2D to 3D: Exploring variation of niche dimensionality across consumers in a coastal Arctic ecosystem and implications on interpretation

2D to 3D: Exploring variation of niche dimensionality across consumers in a coastal Arctic ecosystem and implications on interpretation | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 4 October 2025 V1 Latest version Share on 2D to 3D: Exploring variation of niche dimensionality across consumers in a coastal Arctic ecosystem and implications on interpretation Authors : Paloma Carvalho 0000-0003-3938-6935 [email protected] , Kelsey Johnson , Kyle Elliott 0000-0002-4313-0345 , Steven Ferguson 0000-0002-3794-0122 , Aaron Fisk , Grant Gilchrist , Kevin Hedges , … Show All … , Oliver Love , CJ Mundy , Andrea Niemi , Wesley Ogloff , Bruno Rosenberg , Cortney Watt , and David Yurkowski Show Fewer Authors Info & Affiliations https://doi.org/10.22541/au.175957396.61723617/v1 Published Ecology and Evolution Version of record Peer review timeline 384 views 97 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Each species occupies a distinct ecological niche, defined by a specific set of environmental conditions and resource requirements necessary for its survival and reproduction. However, climate change is altering species distributions, predator-prey relationships and resource partitioning between species with these changes being pronounced in the Arctic. Stable isotope analysis of carbon (δ¹³C) and nitrogen (δ¹⁵N) has been widely used to estimate isotopic niches and quantify niche overlap among species, a two-dimensional approach (2D). However, δ¹³C is not always sufficient to differentiate habitat and resource use among species due to minimal variation between end-members. Incorporating sulfur stable isotopes (δ³⁴S) can enhance resolution in such cases. Using an Arctic coastal food web as a model system, we used a three-dimensional isotopic niche approach (3D: δ¹³C-δ¹⁵N-δ³⁴S) with 717 individuals across 69 species spanning multiple taxonomic groups (invertebrates, fish, seabirds, and marine mammals) that utilize resources from benthic and pelagic habitats. We compared the traditional 2D isotopic niches with a 3D framework using nicheROVER to assess how the addition of a third dimension changes niche size estimates and probability of niche overlap between species. We found that benthic-associated species, such as common eider (Somateria mollissima) and various benthic invertebrates, exhibited greater changes in isotopic niche size with the addition of δ³⁴S than pelagic-associated species. In addition, niche overlap among benthic-associated taxa decreased with the 3D approach, indicating better resolution of habitat use and resource partitioning. This finding likely reflects the greater ecological diversity, foraging specialization and more complex food web structure characteristic of benthic ecosystems. We recommend incorporating δ³⁴S for aquatic studies that involve benthic habitats and emphasize the value of multidimensional approaches in ecological niche analysis. Introduction Food webs are complex and dynamic, and understanding the interactions among species is essential for interpreting ecosystem structure and function. Different species can occupy similar feeding guilds and ecological roles (Blondel 2003), and determining the extent of their niche overlap is important for assessing the level of competition, resource partitioning, and trophic relationships in an ecosystem. Niche theory provides a foundational framework in ecology, describing how each species requires a specific set of environmental conditions and resources to survive and reproduce (Hutchinson 1957). This concept was expanded by Hutchinson (1957), who introduced the notion that the niche is an n-dimensional hypervolume, where each axis represents a different environmental or resource variable necessary for the species to exist. These axes can be categorized as either bionomic— defined by biological resources and interspecific interactions (e.g., competition, predation)—or scenopoetic, which encompasses abiotic factors (e.g., temperature and salinity) (Hutchinson 1978). Within this framework, the fundamental niche encompasses the full range of conditions a species can tolerate in the absence of interspecific interactions such as competition and predation. In contrast, the realized niche reflects the actual conditions under which the species exists, constrained by interspecific interactions with other species that occupy similar habitat and utilize similar resources (Hutchinson 1957). As a result, the realized niche is typically narrower than the fundamental niche (Soberon and Arroyo-Pena 2017). In ecological studies, stable isotopes analysis, particularly of carbon (δ 13 C) and nitrogen (δ 15 N), is widely used to estimate isotopic niches (Newsome et al. 2007; Jackson et al. 2011), identify food sources (Post 2002; Brown et al. 2016; Szpak and Buckley 2020; Cui et al. 2023), determine trophic position (Post 2002), assess spatial and temporal niche variability (Yurkowski et al. 2016), and quantify niche overlap among species (Jackson et al. 2011; Swanson et al. 2015). Stable isotope bi-plots (i.e., δ 13 C and δ 15 N) are mainly used by ecologists to describe the isotopic niche; however the definition of Hutchison’s niche as ‘ ‘n-dimensional hypervolumes’ ’ implies niche as a multidimensional set of resources and therefore could include other stable isotopes or biological variables that would provide more resolution for estimating a species’ niche and niche overlap between species (van Oordt et al. 2024). The use of sulfur isotopes (δ³⁴S) in food web studies has grown in recent years (Raoult et al. 2024). While δ¹⁵N is commonly used to infer trophic level, due to increases in δ 15 N of consumers up the food web (Hobson and Welch 1992; Post 2002), δ¹³C and δ³⁴S are more indicative of habitat use, as they generally exhibit minimal trophic fractionation (typically <1‰). Carbon stable isotope ratios (δ¹³C) are often used to trace carbon sources in the food web. For instance, in a marine system, benthic primary producers (e.g., benthic algae and seagrass) generally have higher δ¹³C values than the pelagic producers like phytoplankton, allowing researchers to distinguish between benthic and pelagic food sources (Hobson and Welch 1992). In Arctic marine ecosystems, carbon sources such as ice algae and phytoplankton also differs in δ¹³C, with ice algae typically enriched in 13 C (Hobson and Welch 1992; Hobson et al. 2002). Similarly, δ³⁴S can differentiate pelagic and benthic food sources in the marine environment. In the water column, sulfur is primarily present as sulfate, while in the sediments, sulfur is often available as sulfides due to the activity of anaerobic bacteria present in the sediments (Peterson and Fry 1987; Peterson 1999). Sulfates and sulfides have distinct δ 34 S values and can therefore be used to distinguish between benthic and pelagic originating food sources, with the former being relatively lower in δ 34 S (Peterson and Fry 1987; Connolly et al. 2004). This isotopic distinction enables the identification of species that utilize resources from pelagic and benthic compartments, thereby revealing benthic–pelagic coupling in aquatic environments. Additionally, δ³⁴S can be used to distinguish between freshwater and marine residency within estuarine systems, as δ³⁴S values often follow a salinity gradient, with marine typically showing higher values compared to freshwater systems (Hesslein et al. 1991; Fry and Chumchal 2011). The application of δ³⁴S has helped clarify habitat use and resource utilization across a wide range of taxa, including marine mammals (Szpak and Buckley 2020; Cani et al. 2024), sea turtles (Weber et al. 2023), sharks (Burke et al. 2024; Besnard et al. 2025), fish (Fry and Chumchal 2011; Cybulski et al. 2022), and invertebrates (Yamanaka et al. 2000; Bopp et al. 2023). In many cases, δ³⁴S has revealed dietary patterns that δ¹³C alone could not detect, highlighting its value in a multi-isotope approach. Understanding food web and isotopic niche dynamics in the