From species traits to protection goals: Individual-based modeling for ecological risk assessment across species

Ecological risk assessment (ERA) is moving toward greater ecological realism, where population-level responses across focal species are evaluated. A central challenge is therefore to extrapolate chemical effects to the population-level responses of numerous non-test species, for which biological and ecological data are often scarce. Trait-based approaches offer a practical means to meet this challenge by representing species through characteristics that determine exposure, toxicological sensitivity, life history, and population resilience. They can be used both to identify potentially vulnerable species and to group functionally similar taxa, supporting the selection of representative, data-rich species for modeling. Individual-based models provide a particularly suitable mechanistic framework for trait-based ERA. They translate individual variation in traits and chemical effects into population-level outcomes while retaining a common model structure across species and expressing interspecific differences through parameter values. This enables comparisons of vulnerability, identification of influential traits, and extrapolation to less-studied taxa. Examples from fish and solitary bees illustrate the potential of this approach. Improved biodiversity observations, trait databases, and machine-learning methods are expanding the data available for such models. However, credible application requires biologically realistic species profiles and model validation. Integrating trait-based and individual-based approaches can therefore strengthen population-relevant ERA and better inform risk assessment and management.