Science of the Total Environment 509–510 (2015) 226–236

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Distant drivers or local signals: Where do mercury trends in western Arctic belugas originate? L.L. Loseto a,b,⁎, G.A. Stern b, R.W. Macdonald b,c a b c

Freshwater Institute/Fisheries and Oceans Canada, 501 University Cres., Winnipeg, MB R3T 2N6, Canada Dept of Environment & Geography, University of Manitoba, 500 University Cres., Winnipeg, MB R3T 2N2, Canada Institute of Ocean Sciences, Fisheries and Oceans Canada, 9860 West Saanich Rd, Sidney, BC V8L 4B2, Canada

H I G H L I G H T S • • • • •

We present a 31-year time series for mercury in beluga whales from the Beaufort Sea A large peak in beluga Hg concentration in the late 1990's remains a key feature Neither diet nor trends in Hg emissions can explain beluga Hg trends The Pacific Decadal Oscillation, lagged by 8 years, provides the best correlation with beluga Hg Beluga Hg trends may reflect distant drivers of climate variability that likely altered dietary exposure in their home range

a r t i c l e

i n f o

Article history: Received 1 February 2014 Received in revised form 30 October 2014 Accepted 30 October 2014 Available online 28 November 2014 Keywords: Arctic Stable isotopes Arctic Oscillation Pacific Decadal Oscillation Sea ice minimum concentration

a b s t r a c t Temporal trends of contaminants are monitored in Arctic higher trophic level species to inform us on the fate, transport and risk of contaminants as well as advise on global emissions. However, monitoring mercury (Hg) trends in species such as belugas challenge us, as their tissue concentrations reflect complex interactions among Hg deposition and methylation, whale physiology, dietary exposure and foraging patterns. The Beaufort Sea beluga population showed significant increases in Hg during the 1990s; since that time an additional 10 years of data have been collected. During this time of data collection, changes in the Arctic have affected many processes that underlie the Hg cycle. Here, we examine Hg in beluga tissues and investigate factors that could contribute to the observed trends after removing the effect of age and size on Hg concentrations and dietary factors. Finally, we examine available indicators of climate variability (Arctic Oscillation (AO), the Pacific Decadal Oscillation (PDO) and sea-ice minimum (SIM) concentration) to evaluate their potential to explain beluga Hg trends. Results reveal a decline in Hg concentrations from 2002 to 2012 in the liver of older whales and the muscle of large whales. The temporal increases in Hg in the 1990s followed by recent declines do not follow trends in Hg emission, and are not easily explained by diet markers highlighting the complexity of feeding, food web dynamics and Hg uptake. Among the regional-scale climate variables the PDO exhibited the most significant relationship with beluga Hg at an eight year lag time. This distant signal points us to consider beluga winter feeding areas. Given that changes in climate will impact ecosystems; it is plausible that these climate variables are important in explaining beluga Hg trends. Such relationships require further investigation of the multiple connections between climate variables and beluga Hg. © 2014 Elsevier B.V. All rights reserved.

1. Introduction Temporal trends in contaminants have provided a cornerstone in the assessment of risks posed to Arctic ecosystems (AMAP, 1998; 2011). Especially important have been the long time series for animals like ⁎ Corresponding author at: Freshwater Institute/Fisheries and Oceans Canada, 501 University Cres., Winnipeg, MB R3T 2N6, Canada. Tel.: +1 204 983 5135; fax: +1 204 984 2403. E-mail address: [email protected] (L.L. Loseto).

http://dx.doi.org/10.1016/j.scitotenv.2014.10.110 0048-9697/© 2014 Elsevier B.V. All rights reserved.

whales, seals and bears because they provide evidence of trends in the most exposed components of the food web and they also inform us of the risks to humans who consume such animals (AMAP, 2011). The leading aim of the Arctic Monitoring and Assessment Programme (AMAP) and its Canadian counterpart, the Northern Contaminants Programme (NCP), has been to determine contaminant burdens and trends in various media for the express purpose of influencing the outcome of discussions on curtailing global emissions (2004 Stockholm Convention on Persistent Organic Pollutants (https://treaties.un.org), 2013 Minimata Convention on Mercury (http://www.mercuryconvention.org/)). Mercury (Hg)

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trends in animals like beluga whales (Delphinapterus leucas) however, provide an exceptional challenge to interpret given that they are products of complex interactions between Hg deposition and methylation, uptake in food webs and foraging behavior (Douglas et al., 2012). Despite these challenges, we need to understand the mechanisms underlying trends to develop confidence in future projections and, especially, to determine the effect of emission reductions. In the 1990s, total Hg concentrations in the livers of the Beaufort Sea beluga whale population tripled in comparison to concentrations in the 1980s, and were the highest relative to any other Canadian Arctic beluga population (Lockhart et al., 2005). Since 2002, the last year of data included in Lockhart et al (2005), consecutive annual samples have been collected and data generated for that population at the same dedicated monitoring site, making this one of the longest data sets available for an Arctic cetacean (AMAP, 2011). As a result of the observed increasing Hg in this beluga population, a series of process studies addressing sources, pathways and fate of Hg and the bioaccumulative form, methyl mercury (MeHg), in Arctic marine ecosystems has been conducted (e.g. Lehnherr et al., 2011; Leitch et al., 2007; Loseto et al., 2008b; Macdonald and Loseto, 2010). The complexities of Hg atmosphere–sea ice–ocean interactions (Burt et al., 2013; Chaulk et al., 2011), ocean methylation of Hg (Lehnherr et al., 2011; Wang et al., 2012), entry of MeHg into food webs (Burt et al., 2013; Macdonald and Loseto, 2010; Pucko et al., 2014) and biomagnifying within food webs (AMAP, 2011; CACAR, 2013; Foster et al., 2012; Loseto et al., 2008a; McMeans et al., 2010) are becoming better understood but, unfortunately, we now have a long list of potential contributing factors to Hg trends in belugas without having determined which are the most important. Superimposed on these challenges is the problem that high trophic level species such as belugas represent both the cumulative processes of the supporting ecosystem and inherent physiological processes and behavior of the whales, none of which are mutually exclusive (Douglas et al., 2012; Loseto et al., 2008b). Within belugas, different tissues contain different forms of Hg (e.g. organic and inorganic species) that reflect different processes (e.g., bioaccumulation over time vs. biomagnification in a food web) (Loseto et al., 2008a; Wagemann et al., 1998). As a result, the evaluation of temporal trends must consider the ways in which the process of Hg sequestration and biotransformation within tissues can confound trends. Understanding the trends of Hg concentrations in beluga tissues also requires some understanding of the underlying biogeochemical processes driving them and the potential variability of these processes produced by climate change (Stern et al., 2012). Independent of climate change, climate forcing on marine systems has resulted in regime shifts (Stenseth et al., 2002); well-known examples include the regime shift in the North Sea related to the NAO (Beaugrand, 2004) and the regime shift in the North Pacific linked to the Aleutian Low Pressure Index (Beamish et al., 1999; Benson and Trites, 2002). Physical forcing related to these atmospheric indices affect ocean processes and thus have the potential to alter nutrient supply, temperature, and other properties, which in turn can affect marine ecosystem function and the fate and transport of contaminants (Macdonald et al., 2005). The interaction among external drivers (environment, ecology), emission sources and processes intrinsic to belugas (physiology, behavior) combine to produce the Hg trend observations. Given our improved understanding of system and biological processes we assess the recent temporal trends in beluga Hg concentrations while considering confounding factors such as age and size between tissue groups to address key drivers of trends. Since 2002 (Lockhart et al., 2005), an additional 10 consecutive years of beluga data have been collected, yielding 18 points in time between 1981 and 2012 where Hg, biological data and diet metrics have been recorded. During this time span Hg emissions have continued to increase at an annual rate of 1.3% (Muntean et al., 2014). Additionally, significant changes in the Arctic Ocean and surrounding drainage basin have been observed and documented in numerous

