A global-scale assessment of fish mercury concentrations and the identification of biological hotspots

Science of the Total Environment 687 (2019) 956–966

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

A global-scale assessment of fish mercury concentrations and the identification of biological hotspots David G. Buck a,b,⁎, David C. Evers b, Evan Adams b, Joseph DiGangi c, Bjorn Beeler c, Jan Samánek d, Jindrich Petrlik d, Madeline A. Turnquist e, Olga Speranskaya f, Kevin Regan b, Sarah Johnson b a

Shoals Marine Laboratory, School of Marine Sciences and Ocean Engineering, University of New Hampshire, Durham, NH 03824, USA Biodiversity Research Institute, 276 Canco Road, Portland, ME 04103, USA IPEN, Första Långgatan 18, 413 28 Göteborg, Sweden d Arnika Association, Chlumova 17, Prague 3 130 00, Czech Republic e The Intelligence Group LLC, 443 North Franklin St., Suite 220, Syracuse, NY 13204, USA f Eco-Accord Center for Environment and Sustainable Development, P.O. Box 43, Moscow 129090, Kuusinena Str. 21 B, Russia b c

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• Fish mercury (Hg) concentrations are determined from 40 waterbodies in 26 countries • Fish Hg, body size, trophic level and latitude are positively correlated • Biological Hg hotspots are identified in regions where Hg data are limited • Methods provide model for Hg monitoring in support of Minamata Convention on Mercury

a r t i c l e

i n f o

Article history: Received 28 January 2019 Received in revised form 10 June 2019 Accepted 10 June 2019 Available online 12 June 2019 Editor: Mae Sexauer Gustin Keywords: Biomonitoring Minamata convention Mercury Human health criteria

a b s t r a c t We present data on a rapid assessment of fish Hg concentrations from 40 different waterbodies in 26 countries that includes data on 451 fish of 92 species. Significant differences in fish Hg concentrations were observed across fish foraging guilds and in general, higher trophic level fish (i.e., piscivores and carnivores) showed the highest mean total Hg (THg) concentrations. However, elevated THg concentrations observed in a lower trophic level, detrivorous species highlights the importance of understanding Hg concentrations across a wide range of trophic levels, and also characterizing site-specific processes that influence methylmercury (MeHg) bioavailability. A linear mixed effects model was used to evaluate the effects of length, trophic level, sampling location, and taxonomy on THg concentrations. A positive, significant relationship between THg in fish and fish size, trophic level, and latitude of the sampling site was observed. A comparison of Hg concentrations across all sites identifies biological mercury hotspots, as well as sites with reduced Hg concentrations relative to our overall sampling population mean Hg concentration. Results from this study highlight the value of rapid assessments on the availability of methylmercury in the environment using fish as bioindicators and the need for expanded biomonitoring efforts in understudied regions of the world. This study also provides insights for the future design and implementation of large-scale Hg biomonitoring efforts intended to evaluate the effectiveness of future Hg reduction strategies instituted by the Minamata Convention on Mercury. © 2019 Elsevier B.V. All rights reserved.

⁎ Corresponding author at: Shoals Marine Laboratory, School of Marine Sciences and Ocean Engineering, University of New Hampshire, Durham, NH 03824, USA. E-mail address: [email protected] (D.G. Buck).

https://doi.org/10.1016/j.scitotenv.2019.06.159 0048-9697/© 2019 Elsevier B.V. All rights reserved.

