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Assessing the utility of sulfur isotope values for understanding mercury concentrations in water and biota from high Arctic lakes

Publication: Arctic Science
19 December 2018


Methylmercury (MeHg) biomagnifies through aquatic food webs resulting in elevated concentrations in fish globally. Stable carbon and nitrogen isotopes are frequently used to determine dietary sources of MeHg and to model its biomagnification. However, given the strong links between MeHg and sulfur cycling, we investigated whether sulfur isotopes (δ34S) would improve our understanding of MeHg concentrations ([MeHg]) in Arctic lacustrine food webs. Delta34S values and total mercury (THg) or MeHg were measured in water, sediments, and biota from six lakes near Resolute Bay, NU, Canada. In two lakes impacted by historical eutrophication, aqueous sulfate δ34S was ∼8‰ more positive than sedimentary δ34S, suggestive of bacterial sulfate reduction in the sediment. In addition, aqueous δ34S showed a significant positive relationship with aqueous [MeHg] across lakes. Within taxa across lakes, [THg] in Arctic char muscle and [MeHg] in their main prey, chironomids, were positively related to their δ34S values across lakes, but inconsistent relationships were found across entire food webs among lakes. Across lakes, nitrogen isotopes were better predictors of biotic [THg] and [MeHg] than δ34S within this dataset. Our results suggest some linkages between Hg and S biogeochemistry in high Arctic lakes, which is an important consideration given anticipated climate-mediated changes in nutrient cycling.


Le méthylmercure (MeHg) se bioamplifie au moyen de réseaux trophiques aquatiques, ce qui entraîne des concentrations élevées chez les poissons à l’échelle planétaire. Les isotopes stables de carbone et d’azote sont fréquemment utilisés pour déterminer les sources alimentaires de MeHg et pour modéliser sa bioamplification. Toutefois, compte tenu des liens étroits entre le MeHg et le cycle du soufre, nous avons cherché à savoir si les isotopes du soufre (δ34S) amélioreraient notre compréhension des concentrations de MeHg ([MeHg]) dans les réseaux trophiques lacustres de l’Arctique. Les valeurs de δ34S et de mercure total (THg) ou de MeHg ont été mesurées dans l’eau, les sédiments et le biote de six lacs près de Resolute Bay, au Nunavut. Dans deux lacs subissant les effets d’eutrophisation historique, le sulfate aqueux δ34S était ~8 ‰ plus positif que le δ34S sédimentaire, suggérant la présence de bactéries sulforéductrices dans les sédiments. En outre, δ34S aqueux a montré une corrélation positive significative avec les [MeHg] dans les lacs. Chez les taxons de tous les lacs, les [THg] dans les muscles de l’omble chevalier arctique et les [MeHg] dans leurs principales proies, les chironomidés, étaient positivement liées à leurs valeurs δ34S dans les lacs, mais des relations incohérentes ont été observées à travers les réseaux trophiques complets des lacs. Dans les lacs, les isotopes d’azote étaient de meilleurs prédicteurs des [THg] et [MeHg] biotiques que les δ34S dans cet ensemble de données. Nos résultats montrent des liens importants entre la biogéochimie du mercure et du soufre dans les lacs du Haut-Arctique, ce qui constitue une considération importante compte tenu des changements climatiques prévus dans le cycle des éléments nutritifs. [Traduit par la Rédaction]