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publications (e.g., sea ice (Stroeve et al., 2012); landfast ice (Yu et al., 2014); rivers and hydrology (Prowse et al., 2011); primary production (Brown and Arrigo, 2012) and organic carbon cycling (McGuire et al., 2009)) with most of these changes linked to declining ice. Here, we examine Hg concentrations in beluga muscle and liver tissue over this 31 year time period and ask the question “Which major drivers might be linked to these observed trends?” We reduce confounding drivers of age and size on Hg concentrations based on physiological evidence of how these affect Hg uptake. We consider the direct impact of dietary Hg exposure as evident in beluga stable isotope data. Lastly, given the location of the Beaufort Sea in the western Arctic and the annual migratory excursions of the belugas, which include the Bering, Beaufort and Chukchi Seas, we examine the potential for indicators of climate variability for the northern range of the belugas (e.g., the Arctic Oscillation (AO)), for the southern range (Pacific Decadal Oscillation (PDO)) and for the ice itself (sea-ice minimum concentration), to provide an explanation for the observed Hg trends. 2. Methods 2.1. Beluga tissue collection The Eastern Beaufort Sea beluga population has one of the largest home ranges among circumpolar beluga populations, wintering in the Bering Sea and summering in the Beaufort Sea (Fig. 1) (Hauser et al., 2014; Richard et al., 2001). These belugas form large summer aggregations in the Mackenzie Estuary where they are harvested by local Inuvialuit (Harwood et al., 2002). In this study, tissues were sampled from beluga whales harvested at East Whitefish (1981 and 1984) and Hendrickson Island (1993–2012) located in the Mackenzie Estuary near the community of Tuktoyaktuk, Northwest Territories (NT) (Fig. 1). These two sampling sites are located in Kugmallit Bay and are within 30 km of one another. The two sites do not imply different animal populations but are simply the locations where hunters bring the belugas for processing. Belugas are brought to shore by hunters where measurements and tissue samples are taken. A total of 379 belugas were measured and tissues sampled for Hg and supporting analyses (Table 1). Typically, the tissue samples are taken by a community monitor who has been trained in the collection process. For consistency we have the monitor take tissue samples from the same location on the animal so as to limit the potential for variability that may occur among tissues. For example, skin/blubber is always sampled from the ventral side just posterior of the arm fin. Once sampled, all tissues are frozen on site in a portable freezer at −20 °C and shipped to Fisheries & Oceans Canada in Winnipeg for analysis. While many tissues are sampled and analyzed for Hg in this study, we present only the results for muscle and liver tissues. The same tissues were also analyzed for stable isotopes of carbon and nitrogen. Ages were determined from a thin section of a tooth by counting growth layer groups in the dentine (Stewart et al., 2006). 2.2. Laboratory analyses 2.2.1. Total mercury analysis Beluga muscle and liver tissues were analyzed for Total Hg (THg). The majority of Hg in beluga liver is inorganic, whereas the majority in muscle is MeHg (Lockhart et al., 2005). Sub-samples (~ 0.15 g, wet weight) were taken from partially thawed tissue after slicing away the outer surface and digested using a hydrochloric/nitric acid mixture (Aqua Regia) heated to 90 °C. The digest was analyzed for THg by Cold Vapour Atomic Absorption spectroscopy (CVAAS) (Armstrong and Uthe, 1971). The detection limit was 0.005 μg/g. Certified standard reference materials (CRM 2976, TORT-2, DOLT-2) were analyzed in duplicate in every run. Recovery within 10% of the certified values was used as a batch validation for samples. Duplicates of tissue samples were taken every ten samples with an average difference of ~5%.

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Fig. 1. Home range for the eastern Beaufort Sea belugas. The seasonal habitat use with summer and winter home range (95% probability) and darkened areas showing the 50% probability contours were determined from tracking data in the Beaufort, Chukchi (Hauser et al., 2014) and Bering Sea (Luque and Ferguson, 2010). Less is known about the habitat use in the Bering Sea, with few satellite tags lasting the duration of the winter season. Migratory routes in spring and fall shown with arrows. Beluga tissue samples were obtained from whales hunted in their summer habitat, in the Mackenzie Estuary area, in particular at East Whitefish and Hendrickson Island where hunters from Inuvik and Tuktoyaktuk NT Canada flense whales and share tissues for harvest monitors.

