Mercury biomagnification in food webs of the northeastern Chukchi Sea, Alaskan Arctic

Author’s Accepted Manuscript Mercury Biomagnification in Food Webs of the Northeastern Chukchi Sea, Alaskan Arctic Austin L. Fox, John H. Trefry, Robert P. Trocine, Kenneth H. Dunton, Brenda K. Lasorsa, Brenda Konar, Carin J. Ashjian, Lee W. Cooper www.elsevier.com/locate/dsr2

PII: DOI: Reference:

S0967-0645(17)30144-3 http://dx.doi.org/10.1016/j.dsr2.2017.04.020 DSRII4241

To appear in: Deep-Sea Research Part II Received date: 18 March 2016 Revised date: 23 January 2017 Accepted date: 25 April 2017 Cite this article as: Austin L. Fox, John H. Trefry, Robert P. Trocine, Kenneth H. Dunton, Brenda K. Lasorsa, Brenda Konar, Carin J. Ashjian and Lee W. Cooper, Mercury Biomagnification in Food Webs of the Northeastern Chukchi Sea, Alaskan Arctic, Deep-Sea Research Part II, http://dx.doi.org/10.1016/j.dsr2.2017.04.020 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Mercury Biomagnification in Food Webs of the Northeastern Chukchi Sea, Alaskan Arctic Austin L. Foxa*, John H. Trefrya, Robert P. Trocinea, Kenneth H. Duntonb, Brenda K. Lasorsac, Brenda Konard, Carin J. Ashjiane, Lee W. Cooperf a

Department of Marine & Environmental Systems, Florida Institute of Technology, Melbourne, FL 32901, USA b

Marine Science Institute, The University of Texas at Austin, Port Aransas, TX 78373, USA c

Battelle Marine Science Laboratory, Sequim, WA 98382, USA

d

School of Fisheries and Ocean Sciences, University of Alaska Fairbanks, Fairbanks, AK 99775, USA e

Woods Hole Oceanographic Institution, Woods Hole, MA 02543, USA

f

Chesapeake Biological Laboratory, University of Maryland Center for Environmental Science, Solomons, MD 20688, USA *

Correspondence to: Department of Marine & Environmental Systems, Florida Institute of Technology, 150 W. University Blvd. Melbourne, FL 32901, USA. Tel.: +1 480 323 0044. [email protected]

ABSTRACT Predictive tools and a large new dataset for the northeastern Chukchi Sea (NECS) are used here to help identify regional differences and potential future shifts in the magnitude of Hg biomagnification in the Arctic. At the base of the food web in the NECS, concentrations of total mercury (THg) in phytoplankton (20-µm mesh) ranged from 4–42 ng g-1 dry weight, partly in response to variations in algal biomass and water temperature. A >3-fold increase in monomethylmercury 1

(MMHg) was observed in zooplankton (4.3 ± 0.7 ng g-1) relative to phytoplankton (<1.5 ng g-1), even though concentrations of THg in zooplankton (150-µm mesh) were not significantly different than in phytoplankton. Concentrations and % MMHg increased with trophic level (TL) by >150-fold and from <10 to >85%, respectively, from phytoplankton to muscle in the whelk Plicifusus kroeyeri (279 ng g-1, TL 4.5). For muscle tissue in 10 species plus whole phytoplankton and zooplankton, the trophic magnification slope (TMS) for MMHg (log10[MMHg] = m(δ15N) + b; where m = TMS) was 0.23 ± 0.02 (SE). No significant differences in TMS were found for the NECS plus three other studies from the eastern Canadian Arctic (average TMS = 0.24 ± 0.02). Nevertheless, all data for MMHg in biota from the NECS plotted below the combined best fit line for all four studies. Results from an ANCOVA showed that statistically different (p = 0.001) intercept values (b), not TMS, best explained the, >2-fold lower concentrations of MMHg in biota from the NECS (b = -1.85) relative to the same species from the eastern Canadian Arctic (b = -1.29). Future changes that affect bioaccumulation of MMHg in the Arctic may impact the biomagnification equation by shifting the TMS, intercept or both. The intercept is more likely to respond to changes in productivity and concentrations of dissolved Hg whereas the TMS may respond to changing growth rates due to fluctuations in productivity and food availability. In either case, small changes in the intercept or TMS coincide with predictably large increases or decreases in MMHg concentrations in apex predators. 2

Keywords: Arctic, Chukchi Sea, Mercury (Hg), Biomagnification, Trophic magnification slope (TMS) ANCOVA – analysis of covariance CVAFS – cold vapor atomic fluorescence spectrometry d. wt. – dry weight df – degrees of freedom DIW – deionized water DOC – dissolved organic carbon FWMF – food web magnification factor Hg – mercury Hginorg – inorganic mercury MDL – method detection limit MMHg – monomethyl mercury NECS – northeastern Chukchi Sea NIST – National Institute of Standards and Technology NS&T – National Status and Trends PDB – Vienna-Pee Dee Belemnite POM – particulate organic matter SD – standard deviation SE – standard error SRM – standard reference material TEF – trophic enrichment factor THg – total mercury THgd – total dissolved mercury TL – trophic level TMS – trophic magnification slope TOC – total organic carbon 3

1. Introduction Recent studies have identified perturbations in arctic ecosystems that can be linked to sea ice retreat, severe coastal erosion and enhanced primary productivity (Grebmeier et al., 2010; Holland et al., 2006; Ping et al., 2011). These three impacts from global climate change have the potential to alter the supply, cycling and fate of contaminants, including mercury (Hg), in the Arctic. In addition to climate-related stressors, a longer open-water season increases the potential for offshore energy development and trans-arctic shipping to contribute contaminants. The goal of this study was to improve our understanding of Hg biomagnification in arctic marine food webs and thereby identify regional differences and predict future shifts in biomagnification in response to environmental changes. Between 1979 and 2011, the ice-free season in the Chukchi Sea increased in duration and size by about three months and ~40%, respectively (Vaughan et al., 2013). The resulting greater fetch and wave energy enhanced coastal erosion of soils and permafrost to yield a 100% increase in the flux of organic carbon and nitrogen to Alaskan seas (Ping et al., 2011). Warmer temperatures and increased supplies of organic carbon and nutrients can stimulate productivity and influence Hg availability and biomagnification in the Arctic. Any future changes in the timing and intensity of phytoplankton blooms will undoubtedly affect benthic productivity because the two systems are tightly coupled. (Dunton et al., 2005; Grebmeier et al., 1989; Iken et al., 2010). Such perturbations are likely to influence 4

the trophic transfer of potentially toxic substances and thereby change concentrations and the degree of Hg biomagnification (Chen and Folt, 2005; Lavoie et al., 2013). Bioaccumulation of Hg in phytoplankton yields cellular Hg concentrations that are thousands of times greater than in surrounding seawater (Moye et al., 2002; Pickhardt and Fisher, 2007). Mercury is then bioaccumulated by primary grazers through dietary uptake that results from a greater rate of assimilation than excretion. Continued bioaccumulation in pelagic and benthic predators yields an exponential increase in Hg concentrations with increasing trophic level (TL) via biomagnification of monomethylmercury (MMHg). In the Arctic, Hg biomagnifies by >50 fold in higher TLs (sea birds and marine mammals) relative to primary consumers (zooplankton and bivalves) (Jaeger et al., 2009). For example, Wagemann et al. (1998) found an increase in total Hg (THg) from ~20 ng g-1 dry weight for arctic zooplankton to >1,500 ng g-1 dry weight in ringed seal muscle. The degree of Hg biomagnification is quantified as the enrichment of Hg averaged over the length of the food web sampled. Measures of biomagnification historically relied on inferred predator-prey relationships or gut-content analysis; however, these methods did not account for variations in diet (Borga et al., 2011). Broman et al. (1992) used stable nitrogen isotopes (δ15N) to characterize food-web structure and thereby better quantify bioaccumulation of contaminants. The degree of biomagnification is now commonly calculated using the slope of a linear 5

regression for contaminant concentrations versus δ15N (e.g., Atwell et al., 1998; Borga et al., 2011; Campbell et al., 2005; Lavoie et al., 2010). Concentrations of Hg increase exponentially with increasing TL, therefore, the magnitude of biomagnification is calculated using the trophic magnification slope (TMS) for log10-tranformed concentrations of THg or MMHg versus δ15N (log10[Hg] = m(δ15N) + b; where m = TMS). Mercury concentrations in biota also have been linked with environmental variables. For example, concentrations of Hg in fish from freshwater lakes are positively correlated with rates of atmospheric Hg deposition (Hammerschmidt and Fitzgerald, 2006a; Lavoie et al., 2013). In freshwater lakes, pH, dissolved organic carbon (DOC), nutrients and dissolved Hg influence concentrations of Hg in biota (Clayden et al., 2013; Clayden et al., 2014; Lavoie et al., 2013); however, less is known about how they affect the TMS. Other variables, including biomass, abundance, growth rate and food-web length, have been suggested to affect the TMS for Hg (Lavoie et al., 2013). Identifying variables that help explain differences in TMS among food webs is complicated by apparently large regional variations in TMS. Reported TMS values for MMHg in the Arctic range from 0.13–0.34, at least partially due to the use of different species and tissues (Clayden et al., 2015). Considerable discussion has evolved regarding regional differences in TMS as well as distinguishing TMS for pelagic and benthic biota (e.g., Chen et al., 2009; 6

