Conclusions
This paper outlines a methodological framework for designing and implementing ABMs tailored to address applied ecological challenges. By integrating diverse datasets, parameterising agents and environments with empirical evidence, and leveraging iterative feedback loops between simulations and field data, this framework provides a robust tool for informing management and policy decisions. The case study on badger movement and bovine tuberculosis risk illustrates the utility of this approach, showcasing how ABMs can uncover unexpected trends, such as the influence of farm density and spatial configuration on disease transmission. These insights demonstrate the power of ABMs to go beyond conventional thinking, providing actionable, evidence-based recommendations that are grounded in real-world data. The continuous feedback between simulation outputs and field monitoring not only improves the model’s accuracy and relevance but also fosters a circular data-economy, ensuring that research investments yield maximum returns by enhancing both field data collection and model refinement.
Despite the significant potential of this framework, challenges remain. The reliance on high-quality data and substantial computational resources highlights the need for investment in data collection infrastructure and access to high-performance computing. However, these challenges also present opportunities to identify critical data gaps, engage stakeholders in meaningful collaborations, and establish long-term monitoring programs. Addressing these challenges ensures that ABMs evolve as trusted, actionable tools for addressing complex ecological scenarios. By providing a transparent, data-driven platform for exploring ecological interactions, this framework enhances decision-making processes, reduces risk in management experiments, and promotes collaboration among researchers, policymakers, and stakeholders. As ABMs continue to evolve, their ability to integrate real-world data, simulate dynamic systems, and offer evidence-based solutions will position them as indispensable tools in applied ecology, driving more sustainable and effective conservation and management strategies.
By providing a transparent, data-driven workflow for implementing these ABMs, this framework aims to enhance decision-making processes, reduce risk in management experiments, and promote collaboration among researchers, policymakers, and stakeholders. As ABMs continue to evolve, their ability to integrate real-world data, simulate dynamic systems, and offer evidence-based solutions will position them as indispensable tools in applied ecology, driving more sustainable and effective conservation and management strategies. We aim to offer this framework as a repeatable protocol to guide researchers in their workflow and to increase the adoption and application of this methodology in future studies. By providing a clear and systematic approach, we hope to inspire broader use of ABMs to tackle pressing conservation challenges and management challenges in applied ecology effectively and efficiently.