MOSAIC_SSD: a new web-tool for the Species Sensitivity Distribution, allowing to include censored data by maximum likelihood

TL;DR

MOSAIC_SSD is a web-based tool that extends Species Sensitivity Distribution analysis by incorporating censored data through maximum likelihood, improving dataset representativeness and hazard prediction accuracy.

Contribution

It introduces a new web-tool that enables SSD analysis with censored data using maximum likelihood, enhancing data utilization and risk assessment.

Findings

Including censored data improves SSD accuracy.

MOSAIC_SSD provides hazard predictions with confidence intervals.

Application to published data demonstrates added value.

Abstract

Censored data are seldom taken into account in Species Sensitivity Distribution (SSD) analysis. However, they are found in virtually every dataset and sometimes represent the better part of the data. Stringent recommendations on data quality often lead to discard a lot of this meaningful data, often resulting in datasets of reduced size, which lack representativeness of any realistic community. However, it is reasonably simple to include censored data into SSD by using an extension of the standard maximum likelihood method. In this paper, we detail this approach based on the use of the R-package \emph{fitdistrplus}, dedicated to the fit of parametric probability distributions. In particular, we introduce the new web-tool MOSAIC$\_$SSD to fit an SSD on datasets containing any kind of data, censored or not. MOSAIC$\_$SSD allows predicting any Hazardous Concentration (HC) and provides in addition bootstrap confidence intervals on the prediction. In the end, taking examples from published data, we illustrate the added value of including censored data in SSD.