2.2.2. Stable isotopes Stable nitrogen isotope analysis was performed on dried homogenized subsamples of beluga liver and muscle. For the analysis of carbon isotopes, a chloroform/methanol extraction, three rinses following

agitation (vortex and sonication), over a three day period on dried samples was performed to remove lipids. Lipids tend to be depleted in 13C relative to proteins and can therefore bias results. Carbon and nitrogen isotopic analyses were accomplished by continuous flow, ion-ratio,

Table 1 Means and standard errors of male and female beluga whales from the Canadian Western Arctic for age (as determined with 1 GLG), length (cm), mercury in muscle and liver (µg/g ww) from 1981 to 2012. Males Year

N

1981 1984 1993 1994 1995 1996 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

15 5 5 24 17 8 20 18 20 21 18 32 18 23 12 19 17 23

Females Age (yrs) Mean 27.07 43.80 45.20 33.17 31.82 27.50 29.95 28.39 31.70 27.52 27.56 23.50 25.39 33.94 29.25 25.53 24.53 26.22

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

Length (cm)

Muscle Hg

Liver Hg

SE

Mean

SE

Mean

SE

Mean

2.25 3.89 3.89 1.78 2.11 3.08 1.95 2.05 1.95 1.90 2.05 1.54 2.05 2.11 2.51 2.00 2.11 1.82

429.06 384.56 426.70 415.51 419.54 417.51 411.74 414.02 410.05 413.17 413.74 403.62 416.63 419.32 415.71 414.82 401.47 405.55

6.45 11.18 11.18 5.10 6.06 8.84 5.74 5.89 5.59 5.46 5.89 4.42 5.89 5.21 7.22 5.74 6.06 5.10

0.91 1.03 1.81 1.40 1.62 1.85 1.65 1.33 1.60 1.31 1.38 1.27 1.16 1.21 0.97 1.06 1.21 1.28

0.15 0.62 0.23 0.12 0.15 0.17 0.14 0.14 0.14 0.14 0.15 0.11 0.15 0.13 0.15 0.14 0.15 0.13

13.97 14.69 39.72 32.51 43.08 31.01 41.90 30.07 40.10 33.81 27.19 23.41 27.51 21.94 20.29 15.21 24.21 25.13

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

N SE

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

6.50 11.99 10.13 5.36 6.50 7.43 5.99 6.15 6.15 5.85 6.50 4.74 6.32 5.59 6.32 6.15 6.50 5.47

Age (yrs)

Length (cm)

Muscle Hg

Liver Hg

Mean

SE

Mean

SE

Mean

Mean SE

3.66 7.32 5.67 5.18

363.42 357.20 404.38 396.67 373.40 401.32 390.75 348.00

7.18 14.94 11.57 10.56

0.72 ± 0.10 1.03 ± 0.18 1.03 ± 0.14

± 18.29 ± 12.93 ± 18.29

1.20 ± 0.25 1.15 ± 0.18 0.88 ± 0.24

8.43 ± 5.99 23.01 ± 12.93 22.18 ± 11.20 44.29 ± 8.46 59.40 53.00 ± 15.84 23.74 ± 11.20 12.58 ± 15.84

± ± ± ±

23.17 39.67 41.20 39.67 37.00 36.00 35.75 28.00

5 4 3

33.20 ± 5.67 39.75 ± 6.34 46.67 ± 7.32

361.20 ± 11.57 375.29 ± 12.93 378.46 ± 14.94

0.64 ± 0.17 0.65 ± 0.18 0.84 ± 0.21

13.70 ± 10.02 24.21 ± 11.20 30.36 ± 12.93

4 6 1 1 4

38.75 ± 6.34 44.17 ± 5.18

363.22 ± 12.93 369.99 ± 10.56 378.46 388.60 365.76 ± 12.93

0.99 ± 0.18 0.72 ± 0.14 0.78 1.20 1.10 ± 0.18

32.47 ± 11.20 27.29 ± 8.46 36.37 51.13 44.17 ± 11.20

± 8.96 ± 6.34 ± 8.96

39.00 42.50 ± 6.34

± ± ± ±

SE

13 3 5 6 1 2 4 2

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mass spectrometry (CF-IRMS) using a GV-Instruments® IsoPrime attached to a peripheral, temperature-controlled, EuroVector® elemental analyzer (EA) (University of Winnipeg Isotope Laboratory, UWIL). One-mg samples were loaded into tin capsules and placed in the EA auto-sampler along with internally calibrated carbon/nitrogen standards. Carbon and nitrogen isotope results are expressed using standard delta (δ) notation in units of per mil or per 1000 (‰). The standards used for carbon and nitrogen isotopic analyses are Vienna PeeDee Belemnite (VPDB) and IAEA-N-1 (IAEA, Vienna), respectively. Analytical precision, determined from the analysis of duplicate samples, was ± 0.16‰ for δ13C and ±0.18‰ for δ15N. Accuracy was obtained through the analysis of laboratory standards used for calibration of results. 2.2.3. Accounting for age and size as confounding factors of Hg concentration To assess temporal trends of Hg concentrations in beluga whales, confounding factors need to be considered and controlled for prior to the consideration of other environmental factors. Specifically, different tissues contain different forms of Hg and reflect different processes (Loseto et al., 2008b; Wagemann et al., 1998). In particular, Hg in liver and muscle differ between speciation due to differences in the physiological processes that govern their concentrations (Loseto et al. 2008a). Mercury in liver is largely inorganic, bound to selenium (Hg:Se), biologically unavailable and is the by-product of demethylation of MeHg. Only ~15% of total Hg is in the form of MeHg with the remaining 85% being inorganic (Dietz et al., 2000; Wagemann et al., 1997). As MeHg is demethylated over time, Hg, in the form of Hg–Se, accumulates in liver. Animal age must, therefore be considered prior to analyses and comparisons (Lockhart et al., 2005; Wagemann et al., 1997). On the other hand, Hg in muscle is largely in the form of MeHg (i.e. [THg] = [MeHg]), which is thought to have a half-life of several months in mammals (Petersson et al., 1991). MeHg is the form of Hg that biomagnifies up food webs and thus best reflects dietary exposure to Hg (Loseto et al., 2008b). Since the size of a given beluga defines the habitat it selects (Loseto et al., 2006) and the prey it feeds on (Loseto et al., 2009), size, rather than age is the confounding factor that requires consideration when investigating Hg concentrations in muscle (Loseto et al., 2008a,b). The beluga age to size relationship follows a Gompertz growth curve whereby an asymptote is reached at age of maturity (Harwood et al., 2002), thus age and length do not follow a linear growth curve. Below we present the approaches taken to account for the effects of age and size on liver and muscle Hg concentrations respectively. 2.2.3.1. Age factor and liver mercury. Temporal trend analyses using beluga liver Hg concentrations require the concentrations to be adjusted to correct for the bioaccumulation of Hg with age. A linear regression reveals a significant positive relationship with all years combined (r = 0.55, p b 0.0001, n = 375). Two approaches (regression and grouping) were used to adjust for age accumulation of Hg. Adjusting liver Hg concentrations to a common age for all animals requires the assumption that accumulation of Hg over time is consistent is met and thus, comparable across years. In other words, the slopes of the relationships between age and Hg concentration over time (for each year of collection) remain constant (homogeneity) to be used to adjust liver Hg concentrations to a specific age for any beluga of known age. We tested the homogeneity of slopes with an Analysis of Covariance (ANCOVA). This approach was used prior to adjust beluga liver Hg concentrations to the mean age of 13.1 years (Lockhart et al., 2005). It is important to note that beluga ages in Lockhart et al. (2005) were based on the assumption that 2 GLG in teeth were equivalent to one year; however, more recent work has revealed that 1 GLG is equal to one year (Luque et al., 2007; Stewart et al., 2006). The second approach used to adjust for the confounding factor of age on liver Hg concentrations was to group belugas by age. Rather than standardizing Hg concentrations to one age, this method uses age groupings that capture the range or variability of Hg for each age category of whales. We began by grouping belugas into five-year age intervals