Arctic is especially critical, as the region is undergoing rapid environmental change, including ocean warming and extended periods of open water (Crawford et al. 2021) that has influenced species distributions and interspecific interactions (Dupont et al. 2024; Stroeve et al. 2024). These shifts have facilitated the northward expansion of sub-Arctic species, potentially increasing interspecific competition and altering trophic relationships and niche dynamics within Arctic food webs (Fossheim et al. 2015; Dupont et al. 2024). Our comprehensive database includes multiple species of marine taxa from the Arctic, including 50 invertebrates, 14 fish, 2 seabirds, and 3 marine mammals species, all of which forage in either pelagic or benthic habitats. The main objective of this study is to demonstrate how the inclusion of δ 34 S alongside δ 13 C and δ 15 N can affect the ecological interpretation of isotopic niche analyses and to determine whether the magnitude of change varies across taxa and habitats for pelagic and benthic-associated species. Specifically, we compared the traditional two-dimensional (2D: δ¹³C-δ¹⁵N) approach to a three-dimensional (3D: δ¹³C- δ¹⁵N-δ³⁴S) framework to examine how isotopic niche overlap shifts with the addition of δ³⁴S, and whether these changes vary between foraging habitats (pelagic and benthic) or among taxonomic groups: invertebrates, fish, seabirds, and marine mammals Sample Collection Samples of marine mammals, seabirds, fish, and invertebrates (n= 717, 69 species; Table A1) were collected around Southampton Island, Nunavut, Canada (64.5999° N, 84.1348° W) during the summer (June to September) over three years (2016, 2018, and 2019). In 2016, benthic and pelagic invertebrates and fish samples were collected using a Campelen 1200 trawl aboard the MV Nuliajuk. In 2018 and 2019, invertebrate and fish samples were collected aboard the RV William Kennedy using benthic beam trawls, Ponar grab and box core (25 x 25 x 50 cm) as part of the Southampton Island Marine Ecosystem Project (SIMEP). A 3-m beam trawl (0.5-cm cod-end mesh) was towed (2-3 knots for 15 min) at the bottom to collect benthic fish and invertebrates. Zooplankton were collected with an obliquely towed bongo net (500-µm mesh). All samples were sorted in the field to the lowest taxonomic group and frozen at -20 °C for further analysis. The samples had their taxonomic identification visually verified in the lab to the lowest taxonomic group possible (i.e., class, genus or species) using taxonomic keys and guides for Arctic fish (Coad and Reist 2018) and invertebrates (Klekowski and Węsławski 1990; Lacasse et al. 2020). A total of 475 invertebrates and 138 fish, consisting of 50 and 14 species/taxa, respectively, were subsampled for further analysis. Marine mammal muscle samples from beluga whale ( Delphinapterus leucas , n=8), narwhal ( Monodon monoceros , n=10) and ringed seal ( Pusa hispida , n=40) subsistence harvest were collected by Naujaat (Arviq) and Coral Harbour (Aiviit) community members (2018 and 2019). Frozen tissues were shipped to the Freshwater Institute (Winnipeg, MB) as part of an ongoing community-based monitoring program with Fisheries and Oceans Canada (DFO). Seabird blood samples (n=46) from common eider ( Somateria mollissima ) and thick-billed murre ( Uria lomvia ) were collected (2018 and 2019) by the long-term monitoring program in East Bay and Coats Islands (Environment and Climate Change Canada - ECCC and McGill University, respectively). Plasma was analyzed for the common eiders sampled during pre-incubation (at arrival; in May), while red blood cells (RBCs) were analyzed for thick-billed murres sampled during chick-rearing (in July). Plasma has a shorter turnover rate compared to RBCs and therefore, samples would reflect local food sources for both species. Blood samples were shipped frozen (-20 °C ) to the Freshwater Institute (Winnipeg, MB) for analysis. Stable isotope analysis Muscle from fish and marine mammals, blood from seabirds and the whole body, gonad, muscle or soft tissues from invertebrates (Table A1) were subsampled and analyzed for carbon, nitrogen and sulfur stable isotopes (δ 13 C, δ 15 N and δ 34 S). Frozen samples were freeze-dried for 48h at -50 °C and homogenized prior to analysis. As higher lipid content can affect δ 13 C (Post et al. 2007), lipids were extracted from the invertebrate tissues and, fish and marine mammal muscles using a modified 2:1 chloroform: methanol method developed by Bligh and Dyer (1959). For δ 13 C and δ 15 N, 400-600 µg of tissue was weighed into tin capsules while for δ 34 S, 3000-6000 µg of tissue was weighed into tin capsules with an additional 300-500 µg of Vanadium Pentoxide. Stable isotope analysis was performed using a Delta V Advantage Mass spectrometer (Thermo Finnigan, San Jose, CA, USA) coupled to a Costech 4010 Elemental Combustion system (Costech, Valencia, CA, USA) and a ConFlo IV gas interface (as described in Amiraux et al. 2023a) at the Chemical Tracers Laboratory, Great Lakes Institute for Environmental Research (GLIER) at the University of Windsor, Ontario. Stable isotope ratios are expressed in per mil (‰) in standard delta (δ) notation relative to the international standards Pee Dee Belemnite (Carbon), atmospheric N 2 (Nitrogen), and Vienna Canon Diablo Triolite (Sulfur). Instrument accuracy ranged from 0.06 to 0.14‰ for δ 15 N (NIST8573, NIST8547, NIST8574); 0.01 to 0.09 ‰ for δ 13 C (NIST8542, NIST8573, NIST8574); and 0.25 to 0.30 ‰ for δ 34 S (NIST8555, NIST8529). Instrument precision was measured as the standard deviation of standard replicates and was ≤0.25‰ for δ 15 N, ≤0.13‰ for δ 13 C (NIST1577c, internal lab standard, tilapia muscle, USGS 40 and Urea) and ≤0.43‰ for δ 34 S (USGS 42, NIST 8555 and NIST 8529). As inorganic carbon can increase δ 13 C and does not reflect dietary sources, species with inorganic carbon present as calcium carbonate (CaCO 3 ) such as sea stars, sea spiders, and brittle stars had their δ 13 C corrected mathematically. We calculated the correct δ 13 C using the linear regression (Eq. 1) derived from Jacob et al. (2005). The carbonate proxy used in the study was 0.9 as the average for echinoderms from Kazanidis et al. (2019). δ 13 C crude - δ 13 C acid =\(0.181+\ 4.08\ \times\ carbonate\ proxy\) (Eq. 1) Data analysis Species across four taxonomic classifications (invertebrates, fishes, marine mammals, and seabirds) were grouped into 26 different taxa (Table 1, Table A1) with each taxa containing an average of 28 individuals (range = 8-140). To ensure a sufficient sample size to estimate the isotopic niche, a minimum sample size of 10 individuals per taxa (Syväranta et al. 2013) was used and in some cases we combined species that were closely related phylogenetically, particularly in cases where only one or two samples were available for a given species. Taxa were further assigned as benthic (n=16) or pelagic (n=10) based on their habitat use as defined in World Register of Marine Species (WoRMS; https://marinespecies.org) and/or FishBase (https://fishbase.net.br). The classification allowed us to explore the magnitude of change in niche size and niche overlap from δ 13 C and δ 15 N (two dimensions) to δ 13 C, δ 15 N, and δ 34 S (three dimensions) isotopic niche analysis among taxa that inhabit and primarily forage on benthic or pelagic-derived resources. We used the nicheROVER package (Swanson et al. 2015) in R (4.3.1) to calculate the 95% niche size estimate of stable isotopes and the probability of niche overlap for each pairwise species combination (see Swanson et al. 2015) using two (2Dniche: δ 13 C and δ 15 N) and three dimensions (3Dniche: δ 13 C, δ 15 N and δ 34 S). We used a threshold of 60% or higher to identify biologically significant niche overlap between taxa (Matley et al. 2017; Cybulski et al. 2022; Raoult et al. 2024). Niche sizes, calculated as Bayesian Ellipse Area (2D ‰ 2 ) and Bayesian Ellipsoid Volume (3D ‰ 3 ), were standardized (mean-centered and scaled) due to their different measurement units for direct comparison between the two isotopic niche sizes within