Clayden et al., 2015; Lavoie et al., 2013). Globally, large ranges in TMS (0.080.53) were reported across an average range of 1.8 ± 0.8 TLs in a review of 205 different aquatic food webs (Lavoie et al., 2013). Therefore, calculated TMS values may not accurately represent biomagnification across the full length of the food web. Recent studies suggest that consistency in experimental design and methods of reporting biomagnification improve estimates of both TMS and Hg concentrations at the base of the food web (Clayden et al., 2015; Lavoie et al., 2013). Biomagnification of Hg in the northeastern Chukchi Sea (NECS) in this study was investigated with the following objectives: (1) determine how TMS values differ for THg versus MMHg in muscle tissue and whole organisms for a complex food web including distinct pelagic and benthic food webs and (2) use biomagnification equations to identify regional differences in TMS and to predict how concentrations of Hg in biota may respond to future environmental stressors. Data and concepts presented here will help improve intercomparisons for THg and MMHg in biota and provide a reference point for tracking future changes in Hg biomagnification in the Arctic. 2. Methods 2.1. Sample collection

7

Water, sediments and biota were collected from the NECS during July and August of 2010 from the R/V Moana Wave and in 2012 and 2013 from the USCGC Healy. Biota were obtained from 35 stations in the NECS in 2010 (Fig. 1). During 2012 and 2013, 21 and 25 stations, respectively, were occupied within 150 km of Hanna Shoal and predominantly northeast of the 2010 survey (Fig. 1). Sampling stations during all three years were selected using a hexagonal tessellation approach to ensure random selection with an even distribution of sites (White et al., 1992). Twenty-six species or groups of organisms (e.g., phytoplankton and zooplankton) were collected based on availability and their potential as bioindicators of Hg contamination (Appendix A). Phytoplankton and zooplankton were obtained by vertical tows using a ring net with 20-µm mesh and a bongo net with 150-µm mesh, respectively. Phytoplankton and zooplankton are operationally defined in this study by mesh size (20-µm and 150-µm, respectively). Benthic biota were collected from one side of a 0.1 m2 double van Veen grab; samples were sieved through a 1-mm mesh to remove sediments and then organisms were sorted to the species level and counted (Schonberg et al., 2014). The second 0.1-m2 section of the grab was used to collect surface sediments for chemical analysis. Biota also were collected using an epibenthic, 3.05-m plum-staff beam trawl, with a 7-mm mesh and a 4-mm cod end liner. Trawl samples were sorted by species, counted and sizes of selected species were measured to the nearest mm (Konar et al., 2014; Ravelo et al., 2014). Biota samples for Hg analysis were dissected using 8

stainless steel blades aboard ship in a laminar flow hood and stored frozen until laboratory analysis. Samples of whole organisms were frozen immediately aboard ship and kept frozen until laboratory analysis. Continuous vertical profiles for temperature, salinity and chlorophyll a were obtained in 2010 using a YSI 6600 data Sonde (YSI Inc., Yellow Springs, OH) and in 2012 and 2013 using a Sea-Bird SBE9+ CTD system (Sea-Bird Electronics, Bellevue, WA) with an attached fluorescence detector for chlorophyll a (Wetlabs ECO-FLRTD fluorometer). Water column samples for total dissolved mercury (THgd) were collected in 2010 using a peristaltic pump and HCl-washed Tygon tubing, and in 2012 using HCl-washed, Teflon-lined, 10-L GoFlo bottles that were mounted on an epoxy-coated rosette and opened at ~10 m by hydrostatic pressure. Seawater samples for THgd analysis were vacuum filtered through polycarbonate filters (Poretics, 47-mm diameter, 0.4-µm pore size) in a laminar flow hood aboard ship. Filters were pre-washed in 5N HNO3 and rinsed three times using 18 MΩ-cm deionized water (DIW). Filtered samples were collected in PFA Teflon bottles and preserved using 1 mL of Fisher Optima HNO3 per 100 mL of sample. Seawater for chlorophyll a analysis was vacuum filtered through glass fiber filters (Whatman, 25-mm diameter, 0.7-µm pore size). Filters were folded and placed in polypropylene centrifuge tubes. Surface sediment samples were weighed and placed in polypropylene centrifuge tubes. Samples were frozen for one hour to 9

lyse cell walls, then chlorophyll a was extracted from filters and sediments into a 90% acetone solution at 4˚C in the dark for 24 hours. Samples were centrifuged and the supernatant was collected. Extracted chlorophyll a was analyzed aboard ship using a Turner Designs 10-AU field fluorometer (Turner Designs, San Jose, CA) following methods by Welschmeyer (1994). The fluorometer was calibrated before and after all sampling using a chlorophyll standard (Turner designs Part No. 10850) and during sampling using a solid secondary standard (Turner Designs Part No. 10-AU-904). 2.2. Laboratory methods Frozen biota samples for Hg analysis were thawed, weighed and freezedried in preparation for analysis. Concentrations of THg in biota were determined by digesting freeze-dried samples using trace metal grade, concentrated HNO3 and H2SO4 (Fisher Scientific). Digested samples were analyzed using a Laboratory Data Control model 1235 cold vapor atomic absorption spectrometer following methods of Trefry et al. (2003). Standard reference material (SRM) #1566b (oyster tissue) obtained from the National Institute of Standards and Technology (NIST) was processed with each batch of samples to determine precision and accuracy. Values for the SRM were within the 95% confidence interval for the certified value. Analytical precision was better than 6% for laboratory replicates. Samples for MMHg analysis were digested using an acid bromide/methylene chloride extraction. The aqueous phase was analyzed using 10

ethylation, isothermal gas chromatography separation and detection by cold vapor atomic fluorescence spectrometry (CVAFS) based on methods from Bloom and Crecelius (1983) and Bloom (1989). The certified reference material DORM-3 from the National Research Council Canada was processed with each batch of samples and all values were within the 95% confidence interval for the certified value. Analytical precision was better than 7% for lab replicates. Concentrations of inorganic Hg (Hginorg) were determined by subtracting concentrations of MMHg from THg (Hginorg = THg - MMHg). Concentration data for Hg from this and other studies are reported on a dry weight (d. wt.) basis to account for variability in water content among species, unless otherwise noted. All values are reported as mean ± standard error (SE) as a measure of uncertainty between the sample mean and an estimate of the population mean unless noted otherwise. Stable carbon (δ13C) and nitrogen (δ15N) isotope values were determined by combusting dried and homogenized samples at 1020°C using an automated system. Subsamples for carbon isotope analysis were placed in 1N HCl to remove carbonates, rinsed in DIW and dried at 60˚C to a constant weight. Subsamples for nitrogen isotope analysis were not acidified. Combustion gases were quantified using a Finnigan MAT Delta Plus mass spectrometer linked to a CE Instruments elemental analyzer (McTigue and Dunton, 2014). Standard reference materials from NIST and the International Atomic Energy Agency were processed with each 11

batch of samples to ensure accuracy. Precision was better than ± 0.20% for both carbon and nitrogen and all standards were within 0.20‰ of certified values. Results are reported using standard δ notation, with values for δ13C and δ15N calculated relative to the Vienna-Pee Dee Belemnite (PDB) and atmospheric nitrogen standards, respectively. Filtered seawater samples for THgd analysis were treated with a bromine monochloride solution to oxidize organic ligands (Szakacs et al., 1980). Samples were then pre-concentrated by gold amalgamation followed by detection using CVAFS. Precision from laboratory duplicates was better than 8% and the method detection limit (MDL) was 0.1 pM. 2.3. Statistical analysis and calculations Trophic levels (TL, Equation 1) for biota were determined using values for δ15N relative to particulate organic matter (POM, from the NECS, δ15N = 5.3‰, McTigue and Dunton, 2014) that was set as TL 1 at the base of the food web. An estimated trophic enrichment factor (TEF) of 3.4‰ per TL was applied to each trophic step (Hobson and Welch, 1992; Equation 1). This value is consistent with the TEF identified for the Chukchi Sea using a known trophic step between POM (δ15N = 5.3‰) and the amphipod Ampelisca macrocephala (δ15N = 8.7‰) (McTigue and Dunton, 2014). TL = ([δ15N] - δ15NPOM)/3.4‰]+1 12

(Equation 1)

Biomagnification of Hg was determined by signification positive correlations between log10-transformed concentrations of THg or MMHg and corresponding values for δ15N (Equation 2). Significance was determined for all statistical tests based on statistical p-values less than the a-priori alpha value of 0.05. Significant linear trends were identified using the linear least squares statistical test, results are reported in the format (R2, p, df). log10[Hg] = m(δ15N) + b

(Equation 2)

The exponential increase in Hg can be quantified as a per-TL magnification factor analogous to [Hg]predator/[Hg]prey averaged over the entire food web. A Food Web Magnification Factor (FWMF) was determined for biomagnification by substituting TL for δ15N in Equation 2 to get Equation 3 and then taking the antilog of the slope (m) using Equation 4. log10[Hg] = m(TL) + b FWMF = 10m

(Equation 3) (Equation 4)

Comparisons of two independent groups of data were carried out using the Students t-test, one- or two-tailed, assuming equal variance. Levene’s test was used to determine the equality of variance between two independent data sets and pooled variance was used for all t-tests in this study based on Levene’s test p-values >0.05. Independent groups of data with p-values >0.05 were considered not significantly different from one another. 13

Size frequency distributions were identified using normal probability plots (Q-Q plots) generated using Microsoft Excel. Data were paired with z-scores calculated for a normal distribution. Normally distributed data were identified by coefficient of determination (R2) values >0.8, statistical p-values <0.05 and no trend for deviations from the prediction line for a linear regression. Analysis of Covariance (ANCOVA) was used to compare intercept values from the biomagnification equation among studies (JMP 12.1.0). Assumptions for the ANCOVA, including the homogeneity of regression slopes (TMS), were verified prior to analysis. The homogeneity of regression slopes (TMS) was verified using a separate ANCOVA for the group*δ15N versus log10-transformed concentrations of Hg.

