The seasonal sea ice cover and the massive influx of river runoff into the Hudson Bay System (HBS) of the Canadian Arctic are critical factors influencing biological production and, ultimately, the dynamics and structure of benthic communities in the region. This study provides the most recent survey of epibenthic communities in Hudson Bay and Hudson Strait and explores their relationships with environmental variables, including mean annual primary production and particulate organic carbon in surface water, bottom oceanographic variables, and substrate type. Epibenthic trawl samples were collected at 46 stations, with a total of 380 epibenthic taxa identified, representing 71% of the estimated taxa within the system. Three communities were defined based on biomass and taxonomic composition. Ordination analyses showed them to be associated primarily with substrate type, salinity, and annual primary production. A first community, associated with coarse substrate, was distributed along the coastlines and near the river mouths. This community was characterized by the lowest density and taxonomic richness and the highest biomass of filter and suspension feeders. A second community, composed mostly of deposit feeders and small abundant epibenthic organisms, was associated with soft substrate and distributed in the deepest waters. A third community, associated with mixed substrate and mostly located near polynyas, was characterized by high diversity and biomass, with no clearly dominant taxon. The overall analysis indicated that bottom salinity and surface-water particulate organic carbon content were the main environmental drivers of these epibenthic community patterns. In the face of climate change, projections of increased river inflow and a longer open water season for the HBS could have major impacts on these epibenthic communities, emphasizing a need to continually improve our ability to evaluate and predict shifts in epibenthic richness and distribution.

The spatial distribution of benthic community structure is predominantly related to variables such as water depth, salinity, substrate type, and food supply (Piepenburg, 2005; Grebmeier et al., 2006a; Cusson et al., 2007; Witman et al., 2008; Roy et al., 2014). In Arctic waters, sea ice cover is an additional variable that influences primary production and thus the efficiency of pelagic-benthic coupling (Piepenburg, 2005; Renaud et al., 2007; Boetius et al., 2013; Roy et al., 2015; Olivier et al., 2020). Most Arctic marine ecosystems are currently responding to climate-induced changes to environmental and ecological variables, such as changing precipitation, river discharge, sea ice cover, and marine biota (Déry et al., 2016; Bring et al., 2017; Osborne et al., 2018; Derksen et al., 2019). In some Arctic regions, such as the Northern Bering Sea, the decline of the sea ice cover has resulted in decline of the clam populations (Grebmeier et al., 2006b; Grebmeier, 2012). Despite a growing number of species inventories being collected by various research projects and programs around the Arctic (Piepenburg et al., 2011; Link et al., 2013; Roy et al., 2014, 2015), baseline knowledge of some Arctic regions is still limited, preventing accurate predictions of how species richness and distribution will respond to climate change (Piepenburg et al., 2011).

Because of its unique attributes, the Hudson Bay System (HBS) nested within the Canadian Arctic (Figure 1) has been identified as one of the most sensitive regions to climate change (Gagnon and Gough, 2005; Tivy et al., 2011; Derksen et al., 2019). The HBS sea ice season has already grown shorter (Andrews et al., 2018) and is projected to continue to shorten, leading to an extended open water (OW) season (Derksen et al., 2019). River inflow is projected to increase up to 50% due to an earlier snowmelt in the surrounding drainage basins and an overall increase in precipitation (Gagnon and Gough, 2005; Bring et al., 2017). These different water inputs lead to variations of nutrient concentrations and salinity within the system that in turn affect biological processes (Déry et al., 2011, 2016). However, little is known about the environmental drivers of benthic communities in the HBS compared to other Arctic regions, such as the Canadian Arctic Archipelago. To date, most of our knowledge on benthic communities comes from relatively old or low spatial resolution diversity data and is based mainly on grab sampling (Atkinson and Wacasey, 1989a, 1989b; Cusson et al., 2007; Kenchington et al., 2011; Piepenburg et al., 2011; Jørgensen et al., 2016; Roy and Gagnon, 2016; Pierrejean et al., 2019; Wei et al., 2019). Most of these studies focused on infaunal organisms, and none related benthic community structure to environmental drivers.

Figure 1.

Map of the study area with locations of stations investigated in the Hudson Bay System (HBS). Color squares on the enlarged map of the HBS indicate sampling stations that are divided into seven geographical groups: purple squares correspond to the Nelson Estuary area; pink squares, to the Southern Bay; green squares, to areas near recurring polynyas; gray squares, to the middle of the Bay; yellow squares, to Hudson Strait; light blue squares, to Ungava Bay; and dark blue squares, to the Québec coasts. DOI: https://doi.org/10.1525/elementa.00044.f1

Figure 1.

Map of the study area with locations of stations investigated in the Hudson Bay System (HBS). Color squares on the enlarged map of the HBS indicate sampling stations that are divided into seven geographical groups: purple squares correspond to the Nelson Estuary area; pink squares, to the Southern Bay; green squares, to areas near recurring polynyas; gray squares, to the middle of the Bay; yellow squares, to Hudson Strait; light blue squares, to Ungava Bay; and dark blue squares, to the Québec coasts. DOI: https://doi.org/10.1525/elementa.00044.f1

This study aimed to assess the influence of various environmental factors on the structure of epibenthic communities in the study area. The specific objectives of this study were to (i) characterize the epifaunal diversity and density patterns according to environmental variables, (ii) delineate epibenthic communities, and (iii) determine which abiotic factors may drive spatial distribution of the epibenthic communities in the study area. We hypothesized that benthic assemblages will differ along a coast-to-offshore gradient and that salinity, food supply, and sea ice cover will be strong drivers of the epibenthic community structure.

### Study area

The HBS is composed of four regions: James Bay, Hudson Strait, Hudson Bay (HB), and the Foxe Basin (Figure 1) occupying an area of 1.3 million km2. Water masses from the Arctic Ocean enter the HBS from the Canadian Arctic via Fury and Hecla Strait and from Baffin Bay via Hudson Strait (Drinkwater, 1986; Prisenberg, 1986). Within HB, water is rotated cyclonically around the Bay and eventually exported through Hudson Strait (Saucier et al., 2004). However, the major water input for the HBS is river discharge, with around 760 km3 discharged per year from 42 rivers (Déry et al., 2011, 2016). Over half of this river discharge enters the southern and eastern portions of HB, with the largest contributions from La Grande (84.22 km3 y–1) and the Nelson River (102.70 km3y–1; Déry et al., 2016). Furthermore, the HBS is covered by a dynamic seasonal ice cover for most of the year (Hochheim and Barber, 2014). Freeze-up progresses from northwest to southeast across the HBS and during recent years has begun in November and formed a complete ice cover by the end of December (Andrews et al., 2018). Within HB, sea ice breakup generally starts in the northwestern and eastern parts of the Bay between May and June and progresses toward the southern region where the last ice typically remains until late July (Andrews et al., 2018; Kirillov et al., 2020). Within the dynamic seasonal ice cover of the HBS, offshore winds generate numerous coastal latent heat polynyas, biologically active areas of OW and thin ice in the dead of winter (Barber and Massom, 2007). The largest polynya is in northwestern HB, but several smaller polynyas are located close to the Nelson River Estuary, the Belcher Islands, Coats and Mansel Islands, and along the coast of Quebec (Barber and Massom, 2007).

### Biological data collection

Benthic organisms were sampled at 46 stations in HB, Hudson Strait, and Ungava Bay (i.e., study area), between May and July in 2010, 2017, and 2018 (Figure 1). These samples were taken onboard the Canadian scientific icebreaker CCGS Amundsen as part of Arcticnet, the Hudson Bay System study (BaySys) and the Bridging Global Change, Inuit Health and the Transforming Arctic Ocean project (BriGHT). Stations were scattered throughout the HBS in geographically and biologically defined regions with depths ranging from 10 to 322 m (Figure 1; Barber and Massom, 2007; Wilkinson et al., 2009; Kenchington et al., 2011). Epifauna samples were collected with an Agassiz trawl (aperture of 1.5 m and net mesh size of 5 mm), with an average trawling time of 3 min and speed of 1.5 knots, respectively. Four coastal stations were sampled with an epibenthic trawl (aperture of 1 m and net mesh size of 3 mm) with an average trawling time and speed of 3 min and 1.3 knots, respectively. Samples were sieved through 2-mm mesh to retain only macrofauna and megafauna, and identifications were made onboard to the lowest possible taxonomic level. Unidentified taxa were fixed with 4% formaldehyde solution and later identified to the lowest possible taxonomic level under a dissecting microscope. Vertebrates (e.g., Actinopterygii) and planktonic invertebrates (e.g., Chaetognatha and Euphausiacea) collected by the trawl were removed from the analyses. Some taxa were only identified to the phylum level because no complete identification keys exist for HB waters (e.g., Nemertea, Nudibranchia, and Porifera), and hence, taxonomic richness could be underestimated in this study. Taxonomic names were checked and updated using the World Register of Marine Species Editorial Board (2020).