taxa. Non-parametric Kruskal-Wallis test was used to analyze differences in isotopic niche sizes among taxa inhabiting benthic and pelagic habitats. Results The mean values across all species for δ 13 C ranged from -20.95 ± 0.07‰ (nudibranch) to -16.84 ± 0.37‰ (sea cucumbers), for δ 15 N from 9.04 ± 0.17‰ (sea urchin) to 17.53 ± 0.20‰ (ringed seal), and for δ 34 S from 14.68 ± 0.93‰ (common eider) to 21.44 ± 0.40‰ (sea stars) (Table 1). The range of mean stable isotopes was greater across species foraging in the benthic habitat (δ 13 C: 4.1‰; δ 15 N: 8.2‰ and δ 34 S: 6.7‰) compared to those in the pelagic habitat (δ 13 C: 2.4‰; δ 15 N: 7.9‰ and δ 34 S: 3.7‰), with δ 13 C and δ 34 S ranges 1.8 times greater in benthic-associated relative to pelagic-associated species. Isotopic niche size of species varied by foraging habitat and taxa. The 2D isotopic niche size (2D niche ) was, on average, higher in taxa that mainly forage in the benthic habitat (mean = 19.70, range: 1.31-33.88 ‰ 2 ) compared to taxa that mainly forage in the pelagic habitat (mean = 6.54, range: 0.65-17.32‰ 2 ; Z=-2.98, p<0.05) (Table 1). The 3D isotopic niche size (3D niche ) was also higher in benthic (mean = 83.81, range: 3.35-207.66 ‰ 3 ) than pelagic species (mean = 17.82, range: 1.46-37.19‰ 3 ; Z=-2.71, p<0.05) (Table 2). When comparing the 2D and 3D standardized isotopic niche sizes (2D std.niche and 3D std.niche ), the degree of change (niche difference) in benthic-associated species was 2 times larger (mean degree of change = 0.45) than in pelagic-associated species (mean degree of change = 0.22) (Fig 1). Common eider had the highest increase (2D std.niche = 0.91; 3D std.niche = 2.59) in isotopic niche size with the addition of δ 34 S, followed by bivalves and decapods, while benthic amphipods had the largest decrease in isotopic niche size (2D std.niche = 1.23; 3D std.niche = 0.19), followed by brittle stars, worms, and pelagic amphipods (Fig 1). Overall, the isotopic niche overlap decreased when including δ 34 S for both pelagic and benthic habitats (Tables 2-4). For taxa in the benthic habitat, 44 out of 120 pairwise comparisons had biologically significant niche overlap (≥60%) when using only δ 13 C and δ 15 N (2D) and this level of overlap decreased to 21 of 120 with the addition of δ 34 S (3D). In the pelagic habitat, 9 out of 45 pairwise comparisons had biologically significant niche overlap when using the 2D approach, which decreased to 5 of 45 with δ 34 S (3D). The probability of overlap of the common eider isotopic niche with other benthic species (benthic amphipods, decapods, sculpins, snails, and worms) decreased to below 53% when using the 3D approach with δ 34 S (2D: ≥ 62%). The benthic amphipod’s standardized isotopic niche decreased with the 3D approach, and had the second highest degree of change (Fig 1), decreasing the probability of isotopic niche overlap with decapods from 80 to 29% (Fig 2). Moreover, decapods, common eiders, isopods, sea cucumbers, and worms, which previously had significant overlap (≥ 65%) with the benthic amphipods, had overlap percentages drop below 45% (Table 2). Worms were another benthic taxa that had a significant reduction in their standardized isotopic niche size between 3D and 2D and their probability of overlap decreased with other benthic species, such as blennies (61% to 14%), decapods (80% to 41%), common eiders (62% to 25%), sculpins (66% to 24%), and snails (72% to 40%). The standardized isotopic niche size of brittle stars also decreased with the addition of δ 34 S (Fig 1), reducing the probability of overlap with bivalves, sea spider, sea urchins from ≥81% to ≤29% (Fig 2). Sea stars showed no significant overlap with any other benthic taxa using either the 2D or 3D approach (Table 2), with minimal change in standardized isotopic niche size. With the 3D isotopic niche approach, the pelagic amphipod was the only taxa that reduced its standardized isotopic niche size and had the highest degree of change within the pelagic group (Fig 1), as reflected in the probability of overlap with other taxa. Species that previously fell within the pelagic amphipod’s standardized isotopic niche, such as hydrozoans, mysids, and copepods, had their overlap percentages drop below 60% (2D: ≥83% to 3D: ≤44%; Table 3; Fig 2.). Additionally, the pelagic amphipod’s probability of falling into the mysid standardized isotopic niche decreased from 68% (2D) to 42% (3D), and the mysid’s overlap with copepods decreased from 60% (2D) to 45% (3D) (Table 3). For fish, including Arctic cod (the only pelagic taxa) and benthic species, the addition of δ 34 S resulted in minimal changes in their standardized isotopic niche size and their probability of niche overlap. Using either the 2D or 3D standardized isotopic niche size approach, banded gunnel had significant isotopic niche overlap with all other fish species (61-98%; Table 4; Fig 3). Blenny and sculpin also had significant isotopic niche overlap when using both the 2D and 3D approaches (75-98%; Table 4; Fig 3). Overall, the isotopic niche overlap between the fish species remained similar regardless of the approach used, except for Arctic cod, which decreased to a non-significant overlap with banded gunnel when using the 3D approach (2D:73% to 3D:16%; Table 4). For marine mammals, the 3D isotopic niche of belugas still had significant overlap with those of narwhals (2D: 79%, 3D: 68%) and ringed seals (2D: 97%, 3D: 92%) (Table 4; Fig 3). A similar pattern was observed with thick-billed murres and Arctic cod, where murres continued to show significant overlap with Arctic cod isotopic niche (2D: 76%, 3D: 71%). Interestingly, narwhals, ringed seals and thick-billed murres had minimal to zero probability to overlap with other species’ isotopic niches, regardless of the approach used. Overall, the addition of δ 34 S affected the isotopic niche overlap amongst the benthic-associated and invertebrate, but had a minimal influence on isotopic niche sizes and overlap of fish and marine mammal. Discussion This is the first study to examine changes to isotopic niche dynamics and potential ecological interpretation when incorporating δ³⁴S alongside traditional δ¹³C and δ¹⁵N values across numerous species and taxa that inhabit pelagic and benthic Arctic marine ecosystem. While previous research focused on a more limited number of species when comparing the use of 2D and 3D approaches (e.g., Rossman et al. 2016; Skinner et al. 2019; Cybulski et al. 2022; Raoult et al. 2024), our study includes multiple taxonomic groups (invertebrates, fishes, birds and marine mammals) and focuses on comparisons between distinct habitats (pelagic vs. benthic). By directly comparing benthic and pelagic environments, we address a key gap in food web ecology and demonstrate the added value of incorporating δ³⁴S, particularly for species or habitat types where its inclusion enhances the resolution of trophic interactions and ecological interpretation. Our analysis revealed that the degree of change between 2D (δ¹³C–δ¹⁵N) and 3D (δ¹³C–δ¹⁵N–δ³⁴S) isotopic niches was more pronounced in invertebrates and benthic-associated foragers. This distinction likely reflects the greater ecological complexity and diversity of foraging strategies characteristic of benthic ecosystems, in contrast to the more linear structure of pelagic food webs. In the Arctic, sympagic–pelagic–benthic coupling plays a key role in supporting benthic ecosystems where carbon inputs from pelagic sources (e.g., phytoplankton, ice algae and detritus) are transferred to the benthos (Stasko et al. 2018; Niemi et al. 2024). In addition, benthic organisms can acquire sulfur either from the water column, where it is present as sulfate with a higher δ³⁴S value, or from the sediment, where it exists as sulfides with a lower δ³⁴S value (Peterson 1999; Yamanaka et al. 2000) Among benthic invertebrates, several taxa exhibit high trophic plasticity, employing diverse feeding strategies such as suspension feeding, surface and subsurface deposit feeding, and predation/scavenging (Legeżyńska et al. 2012; Volage et al. 2021). These varied