Kilian J Murphy¹

¹Laboratory of Wildlife Ecology and Behaviour, SBES, University College Dublin, Ireland

*Corresponding Author: kilian.murphy@murphyecology.com

Abstract1. Agent-based models (ABMs) are powerful tools for exploring ecological systems but have historically been applied predominantly to theoretical research rather than practical management and policy contexts. Traditionally, ABMs rely on simplified agents and environments, often lacking integration with real-world data, limiting their utility in addressing complex, jurisdiction-specific ecological challenges.

2. This research presents a novel framework for building applied ABMs that integrate high-resolution spatial, behavioural, and environmental datasets. By leveraging telemetry data, remote sensing, and site-specific monitoring, the framework creates realistic, data-driven emulations of ecological systems. These models enable researchers and policymakers to test management and policy interventions virtually, offering insights into system-level dynamics and emergent trends without the risks, costs, or delays of field-based experiments.

3. We highlight this framework’s ability to significantly enhance the realism of ABMs, increasing their robustness, transparency, and trustworthiness through rigorous data input and validation. By uncovering critical data gaps and fostering a circular feedback loop between simulation outputs and field data collection, the framework ensures continuous refinement of models and monitoring programs. We demonstrate the efficacy of this approach using a published workflow examining the role of badger movement in a disturbance landscape and its implications for disease transmission, showcasing how the framework supports evidence-based management and policy decisions.

4. This research establishes a repeatable protocol for integrating ABMs into applied ecology, enhancing their capacity to inform evidence-based decisions. By promoting transparency, collaboration, and trust among stakeholders, it positions ABMs as an essential tool within applied ecology for addressing pressing conservation challenges and human-wildlife conflicts in a cost-effective, adaptable, and scientifically rigorous manner.

Introduction In the Anthropocene, ecological systems are shaped by human activity, whether that is through positive forces such as conservation and management of ecosystems or negative impacts such as habitat destruction, climate change, overexploitation of natural resources, pollution, and the introduction of invasive species (Young et al. 2016; Von Essen et al. 2023). These impacts have heightened pressures on biodiversity and ecosystem services, often placing wildlife in direct conflict with human interests (Simkin et al. 2022). This tension is particularly evident in shared production landscapes (e.g. agricultural landscapes, forestry and aquaculture), where wildlife species interact frequently with human interests, leading to a wide variety human-wildlife conflicts across the globe (König et al. 2020; Munguia-Carrara, 2020; Göttert & Stark, 2022). Addressing these challenges demands applied ecological approaches that integrate diverse data sources, including monitoring from remote sensing technologies, systematic field surveys, and citizen science initiatives that are transformed into actionable evidence through state-of-the-art modeling techniques, such as agent-based and predictive ecological models, which capture the complexity of interactions between species, environments, and human activity (Silvy, 2020; Murphy et al. 2020; Morera-Pujol et al. 2023). Crucially, applied ecology extends beyond data analysis; it necessitates transdisciplinary collaboration among scientists, policymakers, and stakeholders to co-design conservation and management strategies (Silvy, 2020; Fuller et al. 2020; Murphy et al. 2022a).

The field of applied ecology aims to foster win-win scenarios where ecological integrity is preserved while supporting human livelihoods and societal needs (Hopkins et al. 2021; Hegwood et al. 2022). Applied ecology is a cornerstone of evidence-based management and policy, but it remains a time-, resource, and cost-intensive endeavour (Manfredo et al. 2021). Shortages in personnel, data availability, or even appetite for evidence-based decision-making can significantly limit its broader implementation, posing challenges to its role in informing wildlife conservation and management (Mafredo et al. 2021). While applied ecology has tested frameworks for addressing conservation challenges (e.g. adaptive wildlife management), its practical implementation faces significant challenges (Dressel et al. 2018; Mansson et al. 2023). One of the foremost challenges lies in the inherent complexity and variability of ecological systems, where universal problems change often require system-specific solutions. Developing effective solutions requires longitudinal data to evaluate the long-term impacts of interventions, which are costly to establish and maintain long-term (Caughlan & Oakley, 2001; Lindenmayer et al. 2022). Without this iterative feedback process, conservation efforts risk oversimplification, leading to plans that fail to capture the nuance of real-world systems and deliver sustainable outcomes.

Another critical challenge lies in the patchy availability and inconsistent quality of ecological data, which can significantly limit applied ecology’s effectiveness. Monitoring efforts are often unevenly distributed, with some regions or taxa receiving extensive attention while others remain poorly understood (Stephenson et al. 2015). This imbalance frequently forces researchers to rely on proxies or indices as substitutes for high-quality data, which may oversimplify ecological complexity and reduce the accuracy of conclusions (Murphy et al. 2022b). Additionally, many ecological datasets are difficult to access or entirely lost—a phenomenon known as ”dark data.” These datasets, often produced by small-scale, investigator-led studies, are rarely curated or shared due to limited funding, lack of incentives, and the complexity of managing heterogeneous data types (Hampton et al. 2013). The issue is compounded by a historical reliance on “long-tail science,” where small projects generate large amounts of data without sufficient resources for data management or sharing (Hampton et al. 2013). As a result, much of the information remains siloed, inaccessible for broader synthesis or reuse. Poor data infrastructure and insufficient collaboration further hinder the development of comprehensive datasets necessary for informing conservation and management decisions. Despite these challenges, applied ecology has made significant strides by integrating cutting-edge technologies and fostering collaboration across disciplines. Addressing these pitfalls is essential to furthering its impact and ensuring that conservation and management efforts are both effective and equitable.