### Environment variables

At each sampling station, a conductivity–temperature–depth probe recorded bottom temperature (°C), bottom dissolved oxygen (µM), and bottom salinity (Table 1). Particulate organic carbon (POC; mg m–3) content measured at the surface of the water column over multiple years (1998–2008) and mean annual surface primary production (PP; mg C m–2 y–1) measured over multiple years (2006–2010) were extracted from interpolated environmental data layers generated at the global scale (Global Marine Environment Datasets), as well as in the Eastern Canadian Arctic and Sub-Arctic regions (https://data.mendeley.com/datasets/zmwyjs222s/2), using the package “raster” in R (Table 1; Basher et al., 2018; Beazley et al., 2019; Hijmans et al., 2020). The substratum type at each station was classified into three separate qualitative classes based on either visual observation from trawls or substratum data presented by Henderson (1989) and Pelletier (1986). The three classes of the substrate are “coarse,” referring to stations composed mostly of gravel, sandy gravel, and pebbles (grain size > 2 mm); “mixed,” referring to stations containing particles ranging in size from silt to boulders; and “mud,” referring to stations characterized by fine-grained sediment (grain size < 0.06 mm).

Table 1.

Environmental variables at the stations investigated in the Hudson Bay system. DOI: https://doi.org/10.1525/elementa.00044.t1

StationsDateDepth (m)BO2a (µM)BTb (°C)BScOWd (Days)POCe (mg m–3)PPf (mg C m–2 y–1)
702 2010 129 255 –1.36 31.78 189 817 NDg
705 2010 71 329 –1.32 31.65 161 458 613
706 2010 77 311 –1.40 32.38 161 174 598
735 2010 185 192 –1.23 32.94 191 380 640
745 2010 184 219 –1.49 33.28 168 113 619
790 2010 38 369 –0.64 31.15 147 485 527
820 2010 53 298 –1.47 31.60 140 413 607
840 2010 174 180 –1.42 33.18 172 127 632
EXTRA 2010 316 269 –1.06 32.99 172 152 637
670 2017 117 372 –0.83 31.72 233 264 628
676 2017 98 351 –1.20 31.98 206 221 530
682 2017 95 328 –1.34 32.22 174 207 ND
684 2017 108 330 –1.24 32.33 169 348 ND
688 2017 107 321 –1.29 32.53 170 255 ND
694 2017 103 330 –0.98 32.34 180 214 ND
720 2017 91 293 –1.10 31.25 176 303 ND
732 2017 120 254 –1.18 31.66 192 817 ND
736 2017 91 251 –1.18 31.28 202 653 ND
15 2018 188 307 –1.72 32.67 193 135 671
16 2018 136 266 –1.07 32.71 173 124 683
17 2018 94 296 –1.63 32.89 212 147 670
17b 2018 61 NDe –1.49 32.63 150 774 ND
18 2018 118 312 –1.80 33.41 198 128 710
19 2018 83 341 –1.01 33.02 205 172 613
21 2018 151 232 –1.24 32.98 166 124 675
22 2018 63 345 –0.99 33.16 214 181 613
22b 2018 33 ND 0.19 33.09 181 736 ND
23 2018 109 292 –1.75 33.08 198 108 712
25 2018 144 269 –1.14 32.72 183 121 663
28 2018 160 303 –1.80 33.17 205 115 746
29 2018 180 249 –1.04 32.81 167 128 658
32 2018 31 340 –1.62 31.13 162 410 517
34 2018 37 335 –1.70 31.68 156 322 580
341 2018 308 276 0.89 33.76 198 133 605
34b 2018 46 ND –1.70 31.55 156 323 584
38 2018 180 191 –1.34 33.10 164 103 738
2018 271 278 0.85 33.76 230 153 605
44 2018 104 266 –1.58 32.85 178 140 703
45 2018 18 345 2.08 28.92 188 458 629
45bis 2018 17 335 1.07 29.62 188 458 616
46 2018 44 335 –1.28 31.72 253 441 669
689 2018 127 301 –1.22 32.41 171 316 ND
10 2018 200 282 –0.96 32.67 184 147 600
736_18 2018 88 231 –1.16 31.84 202 653 ND
736b 2018 10 ND 3.15 24.40 202 ND ND
2018 322 303 –1.69 33.10 179 125 610
StationsDateDepth (m)BO2a (µM)BTb (°C)BScOWd (Days)POCe (mg m–3)PPf (mg C m–2 y–1)
702 2010 129 255 –1.36 31.78 189 817 NDg
705 2010 71 329 –1.32 31.65 161 458 613
706 2010 77 311 –1.40 32.38 161 174 598
735 2010 185 192 –1.23 32.94 191 380 640
745 2010 184 219 –1.49 33.28 168 113 619
790 2010 38 369 –0.64 31.15 147 485 527
820 2010 53 298 –1.47 31.60 140 413 607
840 2010 174 180 –1.42 33.18 172 127 632
EXTRA 2010 316 269 –1.06 32.99 172 152 637
670 2017 117 372 –0.83 31.72 233 264 628
676 2017 98 351 –1.20 31.98 206 221 530
682 2017 95 328 –1.34 32.22 174 207 ND
684 2017 108 330 –1.24 32.33 169 348 ND
688 2017 107 321 –1.29 32.53 170 255 ND
694 2017 103 330 –0.98 32.34 180 214 ND
720 2017 91 293 –1.10 31.25 176 303 ND
732 2017 120 254 –1.18 31.66 192 817 ND
736 2017 91 251 –1.18 31.28 202 653 ND
15 2018 188 307 –1.72 32.67 193 135 671
16 2018 136 266 –1.07 32.71 173 124 683
17 2018 94 296 –1.63 32.89 212 147 670
17b 2018 61 NDe –1.49 32.63 150 774 ND
18 2018 118 312 –1.80 33.41 198 128 710
19 2018 83 341 –1.01 33.02 205 172 613
21 2018 151 232 –1.24 32.98 166 124 675
22 2018 63 345 –0.99 33.16 214 181 613
22b 2018 33 ND 0.19 33.09 181 736 ND
23 2018 109 292 –1.75 33.08 198 108 712
25 2018 144 269 –1.14 32.72 183 121 663
28 2018 160 303 –1.80 33.17 205 115 746
29 2018 180 249 –1.04 32.81 167 128 658
32 2018 31 340 –1.62 31.13 162 410 517
34 2018 37 335 –1.70 31.68 156 322 580
341 2018 308 276 0.89 33.76 198 133 605
34b 2018 46 ND –1.70 31.55 156 323 584
38 2018 180 191 –1.34 33.10 164 103 738
2018 271 278 0.85 33.76 230 153 605
44 2018 104 266 –1.58 32.85 178 140 703
45 2018 18 345 2.08 28.92 188 458 629
45bis 2018 17 335 1.07 29.62 188 458 616
46 2018 44 335 –1.28 31.72 253 441 669
689 2018 127 301 –1.22 32.41 171 316 ND
10 2018 200 282 –0.96 32.67 184 147 600
736_18 2018 88 231 –1.16 31.84 202 653 ND
736b 2018 10 ND 3.15 24.40 202 ND ND
2018 322 303 –1.69 33.10 179 125 610

aBO2 = bottom dissolved oxygen. bBT = bottom temperature. cBS = bottom salinity. dOW = duration of the open water period. ePOC = particulate organic carbon content measured at the surface of the water column. fPP = mean annual surface primary production. gND = no data.

The timing of sea ice breakup and freeze-up and, therefore, the duration of the OW period at each station were extracted from regional ice charts produced weekly by the Canadian Ice Service. Ice charts were produced through expert manual interpretation of remotely sensed imagery, which since 1996 have been based primarily on imagery provided by RADARSAT-1 and RADARSAT-2 (Tivy et al., 2011). In the current study, we defined the sea ice breakup (freeze-up) by the week that the total ice concentration at the study site fell below (surpassed) one-tenth.

### Statistical analyses

#### Epibenthic characteristics of the study area

Epibenthic characteristics determined for each of the 46 stations were wet biomass (g m–2), density (ind m–2), and three biodiversity metrics: taxonomic richness (S, number of taxa), Shannon–Wiener’s diversity index (H’), and Pielou’s evenness index (J ’). The indices H ’ and J ’ were calculated based on biomass data, including colonial taxa. Bryozoa, Porifera, and Cnidaria were excluded from density analysis because whole organisms were not collected by the sampling method. The nonparametric Chao2 index, which represents the expected number of taxa in the study area, was calculated using the “vegan” package (Oksanen et al., 2017). The Chao2 estimator is defined as $Chao2=Sobs+Q122Q2,$ where Sobs is the total number of observed taxa, Q1 is the number of taxa found at only one station, and Q2 is the number of taxa found at exactly two stations (Chao, 1987).

Principal component analysis (PCA) was applied to the environmental variables from 32 stations to reduce the dimensionality of the data set using the “FactoMineR” package (Pages, 2004). Fourteen stations were removed from this analysis due to missing data in primary production and bottom dissolved oxygen (Table 1). Environmental variables used in the PCA were depth, bottom salinity, bottom temperature, bottom dissolved oxygen, mean annual surface primary production, surface POC, and duration of the OW season. The first resulting components (PC1, PC2, and PC3), representing a set of environmental variables, were used in linear regressions to model the relationships between community characteristics (density, biomass, and taxonomic richness) and environmental variables. Homogeneity of variance and normality of residuals had been verified using regression diagnostic plots and the Shapiro–Wilk test on residuals.

