title: Using phylogenetic information and chemical properties to predict species tolerances to pesticides.
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.
authors.affiliation: Département de Sciences Biologiques, Université de Montréal, CP 6128, Succursale Centre-Ville, Montréal, Québec, Canada H3C 3J7 guillaume.guenard@gmail.com.
authors.forename: Guillaume
authors.initials: G
authors.lastname: Guénard
authors.affiliation: Department of Ecological Chemistry, Helmholtz Centre for Environmental Research-UFZ, Permoserstrasse 15, Leipzig 04318, Germany Amalex Environmental Solutions, Kreuzstrasse 1, Leipzig 04103, Germany.
authors.forename: Peter
authors.initials: P
authors.lastname: Carsten von der Ohe
authors.affiliation: Department of Mathematics and Statistics, McMaster University, 1280 Main Street West, Hamilton, Ontario, Canada L8S 4K1.
authors.forename: Steven
authors.identifier: http://orcid.org/0000-0002-4394-9078
authors.initials: S
authors.lastname: Carlisle Walker
authors.affiliation: Laboratoire Évolution et Diversité Biologique (EDB) UMR 5174, CNRS/Université Paul-Sabatier, 118 Rte de Narbonne 4R3-b1, Toulouse Cedex 9 31062, France.
authors.forename: Sovan
authors.initials: S
authors.lastname: Lek
authors.affiliation: Département de Sciences Biologiques, Université de Montréal, CP 6128, Succursale Centre-Ville, Montréal, Québec, Canada H3C 3J7.
authors.forename: Pierre
authors.identifier: http://orcid.org/0000-0002-3838-3305
authors.initials: P
authors.lastname: Legendre
mesh_terms: D000818:Animals; D007928:Lethal Dose 50; D015233:Models, Statistical; D008962:Models, Theoretical; D010575:Pesticides / Q000494:pharmacology / Q000633:toxicity*; D010802:Phylogeny; D015203:Reproducibility of Results; D013045:Species Specificity; D023382:Toxicity Tests, Acute
chemical_list: D010575:Pesticides
keywords: bilinear model; effect prediction model; pesticide; phylogenetic modelling; tolerance