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OPEN ACCESS | Article
Methylmercury dietary pathways and bioaccumulation in
Arctic benthic invertebrates of the Beaufort Sea
Christine McClelland a, John Chételat a, Kathleen Conlanb, Alec Aitkenc,MarkR.Forbes
d, and Andrew Majewskie
aNational Wildlife Research Centre, Environment and Climate Change Canada, Ottawa, ON K1A 0H3, Canada; bCanadian Museum of
Nature, P. O. Box 3443, Station D, Ottawa, ON K1P 6P4, Canada; cDepartment of Geography and Planning, University of
Saskatchewan, 105 Kirk Hall, 117 Science Place, Saskatoon, SK S7N 5C8, Canada; dDepartment of Biology, Carleton University,
Ottawa, ON K1S 5B6, Canada; eFreshwater Institute, Fisheries and Oceans Canada, 501 University Crescent, Winnipeg, MB R3T 2N6 ,
Canada
Corresponding author: John Chételat (email: john.chetelat@ec.gc.ca)
Abstract
This study investigated methylmercury (MeHg) concentrations in Arctic benthic invertebrates from two shelf sites in the
Canadian Beaufort Sea. Carbon, nitrogen, and sulfur stable isotopes and fatty acids were measured to examine diet influences
on MeHg concentrations in 476 individuals from 53 taxa of benthic invertebrates representing three dierent feeding guilds.
Taxonomic identifications were based on DNA-barcoding and traditional taxonomy. MeHg concentrations ranged from 3 to
421 ng/g dry weight and increased over three trophic levels (δ15Nrange=4.4–14.2). Organic matter sources had small but
significant influences on MeHg bioaccumulation in the benthic food web. Carbon stable isotope ratios (δ13C, range =−25.5 to
−19.8) were positively correlated with MeHg concentrations, suggesting greater reliance on benthic carbon contributed to
higher concentrations. Sulfur stable isotopes were unrelated to MeHg concentrations. Fatty acids suggested feeding on diatoms
versus dinoflagellates, and reliance on benthic resources influenced MeHg concentrations. Higher MeHg concentrations were
observed at the site closer to the Mackenzie River mouth than the Cape Bathurst site. This study generated the most taxonom-
ically rich dataset of MeHg concentrations in invertebrates from the Arctic marine benthos to date and provides a basis for
future research on food web MeHg dynamics in the Canadian Beaufort Sea.
Key words: Arctic Ocean, Beaufort Sea, benthic invertebrate, mercury, stable isotope, fatty acid, bioaccumulation
Introduction
Limited information is available on methylmercury (MeHg)
concentrations in benthic invertebrates in the Arctic Ocean
despite their importance for trophic transfer of MeHg to fish,
marine mammals, and seabirds. This is a significant knowl-
edge gap for understanding MeHg dynamics in Arctic marine
food webs (Loria et al. 2020). Benthic fauna live within (in-
fauna) or upon (epifauna) the sea floor and are in contact
with bottom water and sediment, a zone where inorganic
mercury (Hg) is actively methylated (Cossa and Gobeil 2000;
Lehnherr et al. 2011). Benthic and epibenthic invertebrates
are preyed upon by marine fishes (Giraldo et al. 2016), sharks
(McMeans et al. 2015), seals (Wang et al. 2016), diving sea
birds, walruses, gray whales (Lovvorn et al. 2018), and bel-
uga whales (Loseto et al. 2008). Thus, benthic animals are a
conduit for MeHg transfer from the sediment and benthic
boundary layer to the pelagic food web in the Arctic Ocean
(Chen et al. 2008;Griths et al. 2017;Amiraux et al. 2023b;
Li et al. 2022). MeHg is eciently assimilated from diet, and
it bioaccumulates and biomagnifies through food webs, lead-
ing to toxicological risk for some apex predators (Dietz et al.
2022).
The characterization of dietary pathways within food webs
can identify important sources and exposure routes (Hebert
et al. 2006;Chételat et al. 2020). Diet can be characterized
by trophic position and feeding guild made possible by com-
parisons between taxa with respect to carbon, nitrogen, and
sulfur stable isotopes (SIs) along with fatty acids (FA), and FA
biomarkers (FABM) (Lavoie et al. 2013;McMeans et al. 2015;
Elliot and Elliot 2016;Chételat et al. 2020). Trophic position is
a major driver of MeHg biomagnification in marine food webs
(Lavoie et al. 2013). Trophic position is estimated using sta-
ble nitrogen isotopes (δ15N) because the 15 N isotope enriches
at a consistent rate of ∼3.4per trophic level (Minagawa
and Wada 1984;Post 2002). Organic matter sources such as
benthic or pelagic primary production in invertebrate diets
can also influence contaminant uptake pathways (Lavoie et
al. 2010). Carbon SI ratios (δ13C) enrich minimally through
food webs (0.4per trophic level) and thus reflect those of
the food web primary producers (Hecky and Hesslein 1995;
Post 2002). Lower δ13C values indicate reliance on pelagic re-
sources, while higher δ13C values indicate benthic feeding
(Middelburg and Herman 2007;Coelho et al. 2013). Sulfur
SI ratios (δ34S) in marine animals do not fractionate during
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trophic transfer and are influenced by sulfur biogeochemi-
cal processes and the sulfur pool(s) being utilized by biota,
such as sulfate-reducing environments in anoxic sediments
(Peterson and Fry 1987;Habicht et al. 1998). Arctic marine in-
vertebrates vary in their δ34S values (e.g., ∼10–25in Hudson
Bay; Amiraux et al. 2023a), which may reflect habitat varia-
tion in bacterial sulfate reduction with potential implications
for MeHg production and exposure (Gongora et al. 2018). It is
useful to consider both δ13Candδ34 S values of consumers to
assess diet variation.
FAs and FABM can also reveal predator-prey relationships
due to their conservative transfer through food webs (Graeve
et al. 1997;Dalsgaard et al. 2003;Iverson et al. 2004). Pri-
mary producers typically have unique combinations of FA,
such as eicosapentaenoic acid (20:5n-3) and docosahexaenoic
acid (22:6n-3), that allow for tracing of organic carbon sources
through food webs since consumers accumulate them but
generally cannot synthesize them de novo (Iverson 2009). FA
biomarkers are ratios or sums of specific or groups of FAs
and have been widely applied to dierentiate between ter-
restrial and marine energy sources as well as to identify re-
liance on copepod or bacterial food sources (Mohan et al.
2016;Hebert and Popp 2018). Combining SI and FA/FABM sig-
natures provides a means of cross-validating natural history
feeding guild information (e.g., deposit feeder, suspension
feeder, and predator/scavenger), detailing connections within
food webs and identifying which dietary sources are signifi-
cantly contributing to MeHg bioaccumulation (Petersen and
Fry 1987;McMeans et al. 2015;Chételat et al. 2020).
Considering sources and transport of inorganic Hg is essen-
tial for elucidating MeHg dynamics. Long-range atmospheric
transport, riverine output, coastal erosion, and oceanic path-
ways carry predominantly inorganic Hg to the Arctic Ocean
(Dastoor et al. 2022). Oceanic Hg is methylated in sediments
and in the water column through microbial pathways to form
MeHg (Macdonald and Loseto 2010;Lehnherr et al. 2011). Wa-
ter column MeHg can also be produced through the break-
down of dimethlymercury (Jonsson et al. 2022). Higher MeHg
concentrations have been reported for the Canadian Beau-
fort Sea (CBS) compared with other areas of the Arctic Ocean
for water and zooplankton (Wang et al. 2018), ringed seals
(Brown et al. 2016), and polar bears (Routti et al. 2011;St.
Louis et al. 2011). Complex multi-decadal temporal trends
in MeHg accumulation have also been documented for bel-
uga (Loseto et al. 2015). By comparison, benthic invertebrates
have received minimal research focus for the Beaufort Sea
(though see Loseto et al. 2008), despite their relevance for
evaluating environmental and ecological drivers of spatial
and temporal trends of MeHg in marine mammals (Loria et
al. 2020;Li et al. 2022).
The objective of this study was to characterize MeHg con-
centrations in benthic invertebrate fauna of the CBS shelf and
identify dietary influences on MeHg bioaccumulation. Diet
was examined using a suite of tracers, specifically carbon, ni-
trogen, and sulfur SIs and FAs and FABMs. The study is the
most taxonomically comprehensive investigation of MeHg
and dietary tracers in Arctic marine benthic invertebrates,
and it is supported by the use of DNA-barcoding for species
identification. In addition, spatial dierences were examined
by comparing two locations in the study area, a deep-water
site near the Mackenzie River and a shallower site farther
from the influence of the Mackenzie River, at Cape Bathurst.
This study presents new science on the important role of ben-
thic invertebrates for MeHg transfer to marine vertebrates
and advances our understanding of MeHg bioaccumulation
in the CBS, with broader relevance to other areas of the Arc-
tic Ocean with similar dynamic environmental regimes.
Materials and methods
Study area
The CBS is in the western Canadian Arctic Ocean, north
of the Yukon, Northwest Territories and western Nunavut.
The southern extent of the CBS is characterized by a large,
shallow continental shelf ∼120 km wide by 530 km long
(Carmack and MacDonald 2002). Across the shelf and shelf
break, four water masses are oceanographically dieren-
tiated by temperature and salinity (Lansard et al. 2012).
Nearshore and across the surface of the CBS (0-60 m) is a
mix of marine and fresh water from the sea ice melt and
river influx. Below the mixed layer is the Pacific water mass
(60–200 m, cold [0 to −1◦C], low salinity [30–33.5 PSU]) fol-
lowed by the Atlantic water mass (300–900 m, warmer [<1◦
C], higher salinity [33.5–34.95 PSU)). The cold (<0◦C) and most
saline (<34.9 PSU) Arctic Ocean water mass is found beyond
900 m depth (Majewski et al. 2017).
The field work for this study took place at two locations
on the CBS shelf during the summer of 2007. Benthic in-
vertebrates were collected northwest of Cape Bathurst (CB),
at a depth of 22 m (mean coordinates of trawl, 70.69522 N,
−128.83926 W) and o the eastern coast of Herschel Island
in the Mackenzie Trough (MT), at a depth of 116 m (mean co-
ordinates of trawl 69.61249 N, −138.56257 W) (Fig. 1). Both
sites are characterized by extreme variation in ice cover and
periodic wind-driven, topographically enhanced upwelling
events in which nutrient-rich water flows up the shelf break
from deeper waters to the north (Carmack and Kulikov 1998;
Carmack and Macdonald 2002;Williams and Carmack 2008).
The Mackenzie River delivers 249–333 km3of fresh water to
the CBS annually (Dittmar and Kattner 2003). The freshwater
plume extends up to 70 km north of the coastline, carrying
∼130 megatons of sediment annually (Carson et al. 1998)(see
Fig. 1). As a result of the variable environmental conditions
and nutrient rich habitat, fauna in both locations were di-
verse and abundant, thereby permitting collection of inver-
tebrates from a wide variety of taxa (Conlan et al. 2013).