3. Results and discussion 3.1. Mercury in selected biota 3.1.1. Phytoplankton Concentrations of THg in phytoplankton (20-µm mesh) averaged 15 ± 2 ng g-1, ranged from 4–42 ng g-1 (Table 1) and were positively correlated with water temperature following the equation: THg = 1.5 (T˚C) + 12.6 (R2 = 0.25, p = 0.02, df = 21). The two highest concentrations of THg (42 and 27 ng g-1) were found at stations B3 and H29 (Fig. 1), both located east of Hanna Shoal where water 14

temperatures at 10 m were 3.6 and 2.6˚C, respectively. The lowest concentration of THg in phytoplankton (4 ng g-1) was found at station CB11 located west of Hanna Shoal, with a water temperature of -0.1˚C at 10 m. Temperature was previously reported to be (1) an important environmental variable for explaining patterns in algal community structure (Luengen, 2007) and (2) positively correlated with the abundance of small (<20 µm) phytoplankton in the Chukchi Sea (Lee et al., 2014). Related studies found that community composition influenced concentrations of THg in mixed phytoplankton (e.g., Cardenas et al., 2014; Knauer and Martin, 1972; Rizzo et al., 2014). For example, higher concentrations of THg in phytoplankton from freshwater lakes have been found for smaller size classes of plankton (Cardenas et al., 2014; Rizzo et al., 2014). Vertical profiles for THgd collected in the NECS typically followed trends for chlorophyll a, often with lower concentrations of THgd at depths near the chlorophyll a maximum, thereby supporting algal-bloom dilution of dissolved Hg (Fig. 2) (Fox et al., 2014). Dilution of THgd via uptake by more organisms during algal blooms has been previously observed in laboratory experiments (Pickhardt et al., 2002), lakes in the northeastern U.S. (Chen and Folt, 2005), San Francisco Bay (Luengen and Flegal, 2009), the northwest Atlantic Ocean (Hammerschmidt et al., 2013) and the Chukchi Sea (Fox et al., 2014). Increased algal biomass also reduces body burdens of Hg in plankton (Hammerschmidt et al., 2013; Le Faucheur et al., 2014). This bloom-related process may be partially responsible for the observed 15

range of THg concentrations in phytoplankton in the present study. No other significant correlations were found between concentrations of THg in phytoplankton and environmental or site-specific variables including the following: latitude, longitude, water depth and salinity, as well as sediment Hg, total organic carbon (TOC) and chlorophyll a. Concentrations of THg in phytoplankton from the NECS were lower than values reported from most other regions (Table 2). For example, concentrations of THg in phytoplankton from San Francisco and Monterey Bays in California averaged 94 ng g-1 and 207 ng g-1, respectively (Table 2). Lower concentrations of THg in phytoplankton from the NECS most likely resulted from lower atmospheric deposition of Hg in the Arctic (Outridge et al., 2008). The calculated annual atmospheric deposition for mercury in the Alaskan Arctic, 2.8 ± 0.7 (SD) µg m-2 yr1

(Fitzgerald et al., 2005), was low relative to 15–30 µg m-2 yr-1 for coastal

California (Seigneur et al., 2004). In freshwater lakes across the contiguous United States, Hammerschmidt and Fitzgerald (2006a) found a significant positive correlation (R2 = 0.71, p = <0.0001, df = 21) between concentrations of Hg in biota and atmospheric Hg deposition. Globally, atmospheric deposition likely drives large variations in Hg concentrations in phytoplankton. On smaller spatial scales, water temperature, algal blooms, and biogeochemical and physical properties of the water column may have an important influence on community composition and Hg concentrations in phytoplankton. 16

With respect to the arctic marine environment, more data are available for particulate organic matter (POM at <0.7 µm) than phytoplankton (Table 2). Therefore, some investigators have used POM values (e.g., Atwell et al., 1998) to estimate THg values for phytoplankton (e.g., Outridge et al., 2008). The average THg in phytoplankton from this study is less than values reported for arctic POM, yet five times greater than one value of 3 ng g-1 reported for ice algae from Baffin Bay, Canada (Table 2). Higher concentrations of THg in POM may result from the presence of picoplankton or nanoplankton in POM samples. Monomethylmercury concentrations in phytoplankton samples from the NECS were all below the MDL (1.5 ng g-1) and therefore MMHg accounted for <10% of the THg (Table 1). Other studies also have shown that MMHg generally accounts for <10% of the THg in phytoplankton (Table 2). This low % MMHg is observed even at THg concentrations as high as 94–440 ng g-1 at other locations (Table 2). Passive uptake of Hg has been shown to yield ~5% MMHg in phytoplankton, a value similar to that reported for seawater (Hammerschmidt et al., 2014; Mason et al., 1996; Rizzo et al., 2014). The calculated enrichment factor for THg between phytoplankton and water in the NECS was ~3000 [1.5 ng g-1 (wet weight) phytoplankton / 5.6 x 10-4 ng g-1 water]. Using a phytoplankton enrichment factor of 3,000 and 5% MMHg (from previous studies, Table 2), the estimated

17

MMHg value is ~0.8 ng g-1 for phytoplankton (0.05 x 15 ng g-1), consistent with values of <1.5 ng g-1 and <10% MMHg found during this study (Table 1). 3.1.2. Zooplankton Average concentrations of THg in mixed zooplankton (150-µm mesh) were 13 ± 1 ng g-1 and not significantly different (p = 0.54) from values obtained for phytoplankton (Table 1), even though concentrations of MMHg increased from <1.5 ng g-1 in phytoplankton to 4.3 ± 0.7 ng g-1 in zooplankton. An increase in the % MMHg from phytoplankton (<10%) to zooplankton (~40%) has been previously explained by slower depuration of MMHg from zooplankton (Tsui and Wang, 2004). The average enrichment factor of ≥3 for MMHg from phytoplankton to zooplankton in the NECS is consistent with enrichment of MMHg in zooplankton relative to phytoplankton by two- to six- fold in other regions (Cossa et al., 2012; Hammerschmidt and Fitzgerald, 2006b; Hammerschmidt et al., 2013; Sunderland et al., 2010). Among zooplankton species, reduced uptake and retention of Hginorg with increasing TL was shown to increase the % MMHg despite no significant difference in MMHg concentrations (Foster et al., 2012). No significant correlations were identified between MMHg or THg concentrations and the composition of zooplankton samples (the relative abundance of copepods, chaetognaths, meroplankton and larvaceans) and most environmental variables. A significant negative correlation was found for concentrations of THg in zooplankton versus inventories of chlorophyll a in surface sediments (R2 = 0.43, 18

p = 0.008, df = 14, Fig. 3). In the Canadian Arctic, sediment chlorophyll a was positively correlated with the total amount and abundance of larger (>5 µm) phytoplankton in the water column (Roy et al., 2014). Both higher algal-biomass and larger phytoplankton have been previously shown to yield lower concentrations of Hg in phytoplankton, thereby leading to decreased dietary uptake and lower concentrations of Hg in zooplankton (Cardenas et al., 2014; Hammerschmidt et al., 2013; Le Faucheur et al., 2014; Rizzo et al., 2014). Zooplankton that feed on abundant, chlorophyll-rich POM resuspended from sediments, may have lower concentrations of THg, similar to algal-bloom dilution previously described for the water column. In freshwater lakes from the northeastern U.S., Chen et al. (2014) reported that zooplankton feed on sediment microalgae, phytoplankton and POM. Relatively shallow (~50 m) and clear water in the Chukchi Sea facilitate growth of benthic microalgae (McTigue and Dunton, 2014) that contribute to sediment chlorophyll a. The low THg concentrations and δ15N for zooplankton (δ15N = 9.9 ± 0.9 ‰) are more consistent with a diet that includes phytoplankton (δ15N = 7.7 ± 1.1 ‰) as well as POM (δ15N = 5.3 ± 2.5 ‰) and/or sediment organic matter (δ15N = 7.2 ± 0.7) (McTigue and Dunton, 2014). Zooplankton from the NECS had concentrations of THg and MMHg at the low end of a range of values reported for zooplankton in the Arctic (Table 3). For example, Campbell et al. (2005) found an average THg of 25 ± 9 ng g-1 for Calanus hyperboreus from the Northwater Polynya (Table 3) whereas Stern and Macdonald 19

