A species sensitivity distribution (SSD) is a cumulative distribution function of toxicity endpoints for a receptor group. A key assumption when deriving an SSD is that the toxicity data points are independent and identically distributed (iid). This assumption is tenuous, however, because closely related species are more likely to have similar sensitivities than are distantly related species. When the response of 1 species can be partially predicted by the response of another species, there is a dependency or autocorrelation in the data set. To date, phylogenetic relationships and the resulting dependencies in input data sets have been ignored in deriving SSDs. In this paper, we explore the importance of the phylogenetic signal in deriving SSDs using a case studies approach. The case studies involved toxicity data sets for aquatic autotrophs exposed to atrazine and aquatic and avian species exposed to chlorpyrifos. Full and partial data sets were included to explore the influences of differing phylogenetic signal strength and sample size. The phylogenetic signal was significant for some toxicity data sets (i.e., most chlorpyrifos data sets) but not for others (i.e., the atrazine data sets, the chlorpyrifos data sets for all insects, crustaceans, and birds). When a significant phylogenetic signal did occur, effective sample size was reduced. The reduction was large when the signal was strong. In spite of the reduced effective sample sizes, significant phylogenetic signals had little impact on fitted SSDs, even in the tails (e.g., hazardous concentration for 5^th percentile species [HC5]). The lack of a phylogenetic signal impact occurred even when we artificially reduced original sample size and increased strength of the phylogenetic signal. We conclude that it is good statistical practice to account for the phylogenetic signal when deriving SSDs because most toxicity data sets do not meet the independence assumption. That said, SSDs and HC5s are robust to deviations from the independence assumption. Integr Environ Assess Manag 2019;00:1–13. © 2019 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals, Inc. on behalf of Society of Environmental Toxicology & Chemistry (SETAC)

中文翻译：

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校正物种敏感性分布中的系统发育自相关。


物种敏感性分布 (SSD) 是受体组毒性终点的累积分布函数。导出 SSD 时的一个关键假设是毒性数据点是独立同分布 (iid)。然而，这种假设是站不住脚的，因为关系密切的物种比关系疏远的物种更有可能具有相似的敏感性。当一个物种的响应可以通过另一个物种的响应部分预测时，数据集中存在依赖性或自相关性。迄今为止，在推导 SSD 时，输入数据集中的系统发育关系和由此产生的依赖性已被忽略。在本文中，我们使用案例研究方法探讨了系统发育信号在推导 SSD 中的重要性。案例研究涉及暴露于莠去津的水生自养生物以及暴露于毒死蜱的水生和鸟类物种的毒性数据集。包括完整和部分数据集，以探索不同系统发育信号强度和样本量的影响。系统发育信号对于某些毒性数据集（即大多数毒死蜱数据集）很重要，但对于其他数据集（即莠去津数据集、所有昆虫、甲壳类动物和鸟类的毒死蜱数据集）则不重要。当显着的系统发育信号确实出现时，有效样本量就会减少。当信号强时，减少幅度较大。尽管有效样本量减少，显着的系统发育信号对安装的 SSD 几乎没有影响，即使是在尾部（例如，第 5^个百分位物种的危险浓度 [HC5]）。即使我们人为地减少原始样本大小并增加系统发育信号的强度，系统发育信号影响的缺乏也会发生。 我们的结论是，在推导 SSD 时考虑系统发育信号是一种很好的统计做法，因为大多数毒性数据集不满足独立性假设。也就是说，SSD 和 HC5 对于独立性假设的偏差具有鲁棒性。整体环境评估管理2019；00:1–13。 © 2019 作者。综合环境评估和管理由 Wiley periodicals, Inc. 出版。代表环境毒理学与化学学会 (SETAC)