Environmental specimen banks as a resource for mercury and mercury isotope research in marine ecosystems

Abstract

Environmental specimen banks (ESBs) have been a fundamental tool for many nations to monitor contaminant temporal and spatial trends, study fate and transport, and assess the severity and risks of pollution. The specimens archived in ESBs are among the longest time-series, most geographically robust, and highest integrity samples available for performing environmental research. Mercury (Hg) remains one of the world's most ubiquitous environmental contaminants, and ESBs have played a prominent role in Hg research. Historically this has involved measuring concentrations of Hg species in various environmental matrices, but the emerging field of Hg stable isotope research provides a new analytical approach that can augment these traditional techniques. Signatures of Hg isotope fractionation have been effectively used for source apportionment and for elucidating Hg biogeochemical cycling. As the research surrounding Hg stable isotopes continues to mature, ESBs can play a useful role in analytical quality control, provide a robust and economical sample archive to expand and diversify the inventory of Hg isotope measurements, and be used to develop and test hypotheses to evaluate whether broadly prevailing paradigms are supported. Samples archived in ESBs are available for request by external collaborators in order to perform high impact research, and should be utilized more effectively to address emerging global environmental concerns.

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Rusty D. Day

Dr Rusty Day has been a research biologist with the National Institute of Standards and Technology (NIST) at the Hollings Marine Laboratory since 2003. He received his BS in Biology from UNC Chapel Hill, MSc in Marine Biology from the College of Charleston, and PhD in Environmental Chemistry from the University of Pau. Much of his research has focused on mercury biomonitoring, wildlife toxicology, and mercury stable isotope chemistry. He is currently developing banking and research programs for corals and using elemental/isotopic signatures in marine biota as environmental tracers.

Paul R. Becker

Paul R. Becker, a Research Biologist and Director of NIST operations at the Hollings Marine Laboratory, has been involved in environmental specimen banking for almost 30 years. He was responsible for establishing NIST’s Marine Environmental Specimen Bank and developing several ongoing specimen banking programs that have international significance. These include: Alaska Marine Mammal and Tissue Archival Project (1987), National Marine Mammal Tissue Bank and its associated Marine Mammal Health and Stranding Response Program (1992), Seabird Tissue Archival and Monitoring Project (1999), and the recent expansion of NIST banking activities into the Pacific Ocean through the NIST Pacific Islands Program (2010).

Olivier F. X. Donard

Dr Olivier F. X. Donard is a director at the French CNRS working at the University of Pau (France). He is the head of the “Institut des Sciences Analytical et de Physicochimie pour l’Environnement et les Matériaux”. He has pioneered the domain of elemental speciation to unravel the pathways of trace elements in the environment. Currently, he is promoting the cross breeding of elemental speciation and isotopic signature to improve our understanding of origin and reaction of trace elements. He is also promoting time integrated environmental research using Specimen Bank archival storage samples.

Stephen A. Wise

Stephen A. Wise is Associate Chief of the Chemical Sciences Division at the National Institute of Standards and Technology (NIST) in Gaithersburg, Maryland. He received a B.A. in Chemistry from Weber State University and a PhD in Analytical Chemistry from Arizona State University. During his career at NIST, he has been involved in the development of Standard Reference Materials (SRMs) for organic constituents in environmental, clinical, food, and dietary supplement matrices. He has been involved in environmental specimen banking at NIST since its beginning in the late 1970s and was responsible for establishing the marine mammal specimen banking programs.

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Environmental impact

This article reviews how environmental specimen banks (ESBs) have historically been used to enhance environmental research and management, and provides insight into how investigators can utilize archived samples in ESBs for innovative research applications on a global scale. Mercury is used as a case study, exemplifying how the temporal, geographical, and taxonomic robustness of sample archives in ESBs are an invaluable resource to advance research in the emerging field of mercury stable isotope chemistry.

History and rationale for environmental specimen banking

The environmental sciences use a wide variety of tools and approaches in order to characterize ecosystems, study how they function, and assess the impacts of anthropogenic activities on ecosystem condition. Environmental specimen banks (ESBs) are facilities that are dedicated to the systematic, long-term archival of biotic or abiotic environmental samples for monitoring and research. Various types of environmental samples have been collected and used in past centuries to study environmental processes and how they change through time.¹ Zoological and museum specimens were among the first collections that were utilized to retrospectively document time trends in the environment using archived samples such as feathers, fish, bones, teeth, or hair.¹ Recognition of the importance of ESBs as a formal and dedicated monitoring and research tool gained momentum in the late 1970's, punctuated with the International Workshop on Monitoring Environmental Materials and Specimen Banking in Berlin in 1978.² In the decades following this workshop, ESB programs were initiated around the globe and developed more sophisticated and well-defined methodologies to ensure the integrity of specimens. Twenty-three formal ESBs now exist in 15 countries (Table 1), with new facilities in other countries currently under development. The underlying premise is to collect and process samples without altering their original composition, and preserve them in a stable environment over extremely long time-frames. This typically means processing samples under clean room conditions using carefully chosen materials and detailed protocols, storing specimens under cryogenic temperatures, and using well-designed sample inventory and tracking systems.

Table 1 List of formal environmental specimen banks (ESBs) that currently exist or are under development

Country	Name	Location
Canada	Canadian Wildlife Service Specimen Bank	National Wildlife Research Centre
Canada	National Aquatic Biological Specimen Bank and Database	Canada Centre for Inland Waters, Environment Canada
China	Yangtze Environmental Specimen Bank	Tongji University, Jiaxing
Denmark	Tissue and Data Bank for Greenland	National Environmental Research Institute
Denmark	Faroe Islands Environmental Specimen Bank	Environment Agency, Traðagøta, Faroe Islands
Finland	Paljakka Environmental Specimen Bank	Finnish Forest Research Institute
France	Observatoire de Recherche sur l'Environnement (ORQUE)	University of Pau
France	ANDRA Observatoire Perenne de l'Environnement (OPE)	University of Pau
France	Mytilothèque	French Research Institute for Exploitation of the Sea (Ifremer),
Germany	German Environmental Specimen Bank	Federal Environment Agency, Dessau-Roßsslau
Italy	Mediterranean Marine Mammal Tissue Bank	University of Padua
Italy	Antarctic Environmental Specimen Bank (BCAA)	Genoa
Japan	Environmental Specimen Bank for Global Monitoring (es-Bank)	Ehime University
Japan	Time Capsule for Environment and Endangered Wildlife	National Institute of Environmental Studies
Norway	Norwegian Environmental Specimen Bank	Oslo Centre for Interdisciplinary Environmental and Social Research
Spain	Biscay Bay Environmental Biospecimen Bank	University of the Basque Country, Plentzia
Spain	Environmental Specimen Bank of Galicia	University of Santiago De Compostela
South Africa	Biological Resource Bank	National Zoological Gardens
South Korea	National Environmental Specimen Bank	National Institute of Environmental Sciences, Seoul
South Korea	South Sea Research Institute (SSRI)	Geoje
Sweden	Environmental Specimen Bank	Swedish Museum of Natural History
UK	National Fish Tissue Archive	Centre for Ecology and Hydrology
USA	Marine Environmental Specimen Bank	National Institute of Standards and Technology Charleston, SC
USA	CDC and ASTDR Specimen Packaging, Inventory, and Repository	Centers for Disease Control and Prevention
USA	Alaska Frozen Tissue Collection	Museum of the North, University of Alaska, Fairbanks

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In general, archived samples can be used for a variety of purposes, including clinical, medical, and preservation of cell-lines or genetic diversity. Historically, most environmental specimen banks have focused primarily on environmental contaminants. The collection of samples for ESBs may be performed by dedicated field teams, or using networks of trained volunteers with partner institutions or citizen scientists who are geographically dispersed. Specimens collected for banking are typically processed and divided into aliquots which can be individually retrieved for different analyses. Most aliquots will be stored for long periods before they are analyzed, but coordinating long-term banking with real-time analyses of samples can provide valuable data that can feed back into further refining the design of long-term specimen banking programs. These real-time data may be informative for defining the frequency of collection, the types and numbers of specimens needed, and the most appropriate spatial distribution of sampling in order to provide samples that are representative of the diverse conditions in the environment.^3,4 Sample aliquots in ESBs are not only intended for internal use by ESBs and their immediate monitoring or research objectives. Formal tissue access policies typically exist that provide a mechanism to distribute aliquots of archived samples to external researchers who have an appropriate scientific objective. Considering the funding constraints that the research community faces in today's financial environment, leveraging ESBs to conduct research is an economical and under-utilized alternative to obtaining new funding and conducting costly field studies.

ESBs have been created to meet a variety of goals, but some of the most commonly stated objectives for ESBs regarding chemical contaminants are (1) to determine baseline concentrations, (2) to perform analytical quality control, (3) to evaluate compliance with environmental regulations, (4) to assess exposure and health risks to humans or wildlife, (5) to document temporal changes in contaminants due to policy actions, the emergence of new chemical contaminants, or environmental crises, and (6) to study the spatial distribution, transport, and cycling of contaminants. The types of environmental samples archived in ESBs are as diverse as the objectives for which they are used and the countries from which they come. The species, tissue, or matrix that is selected for banking should be carefully chosen so that it integrates the particular environmental compartment, time period, or geographic region of interest. The criteria for selecting appropriate monitoring matrices have been discussed extensively since formal environmental monitoring programs began.² In some cases the choice of what matrix to collect, analyze, or bank may be easy. For example, if the goal is to monitor contaminant concentrations in fish in order to assess human health risks from seafood, then banking and analysis of routinely consumed fish species is clearly required. Similarly, if the mandate is to monitor water or sediment to assess compliance with regulatory guidelines, then these specific matrices must be banked and/or analyzed. If an animal health assessment is the primary objective, then the target species may be selected based on their conservation status and the severity of the presumed threats. The aforementioned examples refer to scenarios where the species or matrix (water or sediment) is in itself the primary target. However in other cases the goal may be to use a biomonitoring species as a passive or active sampling tool to assess the relative differences in environmental contamination in a given area or for a discrete period of time.