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(i.e. 0–5, 6–10, …, 51–55). To reduce variability associated with sexual maturity (Robeck et al., 2005), all animals below the age of 15 were excluded from the analysis (n = 4). Average Hg in each of the age interval groups examined with an analysis of variance (ANOVA) and a post-hoc pairwise test. Outputs revealed the age intervals lumped into two age groups that differed from one another. Liver Hg concentrations in beluga age groups 16–20, 21–25, 26–30 and 31–35 had Hg concentrations that were not significantly different from one another (p N 0.05) but were significantly lower than the older age groups 36–40, 41–45, 46– 50 and 51 to 55, which were also not significantly different from one another (p N 0.05). Based on these results, two beluga age groups, young (16–35 years) and old (36–55 years), were used to assess temporal variability in this study. Mean Hg concentrations of these two age groups were 20.1 ± 1.1 and 53.7 ± 3.9 SE, μg/g ww (p b 0.0001; t-test), respectively. 2.2.3.2. Size factor and muscle Hg. Loseto et al. (2008b) demonstrated that the size of beluga will impact Hg concentrations in muscle. More specifically larger sized belugas will select habitats in the offshore area in heavy ice concentrations whereas smaller sized whales will remain closer to mainland shorelines in open water habitats. Prey Hg concentrations vary among these habitats with nearshore prey generally having lower Hg concentrations relative to offshore and benthic prey (Loseto et al., 2008a). To take size into consideration we used beluga size groups that were previously defined based on habitat use and relationships found with diet (Loseto et al., 2006, Loseto et al., 2008a,b, 2009). Here the medium- (380–420 cm) and large-sized males (N420 cm) were used to examine the changes in Hg concentration over time. Small males (b380 cm) and all females were excluded due to the lack of representation within the data set for all years. Concentrations were higher in the large whales (1.54 ± 0.06 μg/g ww) than in the medium-sized males (1.12 ± 0.05 μg/g ww) (t-test; p b 0.0001). Because temporal trends in muscle Hg will be evaluated for both size groups, the stable isotope data used to address dietary drivers of Hg were also examined in the corresponding size groups. That is, δ13C and δ15N were used to examine diet variability over time for both of the defined size groups to evaluate diet differences and associated Hg exposure (medium: 380–420 cm; large: N 420 cm) (Loseto et al., 2006, 2008b, 2009). 2.2.4. Distant drivers: Arctic Oscillation, Pacific Decadal Oscillation, minimum sea ice concentration Primary exposure of beluga to Hg is via diet. Considering the high trophic level of belugas, it seems clear that their ultimate uptake of Hg will be subject to a series of processes operating within the ocean's Hg cycle and ocean food webs, all of which may be affected by climate variability. To approach the problem of investigating possible relationships between large-scale ecosystem drivers and beluga Hg concentrations we first considered the large home range of the Beaufort Beluga population. They winter in the Bering Sea and migrate to the eastern Beaufort Sea in the spring to summer and begin to migrate back to the Bering in the fall (Fig. 1) (Hauser et al., 2014; Richard et al., 2001). Three well-known climate variables seem most relevant in their potential to alter ecosystem function in ways that would directly or indirectly affect Hg uptake by beluga during these migrations: the Arctic Oscillation, the Pacific Decadal Oscillation (PDO) and sea-ice coverage. These climate variables have previously been related to ecosystem function as represented by species distributions (e.g., regime shifts in the North Pacific linked to the PDO (Benson and Trites, 2002; Grebmeier et al., 2006b); terrestrial shifts linked to the AO (Aanes et al., 2002); and alterations of marine primary productivity driven by changing sea ice (Brown and Arrigo, 2012)). There are a number of ways to express these variables; here we use the winter AO Index (December, January, February, March), the mean annual PDO index and percent sea-ice concentration at the annual

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minimum (SIM). Of these, the SIM and AOI exhibit some correlation, with the positive AOI phase associated with lower sea ice concentration the following year in the Western Arctic (Rigor et al., 2002). Likewise, the PDO and sea-ice cover in the Bering Sea probably exhibit some relationship with each other, and PDO certainly affects water properties like sea surface temperature (e.g., Danielson et al., 2011; Wendler et al., 2013). Although detailed mechanisms connecting climate variability, as represented by the PDO or sea-ice cover, and ecosystem function remain unclear, considerable literature is accumulating for the Bering Sea that decadal scale change occurs, sometimes abruptly, and these changes manifest in populations from algae to whales (e.g., Bi et al., 2011; Frost et al., 2013; Lehodey et al., 2006; Litzow and Mueter, 2014; Moore, 2008; Zhang et al., 2010). Perhaps more important in terms of providing variability in beluga Hg uptake via diet would be the observation that recent trends in sea ice cover or primary production in the Bering Sea have not matched those of the Arctic Ocean (Brown and Arrigo, 2012). As yet it is very unclear how these three leading climate variables might relate to regional biogeochemical cycles and ecosystem function, but there seems little doubt that they have the potential to change marine ecosystem functioning in ways that can propagate through food webs to affect Hg uptake in large foraging animals (Macdonald and Loseto, 2010; Stern et al., 2012). Sea ice is especially important because it can affect ecosystems by top-down (i.e. impact predators and how they feed) or bottom-up processes (i.e. sea ice provides habitat for the lower trophic level species such as sea ice algae and the secondary, tertiary predators they support) (Douglas et al., 2012; Macdonald et al., 2005). Recognizing that physical change in the marine environment, which contains a large inventory of Hg (Outridge et al., 2008) and food, may not immediately manifest its impact in high trophic level species like belugas, we consider lag times of up to 10 years between climate variables (e.g., PDO, sea-ice cover), and Hg in beluga tissue. Data for the mean percent of normal ice concentration at the minimum for the Western Arctic were obtained from Environment Canada Ice Service (http://www.ec.gc.ca/glaces-ice). Data for the Pacific Decadal Oscillation was obtained from the Joint Institute for the Study of the Atmosphere and Ocean (http://jisao.washington.edu/pdo/PDO.latest). Data for the AOI was obtained from the NOAA Weather Service (http://www.cpc.ncep.noaa.gov/products/precip/CWlink/daily_ao_ index/ao_index.html).