Field collection
In collaboration with the Department of Fisheries and
Oceans Canada, benthic invertebrates were collected on July
31 and August 2, 2007, from the two sites on the continen-
tal shelf of the CBS. While aboard the Canadian Coast Guard
Ship Nahidik, a 3 m wide benthic beam trawl was deployed
to the ocean floor for ∼20 min at an average speed of ∼1m/s
(Majewski et al. 2009). Animals were collected from ∼3600 m2
of the seafloor in each location. The trawl’s net and the cod-
end consisted of 3.17 cm stretched nylon mesh, with the cod-
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Arctic Science 10: 305–320 (2024) | dx.doi.org/10.1139/as-2023-0021 307
Fig. 1. Map of Canada with insets of (A) Canadian Beaufort Sea with study sites (yellow points) and (B) Mackenzie River sediment
plume (Map of Canada: Mapsland, insets A and B: NASA Worldview application (https://worldview.earthdata.nasa.gov)and
EOSDIS).
end itself lined with 0.63 cm square nylon mesh (Majewski
et al. 2011). Trawling was conducted under the authority
of a Department of Fisheries and Oceans License to Collect
Fish for Scientific Purposes. An eort was made to select the
same species from both locations to allow for paired com-
parisons between locations. Animals were depurated in salt
water for 24 h before being frozen aboard the ship under ni-
trogen atmosphere at −80◦C. Samples were shipped on dry
ice to the Canadian Museum of Nature and stored long term
at −80◦C.
Laboratory analyses
DNA sequencing and designation to feeding guilds
Prior to homogenization, a small amount of tissue was
taken from representative specimens for DNA-barcoding
analysis. DNA sequencing was performed at the Canadian
Museum of Nature’s Natural Heritage Campus (Gatineau,
Canada) following an adapted version of the method found
in Ivanova et al. (2006). Lobo primers were used to amplify
the 5’ end of the mitochondrial gene cytochrome coxidase
subunit I, the mitochondrial DNA region widely used as the
unique identifier of animal species (COI-5P, Lobo et al. 2013).
Sequencing was conducted on an Applied Biosystems 3500xl
Genetic Analyzer. Forward and backward COI-5P sequences
were combined using Geneious software, version 7.0.6. Gen-
erated sequences were queried for alignment matches within
the Barcode of Life Data System v4 (BOLD) and National Cen-
ter for Biotechnology Information (NCBI) sequence reference
libraries. Invertebrates were identified using a combination
of DNA barcode alignment best matches and field identifi-
cations with the exception of most amphipod, shrimp, and
polychaete species, which were identified primarily based
on DNA barcode matches. In situations where one specimen
matched multiple species within the same genus to a sim-
ilar extent, only the genus name was retained with “spp.”
used to indicate unknown species. The same nomenclature
was applied for barcode queries that returned higher order
taxonomic matches (i.e., phylum, class, order, and family).
For specimens whose DNA was not amplified successfully and
those whose barcodes were inconclusive, the field identifica-
tion to the lowest taxonomic level was used. Once the inver-
tebrate taxa were confirmed, they were grouped into broad
feeding guilds based on previously published literature (Table
S1, which includes species authorities for all taxa included
in this study). Taxa were separated according to their ma-
jor feeding method, categorized as suspension feeders (SF)
which filter suspended matter from the water at the ben-
thic boundary layer, deposit feeders (DF) which feed on sur-
face and buried sediment organics; microbes; diatoms; small
animals and detritus; and finally, predators and scavengers,
broadly termed carnivores (CV), which prey on larger benthic
and epibenthic fauna as well as scavenge carcasses of pelagic
animals that have settled to the seafloor.
Tissue preparation for chemical analysis
Tissues analyzed for MeHg concentration were chosen
based on the typical consumption of the invertebrate by
predators, that is, invertebrates generally consumed whole,
for example arthropods, were left as such, whereas inver-
tebrates whose exoskeleton or shell was generally not con-
sumed or digested, such as bivalves, were dissected. This se-
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lection method was also beneficial in avoiding carbonates
which can be highly enriched in 13C compared to soft tissues
(Klump and Arthur 1999). If present, eggs and juveniles in
brood pouches of peracarid crustacean were removed prior to
homogenization to remove potential variability in SI caused
by shifts in elemental composition during early ontogeny
(see Heilmayer et al. 2008). An assortment of homogenization
techniques was employed because of the diversity of body
structures of the invertebrates in this study. For bivalves,
gastropods, and sea stars, muscle and internal viscera were
dissected, freeze-dried, and homogenized with acid-washed
glass mortars and pestles. Cumaceans, amphipods, small
isopods, small pycnogonids, sea anemones, corals, sea cu-
cumbers, nudibranchs, nemerteans, polychaetes, and sipun-
culids were freeze-dried whole and homogenized using acid-
washed glass mortars and pestles. Larger arthropods, specif-
ically shrimp and larger isopods and pycnogonids, were ho-
mogenized whole using a Retsch MM301 Ball Mill (30 rev/s for
30 s) and then freeze-dried. In all cases, invertebrates were
freeze-dried for a minimum of 48 h.
Three shrimp (Sabinea septemcarinata) and three isopods
(Saduria sabini) were dissected to enable tissue-specific anal-
ysis of MeHg concentrations. Animals were halved longitudi-
nally; one half was further dissected to isolate exoskeleton
from internal muscle and viscera. The exoskeleton and half-
body samples were homogenized via the ball mill and then
freeze-dried. Muscle and viscera samples were freeze-dried
and then homogenized using acid-washed glass mortars and
pestles.
Composite samples for chemical analyses were created by
combining subsamples of individuals of the same species
from the same location. Two sets of composite samples were
created from the same set of individuals: one set for FA anal-
ysis (n=64, two of which were duplicates) and one set for
MeHg analysis (n=147) and analysis of carbon, nitrogen,
and sulfur SI (n=71, 147, and 145, respectively). The in-
clusion of δ13C data in the modeling was limited to sam-
ples that did not contain calcium carbonate exoskeleton to
avoid biased measurement of organic δ13C. A subset of sam-
ples (n=15) representing dierent taxa were tested for po-
tential eects of inorganic carbon on δ13C values by compar-
ing acidified versus non-acidified samples. Based on this test-
ing, several taxa were excluded (amphipods, shrimp, isopods,
coral, brittle stars, and sea cucumber) due to more negative
δ13C values in acidified samples (mean ±SD =−2.4 ±2.0,
n=8), which indicated an eect of inorganic carbonate.
The subset of invertebrate δ13C data included in the statis-
tical models were the soft tissues of bivalves, gastropods, sea
stars, and whole bodies of polychaetes, sea anemones, and
nudibranchs.
FA analysis
FA composition was analyzed at the National Wildlife Re-
search Centre (NWRC, Environment and Climate Change
Canada, Ottawa, Canada) following modified methods de-
scribed by Schlechtriem et al. (2008) and Bligh and Dyer
(1959). Briefly, FAs were extracted using a 2:1 chloroform-
methanol solution containing 0.01% butylated hydroxyl
toluene, methylated using 1% methanolic sulfuric acid and
quantified on a Hewlett-Packard 6890 GC equipped with a
flame ionizing device and Supelco SP-2560 column (100 m ×
0.25 mm i.d. ×0.20 μm thick film). A more detailed version
of the method is described in Hebert et al. (2006).FApeaksin
the samples were compared to those of known FA retention
times using a 37-component FA standard (Supelco; No. 47885-
U). FAs were reported proportionally as % total FA composi-
tion (% TFA) for selected FAs. Eight method blanks were con-
ducted throughout the sample analyses; all showed less than
2 mg/g (dry weight [dw]) contamination. A known amount
of 5α-cholestane was added to each sample for calibration
and recovery calculations. The surrogate 5α-cholestane was
used to estimate recovery of all FAs, which was calculated
by the GC Chemstation software. Only FAs that constituted
greater than 5% of the total FAs in more than one taxon (here-
after referred to as major FAs) were reported (see Tabl e 1). FA
biomarkers were calculated as sums or ratios of FAs, based
on previous use as dietary indicators (see Tab le 1 ). Five dupli-
cate samples were analyzed to assess method precision. The
duplicate values of the 10 major FAs had a mean relative stan-
dard deviation (RSD) of 6.6% (range =4.1%–11.3%), while the
28 FAs with very low % compositions (<2.7% TFA) had lower
duplicate precision with a mean RSD of 22.4% (range =0.0%–
141.4%) because the values were closer to the detection limits
of the GC-FID. To assess the FA method accuracy, an NWRC-
based standard sample of Herring Gull egg was analyzed to
quantify recoveries of 17 FAs. The average recovery across all
17 detectable FAs was 103% (n=5, average recovery range per
analyte =85%–126%).
SI analysis
SI analyses were conducted at the Ján Veizer Stable Iso-
tope Laboratory (University of Ottawa, Ottawa, Canada) us-
ing a Delta Advantage Isotope Ratio Mass Spectrometer inter-
faced to a Conflo III and Vario EL Cube elemental analyzer. SIs
were measured on untreated samples. Tin capsules for carbon
and nitrogen SI analyses contained 1 ±0.1 mg of dry tissue,
while those for sulfur contained 6 ±0.5 mg. Calculations for
δ13C, δ15 N, and δ34 S were based on the standard reference
materials Vienna-PeeDee Belemnite, atmospheric N2gas, and
Vienna-Canyon Diablo Troilite, respectively. The mean stan-
dard deviation for duplicate SI samples (n=14 for each SI
ratio) was δ13C: 0.10 ±0.06,δ15 N: 0.29 ±0.43,andδ34S:
0.44 ±0.44. Analytical accuracies using reference material
samples (glutamic acid for δ13Candδ15 N, n=6; argentite for
δ34S, n=11) were 100.2 ±0.2for δ13 C, 101.3 ±1.8for
δ15 N, and 103.3 ±9.4for δ34S.
Trophic level was calculated using δ15 N ratios:
TLspecies =δ15Nspecies −δ15 NPOM
δ15NFC +1
where δ15Nspecies is the δ15 N value of the species in question,
δ15NPOM is the δ15 N of the corresponding particulate organic
matter (POM) collected at the same location as the species
in question, and the δ15NFC is the trophic fractionation con-
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Table 1. Major fatty acids (FAs) and FA biomarkers (FABMs) used in this
study and their dietary sources.
Major FA/FABM Sources
C14:0 myristic acid Diatomh
C16:0 palmitic acid Carnivoryc,d
C16:1n7 palmitoleic acid Diatomb
C18:0 stearic acid Dinoflagellateb
C18:1n9c oleic acid Carnivory, related to copepodsd,f
C20:1n9 eicosenoic acid Copepoda,d
C20:4n6 arachidonic acid (ARA) Benthic foragingj
C20:5n3 eicosapentaenoic acid (EPA) Diatomb
C22:5n3 docosapentaenoic acid (DPA) Dinoflagellateb
C22:6n3 docosahexaenoic acid (DHA) Dinoflagellateb,f
Omega 3: Omega 6 Aquatic carbon sourcee
(C18:2n-6 +C18:3n3) Terrestrial carbon sourcea,i
C22:6n-3/C20:5n-3 (DHA/EPA) Dinoflagellate dietb,g
C16:1n-7/C16:0 Diatom dietb,i
(C20:1 +C22:1) Copepod dieta,c
(C15:0 +C17:0) Bacteria dietd,i
aConnelly et al. (2014).
bDalsgaard et al. (2003).
cFalk-Petersen et al. (1987).
dGraeve et al. (1997).
eHebert and Popp (2018).
fKelly and Scheibling (2012).
gLege˙
zy ´
nska et al. (2014).
hLéveillé et al. (1997).
iMohan et al. (2016).
jStowasser et al. (2009).
stant of 3.4 (Minagawa and Wada 1984;Vander Zanden and
Rasmussen 2001). Site-specific δ15NPOM was provided by Dr.