D.G. Buck et al. / Science of the Total Environment 687 (2019) 956–966

1. Introduction Human activities have increased concentrations of mercury (Hg) in the Earth's atmosphere by approximately three-fold since the mid1800s (Amos et al., 2013; Fitzgerald et al., 1998). Anthropogenic Hg emissions have an atmospheric lifetime of between 0.5 and 1 year and as such can be transported across broad spatial scales before being redeposited on the Earth's surface (De Simone et al., 2014; Selin, 2009; Slemr et al., 2011). In addition to atmospheric emissions, anthropogenic Hg sources to the environment include direct releases from point sources as well as remobilized Hg from legacy deposits associated with contaminated sites (AMAP and UNEP, 2013; Kocman et al., 2013). Once it enters the environment, Hg can be transformed via complex, microbially-mediated processes into its more bioavailable form methylmercury (MeHg) (Benoit et al., 2002). Methylmercury is more readily absorbed by organisms and easily bioaccumulates and biomagnifies in the environment (Wiener et al., 2003). Mercury exposure in fish and other wildlife can have significant long-term impacts on ecosystem health (Eagles-Smith et al., 2016; Evers, 2018; National Resource Council, 2000; Scheuhammer et al., 2007; Whitney and Cristol, 2018). The primary pathway for human exposure to Hg is from seafood consumption (Sunderland, 2007) and the health impacts of Hg exposure in humans are well-documented and include developmental and neurocognitive delays in infants and young children as well as cardiovascular and other ailments in adults (Basu et al., 2018; Eagles-Smith et al., 2018; Ha et al., 2017; Karagas et al., 2012). In recognition of the human and ecosystem health risks associated with increased concentrations of Hg in the environment, the international community has taken action to protect humans and the environment from Hg exposure. The Minamata Convention on Mercury was signed in October 2013, becoming the first international agreement designed to specifically address contamination from a heavy metal and its potential impacts on human health and the environment. The stated objective of the Minamata Convention is “to protect the human health and the environment from anthropogenic emissions and releases of mercury and mercury compounds” (Article 1, UNEP, 2013). Currently 128 countries are signatory to the Convention, including 108 ratifications. The Convention entered into force on 15 August 2017. Implementation of the Convention will rely on signatory countries adopting a series of measures to limit the amount of Hg used in a variety of commercial and industrial practices, reduce emissions and releases of Hg into the environment, identify sites contaminated by Hg and Hgcontaining compounds and establish legislation and trade policies to further reduce and restrict the amount of Hg available on the global market (UNEP, 2013). The success of the Convention at achieving its ultimate goal of protecting human health and the environment can be assessed using a variety of short-, medium- and long-term metrics that monitor compliance with the stipulations established in the Convention's various articles (Evers et al., 2016). The Convention also recognizes the role that monitoring environmental Hg concentrations will have in assessing its long-term effectiveness. Article 22 identifies the importance of “monitoring data on the presence and movement of mercury and mercury compounds in the environment as well as trends in concentrations of mercury and mercury compounds observed in biotic media and vulnerable [human] populations” (UNEP, 2013). In addition, Article 19 of the Convention identifies potential taxa that can be used to establish baselines and develop geographically representative modeling of Hg in the environment including “biotic media such as fish, marine mammals, sea turtles, and birds” (UNEP, 2013). Fish are widely used as a monitoring and assessment tool for Hg contamination because of their relative ease of collection and identification. Yearling fish (b1 year old) can serve as good indicators of short-term changes in the input of Hg to aquatic ecosystems while adult fish provide information on long-term patterns of mercury inputs and processes related to biomagnification and bioaccumulation (Mason et al., 2005; Wiener et al., 2012). Fish communities also represent multiple

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trophic levels within aquatic ecosystems and community-wide assessments can provide information on biomagnification of toxic substances within aquatic food webs (Barbour et al., 1999). In addition, human exposure to Hg and methylmercury occurs primarily through the consumption of fish and other aquatic organisms from contaminated aquatic ecosystems (Driscoll et al., 2013; Evers et al., 2016; Sunderland, 2007). As a result, monitoring fish Hg concentrations not only provides information on risks to the environment, but can also serve as an indicator of potential human health risks associated with Hg exposure. Patterns of Hg bioaccumulation and biomagnification are consistently observed in freshwater, estuarine, and marine fishes sampled from around the world (Baumann et al., 2017; Chouvelon et al., 2014; Depew et al., 2013; Hanna et al., 2015; Wiener et al., 2003). In freshwater and estuarine systems, landscape-scale variables including watershed size, presence/absence of wetlands, amount of urban area, and vegetation cover can influence the amount of Hg released to an aquatic ecosystem (Buckman et al., 2017; Riva-Murray et al., 2011; Rypel, 2010; St. Louis et al., 1996). Water column variables including the concentration of dissolved oxygen, dissolved organic carbon, and sulfate can influence the bioavailability of Hg in freshwater, estuarine, and marine systems (Chen et al., 2012; Chen and Folt, 2005; Taylor et al., 2019; Ullrich et al., 2001), while growth rates can influence the uptake of Hg at the base of the food chain (Chen and Folt, 2005; Schartup et al., 2018) and in higher trophic level taxa (Ward et al., 2010). A global fish Hg monitoring program designed to monitor the effectiveness of the Minamata Convention on Mercury will need to simultaneously evaluate two different monitoring endpoints. The first endpoint should identify taxa that are vulnerable to Hg exposure and relate that exposure to potential human health exposure risks via consumption of fish elevated in Hg. The second monitoring endpoint should be related to the identification of sampling sites (or habitats, ecosystems, regions, etc.) where Hg bioaccumulation and biomagnification occur at higher rates than others. During a broad-scale rapid assessment, it is possible to envision a scenario under which sampling could inadvertently not include upper trophic level fishes, resulting in the collection and analysis of lower trophic level taxa with Hg concentrations that are below established human and/or wildlife health criteria. Under this scenario, such a sampling site would not be identified as a “biological Hg hotspot” (sensu Evers et al., 2007). In order to reduce any bias from factors (e.g., trophic level or fish size) known to influence Hg concentrations and the identification of biological Hg hotspots, it is also important to incorporate some level of information about the sampling site to help better interpret results. Given that resource constraints will likely limit the capacity for extensive monitoring of both species- and site-specific endpoints, it is important to consider a time efficient and therefore costeffective monitoring approach that can achieve both simultaneously. Here we present results from a study on fish Hg concentrations conducted at the global scale, with a focus on understudied regions and their freshwater fisheries. The study was conducted in collaboration with a network of researchers and non-governmental organizations (NGOs) from 26 countries (Fig. 1) utilizing a standardized sampling approach to collect fish tissue samples for Hg analysis. The goals of the study were three-fold and included (i) characterizing fish Hg concentrations in aquatic ecosystems proximate to known or suspected Hg sources in order to assess risk to both human and ecosystem health at these sites, (ii) develop a model for fish Hg concentrations that incorporates traditionally used metrics of fish length and trophic level with random effects that incorporate site- and speciesspecific variables to better estimate biological mercury hotspots, and (iii) characterize a standardized monitoring approach across a broad spatial scale to explore its effectiveness for long-term monitoring that can be related to evaluating the effectiveness of the Minamata Convention on Mercury.

