This research aims to account for the uncertainty on 50% effective rates (ER₅₀) in species sensitivity distribution (SSD) analyses and to study how including this uncertainty may influence the 5% Hazard Rate (HR₅) estimation. We explored various endpoints (survival, emergence, shoot dry weight) for non-target plants from seven standard greenhouse studies that used different experimental approaches (vegetative vigour vs. seedling emergence) and applied seven herbicides at different growth stages. Firstly for each endpoint of each study, a three-parameter log-logistic model was fitted to experimental toxicity test data for each species under a Bayesian framework to get a posterior probability distribution for ER₅₀. Then in order to account for the uncertainty on the ER₅₀, we explored two censoring criteria to censor ER₅₀ taking the ER₅₀ distribution and the range of tested rates into account. Based on dose-response fitting results and censoring criteria, we considered input ER₅₀ values SSD analyses in three ways (only point estimates chosen as ER₅₀ medians, interval-censored ER₅₀ based on their 95% credible interval and censored ER₅₀ according to one of the two criteria), by fitting a log-normal distribution under a frequentist framework to get the three corresponding HR₅ estimates. We observed that SSD fitted reasonably well when there were at least six distinct ER₅₀ values. By comparing the three SSD curves and the three HR₅ estimates, we found that propagating the uncertainty from ER₅₀ and including censored data into the SSD analysis often leads to smaller point estimates of HR₅, which is more conservative in a risk assessment context. In addition, we recommend not to focus solely on the point estimate of the HR₅, but also to look at the precision of this estimate as depicted by the 95% confidence interval.

中文翻译：

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如何考虑物种敏感性分布中标准毒性测试的不确定性：非目标植物中的一个例子

这项研究旨在解决物种敏感度分布（SSD）分析中50％有效率（ER ₅₀）的不确定性，并研究包括这种不确定性如何影响5％危害率（HR ₅）的估计。我们从七项标准温室研究中探索了非目标植物的各种终点（存活率，出苗率，枝干重），这些研究使用了不同的实验方法（营养活力与幼苗出苗），并在不同的生长阶段施用了七种除草剂。首先，对于每个研究的每个终点，在贝叶斯框架下将三参数对数逻辑模型拟合到每个物种的实验毒性测试数据，以获得ER ₅₀的后验概率分布。。然后，为了考虑到ER ₅₀的不确定性，我们考虑了ER _50的分布和测试速率的范围，探索了两种审查标准来审查ER ₅₀。根据剂量-反应拟合结果和审查的标准，我们认为输入ER _50个值SSD以三种方式（仅点估计选为ER分析_50个中位数，区间删失ER ₅₀基于其95％可信区间与删ER ₅₀根据两个标准之一），通过在常识框架下拟合对数正态分布来获得三个对应的HR ₅估计。我们观察到，当至少有六个不同的ER ₅₀值时，SSD非常合适。通过比较三个SSD曲线和三个HR ₅估计值，我们发现，将ER ₅₀的不确定性传播到SSD分析中并包括审查数据通常会导致HR _5的点估计较小，这在风险评估的情况下更为保守。此外，我们建议不要仅关注HR ₅的点估计，还应考虑95％置信区间所描绘的估计精度。