Methylmercury (MeHg) is a neurotoxin and has been considered a contaminant of concern in Arctic aquatic systems for many years (e.g., Chételat et al. 2015). Due to its tendency to bioaccumulate in biota and biomagnify through food webs, MeHg has been detected in Arctic char (Salvelinus alpinus Linnaeus, 1758, herein referred to as char), the top predator and only fish species in remote high Arctic lakes, sometimes at concentrations in excess of governmental guidelines for safe consumption (e.g., 0.5 ppm, Health Canada 2014; Gantner et al. 2010; Lescord et al. 2015b). These char are an important subsistence food for members of Inuit communities across the Canadian Arctic and a better understanding of [MeHg] in these fish and the food webs that support them is therefore vital (Muir et al. 2009; Lescord et al. 2015b). However, Hg cycling in Arctic lakes is complex and many biological and biogeochemical processes affect its uptake into organisms and biomagnification through food webs. Mercury in Arctic systems is largely the result of long-range atmospheric transport of elemental and inorganic Hg (Hg(II)) from distant sources; this Hg(II) is readily deposited across the Arctic (Angot et al. 2016) and can enter freshwater systems directly during annual spring melting of ice and snow-packs. Inorganic Hg can subsequently be converted to MeHg during the oxidation of organic matter by anaerobic sulfate-reducing and other anaerobic bacteria (SRBs; Gilmour et al. 2013).
The formation of MeHg is tightly linked to sulfur (S) cycling and studies in more southerly freshwater ecosystems have shown that increasing amounts of aqueous sulfate result in higher rates of bacterial Hg methylation (Gilmour et al. 1998) as well as [MeHg] in water (Myrbo et al. 2017) and biota (Sumner 2016). However, this trend is complicated by complexation of the produced MeHg with sulfides, a process known as sulfide inhibition, as this limits bioavailability to food webs and has been linked to lower [MeHg] in water (Benoit et al. 1999; Bailey et al. 2017) and fish (Sumner 2016). Overall, far less is known about the Hg–S relationship in high Arctic freshwater ecosystems, where aqueous S concentrations can be very low, depending on local sources, when compared with those in temperate regions. Various species of Hg form strong complexes with S-containing compounds, including reduced sulfur functional groups within dissolved organic matter (DOM), which enhances DOM-facilitated transport of Hg from upstream or riparian areas to downstream sites (Haitzer et al. 2002; Paranjape and Hall 2017). Within biota, MeHg binds to the sulfur-containing amino acid cysteine in proteins, a process which partly accounts for its biomagnifying and neurotoxic characteristics (Clarkson and Magos 2006; Larose et al. 2008). Changes to the S cycle could therefore impact MeHg formation (i.e., via bacterial sulfate reduction), uptake, and concentrations in top predator fishes and a better understanding of Hg–S relationships in aquatic systems is needed. This is particularly true in the Arctic where evidence from lacustrine sediment cores suggests that warmer temperatures have enhanced algal productivity and the sedimentation of detrital carbon, which accelerates the S cycle and fuels dissimilatory sulfate reduction and, therefore, Hg methylation (Michelutti et al. 2007; Drevnick et al. 2010). These and other climate-related changes anticipated to impact remote Arctic lakes could alter both S and Hg cycling and, therefore, uptake into the food webs supporting Arctic char (Macdonald et al. 2005; Power et al. 2012).
Stable isotope values of S in sediments and water are related to sulfate-reduction (and therefore likely MeHg formation) in both the catchment and lake system, can help delineate food web structure, and may improve our understanding of MeHg uptake in aquatic biota. More specifically, microbial sulfate () reduction in hypoxic or anoxic sediments and waters results in sedimentary S that is depleted in 34S (i.e., low in δ34S values) when compared with that of water column (Canfield 2001; Proulx and Hare 2014). Furthermore, as the δ34S values in a predator generally reflect levels in its diet (McCutchan et al. 2003), this isotope allows one to distinguish the relative importance of sediment versus open water sources of S to consumers, assuming the original compartment values are distinct (Croisetière et al. 2009). As a result, within a given ecosystem, sediment-feeding organisms can have relatively low δ34S values compared with those that feed on particles suspended in the water column (Proulx and Hare 2014) and δ34S values can identify within-lake energy sources to consumers (Croisetière et al. 2009; Karube et al. 2012).
Despite the links between Hg and S cycling, few studies have used δ34S to assess MeHg bioaccumulation in aquatic ecosystems. Sulfur isotopes have mainly been used to quantify marine- versus freshwater-derived S in migratory fish diets (e.g., Fry and Chumchal 2011) and to determine whether these different sources of energy affect total [Hg] in Arctic lacustrine fishes (Swanson et al. 2010, 2011). Other related studies have occurred in more southerly estuaries or temperate lakes (e.g., Ethier et al. 2008; Schmitt et al. 2011; Clayden et al. 2017, Reinhart et al. 2018), which have drastically different limnological properties when compared with high Arctic systems (e.g., higher nutrient and DOC concentrations, and lower pH; see Clayden et al. 2017). In this study we used δ34S, along with the more commonly used stable isotopes nitrogen (δ15N, a measure of relative trophic position) and carbon (δ13C, a measure of littoral vs. pelagic energy sources), to study Hg bioaccumulation and biomagnification in simple food webs of six high Arctic lakes. Specifically, our goals were to: (1) evaluate δ34S as a means of distinguishing between energy sources in the diets of food web organisms against previously-published data on δ13C (Lescord et al. 2015b) and (2) determine whether [MeHg] was related to tissue δ34S in individual groups of organisms (i.e., fishes, invertebrates) and through food webs.