Patricia Ramlal, DFO (unpublished data; surface water sam-
ple, collected on the same day as study invertebrates in the
same general area as benthic trawls; δ15NPOM near the Cape
Bathurst site was 4.01,δ15NPOM near the Mackenzie Trough
site was 4.36).
MeHg analysis
MeHg concentrations in freeze-dried samples were mea-
sured at the NWRC. Samples (20−50 mg) were digested with
17.5% nitric acid at 60 ◦C for 16 h. Sample extracts of 50 μL
were added to ultrapure water with acetate buer. Samples
were ethylated with 30 μL of sodium tetraethyl borate for 30
min before analysis on a Tekran 2700 Methyl Mercury An-
alyzer (gas chromatography cold-vapor atomic fluorescence
spectrophotometer) equipped with a 2621 M Automatic sam-
ple changer. Method blanks, duplicates, and standard refer-
ence materials were included with every set of extractions.
Method precision, given as the mean RSD for duplicate sam-
ples, was 1.8% (range =0%–5.4%, n=17). Method accuracy,
based on the mean recovery of MeHg in four standard refer-
ence materials (NIST 2976 Mussel Tissue, NRC TORT-3 Lobster
Hepatopancreas, NRC DORM-4 Fish, IAEA-436 Fish Flesh), was
95% (range =87%–104%, n=34).
Data analysis
The interpretation of δ13Candδ34 S values of benthic in-
vertebrates was based on general trends reported in other
Arctic marine studies due to the absence of SI data for diet
end members at the study sites. Across the CBS, published
values of pelagic δ13C are more negative (mean =−26.9,
n=11) with a narrow range of −27.4 to −25.8compared
to more positive (mean =−22.9,n=11) and variable val-
ues of sediment organic carbon (−25.9 to −14.7)(Stasko et
al. 2018a). Thus, higher δ13C values of benthic invertebrates
were interpreted as a greater dietary reliance on benthic car-
bon within a “benthic-pelagic continuum” (as per Stasko et al.
2018a). However, it is possible that δ13C variation of benthic
invertebrate diet may in part reflect other processes that al-
ter carbon isotope ratios of organic matter such as microbial
degradation in sediment. Limited information is available on
δ34S dynamics in the CBS, and we assumed that variation in
δ34S values of benthic invertebrates reflected dierences in
the extent of bacterial sulfate reduction within sediments or
the water column, which can alter the isotopic composition
of locally available sulfur (Habicht et al. 1998). Both study
sites are entirely marine, and therefore terrestrial or estuar-
ine influences on δ34S values were not relevant.
Statistical analyses were performed using R, versions 3.6.3–
4.1.0. Normality was evaluated with Shapiro–Wilks tests, and
the equality of variance between locations was assessed with
Levene’s tests. FA, FABM, and MeHg concentrations were
log-transformed prior to modeling to satisfy normality as-
sumptions. Pearson correlations with Holm–Bonferroni cor-
rected p-values were used to determine associations between
FA/FABM and SI values and MeHg concentration. One-way
analyses of variance (ANOVAs) and Tukey’s post hoc honestly
significant dierence (HSD) tests were used to compare SI
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ratios between feeding guilds. Because of the large number
of variables in this study, only statistically significant results
were reported. Variation is reported as standard deviation
of the mean unless indicated otherwise. Four models were
tested with dierent subsets of data (due to dierences in
sample sizes among explanatory variables) to evaluate the
influences of SI, FA, FABM, and location on MeHg concentra-
tions. For the models that included FA/FABM variables, the SI
values and MeHg concentrations reflect a mean value for all
samples of a given species or taxa because there were fewer
FA analyses performed than MeHg and SI analyses. The best
models for each data set were selected using Akaike’s Infor-
mation Criterion (AICc) adjusted for small samples. The mod-
els passed assumptions of variance homogeneity and normal-
ity of residuals.
Principal component analysis (PCA) using log-ratio-
transformed FA and log-transformed FABM variables was
conducted to reduce the dimensionality of the large dataset
and allow for the visualization of dominant FA/FABM trends
as well as correlations between independent variables. Prin-
cipal component loadings represent the correlation between
independent variables and each principal component. Indi-
vidual observations were plotted to visualize the relationship
between the data points and principal components and in-
dependent variables. Assumptions for the PCA were tested
via Bartlett test for collinearity, Kaiser–Meyer–Olkin test for
sampling adequacy, and correlation matrix positivity.
Results
Taxonomic identification of faunal composition
Field identifications and DNA determinations confirmed
the faunal assemblage of 476 animals consisted of 53 identi-
fiable taxa, some of which are shown in Fig. 2. The barcoding
process amplified barcodes in 72 of the 75 specimens with 65
of the DNA barcodes having sucient amplification to match
DNA barcodes found in the BOLD and NCBI DNA libraries.
The % alignment of the matches was variable, as is seen in
Table S2. Replicate specimens received identical taxonomic
matches though alignment percentages varied. A total of 30
species were found at the CB site and 38 species at the MT site,
with 16 species found at both sites. Study taxa, their benthic
habitat zone, and feeding guilds are presented in Table S3.
MeHg concentrations
MeHg concentrations of individual samples ranged over
two orders of magnitude from 3 to 421 ng/g dw. As reported in
Table 2 , the highest mean MeHg concentrations were found
in carnivores from the MT location, specifically the sea star
Crossaster papposus and the pycnogonid Nymphonidae spp. (367
±93 ng/g and 299 ng/g, respectively). Meanwhile, the lowest
mean MeHg concentrations were found in the CB amphipods
Ampelisca spp. and Arctolembos arcticus (4 ±1ng/gand7±
2 ng/g, respectively). The amphipods and the echinoderms
had the greatest intragroup variation in mean MeHg con-
centration (4–231 ng/g and 8–367 ng/g, respectively). For the
tissue-specific measurements in isopods and shrimp, mean
MeHg concentrations were higher in soft tissues (107 ±
Fig. 2. Photographs of a selection of species included in this
study, organized by feeding guild (animals not to scale, size
range 0.5–12 cm). Photo source: C. McClelland.
5 ng/g, and 150 ±80 ng/g, respectively) than exoskeleton tis-
sues (38 ±5 ng/g, and 70 ±30 ng/g, respectively) (see Table
S4 for concentrations).
Trophic position and diet indicators
Nitrogen SI values were the lowest in CB sipunculids
(5.2 ±1.1) and the MT gastropod Tachyrhynchus spp. (5.9)
(Table 2 and Fig. 3B). The highest δ15 N value (14.2)was
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Table 2 . Species, location, feeding guild (FG), tissue, number of individuals (nind) and composites (ncomp) analyzed for carbon,
nitrogen, and sulfur stable isotopes and MeHg concentrations (±standard deviations) with derived trophic position (TP).
Taxon Location FGaTissuebnind (ncomp) δ15N()δ13 C()δ34S() MeHg (ng/g, dw) TP
Amphipoda
Acanthostepheia behringiensis (L)cCB CV WB 7 (3) 12.4 ±0.2 – 20.6 ±0.4 80 ±41 3.5 ±0
MT CV WB 7 (3) 11.4 ±0.3 – 18.6 ±0.4 62 ±53.1±0.1
Acanthostepheia behringiensis (S)cCB CV WB 4 (1) 8.6 – 21.1 8 2.2
Ampelisca spp. CB SF WB 12 (3) 7.6 ±0.6 – 19.7 ±0.2 4 ±12.1±0.2
Anonyx nugax MT CV WB 10 (3) 13.7 ±0.5 – 20.7 ±0.2 231 ±70 3.7 ±0.1
Arctolembos arcticus CB DF WB 7 (3) 8 ±0.7 – 20.5 ±0.8 7 ±22.2±0.2
Paramphithoe spp. MT DF WB 4 (1) 13.2 – – 127 3.3
Pleustes panoplus CB DF WB 7 (3) 9.9 ±0.4 – 21.2 ±0.9 21 ±22.7±0.1
Rhachotropis aculeata CB CV WB 5 (2) 11.3 ±0.7 – 20.1 ±0.2 55 ±33 3.1 ±0.2
Unidentified amphipod spp. MT CV WB 7 (3) 12.6 ±1.3 – 20.6 ±0.1 79 ±18 3.4 ±0.4
Cumacea
Diastylidae spp. CB DF WB 55 (3) 7.3 ±0.2 – 21.7 ±0.2 8 ±12±0.1
MT DF WB 15 (3) 7.5 ±0.7 – 21.7 ±0.5 12 ±11.9±0.2
Isopoda
Munnopsis typica CB DF WB 4 (1) 10.4 – 17 29 2.7
Saduria entomon MT CV WB 7 (3) 11.5 ±0.2 – 20.8 ±0.7 53 ±12 3.1 ±0.1
Saduria sabini CB CV WB 2 (1) 13.6 – 17.6 39 3.5
MT CV WB 6 (3) 10.5 ±0.1 – 20.4 ±0.9 47 ±72.8±0
Synidotea bicuspida CB DF WB 7 (3) 11.1 ±0.1 – 21.7 ±0.4 19 ±13.1±0
MT DF WB 7 (3) 11 ±0.9 – 21.9 ±0.4 28 ±32.9±0.3
Decapoda
Eualus gaimardii CB CV WB 10 (3) 12.6 ±0.2 – 20 ±0.2 52 ±83.5±0.1
MT CV WB 7 (3) 12.7 ±0.2 – 20.7 ±0 106 ±27 3.4 ±0.1
Sabinea septemcarinata CB DF WB 3 (1) 12.9 – 20.9 35 3.3
MT DF WB 6 (3) 12.8 ±0.3 – 17.3 ±0.4 93 ±27 3.5 ±0.1
Spirontocaris spp. MT CV WB 7 (3) 11.3 ±0.3 – 20.1 ±1.5 134 ±26 3 ±0.1
Pycnogonidae
Nymphonidae spp. CB CV WB 18 (1) 12.3 – 20.8 32 3.4
MT CV WB 3 (1) 14.2 – 19.3 299 3.9
Anthozoa
Actiniaria spp. CB CV WB 9 (3) 11.8 ±0.5 −22.4 ±0.4 19.2 ±0.5 29 ±33.3±0.2
MT CV WB 6 (3) 12.3 ±0.8 −24.3 ±0.3 19.6 ±0.4 48 ±93.3±0.2
Gersemia rubiformis MT SF WB 3 (1) 10.4 23.7 17 2.6
Asterozoa
Crossaster papposus MT CV VM 8 (3) 12.8 ±0.2 −21.7 ±0.8 21.2 ±0.6 367 ±93 3.5 ±0.1
Leptasterias littoralis CB CV VM 6 (3) 9.8 ±0.4 −21.9 ±1.1 20.1 ±0.3 66 ±72.7±0.1
Leptasterias spp. MT CV VM 8 (3) 10.7 ±0.7 −20.9 ±0.6 20.5 ±0.8 244 ±111 2.9 ±0.2
Ophiocten sericeum CB DF CD 9 (2) 8.8 ±0.4 – 22 ±0.7 8.±12.4±0.1
MT DF CD 14 (3) 9.7 ±0.1 – 21.8 ±0.9 14 ±12.6±0
Ophiacantha bidentata MT DF CD 5 (2) 12.4 ±0.5 – 23.2 ±0.1 42 ±10 3.4 ±0.1
Stegophiura nodosa CB DF CD 7 (2) 10 ±0.6 – 26.8 ±1.8 13 ±02.8±0.2
Holothuroidea
Psolus spp. MT SF WB 4 (1) 8.3 – 21.9 17 2.2
Nemertea
Amphiporus spp. CB CV WB 6 (2) 10.2 ±0.2 −22.8 ±0.3 19.9 ±0.4 34 ±52.8±0.1
Sipuncula
Unidentified sipunculid sp. A CB DF WB 4 (2) 5.2 ±1.1 – 19.4 ±0.4 11 ±51.4±0.3
MT DF WB 7 (3) 6.2 ±0.6 – 20 ±0.3 13 ±21.5±0.2
Polychaeta
Bylgides spp. CB CV WB 7 (2) 12 ±0.2 −20.6 ±0.2 18.2 24 ±13.4±0
Bylgides sarsi MT CV WB 7 (3) 11.2 ±0.2 −22.2 ±0.2 17.9 ±0.2 54 ±12 3 ±0.1
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Table 2. (concluded).