(Pearson's correlation) to evaluate lag relationships for up to 10 years on those tissues demonstrating a significant temporal trend. All univariate statistical analyses were performed on SYSTAT 12. 3. Results and discussion 3.1. Mercury temporal trend analysis 3.1.1. Liver mercury To evaluate temporal trends in beluga liver Hg we first accounted for age as a confounding factor. There was a significant interaction between liver Hg and age among years (p b 0.05) thus violating the homogeneity of slopes and ability to develop an age-corrected Hg concentration to evaluate temporal trends in liver Hg. The second approach to account for age using age groups revealed that liver Hg concentrations significantly increased from the 1980s to the early 2000s in both the young and the old age groups (young: p = 0.006; r = 0.32; old: p = 0.011; r = 0.5). Using the data from the same animals (1981–2002), Lockhart et al. (2005) also noted a Hg increase peaking in 1996, when Hg concentrations were adjusted to an average age of whale 13.1 years (based on aging whales using two GLGs instead of one GLG, which would make the mean beluga age 26.2 years). After 2002, the Hg concentrations in the young age group fluctuate around 20 μg/g with no observed trend. In contrast, a significant decline in Hg concentrations was observed in the old beluga age group (p = 0.02; r = 0.33) (Fig. 2a, b). Mean Hg concentrations between the two age groups were significantly different with the young belugas approximately 30 μg/g ww lower than the old belugas. Hg concentrations in liver largely reflect the predominant proportion of biologically unavailable Hg bound to selenium (Hg:Se), a

2.3. Statistics No significant differences in liver Hg were found among male and female beluga based on a t-test (p = 0.6). Thus, males and females were grouped using the following statistical analyses. As described above, an ANCOVA was used to assess the effects of year to year collections (temporal trends), age and age ∗ year interactions (homogeneity of the slope between age and log[Hg]) (SAS Institute. Release 9.1.3, TSM3. Copyright © 2002–2003 by SAS Institute Inc., Cary, NC, USA). An ANOVA was used as the second means to address age as a confounding factor for liver Hg by checking for significant differences among the 5 year intervals. This was followed by a t-test to confirm differences between the two age groups described. All temporal trend analyses of age and size groups were performed for two time periods, the first replicating Lockhart et al. (2005) with data spanning from 1981 to 2001 to confirm the approaches supported previous observations; the second time period spanned from 2002 to 2012. A linear regression was used for both time periods for liver and muscle groups. Note that the muscle Hg analyses were performed only on males due to the significant differences between sexes (t-test; p b 0.0001). To address temporal changes of muscle Hg within the size groups, stable isotope data were used to examine potential temporal shifts in diet that could be driving the Hg trends. Recognizing that regional scale drivers of beluga Hg concentrations may act on different time frames due to system inertia we ran a time series correlation

Fig. 2. a, b. Beluga mean liver Hg concentrations in two age groups over three decades. Solid grey line at 2002 signifies when temporal trends were assessed previously by Lockhart et al. (2005) and again here, as well as assessed for recent trends from 2002 to 2012. a: Liver Hg concentrations in the young age group (16 to 35 years). A significant increasing trend observed from 1981 to 2002. From 2002 to 2012 no significant trend was measured. b: Liver Hg concentrations in older age group (36 to 55 years). A significant increasing trend was observed from 1981 to 2002 and a significant decreasing trend was observed from 2002 to 2012.

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complex formed as a result of the demethylation process (Farris et al., 1993; Wagemann et al., 1998). As a result liver is thought to act as a long term storage site (Young et al., 2001). While post 2002 results for the younger and older belugas differed, neither supported an increase in Hg in recent years. Thus, neither the young nor the old liver Hg trends reflected the increasing global Hg emissions (Muntean et al., 2014). The lack of a Hg temporal trend post 2002 in the younger whale group suggests no change in exposure. Assuming then, that the rate of MeHg demethylation remains constant in all belugas, the decline of liver Hg concentrations in this older beluga age group is significant, with concentrations in recent years declining to nearly half of those measured in animals sampled in the late 1990s and early 2000s. This decline implies an overall decrease in dietary Hg exposure driven by a shift in diet items and/or a decrease in Hg concentrations in their prey. This is investigated in muscle Hg trends. 3.1.2. Muscle mercury To evaluate Hg temporal trends in beluga muscle, we accounted for size-driven feeding preference as a confounding factor using size groups. Trends in muscle Hg concentrations up to 2002, which support the increase previously reported by Lockhart et al (2005), were

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observed in both size classes (medium: p = 0.003; r = 0.48; large: p = 0.003; r = 0.4) (Fig. 3a, b). In the following years, from 2002 to 2012, the large beluga size group showed a decline in muscle Hg (p = 0.05; r = 0.2) (Fig. 3b), while the medium size group showed no significant trend (Fig. 3a). The trends in these two size groups did not necessarily relate to or reflect one another; for example, the lowest concentrations for medium sized animals were observed in 2007 (0.6 μg/g ww), a year where high concentrations were observed in larger animals (1.8 μg/g ww). This highlights the need to consider the exposure differences between these two size groups. The Hg concentrations were higher in the large size group relative to the medium with approximately 0.5 μg/g difference between the two size groups. Since the majority of Hg in beluga muscle is in the form of MeHg (Wagemann et al., 1998), the concentrations must reflect the dietary sources of MeHg and feeding preferences related to size (Loseto et al., 2009; Loseto et al., 2008b). The decline of Hg in beluga muscle implies a decrease in exposure of MeHg via dietary sources and supports observations in liver Hg. The larger size group is known to venture into habitats of deeper water, in offshore regions covered by heavy sea ice (Loseto et al., 2006), whereas the medium sized animals tended to associate with ice edges and did not venture as far offshore. Within the eastern Beaufort Sea prey items associated with deeper waters and benthos (e.g. Arctic cod and shrimp) were found to have some of the higher concentrations of Hg whereas prey in coastal areas (e.g., ciscos) had some of the lower concentrations of Hg (Douglas et al., 2012; Loseto et al., 2008a). Spatial variation in Hg concentrations across the Beaufort and Chukchi Seas were measured in zooplankton and Arctic cod (Stern and Macdonald, 2005). At a regional level in the Chukchi Sea, Hg concentrations in benthic food webs were related to chlorophyll biomass that influenced dilution (Fox et al., 2014). Thus, the decline in dietary exposure of Hg may have resulted from either a change in dietary items perhaps driven by a shift in feeding/foraging behavior over their migratory range (e.g., see Stern et al., 2012) or may have resulted from an overall reduction of Hg concentration in preferred diet items. 3.2. Local drivers: diet with stable isotopes

Fig. 3. a, b. Beluga mean muscle Hg concentrations in two size groups over three decades. Solid grey line at 2002 signifies when temporal trends were assessed previously by Lockhart et al. (2005) and again here, as well as assessed for recent trends from 2002 to 2012. a: Muscle Hg concentrations in the medium size beluga group (380 cm to 420 cm) defined by habitat use (Loseto et al., 2006). From 2002 to 2012 no significant trend observed. b: Muscle Hg concentrations in the largest size range defined by habitat use (greater than 420 cm). From 2002 to 2012 a decreasing trend in Hg concentrations was observed. Significant increasing trend in Hg in both size classes was observed from 1981 to 2002.