strategies are reflected in a broader range of stable isotope values (Christianen et al. 2017; Stasko et al. 2018). In our study, amphipods, brittle stars, and worms showed substantial contraction in standardized isotopic niche size with the addition of δ³⁴S. Amphipods, which display both scavenging and predatory behaviors, typically occupy mid-trophic levels (TP: 2-3) (Amiraux et al. 2023a; Ziegler et al. 2023; Bridier et al. 2024). Brittle stars are opportunistic feeders capable of switching between suspension and deposit feeding depending on food availability (Volage et al. 2021), while worms, such as polychaetas, will rely on benthic detritus but exhibit diverse feeding strategies such as surface deposit feeders, suspension feeding, carnivory and omnivory (Jumars et al. 2015). This trophic plasticity allows these taxa to exploit a range of food sources in response to fluctuations in resource availability (e.g., ice algae vs. phytoplankton), which is an important adaptation to the variable Arctic environment. Relying solely on a 2D approach (δ¹³C and δ¹⁵N) may mask niche partition and lead to misinterpretation of ecological data, as the inclusion of δ³⁴S (3D) provided a better resolution, revealing finer-scale resource partitioning among these benthic taxa. In contrast, sea stars showed minimal change in isotopic niche size. Some species, such as those in the Pterasteridae family, function as top predators (TP: 4–5) (Amiraux et al. 2023b) which likely explains their limited overlap with lower-trophic benthic invertebrates. As opportunistic scavengers, sea stars can utilize both benthic and pelagic carrion (Garrido et al. 2021; Amiraux et al. 2023b), which may explain the limited influence of δ³⁴S on their isotopic niche size. Among seabirds, the inclusion of δ³⁴S was more informative for common eiders, whose diet mainly consists of benthic invertebrates such as bivalves, polychaetas, amphipods, decapods and echinoderms (Merkel et al. 2007; Kristjánsson et al. 2013). When δ 34 S was included, the isotopic niche of common eiders showed the most pronounced increase in size and decrease in overlap with other taxa, likely reflecting their specialized benthic foraging strategy focused on invertebrates. As observed in our study, species associated with benthic environments tend to show greater shifts in isotopic niche size and overlap when δ³⁴S is incorporated in the analysis, highlighting the importance of the use of sulfur isotopes in ecological studies focused on species that rely primarily on benthic ecosystems. In contrast, thick-billed murres are primarily pelagic feeders, consuming forage fish such as capelin ( Mallotus villosus ), Arctic cod and sandlance ( Ammodytes spp.), as well as lower trophic-level prey (i.e. copepods, amphipods, mysids) (Gaston and Noble 1985; Moody et al. 2012; Smith and Gaston 2012; Gaston and Elliott 2014; Góngora et al. 2018). Similar to other pelagic species, their standardized isotopic niche size showed minimal change with the addition of δ 34 S. Notably, thick-billed murres had significant isotopic niche overlap with Arctic cod (with both 2D and 3D analyses), likely due to both species feeding on similar pelagic invertebrates such as copepods, amphipods and mysids in the area (Walkusz et al. 2013). In contrast to adults where invertebrates are a large part of the diet, murre chicks are fed primarily fish (Gaston and Noble 1985; Gaston and Elliott 2014). The incorporation of δ³⁴S reduced the isotopic niche size of all three benthic fish species—sculpin (Cottidae), blenny (Stichaeidae), and banded gunnel—without significantly affecting the niche overlap among them. These species primarily forage near the seafloor and likely consume similar prey (e.g., amphipods, decapods, and polychaetas). Their generalist feeding strategies (Keats et al. 1993; Gray et al. 2017; Landry et al. 2018) likely contribute to the high isotopic niche overlap observed in both 2D and 3D analyses, as their broad foraging behaviors allow them to occupy similar isotopic space. In contrast, the standardized isotopic niche size of Arctic cod remained relatively unchanged with the addition of δ³⁴S. Despite occupying a distinct pelagic habitat and feeding primarily on copepods, amphipods, and mysids (Walkusz et al. 2013; Majewski et al. 2016), Arctic cod still fell within the isotopic niche of all benthic fish in the 2D analysis. While generally considered pelagic, Arctic cod exhibit benthic-pelagic behaviour where larger, older individuals (age +1) tend to reside near the seafloor, feeding predominantly on amphipods, whereas smaller, younger fish (age 0) remain in the epipelagic zone, feeding mainly on copepods (Geoffroy et al. 2016; Majewski et al. 2016; Malizia et al. 2023). The individuals analyzed in the study were adults (+1), that can access deeper mesopelagic waters (Geoffroy et al. 2016), which likely contributed to the substantial isotopic niche overlap observed with benthic species such as sculpin (≥92%) and blenny (≥85%) in both 2D and 3D isotopic space. However, the inclusion of δ³⁴S provided greater resolution, reducing Arctic cod’s overlap with the banded gunnel and highlighting subtle differences in resource use between the two species. Specifically, banded gunnel fed on prey with higher δ³⁴S values, suggesting greater reliance on pelagic sources compared to Arctic cod. Similarly, δ³⁴S helped identify a higher proportion of pelagic inputs in sharks, highlighting its value in systems where benthic-pelagic coupling is expected (Raoult et al. 2024). These findings align with Cybulski et al. (2022), who demonstrated that δ³⁴S can help distinguish isotopic niches among fish species that occupy different trophic guilds but share similar trophic positions. For marine mammals, including δ³⁴S slightly increased the standardized isotopic niche size of beluga, narwhal, and ringed seals, compared to some of the invertebrate and benthic-associated species. This small increase suggests limited value in incorporating δ 34 S to assess isotopic niche overlap among the three top marine predators. Regardless of whether a 2D or 3D isotopic space was used, belugas’ isotopic niche fell within both narwhal’s and ringed seal’s isotopic niches, reflecting their shared reliance on pelagic prey. Belugas whales primarily consume pelagic fish, such as Arctic cod and capelin ( Mallotus villosus ) (Loseto et al. 2009; Kelley et al. 2010; Choy et al. 2020). Similarly, narwhals predominantly feed on pelagic fish (e.g., Arctic cod), although some populations are known to feed on benthic fish, like Greenland halibut ( Reinhardtius hippoglossoides ) (Watt et al. 2013; Watt and Ferguson 2015). Ringed seals also primarily feed on pelagic fish (e.g., capelin, Arctic cod) and invertebrates (e.g., mysids, euphausiids, amphipods) (Yurkowski et al. 2016; Ogloff et al. 2019), but their diet is more variable, occasionally including benthic invertebrates (e.g., decapods) and fish (e.g., Stichaeidae) (Labansen et al. 2007; Schiøtt et al. 2024). Given the shared reliance on pelagic fishes, the δ 34 S values across these species were likely to overlap with minimal impact on the degree of change in isotopic niche size area (2D) and volume (3D). Similar to our study, the incorporation of δ 34 S values did not improved niche resolution among marine mammal species in coastal Africa (Cani et al. 2024). It is also important to note that the coupling between pelagic and benthic ecosystems is particularly tighter in the Arctic compared to warmer regions, mainly driven by its strong seasonality in primary production and the rapid sinking of ice algae to the seafloor (Darnis et al. 2012). Values of δ 34 S have been shown to be a valuable indicator to differentiate between benthic and pelagic feeding habitats in the marine ecosystems (Szpak and Buckley 2020; Cybulski et al. 2022; Raoult et al. 2024). It has also been used to assess habitat use in estuaries (Connolly et al. 2004), freshwater lakes (Croisetière et al. 2009; Onishi et al. 2023) and inshore-offshore gradient (Rossman et al. 2016). The interpretation of δ³⁴S values is often