Understanding and Predicting Population Response to Anthropogenic Disturbance: Current Approaches and Novel Opportunities

作者：Cassie N. Speakman, Sarah Bull, Sarah Cubaynes, Katrina Davis, Sébastien Devillard, John M. Fryxell, Cara A. Gallagher, Elizabeth A. McHuron, Kévan Rastello, Isabel M. Smallegange, Roberto Salguero‐Gómez, Elsa Bonnaud, Christophe Duchamp, Patrick Giraudoux, Simon Lacombe, Courtney J. Marneweck, Louis Schroll, Adrien Tableau, Sandrine Ruette, Olivier Giménez · 发表于：Ecology Letters · 年份：2025 · DOI：10.1111/ele.70198 · 被引用次数：5 · 研究领域：Climate change impacts on agriculture、Species Distribution and Climate Change、Climate Change Communication and Perception

Effective conservation of biodiversity depends on the successful management of wildlife populations and their habitats. Successful management, in turn, depends on our ability to understand and accurately forecast how populations and communities respond to human-induced changes in their environments. However, quantifying how these stressors impact population dynamics remains challenging. Another significant hurdle at this interface is determining which quantitative approach(es) are most appropriate given data types, constraints and the intended purpose. Here, we provide a cross-taxa overview of key methodological approaches (e.g., matrix population models) and model elements (e.g., energetics) that are currently used to model the effects of anthropogenic disturbance on wildlife populations. Specifically, we discuss how these modelling approaches differ in their key assumptions, in their structure and complexity, in the questions they are best poised to address and in their data requirements. Our intention is to help overcome some of the methodological biases that might persist across taxonomic specialisations, identify new opportunities to address existing modelling challenges and improve scientific understanding of the direct and indirect impacts of anthropogenic disturbance. We guide users through the identification of appropriate model configurations for different management purposes, while also suggesting key priorities for model development and integration.