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Fig. 1. Map illustrating sampling sites where fish tissue samples were collected.

2. Study design, sample collection and data set This project was conducted during a three-year period (2011–2014). International collaborators were asked to identify water bodies of interest in their respective countries, including information on potential Hg sources and fish species known to be present. The determination of Hg source types relied on local information about industrial/mining practices and the selection of target fish species for sampling was prioritized based on trophic level of the fish and known human consumption patterns in communities living adjacent to the water body. Participants were provided with sampling kits, field data forms, and a field sampling protocol modeled after United States Environmental Protection Agency (US EPA) and US Geological Survey field sampling protocols for Hg in fish (Scudder et al., 2008; U.S. EPA, 2000). In summary, fish were sampled by rod and reel, purchased directly from fisherman on-site or collected from local fish markets. Fish were photographed to confirm identification of species and measured for total length (TL) and total weight (TW). Skin-off fillet samples from the axial muscle were dissected using a pre-cleaned cutting board and fillet knife. Fillet samples were placed in zip-loc bags labeled with unique sample identification labels that included a three-letter code for each country, sample number and date of sample collection. Samples were stored on ice while in the field and stored frozen prior to sample shipment. All participants were provided with 1.5″ thick polystyrene coolers, freezer packs, and boxes for shipment to the laboratory (see below). U.S. Fish and Wildlife Service import documentation was prepared for all samples (i.e., USFWS for 3-177) and all samples were shipped by expedited international delivery. Photographs provided by study participants were used to validate species identifications listed in the sample data sheets. In circumstances where species identification was in question, requests were sent to fisheries researchers working the region, local specialists or natural history/ ichthyology museums for confirmation. In addition to length and weight measurements, trophic level was assigned to each species using FishBase (Froese and Pauly, 2018). When species-specific trophic levels were not available, the trophic level of a species within the same genus with similar foraging habits was used. Fish were further classified into 5 major foraging guilds including piscivore, carnivore, omnivore, planktivore/herbivore, and detritivore. Classification into foraging guilds was done using a heuristic approach that combined the generalized foraging behavior of a given species and its trophic level

along with any known variations in dietary patterns known to occur based on size/age of that species. Table 1 summarizes the general attributes of each foraging guild that were used in the classification. 3. Laboratory analysis All fish tissue samples (except samples collected in Russia) were analyzed at the Biodiversity Research Institute's (BRI) Wildlife Mercury Laboratory in Gorham, Maine (USA). Tissue samples were analyzed as wet weight (ww) following EPA method 7473 by gold-amalgamation atomic absorption spectroscopy following thermal desorption of the sample using a Milestone DMA-80. Instrument response was evaluated immediately following calibration, and thereafter, following every 20 samples and at the end of each analytical run by analyzing two certified reference materials (DORM-3 and DOLT-4) and a check blank. Instrument detection limit is approximately 0.050 ng/g. Matrix recovery ranged from 88.24 to 96.46%. Samples collected in Russia were analyzed at a laboratory certified by the government agency Rosaccreditatsia in Volgograd, Russia. Samples were analyzed by flameless atomic adsorption spectrometry following the Russian Ministry of Health's method MUK 4.1.1472–03 for the determination of mercury in biological materials. Tissues were digested in a concentrated solution of nitric and sulfuric acid with 5% potassium permanganate and heated in a water bath at 95 °C. After cooling, a 15% solution of hydroxylamine hydrochloride was added. Stannous chloride was then added to the sample and the mercury ions

Table 1 Summary of the principle food items and trophic level information that was used to group individual fishes into different foraging classes. Information on food items and trophic level was collected from FishBase (Froese and Pauly, 2018). Foraging class