Biomonitoring, when an organism is used to integrate the bioavailable fraction of a contaminant in the environment, is perhaps the most common type of contaminant field study. Discrete research efforts have collected and analyzed countless species and declared them to be a potential indicator species. However candidate species for systematic real-time monitoring or long-term archival in ESBs should be thoroughly vetted to ensure they have suitable life history and physiological characteristics to integrate the specific segment of the environment and temporal period to meet the desired goals. Species whose biology and ecology have been thoroughly studied are the most appropriate candidates. Even well-studied species may require additional experimental work such as pharmacokinetic studies for dynamic tissues like blood,^5–8 seasonal sampling to assess temporal biases,^9,10 or repeat sample collections to assess the methodological biases of sample collection techniques. Since the goal is to use a selected matrix as an indicator of the contamination in a given environment, other species or matrices from the same ecosystem should be simultaneously sampled during the program design phase in order to corroborate that the trends observed in the indicator species are representative of the environment at large. For example, temporal Hg trends in Germany's major rivers were corroborated using both bream and mussels.¹¹

Systematic monitoring of contaminants in the environment will continue to be a priority for managers, and ESBs can serve as valuable resource toward this goal. ESB sample inventories are under-utilized for research by external collaborators and efforts should be made to improve visibility and increase interactions. Considering the global nature of today's environmental challenges, broad-scale collective efforts utilizing ESBs may provide an effective research tool. The following sections provide a concise review of Hg as a global pollutant, highlight the strengths of ESBs for contaminant research, and discuss factors that should be considered when selecting target matrices and designing banking/monitoring programs. The discussion will focus on the use of ESBs for Hg research, with particular emphasis on the potential utility of banking and biomonitoring in the emerging field of Hg stable isotope research. We will focus primarily on marine ecosystems, and conclude with a discussion of the challenges and limitations for using ESBs for environmental research.

Mercury as a global pollutant

Mercury (Hg) has long been recognized as one of the most ubiquitous and toxic contaminants in the environment, and a serious threat to the health of humans and wildlife. A wide variety of modes of Hg toxicity have been demonstrated in mammals, birds, fish, and reptiles including neurotoxicity, impaired growth and development, reduced reproductive success, liver and kidney damage, immunomodulation, genotoxicity, and endocrine disruption.^12–16 The primary route of MeHg exposure for humans and wildlife is from consumption of contaminated aquatic organisms. In the United States 76% of fish consumption advisories in 2003 were due at least in part to Hg, for a total of 5 289 020 hectares of lakes and 1 986 081 river miles.¹⁷ Unlike many legacy compounds like DDT or PCBs that have been phased out of use in many countries and are declining in the environment, Hg levels have not systematically declined, and have remained stable or increased in many areas.¹⁸ Based on the reliance of much of the world population on fossil fuels for electricity, Hg will likely remain a contaminant of concern for decades to come. Unlike man-made contaminants, Hg is a naturally occurring heavy metal that is part of the Earth's geology and is released into the hydrosphere and atmosphere through normal environmental processes such as weathering, erosion or geothermal activity. While anthropogenic activity does not create new Hg, it does dramatically alter the distribution and cycling of Hg between the lithosphere and more bioactive environmental compartments. Since Hg is present in most geogenic materials, human activities that accelerate erosion, such as mining, will accelerate the liberation of Hg from geogenic reservoirs. Mining of Hg-rich ores such as cinnabar, or gold mining that uses Hg for amalgamation are significant sources of Hg pollution to local environments.^19,20 Mercury has numerous industrial applications such as chlorine production, fluorescent lights, and in the medical and electronics industry. Mercury from these applications may be discharged as waste directly into local watersheds, or released into the atmosphere through medical or refuse incineration, but the largest anthropogenic source of Hg is fossil fuel combustion.¹⁸ Once released into the environment, Hg undergoes complex chemical speciation, biogeochemical cycling, and isotope fractionation which has been thoroughly reviewed elsewhere.^21,22 While in aquatic systems, Hg may undergo biotic or abiotic methylation, to form methylmercury (MeHg). The MeHg species is more lipophilic and toxic than inorganic Hg (iHg), and is efficiently bioaccumulated by organisms and biomagnified up aquatic food webs where concentrations and toxicity are highest at upper tropic levels.

Global sources of Hg emissions have decreased in North America and Europe but are rapidly increasing in developing nations in Asia.¹⁸ Given the propensity for extended residence times and long-range transport,^23,24 atmospheric Hg pollution is a global phenomenon. Increases in emissions from distant Hg sources means that Hg concentrations in local environments may not be immediately responsive to local or regional changes in emissions. This presents a challenge for those charged with assessing the degree to which new management policies improve environmental conditions. Multi-decadal monitoring and specimen banking programs combined with state-of-the-art analytical tools offer an effective means to meet these challenges.

Mercury pollution has traditionally been monitored by measuring the concentrations of Hg species in biotic and abiotic matrices. However the advent of multi-collector inductively coupled plasma mass spectrometry (MC-ICPMS) has led to a growing number of studies documenting natural variability of Hg stable isotope fractionation in environmental samples. Isotope systems of other elements have been used for decades for forensics, source apportionment, or to study environmental processes. However the high atomic mass of Hg results in a much lower relative mass difference between isotopes compared to lighter elements, and a resulting smaller degree of mass dependent isotope fractionation. Therefore the small differences in Hg isotope abundances in natural samples cannot be adequately resolved without the high precision isotope ratio measurement capability of MC-ICPMS. Mercury isotope ratios are reported using delta (δ) notation, which references all measured isotope ratios to a universally used delta standard (NIST SRM 3133), and provides a relative measure of the degree of Hg mass-dependent fractionation (MDF). Mercury is among a small group of elements which also exhibits mass-independent fractionation (MIF) that is based on the parity (odd versus even) of the isotope, which is reported using capital delta (Δ).²⁵ One area of research using Hg isotopes has been source apportionment, which distinguishes Hg from a given source location or source type from ambient Hg by measuring the distinct Hg isotope signature. Various geologic and environmental matrices have been characterized to inventory the isotope signatures of different types of source materials, and document the ranges in Hg MDF and MIF in materials around the globe. This has included host rocks, Hg-bearing minerals, hydrothermal fields, sediments and soils,^26–28 coal,²⁹ and aquatic biota.^30–36 The Hg isotope signatures in these materials were recently reviewed in Yin et al.²⁶ and Sonke,²² and highlight distinct Hg MDF and MIF in a variety of source materials (Fig. 1). Gradients in Hg concentrations and isotope signatures have been associated with Hg point sources such as chlor-alkali plants,³¹ atmospheric Hg emissions,^37–39 mines,^19,20 or natural geogenic sources.⁴⁰ To improve the utility of isotopic source apportionment we need to expand and improve data inventories of Hg MDF and MIF values for a variety of sample types and locations. This is one function where the robust sample archives in ESBs can play a particularly useful role.

Fig. 1 Review of (A) δ²⁰²Hg, representing MDF,²⁶ and (B) Δ¹⁹⁹Hg, representing MIF,²² reported in various types of natural samples and the number of observations for each. ESBs represent a diverse and cost effective means to expand the body of work describing the natural variability of Hg isotope fractionation in the environment.

The cycling of Hg in marine systems has been thoroughly reviewed elsewhere,²¹ and shows the complexity of Hg dynamics in the environment and the lack of adequate data to fully describe these processes. Another application of measuring Hg stable isotopes in the environmental is to study Hg biogeochemical cycling. Isotope fractionation has been observed in numerous physical, chemical, and biological transformations of Hg, including redox reactions (photolysis, microbial, or chemical pathways), evaporation/condensation, volatilization, methylation, absorption, and diffusion.^{32,34,41–45} MDF is the most common form of fractionation, and is driven by small differences in physicochemical properties such as diffusivity and chemical bond strength that results from isotopes having slightly different atomic masses. Some elements such as oxygen, sulfur, and Hg also undergo MIF, which is caused by differences in isotope parity (even versus odd isotopes) instead of mass. MIF is induced by the magnetic isotope effect or nuclear field shift effect which causes preferential enrichment/depletion of odd and even isotopes during some chemical processes, particularly those involving radicals. Photodegradation of methylmercury (MeHg) and photoreduction of inorganic mercury (iHg) have been experimentally shown to induce MIF in the presence of organic ligands.^32,46 The photoreduction of iHg into more volatile Hg⁰ is an important processes mediating the exchange of Hg species between aquatic and atmospheric reservoirs,⁴⁷ and is believed to be the most important mechanism inducing the Hg MIF that has been observed in aquatic biota.^22,32 Measuring Hg fractionation in key compartments of the environment and using experimentally derived rate constants for various fractionation reactions makes Hg isotopes a new tool for modeling global Hg cycles.²²

Utility of ESBs

Analytical quality assurance/quality control

The analytical instrumentation and methodology used to quantify environmental contaminants is constantly evolving as new technology and innovations are developed. This evolution invariably improves the reliability of more recent data, however it may create potential biases when meta-analyses are performed that include different analytical methods. This may be particularly problematic for very long-term datasets that transcend periods where substantial changes in analytical practices occurred. Ideally all environmental samples that are analyzed are accompanied by matrix-matched certified reference materials (CRMs) with certified concentrations for analytes of interest. However, readily available matrix-matched CRMs may not exist for some sample types, which could introduce undetectable matrix-based analytical artifacts during the measurement of environmental samples. CRMs are typically measured by metrology laboratories using multiple independent methods that represent the best available technology at that time. However, these methods too may change over time, and the inventories of some CRM materials may be consumed and replaced by new materials, providing little opportunity to directly compare analytical methods and sample stability in the same material over very long time periods. Some monitoring and banking programs, such as NOAA's National Status and Trends Mussel Watch, are supported by the production of matrix-matched CRMs (e.g., mussels and oysters) that are also archived for long-term QA/QC and stability testing. This provides a high degree of long-term quality assurance/quality control (QA/QC) while avoiding the consumption of precious environmental samples through repeated reanalysis.

Since the modern conception of environmental specimen banking, the value of ESBs for maintaining QA/QC has been recognized. Real-time monitoring programs without some specimen banking component may have a more difficult time confirming the long-term continuity of datasets. This is particularly important where significant analytical advances have been made in a field. Total Hg can be one of the more difficult elements to quantify because of high reactivity, volatility, and long wash-out times. However cold vapor and combustion sample introduction techniques have improved these measurements and increased sample throughput for total Hg quantification, and this measurement is now routine for a wide range of matrices. The past decade has seen significant measurement improvements for organo-Hg species (methylmercury, ethylmercury) using hyphenated techniques such as GC-ICPMS and speciated isotope dilution.⁴⁸ This method allows the inter-conversion of Hg species during sample preparation to be tracked, and corrected, eliminating a significant source of measurement error for some types of sample matrices. Archived samples that were previously analyzed using less precise methods can now be reanalyzed to ensure method biases did not contribute to the observed trends.

