Constructing Time-Resolved Species Sensitivity Distributions Using a Hierarchical Toxico-Dynamic Model.
Identifieur interne : 002D82 ( PubMed/Checkpoint ); précédent : 002D81; suivant : 002D83Constructing Time-Resolved Species Sensitivity Distributions Using a Hierarchical Toxico-Dynamic Model.
Auteurs : Guillaume Kon Kam King [France] ; Marie Laure Delignette-Muller [France] ; Ben J. Kefford [Australie] ; Christophe Piscart [France] ; Sandrine Charles [France]Source :
- Environmental science & technology [ 1520-5851 ] ; 2015.
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Abstract
Classical species sensitivity distribution (SSD) is used to assess the threat to ecological communities posed by a contaminant and derive a safe concentration. It suffers from several well-documented weaknesses regarding its ecological realism and statistical soundness. Criticism includes that SSD does not take time-dependence of the data into account, that safe concentrations obtained from SSD might not be entirely protective of the target communities, and that there are issues of statistical representativity and of uncertainty propagation from the experimental data. We present a hierarchical toxico-dynamic (TD) model to simultaneously address these weaknesses: TD models incorporate time-dependence and allow improvement of the ecological relevance of safe concentrations, while the hierarchical approach affords appropriate propagation of uncertainty from the original data. We develop this model on a published data set containing the salinity tolerance over 72 h of 217 macroinvertebrate taxa, obtained through rapid toxicity testing (RTT). The shrinkage properties of the hierarchical model prove particularly adequate for modeling inhomogeneous RTT data. Taking into account the large variability in the species response, the model fits the whole data set well. Moreover, the model predicts a time-independent safe concentration below that obtained with classical SSD at 72 h, demonstrating under-protectiveness of the classical approach.
DOI: 10.1021/acs.est.5b02142
PubMed: 26406398
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Kefford</name><affiliation wicri:level="1"><nlm:affiliation>Institute for Applied Ecology, University of Canberra , Canberra, ACT 2601, Australia.</nlm:affiliation><country xml:lang="fr">Australie</country><wicri:regionArea>Institute for Applied Ecology, University of Canberra , Canberra, ACT 2601</wicri:regionArea><wicri:noRegion>ACT 2601</wicri:noRegion></affiliation></author><author><name sortKey="Piscart, Christophe" sort="Piscart, Christophe" uniqKey="Piscart C" first="Christophe" last="Piscart">Christophe Piscart</name><affiliation wicri:level="1"><nlm:affiliation>UMR CNRS 6553, Ecobio, Université de Rennes 1 , 263 Avenue du Général Leclerc, 35042 Rennes, France.</nlm:affiliation><country xml:lang="fr">France</country><wicri:regionArea>UMR CNRS 6553, Ecobio, Université de Rennes 1 , 263 Avenue du Général Leclerc, 35042 Rennes</wicri:regionArea><wicri:noRegion>35042 Rennes</wicri:noRegion><wicri:noRegion>35042 Rennes</wicri:noRegion></affiliation></author><author><name sortKey="Charles, Sandrine" sort="Charles, Sandrine" uniqKey="Charles S" first="Sandrine" last="Charles">Sandrine Charles</name><affiliation wicri:level="1"><nlm:affiliation>Université de Lyon , F69000 Lyon, France.</nlm:affiliation><country xml:lang="fr">France</country><wicri:regionArea>Université de Lyon , F69000 Lyon</wicri:regionArea><wicri:noRegion>69000 Lyon</wicri:noRegion><wicri:noRegion>69000 Lyon</wicri:noRegion></affiliation></author></analytic><series><title level="j">Environmental science & technology</title><idno type="eISSN">1520-5851</idno><imprint><date when="2015" type="published">2015</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Animals</term><term>Ecotoxicology (methods)</term><term>Invertebrates</term><term>Models, Theoretical</term><term>Salinity</term><term>Species Specificity</term><term>Toxicity Tests</term><term>Uncertainty</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Animaux</term><term>Incertitude</term><term>Invertébrés</term><term>Modèles théoriques</term><term>Salinité</term><term>Spécificité d'espèce</term><term>Tests de toxicité</term><term>Écotoxicologie ()</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Ecotoxicology</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Animals</term><term>Invertebrates</term><term>Models, Theoretical</term><term>Salinity</term><term>Species Specificity</term><term>Toxicity Tests</term><term>Uncertainty</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Animaux</term><term>Incertitude</term><term>Invertébrés</term><term>Modèles théoriques</term><term>Salinité</term><term>Spécificité d'espèce</term><term>Tests de toxicité</term><term>Écotoxicologie</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Classical species sensitivity distribution (SSD) is used to assess the threat to ecological communities posed by a contaminant and derive a safe concentration. It suffers from several well-documented weaknesses regarding its ecological realism and statistical soundness. Criticism includes that SSD does not take time-dependence of the data into account, that safe concentrations obtained from SSD might not be entirely protective of the target communities, and that there are issues of statistical representativity and of uncertainty propagation from the experimental data. We present a hierarchical toxico-dynamic (TD) model to simultaneously address these weaknesses: TD models incorporate time-dependence and allow improvement of the ecological relevance of safe concentrations, while the hierarchical approach affords appropriate propagation of uncertainty from the original data. We develop this model on a published data set containing the salinity tolerance over 72 h of 217 macroinvertebrate taxa, obtained through rapid toxicity testing (RTT). The shrinkage properties of the hierarchical model prove particularly adequate for modeling inhomogeneous RTT data. Taking into account the large variability in the species response, the model fits the whole data set well. Moreover, the model predicts a time-independent safe concentration below that obtained with classical SSD at 72 h, demonstrating under-protectiveness of the classical approach.