complicated by regional variability in benthic–pelagic coupling, particularly in the Arctic where the flux of sinking pelagic organic matter to the seafloor varies across regions (Stasko et al. 2018). In areas with stronger pelagic input, benthic species may exhibit elevated δ³⁴S values due to the incorporation of pelagic-derived sulfur. Conversely, some pelagic species may feed on resuspended benthic organic matter, resulting in lower δ³⁴S values that can obscure their true foraging strategies. In other aquatic systems, such as estuaries, the gradient of salinity drives the δ³⁴S values as the sulfate present in saltwater is typically higher in δ³⁴S compared to freshwater (Connolly et al. 2004; Fry and Chumchal 2011). A similar pattern is observed along inshore–offshore gradients, where terrestrial inputs tend to lower δ³⁴S values relative to more pelagic, offshore zones (Cani et al. 2023; Karalis et al. 2025). These complexities reinforce the importance of δ³⁴S as a tool for investigating isotopic niche dynamic in aquatic ecosystems, while also emphasizing the need for careful consideration of regional and ecological context when interpreting niche size and overlap among species. Conclusion This study is the first to investigate changes in isotopic niche dynamics and ecological interpretations by integrating δ³⁴S with the more commonly used δ¹³C and δ¹⁵N across multiple taxonomic groups— including invertebrates, fishes, birds, and marine mammals—that utilize resources from benthic and pelagic environments. Our findings show that benthic-associated species were more strongly influenced by the inclusion of δ³⁴S, highlighting its potential to substantially improve resolution in food web studies focused on species that inhabit or rely on benthic environment. It is also important to acknowledge the practical limitations associated with δ³⁴S analysis. Compared to δ¹³C and δ¹⁵N, δ³⁴S typically requires larger sample sizes and incurs higher analytical costs, as it is analyzed separately. In some cases, these additional requirements may not be justified, particularly if δ³⁴S does not substantially enhance ecological interpretations for certain species, taxa, or habitats. Nevertheless, incorporating δ³⁴S remains valuable, especially when estimating isotopic niche overlap among species. This is particularly true for benthic-associated and benthic-pelagic coupling species, where δ³⁴S can offer critical insights into resource partitioning and habitat use that may not be captured by carbon and nitrogen stable isotopes alone. In addition, the use of multiple stable isotopes (i.e. δ³⁴S, δ 2 H, δ 18 O) can enhance trophic resolution in other complex ecosystems such as freshwater, estuarine, coastal, terrestrial environments, broadening the applicability of this approach beyond the Arctic marine environment. Acknowledgments This work was made possible through the support of the Aiviit Hunters and Trappers Organization and Arviq Hunters and Trappers Organization and their hunters, who assisted with the collection of marine mammals. We extend our sincere thanks to the crew and scientists aboard the Nuliajuk 2016 cruise, and R/V William Kennedy 2018 and 2019 SIMEP cruises, for their contributions to data collection and fieldwork. Funding: This study was supported by MEOPAR-NCE, NSERC, Churchill Marine Observatory (CFI), Arctic Research Foundation, and Fisheries and Oceans Canada. Data availability statement: The data that supports the findings of this study are available in the supplementary material of this article. References Amiraux, R., Mundy, C.J., Pierrejean, M., Niemi, A., Hedges, K.J., Brown, T.A., Ehn, J.K., Elliott, K.H., Ferguson, S.H., Fisk, A.T., Gilchrist, G., Harris, L.N., Iken, K., Jacobs, K.B., Johnson, K.F., Kuzyk, Z.A., Limoges, A., Loewen, T.N., Love, O.P., Matthews, C.J.D., Ogloff, W.R., Rosenberg, B., Søreide, J.E., Watt, C.A., and Yurkowski, D.J. 2023a. Tracing carbon flow and trophic structure of a coastal Arctic marine food web using highly branched isoprenoids and carbon, nitrogen and sulfur stable isotopes. Ecol. Indic. 147 (September 2022). doi:10.1016/j.ecolind.2023.109938.Amiraux, R., Yurkowski, D.J., Archambault, P., Pierrejeanb, M., and Mundy, C.J. 2023b. Top predator sea stars are the benthic equivalent to polar bears of the pelagic realm. Proc. Natl. Acad. Sci. 120 (1): e2216701120. doi:10.1073/pnas.Besnard, L., Le Croizier, G., Sonke, J.E., Galván-Magaña, F., Trueman, C., Martínez-Rincón, R.O., Harrod, C., Kraffe, E., Point, D., Soto-López, K., and Schaal, G. 2025. Using Carbon, Nitrogen, Sulfur, and Mercury Isotopes to Trace Elasmobranch Foraging Habitats in Contrasting Biogeochemical Environments. Environ. Sci. Technol. 59 (32): 16972–16984. doi:10.1021/acs.est.5c01680.Bligh, E.G., and Dyer, W.J. 1959. Canadian Journal of Biochemistry and Physiology. Can. J. Biochem. Physiol. 37 (8).Blondel, J. 2003. Guilds or functional groups: Does it matter? Oikos 100 (2): 223–231. doi:10.1034/j.1600-0706.2003.12152.x.Bopp, J., Olin, J.A., Sclafani, M., Peterson, B., Frisk, M.G., and Cerrato, R.M. 2023. Contrasting Trophic Niche and Resource Use Dynamics Across Multiple American Horseshoe Crab (Limulus polyphemus) Populations and Age Groups. Estuaries and Coasts 46 (1): 227–245. Springer US. doi:10.1007/s12237-022-01116-3.Bridier, G., Olivier, F., Chauvaud, L., Le Garrec, V., Droual, G., and Grall, J. 2024. Benthic food web structure of a highly stratified sub-Arctic archipelago on the Newfoundland Shelf (Northwest Atlantic Ocean). Estuar. Coast. Shelf Sci. 310 . doi:10.1016/j.ecss.2024.108982.Brown, T.A., Belt, S.T., Gosselin, M., Levasseur, M., Poulin, M., and Mundy, C.J. 2016. Quantitative estimates of sinking sea ice particulate organic carbon based on the biomarker IP25. Mar. Ecol. Prog. Ser. 546 : 17–29. doi:10.3354/meps11668.Burke, P.J., Meyer, L., Raoult, V., Huveneers, C., and Williamson, J.E. 2024. Multi-disciplinary approach identifies pelagic nutrient linkage by sawsharks. Rev. Fish Biol. Fish. 34 (4): 1567–1584. Springer International Publishing. doi:10.1007/s11160-024-09888-6.Cani, A., Cardona, L., Aguilar, Á., Borrell, A., and Drago, M. 2024. Fine-tuning the isotopic niche of a marine mammal community through a multi-element approach and variable spatial scales. Estuar. Coast. Shelf Sci. 298 . doi:10.1016/j.ecss.2024.108641.Cani, A., Cardona, L., Valdivia, M., González, E.M., and Drago, M. 2023. Niche Partitioning Among Marine Mammals Inhabiting a Large Estuary as Revealed by Stable Isotopes of C, N, S, and O. Estuaries and Coasts 46 (4): 1083–1097. doi:10.1007/s12237-023-01193-y.Choy, E.S., Giraldo, C., Rosenberg, B., Roth, J.D., Ehrman, A.D., Majewski, A., Swanson, H., Power, M., Reist, J.D., and Loseto, L.L. 2020. Variation in the diet of beluga whales in response to changes in prey availability: Insights on changes in the Beaufort Sea ecosystem. Mar. Ecol. Prog. Ser. 647 : 195–210. doi:10.3354/meps13413.Christianen, M.J.A., Middelburg, J.J., Holthuijsen, S.J., Jouta, J., Compton, T.J., van der Heide, T., Piersma, T., Sinninghe Damsté, J.S., van der Veer, H.W., Schouten, S., and Olff, H. 2017. Benthic primary producers are key to sustain the Wadden Sea food web: stable carbon isotope analysis at landscape scale. Ecology 98 (6): 1498–1512. doi:10.1002/ecy.1837.Coad, B.W., and Reist, J.D. 2018. Marine Fishes of Arctic Canada. University of Toronto Press, Toronto.Connolly, R.M., Guest, M.A., Melville, A.J., and Oakes, J.M. 2004. Sulfur stable isotopes separate producers in marine food-web analysis. Oecologia 138 (2): 161–167. doi:10.1007/s00442-003-1415-0.Crawford, A., Stroeve, J., Smith, A., and Jahn, A. 2021. Arctic open-water periods are projected to lengthen dramatically by 2100. Commun. Earth Environ. 