Concerns about Reproducibility, Use of the Akaike Information Criterion, and Related Issues in Hoondert et al. 2019
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To the Editor:
Estimating species sensitivity distributions (SSDs) is a promising approach that is increasingly used to derive predicted no‐effect concentrations for chemicals in ecological risk assessment. Given the difficulty of and limitations in generating ecotoxicological data, seeking quantitative structure–activity relationship (QSAR) approaches to estimating SSDs is valuable. The recent article by Hoondert et al. (2019) is pioneering in that they developed QSAR‐based models to estimate means and standard deviations (SDs) of SSDs based on acute and chronic toxicity data.
Hoondert et al. (2019) selected a total of 4 models to predict means and SDs of acute and chronic SSDs and then discussed the importance of the predictors included in the models. However, we failed to reproduce their model selection results (i.e., Table 3 of Hoondert et al. [2019]) because their description of the method is not sufficiently detailed. Because they state that "the most parsimonious models were...selected...based on the corrected Akaike information criterion as well as the adjusted R2," we infer that they used both the corrected Akaike information criterion (AICc; Burnham et al. [2011]) and adjusted R2 values for model selection. Assuming that this inference is correct, we raise 2 major concerns about the use of statistical methods and the interpretation thereof by Hoondert et al.: 1) If the AIC(c) is used in determining the "best" model, then the (adjusted) R2 should not also be used for model selection; and 2) Even if the AIC(c) is used correctly to determine the "best" model, final conclusions on the importance of predictors should not be based on this "best" model alone. An additional minor concern, which would likely have had no effect on their general findings, is that AICc values cannot be compared between models developed with different response variables.
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