Materials and methods

Study sites

The six lakes were located on Cornwallis Island in the central Canadian Arctic Archipelago near the hamlet of Resolute Bay, NU, Canada; a map is provided in the Supplementary Material (Fig. S5).1 This region has a polar desert climate, with low mean annual precipitation and temperature (150 mm and −16.4 °C; Antoniades et al. 2011). Detailed water chemistry and lake morphological characteristics are reported in Lescord et al. (2015b). Briefly, these lakes range in size from 39 to 130 ha and in maximum depth from 8 to 27 m, and they are ultra-oligotrophic, with mean chlorophyll-a, total phosphorus (TP), and dissolved organic carbon (DOC) concentrations ranging from 1.28 to 2.23 μg/L, 3.6 to 8.0 μg/L, and 0.5 to 1.9 mg/L, respectively. Dissolved oxygen and temperature profiles showed no thermal or oxygen stratification in the water columns of these systems (Lescord et al. 2015b). Mean pH was similar across the lakes, ranging from 7.9 to 8.1, whereas mean aqueous in surface waters was low but varied by approximately six-fold (0.23–1.35 mg/L across lakes). The food webs have low biodiversity; Arctic char are the only fish species, the benthic invertebrate community consists mainly of chironomids, and the zooplankton community is dominated by copepods (Chételat and Amyot 2009). The lakes from this study (and therefore the char that reside in them) were assumed to be land-locked with no direct marine connection (Lescord et al. 2015b).
Four of the lakes are generally considered un-impacted by human activity (Char, Small, North, and 9-Mile lakes), whereas the catchments of Meretta and Resolute Lakes include the Resolute Bay airport and a former military air base; from 1949 to 1998, untreated waste water from the air base was released directly into the catchment of Meretta Lake (Antoniades et al. 2011), which flows into Resolute Lake. Although recovery from this eutrophication has occurred, these inputs affected both the chemical and microbial characteristics of Meretta Lake (Antoniades et al. 2011). Furthermore, the proximity of both Meretta and Resolute Lakes to the local airport has altered their organic contaminant profiles when compared with more remote lakes in the area (Lescord et al. 2015a; Cabrerizo et al. 2018).

Field sampling

All field sampling was conducted between July and August of 2010 and 2011. Details of sampling for water chemistry (e.g., MeHg, total Hg (THg, all forms of Hg in a sample), nutrients, and pH), biota (Arctic char, larval and adult chironomids, and zooplankton) and sediments were reported by Lescord et al. (2015b). Briefly, water samples were collected weekly above the deepest part of each lake, approximately 1 m below the surface, using a Niskin bottle (model 1010, 2.5 L; General Oceanics, Miami, FL, USA). Larval and adult chironomids were collected by kick sweeping shore lines and aspiration from the ice surface, respectively. Periphyton scrapings were taken from littoral rocks in triplicates. Bulk zooplankton samples were collected by towing a net (80 μm mesh) for 3–5 min on each lake; these samples were then size filtered at <250, 250–500, and >500 μm. Large char (>18 cm total length) were collected using gill nets and small char (<18 cm total length) were electrofished along shorelines; these size categories of fish were kept separate for all laboratory and statistical analyses. Fish sampling was done using protocols approved by the Animal Care Committee of the University of New Brunswick (#2010/11-1D-01).
For δ34S analysis, water samples were collected from the surface and deep point of each lake (n = 1 replicate per lake per depth, in each of 2010 and 2011); these data were averaged across depths and years (n = 4 per lake). Samples were kept cool in the field and sulfate was extracted within 6 h of collection at the Polar Continental Shelf (PCSP) base by ion exchange as barium sulfate (Carmody et al. 1998). Surface sediment samples from the Arctic lakes were taken from the top 0.5 cm slice of cores (n = 18, three replicates per lake in 2011) that were collected in the littoral zone of each lake as part of other studies (Lescord et al. 2015a, 2015b).

Laboratory analyses

All laboratory analyses were performed on individual muscle subsamples for large char, whole body homogenates for small char, bulk zooplankton (pooled by sample date, location, and filtration size, n = 2–5 per lake), and benthic invertebrates (all in the family Chironomidae and pooled by sample date and location for adults and larva separately, n = 1–5 per lake). Biotic samples were freeze-dried and hand-ground to a fine powder before all analyses.
Biotic sulfur stable isotope analysis was conducted at the Colorado Plateau Stable Isotope Laboratory (Northern Arizona University, Flagstaff, AZ, USA). For these samples (n = 157), certified reference materials (International Atomic Energy Agency (IAEA) S1, S2, S3, S4, S05, S06) were used and percent differences from expected values (ranging from −34.05‰ for IAEA S06 to 22.67‰ for IAEA S2) averaged 0.82% ± 22.8% (total n = 21). Duplicate samples were also run and the average percent difference between duplicates was 4.21% ± 2.2% (n = 11). Sulfur isotopes (n = 20) and the percentage of sulfur (%S; n = 23; see Table S11) in sediments were measured at IsoAnalytical (Crewe, UK). The percent difference from certified reference materials (CRMS; International Atomic Energy Agency IA-R061, IAEA-SO-5, NBS-1577B) averaged 8.49% ± 18.2% (n = 15) and sample duplicates averaged 1.31% ± 3.42% (n = 5). Delta 34S values in aqueous sulfate were measured as barium sulfate at the University of Waterloo Environmental Isotope Laboratory (Waterloo, ON, Canada; average percent difference between duplicates = 0.41% ± 7.9%).
Procedures for δ13C, δ15N, and Hg analyses in water, sediments, and biota were reported by Lescord et al. (2015b), along with associated quality assurance measures. Briefly, stable C and N isotopes were analyzed at the Stable Isotopes in Nature Laboratory (University of New Brunswick, Fredericton, NB, Canada) via Continuous Flow-Isotope Ratio Mass Spectrometry (CF-IRMS). Data on the elemental content of carbon, nitrogen, and sulfur of biota and sediment samples (as percentages of dry weight) were obtained in association with stable isotope analyses for each element. Percent recoveries of CRMs during CN isotope analysis ranged from 97.5% ± 5.8% to 105.1% ± 4.6% (n = 162) and duplicates were <1% different from each other.
Water samples were analyzed for THg and MeHg at the Low-Level Mercury Analytical Laboratory at the Canadian Centre for Inland Waters (CCIW, Environment Canada) following US EPA Methods 245.7 and 1630, respectively. All chironomids and zooplankton were analyzed for [MeHg] using a potassium hydroxide (KOH) digest and detected using Cold Vapour Atomic Fluorescence Spectroscopy (CVAFS) at the Center for Analytical Research on the Environment (CARE) at Acadia University (Wolfville, NS, Canada). Methyl Hg concentrations in blanks were low and consistent (average concentration = 0.01 ± 0.09 ng/g, n = 17) and percent recoveries of CRMs were high (101.1% ± 18.9%, n = 33). Small and large char were measured for [THg] (92%–99% of which was MeHg; Lescord et al. 2015b) using a Milestone Direct Mercury Analyzer (DMA-80) at the University of New Brunswick (Saint John, NB, Canada). Blanks were also low during THg analysis (average concentration = 0.002 ± 0.002 μg/g, n = 66) and 97.5% ± 11.8% of CRM certified values were obtained (n = 108). Total [Hg] in muscle from large char were converted to whole-body equivalent concentrations as per Peterson et al. (2004).