#### Epibenthic communities

##### Epibenthic community composition

The list of taxa identified at each station were downgraded to the family level (158 taxa) due to an incomplete set of organisms restraining the identification at the species level for the analysis of the epibenthic community composition. Bray–Curtis dissimilarity was calculated on the fourth-root transformed data for the biomass matrix in order to include colonial taxa. The fourth-root transformation was chosen to balance the effects of high and low biomass taxa (Clarke and Warwick, 2001). To define distinct communities in the study area, the dissimilarity matrix was subjected to a hierarchical cluster analysis using Ward’s (1963) minimum variance method. Community clusters were determined by selecting a distance where stations were fused in well-defined clusters. The geographical distribution of these communities in the study area was then mapped using the “ggplot2” package (Wickham, 2016). Similarity percentage analysis (SIMPER) determined which taxa contributed to the dissimilarity between community clusters based on the biomass matrix (cutoff at 70% similarity; Clarke, 1993).

##### Epibenthic community characteristics

Epibenthic community characteristics determined at each station were density (ind m–2), wet biomass (g m–2), and the alpha (α) and beta (β) diversities. Alpha diversity (α) is determined as the mean number of taxa at a given station. Beta diversity (β), also called turnover diversity, provides an indication of species replacement between habitats or along an environmental gradient and indirectly indicates the habitat diversity. The latter is calculated as the ratio between gamma (γ, total number of taxa in a given community) and alpha (α) diversities (Whittaker, 1960).

#### Relationships between community composition and the environment

The relationship between epibenthic community composition based on wet biomass and the environmental variables was evaluated for 32 stations using a multivariate method of constrained ordination, canonical correspondence analysis (CCA; ter Braak and Verdonschot, 1995). To down-weight rare species, we used a stepwise method that removes the species with occurrence 1, then the species with occurrence 2 and so on. At each step, we compared eigenvalues and the total inertia of the analysis. When a marked decrease of ca 4% was observed in these values, we preserved the previous analysis (Legendre and Legendre, 2012). Removing rare species reduced the total number of taxa from 158 to 101, but this reduction had no impact on the outcome of the analyses. Environmental variables entered into the model were similar to the multiple regressions. To avoid redundancy in the model, we tested linear dependencies among constraints. An analysis of variance for CCA was used to assess the significance of variables and axes. We performed these analyses using the “vegan” package (Oksanen et al., 2017) in R (R Core Team, 2020).

### Epibenthic characteristics of the study area

We identified 380 taxa across the 46 stations sampled and 265 to the species level. The Chao2 index reached a value of 539 taxa, exceeding the number of observed taxa. Epifaunal biomass ranged from 0.02 to 45.2 g m–2, density from 0.11 to 29.5 ind m–2, taxa richness from 5 to 71 taxa per station, diversity index H ’ from 0.26 to 2.64, and evenness index J ’ from 0.09 to 0.88 (Figure 2).

Figure 2.

Distributions of epibenthic characteristics at 46 stations in the Hudson Bay System. Characteristics shown are (A) biomass (g m–2), (B) density (individuals m–2), (C) taxonomic richness, and (D) Shannon–Wiener diversity index. Blue dots represent low values and red dots represent high values for these epibenthic characteristics. DOI: https://doi.org/10.1525/elementa.00044.f2

Figure 2.

Distributions of epibenthic characteristics at 46 stations in the Hudson Bay System. Characteristics shown are (A) biomass (g m–2), (B) density (individuals m–2), (C) taxonomic richness, and (D) Shannon–Wiener diversity index. Blue dots represent low values and red dots represent high values for these epibenthic characteristics. DOI: https://doi.org/10.1525/elementa.00044.f2

PC1, 2, and 3 accounted for about 47%, 18%, and 15%, respectively, of the variance in the selected environmental variables, for an approximate combined 80% of the variance (Table 2; Figure S1). PC1 strongly correlated with depth, bottom salinity, bottom dissolved oxygen, surface-water POC, and mean annual primary production (coordinates > 0.6 or < –0.6; Table 2). PC1 reflected an environmental gradient, with low PC1 scores related to shallow depths and low bottom salinity, high POC content, high bottom dissolved oxygen, and low mean annual primary production, whereas high PC1 scores related to deeper waters, high bottom salinity, low POC content, low bottom dissolved oxygen, and high mean primary production. Conversely, PC2 had the strongest correlation with the duration of OW (coordinates > 0.6), while PC3 had the strongest correlation with bottom temperature (Table 2).

Table 2.

Correlation-based principal component analysis (PCA) of environmental variables determined in the Hudson Bay System. DOI: https://doi.org/10.1525/elementa.00044.t2

Results of PCAPC1PC2PC3
Eigenvalues 3.27 1.27 1.03
Variation explained (%) 46.7 18.2 14.7
Linear coefficients by environmental variable
Depth 0.75 0.15 0.52
Temperature –0.43 0.49 0.67
Salinity 0.91 0.05 –0.05
Bottom dissolved oxygen –0.73 0.31 –0.30
POC content –0.89 –0.11 0.03
Mean primary production 0.60 0.20 –0.39
OW duration 0.07 0.93 –0.24
Results of PCAPC1PC2PC3
Eigenvalues 3.27 1.27 1.03
Variation explained (%) 46.7 18.2 14.7
Linear coefficients by environmental variable
Depth 0.75 0.15 0.52
Temperature –0.43 0.49 0.67
Salinity 0.91 0.05 –0.05
Bottom dissolved oxygen –0.73 0.31 –0.30
POC content –0.89 –0.11 0.03
Mean primary production 0.60 0.20 –0.39
OW duration 0.07 0.93 –0.24

Linear models for epibenthic characteristics showed that variables included in the PC1 were significant (P value < .05; Table S1). Biomass, density, and taxonomic richness increased with PC1 scores (Table S1 and Figure 3), which means with deeper water, high bottom salinity, low POC content, low bottom dissolved oxygen, and high mean primary production. The stations located near the Nelson Estuary and in the southern part of HB presented the lowest PC1 scores and the lowest values of biomass, density, and taxonomic richness (Figures 2 and 3). The same observation was noted for Ungava Bay (Figures 2 and 3). Stations located in western HB, northern HB, around the Belcher Islands, and in Hudson Strait presented the highest values of PC1 scores and benthic characteristics (Figures 2 and 3). In contrast, the middle of the Bay was characterized by low values of biomass, density, and taxonomic richness (Figures 2 and 3). Neither PC2 nor PC3 was significant for epibenthic characteristics (Table S1).

Figure 3.

Relationships of epibenthic characteristics with first principal component scores based on environmental variables. Shown are (A) biomass (g m–2), (B) density (individuals m–2), and (C) taxonomic richness, along with coefficients of determination of significant linear regression (P < 0.05) in each case. Gray shaded areas indicate the 95% confidence interval. DOI: https://doi.org/10.1525/elementa.00044.f3

Figure 3.

Relationships of epibenthic characteristics with first principal component scores based on environmental variables. Shown are (A) biomass (g m–2), (B) density (individuals m–2), and (C) taxonomic richness, along with coefficients of determination of significant linear regression (P < 0.05) in each case. Gray shaded areas indicate the 95% confidence interval. DOI: https://doi.org/10.1525/elementa.00044.f3

### Identification of epibenthic communities of the study area

#### Composition of epibenthic community identified

Cluster analysis of all 46 stations based on Bray–Curtis dissimilarity of biomass data highlights three communities independent of the sampling year and the sampling gear (Figure 4A). These communities are distributed along a coastal-to-offshore gradient (Figure 4B). Community 1 is located along coastal areas, whereas Community 3 is mainly located in the middle part of the Bay and Community 2 is generally located between the other two (Figure 4B). Across all of the communities, Echinodermata was dominant with different proportions of Echinoidea, Ophiuroidea, and other Echinodermata (Figure 5). A higher proportion of Echinoidea, predominantly sea urchins (e.g., Strongylocentrotus sp.), was observed in Community 1 (23%), whereas Ophiuroidea presented the opposite pattern to Echinoidea with a higher proportion in Community 3 (63%). Community 2 presented an intermediate value of Ophiuroidea and similar proportions of Echinoidea and other Echinodermata (34%, 13%, and 12%, respectively). Arthropoda decreased from Community 1 to Community 3 (19% and 6%, respectively). Mollusca presented a higher proportion in Communities 1 and 3 (22% and 15%, respectively) than in Community 2 (2%). The latter community presented a higher proportion of Porifera (23%). Other taxa, including tunicates (e.g., Ascidiacea), soft corals (e.g., Nephtheidae), and sea anemones (e.g., Actiniaria), were present in notable quantities in Communities 1 (12%), 2 (4%), and 3 (4%). The proportion of Annelida was low across all communities, which reflects reflecting the nature of sampling by trawl. Thirteen taxa were responsible for the differences among the communities, indicating that a particular set of taxa and their respective biomass are discriminant of community dissimilarity (SIMPER analysis, Table S2).

Figure 4.

Epibenthic communities in the Hudson Bay System. (A) Cluster tree (Ward distance), based on Bray–Curtis dissimilarity matrix using fourth-root transformed species biomass data, showing determination of three epibenthic communities with color-coding for sampling gear and sampling year; and (B) sampling locations color-coded for each of the three communities. DOI: https://doi.org/10.1525/elementa.00044.f4

Figure 4.

Epibenthic communities in the Hudson Bay System. (A) Cluster tree (Ward distance), based on Bray–Curtis dissimilarity matrix using fourth-root transformed species biomass data, showing determination of three epibenthic communities with color-coding for sampling gear and sampling year; and (B) sampling locations color-coded for each of the three communities. DOI: https://doi.org/10.1525/elementa.00044.f4

Figure 5.

