Examining predictors of chemical toxicity in freshwater fish using the random forest technique

Abstract

Chemical pollution is one of the main issues globally threatening the enormous biodiversity of freshwater ecosystems. The toxicity of substances depends on many factors such as the chemical itself, the species affected, environmental conditions, exposure duration, and concentration. We used the random forest technique to examine the factors that mediate toxicity in a set of widespread fishes and analyses of covariance to further assess the importance of differential sensitivity among fish species. Among 13 variables, the 5 most important predictors of toxicity with random forests were, by order of importance, the chemical substance itself (i.e., Chemical Abstracts Service number considered as a categorical factor), octanol-water partition coefficient (log P), pollutant prioritization, ecological structure-activity relationship (ECOSAR) classification, and fish species for 50% lethal concentrations (LC₅₀) and the chemical substance, fish species, log P, ECOSAR classification, and water temperature for no observed effect concentrations (NOECs). Fish species was a very important predictor for both endpoints and with the two contrasting statistical techniques used. Different fish species displayed very different relationships with log P, often with different slopes and with as much importance as the partition coefficient. Therefore, caution should be exercised when extrapolating toxicological results or relationships among species. In addition, further research is needed to determine species-specific sensitivities and unravel the mechanisms behind them.

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