The relatively recent availability of MC-ICPMS has created a growing field of research on Hg stable isotopes. The relatively heavy mass of Hg means that differences in atomic mass among the seven Hg stable isotopes are small relative to the overall mass. Therefore the degree of Hg isotopic fractionation in natural samples is small (maximum range of ≈8‰, and typically much smaller within studies)^26,49–52 compared to lighter isotopes. The high instrumental precision of MC-ICPMS has proven to be adequate to discriminate the variability in Hg isotope ratios in environmental samples. Perhaps the largest analytical challenges stem from the same properties that made Hg problematic for total elemental analysis and speciation; its reactivity. The most commonly used sample preparation method for measuring Hg isotope ratios is an acid digestion (often microwave assisted) to fully oxidize all MeHg into Hg²⁺ followed by a cold vapor sample introduction by reducing Hg²⁺ to volatile Hg⁰ (typically using stannous chloride). Incomplete digestion or reduction can induce MDF,⁴² so it is critical to confirm the recoveries of these reactions to ensure no analytical artifacts are introduced. The chemical constituents in complex biological matrices can impair the efficiency of cold vapor generation if they are present in the digestant at high concentrations, so samples should be diluted so they are similar in matrix content to the delta bracketing standard. One approach to eliminate the issue of the biological matrix is to perform a separation where the Hg is captured on and re-eluted from a column or amalgamator, but these processes must also be 100% efficient to avoid isotope fractionation artifacts. These matrix-related analytical challenges are compounded by the fact that currently the Hg isotope research community relies on inorganic inter-laboratory comparison standards such as the informally distributed UM-Almaden material for QA/QC validation. QA/QC practices typically include reporting the measured δ^xxxHg and Δ^xxxHg in UM-Almaden compared to the accepted values, but there is rarely any isotopically characterized biological control material (and even more rarely a matrix-matched material) measured in conjunction with unknown environmental samples to verify the validity of measurements. Inevitably, the analytical methodologies being used in this field will also continue to evolve and improve. Proper archival of samples in ESBs from defining studies being conducted now will allow future reanalysis and verification of findings using new and improved methods, thereby further advancing this field using the most accurate data possible.

Environmental compliance/management/remediation/response

The well-documented protocols used by ESBs for sample collection, processing, and analyses make them useful tools for environmental programs where management, policy, or litigation action may be involved. This may include permitting, risk assessment, remediation, environmental impact assessments, or responses to major environmental crises. Some ESBs have explicitly stated that one of their primary functions is to monitor and archive samples in order to assess and document compliance with environmental regulations. The German ESB has such a mandate, and collects and analyzes biota, suspended particulate matter, or water for comparison to legislated Hg Environmental Quality Standards (EQS).¹¹ Thus far the preferred tissues to monitor surface waters for this purpose have been bream (Abramis brama L.), zebra mussels (Dreissena polymorpha), and suspended particulate matter. The Galician ESB used macroalgae (Fucus spp.) to monitor Hg and other metals in coastal waters in order to demonstrate compliance with the European Marine Strategy Framework Directive (Directive 2008/56/EC). The Marine Mammal Tissue Bank (NMMTB) housed within the NIST Marine ESB was mandated by the U.S. Congress through the Marine Mammal Health and Stranding Response Act (Public Law 102-587) to cryogenically archive marine mammal tissues. These very pragmatic and clearly defined functions give ESBs strong relevance for both policy development and management action.

Responding to and documenting the impacts and recovery from environmental incidents is also a key role that ESBs can play. The NIST Marine ESB has banked samples collected in response to the Deepwater Horizon/BP oil spill in the Gulf of Mexico in 2010 (personal observation) and the Exxon Valdez Damage Assessment program in 1989.⁵³ Thorough documentation of collection and processing protocols and chain of custody control is critical in these scenarios where samples may be used as evidence in court proceedings to assess financial reparations. In the aftermath of environmental incidents, there is often a realization that archived samples from before the crisis that can serve as a baseline do not exist. This makes assessing damages and documenting recovery difficult.⁵⁴ These lessons provide further justification to maintain, or expand, a comprehensive archive of samples even when funding is not available for real-time analyses.

While Hg is not a priority contaminant in oil spills, other industrial activities such as chlor-alkali plants, gold and Hg mining, and coal-fired power plants are important point sources of Hg. Releases from these point sources are less acute, however systematic sampling prior to (e.g., during permit requests), during, and after these operations allows for an assessment of the Hg pollution associated with these activities. Establishing a definitive link between a presumed Hg source and the ultimate environmental sink can be challenging because of the ubiquitous nature of Hg pollution and the myriad of factors that control its fate and bioavailability. Documenting that elevated Hg concentrations are temporally or spatially associated with a point source provides one line of evidence to make this link. Mercury stable isotope data provide another means to chemically differentiate Hg from a point source from ambient background Hg present in the region. This has been successfully done with mine tailings,¹⁹ coal-fired power plant emissions,³⁷ and chlor-alkali plant pollution³¹ due to the distinct isotopic patterns in the Hg source materials. Using archived samples for isotopic source apportionment of Hg provides a promising new tool for use in environmental impact assessments.

Health risks to humans and wildlife

The impetus for environmental monitoring and regulations is to protect humans and wildlife from the adverse effects of chemical contaminants. The degree of emphasis on human tissue collection and archival in ESBs varies globally. In some cases, agencies such as the U.S. Center for Disease Control whose explicit mandate is human health, take a leading role in the archival of human tissues. However human liver samples were one of the earliest samples housed in the NIST Marine ESB, and the German and Japanese ESBs have ongoing programs archiving blood, urine, and hair for Hg analysis. One underlying assumption is that higher Hg contamination in a local environment will result in higher exposure and health risks to humans in that area. Since food (in particular seafood) is the main route of MeHg exposure, the link between local environmental Hg contamination and human exposure will be strongest for subsistence fishermen in rural regions and indigenous peoples who consume locally caught species instead of imported food products. Monitoring Hg exposure in these demographics provides data on those most closely linked to the local environment and those with the highest Hg exposure and toxicological risks. Other types of human banking and monitoring programs may be more appropriate for assessing occupational, medical, or atmospheric Hg exposure. In the U.S., the FDA has the regulatory responsibility for monitoring food safety, and plays a leading role in screening commercial seafood products such as fish for Hg. However these efforts rarely include subsistence food items such as marine mammal tissues that have Hg concentrations far higher than typically reported in fish.^55,56 ESBs monitoring programs have targeted liver, kidney, and muscle from commonly hunted marine mammals, and eggs from seabirds that make up a routine part of human diets in some northern regions.^55,57,58

Wildlife species with high Hg concentrations are also subject to toxicological risk themselves. In practice, many monitoring programs target species that have relevance for both human and wildlife health. ESBs that include wildlife health among their primary objectives will choose target species for banking that have the highest contaminant exposure, greatest toxicological sensitivity, and most tenuous conservation status. In some cases this means working with protected species that have restricted access to tissues and limited or no direct toxicological data to establish impact thresholds. Instead, data from model species are considered in conjunction with correlative field studies of exposure and health indicators, and in vitro and in vivo experimental work to establish a weight of evidence approach for assessing toxicological risk.¹³ Field-based health assessments, lab-based toxicology, and specimen banking and monitoring can be combined to construct a population-level risk assessment like that performed for persistent organic pollutant exposures in bottlenose dolphins in the southeast U.S.⁵⁹ Samples from ESBs can provide baseline data on contaminant exposure in species of interest to assess risk broadly and determine where additional research is merited.^1,55

Fate, transport, and biogeochemical cycling

Monitoring and banking programs inevitably incorporate a spatial component to their designs. Generating baseline Hg concentration data, assessing toxicological risk, and longitudinal sampling at multiple sites all lend themselves to comparing geographic patterns in Hg contamination among sites. Sampling locations for ESB programs may reflect localized urban or industrial activity or relatively pristine conditions. Monitoring and banking performed on this scale allows for meaningful comparisons within or among watersheds to investigate anthropogenic sources and natural transport and biochemical cycling within regions. This type of approach is not unique to ESBs, as discrete research projects often focus on localized scales to address specific research questions. However, the longevity and broader scope of ESB programs means that the spatial scales at which samples are collected and research questions are asked can be much larger. The Japanese es-Bank has used archived samples collected worldwide to determine regional and global distributions and trends for various contaminant classes in developed and developing nations.⁶⁰ The NIST Marine ESB has seabird egg samples from the Seabird Tissue Archival and Monitoring Program (STAMP) that span from the Arctic Ocean to the Hawaiian islands. These banked samples have recently been used to reveal geographic gradients in Hg fractionation related to photochemical processes and to Hg sources (Fig. 2).^36,40 Seabird eggs from the families Alcidae and Laridae are globally distributed and are routinely banked in the U.S., Canada, Germany, Denmark, Sweden, and Norway. The availability and high quality of archived seabird eggs make it possible to conduct a truly global study on Hg isotopes, butyltins, or other contaminants of concern by utilizing the international network of ESBs (Fig. 3). A similar approach could also be performed using other broadly distributed and commonly banked taxa that represent lower and high trophic levels, such as bivalves and delphinids. There are approximately 300 U.S. Mussel Watch sites that span the entire continental U.S. coastline, Great Lakes, Alaska, Puerto Rico, and Hawaii.⁶¹ European ESBs have excellent broad-scale coverage of this continent using bivalves, and specimen banks in Japan have numerous bivalve collection sites in Asia. Delphinids have a global distribution and a broad latitudinal range and are also routinely banked during strandings, harvest, and health assessments. Tissues from delphinids in the Atlantic have been collected for the NOAA Marine Mammal Health and Stranding Response Program from New Brunswick, Canada to the Gulf of Mexico.⁶² The Japanese es-Bank has an extensive archive of delphinid tissues throughout the Pacific and Indian Oceans (Table 2). Like seabirds, delphinids have ranges that integrate a broad area, and may be representative of regional conditions (opposed to highly localized) in large-scale geographic studies.

Fig. 2 Two studies using Hg isotope fractionation in ESB samples to study biogeochemical cycling and sources of Hg. (A) Influence of sea ice on Hg MIF (indicated by Δ²⁰¹Hg) in seabird eggs across the Alaskan Arctic and subarctic.³⁶ Sea ice in the Arctic acts as a barrier to Hg photochemical reduction and exchange between oceanic and atmospheric reservoirs, resulting in lower Hg MIF. (B) Gradient in Hg MDF (indicated by δ²⁰²Hg) and Hg concentrations in seabird eggs from the coastal embayment of Norton Sound to oceanic colonies in the Bering Sea⁴⁰ (reprinted with permission from ref. 40. Copyright 2012 American Chemical Society). Elevated Hg concentrations in Norton Sound are the result of high fluxes of terrestrially derived Hg from the Yukon River and other watersheds during the spring melt. This is supported by the more terrestrial Hg isotopic source signature, higher Hg concentration, and terrestrial carbon signature in Norton Sound compared to offshore.