Mean biomass composition (%) for the main phyla or classes in the three community clusters. DOI: https://doi.org/10.1525/elementa.00044.f5

Figure 5.

Mean biomass composition (%) for the main phyla or classes in the three community clusters. DOI: https://doi.org/10.1525/elementa.00044.f5

#### Characteristics of epibenthic community identified

The highest α diversity was observed in Community 2 (28.2 ± 3.3 taxa, n = 11), whereas the lowest value was found in Community 1 (20.5 ± 1.9 taxa, n = 9). The highest value of turnover (β) diversity was found in Community 1 (4.87) compared to the others (3.84 and 4.26 for Communities 2 and 3, respectively). Community 1 was also characterized by a low density and an intermediate biomass (3.07 ± 1.52 ind m–2 and 6.81 ± 2.58 g m–2; n = 9). Community 2 had the highest biomass and intermediate density (7.42 ± 1.66 g m–2 and 5.29 ± 6.61 ind m–2; n = 11), whereas Community 3 showed the highest density and the lowest biomass (6.65 ± 1.48 ind m–2 and 4.22 ± 0.89 g m–2; n = 12).

### Relationships between community composition and the environment

For all data, the first two axes generated by CCA explained approximately 43% of the taxa biomass–environment relationship, with the first axis explaining the highest variation of 26% (Table 3). The variables coarse substrate, bottom dissolved oxygen, bottom temperature, POC content, and the duration of OW correlated positively with the first axis. Mud substrate, depth, and mean primary production were inversely correlated with these factors and with the first axis (Table 3; Figure 6A). The mixed substrate was located along the second axis (Table 3; Figure 6A). Among the environmental variables considered, only substrate type, salinity, and mean primary production were significantly correlated to the communities (P value < 0.01; Table 3).

Table 3.

Results of canonical correspondence analysis (CCA) using epibenthic biomass and environmental variables. DOI: https://doi.org/10.1525/elementa.00044.t3

Results of CCAAxis 1Axis 2P Valuea
Eigenvalues 0.66 0.44 ***
Variation explained (%) 25.8 17.3 **
Linear coefficients by environmental variable
Depth –0.65 0.19 NS
Temperature 0.21 0.03 NS
Salinity 0.002 –0.15 **
Oxygen 0.84 0.04
POC content 0.18 –0.07 NS
Mean primary production –0.57 –0.21 **
OW duration 0.58 0.01
Coarse sediment 0.93 –0.39 ***
Mixed sediment –0.32 0.79 ***
Mud sediment –0.66 –0.54 ***
Results of CCAAxis 1Axis 2P Valuea
Eigenvalues 0.66 0.44 ***
Variation explained (%) 25.8 17.3 **
Linear coefficients by environmental variable
Depth –0.65 0.19 NS
Temperature 0.21 0.03 NS
Salinity 0.002 –0.15 **
Oxygen 0.84 0.04
POC content 0.18 –0.07 NS
Mean primary production –0.57 –0.21 **
OW duration 0.58 0.01
Coarse sediment 0.93 –0.39 ***
Mixed sediment –0.32 0.79 ***
Mud sediment –0.66 –0.54 ***

aFrom permutation analysis of variance tests (9,999 permutations), where NS indicates not significant (P > 0.05); *P = 0.05. **P = 0.01. ***P < 0.01.

Figure 6.

Canonical correspondence analysis (CCA) ordination biplots of epibenthic composition based on biomass. (A) Ordination biplot with environmental variables, where quantitative variables are illustrated by arrows and qualitative variables (type of substrate: coarse, mixed, and mud) by blue cross symbols; and (B) ordination biplot with principal taxa (diamond symbols) from similarity percentage analysis: Yoldiidae (YOLD), Asteridae (AST), Ophiuridae (OPHI), Actiniaria (ACT), Solasteridae (SOLAS), Porifera (POR), Nephtheidae (NEP), Gorgonocephalidae (GORG), Strongylocentrotidae (STRONG), Thoridae (THOR), Oregonidae (OREG), Balanidae (BAL), and Pectinidae (PECT). Diamond symbols in both plots are color-coded by community (see Figure 4). DOI: https://doi.org/10.1525/elementa.00044.f6

Figure 6.

Canonical correspondence analysis (CCA) ordination biplots of epibenthic composition based on biomass. (A) Ordination biplot with environmental variables, where quantitative variables are illustrated by arrows and qualitative variables (type of substrate: coarse, mixed, and mud) by blue cross symbols; and (B) ordination biplot with principal taxa (diamond symbols) from similarity percentage analysis: Yoldiidae (YOLD), Asteridae (AST), Ophiuridae (OPHI), Actiniaria (ACT), Solasteridae (SOLAS), Porifera (POR), Nephtheidae (NEP), Gorgonocephalidae (GORG), Strongylocentrotidae (STRONG), Thoridae (THOR), Oregonidae (OREG), Balanidae (BAL), and Pectinidae (PECT). Diamond symbols in both plots are color-coded by community (see Figure 4). DOI: https://doi.org/10.1525/elementa.00044.f6

The arrangement of the samples on the CCA biplot showed three primary aggregates driven by the substrate type (Figure 6). Stations hosting Community 1 were associated with shallower waters with high values of bottom dissolved oxygen, longer duration of OW, and coarse substrate. These stations were more associated to deposit feeders (e.g., Strongylocentrotidae), filter-feeder bivalves and barnacles (e.g., Pectinidae, Balanidae), and opportunist-predator decapods (e.g., Thoridae and Oregoniidae; Figure 6B). Stations hosting Community 2 were spread along the second axis indicating a high variability of the environmental variables. These stations were found at different depths and temperatures with mixed substrate (Figure 6A). Mixed substrate was linked to filter-feeder sponges, basket stars (e.g., Porifera and Gorgonocephalidae), and soft corals (e.g., Nephtheidae; Figure 6B). Conversely, Community 3 was strongly correlated with high values of mean primary production, low dissolved oxygen, longer duration of ice cover, and deeper waters with mud substrate (Figure 6A). This community was associated with deposit- and filter-suspension feeder bivalves (e.g., Yoldiidae and Astartidae, respectively) and opportunist-predator brittle stars (e.g., Ophiuridae; Figure 6B).

Based on bottom trawl bycatch surveys in the HBS, Jørgensen et al. (2016) estimated a benthic megafauna species richness ranging from 3 to 59 species per station. In our study, the macro-megabenthic taxa richness ranged from 5 to 71 taxa per station. Despite a difference in the gear used to sample river mouths, our results showed no influence of the different types of sampling gear. Moreover, our work increases the observed richness of the macro-megabenthic taxa of the study area (i.e., HB, Hudson Strait, and Ungava Bay) to 380, which is 71% of the taxa expected in the study area, indicating that about one third of the expected species pool is still unrecorded. Unlike previous studies, which have concluded that the HB was a less productive ecosystem than other Arctic regions, our results demonstrate that present values of epibenthic characteristics (i.e., biomass, density, and taxonomic richness) are comparable to other Arctic regions (Grebmeier et al., 2006a; Piepenburg et al., 2011; Roy et al., 2015).

### Epibenthic characteristics of the study area

Food supply, salinity gradients, and freshwater discharge are generally considered to be significant environmental drivers for both pelagic and benthic organisms (Remane and Schlieper, 1971; Mayer and Piepenburg, 1996; Piepenburg, 2005; Cusson et al., 2007; Palmer et al., 2011). Univariate characteristics related to biodiversity (density, taxonomic richness, biomass, and Shannon Index) highlighted differences among the 46 stations sampled across the study area. As shown by the PCA, epibenthic biomass, density, and taxonomic richness were driven by the first environmental principal component (PC1), which reflected the following environmental variables: salinity, POC content, depth, dissolved oxygen, and mean annual primary production. Salinity and POC content contributed the most to PC1 and can be considered the main drivers of these benthic community characteristics. Thus, an increase in biomass, density, and taxonomic richness was linked to an increase in bottom salinity and mean annual primary production as well as a decrease in the POC content.

Previously, Pearson and Rosenberg (1978) found that enrichment of organic material (e.g., POC content) generally reduced the number of species and increased both biomass and abundance. Our study showed that high POC content, which we attribute to strong terrestrial contributions, decreased the number of taxa, as well as biomass and density. Stations located in the Nelson River Estuary and along the south coast of HB showed the highest POC content and the lowest values of benthic community characteristics. This area is known to be strongly influenced by river discharge (Prisenberg, 1986; Granskog et al., 2007), which causes a reduction in salinity (to values that range from 28.92 to 32.38) and promotes the deposition of fine riverine particles (Duboc et al., 2017), which alters substrate composition and thus can impact benthic structure significantly (Harrison et al., 2007).

Moreover, the relationships between salinity and primary productivity and benthic biodiversity have already been observed in the Estuary and Gulf of the St. Lawrence River and in the Arctic, especially in the Beaufort Sea (Witman et al., 2008). A drop in salinity is assumed to be responsible for the reduction in benthic richness observed in the Nelson River Estuary and along the south coast of HB. Similarly, stations located in eastern HB showed mainly low values of benthic characteristics. Despite the occurrence of polynyas in the area, increased freshwater runoff and a longer ice season (i.e., late breakup; Kirillov et al., 2020) limit food supply in the area (Granskog et al., 2007; Lapoussière et al., 2009; Ferland et al., 2011; Sibert et al., 2011). Highly biologically productive areas, such as polynyas, generally promote benthic systems in terms of taxa richness, biomass, and secondary production (Ambrose and Renaud, 1995; Link et al., 2011). Despite relatively lower POC content compared to the other geographical areas, stations located near polynyas showed higher diversity, biomass, and density (Figures 2 and 3), hence reflecting strong pelagic-benthic coupling in these areas (Lapoussière et al., 2009; Hochheim et al., 2010).