Statistical analyses

All statistical analyses were conducted using R version 3.4.3 and SigmaPlot version 11, and alpha (α) was set at 0.05 for all tests. Although zooplankton were separated by size factions (<250, 250–500, and >500 μm) in the field, samples were pooled for analysis due to biomass constraints. To account for potential variation in baseline δ15N values (Jardine et al. 2006), δ15N values were adjusted (δ15Nadj) for each biotic sample by subtracting the mean δ15N value of periphyton from a given lake (Lescord et al. 2015b, n = 3–5 per lake). Delta34S and δ13C values were not baseline-adjusted. Because elemental composition has been shown to affect MeHg accumulation in Arctic biota (Chételat et al. 2012), we also considered the percentage of S, C, and N (%S, %C, and %N) of biotic samples in subsequent statistical analyses.
Log10-transformed [Hg] (MeHg in invertebrates, THg in fish) were regressed against corresponding δ34S values within biotic groups as well as in surface waters (MeHg and THg, separately). These relationships were tested using a linear mixed effect model (LME) through the nlme package in R (Pinheiro et al. 2018) including lake as a random factor to account for site-specific variability in the data. To compare the regression slopes of biotic [Hg] versus δ34S between the six lake food webs, analysis of covariance (ANCOVA) models (Log10 Hg [THg (fish, whole-body estimates) and MeHg (invertebrates), dry wt.] = lake + δ34S + δ34S × lake) were run, using biotic Hg as the dependent variable.
To further assess how biotic Hg concentrations related to sulfur isotopes in combination with other elemental measures (δ15Nadj, δ13C, %N, %C, and %S), penalized regression modeling was performed. Specifically, we used the Least Angle Shrinkage and Selection Operator (LASSO) method through the R package lars (Hastie and Efron 2013), similar to Lescord et al. (2018). Four sets of analyses were run: [Hg] in either chironomids (MeHg), zooplankton (MeHg), small char (THg), or large char (THg) were used as dependent variables with six elemental or isotopic predictor variables (δ15Nadj, δ13C, δ34S, %N, %C, and %S). LASSO, a form of least angle regression, performs forward selection of predictors by incrementally reducing a penalty parameter (lambda, λ), allowing more variables to enter the model. Predictors with less influence on a dependent variable retain a coefficient of zero, excluding them from the final model estimates. We chose to use the LASSO method because elemental measures in this study were somewhat correlated (e.g., variance inflation factors of 1.5–27.9 in linear models) and LASSO has been shown to produce more accurate models when sample sizes are low and predictor variables are correlated, compared with other linear methods (Dormann et al. 2013; Hebiri and Lederer 2013; James et al. 2013). Mallow’s Cp values, an information theory measure similar to Akaike Information Criterion (AIC), was used to select the final LASSO models; the model with the lowest Mallow’s Cp value (>2, to prevent overfitting models) was chosen (James et al. 2013). Although the inclusion of a predictor variable in a final LASSO model indicates an effect on the dependent variable (i.e., [Hg] in this study), the R package selectiveInference was also used to estimate the significance of each predictor included in a given model at the corresponding level of λ (Tibshirani et al. 2017). LASSO path plots as well as correlation matrices are included in the Supplementary Material.1