(2005) reported an average of 85 ± 9 ng g-1 for the Canadian Basin (Table 3). The large range of THg values reported for C. hyperboreus are most likely due to seasonal shifts in physiology and regional differences in food availability with the lowest concentrations identified during late summer when lipid and caloric energy content are highest (Percy and Fife, 1981; Pucko et al., 2014). Concentrations of THg and MMHg in zooplankton from the NECS were lower than values reported for non-arctic regions (Table 3). Lower concentrations of Hg in arctic zooplankton from this and other studies likely result from lower deposition rates for atmospheric Hg as discussed previously and lower concentrations of Hg in arctic phytoplankton. 3.1.3. Bivalves Although Hg is known to bioaccumulate in muscle tissue, average concentrations of THg (equally weighted) in whole bivalves for the six species in this study (83 ± 10 ng g-1) were ~75% greater than the average value for THg in muscle (47 ± 7 ng g-1; Table 4 and Fig. 4A). In contrast, MMHg concentrations in whole bivalves (14 ± 3 ng g-1) were ~42% lower than in muscle (24 ± 4 ng g-1). These two differences also yield (1) two-fold greater concentrations of Hginorg in whole organisms (58 ng g-1) than in muscle (29 ng g-1) and (2) only 20 ± 2% MMHg in whole organisms relative to 51 ± 8% MMHg in muscle (Fig. 4A). Higher concentrations of MMHg and a higher % MMHg in bivalve muscle relative to whole organisms are consistent with long-term accumulation of MMHg in muscle relative to more rapid accumulation and excretion of Hginorg by other tissues 20

such as the visceral mass that are more closely linked with digestion of sediments (Inza et al., 1997). In both muscle and whole bivalves, concentrations of Hginorg were more variable among species than concentrations of MMHg (Fig. 4A). Furthermore, the % MMHg in bivalves decreased with increasing THg due to the increased relative abundance of Hginorg (Fig. 4B), an observation similar to that of Apeti et al. (2012) for whole oysters from the Gulf of Mexico. This dependence of THg concentrations on the abundance of Hginorg shows that values for THg in whole bivalves are not a good indicator of the presence and biomagnification potential for MMHg. Although values for THg in biota remain useful as environmental indicators, they do not accurately predict biomagnification of MMHg at higher TLs. Bivalves from the Arctic, including the NECS had THg concentrations in both muscle and whole organisms that were at the lower end of the range of values previously reported for bivalves from lower latitudes. For example, THg concentrations reported for bivalves from Alaska (60-120 ng g-1) were much less than for bivalves from the contiguous U.S. (up to 1,280 ng g-1) during the NOAA National Status & Trends (NS&T), Mussel Watch Program (Kimbrough et al., 2008). Also, as part of the NS&T, Apeti et al. (2012) reported average THg and MMHg concentrations of 119 and 44 ng g-1, respectively, for whole oyster tissue collected from the Gulf of Mexico, 45% and 300% greater, respectively, than average values identified for bivalves in this study (Table 4). Lower Hg 21

concentrations in arctic bivalves likely result from lower concentrations of Hg in arctic plankton and an essentially pristine sedimentary environment with respect to Hg (Fox et al., 2014). No significant correlations were identified between concentrations of THg or MMHg in tissues from any of the six bivalve species and any site-specific or environmental variables determined during this study. The absence of significant trends likely resulted from a narrow range of values for environmental variables including sediment THg concentrations (31 ± 10 (SD) ng g-1, Fox et al., 2014). 3.1.4. Whelk The five whelk species collected had average THg in muscle that ranged from 52–209 ng g-1 (Table 5). Four of these species were predatory whelk from the family Buccinidae (Plicifusus kroeyeri, Colus aphelus, Neptunea borealis, Buccinum spp.) that had an overall average (equally weighted) THg concentration in muscle of 170 ± 30 ng g-1 where MMHg accounted for 91 ± 4% of the THg (Table 5). In contrast, the omnivorous scavenging whelk, Cryptonatica affinis, had a significantly lower average THg concentration (p <0.05) of 52 ± 10 ng g-1. Lower THg concentrations in the omnivorous whelk are consistent with a lower TL (3.3, Equation 1) relative to predatory whelk in this study (TL 4.1 ± 0.4). This difference in Hg concentrations between C. affinis and the Buccinidae whelk reinforces the complexities of feeding strategies that characterize biomagnification of Hg in the benthic food web. In previous studies, omnivorous invertebrates from freshwater 22

lakes had lower protein content relative to predators, thereby supporting both higher concentrations of MMHg and increased % MMHg found for predatory whelk in this study (Clayden et al., 2013). Concentrations of MMHg and THg in the four predatory whelk varied with size. Strong positive correlations were identified between shell length and log10transformed concentrations of THg for P. kroeyeri and C. aphelus with size accounting for ~70% and 60% of the variability in THg concentrations, respectively (Fig. 5). The highest concentrations of MMHg and THg for N. borealis and Buccinum spp. also were found for larger whelk, with significantly greater concentrations for whelk with shell lengths ≥5 cm relative to whelk with shells <5 cm (p = 0.04 and 0.01 respectively, t-test one-tailed, assuming equal variance). This observation reinforces the importance of having a representative sampling of organisms by size when strong Hg versus size relationships exist. The sizefrequency distribution for each species followed a normal distribution (normal probability plots R2 >0.90, p <0.001) and mean Hg values obtained in this study are representative of the most frequently occurring size class within each species. In contrast with the Buccinidae whelk, no significant size versus Hg relationship was identified for the omnivorous, scavenging whelk C. affinis, likely due to a smaller range of shell lengths (1.5–3.5 cm) than found for predatory whelk. 3.1.5. Arctic cod (Boreogadus saida)

23

Concentrations of THg in muscle from Arctic cod (Boreogadus saida) ranged from 6–250 ng g-1 with an overall average of 52 ± 8 ng g-1 (Table 6). Monomethylmercury accounted for an average of 97 ± 6% of the THg in Arctic cod muscle (Table 6) consistent with values of >90% MMHg from previous studies (Douglas et al., 2012; Lavoie et al., 2010). In whole Arctic cod from the NECS, concentrations of THg (23 ± 4 ng g-1) were >50% lower than values for muscle and MMHg accounted for 77 ± 4% of the THg in whole fish. Log10-transfomed concentrations of MMHg and THg in muscle versus fish length showed strong correlations (r = 0.74 and 0.75, respectively) (Fig. 6). No significant difference (ANCOVA, p = 0.203) was identified between slopes for MMHg and THg versus fish length, likely because MMHg accounted for essentially all of the THg in Arctic cod muscle. These observations again indicate the importance of obtaining a representative sampling of organisms by size. No significant correlations were identified between MMHg or THg concentrations and any site specific or environmental variables measured during this study including spatial distribution (i.e., latitude or longitude). Relative to previous studies, average THg concentrations for Arctic cod muscle from this study were 2–4 times lower than concentrations reported for the Canadian Arctic of: (1) 190 ± 30 ng g-1 (Atwell et al., 1998), (2) 270 ± 40 ng g-1 (Stern and Macdonald, 2005), (3) 158 ± 13 ng g-1 (van der Velden et al., 2013) and (4) 363 ± 42 ng g-1 (Clayden et al., 2015). Lower concentrations of Hg in this study 24

versus the Canadian Arctic were not explained by differences in fish size and are consistent with ~4 times lower concentrations of THg (85 ± 5 ng g-1) reported by Stern and Macdonald (2005) for Arctic cod collected from the Chukchi Sea. Lower concentrations of Hg in Arctic cod From the Chukchi Sea are likely related to lower concentrations of Hg at the base of the food web in the NCES relative to the Canadian Arctic as discussed in Section 3.2.2. 3.2. Trophic transfer of mercury 3.2.1. Trophic structure and biomagnification of THg versus MMHg in muscle and whole organisms Biota for this study represent the most abundant groups of epibenthic species and bivalves that were collected from the NECS using benthic trawls and van Veen grabs. A plot of δ15N versus δ13C (Fig. 7) for biota collected during this study shows the complex food web in the NECS as previously described by McTigue and Dunton (2014). The complexities develop because multiple and distinct carbon sources (e.g., POM, phytoplankton, benthic microalgae, ice algae) are introduced into a highly omnivorous food web. Consistent with previous observations, pelagic consumers (e.g., zooplankton, δ13C = -22.2) that are known to feed on carbon derived from phytoplankton or POM were typically more depleted in 13C than benthic consumers (e.g., bivalves δ13C = -18.0 to -19.5) that likely obtain a greater fraction of their carbon from settled ice-algae and benthic