Taxon Location FGaTissuebnind (ncomp) δ15N()δ13 C()δ34S() MeHg (ng/g, dw) TP
Gattyana cirrhosa CB CV WB 4 (2) 11.5 ±0.1 −20.9 ±0.3 20.5 ±0.2 69 ±25 3.2 ±0
Harmothoe imbricata CB CV WB 6 (2) 11 ±0.6 −20.9 ±0.8 17.8 ±1.5 40 ±33.1±0.2
Nephtys spp. MT CV WB 7 (3) 10.8 ±0.4 −20.2 ±0.4 17.3 ±0.5 149 ±58 2.9 ±0.1
Nereis zonata MT DF WB 3 (1) 8.7 −22.1 15.3 20 2.3
Pectinaria hyperborea CB DF WB 4 (1) 7.1 −21.5 16.4 48 1.9
Phyllodoce groenlandica CB CV WB 4 (2) 11.2 ±0.2 −21 ±0.4 17.4 ±0.8 41 ±11 3.1 ±0
Unidentified polychaete spp. MT DF WB 6 (3) 8.6 ±0.7 −21.6 ±0.1 15.3 ±0.3 22 ±22.2±0.2
Bivalvia
Astarte borealis CB SF VM 8 (3) 9.2 ±0.6 −22.4 ±0.3 19.3 ±0.4 32 ±82.5±0.2
Astarte spp. CB SF VM 4 (2) 9.9 ±0−21.1 ±0 20.8 ±0.1 36 ±22.7±0
MT SF VM 7 (3) 9.3 ±0.1 −21.5 ±120±0.9 59 ±12 2.5 ±0
Clinocardium ciliatum CB SF VM 3 (1) 9.2 −23.3 21.1 71 2.5
MT SF VM 9 (3) 9.8 ±1−22.5 ±0.2 19.7 ±0.3 173 ±55 2.6 ±0.3
Cyclocardia spp. MT SF VM 2 (1) 6.9 −21.8 19.9 89 1.8
Nuculana pernula MT DF VM 4 (2) 9.2 ±0.6 −23.3 ±0.4 18.4 ±0.2 47 ±12.4±0.2
Similipecten greenlandicus CB DF VM 4 (1) 10.5 −21.5 21.1 76 2.9
MT DF VM 16 (2) 10.8 ±0.4 −23.8 ±0.1 21.1 ±0.3 146 ±21 2.9 ±0.1
Yoldia hyperborea CB DF VM 3 (1) 8.1 −22.8 19.2 15 2.2
Gastropoda
Boreotrophon truncatus MT CV VM 3 (1) 9.7 −20.0 20.4 109 2.6
Buccinum polare MT CV VM 5 (2) 11.2 ±1.1 −20.5 ±0.4 14.9 ±4.8 172 ±49 3 ±0.3
Buccinum spp. MT CV VM 5 (2) 9.9 ±0.7 −21.7 ±0.1 18.2 ±0.7 89 ±28 2.6 ±0.2
Cryptonatica spp. MT CV VM 6 (2) 10.5 ±0.3 −20.8 ±0.5 18.1 ±0.5 141 ±53 2.8 ±0.1
Cylichna spp. MT CV VM 3 (1) 8.4 −22.7 18.8 26 2.2
Margarites costalis MT DF VM 8 (2) 10.7 ±2.3 −21.6 ±0.6 17.8 ±1.6 61 ±62.9±0.7
Neptunea spp. MT CV VM 5 (2) 10.5 ±0.6 −20.3 ±0.1 20.5 ±0.3 268 ±19 2.8 ±0.2
Tachyrhynchus spp. MT CV VM 2 (1) 5.9 −20.4 19.5 14 1.4
Nudibranchia
Dendronotus frondosus CB CV WB 5 (2) 13.7 ±0.3 −22 ±0.2 18.2 ±0.6 42 ±63.9±0.1
MT CV WB 3 (1) 8.6 −25.5 20.1 32 2.2
aFeeding guild: SF, suspension feeder; DF, deposit feeder; CV, carnivore.
bTissue: WB, whole body; VM, viscera and muscle; CD, central disk.
cA. behringiensis were divided by size: L, large; S, small.
found in the MT pycnogonids Nymphonidae spp. Feeding guild
and δ15N were closely associated (one-way ANOVA, p< 0.0001,
n=147) with deposit feeders and suspension feeders hav-
ing significantly lower δ15N values than carnivores (Tukey
HSD, p< 0.0001 for both). Despite the dierence in depth,
the range of δ15N values was similar at both sites. Log
MeHg concentration showed a strong linear relationship
with δ15N values at both locations combined (Fig. 4A,δ15 N
slope =0.14 ±0.02, r2=0.37, p< 0.001, n=147). There was
no dierence in slope between locations (ANCOVA, interac-
tion of δ15N∗location, p=0.669).
The range of δ13C values was greater at the MT site (−25.5
to −19.8) than at the CB site (−23.2 to −20.3)(Tabl e 2).
There was no dierence (one-way ANOVA, p=0.166, n=71)
in δ13C values among feeding guilds though only a subset of
samples was used in statistical tests owing to the exclusion
of animals containing carbonates. Carbon SI values were pos-
itively correlated with log MeHg concentration although it
was not a strong explanatory variable in linear regressions
(Fig. 4B,δ13Cslope=0.08 ±0.04, r2=0.07, p< 0.05, n=71).
Mean δ34S values ranged from 14.9 ±4.8in the MT snail
Buccinum polare to 26.8 ±1.8in the brittle star Stegophiura
nodosa (Table 2 ). Although much of the δ34S range was shared
between the two sites, the lowest individual δ34S values were
found in MT polychaetes (Unident. polychaete spp., 15.0)
and MT snails (B. polare, 11.5). There was no dierence (one-
way ANOVA, p=0.051, n=145) in δ34S values among feeding
guilds. MeHg concentrations were not correlated with δ34S
values (Fig. 4C).
FA composition varied considerably among the taxa ana-
lyzed. Of the 29 FA analytes, 10 FAs were considered ma-
jor (i.e., constituted greater than 5% of TFA in more than
one taxon), shown in Table 1 . Generally, the major FAs re-
sponsible for the highest proportion of the total FA compo-
sition were C20:5n3 (26.7 ±6.7%), C16:1n7 (17.4 ±10.1%),
C16:0 (15.7 ±2.2%), and C22:6n3 (11.1 ±7.2%). Between
paired species, major FA proportions were similar at the two
locations, with the exceptions of the amphipod Aspergillus
beringiensis, the polychaete Bylgides spp., and the unidentified
sipunculids (Fig. S1).
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Fig. 3. Bargraphs(±SE bars) of (A) mean MeHg concentrations and (B) mean δ15 N values in paired species samples from the
Cape Bathurst (CB) and Mackenzie Trough (MT) collection sites.
Minor dierences in organic matter sources were found be-
tween the two study locations based on PCAs using log-ratio-
transformed major FAs and log-transformed FABMs (Fig. S2).
The location-based confidence ellipses suggested that benthic
invertebrates at the CB site relied more heavily on diatoms,
whereas benthic invertebrates at the MT site incorporated
more dinoflagellates in their diets. Likewise, the location-
based confidence ellipses reflected a more aquatic-origin and
diatom-based food web at CB and more terrestrial-origin and
bacteria-based food sources at MT.
FAs and FABMs were correlated with SI indicators and
also explained MeHg concentrations in invertebrates. Strong
positive Pearson correlations were found between δ15Nand
C18:1n9c (r=0.53, p< 0.001), C20:1n9 (r=0.44, p< 0.05),
and copepod FABM (r=0.42, p< 0.05) (Tabl e 3). The corre-
lation between δ34S values and C22:5n3 was strongly nega-
tive (r=−0.53, p< 0.005). MeHg concentration was nega-
tively correlated with diatom related FA (C16:1n7, r=−0.49,
p< 0.005) and FABM (r=−0.44, p< 0.05). MeHg correla-
tions were both positively and negatively correlated with di-
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Fig. 4. Scatter plots of methylmercury (MeHg) concentrations in relation to (A) δ15N, (B) δ13 Cand(C)δ34 S with linear regression
lines, when significant. See text for statistical details.
Table 3 . Pearson correlations between fatty acid/FA biomarker (FA/FABM)
proportions (percent of total FA) and either stable isotope (SI) values or
methylmercury (MeHg) concentration (n=number of samples), coecients
(r), and Holm–Bonferroni (α=0.05) corrected p-values.
FA/FABM Variable nrp-value
Log C18:1n9c (%TFA) δ15N 62 0.53 0.0006
Log C22:5n3 (%TFA) δ34 S61−0.53 0.0010
Log C16:1n7 (%TFA) Log MeHg 62 −0.49 0.0034
Log C20:1n9 (%TFA) δ15N 62 0.44 0.0179
Log C22:5n3 (%TFA) Log MeHg 62 0.44 0.0196
Log Diatom FABM Log MeHg 62 −0.44 0.0231
Log C20:4n6 (%TFA) Log MeHg 62 0.42 0.0388
Log Copepod FABM (%TFA) δ15N 62 0.42 0.0434
etary indicators (Table 3 , Fig. S3). The two negative correla-
tions both involved variables that related to diatom feeding
(C16:1n7, r=−0.49, p< 0.005; diatom FABM, r=−0.44,
p< 0.05) (Fig. 5). Meanwhile, MeHg concentration was pos-
itively associated with a benthic diet, indicated by C20:4n6
(r=0.42, p< 0.05) and dinoflagellate-based feeding indicated
by C22:5n3 (r=0.44, p< 0.05). No significant relationship was
found between MeHg concentrations and the concentrations
of terrestrial, bacteria, or copepod FABM.
