Advancing causal inference in ecology: Pathways for biodiversity change detection and attribution

Abstract Understanding the causes of biodiversity change is essential for addressing environmental challenges. While causal attribution has advanced in other fields, ecologists remain cautious about causal claims or misinterpret predictive models as causal. With growing spatio‐temporal data, computational power and cross‐disciplinary collaboration, discussions on improving attribution methods in ecology are gaining momentum. However, practical guidance remains limited for non‐experts. Here, we identify the challenges and decisions involved in detecting and attributing biodiversity change and provide an overview of suitable methods based on available data and specific research questions. The first challenge we address pertains to biodiversity and driver data. Unlike controlled experimental data in other disciplines, ecological data often stem from monitoring programs or field samplings with varying degrees of rigour, which complicates the analysis due to sampling biases, interacting drivers, measurement error or spatio‐temporal variations. We specifically outline how data structure (e.g. structured vs. opportunistic data) and data coverage along the spatial and temporal scale impact detection and attribution. The second challenge involves the ability to detect directional change in the system of interest, which is associated with numerous hurdles. We provide an overview of the most relevant approaches to deal with sampling variability, gaps and biases in the data, non‐linearity in the temporal trends and to identify the most appropriate spatio‐temporal resolution. For the third challenge, causal attribution, we focus on data‐driven approaches. We review recent frameworks that draw on methodologies from other disciplines, offering analytical roadmaps and step‐by‐step guidance for causal inference. These include constructing theoretical causal models a priori, full causal models based on data and theory and posterior causal interpretation tailored to specific data and research questions. Moving forward, it is essential to foster interdisciplinary collaboration to adapt and refine methodologies from other fields, ensure robust data collection and sharing practices, promote the integration of advanced computational tools and improve the link between data‐driven and theory‐driven approaches. This approach will enhance our ability to make robust causal inferences; thereby improving our understanding of biodiversity changes and informing effective conservation strategies.