Fig. 3 Map showing sample collection locations for one of the more geographically robust tissues archived in specimen banks worldwide; seabird eggs. Red markers indicate eggs from gulls of the family Laridae, and orange markers indicate eggs from the family Alcidae. Upward triangles are from the NIST Marine ESB, circles from the Canadian Wildlife ESB, downward triangles from the Tissue and Databank for Greenland, diamonds from the Swedish ESB, squares from the Norwegian ESB, and X's from the German ESB.

Table 2 Compilation of the most prominent long-term sample sets currently available in ESBs worldwide

Species	Tissue	Year	Ecosystem	Region	Publications
NIST Marine ESB
Mussels, Mytilus spp., Dreissena spp. and oysters, Crassostrea virginica	Soft tissue	1986–	Coastal/estuarine and Great Lakes	Continental USA, Hawaii, Alaska	http://ccma.nos.noaa.gov/about/coast/nsandt/musselwatch.aspx
Common murre, Uria aalge, and thick-billed murre, U. lomvia	Egg	1999–	Coastal/marine	Alaska, Pacific Northwest, Hawaiian Islands	22, 36, 40, 57 and 75–80
Sea otter, Enhydra lutris	Liver, kidney, serum	1997–2007	Coastal/marine	SE Alaska, Cook Inlet	—
California sea lion, Zalophus californianus	Blubber, liver, kidney	1993–2007	Coastal/marine	California	81
Northern fur seal Callorhinus ursinus	Liver, kidney, muscle	1987–2007	Coastal/marine	St. Paul Island, Alaska	55, 58 and 82–88
Bearded seal, Erignathus barbatus	Liver, kidney, blubber	1989–2007	Coastal/marine	Norton Sound, Chukchi Sea, Alaska	84 and 87–90
Spotted seal, Phoca largha	Liver, kidney, blubber	1991–2007	Coastal/marine	Kotzebue Sound, Alaksa	84 and 90
Harbor seal, Phoca vitulina	Liver, kidney, blubber	1990–2005	Coastal/marine	Cook Inlet, Alaska	55, 87 and 88
Ringed seal, Phoca hispida	Liver, kidney, blubber	1988–2007	Coastal/marine	Chukchi Sea, Norton Sound	55, 82–84, 87 and 89–92
Pacific walrus, Odobenus rosmarus divergens	Liver, kidney, blubber	1993–2006	Coastal/marine	Bering Sea, Chukchi Sea	55, 88 and 92
Harbor porpoise, Phocoena phocoena	Liver, kidney, blubber	1990–2006	Coastal/marine	Northwest Atlantic, (New England, Mid-Atlantic, New Brunwick Canada), Pacific NW	56, 84 and 93
Common dolphin, Delphinus delphis	Liver, kidney, blubber	1996–2007	Coastal/marine	New England (MA) and Mid Atlantic	86
Rough-toothed dolphin, Steno bredanensis	Liver, kidney, blubber	1997–2005	Marine	Florida (GOM, Keys, St. Lucie)	94–97
White-sided dolphin, Lagenorhynchus acutus	Liver, kidney, blubber	1993–2008	Coastal/marine	Massachusetts	56, 84, 93 and 97–99
Bottlenose dolphin, Tursiops truncatus	Liver, kidney, blubber	1997–2010	Coastal/marine	Mid-Atlantic to Gulf of Mexico (most from Carolinas)	100–104
Bowhead whale, Balaena mysticetus	Liver, kidney, blubber	1992–2000	Marine	Chukchi Sea, Alaska	88, 90, 105 and 106
Pygmy sperm whale, Kogia simus	Liver, kidney, blubber	1998–2010	Marine	North Carolina, South Carolina, north/mid Florida	107
Beluga whale Delphinapterus leucas	Blubber, liver, kidney	1989–2006	Marine	Alaska (Cook Inlet, Chukchi Sea)	55, 82–84, 88–90 and 108–112
Pilot whale Globicephala melas	Liver, kidney, blubber	1990–2005	Marine	Northwest Atlantic	56, 84, 93 and 108
Polar bear, Ursus maritimus	Liver, kidney, adipose	1996–2007	Marine	Bering Sea, Chukchi Sea	55, 88 and 91
German ESB
Sediments		1991	Limnic	Germany	—
Brown algae, Fucus spp.	Whole	1982–	Coastal/estuarine	North Sea, Baltic Sea	63 and 113
Zebra mussels, Dreissena polymorpha	Soft tissue	1985–	Limnic	Germany	11
Common mussel, Mytilus edulis	Soft tissue	1985–	Coastal/estuarine	North Sea, Baltic Sea	63 and 113
Bream, Abramis brama	Muscle, liver	1985–	Limnic	Germany	11 and 114–117
Eel-pout, Zoarces viviparus	Muscle, liver	1992–	Coastal/marine	North Sea, Baltic Sea	63 and 118
Herring gull, Larus argentatus	Egg	1988–	Coastal/marine	North Sea, Baltic Sea	63 and 119–121
Swedish ESB
Blue mussels, Mytilus edulis	Soft tissue	1987– (BS), 1980– (NS)	Coastal/marine	North Sea, Baltic Sea	64, 122 and 123
Herring, Clupea harengus	Muscle, liver	1972– (BS), 1980– (NS)	Coastal/marine	North Sea, Baltic Sea	64, 124 and 125
Cod, Gadus morhua	Muscle, liver	1980– (BS), 1979– (NS)	Coastal/marine	North Sea, Baltic Sea	64
Dab, Limanda limanda	Muscle, liver	1981–	Coastal/marine	North Sea	64
Flounder, Platichthys flesus	Muscle, liver	1980–	Coastal/marine	North Sea	64
Perch, Perca fluviatilis	Muscle, liver	1980–	Coastal/marine	Baltic Sea	64
Eelpout, Zoarces viviparus	Muscle, liver	1988– (NS), 1995– (BS)	Coastal/estuarine	North Sea, Baltic Sea	64 and 118
Common murre, Uria aalge	Egg	1968– (BS), 1991– (NS)	Coastal/marine	North Sea (NS), Baltic Sea (BS)	64 and 125–129
Peregrine falcon, Falco peregrinus	Egg	1974–2008	Terrestrial	Sweden	130
Grey seal, Halichoerus grypus	Liver	1974–2008	Coastal/marine	Baltic Sea	131
Canada National Wildlife Specimen Bank
Herring gull, Larus argentatus	Egg	1972– (Atlantic), 1974– (Great Lakes)	Coastal/marine	Canadian Atlantic, Great Lakes	132–136
Thick-billed murre, Uria lomvia	Egg	1975– (PLI), 1993– (HB)	Coastal/marine	Canadian Arctic/Subarctic, Prince Leopold Island (PLI), Hudson Bay (HB)	72, 74 and 137–139
Northern fulmar, Fulmaris glacialis	Egg	1975–	Coastal/marine	Canadian Arctic (PLI)	72, 74, 138 and 139
Black-legged kittiwake, Rissa tridactyla	Egg	1975–	Coastal/marine	Canadian Arctic (PLI)	72, 74 and 137
Ivory gulls, Pagophila eburnea	Egg	1976–2004	Coastal/marine	Canadian Arctic (Seymour Island)	140–142
Black guillemot, Cepphus grylle	Egg	1993–	Coastal/marine	Canadian Arctic (PLI)	Braune, unpublished data
Glaucous gull, Larus hyperboreus	Egg	1993–	Coastal/marine	Canadian Arctic (PLI)	Braune, unpublished data
Double-crested cormorant, Phalacrocorax auritus	Egg	1972–(Atlantic), 1970 (BC)	Coastal/marine	Canadian Atlantic, British Columbia	143
Storm petrel, Oceanodroma furcata	Egg	1971–	Marine	British Columbia	144
Great blue heron, Ardea herodias	Egg	1977–	Coastal	British Columbia	143, 145 and 146
Ancient murrelet, Synthliboramphus antiquus	Egg	1968–		British Columbia	—
Rhinoceros auklet, Cerorhinca monocerata	Egg	1970–	Coastal/marine	British Columbia	142
Osprey, Pandion haliaetus	Egg	1991–		British Columbia	143, 147 and 148
Leach's storm petrel, Oceanodroma leucorhoa	Egg	1968–	Marine	Canadian Atlantic, British Columbia	142
Atlantic puffin,	Egg	1972–	Coastal/marine	Canadian Atlantic	142
Canada's National Aquatic Biological Specimen Bank
Plankton (153 μm)	Bulk sample	1982–	Limnic	Great Lakes	149 and 150
Mysis diluviana	Bulk sample	1981–	Limnic	Great Lakes	149 and 150
Diporeia hoyi	Bulk sample	1983–	Limnic	Great Lakes	149 and 150
Walleye, Sander vitreus	Whole body homogenate	1977– (GL), 2005– (CAN)	Limnic	Great Lakes (GL), Canadian (CAN)	151–154
Lake trout, Salvelinus namaycush	Whole body homogenate	1977– (GL), 2005– (CAN)	Limnic	Great Lakes (GL), Canadian (CAN)	152 and 155–157
Rainbow smelt, Osmerus mordax	Whole body homogenate	1977–	Limnic	Great Lakes	149 and 154
Alewife, Alosa pseudoharengus	Whole body homogenate	1986–	Limnic	Great Lakes	149 and 150
Slimy sculpin, Cottus cognatus	Whole body homogenate	1977–	Limnic	Great Lakes	149 and 150
Japan es-Bank
Black-footed albatross, Diomedea nigripes	Muscle	1998	Marine	Pacific Ocean	—
Laysan albatross, Diomedea immutabilis	Muscle	1998	Marine	Pacific Ocean	—
Northern fulmar, Fulmarus glacialis	Muscle	1999	Marine	Pacific Ocean	—
Black-tailed gull, Larus crassirostris	Muscle	1999	Coastal/marine	Japan	—
Steller's sea-eagle, Haliaeetus pelagicus	Muscle	1994	Coastal/marine	Japan	—
Common cormorant Phalacrocorax carbo	Muscle	2001	Limnic	Japan	—
Striped dolphin, Stenella coeruleoalba	Blubber, muscle, liver, kidney	1978–2007	Coastal/marine	North Pacific, Japan	—
Finless porpoise, Neophocaena phocaenoides	Blubber, muscle, liver kidney	1979–2011	Marine	China, Hong Kong, Japan	—
Pacific white-sided dolphin, Lagenorhynchus obliquidens	Blubber, muscle, liver kidney	1980–2011	Marine	North Pacific, Japan	—
Indo-Pacific humpback dolphin, Sousa chinensis	Blubber, muscle, liver kidney	1989–2001	Marine	India, Bay of Bengal, Hong Kong	—
Japan Time Capsule Program
Bivalves	Soft tissues	1994–	Coastal/marine	Japan	—
Black-tailed gull, Larus crassirostris	Egg	1983–	Coastal/marine	Japan	—

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The ability to look at larger scale patterns is one of the true strengths of ESBs compared to other types of contaminant research efforts. Comprehensive sampling programs combined with detailed documentation and database management allows sample archives to be mined for the most appropriate specimens to infer the distribution, biogeochemical cycling, or isotope fractionation of Hg in the environment. The field of Hg stable isotope research is relatively new, so diverse observations provide valuable data to improve our current understanding of the Hg fractionation in nature. Samples from ESBs are ideal to generate baseline data, provide a broad sense of context, and look for patterns that can frame and test hypotheses regarding the variability and processes that define Hg fractionation.

