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Using phylogenetic information and chemical properties to predict species tolerances to pesticides

Published on Aug 22, 2014
· DOI :10.1098/rspb.2013.3239
GuénardGuillaume8
Estimated H-index: 8
(UdeM: Université de Montréal),
Peter C. von der Ohe26
Estimated H-index: 26
(Helmholtz Centre for Environmental Research - UFZ)
+ 2 AuthorsPierre Legendre85
Estimated H-index: 85
(UdeM: Université de Montréal)
Abstract
Direct estimation of species' tolerance to pesticides and other toxic organic substances is a combinatorial problem, because of the large number of species–substance pairs. We propose a statistical modelling approach to predict tolerances associated with untested species–substance pairs, by using models fitted to tested pairs. This approach is based on the phylogeny of species and physico-chemical descriptors of pesticides, with both kinds of information combined in a bilinear model. This bilinear modelling approach predicts tolerance in untested species–compound pairs based on the facts that closely related species often respond similarly to toxic compounds and that chemically similar compounds often have similar toxic effects. The three tolerance models (median lethal concentration after 96 h) used up to 25 aquatic animal species and up to nine pesticides (organochlorines, organophosphates and carbamates). Phylogeny was estimated using DNA sequences, while the pesticides were described by their mode of toxic action and their octanol–water partition coefficients. The models explained 77–84% of the among-species variation in tolerance (log10 LC50). In cross-validation, 84–87% of the predicted tolerances for individual species were within a factor of 10 of the observed values. The approach can also be used to model other species response to multivariate stress factors.
  • References (28)
  • Citations (16)
References28
Newest
#1Ralf B. Schäfer (Helmholtz Centre for Environmental Research - UFZ)H-Index: 31
#2Nadine V. Gerner (University of Koblenz and Landau)H-Index: 3
Last.Peter C. von der Ohe (Helmholtz Centre for Environmental Research - UFZ)H-Index: 26
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#1Ralph Kühne (Helmholtz Centre for Environmental Research - UFZ)H-Index: 25
#2Ralf-Uwe Ebert (Helmholtz Centre for Environmental Research - UFZ)H-Index: 20
Last.Gerrit Schüürmann (Helmholtz Centre for Environmental Research - UFZ)H-Index: 45
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#1GuénardGuillaume (UdeM: Université de Montréal)H-Index: 8
#2Peter C. von der Ohe (Helmholtz Centre for Environmental Research - UFZ)H-Index: 26
Last.Sovan Lek (Paul Sabatier University)H-Index: 44
view all 5 authors...
#1Eric de Deckere (University of Antwerp)H-Index: 12
#2Ward De Cooman (EA: Environment Agency)H-Index: 5
Last.Peter C. von der Ohe (Helmholtz Centre for Environmental Research - UFZ)H-Index: 26
view all 6 authors...
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#1Manuel E. Ortiz-Santaliestra (CSIC: Spanish National Research Council)H-Index: 18
#2Joao P. Maia (University of Aveiro)H-Index: 2
Last.Isabel Lopes (University of Aveiro)H-Index: 21
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#1Andrew Hylton (USA: University of South Alabama)H-Index: 1
#2Ylenia Chiari (USA: University of South Alabama)H-Index: 18
Last.Scott Glaberman (USA: University of South Alabama)H-Index: 15
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#1Sara M. Esteves (University of Aveiro)H-Index: 1
#2François Keck (INRA: Institut national de la recherche agronomique)H-Index: 8
Last.Frédéric Rimet (INRA: Institut national de la recherche agronomique)H-Index: 29
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#1Elizabeth K. Peterson (SUNY: State University of New York System)H-Index: 3
#2David B. Buchwalter (NCSU: North Carolina State University)H-Index: 25
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#1Roman Ashauer (Ebor: University of York)H-Index: 29
#2Carlo Albert (Eawag: Swiss Federal Institute of Aquatic Science and Technology)H-Index: 11
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