Understanding the causes of biodiversity change is central to improving our predictions of and responses to future changes. While causal attribution has progressed in fields such as epidemiology and economics, ecology has remained cautious, often avoiding causal claims or conflating predictive models with causal inference. However, with the rapid growth of and access to spatio-temporal biodiversity data, increased computational capacity, and interdisciplinary collaboration, there is renewed momentum to strengthen causal reasoning in ecological research.

This session will highlight recent advances in advancing causal inference in biodiversity science, including theoretical approaches, integrating underused and novel modelling perspectives, and applied uses of biodiversity change detection and attribution frameworks. We will highlight the key challenges and opportunities in applying causal approaches to biodiversity change analyses, offering an accessible overview of current methods and decision points for ecologists and applied practitioners.

Our session is aimed at fostering dialogue across disciplines, highlighting pathways towards integrating theory with data-driven approaches to advance robust causal inference in biodiversity science. We particularly welcome contributions on integrating causal and process-based models, as well as novel applications of detection-attribution frameworks, as well as studies addressing the interface of ecological causal monitoring, policy, and conservation planning.