Temporal trends

One of the primary strengths of ESBs is the ability to conduct retrospective time-trend analyses on samples that have been systematically collected and archived. Changing technology and industry can lead to the release of new classes of contaminants, or create the ability to measure previously undetectable or unknown compounds in the environment. No historical data exists for new and emerging contaminants to compare to current data, so archived samples in ESBs provide the most powerful tool available to retrospectively assess temporal trends. Mercury has been included in routine contaminant monitoring for decades, but the new ability to measure Hg stable isotopes has created a completely new analytical target for which no historical data exist. For traditional Hg concentration studies, shifts in urban or industrial activity, regulatory action, or climate change can create a need to generate temporal data from areas previously considered a low priority. Thoughtfully designed banking programs often include samples that do not merit real-time analysis at the time of collection, but become a valuable resource as patterns in human impacts and research priorities change.

As discussed above, selecting an appropriate sample type is critical to maximizing the power of your monitoring program to detect temporal trends. One of the most important criteria to consider is the time-period through which your sample integrates the ambient environmental Hg. On one end of the spectrum is a water sample which provides a snap-shot of Hg in the aquatic environment. Some complications are that water may exhibit low concentrations and high short-term variability, which means that assessing annual differences may require a tremendous number of samples. On the other end of the spectrum is a sample such as marine mammal liver. Mercury accumulates in this organ over decades of the animal's life, which introduces potential confounding effects related to the individual's life history status if one wishes to evaluate temporal trends. This limits sampling to individuals of similar age and sex, makes assumptions and corrections for bioaccumulation with age, or requires extremely long monitoring durations before a reasonable change in environmental Hg levels can be detected.

The ideal specimen for monitoring annual changes in environmental Hg reflects the Hg assimilated during the same period in the same location each year. This requires knowledge of the behavior, physiology, pharmacokinetics, and life history of the species and tissue in question. Seabird eggs are being used by numerous specimen banks to track Hg temporal trends, and have proven to be very effective at tracking long-term changes in environmental Hg concentrations (Fig. 4A). ESBs currently banking seabird eggs include the Canadian Wildlife Service Specimen Bank,⁵⁴ German Environmental Specimen Bank,⁶³ Environmental Specimen Bank at the Swedish Museum of Natural History,⁶⁴ and the NIST Marine Environmental Specimen Bank.⁴⁰ Many seabird species are income breeders,⁶⁵ so the Hg deposited into eggs each year reflects the Hg consumed in prey by nesting females once they arrive at the breeding grounds. Mercury in bird eggs has been found to reflect Hg in the blood or diet of nesting females,^66,67 and eggs are generally a well characterized tissue for banking. Macroalgae have also been used by ESBs in Poland, Germany, Denmark, and Spain.⁶⁸ Macroalgae have a limited ability to regulate metal ion uptake,⁶⁹ and provide a time-integrated measure of the Hg concentrations in their environment. Macroalgae are also sedentary, widespread, and easy to collect. Other matrices that reflect discrete periods of Hg exposure and may be suitable to bank for temporal studies are plankton, yearling fish, blood, or keratinized structures such as feathers or hair that have known growth patterns. Fish filets (Fig. 4B) and mussels from ESBs have also successfully been used to track temporal trends.^11,61 Since the Hg concentrations in fish filets and mussels may be confounded by bioaccumulation with age, the size/age class that is sampled for each species must be restricted to minimize this effect.

Fig. 4 Temporal Hg trends from ESB samples for (A) thick-billed murres (Uria lomvia) eggs from the Canadian Arctic⁷⁴ and (B) bream (Abramis brama) muscle from a German river.¹¹

The temporal studies discussed above have measured trends in Hg concentrations, but these same matrices may also be suitable to track temporal changes in Hg isotope patterns in the environment. Temporal changes in atmospheric Hg isotope ratios have been reconstructed over century time scales using dated cores from peat²⁸ and lake sediments,⁷⁰ and suggest that anthropogenic emissions have caused a shift to isotopically lighter Hg deposition into aquatic and terrestrial environments. This approach can yield valuable data, however, there is also evidence that varying degrees of diagenesis across these cores may alter the source Hg isotope signature, thereby introducing artifacts in the temporal trend. Therefore corroborating these results with independent observations in other sample matrices would further strengthen these findings. ESB samples that are collected and stored in a manner suitable to preserve the Hg isotope ratios offer a complementary alternative to coring to detect potential temporal evolution of Hg isotope signatures in the environment. Dietz et al.⁷¹ compiled temporal data on Hg in hard structures of biota (teeth, hair, and feathers) from the Arctic that dated back to 1200 to estimate the proportion of anthropogenic Hg in present day biota. These sample types that exist in long-term archives present a valuable opportunity to explore Hg isotope temporal trends, and should be priorities for analysis and continued banking. Samples dating back 3 to 4 decades are more common in ESBs, and have been collected, processed, and stored under more controlled conditions. The Swedish ESB, Canadian ESB, and German ESB contain seabird eggs from the 1960s, 1970s and 1980s,^63,64,72 and the NIST Marine ESB maintains the NOAA Mussel Watch archive that represents one of the longest time-series (1986 to present) in the U.S.⁵³ Mercury isotopes in fish samples from the German ESB have recently been measured, and provide a twenty-year temporal trend documenting changing Hg isotope fractionation in rivers and lakes across Germany (personal communication). Ringed seal livers archived in the NIST Marine ESB were also used to document shifts in Hg isotope patterns in sea ice food webs in the Arctic where multi-year ice has decreased.⁷³ A comprehensive compilation of the most prominent sample archives in ESBs worldwide is shown in Table 2. These collections represent the longest ongoing tissue collections that are currently available for request by external collaborators. Selecting the desired region, ecosystem, species, and tissue from this vast library of samples make it possible to investigate temporal trends of Hg isotopes or other emerging contaminants across numerous environmental compartments.

In addition to monitoring changes in anthropogenic Hg sources, Hg isotope fractionation may be useful for tracking temporal changes in the release and cycling of Hg in natural reservoirs in response to climate change. Seabird eggs from the NIST Marine ESB have been effective in using Hg isotope signatures to differentiate whether Hg in marine food webs is derived from terrestrial geogenic sources versus oceanic reservoirs.⁴⁰ Eggs banked from the coastal embayment of the Norton Sound had a distinct terrestrial/geogenic isotope signature for carbon and Hg that reflects the influence of the Yukon River. As permafrost melts in northern watersheds such as the Yukon, increased erosion and flushing of soils and peat is expected to increase the inputs of fluvial Hg into coastal ecosystems. This process could be quantified by measuring long-term changes in Hg concentrations and Hg fractionation patterns in banked seabird eggs. Rising global temperatures have many other anticipated effects on environmental processes, including diminishing the extent of sea ice coverage. Sea ice acts as a physical barrier to the photodegradation of MeHg and photoreduction of iHg that induce MIF. Livers from Arctic ringed seals banked in the NIST Marine ESB have been analyzed for Hg isotopes, providing a temporal trend dating back to 1988 which spans significant reductions in sea ice (personal communication). Alaskan seabird eggs from the NIST Marine ESB were able to detect significant differences in MIF at northern sites that experience seasonal sea ice compared to southern sites that are ice-free all year.³⁶ As sea ice continues to recede, MIF values in banked seabird eggs and other tissues can be used to monitor increases in Hg photoreduction, and model the resulting increase in Hg evasion from oceanic surface waters to the atmosphere.

Challenges and limitations regarding ESBs

The previous discussion highlights how specimen banks can be a valuable resource to investigate Hg and Hg isotopes in the marine environment. However there are practical limitations to the types and scope of research that can be conducted using archived samples. Retrospective temporal trend analysis is one of the strengths of ESBs, but the time frame of most sample sets is currently limited to several decades. Further hind-casting of Hg in the environment requires the use of alternative methods such as coring or identifying informally but suitably stored samples that pre-date ESBs. For some Hg species and matrices, sample stability over century time-scales could be an issue. There is no data available to inform these concerns, but this would probably be most problematic for Hg species in water, sediment, soil, or surrogate air samples, and least problematic for tissues such as fish or eggs where Hg and MeHg are more strongly bound to proteins. Hg concentration data from archived samples should include moisture content analyses to ensure dehydration of fresh-frozen samples does not induce a systematic temporal bias. Mercury stable isotope fractionation in samples archived for extended periods should not be an issue as long as loss of Hg through transpiration or diffusion through the sample container does not occur. Potential isotope fractionation induced by sample preparation procedures such as cryo-homogenization and freeze-drying should be evaluated in a range of sample types to ensure no measurable artifacts are induced.

There are also several challenges regarding data interpretation that must be considered with studies utilizing ESBs. In the case of biological samples from indicator species, the validity of the data interpretation is only as good as the complementary information and/or assumptions regarding the life history, ecology, migratory behavior, and physiology of the organism. These biological factors influence the spatial and temporal scales which the sample integrates and determine whether the Hg measured in the sample reflects the intended environmental compartment and time period. This means that the selection of the indicator species and the availability of comprehensive complementary biological data are critical to maintaining continuity in long-term sample sets. This may require including routine measurements of key parameters such as carbon and nitrogen stable isotopes, fatty acid profiles, or total protein content that inform us of shifts in foraging behavior or nutritional status that could affect Hg uptake, metabolism, and deposition/binding in target tissues.

