, M. G., Rowe, G. T., and Wade, T. L. (2020). Linking Abiotic
581 Variables with Macrofaunal and Meiofaunal Abundance and Community Structure Patterns on the Gulf of
582 Mexico Continental Slope. In Scenarios and Responses to Future Deep Oil Spills, pages 109131. Springer
583 International Publishing.
584 Oberle, F. K. J., Storlazzi, C. D., and Hanebuth, T. J. J. (2016). What a drag: Quantifying the global impact
585 of chronic bottom trawling on continental shelf sediment. Journal of Marine Systems, 159:109119.
586 Pauly, D., Watson, R., and Alder, J. (2005). Global trends in world sheries: impacts on marine ecosystems
587 and food security. Philosophical Transactions of the Royal Society B: Biological Sciences, 360(1453):512.
588 Piepenburg, D., Archambault, P., Ambrose, W. G., Blanchard, A. L., Bluhm, B. A., Carroll, M. L., Conlan,
589 K. E., Cusson, M., Feder, H. M., Grebmeier, J. M., Jewett, S. C., Lévesque, M., Petryashev, V. V.,
590 Sejr, M. K., Sirenko, B. I., and Wªodarska-Kowalczuk, M. (2011). Towards a pan-Arctic inventory of the
591 species diversity of the macro- and megabenthic fauna of the Arctic shelf seas.
592 Pineda, M. C., Strehlow, B., Sternel, M., Duckworth, A., Haan, J. D., Jones, R., and Webster, N. S. (2017).
593 Eects of sediment smothering on the sponge holobiont with implications for dredging management.
594 Scienti c Reports, 7(1):115.
595 QGIS Development Team (2020). QGIS.
596 R Core Team (2018). R: A Language and Environment for Statistical Computing.
597 Rengstorf, A. M., Yesson, C., Brown, C., and Grehan, A. J. (2013). High-resolution habitat suitability mod-
598 elling can improve conservation of vulnerable marine ecosystems in the deep sea. Journal of Biogeography,
599 40(9):17021714.
600 Rice, J. C. and Garcia, S. M. (2011). Fisheries, food security, climate change, and biodiversity: characteristics
601 of the sector and perspectives on emerging issues. ICES Journal of Marine Science, 68(6):13431353.
602 Rosegrant, M. W. and Cline, S. A. (2003). Global Food Security: Challenges and Policies.
603 Scheick, J., Enderlin, E. M., and Hamilton, G. (2019). Semi-automated open water iceberg detection from
604 Landsat applied to Disko Bay, West Greenland. Journal of Glaciology, 65(251):468480.
605 Scott, A. and Harrison, P. L. (2008). Larval settlement and juvenile development of sea anemones that
606 provide habitat for anemone sh. Marine Biology, 154(5):833839.
607 Sebens, K. P. (1991). Habitat structure and community dynamics in marine benthic systems. Habitat
608 structure, pages 211234.
609 Sejr, M. K., Wªodarska-Kowalczuk, M., Lege»y«ska, J., and Blicher, M. E. (2010). Macrobenthic species
610 composition and diversity in the Godthaabsfjord system, SW Greenland. Polar Biology, 33(4):421431.
611 Sexton, J. P., Montiel, J., Shay, J. E., Stephens, M. R., and Slatyer, R. A. (2017). Evolution of Ecological
612 Niche Breadth. Annual Review of Ecology, Evolution, and Systematics, 48:183206.
31 613 Slatyer, R. A., Hirst, M., and Sexton, J. P. (2013). Niche breadth predicts geographical range size: A
614 general ecological pattern.
615 Taiyun, W. and Viliam, S. (2017). R package "corrplot": Visualization of a Correlation Matrix.
616 Taniguchi, H. and Tokeshi, M. (2004). Eects of habitat complexity on benthic assemblages in a variable
617 environment. Freshwater Biology, 49(9):11641178.
618 Team, R. (2019). RStudio: Integrated Development Environment for R.
619 The Economic Council (2017). Nunatta Aningaasaqarnera: Greenland's Economy.Nuuk:.
620 Venables, W. N. and Ripley, B. D. (2002). Modern Applied Statistics with S.
621 Wanamaker, A. D., Kreutz, K. J., Borns, H. W., Introne, D. S., Feindel, S., Funder, S., Rawson, P. D., and
622 Barber, B. J. (2007). Experimental determination of salinity, temperature, growth, and metabolic eects
623 on shell isotope chemistry of Mytilus edulis collected from Maine and Greenland. Paleoceanography,
624 22(2).
625 Watling, L. and Norse, E. A. (1998). Disturbance of the seabed by mobile shing gear: A comparison to
626 forest clearcutting. Conservation Biology, 12(6):11801197.
627 Wood, A. C. L. and Probert, P. K. (2013). Bryozoan-dominated benthos of Otago shelf, New Zealand: its
628 associated fauna, environmental setting and anthropogenic threats. Journal of the Royal Society of New
629 Zealand, 43(4):231249.
630 Yesson, C., Fisher, J., Gorham, T., Turner, C. J., Hammeken Arboe, N., Blicher, M. E., and Kemp, K. M.
631 (2017). The impact of trawling on the epibenthic megafauna of the west Greenland shelf. ICES Journal
632 of Marine Science, 74(3):866876.
633 Yesson, C., Simon, P., Chemshirova, I., Gorham, T., Turner, C. J., Hammeken Arboe, N., Blicher, M. E.,
634 and Kemp, K. M. (2015). Community composition of epibenthic megafauna on the West Greenland Shelf.
635 Polar Biology, 38(12):20852096.
636 Zurowietz, M., Langenkämper, D., Hosking, B., Ruhl, H. A., and Nattkemper, T. W. (2018). MAIAA
637 machine learning assisted image annotation method for environmental monitoring and exploration. PLOS
638 ONE, 13(11):e0207498.
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