Agent-based models in applied ecology: Designing data-informed simulations for wildlife conservation and management

Agent-based models (ABMs) are increasingly recognized as valuable tools in applied ecology for simulating species behavior, ecological interactions, and responses to management. However, their adoption in conservation and policy contexts has been limited by a reliance on simplified representations and a lack of integration with empirical data. This paper presents a structured, data-informed framework for developing applied ABMs using high-resolution spatial, behavioral, and environmental datasets. By incorporating telemetry data, remote sensing products, and site-level ecological monitoring, the framework enables realistic simulations of ecological systems that can be used to virtually test management strategies and policy interventions. These models support real-time scenario testing, guide field data collection by identifying knowledge gaps, and facilitate transparent communication with stakeholders. We demonstrate the utility of this framework using a published case study on badger movement and bovine tuberculosis risk in a disturbance-driven landscape, showing how it reveals emergent behavioral patterns with implications for disease management. By formalizing a repeatable protocol for model development, validation, and stakeholder engagement, this research enhances the accessibility and applicability of ABMs in conservation planning, biodiversity monitoring, and human–wildlife conflict mitigation. The framework supports evidence-based decision-making while promoting transparency, adaptability, and cross-sector collaboration.