Results and discussion

δ34S values in water and sediments

Average values of δ34S in sediment and aqueous ranged from −13.04‰ to −3.43‰ and from −10.03‰ to −3.10‰, respectively, and absolute differences between these compartments were not consistent across lakes (Fig. 1). The values for Arctic lake sediments overlapped with those of boreal lakes (∼−5‰ to −2‰, Quebec lakes; Croisetière et al. 2009), but not with those of coastal hemiboreal systems (9.6‰–12.5‰, Nova Scotian lakes; Clayden et al. 2017). In addition, δ34S values of aqueous sulfate in the Arctic systems fell within the typical range of terrestrial biogenic and anthropogenic sulfur emissions (Wadleigh et al. 1996) and did not overlap with those from either boreal or temperate systems (∼4‰–10‰ and 10.4‰–10.7‰, respectively), the latter of which closely matched that of in precipitation (Croisetière et al. 2009; Clayden et al. 2017). Mean annual precipitation near these temperate lakes is almost 10-fold higher than at the Arctic lakes (1332 mm versus 150 mm, respectively; Zhang et al. 2010; Antoniades et al. 2011); therefore, it is likely that sulfur in precipitation influences the δ34S values of aqueous in more southerly lakes. Differences in aqueous δ34S values may also be related to differences in bedrock geology among regions (Hitchon and Krouse 1972).
Fig. 1.
Fig. 1. Mean values (±1 standard deviation, SD) of δ34S in aqueous sulfate () and sedimentary sulfur (S) from six lakes near Resolute Bay, NU, Canada. Lakes are arranged on the y-axis in order of increasing mean concentration in the surface water (i.e., 1.35 ± 0.22, 0.85 ± 0.10, 0.56 ± 0.14, 0.31 ± 0.12, 0.71 ± 0.09, and 0.23 ± 0.05 mg/L, respectively; Lescord et al. 2015b) within groups (remote versus impacted). Total sulfur content in North Lake sediment was too low to obtain δ34S values. MeHg percentages and concentrations (mean ± SD) in surface waters of these lakes are also plotted (data from Lescord et al. 2015b).
In three of our remote study lakes, mean δ34S values in sediments were more positive than those measured in the water column (Fig. 1). However, in the wastewater-impacted Meretta and Resolute Lakes, aqueous δ34S values of sulfate were 8.4‰ and 7.9‰ more positive than sedimentary values, respectively (Fig. 1). At low temperatures (i.e., 5 °C), δ34S values of sediments where sulfate reduction occurs are expected to be 8‰–14‰ lower than those of water sulfate values due to bacterial fractionation (Canfield 2001); our results may therefore indicate enhanced SRB activity in these two systems, possibly due to the historical wastewater inputs altering the bacterial communities and cycling of nutrients (Antoniades et al. 2011). Indeed, the %S values in the sediments were higher in Meretta and Resolute Lakes (0.19%–0.30%) when compared with three of the remote lakes (0.1%–0.12%), but not 9-Mile Lake, which had sediments with >0.4% (see Table S11). Changes to the microbial community can alter sulfur cycling and different strains of SRB fractionate S isotopes at different rates (Wing and Halevy 2014; Bradley et al. 2016). Although the anthropogenic changes to bacterial communities in Meretta have been relatively well studied, any changes to the microbial community in Resolute Lake have not. Furthermore, the historical eutrophication of Meretta Lake may have created more favorable redox conditions in its sediments, enhancing sulfate reduction (Holmer and Storkholm 2001).
An increase in SRB activity may explain why [MeHg] and % MeHg were 2–6 times higher in water samples from Meretta Lake when compared with the remote lakes and with downstream Resolute Lake (see Fig. 1 and Lescord et al. 2015b). Although [MeHg] in fish and chironomids were not higher in Meretta or Resolute Lakes compared with the remote lakes (Lescord et al. 2015b), levels of MeHg in zooplankton from Meretta were four-fold higher than in all other lakes (Table S1 of the Supplementary Material1). This, in combination with the higher aqueous δ34S values of sulfate and [MeHg], as well as more negative sediment δ34S values, suggests that there could be enhanced Hg(II) methylation in Meretta Lake (Canfield 2001; Alpers et al. 2014). However, recent studies suggest that the rates and degree of fractionation of S isotopes due to SRB vary among bacterial strains/species (Bradley et al. 2016). It is important to note that aqueous sulfate concentrations are very low in these high Arctic lakes (i.e., 0.23–1.35 mg/L across lakes and highest in the unaffected Small Lake); it is, therefore, likely that other processes (e.g., sulfurization; Werne et al. 2008) are also affecting S and Hg cycling, respectively, in these systems.