Multivariate models for MeHg accumulation
The multivariate model that best explained variation in
benthic invertebrate MeHg concentration included log C16:0,
log C18:1n9c, log C22:6n3, mean δ15N values, and location
variables (r2=0.69, p< 0.005, n=62) (Table 4 ). Location and
δ15N values were the strongest explanatory variables across
multiple linear regression models of all possible combina-
tions of explanatory variables. The same three FAs were sig-
nificant explanatory variables in both models that included
FAs: C16:0 (p< 0.005) and C18:1n9c (p< 0.05), both indica-
tive of carnivory; and C22:6n3, indicative of dinoflagellate
diet (p< 0.05). Diatom FABM was the only FABM that ex-
plained a significant amount of variation in MeHg concen-
trations (data set 3, partial r2=0.12, p< 0.05), but it should
be noted that it was only significant in the absence of SI ex-
planatory variables. Note that due to dierences in sample
size, the AICc values in Table 4 simply reflect the best AICc
for model fit of each data set and are not comparable to each
other.
Discussion
This study demonstrated that invertebrate MeHg concen-
trations varied widely among taxa and were strongly influ-
enced by diet and location. Trophic position was the domi-
nant explanatory variable, reflecting the importance of MeHg
biomagnification. Variation in organic matter sources to the
benthic food webs also explained small but significant dier-
ences in MeHg accumulation. This study further showed that
benthic marine invertebrates were exposed to more MeHg at
the MT location, closer to the Mackenzie River mouth than
at the CB location.
Canadian Science Publishing
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Fig. 5. Scatterplots for log mean methylmercury (MeHg) concentrations with respect to (A) Log C16:1n7 (%TFA), (B) Log C20:4n6
(%TFA), (C) Log C22:5n3 (%TFA), and (D) Log Diatom FABM (unitless). See Table 3 for statistical details.
MeHg in CBS benthic food webs
MeHg concentrations in the benthic invertebrates of this
study varied by two orders of magnitude over three trophic
levels. Dierential prey selection could have important con-
sequences for MeHg trophic transfer to marine vertebrate
predators (Li et al. 2022). The highest MeHg concentrations
in this study, found in the carnivorous sunstar C. papposus
(367 ±93 ng/g dw), are higher than most MeHg and total
mercury concentrations in Arctic marine invertebrates re-
ported in other studies, where concentrations are generally
below 210 ng/g dw (Barst et al. 2022). To our knowledge, the
highest published MeHg concentration for Arctic marine in-
vertebrates was observed in shrimp from the eastern Cana-
dian Arctic (870 ng/g dw, converted from a wet weight in
Pedro et al. 2019) . Although limited information is avail-
able for the toxicological eects of MeHg on marine inverte-
brates, the concentrations observed in this study are below
known critical concentrations associated with a risk of ef-
fects documented in invertebrate toxicity studies (Barst et al.
2022).
Total mercury was not measured in this study, though
those concentrations have been previously reported for ma-
rine benthic invertebrates. The proportion of total mercury
as MeHg can vary widely among benthic invertebrate taxa,
for example, 13–85% (Hilgendag et al. 2022), 9–90% (Fox et al.
2017), and 40–72% (Loseto et al. 2008). Percent MeHg in tis-
sue increases with invertebrate trophic position due to the
biomagnifying behavior of MeHg but not inorganic mercury.
Our results for MeHg are thus more toxicologically relevant
than total mercury for evaluating exposure to marine verte-
brate consumers.
The influence of trophic position and feeding
guild on MeHg concentration
Carnivorous invertebrates had higher δ15N values and
MeHg concentrations, which reflects the eect of food web
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Table 4 . Parameters of multiple regression models for MeHg concentration and number of samples in model (n) for four subsets
of the data that included dierent explanatory variables.
Model (variables tested) Variables in best model Variable coecient Partial r2Model r2AICc p-value
Model 1 (MeHg data for all samples
[n=145] vs. location, δ34S, δ15 N)
δ15N 0.13 0.34
Location (MT) 0.35 0.17 0.55 74.7 2.2 ×10−16
δ34S−0.03 0.03
Model 2 (MeHg data for samples with
δ13Cdata[n=70]∗vs. location, δ13C,
δ34S, δ15 N)
Location (MT) 0.40 0.24
δ15N 0.08 0.12 0.58 8.2 1.10 ×10−11
δ13C 0.12 0.11
δ34S 0.08 0.11
Model 3 (MeHg data for samples with
FA and FABM data [n=62] vs. location,
FA, FABM)
Location (MT) 0.35 0.17
Diatom FABM −0.40 0.13
Log C22:6n3 0.32 0.07 0.46 50.3 1.33 ×10−6
Log C16:0 −1.25 0.06
Log C18:1n9c 0.32 0.03
Model 4 (MeHg data for samples with
FA and FABM data [n=62] vs. location,
mean δ15N, mean δ34 S, all FA and
FABM)
Mean δ15N 0.14 0.32
Location (MT) 0.40 0.21
Log C22:6n3 0.37 0.08 0.69 16.2 4.81 ×10−13
Log C16:0 −1.17 0.06
Log C18:1n9c −0.25 0.02
∗δ13C for this model includes values only from invertebrate tissues with low/no carbonate composition.
biomagnification on biota feeding at a higher trophic posi-
tion. The range of δ15N values in CBS invertebrate fauna was
comparable to those in other Arctic Ocean studies (Hobson et
al. 2002;Connelly et al. 2014;Stasko et al. 2018b;Hilgendag
et al. 2022). The linear relationship between log MeHg con-
centration and δ15N had a mean trophic magnification slope
(TMS)of0.14(±0.02 SE), which is lower than the average
MeHg TMS of 0.21 (±0.09 SD) for polar marine ecosystems
(Lavoie et al. 2013) and that of 0.21 for a Chukchi Sea ben-
thic food web (Fox et al. 2017). The dierence in depth be-
tween the two locations (CB, 22 m; MT, 116 m) did not in-
fluence the δ15N values of the animals in this study. In con-
trast, Stasko et al. (2018b)found that δ15N values of benthic
invertebrates generally increased with depth across the CBS
shelf/shelf break. However, their study examined the influ-
ence of water depth over a much greater area with depth
range (20–1000 m) and mentioned that δ15N could vary ac-
cording to bathymetric separation of water masses because of
dierences in age and origin of POM contained therein. The
δ15N of invertebrates was a dominant explanatory variable in
all the multiple linear regression models that included SIs as
a predictor of MeHg concentrations.
The other two feeding guilds, deposit and suspension feed-
ers, had overlapping MeHg concentrations, suggesting that
those dierent feeding strategies did not strongly aect their
MeHg exposure. This may be in part because some benthic
invertebrates, such as ampeliscids, can switch feeding behav-
iors to capitalize on shifts in food availability (Conlan et al.
2019). In addition, the diet of suspension feeders can be more
connected to organic matter in the water column (such as
phytoplankton), while that of deposit feeders contains more
organic matter of sediment origin (such as benthic protists
and bacteria) (Mohan et al. 2016;McMahon et al. 2021a). How-
ever, considerable overlap of organic matter sources likely oc-
curs given their close proximity to the sediment surface. The
δ13C values (albeit limited) for suspension and deposit feed-
ers overlapped among taxa in the CBS assemblage. An exper-
imental feeding study of blue mussels (Mytilus edulis)using
radiolabeled inorganic mercury and MeHg showed that this
suspension feeder obtains its mercury from particles in both
the water column and sediment (Gagnon and Fisher 1997).
Overall, the categorical separation of deposit and suspension
feeder was not informative in explaining MeHg bioaccumu-
lation in this study.
The influence of organic matter sources on
MeHg accumulation
Carbon SI, FAs, and FABMs highlighted variation in the
types of organic matter sources available at the two study lo-
cations and influences on MeHg exposure to benthic inverte-
brates. Dierent organic matter sources, such as planktonic
or benthic microalgae, bacteria, or detritus of marine or ter-
restrial origin, can vary in their MeHg content, with poten-
tial to aect dietary exposure of marine primary consumers
(Cresson et al. 2014;Jonsson et al. 2017;Liu et al. 2020). In
this study, FAs and FABMs indicative of diatom-based diets
were negatively correlated with MeHg concentrations. The
FABM for another main phytoplankton group of the Arctic
Ocean, dinoflagellates, was positively correlated with MeHg
concentration. Possibly, this dierence is because dinoflagel-
lates, unlike diatoms, can be heterotrophic and can use col-
loidal matter as food sources (Lee and Fisher 2016) . There-
fore, dinoflagellates can have increased exposure to metals
bound to terrestrial organic matter used for energy (Tranvik
et al. 1993). Both diatoms and dinoflagellate cysts are present
in sediment on the Mackenzie Shelf of the CBS (McMahon et
al. 2021b). In addition, MeHg concentrations of invertebrates
without a carbonaceous exoskeleton were positively corre-
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Arctic Science 10: 305–320 (2024) | dx.doi.org/10.1139/as-2023-0021 317
lated with δ13C values, suggesting higher exposure to animals
with a greater reliance on carbon from benthic sources rather
than pelagic origin. While the range of δ13C values was wider
for the benthic assemblage at the MT site (−25.5 to −19.8)
compared to the CB site (−23.2 to −20.3), Ehrman et al. (2022)
concluded that sediment organic matter sources to benthic
invertebrates were generally similar across the CBS shelf and
upper slope based on SI. The correlations in this study sug-
gest that organic matter sources influenced MeHg bioaccu-
mulation although the statistical eects were weak. Further
research is needed to examine the MeHg content of organic
matter sources supporting the benthic food web in the CBS.
Sulfur isotope values in the CBS benthic food web did not
explain dierences in MeHg concentrations, in contrast with
correlations between δ34S and mercury concentrations re-
ported in other studies (Elliot and Elliot 2016;Willacker et al.
2017;Góngora et al. 2018). Seawater has a δ34S value of ∼21
(Tostevin et al. 2014), and many of the taxa in this study had
aδ34S signature comparable to that of seawater. There were
some taxa, however, that strayed from the seawater bench-
mark; for instance, the δ34S value of the CB deposit-feeding
brittle star S. nodosa (26.8 ±1.8) was higher than seawater
and higher than the δ34S value of S. nodosa in other inverte-
brate δ34S studies (Góngora et al. 2018;Reinhart et al. 2018).
The lowest δ34S value in this study was found in the MT car-
nivorous snail B. polare (14.9 ±4.8), possibly indicating the
influence of sedimentary sulfides with depleted δ34S resulting
from sulfate reduction.
TheinfluenceoflocationonMeHg
accumulation
Higher MeHg concentrations were observed in benthic in-
vertebrates from the MT site compared with the CB site. Loca-
tion was statistically significant in every predictive model, ac-
counting for 30–41% of the variation in MeHg concentrations.