2 (1): 1–10. doi:10.1038/s43247-021-00183-x.Croisetiere, L., Hare, L., Tessier, A., and Cabana, G. 2009. Sulphur stable isotopes can distinguish trophic dependence on sediments and plankton in boreal lakes. Freshw. Biol. 54 (5): 1006–1015. doi:10.1111/j.1365-2427.2008.02147.x.Cui, L., Jiang, Z., Huang, X., Liu, S., and Wu, Y. 2023. Identification of food sources in tropical seagrass bed food web using triple stable isotopes and fatty acid signatures. Front. Mar. Sci. 10 : 1–12. doi:10.3389/fmars.2023.1093181.Cybulski, J.D., Skinner, C., Wan, Z., Wong, C.K.M., Toonen, R.J., Gaither, M.R., Soong, K., Wyatt, A.S.J., and Baker, D.M. 2022. Improving stable isotope assessments of inter- and intra-species variation in coral reef fish trophic strategies. Ecol. Evol. 12 (9): 1–16. doi:10.1002/ece3.9221.Darnis, G., Robert, D., Pomerleau, C., Link, H., Archambault, P., Nelson, R.J., Geoffroy, M., Tremblay, J.É., Lovejoy, C., Ferguson, S.H., Hunt, B.P.V., and Fortier, L. 2012. Current state and trends in Canadian Arctic marine ecosystems: II. Heterotrophic food web, pelagic-benthic coupling, and biodiversity. Clim. Change 115 (1): 179–205. doi:10.1007/s10584-012-0483-8.Dupont, N., Durant, J.M., Langangen, Ø., and Christian Stige, L. 2024. Changes in prey-predator interactions in an Arctic food web under climate change. Prog. Oceanogr. 229 : 103380. doi:10.1016/j.pocean.2024.103380.Fossheim, M., Primicerio, R., Johannesen, E., Ingvaldsen, R.B., Aschan, M.M., and Dolgov, A. V. 2015. Recent warming leads to a rapid borealization of fish communities in the Arctic. Nat. Clim. Chang. 5 (7): 673–677. doi:10.1038/nclimate2647.Fry, B., and Chumchal, M.M. 2011. Sulfur stable isotope indicators of residency in estuarine fish. Limnol. Oceanogr. 56 (5): 1563–1576. doi:10.4319/lo.2011.56.5.1563.Garrido, I., Pardo, L.M., Johnson, L.E., and Schories, D. 2021. Selective Feeding by a Predatory Sea Star Across a Depth Gradient in Northern Patagonia, Chile. Front. Mar. Sci. 8 : 636208. doi:10.3389/fmars.2021.636208.Gaston, A.J., and Elliott, K.H. 2014. Seabird diet changes in northern Hudson Bay, 1981-2013, reflect the availability of schooling prey. Mar. Ecol. Prog. Ser. 513 : 211–223. doi:10.3354/meps10945.Gaston, A.J., and Noble, D.G. 1985. The diet of thick-billed murres ( Uria lomvia) in west Hudson Strait and north-east Hudson Bay. Can. J. Zool. 63 (5): 1148–1160. doi:10.1139/z85-173.Geoffroy, M., Majewski, A., LeBlanc, M., Gauthier, S., Walkusz, W., Reist, J.D., and Fortier, L. 2016. Vertical segregation of age-0 and age-1+ polar cod (Boreogadus saida) over the annual cycle in the Canadian Beaufort Sea. Polar Biol. 39 (6): 1023–1037. doi:10.1007/s00300-015-1811-z.Góngora, E., Braune, B.M., and Elliott, K.H. 2018. Nitrogen and sulfur isotopes predict variation in mercury levels in Arctic seabird prey. Mar. Pollut. Bull. 135 : 907–914. Elsevier. doi:10.1016/j.marpolbul.2018.07.075.Gray, B.P., Norcross, B.L., Beaudreau, A.H., Blanchard, A.L., Seitz, A.C., and Sea, B. 2017. Deep-Sea Research II Food habits of Arctic staghorn sculpin ( Gymnocanthus tricuspis ) and shorthorn sculpin ( Myoxocephalus scorpius ) in the northeastern Chukchi and western Beaufort Seas. Deep. Res. Part II 135 : 111–123. doi:10.1016/j.dsr2.2016.05.013.Hesslein, R.H., Capel, M.J., Fox, D.E., and Hallard, K.A. 1991. Stable isotopes of Sulfur, Carbon, and Nitrogen as indicators of trophic level and fish migration in the lower Mackenczie River Basin, Canada. Can. J. Fish. Aquat. Sci. 48 : 2258–2265.Hobson, K.A., Fisk, A., Karnovsky, N., Holst, M., Gagnon, J.M., and Fortier, M. 2002. A stable isotope (δ13C, δ15N) model for the North Water food web: Implications for evaluating trophodynamics and the flow of energy and contaminants. Deep. Res. Part II Top. Stud. Oceanogr. 49 : 5131–5150. doi:10.1016/S0967-0645(02)00182-0.Hobson, K.A., and Welch, H.E. 1992. Determination of trophic relationships within a high Arctic marine food web using δ13C and δ15N analysis. Mar. Ecol. Prog. Ser. 84 (1): 9–18. doi:10.3354/meps084009.Hutchinson, G.E. 1957. Concluding Remarks. Ecology 43 (3): 225–237.Hutchinson, G.E. 1978. An introduction to population ecology. In Vol 260. Yale University Press, New Haven.Jackson, A.L., Inger, R., Parnell, A.C., and Bearhop, S. 2011. Comparing isotopic niche widths among and within communities: SIBER - Stable Isotope Bayesian Ellipses in R. J. Anim. Ecol. 80 (3): 595–602. doi:10.1111/j.1365-2656.2011.01806.x.Jacob, U., Mintenbeck, K., Brey, T., Knust, R., and Beyer, K. 2005. Stable isotope food web studies: a case for standardized sample treatment. Mar. Ecol. Prog. Ser. 287 : 251–253. doi:10.1179/joc.2011.23.1.32.Jumars, P.A., Dorgan, K.M., and Lindsay, S.M. 2015. Diet of worms emended: An update of polychaete feeding guilds. Ann. Rev. Mar. Sci. 7 : 497–520. doi:10.1146/annurev-marine-010814-020007.Karalis, P., Dotsika, E., Poutouki, A.E., Diamantopoulos, G., Gkelou, L., Kyropoulou, D., Bellas, S., and Gamaletsos, P.N. 2025. Isotopic Analysis (δ13C, δ15N, and δ34S) of Modern Terrestrial, Marine, and Freshwater Ecosystems in Greece: Filling the Knowledge Gap for Better Understanding of Sulfur Isotope Imprints—Providing Insights for the Paleo Diet, Paleomobility, and Paleoecolo. Appl. Sci. 15 (8). doi:10.3390/app15084351.Kazanidis, G., Bourgeois, S., and Witte, U.F.M. 2019. On the Effects of Acid Pre-treatment on the Elemental and Isotopic Composition of Lightly- and Heavily-calcified Marine Invertebrates. Ocean Sci. J. 54 (2): 257–270. doi:10.1007/s12601-019-0014-x.Keats, D., Steele, D.H., Green, J.M., and Martel, G.M. 1993. Diet and population size structure of the Arctic shanny, Stichaeus punctatus ( Pisces : Stichaeidae ), at sites in eastern Newfoundland and the eastern Arctic. Environ. Biol. Fishes 37 : 173–180. doi:10.1007/BF00000592.Kelley, T.C., Loseto, L.L., Stewart, R.E.A., Yurkowski, M., and Ferguson, S.H. 2010. Importance of Eating Capelin: Unique Dietary Habits of Hudson Bay Beluga BT - A Little Less Arctic: Top Predators in the World’s Largest Northern Inland Sea, Hudson Bay. Edited by S.H. Ferguson, L.L. Loseto, and M.L. Mallory. Springer Netherlands, Dordrecht. pp. 53–70. doi:10.1007/978-90-481-9121-5_3.Klekowski, R.Z., and Węsławski, J.M. 1990. Atlas of the Marine Fauna of Southern Spitsbergen. Ossolineum, Wrocław.Kristjánsson, T.Ö., Jónsson, J.E., and Svavarsson, J. 2013. Spring diet of common eiders (Somateria mollissima) in Breidafjördur, West Iceland, indicates non-bivalve preferences. Polar Biol. 36 (1): 51–59. doi:10.1007/s00300-012-1238-8.Labansen, A.L., Lydersen, C., Haug, T., and Kovacs, K.M. 2007. Spring diet of ringed seals (Phoca hispida) from Northwestern Spitsbergen, Norway. ICES J. Mar. Sci. 64 (6): 1246–1256. doi:10.1093/icesjms/fsm090.Lacasse, O., Roy, V., Nozères, C., Deslauriers, D., and Walkusz, W. 2020. Invertebrate Biodiversity and Photo Catalogue from the 2018 Northern and Striped Shrimp Stock Assessment Survey in Davis Strait, Hudson Strait, and Northern Labrador Coast. Can. Tech. Rep. Fish. Aquat. Sci. 3351 : iv+163 p.Landry, J.J., Fisk, A.T., Yurkowski, D.J., Hussey, N.E., Dick, T., Crawford, R.E., and Kessel, S.T. 2018. Feeding ecology of a common benthic fish, shorthorn sculpin (Myoxocephalus scorpius) in the high arctic. Polar Biol. 41 (10): 2091–2102. doi:10.1007/s00300-018-2348-8.Legeżyńska, J., Kędra, M., and Walkusz, W. 2012. When season does not matter: Summer and winter trophic ecology of Arctic amphipods. Hydrobiologia 684 (1): 189–214. doi:10.1007/s10750-011-0982-z.Loseto, L.L., Stern, G.A., Connelly, T.L., Deibel, D., Gemmill, B., Prokopowicz, A., Fortier, L., and Ferguson, S.H. 2009. Journal of Experimental Marine Biology and Ecology Summer diet of beluga whales inferred by fatty acid analysis of the eastern Beaufort Sea food web. J. Exp. Mar. Bio. Ecol. 374 (1): 12–18. doi:10.1016/j.jembe.2009.03.015.Majewski, A.R., Walkusz, W., Lynn, B.R., Atchison, S., Eert, J., and Reist, J.D. 2016. Distribution and diet of demersal Arctic Cod, Boreogadus saida, in relation to habitat characteristics in the Canadian Beaufort Sea. Polar Biol. 39 (6): 1087–1098. doi:10.1007/s00300-015-1857-y.Malizia, J., Launay, M., Bruvold, I.M., Quintela, M., Johansen, T., Reist, J.D., Majewski, A.R., and Roy, D. 2023. Morphology of Arctic cod (Boreogadus saida) assessed according to habitat preference and age in the Beaufort Sea. Arct. Sci. 9 (4): 825–837. doi:10.1139/as-2022-0043.Matley, J.K., Tobin, A.J., Simpfendorfer, C.A., Fisk, A.T., and Heupel, M.R. 2017. Trophic niche and spatio-temporal changes in the feeding ecology of two sympatric species of coral trout (Plectropomus leopardus and P. laevis). Mar. Ecol. Prog. Ser. 563 : 197–210. doi:10.3354/meps11971.Merkel, F.R., Jamieson, S.E., Falk, K., and Mosbech, A. 2007. The diet of common eiders wintering in Nuuk, Southwest Greenland. Polar Biol. 30 (2): 227–234. doi:10.1007/s00300-006-0176-8.Moody, A.T., Hobson, K.A., and Gaston, A.J. 2012. High-arctic seabird trophic variation revealed through long-term isotopic monitoring. J. Ornithol. 