Principle food items

Trophic level

Herbivore/Planktivore Plankton; grazing on aquatic plants; browsing 2.0–2.51 on substrate Detritivore Detritus; plant material 2.52 Omnivore Zoobenthos; benthic invertebrates; plankton; 2.76–4.0 finfish Carnivore Benthic crustaceans; other invertebrates; 2.87–4.48 finfish Piscivore Finfish 3.8–4.5

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were measured at 253.6 nm. The Russian State Standard Sample 7343–96 was used as a calibration standard. Instrument detection limit was 0.001 mg/kg. 4. Statistical analysis For each fish species at each sampling location, we calculated the mean, standard deviation and range of Hg concentrations. These data are presented in Supplemental data, Table S1. A one-way ANOVA followed by a Holm-Sidak t-test for pairwise comparisons was used to test for differences in THg concentrations between foraging guilds. To evaluate the effects of length, trophic level, sampling location and taxonomy on THg concentrations, we used a generalized linear mixed model where error of the response is assumed to be normally distributed. Length, trophic level, and latitude (as an absolute value) of each sampling site were set as fixed effects while site name and taxonomy were set as random effects. Order and site were selected as random effects primarily to increase modeling efficiency as the covariates and nine and 40 levels, respectively, and including them as fixed effects would use considerably more degrees of freedom. This parameterization does assume that the Hg concentrations of each level of the covariate are independent, but it also allows sites or orders with relatively few samples to be included in the model without imprecisely estimated parameters. The model was implemented by the ‘lme4’ function (Bates et al., 2015) in the R statistical computing environment (R Core Team, 2016). The model can be written in the syntax of the lme4 function in R as follows: logðHg þ 1e−03Þ  ð1jorderÞ þ ð1jsiteÞ þ total length þ trophic level þ latitude Mercury concentrations were normally distributed after adding 0.001 to remove zeroes and being log transformed. Many fish species were collected only at a single sampling location. Because of this, we aggregated the taxonomic information to order (instead of genus or species) to have more taxa (i.e., orders) found across multiple sites. This made the taxa and site random effects easier to disentangle from one another and produced a model with more easily defined estimates of THg at sampling sites. The model was fit through restricted maximum likelihood to obtain coefficient estimates (and their 95% confidence intervals using the ‘profile’ method in ‘lme4’) as well as marginal and conditional R2 values. Marginal R2 represents the variance explained by fixed factors and conditional R2 represents the variance explained by both fixed and random effects (Nakagawa and Schielzeth, 2013). Model goodness-of-fit was assessed using R2 and checking for evidence of heteroscedasticity and non-normally distributed error. 5. Results 5.1. Comparing mercury concentrations across foraging guilds A total of 451 individual fish of 92 species from 26 different countries were analyzed. Fish were separated into five different foraging guilds: Herbivore/Piscivore, Detritivore, Omnivore, Carnivore, and Piscivore. A one-way ANOVA (with Holm-Sidak posthoc test at p b 0.05) showed significant differences among foraging guilds (F = 17.453, df = 4, p b 0.001) (Fig. 2). Omnivores (t = 2.803, p = 0.031), Carnivores (t = 3.230, p = 0.009), and Piscivores (t = 6.928, p b 0.001) had THg concentrations greater than Herbivore/Planktivores. Piscivores also had THg concentrations greater than Omnivores (t = 5.992, p b 0.001) and Carnivores (t = 6.719; p b 0.001). No significant difference was observed between the other foraging guilds (Fig. 2). One detritivorous species was collected as part of the study - the swamp barb (Puntius titius) from Fewa Lake in Nepal. Mean THg in P. titius was 0.14 ± 0.06 ppm (ww). Absolute THg concentrations for

Fig. 2. Comparison of THg concentrations in fish tissue across 5 foraging guilds. Different letters denote statistically significant differences (95% CI) between foraging guilds. ** Sample size of Detritivores was low and limited statistical comparisons with other foraging guilds.