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">26406398</PMID><DateCreated><Year>2015</Year><Month>10</Month><Day>20</Day></DateCreated><DateCompleted><Year>2016</Year><Month>05</Month><Day>27</Day></DateCompleted><DateRevised><Year>2015</Year><Month>10</Month><Day>20</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1520-5851</ISSN><JournalIssue CitedMedium="Internet"><Volume>49</Volume><Issue>20</Issue><PubDate><Year>2015</Year><Month>Oct</Month><Day>20</Day></PubDate></JournalIssue><Title>Environmental science & technology</Title><ISOAbbreviation>Environ. Sci. Technol.</ISOAbbreviation></Journal><ArticleTitle>Constructing Time-Resolved Species Sensitivity Distributions Using a Hierarchical Toxico-Dynamic Model.</ArticleTitle><Pagination><MedlinePgn>12465-73</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1021/acs.est.5b02142</ELocationID><Abstract><AbstractText>Classical species sensitivity distribution (SSD) is used to assess the threat to ecological communities posed by a contaminant and derive a safe concentration. It suffers from several well-documented weaknesses regarding its ecological realism and statistical soundness. Criticism includes that SSD does not take time-dependence of the data into account, that safe concentrations obtained from SSD might not be entirely protective of the target communities, and that there are issues of statistical representativity and of uncertainty propagation from the experimental data. We present a hierarchical toxico-dynamic (TD) model to simultaneously address these weaknesses: TD models incorporate time-dependence and allow improvement of the ecological relevance of safe concentrations, while the hierarchical approach affords appropriate propagation of uncertainty from the original data. We develop this model on a published data set containing the salinity tolerance over 72 h of 217 macroinvertebrate taxa, obtained through rapid toxicity testing (RTT). The shrinkage properties of the hierarchical model prove particularly adequate for modeling inhomogeneous RTT data. Taking into account the large variability in the species response, the model fits the whole data set well. Moreover, the model predicts a time-independent safe concentration below that obtained with classical SSD at 72 h, demonstrating under-protectiveness of the classical approach.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Kon Kam King</LastName><ForeName>Guillaume</ForeName><Initials>G</Initials><AffiliationInfo><Affiliation>Université de Lyon , F69000 Lyon, France.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Université de Lyon , Université Lyon 1, CNRS, UMR 5558, Laboratoire de Biométrie et Biologie Evolutive, Bâtiment Grégor Mendel, 43 Boulevard du 11 novembre 1918, F69622 CEDEX Villeurbanne, Villeurbanne, France.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Delignette-Muller</LastName><ForeName>Marie Laure</ForeName><Initials>ML</Initials><AffiliationInfo><Affiliation>Université de Lyon , F69000 Lyon, France.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Université de Lyon , Université Lyon 1, CNRS, UMR 5558, Laboratoire de Biométrie et Biologie Evolutive, Bâtiment Grégor Mendel, 43 Boulevard du 11 novembre 1918, F69622 CEDEX Villeurbanne, Villeurbanne, France.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>VetAgro Sup Campus Vétérinaire de Lyon , F69280 Marcy l'Etoile, France.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Kefford</LastName><ForeName>Ben J</ForeName><Initials>BJ</Initials><AffiliationInfo><Affiliation>Institute for Applied Ecology, University of Canberra , Canberra, ACT 2601, Australia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Piscart</LastName><ForeName>Christophe</ForeName><Initials>C</Initials><AffiliationInfo><Affiliation>UMR CNRS 6553, Ecobio, Université de Rennes 1 , 263 Avenue du Général Leclerc, 35042 Rennes, France.</Affiliation></AffiliationInfo></Author><Author 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name="France"><region name="Auvergne-Rhône-Alpes"><name sortKey="Kon Kam King, Guillaume" sort="Kon Kam King, Guillaume" uniqKey="Kon Kam King G" first="Guillaume" last="Kon Kam King">Guillaume Kon Kam King</name></region><name sortKey="Charles, Sandrine" sort="Charles, Sandrine" uniqKey="Charles S" first="Sandrine" last="Charles">Sandrine Charles</name><name sortKey="Delignette Muller, Marie Laure" sort="Delignette Muller, Marie Laure" uniqKey="Delignette Muller M" first="Marie Laure" last="Delignette-Muller">Marie Laure Delignette-Muller</name><name sortKey="Piscart, Christophe" sort="Piscart, Christophe" uniqKey="Piscart C" first="Christophe" last="Piscart">Christophe Piscart</name></country><country name="Australie"><noRegion><name sortKey="Kefford, Ben J" sort="Kefford, Ben J" uniqKey="Kefford B" first="Ben J" last="Kefford">Ben J. 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