There are additional limitations to the types of conclusions that can be drawn from analysis of ESB samples. A well-characterized indicator species and comprehensive complementary data can provide conclusive evidence that differences in Hg concentrations or Hg isotope patterns has occurred in the environment. However our ability to deduce the cause of the observed difference is often limited to inspecting the correlative relationships that we observe with other parameters measured in the same sample, or information that is available about the study site or other components of the ecosystem from which our sample came. Robust, multi-compartmental sampling in an ecosystem is typically too costly and time-consuming to repeat year after year as part of routine banking. Therefore in most cases long-term monitoring and banking programs choose one, or possibly two, key matrices to represent a given environment. The benefits of this stream-lined strategy include the potential for an increased spatial and temporal scope. However one of the costs is that a comprehensive understanding of the processes and interactions that occur are more difficult to discern compared to a discrete, intensive ecosystem-based study. Definitively characterizing mechanisms of Hg biogeochemical cycling, or Hg isotope fractionation using limited correlative data alone is problematic because of the host of biotic and abiotic covariates that exist. Intensive ecosystem-based field campaigns, manipulative field experiments, and controlled laboratory studies are needed to refine our understanding of Hg cycling and fractionation and demonstrate causative relationship between processes and analytical endpoints. To address these limitations, ESBs should consider periodically (every 5 or 10 years) collecting and banking a more comprehensive suite of samples from representative ecosystems/sites in order to “ground-truth” the observations made in key indicators species or matrices, and allow for retrospective investigation of complex ecosystem interactions.

Despite this limitation, designing exploratory studies that mine the vast sample archives in ESBs can provide invaluable data to investigate relationships between Hg and other environmental parameters and to develop and test hypotheses regarding the processes driving these trends. These data can provide a broad context from a variety of systems and frame the objectives of more experimental field and laboratory studies. Research on Hg stable isotopes is fairly new, and therefore this function is particularly important for this field in generating and testing cohesive theories and directing future research. For example, ESBs provide a readily available resource of samples to expand the inventory of Hg stable isotope measurements across a wide range of sample types and locations to evaluate how broadly prevailing hypotheses are supported. A more robust inventory of Hg isotope data is also very informative for characterizing materials for source apportionment and biota that accumulate Hg. It is in the best interests of ESBs managers to promote their sample archives as a resource for external collaborators, have a clearly defined and efficient sample access policy to improve availability for high priority research objectives, and explore how complementary sample collections in ESBs worldwide could be used to investigate global-scale research questions. ESBs represent a particularly valuable resource for researchers in the new area of Hg stable isotopes, and should be more fully utilized for this purpose.