Of the 30 samples with the highest MeHg concentrations in
this study, 29 were collected at the MT site. The dierences in
the origin of organic matter sources supporting the benthic
fauna between the two study sites may have contributed to
the importance of location in the models explaining MeHg
variance. The FABMs for terrestrial organic matter and bac-
teria were higher in MT invertebrates, suggesting that the
MT food web relied to some extent on energy sources trans-
ported by the Mackenzie River. Connelly et al. (2012) simi-
larly found that terrestrial FAs were the highest at stations
near the Mackenzie River and continuously decreased with
distance from the river outflow across the shelf. FA biomark-
ers indicated that the CB invertebrates relied more heavily
on aquatic (autochthonous) energy sources, with significant
reliance on diatoms which were negatively correlated with in-
vertebrate MeHg concentrations. It is possible that since the
CB site was shallow and within the euphotic zone, both ben-
thic and sympagic diatoms of lower MeHg content may have
been significant organic matter resources.
The bathymetrically stratified water masses of the Arctic
Ocean have variable concentrations of seawater MeHg, with
subsurface maxima at depths of 100–300 m (Wang et al. 2018).
Therefore, the MT site with a depth of 116 m may have been
exposed to higher seawater MeHg concentrations associated
with subsurface layers, in comparison to the CB site at a shal-
low depth of 22 m. Wang et al. (2018) also suggest that high
seawater MeHg concentrations of the CBS may be due to wa-
ter masses from the Bering Strait and Chukchi Sea that en-
tered the CBS within the upper haloclines. Mercury in up-
welling water masses may contribute to MeHg concentra-
tion dierences as well. Upwelling water at the CB site is
drawn from the Pacific layer (Carmack and McDonald 2002),
whereas it is the deeper Atlantic water that upwells at the
MT site (Williams et al. 2006). Soerenson et al. (2016)indi-
cated that the Atlantic Ocean water mass delivered almost
three times more MeHg to the subsurface Arctic Ocean than
the Pacific Ocean water mass.
The biogeochemical processes controlling mercury methy-
lation rates in benthic habitats were not examined in this
study. Recent findings suggest that the sediment production
of MeHg on the Arctic Ocean shelf may be important (Kim et
al. 2020;Jonsson et al. 2022). Thus, MeHg exposure to the ben-
thic invertebrate food webs may have occurred via porewa-
ter diusion from sediment. The influx of THg to the Arctic
Ocean is strongly associated with river discharge, and prox-
imity to major rivers has been shown to increase the amount
of THg in marine sediments (Fitzgerald et al. 2007;Leitch et
al. 2007). The proximity to the Mackenzie River delta there-
fore could plausibly contribute to the higher MeHg concen-
trations in the MT invertebrates of this study. Although this
study was limited to two locations, it is consistent with the
findings of other studies of spatially variable MeHg concen-
trations in the Arctic Ocean (Brown et al. 2016;St. Louis et
al. 2011;Wang et al. 2018). Future studies integrating THg
concentrations and mercury methylation in sediment with
invertebrate MeHg concentrations may further clarify spatial
patterns of bioaccumulation within the CBS.
Conclusion
This study generated the most comprehensive dataset to
date on MeHg concentrations in a benthic marine faunal as-
semblage in the Arctic Ocean. Diet, as indicated by trophic
position and organic matter sources, influenced MeHg con-
centrations of the benthic invertebrates. Spatial variation in
MeHg concentrations between two sites is indicative of im-
portant environmental processes controlling food web expo-
sure. Future research should focus on evaluating the role of
biogeochemical processes in controlling MeHg uptake in the
benthic food web of the CBS shelf, specifically contributions
from sediment and water-column mercury methylation as
well as riverine transport of mercury.
Acknowledgements
This research was funded by the Northern Scientific Train-
ing Program (Polar Knowledge Canada), the Canadian Mu-
seum of Nature (CMN, Dr. Kathleen Conlan), the University
of Saskatchewan (Dr. Alec Aitken), the Department of Fish-
eries and Oceans (DFO, Don Cobb), and the Geological Survey
of Canada (GSC, Dr. Steve Blasco) for the Northern Coastal
Marine Studies Program. Additional funding was provided by
Canadian Science Publishing
318 Arctic Science 10: 305–320 (2024) | dx.doi.org/10.1139/as-2023-0021
Environment and Climate Change Canada (ECCC). We recog-
nize the support of Fisheries Joint Management Committee
of the Inuvialuit Settlement Region, Dr. Bill Williams (DFO),
Dr. Chris Parrish (Memorial University), Dr. Stacey Robinson
(ECCC), Dr. Frances Pick (University of Ottawa), the Aurora
Research Institute (Inuvik), the Canadian Hydrographic Ser-
vice, and the captain and crew of CCGS Nahidik (2007). Chem-
ical analyses, advice, and support were provided by Dr. Craig
Hebert, David Carpenter, Michelle Zanuttig and Francois Cyr
(National Wildlife Research Centre, ECCC), Dr. Virginie Roy
(DFO), Paul Middlestead, Wendy Abdi and Patricia Wickham
(Ján Veizer Stable Isotope Laboratory, University of Ottawa),
and Roger Bull (Laboratory of Molecular Biodiversity, CMN).
We also appreciate the constructive comments of two anony-
mous reviewers.
Article information
History dates
Received: 13 April 2023
Accepted: 24 October 2023
Version of record online: 25 January 2024
Copyright
© 2024 Authors Conlan, Aitken, and Forbes, and The Crown.
This work is licensed under a Creative Commons Attribution
4.0 International License (CC BY 4.0), which permits unre-
stricted use, distribution, and reproduction in any medium,
provided the original author(s) and source are credited.
Data availability
Data generated or analyzed during this study are avail-
able in the ECCC Data Catalogue repository (identi-
fier: 6a104e8f-bfa8-442d-87a9-3c85e7b31deb) at: https:
//catalogue.ec.gc.ca/geonetwork/srv/eng/catalog.search#/
metadata/6a104e8f-bf a8-442d-87a9-3c85e7b31deb.
Author information
Author ORCIDs
Christine McClelland https://orcid.org/0000-0003-2645-0221
John Chételat https://orcid.org/0000-0002-9380-7203
Author contributions
Conceptualization: CM, JC, MF
Data curation: CM
Formal analysis: CM
Funding acquisition: KC, AA, AM
Investigation: CM, AA, AM
Methodology: CM, JC, MF, AM
Project administration: CM, KC, AA, AM
Resources: CM, JC, KC, AA, AM
Supervision: JC, MF
Writing – original draft: CM, JC
Writing – review & editing: JC, KC, AA, MF, AM
Competing interests
The authors declare there are no competing interests.
Supplementary material
Supplementary data are available with the article at https:
//doi.org/10.1139/as-2023-0021.
References
Amiraux, R., Mundy, C.J., Pierrejean, M., Niemi, A., Hedges, K.J., Brown,
T.A., et al. 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:
109938. doi:10.1016/j.ecolind.2023.109938.
Amiraux, R., Yurkowski, D.J., Archambault, P., Pierrejean, 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: e2216701120.
doi:10.1073/pnas.2216701120.
Barst, B.D., Chételat, J., Basu, N., Tsui, M., Fisher, N., Øverjordet, I., et al.
2022. Toxicological risk of mercury for fish and invertebrate prey
in the Arctic. Sci. Total Environ. 836: 155702. doi:10.1016/j.scitotenv.
2022.155702. PMID: 35523325.
Bligh, E.G., and Dyer, W.J. 1959. A rapid method of total lipid extraction
and purification. Can. J. Biochem. Physiol. 37: 911–917. doi:10.1139/
o59-099. PMID: 13671378.
Brown, T.M., Fisk, A.T., Wang, X., Ferguson, S.H., Young, B.G., Reimer,
K.J., and Muir, D.C.G. 2016. Mercury and cadmium in ringed seals
in the Canadian Arctic: influence of location and diet. Sci. Total En-
viron. 545–546: 503–511. doi:10.1016/j.scitotenv.2015.12.030. PMID:
26760271.
Carmack, E.C., and Kulikov, E.A. 1998. Wind-forced upwelling and in-
ternal Kelvin wave generation in Mackenzie Canyon, Beaufort Sea. J.
Geophys. Res. Ocean. 103: 18447–18458. doi:10.1029/98JC00113.
Carmack, E.C., and Macdonald, R.W. 2002. Oceanography of the Canadian
shelf of the Beaufort Sea: a setting for marine life. Arctic 55: 29–45.
doi:10.14430/arctic733.
Carson, M.A., Jasper, J.N., and Conly, P.M. 1998. Magnitude and sources of
sediment input to the Mackenzie Delta, Northwest Territories, 1974–
94. Arctic 51: 116–124. doi:10.14430/arctic1053.
Chen, C., Amirbahman, A., Fisher, N., Harding, G., Lamborg, C., Nacci,
D., and Taylor, D. 2008. Methylmercury in marine ecosystems: spatial
patterns and processes of production, bioaccumulation, and biomag-
nification. Ecohealth 5: 399–408. doi:10.1007/s10393-008- 0201-1.
Chételat, J., Ackerman, J.T., Eagles-Smith, C.A., and Hebert, C.E. 2020.
Methylmercury exposure in wildlife: a review of the ecological and
physiological processes aecting contaminant concentrations and
their interpretation. Sci. Total Environ. 711: 135117. doi:10.1016/j.
scitotenv.2019.135117. PMID: 31831233.
Coelho, J.P., Mieiro, C.L., Pereira, E., Duarte, A.C., and Pardal, M.A. 2013.
Mercury biomagnification in a contaminated estuary food web: ef-
fects of age and trophic position using stable isotope analyses. Mar.
Pollut. Bull. 69: 110–115. doi:10.1016/j.marpolbul.2013.01.021. PMID:
23433553.
Conlan, K., Hendrycks, E., Aitken, A., Williams, B., Blasco, S., and Craw-
ford, E. 2013. Macrofaunal biomass distribution on the Canadian
Beaufort Shelf. J. Mar. Syst. 127: 76–87. doi:10.1016/j.jmarsys.2013.
07.013.
Conlan, K.E., Hendrycks, E.A., and Aitken, A.E. 2019. Dense ampeliscid
bed on the Canadian Beaufort Shelf: an explanation for species pat-
terns. Polar Biol. 42: 195–215. doi:10.1007/s00300-018- 2417-z.
Connelly, T.L., Deibel, D., and Parrish, C.C. 2012. Biogeochemistry of near-
bottom suspended particulate matter of the Beaufort Sea shelf (Arctic
Ocean): C, N, P, δ13C and fatty acids. Cont. Shelf Res. 43: 120–132.
doi:10.1016/j.csr.2012.05.011.
Connelly, T.L., Deibel, D., and Parrish, C.C. 2014. Trophic interactions
in the benthic boundary layer of the Beaufort Sea shelf, Arctic
Ocean: combining bulk stable isotope and fatty acid signatures. Prog.
Oceanogr. 120: 79–92. doi:10.1016/j.pocean.2013.07.032.
Cossa, D., and Gobeil, C. 2000. Mercury speciation in the lower St.
Lawrence estuary. Can. J. Fish. Aquat. Sci. 57: 138–147. doi:10.1139/
cjfas-57- s1-138.
