Estimation of Chemical Toxicity to Wildlife Species Using Interspecies Correlation Models

Abstract

Ecological risks to wildlife are typically assessed using toxicity data for relatively few species and with limited understanding of differences in species sensitivity to contaminants. Empirical interspecies correlation models were derived from LD50 values for 49 wildlife species and 951 chemicals. The standard wildlife test species Japanese quail (Coturnix japonica) and mallard (Anas platyrhynchos) were determined to be good surrogates for many species within the database. Cross-validation of all models predicted toxicity values within 5-fold and 10-fold of the actual values with 85 and 95% certainty, respectively. Model robustness was not consistently improved by developing correlation models within modes of action (MOA); however, improved models for neurotoxicants, carbamates, and direct acting organophosphorous acetylcholenesterase inhibiting compounds indicate that toxicity estimates may improve if MOA-specific models are built with robust datasets. There was a strong relationship between taxonomic distance and cross-validation prediction success (chi2 = 297, df = 12, p < 0.0001), with uncertainty increasing with larger taxonomic distance between the surrogate and predicted species. Interspecies toxicity correlations provide a tool for estimating contaminant sensitivity with known levels of uncertainty for a diversity of wildlife species.