References

1. Environmental Biomonitoring, ed. K. S. Subramanian and G. V. Iyengar, American Chemical Society, Washington, DC, 1997.
2. Monitoring Environmental Materials and Specimen Banking, ed. N. P. Luepke, Martinus Hijhoff Publishers, The Hague, 1979.
3. C. E. Hebert and D. V. C. Weseloh, Ecotoxicology, 2003, 12, 141–151.
4. F. Riget, R. Dietz and M. Cleeman, Sci. Total Environ., 2000, 245, 249–259.
5. S. Bearhop, G. D. Ruxton and R. W. Furness, Environ. Toxicol. Chem., 2000, 19, 1638–1643.
6. F. Fournier, W. H. Karasov, K. P. Kenow, M. W. Meyer and R. K. Hines, Comp. Biochem. Physiol., Part A: Mol. Integr. Physiol., 2002, 133, 703–714.
7. L. R. Monteiro and R. W. Furness, Environ. Sci. Technol., 2001, 35, 739–746.
8. J. A. Schwenter, Master of Science, College of Charleston, 2007.
9. L. Zhang, L. M. Campbell and T. B. Johnson, Environ. Pollut., 2012, 161, 178–184.
10. G. Blanvillain, J. A. Schewenter, R. D. Day, D. Point, S. J. Christopher, W. A. Roumillat and D. W. Owens, Environ. Toxicol. Chem., 2007, 26, 1441–1450.
11. P. Lepom, U. Irmer and J. Wellmitz, Chemosphere, 2012, 86, 202–211.
12. J. Wiener, D. Krabbenhoft, G. Heinz and A. Scheuhammer, in Handbook of Ecotoxicology, ed. D. Hoffman, B. A. Rattner, G. A. Burton Jr and J. Cairns Jr, CRC Press, Boca Raton, 2nd edn, 2003, pp. 409–463.
13. R. D. Day, A. Segar, M. D. Arendt, A. M. Lee and M. M. Peden-Adams, Environ. Health Perspect., 2007, 115, 1421–1428.
14. C. S. A. Pereira, S. I. A. G. Guilherme, C. M. M. Barroso, L. Verschaeve, M. G. G. Pacheco and S. A. L. V. Mendo, Arch. Environ. Contam. Toxicol., 2010, 58, 112–122.
15. P. E. Drevnick and M. B. Sandheinrich, Environ. Sci. Technol., 2003, 37, 4390–4396.
16. J. T. Zelikoff, R. Smialowicz, P. E. Bigazzi, R. A. Goyer, D. A. Lawrence, H. I. Maibach and D. Gardner, Fundam. Appl. Toxicol., 1994, 22, 1–7.
17. USEPA, Office of Water, Fact Sheet: National Listing of Fish Advisories, EPA-823-F-04-016, Washington, DC, 2004.
18. AMAP, AMAP Assessment 2011: Mercury in the Arctic, Arctic Monitoring and Assessment Programme (AMAP), Olso, Norway, 2011.
19. G. E. Gehrke, J. D. Blum and M. Marvin-DiPasquale, Geochim. Cosmochim. Acta, 2011, 75, 691–705.
20. D. Foucher, N. Ogrinc and H. Hintelmann, Environ. Sci. Technol., 2009, 43, 33–39.
21. W. F. Fitzgerald, C. H. Lamborg and C. R. Hammerschmidt, Chem. Rev., 2007, 107, 641–662.
22. J. E. Sonke, Geochim. Cosmochim. Acta, 2011, 75, 4577–4590.
23. W. H. Schroeder and J. Munthe, Atmos. Environ., 1998, 32, 809–822.
24. N. E. Selin, D. J. Jacob, R. J. Park, R. M. Yantosca, S. Strode, L. Jaegle and D. Jaffe, J. Geophys. Res., 2007, 112, D02308.
25. J. D. Blum and B. A. Bergquist, Anal. Bioanal. Chem., 2007, 388, 353–359.
26. R. Yin, X. Feng and W. Shi, Appl. Geochem., 2010, 25, 1467–1477.
27. X. Feng, D. Foucher, H. Hintelmann, H. Yan, T. He and G. Qiu, Environ. Sci. Technol., 2010, 44, 3363–3368.
28. S. WenFang, X. Feng, G. Zhang, L. Ming, R. Yin, Z. Zhao and J. Wang, Chin. Sci. Bull., 2011, 56, 877–882.
29. A. Biswas, J. D. Blum, B. A. Bergquist, G. J. Keeler and Z. Xie, Environ. Sci. Technol., 2008, 42, 8303–8309.
30. N. Gantner, H. Hintelmann, W. Zheng and D. C. G. Muir, Environ. Sci. Technol., 2009, 43, 9148–9154.
31. V. Perrot, V. N. Epov, M. V. Pastukhov, V. I. Grebenshchikova, C. Zouiten, J. E. Sonke, S. Husted, O. F. X. Donard and D. Amouroux, Environ. Sci. Technol., 2010, 44, 8030–8037.
32. B. A. Bergquist and J. D. Blum, Science, 2007, 318, 417–420.
33. D. B. Senn, E. J. Chesney, J. D. Blum, M. S. Bank, A. Maage and J. P. Shine, Environ. Sci. Technol., 2010, 44, 1630–1637.
34. L. Laffont, J. E. Sonke, L. Maurice, H. Hintelmann, M. Pouilly, Y. S. Bacarreza, T. Perez and P. Behra, Environ. Sci. Technol., 2009, 43, 8985–8990.
35. V. Perrot, M. V. Pastukhov, V. N. Epov, S. Husted, O. F. X. Donard and D. Amouroux, Environ. Sci. Technol., 2012, 46, 5902–5911.
36. D. Point, J. E. Sonke, R. D. Day, D. G. Roseneau, K. A. Hobson, S. S. Vander Pol, A. J. Moors, R. S. Pugh, O. F. X. Donard and P. R. Becker, Nat. Geosci., 2011, 4, 188–194.
37. T. A. Jackson and D. C. G. Muir, Sci. Total Environ., 2012, 417–418, 189–201.
38. N. Estrade, J. Carignan and O. F. X. Donard, Environ. Sci. Technol., 2010, 44, 6062–6067.
39. N. Estrade, J. Carignan and O. F. X. Donard, Environ. Sci. Technol., 2011, 45, 1235–1242.
40. R. D. Day, D. G. Roseneau, S. Berail, K. A. Hobson, O. F. X. Donard, S. S. Vander Pol, R. S. Pugh, A. J. Moors, S. E. Long and P. R. Becker, Environ. Sci. Technol., 2012, 46, 5327–5335.
41. K. Kritee, J. D. Blum, M. W. Johnson, B. A. Bergquist and T. Barkay, Environ. Sci. Technol., 2007, 41, 1889–1895.
42. L. Yang and R. E. Sturgeon, Anal. Bioanal. Chem., 2009, 393, 377–385.
43. W. Zheng, D. Foucher and H. Hintelmann, J. Anal. At. Spectrom., 2007, 22, 1097–1104.
44. N. Estrade, J. Carignan, J. E. Sonke and O. F. X. Donard, Geochim. Cosmochim. Acta, 2009, 73, 2693–2711.
45. P. Rodriguez-Gonzalez, V. N. Epov, R. Bridou, E. Tessier, R. Guyoneaurd, M. Monperrus and D. Amouroux, Environ. Sci. Technol., 2009, 43, 9183–9188.
46. W. Zheng and H. Hintelmann, Geochim. Cosmochim. Acta, 2009, 73, 6704–6715.
47. E. M. Sunderland and R. P. Mason, Global Biogeochem. Cycles, 2007, 21, GB4022.
48. D. Point, W. C. Davis, J. I. G. Alonso, M. Monperrus, S. J. Christopher, O. F. X. Donard, P. R. Becker and S. A. Wise, Anal. Bioanal. Chem., 2007, 389, 787–798.
49. D. Foucher and H. Hintelmann, Anal. Bioanal. Chem., 2006, 384, 1470–1478.
50. C. N. Smith, S. E. Kesler, J. D. Blum and J. J. Rytuba, Earth Planet. Sci. Lett., 2008, 269, 399–407.
51. C. N. Smith, B. Klaue, S. E. Kesler and J. D. Blum, Geology, 2005, 33, 825–828.
52. J. E. Sonke, J. Schafer, J. Chmeleff, S. Audry, G. Blanc and B. Dupre, Chem. Geol., 2010, 279, 90–100.
53. P. R. Becker, S. A. Wise, L. K. Thorsteinson, B. J. Koster and T. Rowles, Chemosphere, 1997, 34, 1889–1906.
54. B. J. Wakeford and M. T. Kasserra, Chemosphere, 1997, 34, 1933–1938.
55. P. R. Becker, Mar. Pollut. Bull., 2000, 40, 819–829.
56. E. A. Mackey, R. Demiralp, P. R. Becker, R. R. Greenberg, B. J. Koster and S. A. Wise, Sci. Total Environ., 1995, 175, 25–41.
57. R. D. Day, D. G. Roseneau, S. S. Vander Pol, K. A. Hobson, O. F. X. Donard, R. S. Pugh, A. J. Moors and P. R. Becker, Environ. Pollut., 2012, 166, 226–232.
58. P. R. Becker, B. J. Koster, S. A. Wise and R. Zeisler, Biol. Trace Elem. Res., 1990, 26–27, 329–334.
59. L. H. Schwacke, E. O. Voit, L. J. Hansen, R. S. Wells, G. B. Mitchum, A. A. Hohn and P. A. Fair, Environ. Toxicol. Chem., 2002, 12, 2752–2764.
60. S. Tanabe, J. Environ. Monit., 2006, 8, 782–790.
61. K. L. Kimbrough, W. E. Johnson, G. G. Lauenstein, J. D. Christensen and D. A. Apeti, An Assessment of Two Decades of Contaminant Monitoring in the Nation's Coastal Zone, NOAA Technical Memorandum NOS NCCOS 74, Silver Spring, MD, 2008.
62. R. S. Pugh, M. B. Ellisor, A. J. Moors, P. R. Becker, B. J. Porter, E. A. Mackey, M. M. Schantz, R. Demiralp and S. A. Wise, The National Marine Mammal Tissue Bank Specimen Inventory, NISTIR 7372, National Institute of Standards and Technology, Gaitersburg, MD, 2006.
63. H. Emons, J. D. Schladot and M. J. Schwuger, Chemosphere, 1997, 34, 1875–1888.
64. T. Odsjo, A. Bignert, M. Olsson, L. Asplund, U. Eriksson, L. Haggberg, K. Litzen, C. de Wit, C. Rappe and K. Alslund, Chemosphere, 1997, 34, 2059–2066.
65. K. A. Hobson, J. Sirois and M. L. Gloutney, Auk, 2000, 117, 760–774.
66. A. Kambamamonli-Dimou, A. Kamarianos and S. Kilikidis, Bull. Environ. Contam. Toxicol., 1991, 46, 128–133.
67. D. C. Evers, K. M. Taylor, A. Major, R. J. Taylor, R. H. Poppenga and A. M. Scheuhammer, Ecotoxicology, 2003, 12, 69–81.
68. I. G. Viana, J. R. Aboal, J. A. Fernandez, C. Real, R. Villares and A. Carballeira, Water Res., 2010, 44, 1713–1724.
69. R. Fuge and K. H. James, Mar. Pollut. Bull., 1974, 5, 9–12.
70. T. A. Jackson and D. C. G. Muir, Environ. Sci. Technol., 2004, 38, 2813–2821.
71. R. Dietz, P. M. Outridge and K. A. Hobson, Sci. Total Environ., 2009, 407, 6120–6131.
72. B. Braune, G. M. Donaldson and K. A. Hobson, Environ. Pollut., 2001, 114, 39–54.
73. J. Masbou, D. Point, J. E. Sonke and P. R. Becker, Mineral. Mag., 2012, 76, 2077.
74. B. Braune, Environ. Pollut., 2007, 148, 599–613.
75. S. S. Vander Pol, K. A. Hobson, P. R. Becker, R. D. Day, M. B. Ellisor, R. S. Pugh and D. G. Roseneau, J. Environ. Monit., 2011, 13, 699–705.
76. R. D. Day, S. S. Vander Pol, S. J. Christopher, W. C. Davis, R. S. Pugh, K. S. Simac, D. G. Roseneau and P. R. Becker, Environ. Sci. Technol., 2006, 40, 659–665.
77. S. J. Christopher, S. S. Vander Pol, R. S. Pugh, R. D. Day and P. R. Becker, J. Anal. At. Spectrom., 2002, 17, 780–785.
78. S. S. Vander Pol, P. R. Becker, B. Sylvain, R. D. Day, O. F. X. Donard, K. A. Hobson, A. J. Moors, R. S. Pugh, L. B. Rust and D. G. Roseneau, Seabird Tissue Archival and Monitoring Project: Egg Collections and Analytical Results for 2006–2009, NIST Interagency/Internal Report NISTIR 7872, 2012.
79. S. S. Vander Pol, P. R. Becker, J. R. Kucklick, R. S. Pugh, D. G. Roseneau and K. S. Simac, Environ. Sci. Technol., 2004, 38, 1305–1312.
80. S. S. Vander Pol, P. R. Becker, R. D. Day, M. B. Ellisor, A. Guichard, A. J. Moors, D. Point, R. S. Pugh and D. G. Roseneau, Seabird Tissue Archival and Monitoring Project: Egg collections and analytical results for 2002–2005, NISTIR 7562, National Institute of Standards and Technology, Gaithersburg, MD, 2009, 145 p.
81. H. Stapleton, N. G. Dodder, J. R. Kucklick, C. M. Reddy, M. M. Schantz, P. R. Becker, F. Gulland, B. J. Porter and S. A. Wise, Mar. Pollut. Bull., 2006, 52, 522–531.
82. R. Zeisler, R. Demiralp, B. J. Koster, P. R. Becker, M. Burow, P. Ostapczuk and S. A. Wise, Sci. Total Environ., 1993, 139/140, 365–386.
83. M. M. Schantz, B. J. Koster, S. A. Wise and P. R. Becker, Sci. Total Environ., 1993, 139–140, 323–345.
84. P. R. Becker, E. A. Mackey, R. Demiralp, M. M. Schantz, B. J. Koster and S. A. Wise, Chemosphere, 1997, 34, 2067–2098.
85. S. Mossner, T. R. Spraker, P. R. Becker and K. Ballschmiter, Chemosphere, 1992, 24, 1171–1180.
86. S. Mossner, I. Barudio, T. S. Spraker, G. Antonelis, G. Early, J. R. Geraci, P. R. Becker and K. Ballschmiter, Fresenius' J. Anal. Chem., 1994, 349, 708–716.