Central HB is deeper and less productive than both western and eastern HB due to, among other factors, local hydrodynamics (e.g., strong haline stratification in summer) and a longer ice season (Ferland et al., 2011; Kenchington et al., 2011; Sibert et al., 2011). In central HB, stations were associated with lower values of biomass, density, and taxonomic richness. Previous studies have shown that Hudson Strait was more productive than HB during summer and fall (Lapoussière et al., 2009; Ferland et al., 2011). However, our results suggest that HB could be as productive as Hudson Strait in spring because of higher stratification in Hudson Strait confining phytoplankton to the euphotic zone and thus limiting carbon export toward benthic organisms (L Matthes, personal communication). Despite a limited number of stations sampled in Hudson Strait, we observed diversity and biomass similar to HB. Remarkably, stations located in Ungava Bay showed low values of epibenthic characteristics that were similar to those observed near the Nelson River Estuary and southern part of HB. This pattern could be because Ungava Bay is subject to a higher stratification due to large freshwater inputs and intense tidal mixing (up to 17 m) that ultimately reduce food supply by flushing out local production (Markham, 1986; Drinkwater and Jones, 1987).

Our results highlight that epibenthic characteristics are spatially segregated by salinity and food supply (i.e., surface-water POC content and mean annual primary production). Thus, we have shown that epibenthic biomass, density, and diversity increase further off the coast as salinity increases and POC content decreases, until the waters become too deep and these epibenthic variables decline again in central HB.

### Relationships between community composition and the environment

Salinity, food supply, depth, temperature, and substrate type are known as critical environmental factors that explain the distribution and composition of benthic macrofauna (Mayer and Piepenburg, 1996; Cusson et al., 2007; Bluhm et al., 2009; Palmer et al., 2011; Roy et al., 2014). Data on substrate type are relatively scarce, rather dated and at low spatial resolution in the HBS (e.g., Pelletier, 1986; Henderson, 1989; Misiuk and Aitken, 2020). Despite the difficulty in obtaining recent information on substrate type in the study area, the CCA revealed noticeable changes in diversity and composition of benthic macro-megafauna along an environmental gradient mostly driven by the substrate type, thus also reflecting different feeding types. Epibenthic community structure was divided into three communities based on the substrate (coarse to soft) that characterizes their habitat. Soft substrate mainly characterized the deeper and more productive stations of the study area and the middle of the Bay with low POC content, whereas coarse substrate mainly characterized the shallower and oxygenated stations along the coasts with high POC content.

#### Coarse substrate community

Community 1 was associated with coarse substrate and dominated by deposit feeders such as echinoderms (e.g., Echinoidea and Strongylocentrotidae) and filter feeders such as bivalves (e.g., Pectinoidae), arthropods (e.g., Balanidae), and ascidians (Ascidiacea). Generally, coarse substrate occurs in areas with strong currents that resuspend sediments and therefore provide food for filter and suspension feeders (Bluhm et al., 2009). This type of benthic community (Community 1), typically attached to rocks and cobbles, was found in coastal areas where river discharge maintained high POC content and high dissolved oxygen, along with strong currents and a longer OW season. Moreover, this community had the lowest density and α diversity values but the highest value for β diversity. Turnover diversity (β) provides an indication of species replacement and, indirectly, of habitat diversity (Whittaker, 1960; Cusson et al., 2007). A high β value indicates a large difference in community composition among the stations in this community. We attribute this difference to the environmental heterogeneity found in this coastal community. These coastal stations encompass a wide range of depths (from 10 to 127 m), salinity (from 24.4 to 33), dissolved oxygen (from 298 to 369 µM), and POC content (from 171 to 774 mg m–3), reflecting the specific characteristics of each river in the HBS. The strong influence of rivers causes salinity to vary between stations, leading to low values of α diversity (24.2 ± 2.4 species, n = 9) and density (3.07 ± 1.52 ind m–2, n = 9).

#### Soft substrate community

Community 3 was associated with soft substrate (i.e., clay and mud) and dominated by deposit and suspension feeders such as bivalves (e.g., Yoldiidae, Astartidae, and Arcidae), deposit feeders such as holothurians (e.g., Myriotrochus rinkii and Eupyrgus scaber), and brittle stars (e.g., Ophiuridae). Ophiuroids are common in Arctic shelf and slope habitats compared to coastal habitats (Piepenburg, 2000, 2005; Piepenburg et al., 2011). Not surprisingly, brittle stars dominated deeper communities in this study. Moreover, stations characterized by Community 3 were strongly influenced by mean primary production and presented high epifaunal density. However, the prolonged ice cover observed in this area indicates a dominant sympagic system and delayed pelagic primary production (Sibert et al., 2011), suggesting a pulsed food supply. Holothurians are known to exploit fresh phytodetrital pulses on soft sediment (Bluhm et al., 2009; Boetius et al., 2013; Kirillov et al., 2020). Their presence and the presence of deposit and suspension feeders as well as the low biomass observed (4.22 ± 0.89 g m–2, n = 12) are consistent with a discontinuous food supply in this area. Furthermore, this community was found on average in deeper waters than the coarse substrate community (i.e., Community 1) and presented a high α diversity. As the number of taxa generally increases at depths between 0 and 1,000 m (Levin et al., 2001), the difference in mean depth between Communities 1 and 3 (58 ± 8 m, n = 9, and 160 ± 16 m, n = 12, respectively) could explain the difference in α diversity.

#### Mixed substrate community

Community 2 was associated with mixed substrate (i.e., particles ranging from coarse to soft sediment) and combined taxa from the other two communities without exhibiting a specific dominant group or class. For example, filters and suspension feeders, such as sponges and soft corals (i.e., Porifera and Nephtheidae), have been associated with poorly sorted sediment (coarse to soft substratum; Hogg et al., 2010). As a consequence, substrate heterogeneity that includes pebbles, cobbles, and/or boulders likely explains why sponges and soft corals were present more in Community 2 than in the other communities. Deposit feeders, however, such as Echinoidea (i.e., Strongylocentrotidae) and Ophiuroidea (e.g., Ophiuridae and Gorgonocephalidae), presented intermediate values of biomass. Moreover, stations supporting these communities were mostly located in deep waters (163 ± 23 m, n = 11) and near recurrent polynyas, such as the large polynya in northwestern HB (Barber and Massom, 2007; Landy et al., 2017). These areas are generally characterized by high food supply and strong pelagic-benthic coupling (Kenchington et al., 2011). Unfortunately, due to the substrate type and sampling method, quantifying sediment pigment concentrations in this study was not possible. Nevertheless, this community showed high α diversity, density, and biomass, highlighting niche diversification (coarse to soft substratum) and an important food supply to this benthic community.

This study presents results from the most recent survey of epibenthic organisms in part of the HBS. We identified 380 epibenthic taxa, representing 71% of the total taxa that are estimated to be present within the study area (i.e., HB, Hudson Strait, and Ungava Bay). The overall analysis of epibenthic characteristics showed that bottom salinity and surface-water POC content were the main environmental drivers. We showed that coastal waters, directly influenced by rivers, harbored the lowest epibenthic density, biomass, and taxonomic richness. These low values were located in shallower depths with low salinity and high POC content. In accordance with previous studies, we also showed that central HB was less productive than the other regions of HB productive than Hudson Strait and we showed that some areas of HB can be as productive as Hudson Strait (i.e., high density, biomass, and taxonomic richness). Further benthic sampling between these two regions would be necessary to confirm this result conclusively.

Based on biomass data, three epibenthic community clusters have been identified and broadly related to the substrate type reflecting food supply proxies (i.e., mean annual primary production and surface-water POC content). More specifically, coarse substrate along the coastlines hosted a higher biomass of filter and suspension feeders, whereas soft substrate in deeper water was mostly associated with deposit and suspension feeders. We showed that the low density and taxonomic richness occurring in the coarse substrate community could be attributed to high POC content and freshwater discharge from rivers. In contrast, the soft substrate community likely received a pulsed food supply because of the longer duration of ice cover and dominant sympagic system. The mixed substrate type did not show dominant taxa or classes and was characterized by large and diverse epibenthic organisms. This last community showed the highest biomass and diversity, which was attributed to a high food supply and strong pelagic-benthic coupling near polynyas.

Benthic organisms respond to natural and anthropogenic changes occurring in their environment, leading to changes in their distribution. Given our results, high POC content of overlying surface water and low salinity bottom water lead to a decrease in biodiversity and change in epibenthic community composition in the study area. Projections toward a longer OW season and increased river discharge as a result of climate change may have a major impact on these epibenthic communities due, in particular, to increases in OW primary production, freshwater inflow, and inputs of terrestrial organic matter from permafrost thaw and forest growth upstream of the HBS.

All data are accessible at the Polar Data Catalogue (https://www.polardata.ca/pdcinput/) and will be made public prior to publication.