25

microalgae (Fig. 7; Chen et al., 2009; France, 1995; Fisk et al., 2003; Hobson et al., 2002; Iken et al., 2010). When all 13 organisms from the NECS with data for MMHg in muscle, plus whole phytoplankton and zooplankton, were considered, the TMS was 0.19 ± 0.03 (R2 = 0.79, p <0.001, df = 14, Fig. 8A); however, the best fit line did not closely represent MMHg concentrations in biota obtained in the NECS, especially at low TLs (R2 = 0.79, Fig. 8A). Few studies, including the present one, sample every species in an ecosystem; therefore, the TMS for an overall food web is likely a function of the relative abundance of species and TLs included in each study. For example, three deposit-feeding bivalves in this study (Macoma calcarea, Nuculana pernula and Yoldia Hyperborea) that obtain carbon from sediment organic matter that has undergone continued microbial processing and isotopic fractionation have distinctly different concentrations of MMHg relative to their δ15N values (triangles in Fig. 8A). These species represent two different trophic guilds of deposit feeders in the NECS as described by McTigue and Dunton, (2014). Predators (e.g., whelk) and scavengers (e.g., crabs) investigated in our Hg study had δ15N and δ13C values consistent with a diet of primarily epibenthic and surface macrofauna, suggesting that deposit-feeding bivalves are not likely a major food source (McTigue and Dunton, 2014). If these deposit-feeding bivalves are removed from the composite analysis, the TMS increases to 0.23 ± 0.02, a value that better represents concentrations of MMHg throughout a contiguous food web in the NECS (R2 = 26

0.95, p < 0.001, df = 11, Fig. 8B). The complexity of arctic marine food webs enhances the need for careful consideration of trophic interactions when investigating contaminant transfer and calculating TMS values. Trophic Magnification Slopes for THg are almost always lower than those for MMHg (Table 7), especially when the food web sampled has a predominance of biota at TLs <3. This observation is directly related to an increase in the % MMHg with increasing TL as shown on an idealized curve for % MMHg versus δ15N and TL (Fig. 9A). The resulting curve shows that the % MMHg approaches 100% by TL 4–5 (Fig. 9A). Data for % MMHg in muscle from this study fit the idealized curve relatively well (Fig. 9A; R2 = 0.57 predicted versus actual, p <0.001, df = 15). A strong bias is incurred at low TLs when THg is used instead of MMHg to calculate the TMS (Fig. 9B). For example, the calculated line for THg (solid line labeled THg) has a lower slope (0.10) than the line for MMHg (0.23 for line labeled MMHg, Fig. 9B, Table 7). The linear best fit line for THg is tangent to a curve of THg values (squares and dashed line) and dependent on the TL and % MMHg of biota included (Fig. 9B). When more, lower trophic level biota (TL <3) are included, the TMS for THg will be distinctly different from the TMS for MMHg (Fig. 9B). For the overall food web in this study, the TMS for THg in muscle was >50% lower than the TMS for MMHg. Lower TMS values for THg relative to MMHg are consistent with data from other studies (Clayden et al., 27

2015). The TMS for THg depends on the range of TLs in the study. When mainly lower TL biota (TL <3) are included, the TMS for MMHg is consistently a more reliable measure of Hg biomagnification. A pelagic component of the food web from the NECS in this study was operationally defined using δ15N and δ13C values consistent with a diet of pelagic phytoplankton or POM (left ellipse, Fig. 7). Using available concentrations of MMHg (muscle from fish and shrimp and whole phytoplankton and zooplankton), the TMS was 0.22, (Fig. 10A). The very strong coefficient of determination (R2 = 0.998) was likely due to a low degree of omnivorous species interactions. The TMS for MMHg in whole pelagic organisms from the NECS was 0.15, ~30% lower than the TMS for muscle. The benthic component of the food web for the NECS includes organisms (right ellipse, Fig. 7) characterized by δ13C values that were less negative than predicted for a diet of pelagic phytoplankton (Hobson et al., 2002; Horton et al., 2009; Iken et al., 2010; McTigue and Dunton, 2014). The calculated TMS values for benthic species, excluding three deposit-feeding bivalves, were 0.21 for MMHg in muscle (Fig. 10B) and 0.23 (not significant, p = 0.077) for MMHg in whole organisms. No significant trend was identified for MMHg versus δ15N in whole organisms due in part to large variability in Hg concentrations among species with different compositions of tissues (Appendix B and C). For example, concentrations of MMHg in whole brittle stars (Ophiura sarsii) at TL 3.0 averaged only 3 ± 1 ng 28

g-1, >4 times lower than concentrations found for bivalves (14 ± 3 ng g-1) at TL 2.4 due to dilution by the mass of Hg-poor carbonate skeletal material. 3.2.2 A regional perspective for biomagnification of MMHg in the Arctic Data for MMHg in biota from this study in the NECS plus three studies of Hg biomagnification from the eastern Canadian Arctic (Campbell et al., 2005; Clayden et al., 2015; van der Velden et al., 2013) were plotted versus δ15N (Fig. 11) and then used to provide a regional assessment for MMHg biomagnification in the North American Arctic. The overall TMS was 0.24 ± 0.02 (R2 = 0.72, p < 0.0001, 52) for a range of δ15N from 3.8-17.7 (TL 0.5-4.7). To validate this TMS over a wider range of TLs (TL 0.5-5.5, δ15N 3.8-23.5), additional data for zooplankton and apex predators (polar bears) were added (Horton et al., 2009; Pucko et al., 2014) with no change in the TMS (0.24 ± 0.02, R2 = 0.74, p <0.0001; Fig. 11). No significant difference was identified for TMS from these four studies (ANCOVA, p = 0.400, F = 1.00, df = 52); therefore, an overall TMS of 0.24 will be used here as a regional value for MMHg in biota in the marine environment of the North American Arctic. Although no significant differences for TMS were found among the four studies, all values for MMHg in biota from the NECS plotted below the overall best fit line (Fig. 11). When data for the NECS were included, a significant difference in the intercept was identified among studies for the same TMS (ANCOVA, p = 29

0.001, F = 6.06, df = 52). When only data for the eastern Canadian Arctic were included (3 studies), no significant difference in the intercept was observed (ANCOVA, p = 0.353, F = 1.07, df = 40). Therefore, ANCOVA (p < 0.001, F = 15.2, df = 52) showed a significant difference in intercept values for data from the NECS (b = -1.85) relative to grouped data from the eastern Canadian Arctic (b = 1.29). The difference in intercepts is consistent with greater than 2 times lower concentrations of MMHg found for zooplankton (C. hyperboreus), bivalves (M. Calcarea), fish (Arctic cod, B. saida) and polar bears (Ursus maritimus) from the Chukchi Sea relative to the same species from the eastern Canadian Arctic (Atwell et al., 1998; Clayden et al., 2015; Routti et al., 2012; Stern and Macdonald, 2005). 3.2.3. Using biomagnification equations to predict changing Hg concentrations The combined regional equation for the NECS and eastern Canadian Arctic (Log10[MMHg] = 0.24(δ15N) – 1.18; Fig. 11) is used here to investigate differences in Hg biomagnification among arctic regions and to predict how biomagnification may respond to a changing arctic climate. Changes to inputs of Hg, rates of methylation and demethylation, and rates of primary productivity and total benthic biomass will likely influence the intercept of biomagnification equations as previously discussed for the NECS and the eastern Canadian Arctic. Small changes in the intercept, with the same TMS, result in large differences in Hg concentrations at higher trophic levels. For example, if MMHg concentrations in primary producers decreased by 20% from algal-bloom dilution (i.e., from 1.5 ng g30

1

to 1.2 ng g-1, much less than the ~200% difference between the NECS and the

eastern Canadian Arctic), concentrations of MMHg in biota at each TL are predicted to decrease by 20% with greater absolute changes at higher TLs (i.e., 20% of 5,800 ng g-1 yields a 1,200 ng g-1 decrease at TL 5.5) (Fig. 12A). In contrast, a hypothetical 20% increase in MMHg in phytoplankton, possibly from increased atmospheric deposition of Hg, would produce the opposite result with a 20% increase in MMHg concentrations in biota (e.g., +1,200 ng g-1 at TL 5.5). In contrast with the previous example, enhanced primary productivity and food availability may lead to changes in the trophic transfer efficiency and thereby decrease the TMS for MMHg. Increased food availability can enhance growth rates such that the increase in mass of tissues outpaces the accumulation of MMHg (Pickhardt et al., 2002). A decrease in the TMS of 0.01 corresponds to a decrease in the FWMF of 7.5% (e.g., if TMS decreases from 0.24 to 0.23, the FWMF decreases by 7.5% from 6.54 to 6.05). If concentrations of MMHg in primary producers (TL 1) remained constant, a decrease in the TMS of 0.01 corresponds to a 7.5% decrease in MMHg concentrations per TL for a compounded decrease of ~30% by TL 5.5 (i.e., a 30% decrease of 1,800 ng g-1 in MMHg concentrations in polar bears at TL 5.5, Fig. 12B). Clearly, small changes to the TMS correspond to large increase or decreases in concentration of MMHg in biota. In addition to changes in existing food webs, a changing arctic climate may lead to complex shifts in food web structure. For example, Horton et al. (2009) 31

