TY - JOUR
T1 - EStimating Contaminants tRansfers Over Complex food webs (ESCROC)
T2 - An innovative Bayesian method for estimating POP's biomagnification in aquatic food webs
AU - Ballutaud, Marine
AU - Drouineau, Hilaire
AU - Carassou, Laure
AU - Munoz, Gabriel
AU - Chevillot, Xavier
AU - Labadie, Pierre
AU - Budzinski, Hélène
AU - Lobry, Jérémy
N1 - Funding Information:
This study has been carried out with financial support from the French National Research Agency (ANR) in the frame of the Investments for the future Programme, within the Cluster of Excellence COTE ( ANR-10-LABX-45 ). We would like to thank Marc Babut for early discussions and insights on this subject.
Publisher Copyright:
© 2018 Elsevier B.V.
PY - 2019/3/25
Y1 - 2019/3/25
N2 - Pollution greatly impacts ecosystems health and associated ecological functions. Persistent Organic Pollutants (POPs) are among the most studied contaminants due to their persistence, bioaccumulation, and toxicity potential. Biomagnification is often described using the estimation of a Trophic Magnification Factor (TMF). This estimate is based on the relationship between contamination levels of the species and their trophic level. However, while the estimation can be significantly biased in relation to multiple sources of uncertainty (e.g. species physiology, measurement errors, food web complexity), usual TMF estimation methods typically do not allow accounting for these potential biases. More accurate and reliable assessment tool of TMFs and their associated uncertainty are therefore needed in order to appropriately guide chemical pollution management. The present work proposes a relevant and innovative TMF estimation method accounting for its many variability sources. The ESCROC model (EStimating Contaminants tRansfers Over Complex food webs), which is implemented in a Bayesian framework, allows for a more reliable and rigorous assessment of contaminants trophic magnification, in addition to accurate estimations of isotopes trophic enrichment factors and their associated uncertainties in food webs. Similar to classical mixing models used in food web investigations, ECSROC computes diet composition matrices using isotopic composition data while accounting for contamination data, leading to more robust food web descriptions. As a demonstration of the practical application of the model, ESCROC was implemented to revisit the trophic biomagnification of 5 polyfluoroalkyl substances (PFAS) in a complex estuarine food web (the Gironde, SW France). In addition to the TMF estimate and 95% confidence intervals, the model provided biomagnification probabilities associated to the investigated contaminants—for instance, 92% in the case of perfluorooctane sulfonate (PFOS)—that can be interpreted in terms of risk assessment in a precautionary approach, which should prove useful to environmental managers.
AB - Pollution greatly impacts ecosystems health and associated ecological functions. Persistent Organic Pollutants (POPs) are among the most studied contaminants due to their persistence, bioaccumulation, and toxicity potential. Biomagnification is often described using the estimation of a Trophic Magnification Factor (TMF). This estimate is based on the relationship between contamination levels of the species and their trophic level. However, while the estimation can be significantly biased in relation to multiple sources of uncertainty (e.g. species physiology, measurement errors, food web complexity), usual TMF estimation methods typically do not allow accounting for these potential biases. More accurate and reliable assessment tool of TMFs and their associated uncertainty are therefore needed in order to appropriately guide chemical pollution management. The present work proposes a relevant and innovative TMF estimation method accounting for its many variability sources. The ESCROC model (EStimating Contaminants tRansfers Over Complex food webs), which is implemented in a Bayesian framework, allows for a more reliable and rigorous assessment of contaminants trophic magnification, in addition to accurate estimations of isotopes trophic enrichment factors and their associated uncertainties in food webs. Similar to classical mixing models used in food web investigations, ECSROC computes diet composition matrices using isotopic composition data while accounting for contamination data, leading to more robust food web descriptions. As a demonstration of the practical application of the model, ESCROC was implemented to revisit the trophic biomagnification of 5 polyfluoroalkyl substances (PFAS) in a complex estuarine food web (the Gironde, SW France). In addition to the TMF estimate and 95% confidence intervals, the model provided biomagnification probabilities associated to the investigated contaminants—for instance, 92% in the case of perfluorooctane sulfonate (PFOS)—that can be interpreted in terms of risk assessment in a precautionary approach, which should prove useful to environmental managers.
KW - Bayesian mixing model
KW - Food webs
KW - Gironde estuary
KW - Organic micropollutants
KW - Stable isotopes
KW - Trophic magnification
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UR - http://www.scopus.com/inward/citedby.url?scp=85058958632&partnerID=8YFLogxK
U2 - 10.1016/j.scitotenv.2018.12.058
DO - 10.1016/j.scitotenv.2018.12.058
M3 - Article
C2 - 30580218
AN - SCOPUS:85058958632
SN - 0048-9697
VL - 658
SP - 638
EP - 649
JO - Science of the Total Environment
JF - Science of the Total Environment
ER -