Figure S1. Principal component analysis (PCA) plots among 32 stations. (A) PCA in terms of environmental variables. (B) PCA in terms of individual stations. OW indicates duration of open water, and PP indicates primary production based on interpolation from satellite data.

Table S1. Results of linear regression tests of density, biomass, and taxonomic richness related to principal component analysis (PCA) axes.

Table S2. Results of similarity of percentage (SIMPER) analysis of the taxa contributing to 70% of the dissimilarity in epibenthic composition based on biomass (g m–2) of the three identified communities.

This project is part of the Natural Sciences and Engineering Research Council (NSERC)-Manitoba Hydro funded Collaborative Research and Development program known as Hudson Bay System study. Data collection for this research would not be possible without the support and hospitality of the CCGS Amundsen crew during the 2018 field season. D. G. Babb was supported through an NSERC PGS-D, the Canadian Meteorological and Oceanographic Society, and D. Barber’s Canada Research Chair funding. NSERC discovery grant funding (DI) supported research specific to this work, as did the Northern Scientific Training Program. This work is a contribution to the ArcticNet Networks of Centres of Excellence and the Arctic Science Partnership (asp-net.org). We would like to thank the Sentinel North Research Project BriGHT, Amundsen Science, and Québec-Océan for their contribution in sampling time, polar logistic, and scientific equipment. We thank all the participants in the Hudson Bay System study campaign for their contribution to the field work and data collection.

Funding for this research was graciously provided by Manitoba Hydro, the Natural Sciences and Engineering Research Council of Canada, Amundsen Science, and the Canada Research Chairs program.

The authors declare that they have no competing interests.

MP led the design of the study, performed the experiments and taxonomic identification, analyzed the data, prepared figures and tables, and authored and reviewed drafts of this article. DGB performed the sea ice extraction analysis and authored and reviewed drafts of this article. PA and CN led the design of the study, authored and reviewed drafts of this article, and obtained the funding. FM authored and reviewed drafts of this article.

