A global, cross-system meta-analysis of polychlorinated biphenyl biomagnification
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https://datadryad.org/dataset/doi:10.5061/dryad.b2rbnzsbn
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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.
提供机构:
Dryad
创建时间:
2020-12-03



