A Blueprint Workflow for Quantifying Chemical Impacts on Freshwater Biodiversity

Chemical pollution is a driver of biodiversity decline, yet quantitative frameworks capable of linking chemical exposure to field­-observed biodiversity impacts remain underdeveloped. To address this gap, we present a “blueprint” workflow developed under the NORMAN Joint Programme of Activities (2025), which is designed as a template for systematically analysing the presence and magnitude of ecotoxicity effects of chemical mixtures in multiple-stressed ecosystems, and express the biodiversity damage of chemical pollution in freshwater ecosystems.

The workflow synthesizes existing data, models, and lines of evidence into an integrated approach that translates environmental chemical data, organism-level toxicity, and mixture effects into ecologically meaningful biodiversity metrics across structural, functional, genetic, and ecosystem-service dimensions. We reviewed existing hazard metrics and identified key limitations in current environmental risk assessment, emphasizing the need for biodiversity-relevant units of damage and improved data streams. To illustrate this approach, we evaluated several datasets such as NAIADES (France) and River Rhine (Germany) combining freshwater chemical monitoring and biological monitoring at geographically large scales in Europe. We used them sequentially to address specific questions regarding the link between mixture toxicity pressure and ecosystem level biodiversity damage.

Initial analyses highlight the inherent difficulty in establishing clear correlations between the toxic pressure of chemical mixtures and biodiversity indices. Key confounding factors include historically degraded species communities, absent baseline data, substantial ecological variation across regions, and poor spatiotemporal alignment of chemical and biological datasets. Field studies that do achieve spatial–temporal overlap between chemical exposure and ecological data typically focus only on changes in species richness or abundance, providing a narrow view of biodiversity loss. These findings reinforce the need for refined metric selection, consideration of ecosystem-specific sensitivity, and approaches that leverage large spatial or temporal gradients. We propose a structured set of decisions for selecting datasets, biodiversity, toxic pressure indicators, and statistical methods to test causal pathways and quantify damage levels.

The resulting workflow will guide reproducible chemical–biodiversity linkages across regions and scales, supporting both scientific understanding and regulatory decisions, and paving the way toward operational biodiversity damage metrics and more effective chemical pollution prevention, assessment and management in freshwater ecosystems.