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.