The use of δ34S to differentiate between pelagic and benthic energy sources in Arctic lakes

Although δ13C values indicated differences in the habitat use of zooplankton and chironomids in our study lakes (see Lescord et al. 2015b), there was no consistent difference in δ34S values between these groups (Fig. 2A), which precluded the use of isotope mixing models to assess their relative importance in the diets of Arctic char. Delta34S values were also not useful in distinguishing between benthic invertebrates and zooplankton as prey for fish in temperate coastal lakes (Clayden et al. 2017). In our Arctic lakes, δ13C values were more negative in pelagic zooplankton than benthic invertebrates in each lake (Fig. 2B) and mixing models indicated that most or all of the carbon (≥76% across lakes) in the diet of char in these systems is derived from benthic rather than pelagic sources (Lescord et al. 2015b), agreeing with the results of Muir et al. (2005) and Gantner et al. (2010) from the same lakes, and of Swanson et al. (2010) for resident fishes from other coastal Arctic lakes. Our current results, therefore, suggest that δ13C is a more useful indicator of diet sources for biota from high Arctic lakes than δ34S, which contrasts with results from lakes in boreal Québec, Canada (Croisetière et al. 2009) and those of estuaries in the western United States (Willacker et al. 2017). Overall, far less is known about how δ34S values respond to changes in S cycling and biogeochemical conditions when compared with δ13C and carbon cycling. It is possible that physico-chemical factors such as temperature and dissolved oxygen concentrations (and, therefore, thermal and oxic stratification patterns), which vary greatly among temperate, boreal, and Arctic regions, influence S isotope fractionation and therefore its applications to food web delineation. Indeed, these ultra-oligotrophic lakes likely do not undergo seasonal stratification during summer months, which would change the anoxic cycling of S, Hg, and other variables when compared with more southerly systems.
Fig. 2.
Fig. 2. A comparison of mean δ34S and δ13C values (±SD) in chironomids and zooplankton from six lakes near Resolute Bay, NU, Canada. Shaded and white areas show impacted and remote lakes, respectively.

Relationship between δ34S values and Hg concentrations in Arctic water and biota

Across all six Arctic lakes, average [MeHg] values in surface waters were positively related to the corresponding δ34S values of aqueous (LME p = 0.022; Fig. 3), which implies that lakes with 34S-enriched had higher production of MeHg due to enhanced SRB activity using more of the 32S isotope, as discussed previously. MeHg concentrations from deep water samples, however, were not significantly related to their corresponding δ34S values (p = 0.645 and 0.487, r2 = 0.027 and 0.049, respectively; data not shown). Because of their oligotrophic nature and lack of riparian cover, these high Arctic lakes do not stratify during the summer months (Lescord et al. 2015b). It is possible that the relationship between δ34S and [MeHg] in surface (but not deep) waters is due to the influence of spring snow- and ice-melt, which are important sources of Hg to high Arctic systems and alter concentrations and δ34S values in receiving waters (Loseto et al. 2004; Mörth et al. 2008; Giesler et al. 2009). Our water samples were collected after the annual spring melt so it is possible the relationship between aqueous [MeHg] and δ34S values of sulfate would change if samples were collected before ice-off. It is also unclear how photochemical reactions in snow or surface waters, which affect Hg retention and loss (Mann et al. 2014), may affect concentrations and δ34S values of spring melt-water.
Fig. 3.
Fig. 3. Relationships between log10 mercury concentrations ([MeHg] in invertebrates and water, [THg] in fish and water), and sulfur stable isotope values (δ34S) in (A) and (B) surface waters, (C) chironomids, (D) zooplankton, (E) small char, and (F) large char from six lakes near Resolute Bay, NU, Canada. The shaded area around the regression lines represents the 95% confidence intervals. Note the differences in scale on the x-axis. Data points for surface waters (A and B) represent one value taken per lake in 2010 and 2011 for Hg and δ34S simultaneously.
Total [Hg] in water showed no significant relationship with δ34S values of aqueous (LME p = 0.129; Fig. 3). However, this relationship is largely influenced by Meretta Lake and when removed, the negative relationship between aqueous THg concentrations and δ34S became significant (LME p = 0.040, r2 = 0.506; not shown). Because the number of lakes examined in this study was low, we were not able to determine whether Meretta Lake’s influential data were indeed statistical outliers. Studies have shown that the lighter δ34S of DOM complexes are reflective of a terrestrial-based carbon source and to aerobic conditions in riparian soils and limited S isotope fractionation by shoreline plants (Giesler et al. 2009). DOM complexes are important transporters of Hg in aquatic systems; therefore, terrestrial organic matter increases aqueous [THg] in boreal streams and lakes presumably due to enhanced transport from riparian areas (Teisserenc et al. 2011, 2014; Eklöf et al. 2012; Lescord et al. 2018). The negative relationship between δ34S values of sulfate and aqueous THg in the current study may therefore reflect higher Hg input with greater terrestrial DOM. However, sulfur compounds, DOM, and Hg have complex biogeochemical interactions (e.g., Haitzer et al. 2002; Ravichandran 2004) and further study is warranted to understand how these processes alter δ34S values, particularly in ultra-oligotrophic Arctic systems.
Relationships between [MeHg] or [THg] and δ34S values in biota varied among organisms in these high Arctic lakes. Benthic chironomids had positive relationships between their tissue [MeHg] and δ34S (LME p = 0.008; Fig. 3), which is in contrast with the negative relationships between [MeHg] and δ34S for several benthic macroinvertebrate taxa from temperate systems (Clayden et al. 2017). However, zooplankton from these Arctic lakes showed a negative relationship between their [MeHg] and δ34S values (Fig. 3D). This relationship was significant after the removal of Meretta Lake, where Daphnia sp. have been detected, which elevate MeHg in bulk zooplankton (Chételat and Amyot 2009). Daphnia sp. are only found in higher-nutrient Arctic systems and likely thrive in Meretta because of the historical anthropogenic eutrophication (Chételat and Amyot 2009). Similar to benthic chironomids, both large and small char showed positive relationships between [THg] and δ34S (LME p = 0.008 and 0.027, respectively; Figs. 3E and 3F). Other fish studies showed opposing log-THg versus δ34S relationships between species from the same streams (Schmitt et al. 2011), no relationships between these variables across lakes (Ethier et al. 2008; Clayden et al. 2017), or that δ34S was a strong positive predictor of [THg] in fishes in estuarine wetlands (Willacker et al. 2017). In the latter study, the authors attributed the positive relationship to the influence of marine S values as well as the presumed higher rates of Hg methylation and 34S enrichment due to SRB activity in impounded sites (Willacker et al. 2017). The consistent, positive relationships between water [MeHg], invertebrate [MeHg], or fish [THg] and δ34S values may also be related to SRB activity and suggest linkages between Hg and S cycling in these remote high Arctic systems. Studies examining the mechanisms behind these positive relationships are warranted and sampling of additional Arctic lakes would help determine whether the positive trends between biotic [THg] or [MeHg] and δ34S found in this study occur more broadly.
The relationships between log10[THg or MeHg] versus δ34S values of food web organisms varied among lakes (ANCOVA: Lake × δ34S interaction term p = 0.002, F = 4.240), with a positive slope in North Lake, a negative slope in Meretta Lake, and no significant slopes in the other four systems (Fig. 4). These mixed relationships contrast with our previous findings in seven coastal temperate lakes, where relationships were consistently negative (Clayden et al. 2017). It is important to note that the δ34S values in temperate coastal lakes were generally more positive than those presented herein, which would extend the range in values on the x-axis assessed. However, the mixed Hg–δ34S relationships herein also contrast with the strong and consistent relationships between log-THg or MeHg and δ15N through these food webs (Lescord et al. 2015b) and other aquatic food webs worldwide (Lavoie et al. 2013). Few studies have related [Hg] to δ34S values in biota across a range of trophic levels and, in those that have, both the direction and strength of the relationships varied, particularly among regions. Our results from Arctic systems add to this mixed body of evidence and show that the [Hg]–δ34S relationships in food webs can differ between similar lake ecosystems within a relatively small geographic area (i.e., ∼12 km). It is possible that various physical and chemical factors (e.g., nutrient content and anoxic habitat) may contribute to these differences, and more work is needed to investigate these effects. Interestingly, the two lake food webs that showed the strongest relationships between [Hg] and δ34S had considerably different morphology: Meretta Lake was one of the smallest (0.39 km2) and shallowest lakes (max depth = 9.2 m), whereas Resolute Lake was the largest (1.3 km2) and one of the deepest systems (14.7 m) in our dataset.
Fig. 4.
Fig. 4. Relationships between log10 Hg concentrations ([MeHg] in chironomids and [THg] in fish) and δ34S values through food webs from six lakes near Resolute Bay, NU, Canada. Note the differences in scale among lakes. Only organisms that are involved in aquatic food webs should be included in biomagnification models (Borgå et al. 2012); as such, zooplankton were not included in these analyses. Within-food-web regressions including zooplankton are shown in Fig. S6.1