individual species within the other foraging guild are shown in Figs. 3–4. Mercury concentrations in herbivore/planktivore species were all below the established general fish consumption threshold by the U.S. EPA (i.e., 0.30 ppm, ww) and World Health Organization (WHO) (i.e., 0.50 ppm, ww) (Fig. 3a). Within the omnivore foraging guild, THg values were elevated in three different species relative to fish consumption criteria (Fig. 3b). These included Crucian carp (Carassius carassius) and freshwater bream (Abramis brama) collected from Labe River in the Czech Republic, and spotted pim (Pimelodus maculatus) collected from the Rio Tebicuarymí in Paraguay. Within the group of carnivorous fishes, 78% of all fishes had THg concentrations below the U.S. EPA human health criteria of 0.30 ppm (ww) (Fig. 4a). This group of low mercury carnivorous fishes includes several commercially important marine species of snapper (genus Lutjanus) and grouper (order Epinephelinae). Other notable low-mercury fish within this group of carnivorous fishes includes the freshwater Barramundi perch (Lates calcarifer), a widely distributed and commonly consumed fish across the Indo-Pacific region (Grey, 1987). (Fig. 4a). Carnivorous marine fishes with elevated THg included marbled rockfish (Sebastiscus marmoratus) from the Yatsushiro Sea of Japan (absolute THg = 0.35 ± 0.37 ppm, ww) and Pacific halibut (Hippoglossus stenolepis) collected off the coast of Alaska, USA (absolute THg = 0.37 ± 0.48 ppm, ww). The common snakehead (Channa striata), collected from freshwater habitats in both Thailand and Bangladesh, also had elevated absolute THg concentrations of 0.33 ± 0.11 ppm (ww) (Fig. 4a). The long-whiskered catfish Pimelodus maculatus from the Rio Tebicuarymí, Paraguay, had the most elevated mean THg concentration of the carnivorous fishes (0.43 ± 0.09 ppm, ww) (Fig. 4a). THg values in piscivorous fish exhibited a wide range of concentrations from 0.01 ppm (ww) to over 1.5 ppm (ww) (Fig. 4b). The most elevated THg concentration in the study was observed in swordfish (Xiphias gladius) from the southeastern Atlantic Ocean, with a mean THg concentration of 1.34 ± 0.15 ppm (ww). The black scabbardfish (Aphanopus carbo) collected from a market in the Azores Islands of the eastern equatorial Atlantic Ocean had a mean absolute THg concentration of (0.75 ± 0.04 ppm, ww). Several other marine species had elevated THg concentrations including albacore (Thunnus allunga) and Pacific bluefin tuna (T. orientalis) (Fig. 4b). Other species of tuna, including longtail tuna (T. tonggol) and yellowfin tuna (T. albacares) had mean

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Fig. 3. Bar graph showing mean (SD) total Hg concentrations in (a) herbivorous/planktivorous and (b) omnivorous fishes. Reference lines correspond to fish consumption guidelines for the U.S. EPA (0.3 ppm, ww) and World Health Organization (WHO, 0.5 ppm,ww).

THg concentrations well below the U.S. human health criteria for fish mercury of 0.3 ppm (ww) (Fig. 4b). Several piscivorous fishes from freshwater habitats also had elevated THg concentrations. Traihira (Hoplias malabaricus) collected from streams in Uruguay and Paraguay had a mean THg of 0.38 ± 0.27 ppm (ww). European catfish (Silurus glanis) from the Volga River also had elevated THg concentrations (0.46 ± 0.20 ppm, ww) (Fig. 4b). 5.2. Modeling fish mercury across multiple sites The model used for estimating fish Hg concentrations included total length, trophic level and the absolute value of the latitude from each sampling site as fixed effects with order and sampling site as random effects. Overall model fit was good with a high pseudo-R2 of 0.84 and strong evidence of normally distributed response error. The marginal pseudo-R2 for the fixed effects was relatively weak, only achieving an R2 of 0.17. The individual effects of total length, trophic level and latitude were estimated with precision and had ecologically significant effects on mercury concentrations. Total length, trophic level and latitude were positively related to Hg concentration (β = 0.32, 95% CI: 0.19, 0.45, β = 0.52, 95% CI: 0.31, 0.75, and β = 0.02, 95% CI: 0.003, 0.041, respectively; Supplemental data Table S2). These relationships translate into an approximately 100% increase in mercury concentrations with

increasing total length from 800 to 1600 mm, with a similar response observed when trophic level increases from a level 2 to 4 and absolute latitude increases from 10 to 40 degrees (Fig. 5). The addition of the random effects (i.e., order and sampling site) helped to characterize the variation in Hg. The marginal R2 increase for including both random effects 0.67 and represented a much larger amount of the total variance than the fixed effects. Both random effects had similarly large effects. Order explained 71% of the total variance while sampling site also explained 72% of the total variance observed in the data. The residual variance (i.e., the unexplained variability in the model) was 0.33. An analysis of the taxonomic- and site-related residuals shows the importance of these random effects in estimating fish mercury concentrations in our data set. Caterpillar plots comparing the estimated intercepts (±2 standard deviations) for each level of order (Fig. 6) and sampling site (Fig. 7) highlight the importance of these random effects in this model specification. Three of the nine confidence intervals for the taxonomic-related (i.e., order) residuals do not intersect the mean mercury concentration (i.e., conditional mean) of the modeled population (Fig. 6). In addition, 17 of the 40 confidence intervals for the siterelated residuals also do not overlap with the conditional mean mercury concentration (Fig. 7). Thus, order and sampling site are explaining considerable variation in the distribution of fish Hg concentrations, even

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Fig. 4. Bar graph showing mean (SD) total Hg concentrations for (a) carnivorous and (b) piscivorous fishes. Reference lines correspond to fish consumption guidelines for the U.S. EPA (0.3 ppm, ww) and World Health Organization (WHO, 0.5 ppm, ww).