87. M. M. Krahn, P. R. Becker, K. L. Tilbury and J. E. Stein, Chemosphere, 1997, 34, 1889–1906.
88. R. A. Ponce, G. M. Egeland, J. P. Middaugh and P. R. Becker, Twenty years of trace element analyses of marine mammals: Evaluation and summation of data from Alaska and other Arctic regions, State of Alaska Epidemiology Bulletin, Anchorage, Alaska, 1997, vol. 1, no. 3, 15 p.
89. E. A. Mackey, P. R. Becker, R. Demiralp, R. R. Greenberg, B. J. Koster and S. A. Wise, Arch. Environ. Contam. Toxicol., 1996, 30, 503–512.
90. P. R. Becker, E. A. Mackey, M. M. Schantz, R. Demiralp, R. R. Greenberg, B. J. Koster, S. A. Wise and D. C. G. Muir, Concentrations of chlorinated hydrocarbons, heavy metals and other elements in tissues banked by the Alaska Marine Mammal Tissue Archival Project, NISTIR 5620, National Institute of Standards and Technology, Gaithersburg, MD, 1995, 115 p.
91. J. R. Kucklick, W. D. J. Struntz, P. R. Becker, G. W. York and J. E. Bohonowych, Sci. Total Environ., 2002, 287, 45–49.
92. J. R. Kucklick, M. M. Krahn, P. R. Becker, B. J. Porter, M. M. Schantz, G. W. York, T. M. O'Hara and S. A. Wise, J. Environ. Monit., 2006, 8, 848–854.
93. P. R. Becker, B. J. Porter, E. A. Mackey, M. M. Schantz, R. Demiralp and S. A. Wise, National Marine Mammal Tissue Bank and Quality Assurance Program: Protocols, Inventory, and Analytical Results, NISTIR 6279, National Institute of Standards and Technology, Gaithersburg, MD, 1999, 183 p.
94. J. R. Kucklick, W. D. J. Struntz, P. R. Becker, M. M. Schantz, E. A. Mackey, R. Demiralp, M. Epstein, B. J. Porter, S. A. Wise, W. E. McFee and M. K. Stolen, Persistent organochlorine pollutants and elements determined in tissues of rough-toothed dolphin (Steno bradendensis) banked from a mass stranding event, NISTIR 6857, National Institute of Standards and Technology, Gaithersburg, MD, 2002, 57 p.
95. E. A. Mackey, R. D. Oflaz, M. Epstein, B. Buehler, B. J. Porter, T. Rowles, S. A. Wise and P. R. Becker, Arch. Environ. Contam. Toxicol., 2003, 44, 523–532.
96. W. D. J. Struntz, J. R. Kucklick, M. M. Schantz, P. R. Becker, W. E. McFee and M. K. Stolen, Mar. Pollut. Bull., 2004, 48, 1305–1312.
97. K. J. Tuerk, J. R. Kucklick, P. R. Becker, H. Stapleton and J. E. Baker, Environ. Sci. Technol., 2005, 39, 692–698.
98. K. J. Tuerk, J. R. Kucklick, W. E. McFee, R. S. Pugh and P. R. Becker, Environ. Toxicol. Chem., 2005, 24, 1079–1087.
99. A. M. Peck, R. S. Pugh, A. J. Moors, M. B. Ellisor, B. J. Porter, P. R. Becker and J. R. Kucklick, Environ. Sci. Technol., 2008, 42, 2650–2655.
100. J. E. Yordy, R. S. Wells, B. C. Balmer, L. H. Schwacke, T. Rowles and J. R. Kucklick, Sci. Total Environ., 2010, 408, 2163–2172.
101. J. E. Yordy, R. S. Wells, B. C. Balmer, L. H. Schwacke, T. Rowles and J. R. Kucklick, Environ. Sci. Technol., 2010, 44, 4789–4795.
102. J. E. Yordy, D. A. Pabst, W. A. McLellan, R. S. Wells, T. Rowles and J. R. Kucklick, Environ. Toxicol. Chem., 2010, 29, 1263–1273.
103. B. C. Balmer, L. H. Schwacke, R. S. Wells, R. C. George, J. Hoguet, J. R. Kucklick, S. M. Lane, A. Martinez, W. A. McClellan, P. E. Rosel, K. Sparks, T. Speakman, E. S. Zolman and D. A. Pabst, Sci. Total Environ., 2011, 409, 2094–2101.
104. J. R. Kucklick, L. H. Schwacke, R. S. Wells, A. A. Hohn, A. Guichard, J. E. Yordy, L. J. Hansen, E. S. Zolman, R. Wilson, J. Litz, D. Nowacek, T. Rowles, R. S. Pugh, B. C. Balmer, C. Sinclair and P. E. Rosel, Environ. Sci. Technol., 2011, 45, 4270–4277.
105. C. A. Krone, P. A. Robisch, K. L. Tilbury, J. E. Stein, E. A. Mackey, P. R. Becker, T. M. O'Hara and L. M. Philo, Mar. Mamm. Sci., 1999, 15, 123–142.
106. T. M. O'Hara, M. M. Krahn, D. Boyd and P. R. Becker, J. Wildl. Dis., 1999, 35, 741–752.
107. C. E. Bryan, W. C. Davis, W. E. McFee, C. Neumann, G. D. Bossart and S. J. Christopher, Chemosphere, 2012, 89, 556–562.
108. P. R. Becker, E. A. Mackey, R. Demiralp, R. Suydam, R. Early, B. J. Koster and S. A. Wise, Mar. Pollut. Bull., 1995, 30, 262–271.
109. M. M. Krahn, D. G. Burrows, J. E. Stein and P. R. Becker, Journal of Cetacean Resource Management, 1999, 1, 239–249.
110. P. R. Becker, M. M. Krahn, E. A. Mackey, R. Demiralp, M. M. Schantz, M. Epstein, M. K. Donais, B. J. Porter, D. C. G. Muir and S. A. Wise, J. Wildl. Dis., 2000, 62, 81–98.
111. J. L. Reiner, S. G. O'Connel, J. R. Kucklick, P. R. Becker and J. M. Keller, Environ. Sci. Technol., 2011, 45, 8129–8136.
112. J. Hoguet, J. L. Reiner, J. M. Keller, J. R. Kucklick, C. E. Bryan, A. J. Moors, R. S. Pugh and P. R. Becker, Sci. Total Environ., 2013, 449, 285–294.
113. P. Ostapczuk, M. Burow, K. May, C. Mohl, M. Froning, B. Subenbach, E. Waidmann and H. Emons, Chemosphere, 1997, 34, 2049–2058.
114. A. Wenzel, W. Bohmer, J. Muller, H. Rudel and C. Schroter-Kermani, Environ. Sci. Technol., 2004, 38, 1654–1661.
115. H. Rudel, W. Bohmer and C. Schroter-Kermani, J. Environ. Monit., 2006, 8, 812–823.
116. R. Klein, M. Bartel, X. He, J. Muller and M. Quack, Environ. Res., 2005, 98, 55–63.
117. D. Teubner, K. Tarricone, M. Veith, G. Wagner, M. Bartel-Steinbach, R. Klein, A. Korner, M. Quack and M. Paulus, Ecol. Indic., 2011, 11, 1487–1489.
118. J. E. Hedman, H. Rudel, J. Gercken, S. Bergek, J. Strand, M. Quack, M. Appelberg, L. Forlin, A. Tuvikene and A. Bignert, Mar. Pollut. Bull., 2011, 62, 2015–2029.
119. S. Esslinger, R. Becker, C. Jung, C. Schroter-Kermani, W. Bremser and I. Nehl, Chemosphere, 2011, 83, 161–167.
120. C. Schroter-Kermani, E. Schmolz, T. Herrmann and O. Papke, Organohalogen Compd., 2005, 67, 1295–1299.
121. R. Klein, M. Bartel-Steinbach, J. Koschorreck, M. Paulus, K. Tarricone, D. Teubner, G. Wagner, T. Weimann and M. Veith, Environ. Sci. Technol., 2012, 46, 5273–5284.
122. K. Lofstrand, A. Malmvarn, P. Haglund, A. Bignert, A. Bergman and L. Asplund, Environ. Sci. Pollut. Res., 2010, 17, 1460.
123. A. Malmvarn, Y. Zebuhr, S. Jensen, L. Kautsky, E. Greyerz, T. Nakano and L. Asplund, Environ. Sci. Technol., 2005, 39, 8235–8242.
124. K. Lundstedt-Enkel, R. Bjerselius, L. Asplund, K. Nylund, Y. Liu and M. Sodervall, Environ. Sci. Technol., 2010, 44, 9018–9023.
125. K. Lundstedt-Enkel, M. Tysklind, J. Trygg, P. Schuller, L. Asplund, U. Eriksson, L. Haggberg, T. Odsjo, M. Hjelmberg, M. Olsson and J. Orberg, Environ. Sci. Technol., 2006, 39, 8395–8402.
126. H. Jorundsdottir, A. Bignert, J. Svararsson, T. Nygard, P. Weihe and A. Bergman, Sci. Total Environ., 2009, 407, 4174–4183.
127. K. Lundstedt-Enkel, L. Asplund, K. Nylund, A. Bignert, M. Tysklind, M. Olsson and J. Orberg, Chemosphere, 2006, 65, 1591–1599.
128. K. Lundstedt-Enkel, A. Johansson, M. Tysklind, L. Asplund, K. Nylund, M. Olsson and J. Orberg, Environ. Sci. Technol., 2005, 39, 8630–8637.
129. A. Bignert, K. Litzen, T. Odsjo, M. Olsson, W. Persson and L. Reutergardh, Environ. Pollut., 1995, 89, 27.
130. K. Holmstrom, A. Johansson, A. Bignert, P. Lindberg and U. Berger, Environ. Sci. Technol., 2010, 44, 4083–4088.
131. J. Kratzer, L. Ahrens, A. Roos, B.-M. Backlin and R. Ebinghaus, Chemosphere, 2011, 85, 253–261.
132. C. E. Hebert, Ecol. Appl., 1998, 8, 669–679.
133. R. J. Norstrom, M. Simon, J. Moisey, B. J. Wakeford and D. V. C. Weseloh, Environ. Sci. Technol., 2002, 36, 4783–4789.
134. W. A. Gebbink, C. E. Hebert and R. J. Letcher, Environ. Sci. Technol., 2009, 43, 7443–7449.
135. L. T. Gauthier, C. E. Hebert, D. V. C. Weseloh and R. J. Letcher, Environ. Sci. Technol., 2008, 42, 1524–1530.
136. N. M. Burgess, A. L. Bond, C. E. Hebert, E. Neugebauer and L. Champoux, Environ. Pollut., 2013, 172, 216–222.
137. B. Braune, G. M. Donaldson and K. A. Hobson, Environ. Pollut., 2002, 117, 133–145.
138. C. M. Butt, S. A. Mabury, D. C. G. Muir and B. Braune, Environ. Sci. Technol., 2007, 41, 3521–3528.
139. B. M. Braune and R. J. Letcher, Environ. Sci. Technol., 2013, 47, 616–624.
140. B. Braune, M. L. Mallory and H. G. Gilchrist, Mar. Pollut. Bull., 2006, 52, 969–987.
141. B. Braune, M. L. Mallory, H. G. Gilchrist, R. J. Letcher and K. G. Drouillard, Sci. Total Environ., 2007, 378, 403–417.
142. Patterns and Trends of Organic Contaminants in Canadian Seabird Eggs 1968–90, ed. J. E. Elliot, D. G. Noble, R. J. Norstrom, P. E. Whitehead, M. Simon, P. A. Pearce and D. B. Peakall, Pergamon Press, Oxford, 1992, ch. 8, pp. 181–194.
143. J. E. Elliot, L. Wilson and B. J. Wakeford, Environ. Sci. Technol., 2005, 39, 5584–5591.
144. J. E. Elliot and A. M. Scheuhammer, Mar. Pollut. Bull., 1997, 34, 794–801.
145. J. E. Elliot, M. L. Harris, L. Wilson, P. E. Whitehead and R. J. Norstrom, Ambio, 2001, 30, 416–428.
146. M. L. Harris, J. E. Elliot, R. W. W. Butler and L. K. Wilson, Environ. Pollut., 2003, 121, 207–227.
147. M. F. Guigueno, K. H. Elliot, J. Levan, M. Wayland and J. E. Elliot, Environ. Int., 2012, 40, 24–32.
148. J. E. Elliot, J. Levac, M. F. Guigueno, D. P. Shaw, M. Wayland, C. A. Morrissey, D. C. G. Muir and K. H. Elliot, Environ. Sci. Technol., 2012, 46, 9681–9689.
149. G. T. Tomy, W. Budakowski, T. Halldorson, D. M. Whittle, M. J. Keir, C. H. Marvin, G. MacInnis and M. Alaee, Environ. Sci. Technol., 2004, 38, 2298–2303.
150. J. W. Martin, D. M. Whittle, D. C. G. Muir and S. A. Mabury, Environ. Sci. Technol., 2004, 38, 5379–5385.
151. S. B. Gewurtz, D. J. McGoldrick, M. G. Clark, M. J. Keir, M. M. Malecki, M. Gledhill, M. Sekela, J. Syrgiannis, M. S. Evans, A. Armellin, J. Pomeroy, J. Waltho and S. M. Backus, Environ. Toxicol. Chem., 2011, 30, 1564–1575.
152. S. P. Bhavsar, S. B. Gewurtz, D. J. McGoldrick, M. J. Keir and S. M. Backus, Environ. Sci. Technol., 2010, 44, 3273–3279.
153. B. A. Monson, D. F. Staples, S. P. Bhavsar, T. M. Holsen, C. S. Schrank, S. K. Moses, D. J. McGoldrick, S. M. Backus and K. A. Williams, Ecotoxicology, 2011, 20, 1555–1567.
154. D. V. C. Weseloh, D. J. Moore, C. E. Hebert, S. R. de Solla, B. M. Braune and D. J. McGoldrick, Ecotoxicology, 2011, 20, 1644–1658.
155. S. B. Gewurtz, A. O. De Silva, S. M. Backus, D. J. McGoldrick, M. J. Keir, J. Small, L. Melymuk and D. C. G. Muir, Environ. Sci. Technol., 2012, 46, 5842–5850.
156. J. W. Martin, D. M. Whittle, D. C. G. Muir and S. A. Mabury, Environ. Sci. Technol., 2004, 38, 5379.
157. S. B. Gewurtz, R. Lega, P. W. Crozier, D. M. Whittle, L. Fayez, E. J. Reiner, P. A. Helm, C. H. Marvin and G. T. Tomy, Environ. Toxicol. Chem., 2009, 28, 921.

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