Cresson, P., Fabri, M.C., Bouchoucha, M., Brach Papa, C., Chavanon, F.,
Jadaud, A., et al. 2014. Mercury in organisms from the Northwest-
ern Mediterranean slope: importance of food sources. Sci. Total En-
Canadian Science Publishing
Arctic Science 10: 305–320 (2024) | dx.doi.org/10.1139/as-2023-0021 319
viron. 497–498: 229–238. doi:10.1016/j.scitotenv.2014.07.069. PMID:
25129158.
Dalsgaard, J., St. John, M., Kattner, G., Müller-Navarra, D., and Hagen,
W. 2003. Fatty acid trophic markers in the pelagic marine envi-
ronment. Adv. Mar. Biol. 46: 225–340. doi:10.1016/S0065-2881(03)
46005-7. PMID: 14601414.
Dastoor, A., Angot, H., Bieser, J., Christensen, J.H., Douglas, T.A.,
Heimbürger-Boavida, L.E., et al. 2022. Arctic mercury cycling. Nat.
Rev. Earth Environ. 3: 270–286. doi:10.1038/s43017-022-00269- w.
Dietz, R., Letcher, R.J., Aars, J., Andersen, M., Boltunov, A., Born, E.W.,
et al. 2022. A risk assessment review of mercury exposure in Arc-
tic marine and terrestrial mammals. Sci. Total Environ. 829: 154445.
doi:10.1016/j.scitotenv.2022.154445. PMID: 35304145.
Dittmar, T., and Kattner, G. 2003. The biogeochemistry of the river and
shelf ecosystem of the Arctic Ocean: a review. Mar. Chem. 83: 103–
120. doi:10.1016/S0304-4203(03)00105- 1.
Ehrman, A.D., Swanson, H., MacPhee, S., Majewski, A., Archambault, P.,
Eert, J., et al. 2022. Isotopic and traits-based trophic diversity of Cana-
dian Beaufort Sea benthic communities in relation to food supply.
Arct. Sci. 8: 1148–1164. doi:10.1139/as-2021- 0040.
Elliott, K.H., and Elliott, J.E. 2016. Origin of sulfur in diet drives spatial
and temporal mercury trends in seabird eggs from Pacific Canada
1968−2015. Environ. Sci. Technol. 50: 13380–13386. doi:10.1021/acs.
est.6b05458.
Falk-Petersen, S., Sargent, J.R., and Tande, K.S. 1987. Lipid composition
of zooplankton in relation to the sub-arctic food web. Polar Biol. 8:
115–120. doi:10.1007/BF00297065.
Fitzgerald, W.F., Lamborg, C.H., and Hammerschmidt, C.R. 2007. Marine
biogeochemical cycling of mercury. Chem. Rev. 107: 641–662. doi:10.
1021/cr050353m.
Fox, A.L., Trefry, J.H., Trocine, R.P., Dunton, K.H., Lasorsa, B.K., Konar,
B., et al. 2017. Mercury biomagnification in food webs of the north-
eastern Chukchi Sea, Alaskan Arctic. Deep. Res. Part II Top. Stud.
Oceanogr. 144: 63–77. doi:10.1016/j.dsr2.2017.04.020.
Gagnon, C., and Fisher, N.S. 1997. Bioavailability of sediment-bound
methyl and inorganic mercury to a marine bivalve. Environ. Sci. Tech-
nol. 31: 993–998. doi:10.1021/es960364k.
Giraldo, C., Stasko, A., Choy, E.S., Rosenberg, B., Majewski, A., Power,
M., et al., 2016. Trophic variability of Arctic fishes in the Canadian
Beaufort Sea: a fatty acids and stable isotopes approach. Polar Biol.
39: 1267–1282. doi:10.1007/s00300-015- 1851-4.
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. doi:10.1016/j.marpolbul.2018.07.075.
Graeve, M., Kattner, G., and Piepenburg, D. 1997. Lipids in Arctic
benthos: does the fatty acid and alcohol composition reflect feed-
ing and trophic interactions? Polar Biol. 18: 53–61. doi:10.1007/
s003000050158.
Griths, J.R., Kadin, M., Nascimento, F.J.A., Tamelander, T., Törnroos, A.,
Bonaglia, S., et al. 2017. The importance of benthic–pelagic coupling
for marine ecosystem functioning in a changing world. Glob. Chang.
Biol. 23: 2179–2196. doi:10.1111/gcb.13642.
Habicht, K.S., Canfield, D.E., and Rethmeier, J. 1998. Sulfur isotope frac-
tionation during bacterial reduction and disproportionation of thio-
sulfate and sulfite. Geochim. Cosmochim. Acta 62: 2585–2595. doi:10.
1016/S0016-7037(98)00167- 7.
Hebert, C.E., and Popp, B.N. 2018. Temporal trends in a biomagnifying
contaminant: application of amino acid compound-specific stable ni-
trogen isotope analysis to the interpretation of bird mercury levels.
Environ. Toxicol. Chem. 37: 1458–1465. doi:10.1002/etc.4092.
Hebert, C.E., Arts, M.T., and Weseloh, D.V.C. 2006. Ecological tracers can
quantify food web structure and change. Environ. Sci. Technol. 40:
5618–5623. doi:10.1021/es0520619.
Hecky, R.E., and Hesslein, R.H. 1995. Contributions of benthic algae to
lake food webs as revealed by stable isotope analysis. J. North Am.
Benthol. Soc. 14: 631–653. doi:10.2307/1467546.
Heilmayer, O., Thatje, S., McClelland, C., Conlan, K., and Brey, T., 2008.
Changes in biomass and elemental composition during early on-
togeny of the Antarctic isopod crustacean Ceratoserolis trilobitoides.Po-
lar Biol. 31: 1325–1331. doi:10.1007/s00300-008- 0470-8.
Hilgendag, I.R., Swanson, H.K., Lewis, C.W., Ehrman, A.D., and Power,
M. 2022. Mercury biomagnification in benthic, pelagic, and ben-
thopelagic food webs in an Arctic marine ecosystem. Sci. Total En-
viron. 841: 156424. doi:10.1016/j.scitotenv.2022.156424.
Hobson, K.A., Fisk, A., Karnovsky, N., Holst, M., Gagnon, J.M., and Fortier,
M., 2002. A stable isotope (δ13C, δ15 N) model for the North Water food
web: implications for evaluating trophodynamics and the flow of en-
ergy and contaminants. Deep Sea Res. Part II Top. Stud. Oceanogr. 49:
5131–5150. doi:10.1016/S0967-0645(02)00182- 0.
Ivanova, N.V., Dewaard, J.R., and Hebert, P.D.N. 2006. An inexpensive,
automation-friendly protocol for recovering high-quality DNA. Mol.
Ecol. Notes 6: 998–1002. doi:10.1111/j.1471-8286.2006.01428.x.
Iverson, S.J., 2009. Tracing aquatic food webs using fatty acids: from qual-
itative indicators to quantitative determination. In Lipids in aquatic
ecosystems. Edited by Kainz, M., Brett, M. and Arts, M. Springer, New
York, NY.pp. 281–308. doi:10.1007/978-0-387-89366-2_12.
Iverson, S.J., Field, C., Bowen, W.D., and Blanchard, W. 2004. Quantitative
fatty acid signature analysis: a new method. Ecol. Monogr. 74: 211–
235. doi:10.1890/02-4105.
Jonsson, S., Andersson, A., Nilsson, M.B., Skyllberg, U., Lundberg, E.,
Schaefer, J.K., et al. 2017. Terrestrial discharges mediate trophic shifts
and enhance methylmercury accumulation in estuarine biota. Sci.
Adv. 3: e1601239. doi:10.1126/sciadv.1601239.
Jonsson, S., Mastromonaco, M.N., Wang, F., Bravo, A.G., Cairns, W.R.L.,
Chételat, J., et al. 2022. Arctic methylmercury cycling. Sci. Total Env-
iron. 850: 157445. doi:10.1016/j.scitotenv.2022.157445.
Kelly, J.R., and Scheibling, R.E. 2012. Fatty acids as dietary tracers in
benthic food webs. Mar. Ecol. Prog. Ser. 446: 1–22. doi:10.3354/
meps09559.
Kim, J., Soerensen, A.L., Kim, M.S., Eom, S., Rhee, T.S., Jin, Y.K., and Han, S.
2020. Mass budget of methylmercury in the east Siberian sea: the im-
portance of sediment sources. Environ. Sci. Technol. 54: 9949–9957.
doi:10.1021/acs.est.0c00154.
Klump, L.R., and Arthur, M.A. 1999. Interpreting carbon-isotope excur-
sions: carbonates and organic matter. Chem. Geol. 161: 181–198.
doi:10.1016/S0009-2541(99)00086- 8.
Lansard, B., Mucci, A., Miller, L.A., MacDonald, R.W., and Gratton, Y. 2012.
Seasonal variability of water mass distribution in the southeastern
Beaufort Sea determined by total alkalinity and δ18O. J. Geophys. Res.
Ocean. 117: C03003. doi:10.1029/2011JC007299.
Lavoie, R.A., Hebert, C.E., Rail, J.F., Braune, B.M., Yumvihoze, E., Hill, L.G.,
and Lean, D.R.S. 2010. Trophic structure and mercury distribution in
a Gulf of St. Lawrence (Canada) food web using stable isotope anal-
ysis.Sci.TotalEnviron.408: 5529–5539. doi:10.1016/j.scitotenv.2010.
07.053.
Lavoie, R.A., Jardine, T.D., Chumchal, M.M., Kidd, K.A., and Campbell,
L.M. 2013. Biomagnification of mercury in aquatic food webs: a world-
wide meta-analysis. Environ. Sci. Technol. 47: 13385–13394. doi:10.
1021/es403103t.
Lee, C.S., and Fisher, N.S. 2016. Methylmercury uptake by diverse ma-
rine phytoplankton. Limnol. Oceanogr. 61: 1626–1639. doi:10.1002/
lno.10318.
Lege˙
zy ´
nska, J., K˛
edra, M., and Walkusz, W. 2014. Identifying trophic
relationships within the high Arctic benthic community: how
much can fatty acids tell? Mar. Biol. 161: 821–836. doi:10.1007/
s00227-013- 2380-8.
Lehnherr, I., St. Louis, V.L., Hintelmann, H., and Kirk, J.L. 2011. Methy-
lation of inorganic mercury in polar marine waters. Nat. Geosci. 4:
298–302. doi:10.1038/ngeo1134.
Leitch, D.R., Carrie, J., Lean, D., Macdonald, R.W., Stern, G.A., and Wang,
F. 2007. The delivery of mercury to the Beaufort Sea of the Arc-
tic Ocean by the Mackenzie River. Sci. Total Environ. 373: 178–195.
doi:10.1016/j.scitotenv.2006.10.041.
Léveillé, J.C., Amblard, C., and Bourdier, G. 1997. Fatty acids as specific
algal markers in a natural lacustrian phytoplankton. J. Plankton Res.
19: 469–490. doi:10.1093/plankt/19.4.469.
Li, M.L., Gillies, E.J., Briner, R., Hoover, C.A., Sora, K.J., Loseto, L.L., et al.
2022. Investigating the dynamics of methylmercury bioaccumulation
in the Beaufort Sea shelf food web: a modeling perspective. Environ.