under conditions where fixed effects (i.e., total length, trophic level and latitude) are known. As order and sampling site are proxies for information about the species and sampling locations that aren't fully quantified in this data set (e.g., factors ranging from within-species Hg accumulation to site-specific Hg methlylation rates) it is difficult to ascribe causality to these factors. However, we know that they are important to consider when describing differences in mercury at a global scale. 6. Discussion 6.1. Large-scale trends in fish mercury This global data set captured a wide range of fish Hg concentrations both within and across foraging guilds and fish generally considered to be higher trophic level fishes (e.g., carnivores and piscivores) had higher overall Hg concentrations (Fig. 2). However, the traditional predictors of fish Hg concentrations (i.e., fish size and trophic level) were not independently robust predictors in our model (Supplemental data Table S2). This could be, in part, that no morphometric data were collected for some of the larger apex predators with elevated Hg concentrations (e.g., X. gladius and H. stenolepis) that were sampled from fish

markets as fillets. In our data set where sample sizes for individual species are relatively large, the relationship between Hg and fish size is apparent. Mullet fish (Mullus surmuletus) from the Adriatic Sea in Albania exhibited a strong correlation between log-Hg and total length (n = 21; logHg = 8.77(total length) − 2.36; r2 = 0.51; Supplemental data Fig. S1). Although collected at multiple sites, the European perch (Perca fluviatilis) also exhibited a strong relationship between Hg concentrations and fish length (n = 32; logHg = 4.14(total length) − 1.68; r2 = 0.52; Supplemental data Fig. S2). In addition, our data set revealed a positive, significant relationship between latitude and fish Hg concentrations (Supplemental data Table S2). Sampling sites spanned across Northern and Southern hemispheres and this observed poleward increase in fish Hg concentrations has also been documented in other studies. Cutshall and Naidu (1978) documented an increase in Hg concentrations in Pacific hake (Merluccius productus) with latitude in the Pacific Ocean. More recently, (Baumann et al., 2017) found a positive relationship between Hg and MeHg concentrations in the Atlantic silverside (Menidia menidia) and latitude across the Atlantic seaboard of North America. Latitude appears to influence larger scale processes related to the biomagnification of Hg in food webs. A meta-analysis of studies combining Hg and stable

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Fig. 5. Relationship between fixed effects fish length (a) trophic level (b), latitude (c) and total Hg concentrations in fish. Note the y-axis has been back-transformed to absolute THg values from the log-transformed Hg used in model. Shaded areas represent the 95% confidence interval of the prediction.

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Fig. 6. Caterpillar plot comparing ranked order-level log-transformed conditional means of the random effect for 9 different orders with error bars showing the 95% confidence intervals of the means. Orders represented by open circles indicate a confidence interval that does not include zero while closed circles indicate a confidence interval that does include zero.

nitrogen isotopic concentrations (δ15N) in fishes revealed that the rate of Hg biomagnification is greater in waterbodies from higher latitudes (Lavoie et al., 2013). Mechanisms for this relationship between Hg concentrations in fish and latitude are not fully understood but may be related to large-scale variations in growth rates in tropical versus temperate/polar aquatic ecosystems (Baumann et al., 2017; Lavoie et al., 2013; Ward et al., 2010). Colder temperatures also inhibit Hg depuration thus increasing the overall body burden of fishes from year

to year (Trudel and Rasmussen, 2006), while greater food web complexity in warmer climates may serve to depress Hg bioaccumulation within lower-latitude ecosystems (Cabana and Rasmussen, 1994). Although outside of the scope of this study, variations in atmospheric Hg deposition between lower- and higher-latitudinal areas (Cole et al., 2013; Durnford et al., 2010) may also be driving some of the observed variations in fish Hg concentrations across latitudes observed in this and other studies.

Fig. 7. Caterpillar plot comparing ranked site-level log-transformed conditional means of the random effect for 41 sites with error bars representing the 95% confidence intervals. Sites represented by open circles indicate a confidence interval that does not include zero while closed circles indicate a confidence interval that does include zero.