Multiple isotopes as predictors of Hg concentrations in Arctic biota

Overall, the predictive models showed that various combinations of isotope and elemental measures (including the commonly used δ15Nadj, and δ13C as well as δ34S, %N, %C, and %S) had a significant relationship with [THg] or [MeHg] in biota from these Arctic lakes. Not surprisingly, δ15N values were a consistent positive predictor of all biotic [Hg], although this relationship was only significant in zooplankton and large char (Table 1). In both large and small char models, the %S in biotic tissue was a positive predictor of [THg] (Table 1, Fig. S21), which may be related to the amount of S-containing amino acids, the dominant S-containing compounds in organisms (Brosnan and Brosnan 2006), as they are strong protein-based binding sites for MeHg in tissues (Peng et al. 2016; Bradley et al. 2017). In addition, [THg] or [MeHg] concentrations were positively related to %S through the food webs of five lakes (linear models, p < 0.001–0.003, r2 = 0.396–0.894; Supplementary Material1); the sixth lake, Small Lake, showed no significant relationship between [THg] or [MeHg] and %S (p = 0.130, r2 = 0.162; Fig. S41). Interestingly, δ15N values were positively related to %S measures in each of the six food webs (p < 0.001–0.003, r2 = 0.375–0.660; Fig. S31), implying higher S-containing amino acid content with increasing trophic level. Delta34S values were identified as predictors of [MeHg] or [THg] in invertebrates and small char, although their effect was weak (i.e., coefficients were small) and varied in direction (Table 1). For example, [MeHg] in chironomids and small char were positively related to their tissue δ34S, whereas zooplankton showed a negative relationship between the two variables (Fig. 3). Other elemental measures were excluded from all models (i.e., %N; Table 1) or had mixed relationships between models. Delta13C values, for example, were only predictive of fish Hg, with higher [THg] in large char with more positive δ13C values; this suggests increased Hg uptake in individuals with more benthic feeding.
Table 1.
Table 1. A summary of Least Angle Shrinkage and Selection Operator (LASSO) regression results of isotopic and elemental predictors of log10 THg or MeHg concentrations in biota across six lake food webs near Resolute, NU, Canada.

