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      Trophic Dynamics of Mercury in the Baltic Archipelago Sea Food Web: The Impact of Ecological and Ecophysiological Traits

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          Abstract

          We investigated trophic dynamics of Hg in the polluted Baltic Archipelago Sea using established trophic magnification (TMFs) and biomagnification factors (BMFs) on a comprehensive set of bird, fish, and invertebrate species. As different ecological and ecophysiological species traits may affect trophic dynamics, we explored the effect of food chain (benthic, pelagic, benthopelagic) and thermoregulatory strategy on trophic total Hg (THg) dynamics, using different approaches to accommodate benthopelagic species and normalize for trophic position (TP). We observed TMFs and most BMFs greater than 1, indicating overall THg biomagnification. We found significantly higher pelagic TMFs (3.58–4.02) compared to benthic ones (2.11–2.34) when the homeotherm bird species were excluded from models, but not when included. This difference between the benthic and pelagic TMFs remained regardless of how the TP of benthopelagic species was modeled, or whether TMFs were normalized for TP or not. TP-corrected BMFs showed a larger range (0.44–508) compared to BMFs representing predator–prey concentration ratios (0.05–82.2). Overall, the present study shows the importance of including and evaluating the effect of ecological and ecophysiological traits when investigating trophic contaminant dynamics.

          Abstract

          Ecophysiological and ecological traits impact the output of established models for trophic contaminant dynamics, potentially having consequences for regulatory decisions based thereon.

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          Most cited references39

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          The coefficient of determination R2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded

          The coefficient of determination R 2 quantifies the proportion of variance explained by a statistical model and is an important summary statistic of biological interest. However, estimating R 2 for generalized linear mixed models (GLMMs) remains challenging. We have previously introduced a version of R 2 that we called for Poisson and binomial GLMMs, but not for other distributional families. Similarly, we earlier discussed how to estimate intra-class correlation coefficients (ICCs) using Poisson and binomial GLMMs. In this paper, we generalize our methods to all other non-Gaussian distributions, in particular to negative binomial and gamma distributions that are commonly used for modelling biological data. While expanding our approach, we highlight two useful concepts for biologists, Jensen's inequality and the delta method, both of which help us in understanding the properties of GLMMs. Jensen's inequality has important implications for biologically meaningful interpretation of GLMMs, whereas the delta method allows a general derivation of variance associated with non-Gaussian distributions. We also discuss some special considerations for binomial GLMMs with binary or proportion data. We illustrate the implementation of our extension by worked examples from the field of ecology and evolution in the R environment. However, our method can be used across disciplines and regardless of statistical environments.
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            Mercury as a Global Pollutant: Sources, Pathways, and Effects

            Mercury (Hg) is a global pollutant that affects human and ecosystem health. We synthesize understanding of sources, atmosphere-land-ocean Hg dynamics and health effects, and consider the implications of Hg-control policies. Primary anthropogenic Hg emissions greatly exceed natural geogenic sources, resulting in increases in Hg reservoirs and subsequent secondary Hg emissions that facilitate its global distribution. The ultimate fate of emitted Hg is primarily recalcitrant soil pools and deep ocean waters and sediments. Transfers of Hg emissions to largely unavailable reservoirs occur over the time scale of centuries, and are primarily mediated through atmospheric exchanges of wet/dry deposition and evasion from vegetation, soil organic matter and ocean surfaces. A key link between inorganic Hg inputs and exposure of humans and wildlife is the net production of methylmercury, which occurs mainly in reducing zones in freshwater, terrestrial, and coastal environments, and the subsurface ocean. Elevated human exposure to methylmercury primarily results from consumption of estuarine and marine fish. Developing fetuses are most at risk from this neurotoxin but health effects of highly exposed populations and wildlife are also a concern. Integration of Hg science with national and international policy efforts is needed to target efforts and evaluate efficacy.
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              USING STABLE ISOTOPES TO ESTIMATE TROPHIC POSITION: MODELS, METHODS, AND ASSUMPTIONS

              David Post (2002)
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                Author and article information

                Journal
                Environ Sci Technol
                Environ Sci Technol
                es
                esthag
                Environmental Science & Technology
                American Chemical Society
                0013-936X
                1520-5851
                03 August 2022
                16 August 2022
                : 56
                : 16
                : 11440-11448
                Affiliations
                []Department of Biology, University of Turku , FI-20014 Turku, Finland
                []Department of Ecoscience, Aarhus University , Frederiksborgvej 399, Postbox 358, DK-4000 Roskilde, Denmark
                [§ ]iES Landau, Institute for Environmental Sciences, University of Koblenz-Landau , Fortstrasse 7, DE-76829 Landau, Germany
                []Zubrod Environmental Data Science , Friesenstrasse 20, DE-76829, Landau, Germany
                []Norwegian Polar Institute, FRAM Centre , Postboks 6606 Stakkevollan, NO-9296 Tromsø, Norway
                Author notes
                Author information
                https://orcid.org/0000-0003-2649-6799
                https://orcid.org/0000-0002-6348-6971
                Article
                10.1021/acs.est.2c03846
                9387095
                35921287
                3f87593c-e3a6-44ae-a94c-042ca66405b1
                © 2022 The Authors. Published by American Chemical Society

                Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained ( https://creativecommons.org/licenses/by/4.0/).

                History
                : 29 May 2022
                : 22 July 2022
                : 22 July 2022
                Funding
                Funded by: Stiftelsen för Miljöstrategisk Forskning, doi 10.13039/100007633;
                Award ID: NA
                Funded by: Union of the Baltic Cities, doi NA;
                Award ID: NA
                Funded by: Suomen Luonnonsuojelun Säätiö, doi 10.13039/501100019228;
                Award ID: NA
                Funded by: European Regional Development Fund, doi 10.13039/501100008530;
                Award ID: NA
                Funded by: Turun Yliopisto, doi 10.13039/501100005609;
                Award ID: 080552
                Funded by: Varsinais-Suomen Rahasto, doi 10.13039/501100005424;
                Award ID: 85201728
                Funded by: Forschungszentrum Jülich, doi 10.13039/501100003163;
                Award ID: NA
                Funded by: Bundesministerium für Bildung und Forschung, doi 10.13039/501100002347;
                Award ID: FKZ 03F0767A
                Funded by: Academy of Finland, doi 10.13039/501100002341;
                Award ID: 311966
                Funded by: Innovationsfonden, doi 10.13039/100012774;
                Award ID: 6180-00002B
                Funded by: Innovationsfonden, doi 10.13039/100012774;
                Award ID: 6180-00001B
                Categories
                Article
                Custom metadata
                es2c03846
                es2c03846

                General environmental science
                stable isotopes,food web magnification factor,biomagnification factor,trophic position,hg,food chain

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