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6.2. Sensitive species and biological mercury hotspots Many Hg monitoring programs focus on upper trophic level species that are commonly consumed by humans because of the public health risks associated with consuming Hg-contaminated fish and that fish consumption is the primary vector through which humans are exposed to MeHg (Sunderland, 2007; Wiener and Spry, 1996). This study adopted a similar approach and many of the upper trophic level species within our data set were consistently elevated across sampling sites (Fig. 4a and b). However, this data set also included examples where these traditional relationships between Hg concentration and trophic level clearly do not apply. Fish generally considered to occupy lower trophic levels (i.e., those classified as omnivores and detritivores) had elevated Hg concentrations relative to the other foraging categories. P. titius forages within the benthos and its diet can also include benthic invertebrates. In environments where Hg is sequestered in the sediments, fish species that rely on benthic primary production can have elevated Hg concentrations relative to pelagic species (Châteauvert et al., 2015), but are usually lower than higher trophic level species. Despite having relatively few samples from detritivores in this study, by using a continuous linear relationship with trophic level we were able to infer that these species should be fairly low in Hg exposure and identify those sampling locations as potential hotspots. To determine if our estimate of Hg exposure in detritivores deviates from the expected values based on trophic level and length, we would have to increase sampling effort for that foraging guild. These examples highlight the value of incorporating other ancillary information about the sampling site when trying to understand patterns of Hg accumulation in fishes. Site-specific water column variables (e.g., pH, dissolved organic carbon, nutrient status) are frequently used as predictors of Hg concentrations in fish (Wiener et al., 2003). However, these data can vary temporally within a system and the collection of such data can require additional resources not always available, particularly in certain regions of the world (Hanna et al., 2015). Information on potential Hg sources can also be useful when trying to identify biological Hg hotspots but variations in the mobility of Hg, whether the Hg is released directly to land or water or emitted to air, and the site's proximity to the Hg source can all be confounding variables. The application of Hg stable isotopes for source partitioning represents a novel and powerful application for identifying Hg sources (Blum et al., 2014), but requires existing knowledge of the Hg isotopic concentration of all sources to be properly applied. In the absence of this level of information, our model attempts to capture any variation in Hg concentrations related to each site by incorporating it into the model as a random effect. The inclusion of site (and order) as random effects clearly improves the model's ability to explain variability observed in fish Hg concentrations. This parameterization also allows sites or orders with relatively few samples to be included in the analysis. Orders or sites with few samples will have predicted values similar to the overall mean with increasing effort providing more evidence as to the true value of the site. The comparison of random effect estimates for each site also helps to identify hotspots where Hg concentrations are elevated relative to the sampling population average. Sites that are identified as hotspots include the Vlora Bay of the Adriatic Sea, Lake Sarpa (downstream of Volgograd, Russia), the Shalongwaeng Canal and Pongphai Swamp in Thailand, Rio Tebicuarymi, Paraguay, Rio Coatzecoalcos in Mexico and the Mesoamerican Barrier Reef (Fig. 7). Our definition of a biological mercury hotspot is a relative one. In several cases, sites that include taxa such as tuna (i.e., Pacific Ocean, Hawaii) and swordfish (i.e., Uruguay market) have Hg concentrations that are elevated relative to human health criteria (Fig. 4), but are also close to what our model predicts as average for a fish of similar size and trophic level (Figs. 6, 7). Because we are allowing Hg concentrations to vary with order and trophic level, these sites are not necessarily considered hotspots simply because of their high Hg concentrations. Notably, we

can still predict the Hg concentrations of high trophic level pelagic fish at these sites so we can identify areas that might not be hotspots yet have Hg levels that are elevated relative to human health criteria. The model also identifies sites where Hg concentrations are effectively below a model-predicted average (Fig. 7). These sites include the Gulf of Thailand where Great barracuda (S. barracuda) and Longtail tuna (T. tonggol) were collected, the Cook Island where Bluefin trevally (C. melampygus) were collected, as well as the Gulf of Nicoya in Costa Rica where Red snapper (L. campechanus) and Whitefin weakfish (M. vanicolensis) were collected. A similar descriptive term was not given to the low outliers in the model,but identifying these sites where Hg concentrations are below predicted values is an additional benefit of this model. 6.3. Biomonitoring and the Minamata Convention Fish Hg monitoring has been previously conducted across broad spatial scales, primarily in North America and Europe (Åkerblom et al., 2014; Depew et al., 2013; Munthe et al., 2007; Scudder et al., 2008; Wathen et al., 2015). In many cases, thousands of samples from hundreds and in some cases thousands of locations collected during decades of field work were summarized to examine spatio-temporal trends in Hg within targeted regions (Eagles-Smith et al., 2016; Gandhi et al., 2014; Kamman et al., 2005; Monson et al., 2011). By contrast, a continent-wide meta-analysis of fish Hg concentrations in Africa summarized all the available data on fish Hg concentrations from the continent – a total of 30 published and government data sources representing 407 reported Hg concentrations from b4% of the waterbodies across the African continent (Hanna et al., 2015). The above-mentioned examples highlight two primary challenges for long-term Hg monitoring related to the Minamata Convention. First, there is a need to develop a monitoring program that is simultaneously cost- and time-effective while also providing replicable and statistically relevant information on spatial gradients, temporal changes, and potential human and environmental health risks (Evers et al., 2016). Second, there exists a clear data gap in lesser developed countries and countries with economies in transition. Perhaps with the exception of Brazil (Costa et al., 2012; Hylander et al., 2000; Kehrig et al., 2002), there is a general lack of information on freshwater fish Hg concentrations in much of the world outside of North America and western Europe. To obtain information in lesser known places, a combination of approaches will likely be most effective. Rapid sampling efforts could be applied to identify areas that are modeled as ecologically sensitive spots with unusual rates and levels of potential MeHg availability. Therefore, focused ecosystem-level assessments could then be designed and implemented to characterize the sources, causes and extent in the availability of MeHg to high trophic level fish and wildlife (i.e., trophic level 4 or higher). More data, and the synthesis of available data from these understudied regions of the world is necessary to provide a better understanding of potential risks associated with MeHg availability in the environment. Based on global and regional risk models (Gustin et al., 2016; Sunderland et al., 2009), the potential impacts on ecological and human health will vary greatly across time and space, and thereby require a need for establishing a baseline of environmental conditions of Hg from which the effectiveness of future interventions related to the Minamata Convention can be evaluated (Evers et al., 2016). There is already evidence that suggests reductions in Hg air emissions can have positive impacts in lowering fish Hg concentrations. In the northeastern United States localized rapid reductions of Hg emissions from municipal and medical incinerators, reflecting a change of ~6000 pounds of Hg emitted annually to negligible levels within a few years, were related to local downwind declines of over 50% of the Hg concentrations in fish and bird bioindicators (Evers et al., 2007; Hutcheson et al., 2008). Even within large ocean basins, declining fish Hg concentrations have been attributed to decreasing Hg inputs (e.g., in Atlantic Bluefin Tuna Thunnus thynnus in

