Expert judgment based multicriteria decision models to assess the risk of pesticides on reproduction failures of grey partridge

Abstract

A suite of models is proposed for estimating the risk of pesticides against the grey partridge (Perdix perdix) and their clutches. Radio-tracked data of females, description and location of the clutches, and data on the pesticide treatments during the laying periods of the partridges were used as basic information. Quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) modelling allowed us to characterize the pesticides by their 1-octanol/water partition coefficient (log P), vapour pressure, primary and ultimate biodegradation potential, acute toxicity (LD) on P. perdix, and endocrine disruption potential. From these physicochemical and toxicological data, the system of integration of risk with interaction of scores (SIRIS) method was used to design scores of risk for pesticides, alone or in mixture. A program, written in R (version 3.1.1), called Simulation of Toxicity in Perdix perdix (SimToxPP), was designed for estimating the risk of substances, considered alone or in mixture, against the grey partridge during breeding. The software tool is flexible enough to simulate realistic in situ scenarios. Different examples of applications are shown. The advantages and limitations of the approach are briefly discussed.

Exploring regression-based QSTR and i-QSTR modeling for ecotoxicity prediction of diverse pesticides on multiple avian species

“…[17][18][19] However, the reported in silico models for avian species are very less in number. 4,5,[20][21][22][23][24][25][26] Aer a thorough analysis of the reported models, we have seen that until now all the models were built using a small dataset except Zhang et al, 2015, 5 who used 663 diverse chemicals (pesticides) to build models for 17 different avian species. Until now, all QSTR models were built for single or multiple species, 5,27,28 using different machine learning approaches such as classication-based applications using LDA 29 and GFA followed by PLS.…”