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Correcting for Phylogenetic Autocorrelation in Species Sensitivity Distributions

A species sensitivity distribution (SSD) is a cumulative distribution function of toxicity endpoints for a receptor group. A key assumption when deriving an SSD is that the toxicity data points are independent and identically distributed (iid). This assumption is tenuous, however, because closely re...

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Autores principales: Moore, Dwayne RJ, Priest, Colleen D, Galic, Nika, Brain, Richard A, Rodney, Sara I
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6972980/
https://www.ncbi.nlm.nih.gov/pubmed/31433110
http://dx.doi.org/10.1002/ieam.4207
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author Moore, Dwayne RJ
Priest, Colleen D
Galic, Nika
Brain, Richard A
Rodney, Sara I
author_facet Moore, Dwayne RJ
Priest, Colleen D
Galic, Nika
Brain, Richard A
Rodney, Sara I
author_sort Moore, Dwayne RJ
collection PubMed
description A species sensitivity distribution (SSD) is a cumulative distribution function of toxicity endpoints for a receptor group. A key assumption when deriving an SSD is that the toxicity data points are independent and identically distributed (iid). This assumption is tenuous, however, because closely related species are more likely to have similar sensitivities than are distantly related species. When the response of 1 species can be partially predicted by the response of another species, there is a dependency or autocorrelation in the data set. To date, phylogenetic relationships and the resulting dependencies in input data sets have been ignored in deriving SSDs. In this paper, we explore the importance of the phylogenetic signal in deriving SSDs using a case studies approach. The case studies involved toxicity data sets for aquatic autotrophs exposed to atrazine and aquatic and avian species exposed to chlorpyrifos. Full and partial data sets were included to explore the influences of differing phylogenetic signal strength and sample size. The phylogenetic signal was significant for some toxicity data sets (i.e., most chlorpyrifos data sets) but not for others (i.e., the atrazine data sets, the chlorpyrifos data sets for all insects, crustaceans, and birds). When a significant phylogenetic signal did occur, effective sample size was reduced. The reduction was large when the signal was strong. In spite of the reduced effective sample sizes, significant phylogenetic signals had little impact on fitted SSDs, even in the tails (e.g., hazardous concentration for 5(th) percentile species [HC5]). The lack of a phylogenetic signal impact occurred even when we artificially reduced original sample size and increased strength of the phylogenetic signal. We conclude that it is good statistical practice to account for the phylogenetic signal when deriving SSDs because most toxicity data sets do not meet the independence assumption. That said, SSDs and HC5s are robust to deviations from the independence assumption. Integr Environ Assess Manag 2019;00:1–13. © 2019 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals, Inc. on behalf of Society of Environmental Toxicology & Chemistry (SETAC)
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spelling pubmed-69729802020-01-27 Correcting for Phylogenetic Autocorrelation in Species Sensitivity Distributions Moore, Dwayne RJ Priest, Colleen D Galic, Nika Brain, Richard A Rodney, Sara I Integr Environ Assess Manag Health & Ecological Risk Assessment A species sensitivity distribution (SSD) is a cumulative distribution function of toxicity endpoints for a receptor group. A key assumption when deriving an SSD is that the toxicity data points are independent and identically distributed (iid). This assumption is tenuous, however, because closely related species are more likely to have similar sensitivities than are distantly related species. When the response of 1 species can be partially predicted by the response of another species, there is a dependency or autocorrelation in the data set. To date, phylogenetic relationships and the resulting dependencies in input data sets have been ignored in deriving SSDs. In this paper, we explore the importance of the phylogenetic signal in deriving SSDs using a case studies approach. The case studies involved toxicity data sets for aquatic autotrophs exposed to atrazine and aquatic and avian species exposed to chlorpyrifos. Full and partial data sets were included to explore the influences of differing phylogenetic signal strength and sample size. The phylogenetic signal was significant for some toxicity data sets (i.e., most chlorpyrifos data sets) but not for others (i.e., the atrazine data sets, the chlorpyrifos data sets for all insects, crustaceans, and birds). When a significant phylogenetic signal did occur, effective sample size was reduced. The reduction was large when the signal was strong. In spite of the reduced effective sample sizes, significant phylogenetic signals had little impact on fitted SSDs, even in the tails (e.g., hazardous concentration for 5(th) percentile species [HC5]). The lack of a phylogenetic signal impact occurred even when we artificially reduced original sample size and increased strength of the phylogenetic signal. We conclude that it is good statistical practice to account for the phylogenetic signal when deriving SSDs because most toxicity data sets do not meet the independence assumption. That said, SSDs and HC5s are robust to deviations from the independence assumption. Integr Environ Assess Manag 2019;00:1–13. © 2019 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals, Inc. on behalf of Society of Environmental Toxicology & Chemistry (SETAC) John Wiley and Sons Inc. 2019-11-18 2020-01 /pmc/articles/PMC6972980/ /pubmed/31433110 http://dx.doi.org/10.1002/ieam.4207 Text en © 2019 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals, Inc. on behalf of Society of Environmental Toxicology & Chemistry (SETAC) This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Health & Ecological Risk Assessment
Moore, Dwayne RJ
Priest, Colleen D
Galic, Nika
Brain, Richard A
Rodney, Sara I
Correcting for Phylogenetic Autocorrelation in Species Sensitivity Distributions
title Correcting for Phylogenetic Autocorrelation in Species Sensitivity Distributions
title_full Correcting for Phylogenetic Autocorrelation in Species Sensitivity Distributions
title_fullStr Correcting for Phylogenetic Autocorrelation in Species Sensitivity Distributions
title_full_unstemmed Correcting for Phylogenetic Autocorrelation in Species Sensitivity Distributions
title_short Correcting for Phylogenetic Autocorrelation in Species Sensitivity Distributions
title_sort correcting for phylogenetic autocorrelation in species sensitivity distributions
topic Health & Ecological Risk Assessment
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6972980/
https://www.ncbi.nlm.nih.gov/pubmed/31433110
http://dx.doi.org/10.1002/ieam.4207
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