Agent-based modelling (ABMs) is a method that holds immense potential to address many of the pitfalls inherent in conducting research in applied ecology, offering a framework to integrate diverse data sources and explore the complexity of ecological systems in unprecedented detail (Murphy et al. 2020). By simulating individual agents (e.g., animals, plants, humans) and their interactions with the environment, ABMs can capture the emergent dynamics of ecosystems, allowing researchers to predict the outcomes of conservation interventions under various scenarios (McClane et al. 2011). These models are particularly well-suited to tackle system-specific challenges, such as species-specific responses to habitat management, or to assess the cascading impacts of management actions across landscapes (McClane et al. 2011; Murphy et al. 2020). However, while their use is increasing in applied ecology (Recio et al. 2020; Gritter et al. 2024; Murphy et al. 2024; Thompson et al. 2024), the application of ABMs to real-world applied ecological management and decision-making has historically been limited relative to the potential of the method.

Traditionally, ABMs have been used predominantly in theoretical or ”blue-sky” ecological research, focusing on advancing fundamental ecological theory rather than providing actionable insights for applied contexts (Chivers et al. 2014; Ringelman, 2014; Kane et al. 2016; Teckentrup et al. 2018). This stems, in part, from the perception that ABMs lack the nuance and complexity needed to reflect real-world systems accurately. ABMs in ecology have traditionally not been parameterised with the level of realism required for evidence-based management. Instead, they often use oversimplified agents and environments, aiming to extrapolate patterns and mechanisms that can be generalised add nuanced evidence about the ecology of the species but not necessarily how to management them within a specific jurisdiction (Chivers et al. 2014; Carter et al. 2015). While this approach has provided valuable insights into ecological processes, it has limited the ability of ABMs to address system-specific management questions directly. Many ABMs assess their efficacy based on their ability to recreate patterns observed in the field, yet they often lack validation using independent, external, and longitudinal datasets (Carter et al. 2015). This absence of robust validation, which would confirm that model outputs align with real-world outcomes over time, has been a significant barrier to their application in high-stakes decision-making. Without this level of proof, ABMs struggle to gain the trust needed to inform contentious management scenarios or resolve conflicts involving multiple stakeholders, who often have significant stakes in the outcomes and cannot afford to rely on models that lack the realism to address their specific circumstances.

Recent advancements, however, are rapidly transforming the potential of ABMs for applied ecology. The growth of big data repositories (e.g. MoveBank), high-resolution remote sensing (e.g. Google Earth Engine), and satellite imagery (e.g. Copernicus) now provides a wealth of spatially and temporally detailed datasets that can be used to parameterise ABMs with new levels of realism. Furthermore, the integration of artificial intelligence, including machine learning techniques, has enhanced the ability to develop ABMs capable of capturing complex, non-linear dynamics and emergent behaviours in the model. Improvements in computational power and software capabilities have also significantly reduced the time and resource requirements for running large-scale simulations with millions of agents and hundreds of procedures. Crucially, the community-driven, continual development of popular modeling environments such as R (R Core Team, 2024) and NetLogo (Wilensky, 1999) has played a pivotal role in advancing the capabilities of ABMs. These platforms now enable the seamless integration of diverse datasets and sub-models through extensions (e.g. gis, csv) , facilitating the creation of hyper-realistic agents and environments. This progress has allowed ABMs to scale up their applications, testing applied ecological systems in ways that were previously unfeasible. Combined with improvements in computational power and software efficiency, these advancements have made large-scale simulations increasingly accessible and practical for addressing real-world ecological challenges.

In this paper, we present a methodological framework for building realistic agent-based models tailored to informing applied ecological challenges, hereafter referred to as “applied ABMs.” We demonstrate how to effectively parameterise agents and their environments using diverse input datasets and outline methods for generating data to feed into multivariable modelling and external validation protocols. This approach aims to deliver evidence-based insights for management and policy while integrating feedback loops that inform field data collection systems through applied ABMs. By fostering an adaptive data economy, this framework addresses issues such as dark data and long-tail science, promoting iterative improvement in applied ecological management. To illustrate the framework’s practical application, we refer to its use in a previously published case study (Murphy et al. 2024), which investigates the role of wildlife hosts in the transmission of a zoonotic disease within a disturbance-driven landscape. This example highlights how applied ABMs can address pressing ecological and management questions with enhanced realism and practical utility.