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Dryad

A global, cross-system meta-analysis of polychlorinated biphenyl biomagnification

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Dec 03, 2020 version files 171.06 KB

Abstract

Studies evaluating the mechanisms underpinning the biomagnification of polychlorinated biphenyls (PCBs), a globally prevalent group of regulated persistent organic pollutants, commonly couple chemical and stable isotope analyses to identify bioaccumulation pathways. Due to analytical costs constraining the taxonomic and geographic scope, sample size, and the range of compounds analyzed for most studies, and study-to-study variation in methodologies and analytical resolution, how PCBs biomagnify at food web, regional, and global scales remains uncertain. To overcome these constraints, we compiled diet (stable isotopes) and lipid-normalized PCB data from peer-reviewed studies reporting both values and used complementary analyses to evaluate the relative importance of global key PCB drivers and assess ecosystem- and ocean-wide biomagnification trends of sum total PCB concentrations (PCBST), and the concentrations of seven individual PCB congeners, and their sum (PCBå7). We discovered that the number of congeners analyzed, region, and class were the most important factors predicting PCBST, while, similarly, region, class and feeding location were the best predictors of PCBå7 and all seven congeners. In addition, biomagnification analyses revealed that PCBST, PCBΣ7 and the seven individual PCBs all demonstrate a higher propensity for biomagnification in marine relative to freshwater food webs and within the Atlantic Ocean relative to the Pacific. We further found that some congeners exhibiting relatively high trophic magnification factors (TMFs) in the Atlantic exhibited low TMFs in the Pacific (such as PCB 118), while the order of individual congener TMFs relative to one another remained consistent across marine and freshwater ecosystems. Our analyses demonstrate that novel insights regarding PCB concentrations across taxonomic, food webs, regional and global scales can be gleaned by leveraging existing data to overcome analytical constraints.