To investigate the relationship of muscle Hg and diet, δ15N and δ13C were examined over time within each size group, using years as an interaction variable. Hg concentrations in medium sized belugas showed significant relationships with δ15N (p b 0.001; r = 0.57) and δ13C (p = 0.001; r = 0.56) with years as an interaction. Conversely, no significant relationships were observed in the large size group. Thus, the often expected relationships between stable isotopes and Hg (Cabana and Rasmussen, 1994; Jardine et al., 2006) are not always evident (e.g., Arcagni et al., 2013; Bisi et al., 2012). The lack of a relationship in the larger animals between Hg and δ15N and δ13C may be attributed, at least in part, to what they prey on and the differences in how prey accumulate Hg relative to the δ15N and δ13C signals. The large whales are thought to have a wider assortment of prey items that may vary with the δ15N and δ13C relative to Hg concentrations. That is, the typical linear trend observed for δ15N and Hg (or other bioaccumulative contaminants) can become distorted, which is sometimes observed in benthic food webs (Hobson et al., 2002; Lavoie et al., 2010). For example, in the Gulf of St. Lawrence low trophic level benthic organisms had high Hg concentrations yet the biotransfer of Hg up the food web was less efficient than for pelagic food webs (Lavoie et al., 2010). Differences among food webs may be driven by the species of Hg, its accumulation properties as well as the tissue matrices of lipid/protein content that can affect biomagnification (Jardine et al., 2006). Temporal trends in beluga δ15N and δ13C were examined independently to evaluate potential temporal changes in diet (note data not available for the 1980s). The δ15N decreased from the 1990s to the 2000s in both the medium (p = 0.01; r = 0.59) and the large beluga size group (p = 0.002; r = 0.61). From 2002 onwards, δ15N increased in the medium size group (p = 0.045; r = 0.23) driven primarily by

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the last three years, while no trend was observed in the larger size group (Fig. 4a). The δ13C values for both the medium and large size groups declined from the 1990s to 2002 (medium: p b 0.001; r = 0.62; large: p = 0.016; r = 0.54). No significant trends were observed in either size group beyond 2002 (Fig. 4b). The temporal trends observed in beluga stable isotopes imply weak connections between diet shifts and observed Hg trends, and the trends cannot be interpreted in any conventional way. That is, based on known biomagnification of Hg (e.g., Atwell et al., 1998; Cabana and Rasmussen, 1994) one would expect an increase/decrease in δ15N to be accompanied by an increase/decrease in Hg, but this is not the case with our data. In fact the opposite was observed for the 1990s to 2002; δ15N declined at a time when Hg concentrations were rising. The increasing δ15N trend post 2002 did not support a diet shift to accompany the declining Hg in large whales. There may be several factors at play and, as described above, δ15N and Hg may not always demonstrate a positive linear trend as one moves up the food web. One challenge in using δ15N (as well as δ13C) specifically for this population, is that these belugas have a large home range where δ15N and δ13C baselines are known to differ between regions (Schell et al., 1998). Base values of δ15N demonstrated a complex spatial variation across the Beaufort, Chukchi and Bering Seas making it difficult to assign feeding areas with higher or lower δ15N (Schell et al., 1998). As such the decline in δ15N observed prior to 2002 when Hg had increased, may have been driven by a combination of feeding at lower trophic levels as well as spatial variability in prey signals across their migratory path.

The decrease of δ13C in the 1990s and 2000s may indicate shifts in feeding preferences; that is, generally a decrease in δ13C supports moving from feeding in a nearshore/benthic habitat to an offshore/pelagic area (France, 1995; Hobson et al., 2002). However, the Bering, Chukchi and Beaufort exhibit a well-known geographical variation in δ13C, whereby concentrations become depleted moving eastward (Schell et al., 1998; Stern and Macdonald, 2005). Stern and Macdonald (2005) found that the eastward depletion also related to the Hg concentrations in both zooplankton and Arctic cod whereby Hg increased with depleted δ13C along an eastern gradient. Those observations support results here; as beluga Hg increased from the 1990s to the 2000s, δ13C became depleted. Given that belugas migrate across the δ13C gradient from the Bering to the Beaufort, the assignment of relationship between δ13C and Hg becomes even more complicated. We propose that prior to 2002 the rise observed in beluga Hg might have been due to feeding on prey depleted in δ13C associated with the Beaufort food web as well as feeding in more pelagic food webs. The decline in beluga Hg following 2002 does not appear to be driven by shifts related to δ13C dietary sources. The weak relationship between stable isotopes of carbon and nitrogen and beluga Hg suggest that other processes must be considered when trying to interpret the Hg temporal trend data. Specifically, either the Hg concentrations in prey items have generally changed or belugas have migrated their primary feeding locations to different domains having different Hg exposures or different baseline isotope compositions. Further complicating this, is the fact that stable isotope values and Hg concentrations in belugas can result from multiple processes related to their diet, food web dynamics and bioavailable and bioaccumulative processes. Secondly, interpretation of Hg or stable isotopes in this beluga population is complicated by its large extensive home range known to support perennial differences in base δ13C and δ15N values (Dunton et al., 1989; Schell et al., 1998). Given these underlying complications noted above, stable isotopes of nitrogen and carbon may not be the best tracers of dietary changes in association with trends in beluga Hg. Our results do not present an unequivocal link between shifts in diet and consequent shifts in Hg concentrations over the three decades. These observations highlight the difficulty of using traditional dietary indicators such as δ13C and δ15N to account for contaminant uptake by animals that migrate widely among very different feeding habitats. While diet must play a key role in defining exposure and Hg concentrations in top predators like belugas, the weak statistical relationship between stable isotope data and Hg data imply that other factors need to be considered. It seems to us that the large-scale change in sea-ice climate in the polar ocean during the past two decades is a strong candidate to produce change given that it can operate both from the bottom up (e.g., by changing the nutrient and Hg exposure through enhanced coastal upwelling (Wang et al., 2012)) and by the top-down processes (e.g., by changing the foraging range and prey opportunities (Douglas et al., 2012; Macdonald et al., 2005)). Indeed these sorts of climate shifts may be manifested either as trends within domains (e.g., upwelling) or shifting of domain boundaries (e.g., ice distribution) (Sarmiento et al., 2004). 3.3. Distant drivers: climate variables