Ambrose
,
WG
,
Renaud
,
PE
.
1995
.
Benthic response to water column productivity patterns: Evidence for benthic-pelagic coupling in the Northeast Water Polynya
.
J Geophys Res
100
:
4411
4421
. DOI: https://dx.doi.org/10.1029/94JC01982.
Andrews
,
JS
,
Babb
,
D
,
Barber
,
DG
.
2018
.
Climate change and sea ice: Marine shipping access in Hudson Bay and Hudson Strait (1980–2014)
.
Elem Sci Anth
5
:
19
. DOI: https://dx.doi.org/10.1525/elementa.130.
Atkinson
,
EG
,
Wacasey
,
JW
.
1989
a.
Benthic invertebrates collected from Hudson Strait, Foxe Channel and Foxe Basin, Canada, 1949 to 1970
.
Can Data Rep Fish Aquat Sci
746
.
Atkinson
,
EG
,
Wacasey
,
JW
.
1989
b.
Benthic invertebrates collected from Hudson Bay, Canada, 1953 to 1965
.
Can Data Rep Fish Aquat Sci
.
744
.
Barber
,
DG
,
Massom
,
R
.
2007
. A bipolar assessment of modes of polynya formation, in
Smith
,
WO
,
Barber
,
DG
eds.,
Polynyas: Windows to the world
.
Amsterdam, The Netherlands
:
Elsevier
:
1
54
.
Basher
,
Z
,
Bowden
,
DA
,
Costello
,
MJ
.
2018
.
Global Marine Environment Datasets (GMED). [WWW Document]
.
World Wide Web Electron. Publ. Version 2.0
.
Available at
http://gmed.auckland.ac.nz.
Accessed 2 March 2020
.
Beazley
,
L
,
Guijarro-Sabaniel
,
J
,
Lirette
,
C
,
Wang
,
Z
,
Kenchington
,
E
.
2019
.
Characteristics of environmental data layers for use in species distribution modelling in the eastern Canadian Arctic and sub-Arctic regions
.
Can Tech Rep Fish Aquat Sci
3248
:
vi
488
.
Bluhm
,
B
,
Iken
,
K
,
Mincks Hardy
,
S
,
Sirenko
,
B
,
,
B
.
2009
.
Community structure of epibenthic megafauna in the Chukchi Sea
.
Aquat Biol
7
:
269
293
. DOI: https://dx.doi.org/10.3354/ab00198.
Boetius
,
A
,
Albrecht
,
S
,
Bakker
,
K
,
Bienhold
,
C
,
Felden
,
J
,
Fernández-Méndez
,
M
,
Hendricks
,
S
,
Katlein
,
C
,
Lalande
,
C
,
Krumpen
,
T
,
Nicolaus
,
M
,
Peeken
,
I
,
Rabe
,
B
,
Rogacheva
,
A
,
Rybakova
,
E
,
Somavilla
,
R
,
Wenzhöfer
,
F
,
Felden
,
J
.
2013
.
Export of algal biomass from the melting arctic sea ice
.
Science
339
:
1430
1432
. DOI: https://dx.doi.org/10.1126/science.1231346.
Bring
,
A
,
Shiklomanov
,
A
,
Lammers
,
RB
.
2017
.
Pan-Arctic river discharge: Prioritizing monitoring of future
.
Earth’s Future
5
:
72
92
. DOI: https://dx.doi.org/10.1002/eft2.175.
Chao
,
A
.
1987
.
Estimating the population size for capture-recapture data with unequal catchability
.
Biometrics
43
:
783
791
. DOI: https://dx.doi.org/10.2307/2531532.
Clarke
,
KR
,
Warwick
,
RM
.
2001
.
Change in marine communities: An approach to statistical analysis and interpretation
. 2nd ed.
Plymouth, UK
:
PRIMER-E Ltd
.
Clarke
,
KR
.
1993
.
Non-parametric multivariate analyses of changes in community structure
.
Aust J Ecol
18
:
117
143
. DOI: https://dx.doi.org/10.1111/j.1442-9993.1993.tb00438.x.
Cusson
,
M
,
Archambault
,
P
,
Aitken
,
A
.
2007
.
Biodiversity of benthic assemblages on the Arctic continental shelf: Historical data from Canada
.
Mar Ecol Prog Ser
331
:
291
304
. DOI: https://dx.doi.org/10.3354/meps331291.
Derksen
,
C
,
Burgess
,
D
,
Duguay
,
C
,
Howell
,
S
,
Mudryk
,
L
,
Smith
,
S
,
Thackeray
,
C
,
Kirchmeier-Young
,
M
.
2019
. Changes in snow, ice, and permafrost across Canada, in
Bush
,
E
,
Lemmen
,
DS
eds.,
.
Ottawa, Ontario
:
:
194
260
.
Déry
,
SJ
,
Mlynowski
,
TJ
,
Hernández-Henríquez
,
MA
,
Straneo
,
F
.
2011
.
Interannual variability and interdecadal trends in Hudson Bay streamflow
.
J Mar Syst
88
:
341
351
. DOI: https://dx.doi.org/10.1016/j.jmarsys.2010.12.002.
Déry
,
SJ
,
,
TA
,
MacDonald
,
MK
,
Gauli-Sharma
,
B
.
2016
.
Recent trends and variability in river discharge across northern Canada
.
Hydrol Earth Syst Sci Discuss
20
:
1
48
. DOI: https://dx.doi.org/10.5194/hess-2016-461.
Drinkwater
,
KF
,
Jones
,
EP
.
1987
.
Density stratification, nutrient and chlorophyll distributions in the Hudson Strait region during summer and their relation to tidal mixing
.
Cont Shelf Res
7
:
599
607
. DOI: https://dx.doi.org/10.1016/0278-4343(87)90025-2.
Drinkwater
,
KF
.
1986
.
Chapter 13: Physical oceanography of Hudson Strait and Ungava Bay
, in
Martini
,
IP
ed.,
.
44
:
237
264
. (
Elsevier oceanography series
) DOI: https://doi.org/10.1016/S0422-9894(08)70906-1.
Duboc
,
Q
,
St-Onge
,
G
,
Lajeunesse
,
P
.
2017
.
Sediment records of the influence of river damming on the dynamics of the Nelson and Churchill Rivers, western Hudson Bay, Canada, during the last centuries
.
Holocene
27
:
712
725
. DOI: https://dx.doi.org/10.1177/0959683616670465.
Ferland
,
J
,
Gosselin
,
M
,
Starr
,
M
.
2011
.
Environmental control of summer primary production in the Hudson Bay system: The role of stratification
.
J Mar Syst
88
:
385
400
. DOI: https://dx.doi.org/10.1016/j.jmarsys.2011.03.015.
Gagnon
,
A
,
Gough
,
W
.
2005
.
Climate change scenarios for the Hudson Bay region: An intermodel comparison
.
Clim Change
69
:
269
297
. DOI: https://dx.doi.org/10.1007/s10584-005-1815-8.
Granskog
,
MA
,
Macdonald
,
RW
,
Mundy
,
CJ
,
Barber
,
DG
.
2007
.
Distribution, characteristics and potential impacts of chromophoric dissolved organic matter (CDOM) in Hudson Strait and Hudson Bay, Canada
.
Cont Shelf Res
27
:
2032
2050
. DOI: https://dx.doi.org/10.1016/j.csr.2007.05.001.
Grebmeier
,
JM
,
Cooper
,
LW
,
Feder
,
HM
,
Sirenko
,
BI
.
2006
a.
Ecosystem dynamics of the Pacific-influenced Northern Bering and Chukchi Seas in the Amerasian Arctic
.
Prog Oceanogr
71
:
331
361
. DOI: https://dx.doi.org/10.1016/j.pocean.2006.10.001.
Grebmeier
,
JM
,
Overland
,
JE
,
Moore
,
SE
,
Farley
,
E V
,
Carmack
,
EC
,
Cooper
,
LW
,
Frey
,
KE
,
Helle
,
JH
,
McLaughlin
,
FA
,
McNutt
,
SL
.
2006
b.
A major ecosystem shift in the northern Bering Sea
.
Science
311
:
1461
1464
. DOI: https://dx.doi.org/10.1126/science.1121365.
Grebmeier
,
JM
.
2012
.
Shifting patterns of life in the Pacific Arctic and sub-Arctic seas
.
Ann Rev Mar Sci
4
:
63
78
. DOI: https://dx.doi.org/10.1146/annurev-marine-120710-100926.
Harrison
,
E
,
Norris
,
R
,
Wilkinson
,
S
.
2007
. The impact of fine sediment accumulation on benthic macroinvertebrates: Implications for river management, in
Wilson
,
AL
,
Dehaan
,
RL
,
Watts
,
RJ
,
Page
,
KJ
,
Bowmer
,
KH
,
Curtis
,
A
eds.,
Proceedings of the 5th Australian Stream Management Conference. Australian rivers: Making a difference
.
Thurgoona, Australia
:
Charles Sturt University
:
139
144
.
Henderson
,
P
.
1989
. Provenance and depositional facies of surficial sediments in Hudson Bay, a glaciated Epeiric sea [PhD thesis].
:
University of Ottawa
.
Hijmans
,
RJ
,
van Etten
,
J
,
Sumner
,
M
,
Cheng
,
J
,
Baston
,
D
,
Bevan
,
A
,
Bivard
,
R
,
Busetto
,
L
,
Canty
,
M
,
Fasdi
,
B
,
Forrest
,
D
,
Ghosh
,
A
,
Golicher
,
D
,
Cray
,
J
,
Greenberg
,
JA
,
Hiemstra
,
P
,
Hingee
,
k
,
Karney
,
C
,
Mattiuzzi
,
M
,
Mosher
,
S
,
,
J
,
Pebesma
,
E
,
perpinan Lamigueiro
,
O
,
Racine
,
EB
,
Rowlingson
,
B
,
Shortridge
,
A
,
Venables
,
B
,
Wueest
,
R
.
2020
.
raster: Geographic Data Analysis and Modeling. R Package Version 2.4-15
.
Available at
https://cran.r-project.org/web/packages/raster/raster.pdf.
Hochheim
,
KP
,
Barber
,
DG
,
Lukovich
,
J V
.
2010
.
Changing sea ice conditions in Hudson Bay, 1980–2005
, in
Ferguson
,
SH
,
Loseto
,
L
,
Mallory
,
M
eds.,
A little less Arctic: Top predators in the world’s largest Northern Inland Sea, Hudson Bay
.
:
39
51
. DOI: https://dx.doi.org/10.1007/978-90-481-9121-5.
Hochheim
,
KP
,
Barber
,
DG
.
2014
.
An update on the ice climatology of the Hudson Bay system
.
Arctic Antarct Alp Res
46
:
66
83
. DOI: https://dx.doi.org/https://doi. org/10.1657/1938-4246-46.1.66.
Hogg
,
MM
,
Tendal
,
OS
,
Conway
,
KW
,
Pomponi
,
SA
,
Van Soest
,
RWM
,
Gutt
,
J
,
Krautter
,
M
,
Roberts
,
JM
.
2010
.
Deep-sea sponge grounds: Reservoirs of biodiversity
.
Cambridge
:
World Conservation Monitoring Centre
(UNEP regional seas report and studies; no. 189). (UNEP-WCMC Biodiversity Series No. 32). ISBN: 978-92-807-3081-4
.
Jørgensen
,
LL
,
Archambault
,
P
,
Blicher
,
M
,
Denisenko
,
N V
,
Guðmundsson
,
G
,
Iken
,
K
,
Roy
,
V
,
Sørensen
,
J
,
Anisimova
,
N
,
Behe
,
C
,
Bluhm
,
BA
,
Denisenko
,
S
,
Metcalf
,
V
,
Olafsdóttir
,
S
,
Schiøtte
,
T
,
Tendal
,
O
,
Ravelo
,
AM
,
Kedra
,
M
,
Piepenburg
,
D
.
2016
.
Benthos
, in
CAFF
ed.,
State of the Arctic Marine biodiversity report
.
Akureyri, Iceland
:
85
108
. DOI: https://dx.doi.org/978-9935-431-63-9.
Kenchington
,
EL
,
,
H
,
Roy
,
V
,
Archambault
,
P
,
Siferd
,
T
,
Treble
,
M
,
Wareham
,
V
.
2011
.
Identification of mega- and macrobenthic Ecologically and Biologically Significant Areas (EBSAs) in the Hudson Bay system, the Western and Eastern Canadian Arctic
.
DFO Can Sci Advis Secr Res Doc
2011/071
:
vi + 52 p
.
Kirillov
,
S
,
Babb
,
D
,
Dmitrenko
,
I
,
Landy
,
J
,
Lukovich
,
J
,
Ehn
,
J
,
Sydor
,
K
,
Barber
,
D
,
Stroeve
,
J
.
2020
.
Atmospheric forcing drives the winter sea ice thickness asymmetry of Hudson Bay
.
J Geophys Res Ocean
125
:
1
12
. DOI: https://dx.doi.org/10.1029/2019JC015756.
Landy
,
JC
,
Ehn
,
JK
,
Babb
,
DG
,
Thériault
,
N
,
Barber
,
D
.
2017
.
Sea ice thickness in the Eastern Canadian Arctic: Hudson Bay complex and Baffin Bay
.
