Influence of Taxonomic Relatedness and Chemical Mode of Action in Acute Interspecies Estimation Models for Aquatic Species

Ecological risks to aquatic organisms are typically assessed using acute toxicity data for relatively few species and with limited understanding of relative species sensitivity. We developed a comprehensive set of interspecies correlation estimation (ICE) models based on acute toxicity data for aquatic organisms and evaluated three key sources of model uncertainty: taxonomic relatedness, chemical mode of action (MOA), and model parameters. Models are least-squares regressions of acute toxicity of surrogate and predicted species. A total of 780 models were derived from acute values for 77 species of aquatic organisms and over 550 chemicals. Cross-validation of models showed that accurate model prediction was greatest for models with surrogate and predicted taxa within the same family (91% of predictions within 5-fold of measured values). Recursive partitioning provided user guidance for selection of robust models using model mean square error and taxonomic relatedness. Models built with a single MOA were more robust than models built using toxicity values with multiple MOAs, and improve predictions among species pairs with large taxonomic distance (e.g., within phylum). These results indicate that between-species toxicity extrapolation can be improved using MOA-based models for less related taxa pairs and for those specific MOAs.

水生生物的生态风险评估通常仅基于少数物种的急性毒性数据，且对物种间相对敏感性的认知较为有限。本研究基于水生生物急性毒性数据构建了一套完整的物种间相关性估计（interspecies correlation estimation, ICE）模型，并对模型不确定性的三大关键来源进行了评估：分类学相关性、化学品作用模式（mode of action, MOA）以及模型参数。此类模型为替代物种与预测物种急性毒性的最小二乘回归模型。本研究共基于77种水生生物的急性毒性数据与550余种化学品，构建了780个模型。模型交叉验证结果显示，当替代物种与预测物种属于同一科时，模型预测准确率最高——此时91%的预测值与实测值的偏差在5倍以内。递归分区方法可基于模型均方误差与分类学相关性，为用户筛选稳健模型提供指导。基于单一作用模式构建的模型，相较于整合多种作用模式毒性数据构建的模型更为稳健，且可提升分类学距离较远的物种对（例如同门物种间）的预测精度。上述结果表明，针对分类学关联较弱的物种对以及特定作用模式的物种，采用基于作用模式的模型可优化物种间毒性外推效果。