Fig. 4. a, b: Temporal trends of the stable isotopes (δ15N and δ13C) plotted among the two beluga size groups (medium and large). A solid grey line at 2002 signifies when temporal trends in Hg were assessed prior to 2012 and from 2002 to 2012. Both the medium and large whales were assessed for each isotope. a: δ15N in muscle for two beluga size groups. A decrease was observed from the 1990s leading to 2002. From 2002 to 2012 the medium whales showed an increase in δ15N and no trend was observed for the large whales. b: δ13C in muscle for two beluga size groups. A decrease was observed from the 1990s to 2002. From 2002 to 2012 the medium and large whales did not show a significant linear trend.

To consider large-scale ecosystem shifts and decadal changes, the AO, PDO and SIM were used as indicators of regional change that could influence temporal trends in beluga Hg. Correlations for the beluga liver Hg in older whales and muscle Hg in larger whales were carried out using lag times given that impacts of regional drivers likely take a year to several years to manifest themselves in high trophic level consumers. Evaluation of the SIM concentration for the Western Arctic as a driver for Hg trends in beluga revealed opposing trends for muscle and liver, whereby a significant positive relationship with a two-year time lag was observed for muscle Hg in larger sized whales (p = 0.046; r = 0.49) and negative relationship for liver Hg with a

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six-year time lag in the older beluga group (p = 0.06; r = − 0.45) (Fig. 5a). These contradictory trends may reflect different underling processes at both the ecosystem and physiological level in belugas. The liver reflects longer term accumulation whereas muscle reflects the short term. The AO exhibited positive relationships for both the muscle (large size group) and liver (old group) Hg. Beluga muscle Hg had the most significant relationship with a three-year time lag to the AO (p = 0.02; r = 0.55). Among the lag series the best fit for liver Hg was a non-significant six-year time lag (p = 0.13; r = 0.37; Fig. 5b). Lastly the PDO showed a significant positive trend for an eight-year time lag for both Hg in muscle and liver (muscle: p = 0.036; r = 0.51; liver: p = 0.001; r = 0.72) (Fig. 5c).

Fig. 5. a, b, c: Best fit correlations of three regional global ecosystem drivers to Hg concentrations in liver from older age class (36–55) displaced with a time lag. a: Western Arctic Sea Ice minimum (SIM) concentration demonstrating a negative relationship at a six year lag phase; b: Arctic Oscillation Index for January, February, March (AOI (DJFM)) demonstrating a positive, non-significant relationship at a six year lag and c: Pacific Decadal Oscillation (PDO) Index demonstrating a significant positive relationship at an eight year lag.

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Results are complex and show variability among the tissues and different response times to these selected climate drivers. The liver Hg relationship with the AO and SIM were inverse to one another whereby Hg was highest during low SIM and positive AO. Positive AOs in the winter co-occur with decreased ice concentration the following fall thus affecting the ice minimums (Rigor et al., 2002). While the AO and SIM are known to be related, this is the first study to show a common relationship with both to Hg levels in a high trophic level species. A positive PDO was associated with higher Hg concentrations in both muscle and liver at a common lag time of eight years. Among the climate variables, the PDO had the most significant relationship with beluga Hg. That we know of, PDO has not been considered as an important climate variable for Arctic ecosystems, but our analysis suggests that migratory animals with foraging ranges extending into the Bering/Pacific may manifest in part an imported response to climate variability. With the present data we cannot describe with any certainty how the lag between Hg and sea ice or atmospheric states occurs: however, the potential for climate variability to affect numerous pathways and processes that contribute to Hg uptake in beluga whales provide ample room for large-scale climate variability to manifest itself in whale Hg trends (Fig. 6). While beluga habitat use patterns can keep up with changes in ice cover, requiring little or no lag, the changes in the habitats themselves (e.g., shifts between epontic, pelagic and benthic contributions to beluga diet) would take longer, as would the alteration to Hg inventories in large foraging whales. Given that belugas forage in two very different oceans, the fluctuating PDO in the Bering Sea, or the AO and SIM in the Beaufort Sea could lead to cascading, direct and indirect ecosystem changes that affect Hg in belugas (AMAP, 2011; Douglas et al., 2012). These belugas spend time in the Beaufort Sea and surrounding waters, Chukchi and the Bering Sea (Hauser et al., 2014; Richard et al., 2001). The three large scale drivers (PDO, AO, SIM) act regionally across the home range that can alter habitat, food web structure and Hg methylation that together define beluga dietary exposure to Hg. More specifically, the sea-ice minimum will directly define beluga habitat availability across the home range that will affect access to food and thus dietary exposure to Hg. The PDO and/or AO are indirectly important to this pathway because they affect sea ice and the ecosystems they support. It is important to note that belugas are not homogenous in their resource needs and habitat use and thus individuals are affected differently depending on their foraging preferences, which operate over very large ranges (Loseto et al., 2006). That is, with reduced sea ice whales typically selecting heavy sea-ice concentration and ice edges may need to travel farther to reach this habitat as it withdraws to the interior ocean, and whales selecting open water areas would have a wider range to access, directly altering access to prey. There is evidence that these sorts of habitat changes affect diet and feeding patterns in ways that would affect Hg uptake (Loseto et al., 2009). The PDO and AO are associated with ocean changes that impact both food webs and Hg methylation processes; with the PDO having a more significant influence in the Bering Sea and the AO in the Beaufort Sea (Fig. 6). Irrespective of the PDO and AO, Hg processes across the oceans are influenced by different factors. For example, atmospheric sources of Hg to the Bering Sea are largely derived from Asia (AMAP, 2011). Significant inputs of Hg to the ocean and sediments (and subsequently the food web) in the Bering Sea include terrestrially derived Hg from the Yukon River (Day et al., 2012) as well as Hg arriving from the North Pacific to the Bering (Fox et al., 2014). The southern latitude of the Bering has high photo reduction of MeHg that is modulated by sea ice cover (Point et al., 2011). On the other hand, atmospheric sources of Hg in the Beaufort Sea derive predominantly from Eurasia (AMAP, 2011). Furthermore, the Mackenzie River, which impinges on exceptionally important beluga habitat (Fig. 1) is a key source of Hg (Leitch et al., 2007), and atmospheric mercury depletion events and advection of MeHg are key processes governing ocean Hg in the Beaufort Sea (Steffen et al., 2008; Wang et al., 2012). Lastly, the presence of the