Remote Sensing of Environment
200
:
281
294
. DOI: https://doi.org/10.1016/j.rse.2017.08.019.
Lapoussière
,
A
,
Michel
,
C
,
Gosselin
,
M
,
Poulin
,
M
.
2009
.
Spatial variability in organic material sinking export in the Hudson Bay system, Canada, during fall
.
Cont Shelf Res
29
:
1276
1288
. DOI: https://dx.doi.org/10.1016/j.csr.2009.02.004.
Legendre
,
P
,
Legendre
,
L
.
2012
. Canonical analysis, in
Legendre
,
P
,
Legendre
,
L
eds.,
Numerical ecology
.
Amsterdam, The Netherlands
:
Elsevier
:
625
710
.
Levin
,
LA
,
Etter
,
RJ
,
Rex
,
MA
,
Gooday
,
AJ
,
Smith
,
CR
,
Pineda
,
J
,
Stuart
,
CT
,
Hessler
,
RR
,
Pawson
,
D
.
2001
.
Environmental influences on regional deep-sea species diversity
.
Annu Rev Ecol Evol Syst
32
:
51
93
. DOI: https://dx.doi.org/https://doi.org/10.1146/annurev.ecolsys.32.081501.114002.
,
H
,
Archambault
,
P
,
Tamelander
,
T
,
Renaud
,
PE
,
Piepenburg
,
D
.
2011
.
Spring-to-summer changes and regional variability of benthic processes in the western Canadian Arctic
.
Polar Biol
34
:
2025
2038
. DOI: https://dx.doi.org/10.1007/s00300-011-1046-6.
,
H
,
Chaillou
,
G
,
Forest
,
A
,
Piepenburg
,
D
,
Archambault
,
P
.
2013
.
Multivariate benthic ecosystem functioning in the Arctic—benthic fluxes explained by environmental parameters in the southeastern Beaufort Sea
.
Biogeosciences
10
:
1
10
. DOI: https://dx.doi.org/10.5194/bg-10-1-2013.
Markham
,
WE
.
1986
.
Chapter 6: The ice cover
, in
Martini
,
IP
ed.,
.
44
:
101
116
. (
Elsevier oceanography series
) DOI: https://doi.org/10.1016/S0422-9894(08)70899-7.
Mayer
,
L
,
Piepenburg
,
D
.
1996
.
Epibenthic community patterns on the continental slope off East Greenland at 75°N
.
Mar Ecol Prog Ser
143
:
151
164
. DOI: https://dx.doi.org/10.3354/meps143151.
Misiuk
,
B
,
Aitken
,
A
.
2020
.
Benthic habitats of Chesterfield Inlet, Nunavut: A report prepared for the Government of Nunavut
.
Fisheries and Sealing Division
.
21
p.
Oksanen
,
J
,
Blanchet
,
FG
,
Friendly
,
M
,
Kindt
,
R
,
Legendre
,
P
,
Mcglinn
,
D
,
Minchin
,
PR
,
O’Hara
,
RB
,
Simpson
,
GL
,
Solymos
,
P
,
Stevens
,
MHH
,
Szoecs
,
E
,
Wagner
,
H
.
2017
.
Vegan: Community ecology package. R package vegan, vers. 2.4–2
.
Available at
https://cran.r-project.org/web/packages/vegan/index.html.
Olivier
,
F
,
Gaillard
,
B
,
Thébault
,
J
,
Meziane
,
T
,
Tremblay
,
R
,
Dumont
,
D
,
Bélanger
,
S
,
Gosselin
,
M
,
Jolivet
,
A
,
Chauvaud
,
L
,
Martel
,
AL
,
Rysgaard
,
S
,
Olivier
,
AH
,
Pettré
,
J
,
Mars
,
J
,
Gerber
,
S
,
Archambault
,
P
.
2020
.
Shells of the bivalve Astarte moerchi give new evidence of a strong pelagic-benthic coupling’s shift occurring since the late 70s in the NOW Polynya
.
Philos Trans R Soc A
378
. DOI: https://dx.doi.org/http://dx.doi.org/10.1098/rsta.2019.0353.
Osborne
,
E
,
Richter-Menge
,
J
,
Jeffries
,
M
.
2018
.
NOAA Arctic Report Card 2018
.
Available at
https://www.arctic.noaa.gov/Report-Card.
Accessed 16 January 2020
.
Pages
,
J
.
2004
.
Analyse factorielle de données mixtes
.
Rev Stat Appliquée
LII
:
93
111
.
Palmer
,
TA
,
Montagna
,
PA
,
Pollack
,
JB
,
Kalke
,
RD
,
DeYoe
,
HR
.
2011
.
The role of freshwater inflow in lagoons, rivers, and bays
.
Hydrobiologia
667
:
49
67
. DOI: https://dx.doi.org/10.1007/s10750-011-0637-0.
Pearson
,
TH
,
Rosenberg
,
R
.
1978
.
Macrobenthic succession in relation to organic enrichment and pollution of the marine environment
.
Oceanogr Mar Biol Annu Rev
16
:
229
331
.
Pelletier
,
BR
.
1986
.
Seafloor morphology and sediments
.
Elsevier Oceanogr Ser
44
:
143
162
. DOI: https://dx.doi.org/10.1016/S0422-9894(08)70901-2.
Piepenburg
,
D
.
2000
.
Arctic brittle stars (Echinodermata: Ophiuroidea)
.
Oceanogr Mar Biol
38
:
189
256
.
Piepenburg
,
D
.
2005
.
Recent research on Arctic benthos: Common notions need to be revised
.
Polar Biol
28
:
733
755
. DOI: https://dx.doi.org/10.1007/s00300-005-0013-5.
Piepenburg
,
D
,
Archambault
,
P
,
Ambrose
,
WG
,
Blanchard
,
AL
,
Bluhm
,
BA
,
Carroll
,
ML
,
Conlan
,
KE
,
Cusson
,
M
,
Feder
,
HM
,
Grebmeier
,
JM
,
Jewett
,
SC
,
Lévesque
,
M
,
Petryashev
,
VV
,
Sejr
,
MK
,
Sirenko
,
BI
,
Włodarska-Kowalczuk
,
M
.
2011
.
Towards a pan-Arctic inventory of the species diversity of the macro- and megabenthic fauna of the Arctic shelf seas
.
Mar Biodivers
41
:
51
70
. DOI: https://dx.doi.org/10.1007/s12526-010-0059-7.
Pierrejean
,
M
,
Grant
,
C
,
Nozais
,
C
,
Archambault
,
P
.
2019
. Communities of Benthic invertebrates in the Hudson Bay Marine Region, in
Bell
,
T
,
Brown
,
TM
eds.,
From science to policy in the greater Hudson Bay Marine region: An Integrated Regional Impact Study (IRIS) of climate change and modernization
.
:
ArcticNet
:
245
253
.
Prisenberg
,
SJ
.
1986
.
Salinity and temperature distributions of Hudson Bay and James Bay
, in
Martin
,
E
ed.,
.
44
:
163
186
. (
Elsevier oceanography series
).
R Core Team
.
2020
.
R: A language and environment for statistical computing
.
Remane
,
A
,
Schlieper
,
C
.
1971
.
Biology of brackish water
.
Stuttgart, Germany
:
E. Schweizerbart’sche Verlagsbuchhandlung
.
Renaud
,
PE
,
Riedel
,
A
,
Michel
,
C
,
Morata
,
N
,
Gosselin
,
M
,
Juul-Pedersen
,
T
,
Chiuchiolo
,
A
.
2007
.
Seasonal variation in benthic community oxygen demand: A response to an ice algal bloom in the Beaufort Sea, Canadian Arctic?
J Mar Syst
67
:
1
12
. DOI: https://dx.doi.org/10.1016/j.jmarsys.2006.07.006.
Roy
,
V
,
Gagnon
,
JM
.
2016
.
Natural history museum data on Canadian Arctic marine benthos
.
Mar Biodivers
48
:
1357
1367
. DOI: https://dx.doi.org/10.1007/s12526-016-0610-2.
Roy
,
V
,
Iken
,
K
,
Archambault
,
P
.
2014
.
Environmental drivers of the Canadian Arctic megabenthic communities
.
PLoS One
9
. DOI: https://dx.doi.org/10.1371/journal.pone.0100900.
Roy
,
V
,
Iken
,
K
,
Archambault
,
P
.
2015
.
Regional variability of megabenthic community structure across the Canadian Arctic Regional
.
Arctic
68
:
180
192
. DOI: https://dx.doi.org/10.14430/arctic4486.
Saucier
,
FJ
,
Senneville
,
S
,
Prinsenberg
,
S
,
Roy
,
F
,
Smith
,
G
,
Gachon
,
P
,
Caya
,
D
,
Laprise
,
R
,
2004
.
Modelling the sea ice-ocean seasonal cycle in Hudson Bay, Foxe Basin and Hudson Strait, Canada
.
Clim Dyn
23
:
303
326
. DOI: https://doi.org/10.1007/s00382-004-0445-6.
Sibert
,
V
,
Zakardjian
,
B
,
Gosselin
,
M
,
Starr
,
M
,
Senneville
,
S
,
LeClainche
,
Y
.
2011
.
3D Bio-physical model of the sympagic and planktonic productions in the Hudson Bay System
.
J Mar Syst
88
:
401
422
. DOI: https://dx.doi.org/10.1016/j.jmarsys.2011.03.014.
ter Braak
,
CJF
,
Verdonschot
,
PFM
.
1995
.
Canonical correspondence analysis and related multivariate methods in aquatic ecology
.
Aquat Sci
57
:
255
289
. DOI: https://dx.doi.org/10.1007/BF00877430.
Tivy
,
A
,
Howell
,
SEL
,
Alt
,
B
,
McCourt
,
S
,
Chagnon
,
R
,
Crocker
,
G
,
Carriere
,
T
,
Yackel
,
JJ
.
2011
.
Trends and variability in summer sea ice cover in the Canadian Arctic based on the Canadian Ice Service Digital Archive, 1960–2008 and 1968–2008
.
J Geophys Res Ocean
116
. DOI: https://dx.doi.org/https://doi.org/10.1029/2009JC005855.
Ward
,
JH
.
1963
.
Hierarchical grouping to optimize an objective function
.
J Am Stat Assoc
58
:
236
244
. DOI: https://dx.doi.org/10.2307/2282967.
Wei
,
CL
,
Cusson
,
M
,
Archambault
,
P
,
Belley
,
R
,
Brown
,
T
,
Burd
,
BJ
,
Edinger
,
E
,
Kenchington
,
E
,
Gilkinson
,
K
,
Lawton
,
P
,
,
H
,
Ramey-Balci
,
PA
,
Scrosati
,
RA
,
Snelgrove
,
PVR
.
2019
.
Seafloor biodiversity of Canada’s three oceans: Patterns, hotspots and potential drivers
.
Divers Distrib
26
:
226
241
. DOI: https://dx.doi.org/10.1111/ddi.13013.
Whittaker
,
RM
.
1960
.
Vegetation of the Siskiyou Mountains, Oregon and California
.
Ecol Monogr
30
:
279
338
. DOI: https://dx.doi.org/https://doi.org/10.2307/1943563.
Wickham
,
H
.
2016
.
ggplot2: Elegant graphics for data analysis
.
New-York
:
Springer-Verlag New York
.
Wilkinson
,
T
,
Wiken
,
E
,
Bezaury-Creel
,
J
,
Hourigan
,
T
,
Agardy
,
T
,
Hermann
,
H
,
Janishevski
,
L
,
,
C
,
Morgan
,
L
,
,
M
.
2009
.
Marine ecoregions of North America
.
:
Commission for Environmental Cooperation
.
Witman
,
JD
,
Cusson
,
M
,
Archambault
,
P
,
Pershing
,
AJ
,
Mieszkowska
,
N
.
2008
.
The relation between productivity and species diversity in temperate-Arctic marine ecosystems
.
Ecology
89
:
S66
S80
. DOI: https://dx.doi.org/10.1890/07-1201.1.
World Register of Marine Species Editorial Board
.
2020
.
World Register of Marine Species [WWW Document]
.
Available at
http://www.marinespecies.org/index.php.
Accessed 17 January 2020
. DOI: https://dx.doi.org/10.14284/170.

How to cite this article: Pierrejean, M, Babb, DG, Maps, F, Nozais, C, Archambault, P, 2020. Spatial distribution of epifaunal communities in the Hudson Bay System: Patterns and drivers. Elem Sci Anth. 8: 1. DOI: https://doi.org/10.1525/elementa.00044

Domain Editor-in-Chief: Jody W. Deming, University of Washington, WA, USA

Associate Editor: Laurenz Thomsen, Jacobs University Bremen, Germany

Knowledge Domain: Ocean Science

Part of an Elementa Special Feature: BaySys

This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See http://creativecommons.org/licenses/by/4.0/.