noted a trend of shifting diet for polar bears from a dominantly ice-algae derived carbon source to a marine plankton derived carbon source based on tissue δ13C and δ15N values. This shift is likely the result of a reduction in the extent of summer sea ice. In their study, a more pelagic diet was thought to result in higher Hg concentrations. These observations are consistent with lower concentrations of Hg previously reported for ice-algae relative to pelagic phytoplankton and the strong dependence of Hg concentrations in biota at high TLs on concentrations at the base of the food web. 4. Conclusions The overall TMS for MMHg from this study (0.23 ± 0.02) was not significantly different from the TMS obtained using data from three additional arctic studies with an overall combined TMS of 0.24 ± 0.2 for a range of 5.0 TLs. Despite no significant difference in TMS, >2-fold lower concentrations of MMHg in biota and a significantly lower intercept (p = 0.001) were identified for the NECS (b = -1.29) relative to the eastern Canadian Arctic (b = -1.85). Lower concentrations of Hg at the base of the food web in the future may result from either lower inputs of Hg to the system, or from enhanced algal-bloom dilution and higher benthic biomass. Such a change is predicted to yield a lower intercept for the biomagnification equation with no change in the TMS. In contrast, a decrease of only 0.01 in the TMS with no change in MMHg at TL 1, possibly due to enhanced growth rates of biota, corresponds to decreased concentrations of Hg at all TLs >1, 32

with a 7.5% decrease in Hg concentrations per TL compounding to a 30% decrease by TL 5.5, that of apex predators. Acknowledgements We thank Heather Crowley of the Bureau of Ocean Energy Management (BOEM), U.S. Department of Interior, for her interest and enthusiasm for studies of metals in biota. We thank Captain John Seville and his crew aboard the R/V Moana Wave and the US Coast Guard crew, officers and commanding officers Beverly A. Havlik and John D. Reeves aboard the USCGC Healy for logistical and sampling support. We thank Susan Schonberg, Heather McEachen, Philip Alatalo, Stephen Elliott, Nathan McTigue, Yuchao Yan, Brenna O’Neill, Kim Powell, Alexandra Ravelo, Tanja Schollmeier, Jordann Young, Carrie Harris and Christina Bonsell for collecting and sorting biota samples. Comments from John Windsor, Kevin Johnson and three anonymous reviewers were greatly appreciated. The field and laboratory study was funded by the U.S. Department of the Interior, BOEM, Alaska Outer Continental Shelf Region, Anchorage, Alaska under Cooperative Agreement Number M11AC00007 as part of the Hanna Shoal Ecosystem Project and the BOEM Alaska Environmental Studies Program. Support also was provided by the North Pacific Research Board, Project A01/T2201-T2207, Pacific Marine Arctic Regional Synthesis (PacMARS).

33

Appendix A. Species codes and taxonomic names for 26 species collected from the NECS, plus number of samples (n) analyzed as muscle tissue or whole soft tissue. Species PHY ZOO AMP OCN GER SAD CTE PAN BOR ANX MAC YOL NUC SER CLI HYO OPH AST CRY BUC NEY LEP CHI HYA NEP PLI

n n (muscle) (whole) Phytoplankton Phytoplankton 22 Zooplankton Zooplankton 22 Ampelisca macrocephala Amphipod 9 Ocnus glacialis Sea cucumber 4 7 Gersemia rubiformis Soft coral 5 Saduria sabini Isopod 3 6 Ctenodiscus sp. Sea Star 6 Pandalidae spp. Shrimp 33 30 Boreogadus saida Arctic Cod 56 11 Anonyx sp. Amphipod 9 Macoma calcarea Macoma Clam 6 6 Yoldia hyperborea Yoldia Clam 6 4 Nuculana pernula Nuculana Clam 13 9 Serripes groenlandicus Serripes Clam 7 10 Clinocardium ciliatum Clinocardium Clam 7 6 Sertularia sp. Hydroid 6 Ophiura sarsii Brittle star 18 Astarte borealis Astarte clam 18 31 Cryptonatica affinis Whelk 17 3 Buccinum spp. Whelk 19 Nephthys ciliata Scale worm 7 Leptasterias groenlandica Sea star 7 Chionoecetes opilio Snow crab 39 12 Hyas coarctatus Hyas crab 5 6 Neptunea borealis Whelk 76 7 Plicifusus kroeyeri Whelk 19 Total 346 259 Taxonomic name

Common name

Appendix B. Values for THg, MMHg and % MMHg for muscle and whole organisms and n, δ15N, δ13C and trophic level (TL) for pelagic biota collected 34

during this study. All concentrations are reported on a dry weight basis (n, mean ± standard error (SE)). Trophic Levels are calculated using the equation: TLconsumer = ([δ15N]consumer – δ15NPOM)/3.4]+1 (δ15NPOM = 5.28). Muscle Whole Speci MM % MM % es n THg n Hg n MMHg n THg n Hg n MMHg n

15

δ N

PHY -

-

-

-

-

-

2 15 ± 1 1 2 2 0 <1.5 0

2 5

7.70 ± 0.3

ZOO -

-

-

-

-

-

2 13 ± 1 1 1 2 1 7 4 ± 1 7 37 ± 7 3

9.89 ± 0.8

AMP -

OCN 4

GER -

<10

-

-

-

-

22 ± 11.10 ± 9 5 2 8 ± 1 2 45 ± 4 8 0.5

96 ± 28 -

-

-

-

7

50 ± 8 -

-

-

-

7

11.26 ± 0.5

-

-

-

5

20 ± 3 -

-

-

-

8

11.86 ± 0.3

-

-

-

1 12.70

92 ± 35 -

-

-

-

2

-

-

-

62 ± SAD 3 32 -

-

-

-

28 ± 6 2 -

CTE -

-

-

-

6

-

-

12.73 ± 0.4

3 76 ± 1 59 ± 1 3 53 ± 1 29 ± 1 PAN 3 11 5 12 5 72 ± 6 0 5 1 2 1 64 ± 7 1 14.96 5 52 ± 2 77 ± 2 1 23 ± 17 ± 4 15.40 ± BOR 6 8 1 16 1 97 ± 6 1 4 5 3 5 77 ± 4 2 0.1

ANX -

-

-

-

-

-

9

63 ± 32 ± 15.55 ± 8 4 6 4 73 ± 6 2 0.1

35

δ13 C 22. 95 22. 20 23. 69 23. 38 21. 88 21. 81 23. 03 21. 94 20. 51 21. 02

T L 1. 7 2. 4 2. 7 2. 8 2. 9 3. 2 3. 2 3. 8

4

4

Phytoplankton (PHY), Zooplankton (ZOO), Ampelisca macrocephala (AMP), Ocnus glacialis (OCN), Gersemia rubiformis (GER), Saduria sabini (SAD), Ctenodiscus sp. (CTE), Pandalidae spp. (PAN), Boreogadus saida (BOR), Anonyx sp. (ANX).

Appendix C. Values for THg, MMHg and % MMHg for muscle and whole organisms and n, δ15N, δ13C and trophic level (TL) for benthic biota collected during this study. All concentrations are reported on a dry weight basis (n, mean ± standard error (SE)). Trophic Levels are calculated using the equation: TLconsumer = ([δ15N]consumer – δ15NPOM)/3.4]+1 (δ15NPOM = 5.28). Muscle

Whole % % Spec MMH MM MMH ies n THg n MMHg n g n THg n Hg n g n

δ N

MA 47 ± C 6 6 3 20 ± 6 3

9.51 ± 0.3

54 ± 10 ± 1 21 6 60 ± 5 4 2 4 18 ± 2 4

15

50 ± 8 5 23 ± 3 5

54 ± 69 ± 14 ± 10.06 ± 8 4 10 2 2 2 24 ± 2 2 0.4

1 73 ± NUC 3 9 5 46 ± 6 4

45 ± 129 ± 28 ± 1 10.61 ± 4 9 25 5 8 5 22 ± 4 2 0.2

YOL 6

17 ± SER 7 3 5

CLI 7

1 95 ± 8 ± 1 5 63 ± 3 0 10 2

4± 1 2

10.67 ± 9±0 4 0.5

48 ± 14 ± 10.97 ± 7 3 22 ± 2 3 47 ± 8 6 99 ± 4 2 2 2 15 ± 1 3 0.4

HYO -

-

-

-

-

-

OPH -

-

-

-

-

-

79 ± 6 20 1 8 19 ± 1 3 36

11.76 ± - 6 0.3 3± 1 12.02 ± 1 3 18 ± 5 4 0.7

δ13 C 19. 10 18. 00 18. 90 19. 50 19. 08 20. 50 18.

T L 2. 2 2. 4 2. 6 2. 6 2. 7 2. 9 3. 0

1 54 ± 3 1 13 ± 1 12.10 ± AST 8 9 4 24 ± 7 4 35 ± 8 1 48 ± 3 0 3 0 31 ± 4 7 0.3 1 52 ± CRY 7 10 1

28

1 133 ± BUC 9 35 6

93 ± 27 6 92 ± 4 -

1 65 ± 0 3

171 ± 54 -

-

-

NEY -

-

-

-

-

-

75 ± 7 12 -

LEP -

-

-

-

-

-

7

-

-

-

2

13.21 ± 0.5

-

-

-

2

14.32 ± 0.1

-

-

-

15.13 ± 9 0.2

114 ± 48 -

-

-

-

6

3 112 ± 3 3 1 CHI 9 8 0 89 ± 7 0 76 ± 1 2 32 ± 4 -

-

-

-

2 15.39 ± 8 0.2

-

15.80 ± 4 0.2

HYA 5 89 ± 9 -

-

-

-

6 43 ± 4 -

-

-

15.30 ± 0.6

7 163 ± 2 173 ± 2 165 ± 99 ± 54 ± 2 16.09 ± NEP 6 15 6 23 6 90 ± 4 7 37 5 13 5 10 8 0.3 1 173 ± 279 ± PLI 9 40 5 100 5 86 ± 6 -

-

-

-

-

-

17.09 ± 9 0.3

92 19. 50 17. 58 18. 57 19. 73 17. 90 18. 40 18. 60 17. 44 17. 42

3. 0 3. 3 3. 7 3. 9 3. 9 4. 0 4. 1 4. 2 4. 5

Macoma calcarea (MAC), Yoldia hyperborea (YOL), Nuculana pernula (NUC), Serripes groenlandicus (SER), Clinocardium ciliatum (CLI), Sertularia sp. (HYO), Ophiura sarsii (OPH), Astarte borealis (AST), Cryptonatica affinis (CRY), Buccinum spp. (BUC), Nephthys ciliate (NEY), Leptasterias groenlandica (LEP), Chionoecetes opilio (CHI), Hyas coarctatus (HYA), Neptunea borealis (NEP), Plicifusus kroeyeri (PLI).

