Species Sensitivity Distribution revisited: a Bayesian nonparametric approach

TL;DR

This paper introduces a Bayesian nonparametric framework for Species Sensitivity Distribution, enhancing ecological risk assessment by addressing limitations of traditional parametric models, especially with small or censored datasets.

Contribution

The paper develops a novel BNP-based SSD method that improves density estimation, uncertainty quantification, and species clustering analysis in ecological risk assessments.

Findings

BNP-SSD outperforms classical methods in simulations

Effective handling of small and censored datasets

Provides a user-friendly Shiny application for ecotoxicology

Abstract

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.