We present a novel approach to ecological risk assessment by recasting the Species Sensitivity Distribution (SSD) method within a Bayesian nonparametric (BNP) framework. Widely mandated by environmental regulatory bodies globally, SSD has faced criticism due to its historical reliance on parametric assumptions when modeling species variability. By adopting nonparametric mixture models, we address this limitation, establishing a statistically robust foundation for SSD. Our BNP approach offers several advantages, including its efficacy in handling small datasets or censored data, which are common in ecological risk assessment, and its ability to provide principled uncertainty quantification alongside simultaneous density estimation and clustering. We utilize a specific nonparametric prior as the mixing measure, chosen for its robust clustering properties, a crucial consideration given the lack of strong prior beliefs about the number of components. Through simulation studies and analysis of real datasets, we demonstrate the superiority of our BNP-SSD over classical SSD methods. We also provide a BNP-SSD Shiny application, making our methodology available to the Ecotoxicology community. Moreover, we exploit the inherent clustering structure of the mixture model to explore patterns in species sensitivity. Our findings underscore the effectiveness of the proposed approach in improving ecological risk assessment methodologies.

翻译：本文提出了一种生态风险评估的新方法，通过将物种敏感性分布方法重新置于贝叶斯非参数框架中。SSD作为全球环境监管机构广泛采用的方法，长期以来因在模拟物种变异性时依赖参数化假设而受到批评。通过采用非参数混合模型，我们克服了这一局限，为SSD建立了统计稳健的基础。我们的BNP方法具有多重优势：能有效处理生态风险评估中常见的小样本数据集或删失数据；能够在进行密度估计与聚类的同时提供理论严谨的不确定性量化。我们选用具有稳健聚类特性的特定非参数先验作为混合测度，这一选择至关重要，因为对组分数量通常缺乏强先验信念。通过模拟研究和实际数据集分析，我们证明了BNP-SSD相较于经典SSD方法的优越性。我们还开发了BNP-SSD交互式应用，使生态毒理学界能够便捷使用该方法。此外，我们利用混合模型固有的聚类结构探索物种敏感性的模式特征。研究结果充分证明了所提方法在改进生态风险评估方法论方面的有效性。