153 (4): 1067–1078. doi:10.1007/s10336-012-0836-0.Newsome, S.D., Rio, C.M. del, Bearhop, S., and Phillips, D.L. 2007. A niche for isotopic ecology. Front. Ecol. Environ. (1957). doi:10.1890/060150.01.Niemi, A., Bluhm, B.A., Juul-Pedersen, T., Kohlbach, D., Reigstad, M., Søgaard, D.H., and Amiraux, R. 2024. Ice algae contributions to the benthos during a time of sea ice change: a review of supply, coupling, and fate. Front. Environ. Sci. 12 : 1–10. doi:10.3389/fenvs.2024.1432761.Ogloff, W.R., Yurkowski, D.J., Davoren, G.K., and Ferguson, S.H. 2019. Diet and isotopic niche overlap elucidate competition potential between seasonally sympatric phocids in the Canadian Arctic. Mar. Biol. 166 (8): 1–12. doi:10.1007/s00227-019-3549-6.Onishi, Y., Yamanaka, T., and Koba, K. 2023. Major contribution of sulfide-derived sulfur to the benthic food web in a large freshwater lake. Geobiology 21 (5): 671–685. doi:10.1111/gbi.12569.van Oordt, F., Cuba, A., Choy, E.S., Elliott, J.E., and Elliott, K.H. 2024. Amino acid-specific isotopes reveal changing five-dimensional niche segregation in Pacific seabirds over 50 years. Sci. Rep. 14 (1): 1–12. doi:10.1038/s41598-024-57339-w.Peterson, B.J. 1999. Stable isotopes as tracers of organic matter input and transfer in benthic food webs: A review. Acta Oecologica 20 (4): 479–487. doi:10.1016/S1146-609X(99)00120-4.Peterson, B.J., and Fry, B. 1987. Stable Isotopes in Ecosystem Studies. Annu. Rev. Ecol. Syst. 18 (1987): 293–320.Post, D.M. 2002. Using stable isotopes to estimate trophic position: Models, methods, and assumptions. Ecology 83 (3): 703–718. doi:10.1890/0012-9658(2002)083[0703:USITET]2.0.CO;2.Post, D.M., Layman, C.A., Arrington, D.A., Takimoto, G., Quattrochi, J., and Montaña, C.G. 2007. Getting to the fat of the matter: Models, methods and assumptions for dealing with lipids in stable isotope analyses. Oecologia 152 (1): 179–189. doi:10.1007/s00442-006-0630-x.Raoult, V., Phillips, A.A., Nelson, J., Niella, Y., Skinner, C., Tilcock, M.B., Burke, P.J., Szpak, P., James, W.R., and Harrod, C. 2024. Why aquatic scientists should use sulfur stable isotope ratios (ẟ34S) more often. Chemosphere 355 : 141816. Elsevier Ltd. doi:10.1016/j.chemosphere.2024.141816.Rossman, S., Ostrom, P.H., Gordon, F., and Zipkin, E.F. 2016. Beyond carbon and nitrogen: Guidelines for estimating three-dimensional isotopic niche space. Ecol. Evol. 6 (8): 2405–2413. doi:10.1002/ece3.2013.Schiøtt, S., Holland, D.M., Holland, D., Rysgaard, S., and Rosing-Asvid, A. 2024. The diet and diving behaviour of the ringed seal (Pusa hispida) in Kangia (Ilulissat Icefjord), Greenland. Polar Biol. 47 (12): 1409–1422. doi:10.1007/s00300-024-03310-4.Skinner, C., Mill, A.C., Newman, S.P., Newton, J., Cobain, M.R.D., and Polunin, N.V.C. 2019. Novel tri-isotope ellipsoid approach reveals dietary variation in sympatric predators. Ecol. Evol. 9 (23): 13267–13277. doi:10.1002/ece3.5779.Smith, P.A., and Gaston, A.J. 2012. Environmental variation and the demography and diet of thick-billed murres. 454 : 237–249. doi:10.3354/meps09589.Soberon, and Arroyo-Pena, B. 2017. Are fundamental niches larger than the realized? Testing a 50-year-old prediction by Hutchinson. PLoS One (4): 1–14.Stasko, A.D., Bluhm, B.A., Michel, C., Archambault, P., Majewski, A., Reist, J.D., Swanson, H., and Power, M. 2018. Benthic-pelagic trophic coupling in an Arctic marine food web along vertical water mass and organic matter gradients. Mar. Ecol. Prog. Ser. 594 : 1–19. doi:10.3354/meps12582.Stroeve, J., Crawford, A., Ferguson, S., Stirling, I., Archer, L., York, G., Babb, D., and Mallett, R. 2024. Ice-free period too long for Southern and Western Hudson Bay polar bear populations if global warming exceeds 1.6 to 2.6 °C. Commun. Earth Environ. 5 (1): 1–12. Springer US. doi:10.1038/s43247-024-01430-7.Swanson, H.K., Lysy, M., Power, M., Stasko, A.D., Johnson, J.D., and Reist, J. 2015. A new probabilistic method for quantifying n-dimensional ecological niches and niche overlap. Ecology 96 (2): 318–324.Syväranta, J., Lensu, A., Marjomäki, T.J., Oksanen, S., and Jones, R.I. 2013. An Empirical Evaluation of the Utility of Convex Hull and Standard Ellipse Areas for Assessing Population Niche Widths from Stable Isotope Data. PLoS One 8 (2): 1–8. doi:10.1371/journal.pone.0056094.Szpak, P., and Buckley, M. 2020. Sulfur isotopes (δ34S) in Arctic marine mammals: Indicators of benthic vs. pelagic foraging. Mar. Ecol. Prog. Ser. 653 : 205–216. Inter-Research Science Center. doi:10.3354/meps13493.Volage, F., Hamel, J.F., and Mercier, A. 2021. Population structure, habitat preferences, feeding strategies, and diet of the brittle star Ophiopholis aculeata in nearshore and offshore habitats of the northwest Atlantic. Invertebr. Biol. 140 (3): 1–13. doi:10.1111/ivb.12346.Walkusz, W., Majewski, A., and Reist, J.D. 2013. Distribution and diet of the bottom dwelling Arctic cod in the Canadian Beaufort Sea. J. Mar. Syst. 127 : 65–75. Elsevier B.V. doi:10.1016/j.jmarsys.2012.04.004.Watt, C.A., and Ferguson, S.H. 2015. Fatty acids and stable isotopes (δ13C and δ15N) reveal temporal changes in narwhal (Monodon monoceros) diet linked to migration patterns. Mar. Mammal Sci. 31 (1): 21–44. doi:10.1111/mms.12131.Watt, C.A., Heide-Jørgensen, M.P., and Ferguson, S.H. 2013. How adaptable are narwhal? A comparison of foraging patterns among the world’s three narwhal populations. Ecosphere 4 (6): 1–15. doi:10.1890/ES13-00137.1.Weber, S., Cullen, J.A., and Fuentes, M.M.P.B. 2023. Isotopic niche overlap among foraging marine turtle species in the Gulf of Mexico. Ecol. Evol. 13 (11): 1–12. doi:10.1002/ece3.10741.Yamanaka, T., Shimoyama, S., and Mizota, H. 2000. An Evaluation of Source Sulfur in Soft Tissues of Marine and Freshwater Benthic Animals from Japan Using Stable Isotope Analysis. 55 (1): 17–22.Yurkowski, D.J., Ferguson, S.H., Semeniuk, C.A.D., Brown, T.M., Muir, D.C.G., and Fisk, A.T. 2016. Spatial and temporal variation of an ice-adapted predator’s feeding ecology in a changing Arctic marine ecosystem. Oecologia 180 (3): 631–644. doi:10.1007/s00442-015-3384-5.Ziegler, A.F., Bluhm, B.A., Renaud, P.E., and Lis, L.J. 2023. Progress in Oceanography Weak seasonality in benthic food web structure within an Arctic inflow shelf region. 217 : 103109. doi:10.1016/j.pocean.2023.103109. Figure 1. Degree of change between standardized isotopic niches (2D and 3D) for each taxa in descending order. Benthic taxa are shown in red and pelagic taxa in blue. Figure 2. Two-dimensional (2D: δ 13 C, δ 15 N) isotopic niche ellipses (left) and three-dimensional (3D: δ 13 C, δ 15 N, δ 34 S) isotopic niche ellipsoids (right) representation of selected benthic and pelagic invertebrates that showed greater differences in niche overlap between the two approaches. Figure 3. Two-dimensional (2D: δ 13 C, δ 15 N) isotopic niche ellipses (left) and three-dimensional (3D: δ 13 C, δ 15 N, δ 34 S) isotopic niche ellipsoids (right) representation of marine mammals (top), seabirds (center) and benthic fish (bottom) showing minimal change in niche overlap when using either 2D or 3D framework. Table 1. Carbon, nitrogen and sulfur stable isotope ratios (mean ± sd ‰) and two-dimensional (‰ 2 ) and three-dimensional (‰ 3 ) isotopic niche size (mean and scaled) for each taxa (n = sample size) by foraging habitat. habitat Niche size Scaled niche size Niche size Scaled niche size Pelagic Ringed seal 40 7.36 -0.70 17.87 -0.70 -18.26 ± 0.06 17.53 ± 0.20 17.40 ± 0.09 Narwhal 10 4.27 -1.00 10.84 -0.83 -18.13 ± 0.10 15.57 ± 0.44 17.53 ± 0.24 Beluga 8 0.65 -1.35 1.46 -0.99 -18.28 ± 0.03 16.13 ± 0.15 17.46 ± 0.20 Thick-billed