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Fig. 1. Stations with year(s) sampled in the northeastern Chukchi Sea. The left insert shows location of study area off the northwestern coast of Alaska, the right insert shows stations located south of the main study area. Identifications are listed for stations cited in the text and in Fig. 2. (MSL = mean sea level) Fig. 2. Vertical profiles for total dissolved mercury (THgd) and chlorophyll a for stations (A) H4 and (B) H32 from 2012 and (C) station C103 from 2010. Fig. 3. Concentrations of THg in zooplankton versus inventories of sediment chlorophyll a. 50

Fig. 4. (A) Concentrations of THg (± SE) (bar height) with MMHg (± SE) and Hginorg in muscle and whole organisms for six bivalve species. (B) Concentrations of THg in bivalves versus % MMHg and % Hginorg. for muscle tissue (triangles) and whole organisms (circles). Error bars show standard error (SE). Abbreviations: Astarte borealis (AST), Clinocardium ciliatum (CLI), Serripes groenlandicus (SER), Nuculana pernula (NUC) Macoma calcarea (MAC) and Yoldia hyperborea (YOL). Fig. 5. Concentrations of (A) THg (circles) and MMHg (squares) in whelk muscle for Plicifusus kroeyeri and (B) THg (triangles) in muscle for Colus aphelus versus shell length (apex to anterior canal). Both best-fit lines (solid), 95% prediction intervals (dashed lines), equations and values for R2, p are from linear regression analysis for THg. Fig. 6. Concentrations of (A) THg and (B) MMHg in muscle versus fish length (cm) for Boreogadus saida (Arctic cod). Best-fit lines (solid), 95% prediction intervals (dashed lines), equations and values for R2, p are from linear regression analysis. Horizontal error bars indicate a subset of samples that were grouped into 2-cm size classes prior to analysis. Fig. 7. δ15N vs. δ13C for biota collected during this study. Ellipses group organisms into pelagic (left) and benthic (right) trophic pathways of the food web in the northeastern Chukchi Sea based on carbon enrichment from pelagic phytoplankton or POM (left) and carbon enrichment from settled ice-algae and benthic microalgae 51

(right). Triangles indicate that data are available for MMHg, circles indicate data are available for THg, solid fill circles or triangles indicate data are available for muscle. Data are available for whole organisms with the exception of THg and MMHg in PLI and BUC and MMHg in CHI. Abbreviations: Macoma calcarea (MAC), Yoldia hyperborea (YOL), Nuculana pernula (NUC), Serripes groenlandicus (SER), Clinocardium ciliatum (CLI), Sertularia sp. (HYO), Ophiura sarsii (OPH), Astarte borealis (AST), Cryptonatica affinis (CRY), Buccinum spp. (BUC), Nephthys ciliate (NEY), Leptasterias groenlandica (LEP), Chionoecetes opilio (CHI), Hyas coarctatus (HYA), Neptunea borealis (NEP), Plicifusus kroeyeri (PLI), Phytoplankton (PHY), Zooplankton (ZOO), Ampelisca macrocephala (AMP), Ocnus glacialis (OCN), Gersemia rubiformis (GER), Saduria sabini (SAD), Ctenodiscus sp. (CTE), Pandalidae spp. (PAN), Boreogadus saida (BOR), Anonyx sp. (ANX). Fig. 8. Concentrations of MMHg versus δ15N and TL for combined pelagic and benthic food webs in the northeastern Chukchi Sea (NECS) (A) including three deposit-feeding bivalve species (triangles) that are not likely a major food source for predators or scavengers in this study and (B) one overall contiguous food web for the NECS, excluding deposit-feeding bivalves. Equations and best-fit lines are for MMHg biomagnification in muscle (except whole phytoplankton and zooplankton) for the overall (pelagic + benthic) food web from the NECS. Error bars indicate standard error (SE). 52

Fig. 9. (A) Idealized plot for % MMHg versus δ15N and TL in muscle tissue of biota from the northeastern Chukchi Sea (TMS = 0.23, FWMF = 6.05). Monomethylmercury concentrations at each TL (circles) were calculated using the overall biomagnification equation (Log10[MMHg] = 0.78(TL) – 1.07). Concentrations of THg used to determine the % MMHg were determined at each TL by adding the calculated MMHg to a fixed average value of 20 ng g-1 for Hginorg (THg at TL 1 = 0.5 ng g-1 MMHg + 20 ng g-1 Hginorg = 20.5 ng g-1 and so on). The % MMHg was determined from MMHg/THg (e.g., % MMHg at TL 1 = (0.5 ng g-1 / 20.5 ng g-1) x 100 = 2.4%). (B) Idealized curves for (1) Hginorg constant at 20 ng g1

(triangles and best fit line), (2) calculated concentrations of MMHg (TMS of 0.23,

FWMF = 6.05) for the overall food web (circles and best fit line), (3) calculated concentrations of THg (THg = MMHg + Hginorg; blue squares and dashed blue line) and (4) a line showing TMS = 0.10 (FWMF = 2.19) for THg from a linear regression for the overall food web in this study (solid best fit line). Vertical dotted lines indicate the TL range in this study (1.7–4.5). Fig. 10. Concentrations of MMHg in muscle (except whole phytoplankton and zooplankton) versus δ15N and TL for: (A) the pelagic food web and (B) the benthic food web from the northeastern Chukchi Sea. Best-fit lines and equations are from linear regressions. Error bars indicate standard error (SE). Abbreviations: Serripes groenlandicus (SER), Clinocardium ciliatum (CLI), Astarte borealis (AST), Cryptonatica affinis (CRY), Buccinum spp. (BUC), Chionoecetes opilio (CHI), 53

Neptunea borealis (NEP), Plicifusus kroeyeri (PLI). Phytoplankton (PHY), Zooplankton (ZOO), Pandalidae spp. (PAN), Boreogadus saida (BOR). Fig. 11. Average concentrations of MMHg in biota from this and five other Arctic studies versus δ15N (Campbell et al., 2005; Horton et al., 2009; Van der Velden et al., 2013; Pucko et al., 2014; Clayden et al., 2015). Values for polar bears (hair) from Horton et al., (2009) are for THg and assume ~100% MMHg based on TL > 4. Fig. 12. (A) Idealized plot showing calculated concentrations of MMHg at TLs 1-6 using the regional equation for biomagnification (solid line with circles) and a hypothetical line for a 20% decrease in MMHg concentrations and no change in TMS (dashed line with triangles). The absolute change in MMHg concentrations is greater at higher TLs due to the log scale for MMHg (e.g., 0.25 ng g-1 decrease in MMHg at TL 1 versus ~1,200 ng g-1 decrease at TL 5.5). (B) Idealized plots showing calculated concentrations of MMHg at TLs 1-6 using the overall equation for MMHg biomagnification in the North American Arctic (solid line and circles) and a hypothetical line for MMHg biomagnification if the TMS decreased by 0.01 with no change in MMHg at TL 1 (dashed line with triangles). The absolute change in MMHg concentrations are greater at higher TLs due to both a larger % difference and the log scale for MMHg.

54

Table 1. Values for THg, MMHg and % MMHg in whole phytoplankton (PHY) and zooplankton (ZOO) from the northeastern Chukchi Sea. All concentrations are reported on a dry weight (d. wt.) basis (n, mean ± standard error (SE), range, and median values). THg (ng g-1, d. wt.) MMHg (ng g-1, d. wt.) % MMHg Species n Mean Range Median n Mean Range Median n Mean Range Median ± SE ± SE ± SE PHY 22 14.7 ± 4.0– 13.7 10 <1.5 10 <10 1.7 42.2 ZOO 15 13.4 ± 6.8– 11.8 14 4.3 ± <1.5– 4.0 14 37.2 ± 10–79 32.6 1.3 22.5 0.7 8.7 6.5

Table 2. Values for THg, MMHg and % MMHg in phytoplankton from various locations. All concentrations are reported on a dry weight (d. wt.) basis (n, mean ± standard error (SE) and % MMHg). Region

Location

Species

n THg MMHg % Source -1 (ng (ng g ) MMHg g-1) Arctic Chukchi Phytoplankton 22 15 ± <1.5 <10 This study Sea 2 Arctic Lancaster POMa 1 <20 Atwell et al., Sound 1998 Arctic Beaufort POMa 26 36 ± <0.15 <1 Pucko et al., Sea 5 2014 Arctic Baffin Ice algae 1 3b Campbell et al., Bay 2005 Temperate Long Phytoplankton 25 60b 5.4 9 Hammerschmidt Island et al., 2013 Sound Temperate San Phytoplankton - 94b 1.3 1 Luengen and Francisco Flegal, 2009 Bay 55

Temperate Monterey Phytoplankton 29 207 Bay ± 20 Temperate North Phytoplankton - 440b Sea Antarctic Terra Phytoplankton 6 39 ± Nova 3 Bay a POM = Particulate Organic Matter. b

-

-

13.2

3

-

-

Knauer and Martin, 1972 Baeyens et al., 2003 Bargagli et al., 1998

Standard error (SE) unavailable.