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Fig. 6. A schematic diagram showing how climate variability might operate on the pathways, processes and interactions involving Hg and food webs. At the top of the schematic, large scale climate variability, represented by the PDO, AO and SIM, affects the habitat across which Beaufort Sea beluga migrate. Change to sea ice directly impacts beluga access to habitat as well as affecting the food webs supporting beluga prey. Both AO and PDO can influence SIM as well as the ecosystems in summer and wintering regions. The two oceans exhibit distinct features in Hg processes and food web structure (text boxes below the migration schematic). Change occurring at the large scale affects both the Hg processes in the ocean and subsequent transfer of Hg up the food web to beluga. Inherent differences between Hg processes and food web structure in the Bering and Beaufort Seas, like those listed in the text boxes, complicate the task of inferring how climate variability in each of these oceans contributes to observed temporal beluga Hg trends (bottom panels, Fig. 6). Supporting references: 1. AMAP (2011), 2. Point et al. (2011), 3. Fox et al. (2014), 4. Day et al. (2012), 5. Steffen et al. (2008), 6. Wang et al. (2012), 7. Leitch et al. (2007), 8. Beattie et al. (2014), 9. Mathis et al. (2014), 10. Moran et al. (2005), 11. Grebmeier et al. (1988), 12. Grebmeier et al. (2006a, 2006b), 13. Grebmeier (2012), 14. Moore et al. (2014), 15. Lee et al. (2010), 16. Darnis et al. (2008), 17. Hunt et al. (2014), 18. Benoit et al. (2008), 19. Rand and Logerwell (2011).

multiyear sea ice introduces another potential source of MeHg to the marine food web that is absent in the Bering Sea (Beattie et al., 2014). It is important to consider these regional differences among oceans and how complex the interactions between climate and Hg may be: for example, the shift in organic carbon cycling produced by a change in ice climate can alter Hg methylation (Macdonald and Loseto, 2010), the change in light climate of the upper ocean can destroy methyl Hg (Bisi et al., 2012), and change in water mass disposition or upwelling can change the MeHg concentration in a given region (Wang et al., 2012). These are all operating over the large home range occupied by beluga, thus the landscape of Hg sources, pathways and processes across

the Bering to Beaufort Sea complicate direct interpretation of Hg trends in belugas. Superimposed on regional variability of Hg methylation processes are the food web structures and processes that govern energy and Hg biotransfer. The Bering Sea is highly productive and the Beaufort Sea is much less so, and has been described as oligotrophic (Lee et al., 2010; Mathis et al., 2014). The Bering Sea is shallow and has a strong benthic–pelagic coupling that supports a diverse benthic ecosystem with high biomass (Grebmeier, 2012; Grebmeier et al., 2006a; Grebmeier et al., 1988; Moran et al., 2005). The deep basin in the Beaufort does not sustain such strong coupling and the associated diversity is low at lower trophic

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levels (Darnis et al., 2008; Hunt et al., 2014). The productive Bering Sea supports a high diversity and biomass of forage fish species (e.g. Pollock, herring, capelin (Moore et al., 2014)) whereas the Beaufort Sea is largely dominated by one key forage species; Arctic cod (Boreogadus saida) (Benoit et al., 2008; Rand and Logerwell, 2011). The difference in biomass is striking whereby walleye pollock occurred at 10 times the biomass in the Bering Sea to that of Arctic cod in the Beaufort Sea (Moore et al., 2014). The differences in prey availability, diversity and the ecosystem that sustains them varies across the beluga home range in a manner that complicates the determination of prey sources and thus the dietary sources of Hg (Fig. 6). Taken together, the factors discussed above impede our ability to link environmental mechanisms with temporal trends in beluga Hg, partly because we have no parallel time series for these mechanisms, and partly because we do not have an accurate representation of foraging locations over the past three decades, especially in the Bering Sea during winter. These sorts of unknowns need to be more clearly understood before we can explain exactly why the Western beluga population exhibits Hg trends that are dissimilar to the global Hg trends (see reviews in AMAP, 2011). 4. Conclusions Here we have examined one of the longest time series for contaminants in an Arctic marine mammal. This data set indicates periods of high Hg exposure in belugas that do not follow patterns of global emissions for the observed time period. We contend here that higher trophic level species like beluga are subject to many processes leading to Hg uptake other than long-term global Hg emissions. Furthermore, these other processes, which we do not understand and for which we do not have representative time series, can lead to Hg concentrations in beluga of concern for beluga health and the health of humans that consume them. The variability inherent in these processes loads on top of any long-term trend, which makes it difficult to identify the latter, but the system variability increases the likelihood and frequency of exceeding thresholds in a system generally receiving increasing contaminant loads. In the case of the Beaufort Sea beluga population, and many other migratory Arctic species, one has to consider the life course that spans across oceans and outside of the Arctic. All of our observations in the Arctic during the past several decades have occurred during a time of large-scale change with sea-ice and permafrost/snow providing the clear manifestations since the early 1990s. In our first examination of leading climate variables in the Arctic, we have found plausible relationships between Hg in beluga and the PDO and to a lesser extent the SIM. These relationships imply that some of the variability in beluga Hg loads is associated with Bering Sea climate variability, and we need more complete data on winter foraging patterns and food webs to understand the processes and linkages between climate variables, diet and Hg concentrations in belugas caught in the Beaufort Sea. The lags between the observed beluga Hg concentrations and the climate drivers investigated here (5 ± 3 years), if confirmed with longer time series, would be helpful because they provide predictability of future Hg concentrations. Acknowledgments This project was supported by multiple funding agencies including the Northern Contaminants Program, Fisheries Joint Management Committee, Northern Students Training Program, Cumulative Impacts Monitoring Program and ArcticNet. We thank F and N. Pokiak for their years of dedication to the monitoring program, collecting samples in a consistent and concise manner at Hendrickson Island. We thank J. DeLaronde, A. MacHutchon, G. Boila, S. Friesen, and B. Steward for laboratory support. We are grateful for the partnerships and support of Hunters and Trappers Committees of Inuvik and Tuktoyaktuk for beluga tissue collections.

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