murre 31 2.84 -1.14 3.07 -0.96 -20.20 ± 0.07 14.84 ± 0.09 17.28 ± 0.05 Arctic cod 14 7.28 -0.71 21.04 -0.65 -19.60 ± 0.21 14.28 ± 0.15 17.66 ± 0.19 Mysid/Euphausiid 69 9.90 -0.46 37.19 -0.37 -19.88 ±0.07 10.75 ± 0.28 19.26 ±0.04 Hydrozoan 11 8.13 -0.63 28.76 -0.51 -20.55 ±0.26 11.44 ± 0.19 20.56 ± 0.28 Copepod 22 3.70 -1.06 14.80 -0.76 -20.08 ± 0.09 9.59 ± 0.11 16.84 ± 0.20 Chaetognath 21 3.36 -1.09 8.37 -0.87 -19.79 ± 0.13 13.35 ± 0.09 17.05 ± 0.14 Pelagic amphipod 44 17.32 0.26 34.80 -0.41 -19.87 ± 0.12 10.82 ± 0.18 19.43 ± 0.07 Benthic Common eider 15 24.06 0.91 207.66 2.59 -16.99 ± 0.25 13.26 ± 0.41 14.68 ± 0.93 Blenny (Stichaeidae) 46 20.17 0.54 81.54 0.40 -18.67 ± 0.14 15.12 ±0.17 16.61 ± 0.18 Banded gunnel 14 7.14 -0.72 7.56 -0.88 -19.58 ± 0.15 14.57 ±0.24 18.67 ± 0.07 Sculpin (Cottidae) 64 28.38 1.33 115.96 1.00 -18.62 ± 0.19 14.68 ± 0.13 16.99 ± 0.26 Worm 14 23.95 0.90 69.10 0.19 -17.66 ± 0.39 14.09 ± 0.31 17.75 ± 0.25 Snail 16 33.88 1.86 181.86 2.14 -18.40 ± 0.44 12.91 ± 0.40 18.51 ± 0.33 Sea urchin 12 8.75 -0.57 23.85 -0.60 -19.15 ± 0.26 9.04 ± 0.17 18.74 ± 0.21 Sea star 14 21.47 0.66 102.16 0.76 -20.84 ± 0.30 17.24 ± 0.35 21.44 ± 0.39 Sea spider 11 12.19 -0.23 24.77 -0.58 -20.92 ± 0.24 11.10 ± 0.28 17.79 ± 0.16 Brittle star 14 32.11 1.69 110.29 0.90 -19.34 ± 0.32 9.79 ± 0.36 21.39 ± 0.27 Sea cucumber 16 23.73 0.88 87.72 0.51 -16.84 ± 0.37 11.24 ± 0.28 17.97 ± 0.26 Nudibranch 10 1.31 -1.29 3.35 -0.96 -20.95 ± 0.07 13.19 ± 0.13 19.61 ± 0.19 Isopod 14 6.77 -0.76 12.18 -0.80 -18.07 ± 0.19 9.90 ± 0.16 20.02 ± 0.12 Decapod 140 29.38 1.43 162.14 1.80 -17.77 ± 0.13 13.38 ± 0.09 16.55 ± 0.18 Bivalve 10 14.55 -0.01 81.62 0.40 -19.22 ± 0.45 9.86 ± 0.44 18.42 ± 0.43 Benthic amphipod 47 27.31 1.23 69.13 0.19 -16.89 ± 0.12 12.98 ± 0.26 18.87 ± 0.08 Table 2. Probability of isotopic niche overlap between taxa A (rows) with taxa B (columns) using 2D (δ 15 N and δ 13 C) and 3D (δ 15 N, δ 13 C and δ 34 S) approach in benthic invertebrates. 2D Benthic Amphipod 27 18 80 8 0 54 1 0 3 71 59 3D Benthic Amphipod 29 3 29 3 0 34 0 0 0 74 25 2D Bivalve 35 81 25 35 0 66 13 0 50 41 7 3D Bivalve 22 17 22 8 0 53 7 0 0 38 3 2D Brittle stars 18 47 20 30 1 45 31 0 43 43 11 3D Brittle stars 2 21 4 11 0 7 0 0 0 18 0 2D Decapod 73 13 21 2 1 29 10 1 0 89 80 3D Decapod 11 9 1 0 1 19 4 1 2 62 41 2D Isopod 65 58 96 10 0 88 6 0 59 24 3 3D Isopod 35 54 82 6 0 54 0 0 0 26 0 2D Nudibranch 0 0 36 83 0 0 43 4 0 46 42 3D Nudibranch 0 0 0 94 0 0 11 7 0 54 25 2D Sea cucumber 76 44 42 44 24 0 3 0 14 34 11 3D Sea cucumber 45 43 5 33 5 0 2 0 0 32 8 2D Sea spider 1 9 85 38 4 3 9 0 4 82 24 3D Sea spider 1 11 0 23 0 1 12 1 0 71 22 2D Sea star 0 0 0 1 0 0 0 0 0 1 1 3D Sea star 0 0 0 1 0 0 0 0 0 1 1 2D Sea urchin 15 68 98 1 34 0 55 8 0 11 0 3D Sea urchin 18 71 29 1 12 0 63 5 0 0 15 0 2D Snail 57 14 39 84 5 1 24 24 1 2 72 3D Snail 37 12 5 65 2 1 12 10 1 1 40 2D Worms 72 4 13 91 1 1 11 7 1 0 89 3D Worms 36 4 0 70 0 1 10 5 2 2 87 * Biologically significant overlap (≥60%) are highlighted. Table 3. Probability of isotopic niche overlap between taxa A (rows) with taxa B (columns) using 2D (δ 15 N and δ 13 C) and 3D (δ 15 N, δ 13 C and δ 34 S) approach in pelagic invertebrates. 2D Chaetognath 0 14 0 58 3D Chaetognath 0 0 0 1 2D Copepod 0 12 99 99 3D Copepod 0 1 73 5 2D Hydrozoan 4 6 46 89 3D Hydrozoan 0 0 9 44 2D Mysid/Euphausiid 0 60 25 93 3D Mysid/Euphausiid 0 45 9 30 2D Pelagic Amphipod 5 27 49 68 3D Pelagic Amphipod 0 5 42 42 * Biologically significant overlap (≥60%) are highlighted. Table 4. Probability of isotopic niche overlap between taxa A (rows) with taxa B (columns) using 2D (δ 15 N and δ 13 C) and 3D (δ 15 N, δ 13 C and δ 34 S) approach in seabirds, fish and marine mammals. SEABIRDS 2D Common eider 0 3D Common eider 0 2D Thick-billed murre 5 3D Thick-billed murre 5 Arctic Cod Blenny Gunnel Sculpin FISH 2D Arctic Cod 86 73 97 3D Arctic Cod 85 16 92 2D Blenny 40 36 92 3D Blenny 27 5 87 2D Gunnel 71 95 98 3D Gunnel 61 73 92 2D Sculpin 39 80 37 3D Sculpin 31 75 10 Beluga Narwhal Ringed seal MARINE 2D Beluga 79 97 MAMMALS 3D Beluga 68 92 2D Narwhal 16 51 3D Narwhal 15 51 2D Ringed seal 11 24 3D Ringed seal 10 19 * Biologically significant overlap (≥60%) are highlighted. Appendix Table A1. List of species/taxa with their sample size (n) and type of tissue sampled for stable isotope analysis classified by foraging habitat. Pelagic Ringed seal Muscle Pusa hispida 40 Narwhal Muscle Monodon monoceros 10 Beluga Muscle Delphinapterus leucas 8 Thick-billed murre Blood Uria lomvia 31 Arctic cod Muscle Boreogadus saida 14 Mysids/Euphausiids Whole body Meganyctiphanes norvegica 3 Mysis oculata 17 Thysanoessa inermis 10 Thysanoessa raschii 29 Hydrozoan Whole body Hydrozoa 11 Copepod Whole body Calanus hyperboreus 20 Metridia sp. 2 Chaetognath Whole body Chaetognatha 21 Pelagic amphipod Whole body Themisto abyssorum 6 Themisto libellula 38 Benthic Common eider Plasma Somateria mollissima 15 Blenny (Stichaeidae) Muscle Eumesogrammus praecisus 9 Stichaeus punctatus 20 Leptoclinus maculatus 4 Lumpenus fabricii 13 Banded gunnel Muscle Pholis fasciata 14 Sculpin (Cottidae) Muscle Triglops sp. 3 Triglops pingelii 5 Triglops murrayi 20 Myoxocephalus sp 2 Myoxocephalus scorpius 9 Myoxocephalus scorpioides 7 Icelus spatula 1 Gymnocanthus tricuspis 17 Worm Whole body Eunoe oerstedi 8 Nereis pelagica 2 Polychaeta 4 Snail Soft parts Buccinum sp. 9 Colus sp. 2 Littorinidae 2 Margarites helicinus 3 Sea urchin Gonads Strongylocentrotus droebachiensis 12 Sea star Whole body Crossaster papposus 1 Diplopteraster multipes 4 Henricia sp. 2 Pteraster militaris 3 Solaster sp. 2 Boreonymphon abyssorum 2 Sea spider Whole body Nymphonidae 9 Brittle star Disk,arms Ophiopholis aculeata 16 Sea cucumber Muscle, Whole body Cucumaria frondosa 2 Molpadia sp. 13 Psolus fabricii 1 Nudibranch Whole body Dendronotus sp. 10 Isopod Whole body Arcturus baffini 14 Decapod Muscle Argis dentata 23 Eualus fabricii 10 Eualus gaimardii 28 Lebbeus groenlandicus 9 Lebbeus polaris 54 Pagarus sp. 1 Pandalus montagui 1 Sclerocrangon boreas 3 Spirontocaris phippsi 1 Spirontocaris spinus 10 Bivalve Soft parts Bivalvia 1 Hiatella arctica 5 Pectinidae 1 Similipecten greenlandicus 3 Benthic amphipod Whole body Anonyx sp. 31 Eusirus cuspidatus 3 Haploops tubicula 1 Paramphithoe hystrix 1 Rhachotropis aculeata 11 Supplementary Material File (table a1.docx) Download 36.13 KB Information & Authors Information Version history V1 Version 1 04 October 2025 Peer review timeline Published Ecology and Evolution Version of Record 13 May 2026 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords comparative ecosystem ecosystem ecology ecosystem function marine statistical theory Authors Affiliations Paloma Carvalho 0000-0003-3938-6935 [email protected] Fisheries and Oceans Canada View all articles by this author Kelsey Johnson Fisheries and Oceans Canada View all articles by this author Kyle Elliott 0000-0002-4313-0345 McGill University View all articles by this author Steven Ferguson 0000-0002-3794-0122 Fisheries and Oceans Canada View all articles by this author Aaron Fisk University of Windsor View all articles by this author Grant Gilchrist Canadian Wildlife Service View all articles by this author Kevin Hedges Fisheries and Oceans Canada View all articles by this author Oliver Love University of Windsor View all articles by this author CJ Mundy University of Manitoba View all articles by this author Andrea Niemi Fisheries and Oceans Canada View all articles by this author Wesley Ogloff University of Windsor View all articles by this author Bruno Rosenberg Fisheries and Oceans Canada View all articles by this author Cortney Watt Fisheries and Oceans Canada View all articles by this author David Yurkowski Fisheries and Oceans Canada View all articles by this author Metrics & Citations Metrics Article Usage 384 views 97 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Paloma Carvalho, Kelsey Johnson, Kyle Elliott, et al. 2D to 3D: Exploring variation of niche dimensionality across consumers in a coastal Arctic ecosystem and implications on interpretation. 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