Sci. Process. Impacts 24: 1010–1025. doi:10.1039/d2em00108j.
Liu, M., Xiao, W., Zhang, Q., Shi, L., Wang, X., and Xu, Y. 2020. Methylmer-
cury bioaccumulation in deepest ocean fauna: implications for ocean
mercury biotransport through food webs. Environ. Sci. Technol. Lett.
7: 469–476. doi:10.1021/acs.estlett.0c00299.
Canadian Science Publishing
320 Arctic Science 10: 305–320 (2024) | dx.doi.org/10.1139/as-2023-0021
Lobo, J., Costa, P.M., Teixeira, M.A.L., Ferreira, M.S.G., Costa, M.H., and
Costa, F.O. 2013. Enhanced primers for amplification of DNA bar-
codes from a broad range of marine metazoans. BMC Ecol. 13: 34.
doi:10.1186/1472-6785- 13-34.
Loria, A., Archambault, P., Burt, A., Ehrman, A., Grant, C., Power, M., and
Stern, G.A. 2020. Mercury and stable isotope (δ13Candδ15 N) trends
in decapods of the Beaufort Sea. Polar Biol. 43: 443–456. doi:10.1007/
s00300-020- 02646-x.
Loseto, L.L., Stern, G.A., and Macdonald, R.W. 2015. Distant drivers or lo-
cal signals: where do mercury trends in western Arctic belugas orig-
inate? Sci. Total Environ. 509–510: 226–236. doi:10.1016/j.scitotenv.
2014.10.110.
Loseto, L.L., Stern, G.A., Deibel, D., Connelly, T.L., Prokopowicz, A., Lean,
D.R.S., et al. 2008. Linking mercury exposure to habitat and feeding
behaviour in Beaufort Sea beluga whales. J. Mar. Syst. 74: 1012–1024.
doi:10.1016/j.jmarsys.2007.10.004.
Lovvorn, J.R., North, C.A., Grebmeier, J.M., Cooper, L.W., and Kolts, J.M.
2018. Sediment organic carbon integrates changing environmental
conditions to predict benthic assemblages in shallow Arctic seas.
Aquat. Conserv. Mar. Freshw. Ecosyst. 28: 861–871. doi:10.1002/aqc.
2906.
Macdonald, R.W., and Loseto, L.L. 2010. Are Arctic Ocean ecosystems ex-
ceptionally vulnerable to global emissions of mercury? A call for em-
phasised research on methylation and the consequences of climate
change.Environ.Chem.7: 133–138. doi:10.1071/EN09127.
Majewski, A.R., Atchison, S., MacPhee, S., Eert, J., Niemi, A., Michel,
C., and Reist, J.D. 2017. Marine fish community structure and habi-
tat associations on the Canadian Beaufort shelf and slope. Deep.
Res. Part I Oceanogr. Res. Pap. 121: 169–182. doi:10.1016/j.dsr.2017.
01.009.
Majewski, A.R., Lowdon, M.K., Reist, J.D., and Park, B.J. 2011. Fish catch
data from Herschel Island, Yukon Territory, and other oshore sites
in the Canadian aboard the CCGS Nahidik. Canadian Data Report of
Fisheries and Aquatic Sciences.
Majewski, A.R., Reist, J.D., Park, B.J., Sareault, J.E., and Lowdon, M. 2009.
Fish catch data from oshore sites in the Mackenzie River estuary
and Beaufort Sea during the open water season, July and August,
2005, aboard the CCGS Nahidik. Canadian Data Report of Fisheries
and Aquatic Sciences.
McMahon, K.W., Ambrose, W.G., Reynolds, M.J., Johnson, B.J., Whiting,
A., and Clough, L.M. 2021a. Arctic lagoon and nearshore food webs:
relative contributions of terrestrial organic matter, phytoplankton,
and phytobenthos vary with consumer foraging dynamics. Estuar.
Coast. Shelf Sci. 257: 107388. doi:10.1016/j.ecss.2021.107388.
McMahon, R., Taveras, Z., Neubert, P., and Harvey, H.R. 2021b. Organic
biomarkers and meiofauna diversity reflect distinct carbon sources
to sediments transecting the Mackenzie continental shelf. Cont. Shelf
Res. 220: 104406. doi:10.1016/j.csr.2021.104406.
McMeans, B.C., Arts, M.T., and Fisk, A.T. 2015. Impacts of food web
structure and feeding behavior on mercury exposure in Greenland
Sharks (Somniosus microcephalus). Sci. Total Environ. 509–510: 216–225.
doi:10.1016/j.scitotenv.2014.01.128.
Middelburg, J.J., and Herman, P.M.J. 2007. Organic matter processing in
tidal estuaries. Cont. Shelf Res. 106: 127–147. doi:10.1016/j.marchem.
2006.02.007.
Minagawa, M., and Wada, E. 1984. Stepwise enrichment of 15Nalong
food chains: further evidence and the relation between δ15Nand
animal age. Geochim. Cosmochim. Acta 48: 1135–1140. doi:10.1016/
0016-7037(84)90204- 7.
Mohan, S.D., Connelly, T.L., Harris, C.M., Dunton, K.H., and McClelland,
J.W. 2016. Seasonal trophic linkages in Arctic marine invertebrates
assessed via fatty acids and compound-specific stable isotopes. Eco-
sphere. 7: e01429. doi:10.1002/ecs2.1429.
Pedro, S., Fisk, A.T., Ferguson, S.H., Hussey, N.E., Kessel, S.T., and McKin-
ney, M.A. 2019. Limited eects of changing prey fish communities on
food quality for aquatic predators in the eastern Canadian Arctic in
terms of essential fatty acids, methylmercury and selenium. Chemo-
sphere, 214: 855–865. doi:10.1016/j.chemosphere.2018.09.167.
Peterson, B.J., and Fry, B. 1987. Stable isotopes in ecosystem studies.
Annu. Rev. Ecol. Syst. Vol. 18 18: 293–320. doi:10.1146/annurev.es.18.
110187.001453.
Post, D.M. 2002. Using stable isotopes to estimate trophic position: mod-
els, methods, and assumptions. Ecology 83: 703–718. doi:10.2307/
3071875.
Reinhart, B.L., Kidd, K.A., Curry, R.A., O’Driscoll, N.J., and Pavey, S.A.,
2018. Mercury bioaccumulation in aquatic biota along a salinity gra-
dient in the Saint John River estuary. J. Environ. Sci. 68: 41–54.
doi:10.1016/j.jes.2018.02.024.
Routti, H., Letcher, R.J., Born, E.W., Branigan, M., Dietz, R., Evans,
T.J., et al. 2011. Spatial and temporal trends of selected trace ele-
ments in liver tissue from polar bears (Ursus maritimus) from Alaska,
Canada and Greenland. J. Environ. Monit. 13: 2260–2267. doi:10.
1039/c1em10088b.
Schlechtriem, C., Henderson, R.J., and Tocher, D.R. 2008. A critical assess-
ment of dierent transmethylation procedures commonly employed
in the fatty acid analysis of aquatic organisms. Limnol. Oceanogr.
Methods 6: 523–531. doi:10.4319/lom.2008.6.523.
Soerensen, A.L., Jacob, D.J., Schartup, A.T., Fisher, J.A., Lehnherr, I., St.
Louis, V.L., et al. 2016. A mass budget for mercury and methylmercury
in the Arctic Ocean. Global Biogeochem. Cycles 30: 560–575. doi:10.
1002/2015GB005280.
St. Louis, V.L., Derocher, A.E., Stirling, I., Graydon, J.A., Lee, C.,
Jocksch, E., et al. 2011. Dierences in mercury bioaccumulation be-
tween polar bears (Ursus maritimus) from the Canadian high- and
sub-arctic. Environ. Sci. Technol. 45: 5922–5928. doi:10.1021/
es2000672.
Stasko, A.D., Bluhm, B.A., Michel, C., Archambault, P., Majewski, A., Reist,
J.D., et al. 2018a. Benthic-pelagic trophic coupling in an Arctic marine
food web along vertical water mass and organic matter gradients.
Mar. Ecol. Pr og. Ser. 594: 1–19. doi:10.3354/meps12582.
Stasko, A.D., Bluhm, B.A., Reist, J.D., Swanson, H., and Power, M. 2018b.
Relationships between depth and δ15N of Arctic benthos vary among
regions and trophic functional groups. Deep. Res. Part I Oceanogr.
Res. Pap. 135: 56–64. doi:10.1016/j.dsr.2018.03.010.
Stowasser, G., McAllen, R., Pierce, G.J., Collins, M.A., Moat, C.F.,
Priede, I.G., and Pond, D.W. 2009. Trophic position of deep-sea fish-
assessment through fatty acid and stable isotope analyses. Deep. Res.
Part I Oceanogr. Res. Pap. 56: 812–826. doi:10.1016/j.dsr.2008.12.016.
Tostevin, R., Turchyn, A.V., Farquhar, J., Johnston, D.T., Eldridge, D.L.,
Bishop, J.K.B., and McIlvin, M. 2014. Multiple sulfur isotope con-
straints on the modern sulfur cycle. Earth Planet. Sci. Lett. 396: 14–21.
doi:10.1016/j.epsl.2014.03.057.
Tranvik, L.J., Sherr, E.B., and Sherr, B.F. 1993. Uptake and utilization of
“colloidal DOM’’ by heterotrophic flagellates in seawater.” Mar. Ecol.
Prog. Ser. 92: 301–309. doi:10.3354/meps092301.
Vander Zanden, M.J., and Rasmussen, J.B. 2001. Variation in δ15Nand
δ13C trophic fractionation: implications for aquatic food web studies.
Limnol. Oceanogr. 46: 2061–2066. doi:10.4319/lo.2001.46.8.2061.
Wang, K., Munson, K.M., Beaupré-Laperrière, A., Mucci, A., Macdonald,
R.W., and Wang, F. 2018. Subsurface seawater methylmercury maxi-
mum explains biotic mercury concentrations in the Canadian Arctic.
Sci. Rep. 8: 14465. doi:10.1038/s41598-018- 32760-0.
Wang, S., Frost, K.J., and Whiting, A.V. 2016. Foraging ecology of ice seals
in Kotzebue Sound, Alaska: insights from fatty acid markers. Mar.
Mamm. Sci. 32: 765–776. doi:10.1111/mms.12284.
Willacker, J.J., Eagles-Smith, C.A., and Ackerman, J.T. 2017. Mercury
bioaccumulation in estuarine fishes: novel insights from sulfur sta-
ble isotopes. Environ. Sci. Technol. 51: 2131–2139. doi:10.1021/acs.
est.6b05325.
Williams, W.J., and Carmack, E.C. 2008. Combined eect of wind-forcing
and isobath divergence on upwelling at Cape Bathurst, Beaufort Sea.
J. Mar. Res. 66: 645–663. doi:10.1357/002224008787536808.
Williams, W.J., Carmack, E.C., Shimada, K., Melling, H., Aagaard, K.,
Macdonald, R.W., and Ingram, R.G. 2006. Joint eects of wind and
ice motion in forcing upwelling in Mackenzie Trough, Beaufort Sea.
Cont. Shelf Res. 26: 2352–2366. doi:10.1016/j.csr.2006.06.012.
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