Note: Variables included in the models were carbon, nitrogen, and sulfur isotopic and elemental measures (δ34S, δ15Nadj, δ13C, %S, %N, %C). The results presented are considered the best-fit model parameters and were selected based on Mallow’s Cp values at a given level of the penalty parameter (λ).

Overall, these results imply that [Hg] values in Arctic biota are most strongly related to an organism’s trophic position (i.e., δ15N) but that measures of S content and δ13C also influence [Hg] in some groups of organisms. They further demonstrate the utility of modeling trophic interactions and contaminant transfer using multiple isotopic and elemental tracers, as they simultaneously suggest an increase in %S and [Hg] with trophic level, as well as the influence of S-cycling on Hg bioaccumulation in food webs of high Arctic lakes.


In this study the δ34S of benthic invertebrates and zooplankton across our Arctic study lakes were not significantly different, in contrast with what has been observed in boreal lakes. As such, tissue δ34S values did not help to distinguish between dietary sulfur derived from sediments versus aqueous in these food webs. These results suggest that SRB activity is likely not the primary driver of δ34S values in these ecosystems. However, δ34S values were consistently and positively related to [THg] or [MeHg] in benthic invertebrates and fish, linking S isotope fractionation and Hg bioaccumulation. The mechanisms behind these relationships warrant further study, particularly in high Arctic systems where primary productivity is increasing (Michelutti et al. 2007) and changes to microbial S cycling have been reported (Drevnick et al. 2010).


Funding and logistical support for this study was provided by the Natural Sciences and Engineering Research Council of Canada through their Canada Research Chair (#950-203477 and #950-211803) and Discovery (#341960-2013 and #312237) Programs, the Northern Contaminants Program of Indigenous and Northern Affairs Canada, Environment and Climate Change Canada (Water Science and Technology Directorate, #1114181), the Stephen A. Jarislowsky Foundation, and the Polar Continental Shelf Program (#M26) in Resolute, NU, Canada. The authors sincerely thank Debbie Iqaluk, Brandy Iqaluk, Angella Mercer, Amber Gleason, Greg Lawson, Sam Edmonds, Paul Drevnick, Benjamin Barst, and Günter Köck for assistance with field work and laboratory analyses.


Supplementary material is available with the article through the journal Web site at Supplementary Material.


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Published In

cover image Arctic Science
Arctic Science
Volume 5Number 2June 2019
Pages: 90 - 106


Received: 20 July 2018
Accepted: 3 December 2018
Accepted manuscript online: 19 December 2018
Version of record online: 19 December 2018

Key Words

  1. sulfate
  2. food webs
  3. methylmercury
  4. oligotrophic
  5. bioaccumulation


  1. sulfate
  2. réseaux trophiques
  3. méthylmercure
  4. oligotrophe
  5. bioaccumulation



Gretchen L. Lescord* [email protected]
Department of Biology, Canadian Rivers Institute, University of New Brunswick, 100 Tucker Park Road, Saint John, NB E2L 4L5, Canada.
Meredith G. Clayden
Department of Biology, Canadian Rivers Institute, University of New Brunswick, 100 Tucker Park Road, Saint John, NB E2L 4L5, Canada.
Karen A. Kidd
Department of Biology, Canadian Rivers Institute, University of New Brunswick, 100 Tucker Park Road, Saint John, NB E2L 4L5, Canada.
Department of Biology, School of Geography and Earth Sciences, McMaster University, 1280 Main Street West, Hamilton, ON L8S 4K1, Canada.
Jane L. Kirk
Environment and Climate Change Canada, Aquatic Contaminants Research Division, 867 Lakeshore Road, Burlington, ON L7S 1A1, Canada.
Xiaowa Wang
Environment and Climate Change Canada, Aquatic Contaminants Research Division, 867 Lakeshore Road, Burlington, ON L7S 1A1, Canada.
Nelson J. O’Driscoll
Department of Earth and Environmental Science, Acadia University, 32 University Avenue, Wolfville, NS B4P 2R6, Canada.
Derek C.G. Muir
Environment and Climate Change Canada, Aquatic Contaminants Research Division, 867 Lakeshore Road, Burlington, ON L7S 1A1, Canada.


Current address: Freshwater Cooperative Unit, Vale Living with Lakes Centre, Laurentian University, 935 Ramsey Lake Road, Sudbury, ON P3B 2C6, Canada.
This article is open access. This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

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3. Mercury bioaccumulation in relation to changing physicochemical and ecological factors across a large and undisturbed boreal watershed

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