Table 3. Values for THg, MMHg and % MMHg in zooplankton from various locations. All concentrations are reported on a dry weight (d. wt.) basis (n, mean ± standard error (SE) and % MMHg). Regio Location n

Species n

THg (ng g-1)

MMHg (ng g-1)

Arcti c Arcti c Arcti c

Mixed

13 ± 1

4.3 ± 0.7

Arcti c Arcti c Arcti c Arcti

Chukchi Sea

15

Northwater Mixed 4 6±1 Polynya Amundsen Gulf / C. 65/ 14 ± 1 Beaufort Sea hyperbo 47 reus Amundsen Gulf / Chaetho 55/ 24 ± 1 Beaufort Sea gnaths 30 Amundsen Gulf / P. 48/ 56 ± 3 Beaufort Sea glacialis 13 Amundsen Gulf / T. 31/ 159 ± 18 Beaufort Sea abyssor 3 um Lancaster Sound C. 3 60 ± 10 56

4±3 7 ± 0.1

17 ± 1 40 ± 2 88 ± 6

-

% MM Hg 37 ± 7 70 ± 19 51 ± 2

Source

This study Campbell et al., 2005 Pucko et al., 2014

74 ± 3 84 ± 3 58 ± 8

Pucko et al., 2014 Pucko et al., 2014 Pucko et al., 2014

-

Atwell et al.,

c

hyperbo reus Arcti Northwater C. c Polynya hyperbo reus Arcti Chukchi Shelf C. c hyperbo reus Arcti Canadian Basin C. c hyperbo reus Temp Monterey Bay - Mixed erate Hawaii trans. Temp Oregon - Hawaii Mixed erate transect Temp Minamata Bay Mixed erate Temp Yatsushiro Sea Mixed erate Temp Northern Atlantic Mixed erate Temp Long Island Mixed erate Sound Tropi Tropical Pacific Mixed cal Antar Southern Ocean Mixed ctic Antar Terra Nova Bay Mixed ctic

1998 3

25 ± 9

2±1

-

48 ± 3

-

-

-

85 ± 9

-

-

26 119 ± 9

-

-

16 130 ± 24

-

28 ± 13 49 ± 7 15 ± 1 -

5/4

744

158 ± 5

10/ 9 78

114

62 ± 2

40

6 ± 0.7

4

-

12 ± 1.0

17/ 5 9

42

17 ± 4

116

30 ± 3.7

-

65 ± 16

-

8 ± Campbell et al., 0.1 2005

45 ± 12 26 ± 9 -

Stern and Macdonald, 2005 Stern and Macdonald, 2005 Knauer and Martin, 1972 Knauer and Martin, 1972 Hirota et al., 1983 Hirota et al., 1983 Hammerschmidt et al., 2013 Hammerschmidt et al., 2006b Hirota et al., 1979 Hirota et al., 1989 Bargagli et al., 1998

Table 4. Values for THg, MMHg and % MMHg in bivalves from the northeastern Chukchi Sea. All concentrations are reported on a dry weight (d. wt.) basis (n, mean ± standard error (SE), range and median values). Species are: Astarte borealis (AST), Clinocardium ciliatum (CLI), Serripes groenlandicus (SER), Nuculana pernula (NUC) Macoma calcarea (MAC) and Yoldia hyperboreus (YOL).

57

Speci Tissu es e Overa Musc ll le Whol e Musc AST le Whol e Musc CLI le Whol e Musc SER le Whol e Musc NUC le Whol e Musc MAC le Whol e Musc YOL le Whol e

THg (ng g-1, d. wt.) Mean ± Rang Medi n SE e an 5 17– 7 47 ± 7 79 44 6 49– 6 83 ± 10 137 78 1 8 47 ± 6 3–96 44 3 26– 1 48 ± 3 120 42 18– 7 48 ± 7 75 47 87– 6 99 ± 4 115 99 7 17 ± 3 1 0 95 ± 10 1 3 73 ± 9 9 129 ± 25 6 47 ± 6 6 60 ± 5 6 50 ± 8 4 69 ± 9

MMHg (ng g-1, d. wt.) Mean ± Ran Medi n SE ge an 2 17– 5 24 ± 4 35 22 2 10– 5 14 ± 3 24 12 14– 4 24 ± 6 43 21 1 0 13 ± 3 8–36 10 20– 3 22 ± 2 25 22 12– 2 14 ± 2 16 14

8–29 17 5 8 ± 1 44– 148 93 2 4 ± 0 12– 130 68 5 46 ± 6 43– 262 113 5 28 ± 8 30– 69 44 3 20 ± 6 50– 84 55 4 10 ± 1 30– 76 47 5 23 ± 3 45– 91 69 2 14 ± 2

n 2 4 2 5 4 1 0 3 2

% MMHg Mean ± Rang SE e 34– 51 ± 8 66 15 – 20 ± 2 28 35– 44 ± 3 49 16– 31 ± 4 60 33– 47 ± 8 54 13– 15 ± 1 17 57– 63 ± 3 74

5–13

8

5

4–5 33– 68 15– 61 11– 31

4

2

43

4 45 ± 4

22

5 22 ± 4

18

3 54 ± 21

8–14 10 18– 32 23 12– 15 14

4 18 ± 2

9±0

5 54 ± 8 2 24 ± 2

9–9 36– 52 15– 35 16– 88 15– 23 27– 78 22– 26

Medi an 54 18 46 24 54 15 60 9 46 19 58 18 58 24

Table 5. Values for THg, MMHg and % MMHg in whelk from the northeastern Chukchi Sea. All concentrations are reported on a dry weight (d. wt.) basis (n, mean ± standard error (SE), range and median values). Species are: Neptunea

58

borealis (NEP), Plicifusus kroeyeri (PLI), Buccinum spp. (BUC), Colus aphelus (COL) and Cryptonatica affinis (CRY).

Spec Tiss ies ue Mus NEP cle Who le Mus PLI cle BU Mus C cle Mus COL cle CR Mus Y cle Who le

THg (ng g-1, d. wt.) Mean Ran Med n ± SE ge ian 7 163 ± 19– 6 15 677 132 165 ± 73– 7 37 349 125 1 173 ± 19– 9 40 641 100 1 133 ± 10– 9 35 532 55 1 209 ± 43– 8 29 468 204 1 52 ± 15– 7 10 203 40 171 ± 65– 3 54 245 203

MMHg (ng g-1, d. wt.) Mean Rang Med n ± SE e ian 2 173 ± 25– 6 23 525 145 99 ± 49– 5 13 119 111 279 ± 85– 5 100 641 256 93 ± 27– 6 27 183 76 354 ± 295– 2 59 412 354

n 2 6 5 5 6 2

% MMHg Mean Ran Med ± SE ge ian 57– 90 ± 4 100 89 54 ± 32– 10 90 52 72– 86 ± 6 100 82 77– 92 ± 4 100 96 95– 97 ± 2 100 97

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

Table 6. Values for THg, MMHg and % MMHg in Arctic cod (Boreogadus saida) from the northeastern Chukchi Sea. All concentrations are reported on a dry weight (d. wt.) basis (n, mean ± standard error (SE), range and median values). THg (ng g-1, d. wt.) MMHg (ng g-1, d. wt.) Tissu Mean ± Rang Medi Mean ± Rang Medi e n SE e an n SE e an Whol 1 10– e 1 23 ± 4 2–47 23 5 17 ± 3 26 18 Musc 5 6– 2 12– le 6 52 ± 8 250 30 1 77 ± 16 228 35

n 5 2 1

% MMHg Mean ± Rang Medi SE e an 65– 77 ± 4 90 77 57– 97 ± 6 100 91

Table 7. Trophic Magnification Slopes (TMS), intercept values (Int.), Food Web Magnification Factors (FWMF), statistical p-values and coefficients of 59

determination (R2) for the linear regression between log10[Hg] versus δ15N for MMHg and THg in muscle tissue and whole organisms from the northeastern Chukchi Sea. MMHg Food web TMS Int. FWMF p R2 Overall – Muscle 0.23 -1.50 6.1 <0.001 0.95 Overall – Whole 0.15 -0.92 3.2 0.014 0.64

60

TMS 0.10 0.05

THg Int. FWMF p R2 0.44 2.2 <0.001 0.66 1.07 1.5 0.261 0.11

61

62

63

64

65

66

67

68

69

70

71

72