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the North Atlantic Ocean; (Lee et al., 2016)). While declines in fish Hg concentrations may be observed in some parts of the world, current projections for atmospheric Hg emissions in Asia and associated deposition in the western Pacific Ocean point to decades of increases in basin-wide Hg concentrations (Sunderland et al., 2009) and these increases may already be reflected in certain pelagic marine fishes of the North Pacific Ocean (e.g., Yellowfin and Bigeye Tuna; Drevnick et al., 2015; Drevnick and Brooks, 2017, respectively). These and other local, regional, and global assessments will become increasingly important contributions for decisionmaking through the Minamata Convention to track and further reduce the current widespread adverse impacts of MeHg measured in fish, wildlife and human communities around the world. CRediT authorship contribution statement David G. Buck: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing - review & editing, Visualization, Supervision, Project administration, Writing original draft. David C. Evers: Conceptualization, Methodology, Investigation, Resources, Writing - review & editing, Supervision, Project administration, Funding acquisition, Writing - original draft. Evan Adams: Validation, Formal analysis, Writing - review & editing, Visualization, Writing - original draft. Joseph DiGangi: Methodology, Project administration. Bjorn Beeler: Conceptualization, Methodology, Resources, Supervision, Project administration, Funding acquisition. Jan Samánek: Methodology, Investigation, Supervision, Project administration. Jindrich Petrlik: Methodology, Investigation, Supervision, Project administration. Madeline A. Turnquist: Methodology, Validation, Formal analysis, Investigation, Data curation, Supervision, Project administration. Olga Speranskaya: Methodology, Investigation, Resources, Funding acquisition. Kevin Regan: Methodology, Data curation. Sarah Johnson: Methodology, Software, Data curation, Project administration. Declaration of Competing Interest The authors declare no conflicts of interest. Acknowledgements We thank the extensive network of dedicated non-governmental organizations that participate in the International POPs Elimination Network (IPEN) around the work who participated in the sample collection for this project. These organizations, organized alphabetically by country, include: EDEN Center (Albania), ESDO (Bangladesh), Center for Environmental Solutions (Belarus), Environmental Research Institute (Belize), CREPD (Cameroon), Universidad del Norte (Colombia), ISACI (Cook Islands), IRET (Costa Rica), Arnika (Czech Republic), Toxics Link (India), WALHI Central Sulawesi and Balifokus (Indonesia), CACP (Japan), IndyAct (Lebanon), Eko-svest (Macedonia), Ecología y Desarrollo Sostenible en Coatzacoalcos, A.C. and Centro de Análisis y Acción en Tóxicos y sus Alternativas - CAATA, (Mexico), ECOTOX (Moldova), CEPHED (Nepal), ALTER VIDA (Paraguay), Eco-Accord (Russia), Centre for Environmental Justice (Sri Lanka), EARTH (Thailand), RAPAL (Uruguay), and ACAT (USA). The study was supported by the Swedish Environmental Protection Agency as well as internal funding from IPEN and BRI. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.scitotenv.2019.06.159. References Åkerblom, S., Bignert, A., Meili, M., Sonesten, L., Sundbom, M., 2014. Half a century of changing mercury levels in Swedish freshwater fish. AMBIO 43, 91–103. https://doi. org/10.1007/s13280-014-0564-1.

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