Fig. 5: Schematic of the applied ABM framework for informing
ecological management decisions. The workflow starts with defining
objectives, followed by data collection, parameterisation,
experimentation, analysis, and validation. The final step, evaluation
and feedback, translates model insights into policy recommendations,
research directions, and improved monitoring methods, creating a
continuous improvement loop between simulations and real-world
management practices.
Discussion The findings of this study demonstrate the potential of agent-based
models (ABMs) to cross-over from the field of theoretical ecology and
become more readily used in the field of applied ecology, particularly
in the scope of management and policy for conservation and conflict
resolution. By integrating high-resolution spatial and behavioural data,
the presented framework enabled simulations that reflected real-world
dynamics with unprecedented accuracy. For example, the case study
revealed how clearfell forestry operations influence badger movement
patterns, generating actionable insights into the management of bovine
tuberculosis risk across a national landscape and measurable reporters
for policy makers to assess and implement as required. These results
underscore the utility of ABMs as tools for exploring complex ecological
interactions, predicting outcomes under different management scenarios,
and identifying gaps in field data which can maximise the return on
investment for field based research.
Recent advancements in computational power, big data availability, and
remote sensing technologies have expanded the scope of ABMs in applied
ecology (Hampton et al. 2013; Skidmore et al. 2021). High-resolution
telemetry datasets, such as those used in this study, now allow for the
parameterization of agents with data-driven behaviours rather than
relying on rule-based or arbitrary processes. Furthermore, the
availability of global spatial data repositories such as MOVEBANK and
the EUROMAMMALS initiative have made this data even more accessible to
researchers who lack this information in their jurisdictions (Ferrari et
al. 2022; Kays et al. 2022). Similarly, dynamic environmental data,
including climate models and habitat indices, can transform static
landscapes into systems that respond to temporal and spatial
variability, with powerful new technology such as the Google Earth
Engine and the Copernicus Initiative offering open-access spatial data
(Tamiminia et al. 2020; Buhler et al. 2021). These advancements position
ABMs as essential tools for tackling challenges in wildlife conservation
and human-wildlife conflict, providing nuanced, system-specific insights
that traditional methods often lack.
In applied contexts, ABMs offer a unique advantage: the ability to
synthesize complex, multi-dimensional data into cohesive simulations
that capture emergent dynamics. This capability enables researchers and
managers to test interventions, such as altering forestry practices or
adjusting hunting quotas, in a virtual environment before implementing
them in the field. By simulating the potential outcomes of various
scenarios, ABMs reduce the risks and costs associated with experimental
management while ensuring that decisions are informed by robust,
data-driven evidence (Murphy et al. 2020). As demonstrated here, ABMs
are no longer theoretical tools but are increasingly integral to solving
real-world ecological challenges. Field-based management experiments,
while invaluable, often come with prohibitive costs, significant risks,
and long timelines (Silvy, 2020). Poorly executed actions can lead to
unintended ecological consequences, economic losses, and even conflicts
among stakeholders (Silvy, 2020). For example, decisions such as whether
to cull or vaccinate badgers as a means of controlling bovine
tuberculosis often polarise stakeholders, with little consensus on the
most effective course of action due to gaps in evidence or differing
values (Martin et al. 2020). Moreover, the outcomes of management
actions can take years to manifest, delaying the ability to evaluate
their efficacy and adapt strategies accordingly.
ABMs driven by high-resolution real-world data and tailored to specific
jurisdictions, provide a risk-free platform to explore these management
scenarios. These models allow decision-makers to simulate the impacts of
various strategies, such as altering forestry practices or implementing
targeted interventions, without the ecological or economic risks of
testing them in the field. By capturing the complexity of
species-environment interactions and the emergent dynamics of
ecosystems, ABMs offer a level of precision that can significantly
enhance management planning. For instance, running simulations on the
effects of vaccination versus culling in specific landscapes can reveal
potential outcomes, such as reductions in disease transmission or
unintended population shifts, offering a clear comparison of strategies.
Importantly, this virtual experimentation fosters collaboration by
providing a neutral, evidence-based tool to evaluate contentious
decisions. ABMs can help stakeholders and policymakers converge on
optimal solutions by demonstrating the potential consequences of various
actions, thereby reducing uncertainty and facilitating consensus. By
eliminating the risks associated with field trials and compressing the
timeline for understanding management outcomes, ABMs empower
stakeholders to make informed decisions with greater confidence.
The ability to visualise and analyse these simulated outcomes feeds
directly into the need for clear and digestible evidence for
stakeholders, ensuring that complex ecological dynamics are translated
into actionable insights that resonate with diverse technical and lay
audiences. When models are developed within platforms like NetLogo,
which offer a unique advantage in their ability to communicate complex
ecological processes through accessible graphical user interfaces (GUIs)
and parameterised with high-resolution field data, these models provide
stakeholders with a recognisable representation of their landscape,
complete with agents (e.g., wildlife, humans) moving and behaving based
on empirical GPS data collected within their jurisdiction. This
recognisability bridges the gap between abstract modeling and real-world
systems, enhancing transparency and fostering trust in the model’s
outputs (Murphy et al. 2020).
Moreover, this framework allows stakeholders to see not only how agents
interact with their environment but also why certain behaviours emerge
(McClane et al. 2013). By demonstrating that these behaviours are
grounded in real-world data, stakeholders can develop confidence in the
model’s validity and relevance to their concerns. For example,
stakeholders can directly observe how local wildlife responds to
management interventions, such as altered forestry practices or the
creation of new habitat corridors. This interactive and visual approach
demystifies the modeling process, making it more accessible to
non-expert audiences. Crucially, ABMs also offer the ability to run
management experiments in real-time during stakeholder discussions. For
instance, decision-makers can observe the immediate effects of a
proposed intervention, such as a cull or habitat restoration, on key
metrics like population density, resource use, or disease transmission.
This interactive capability not only strengthens the rationale for
proposed actions but also serves as a communication tool to illustrate
why specific management strategies are being considered for field
trials. By providing an evidence-based, visual platform for exploring
ecological scenarios, ABMs facilitate more informed, collaborative, and
transparent decision-making processes.
For policymakers and stakeholders—often the primary funders of
research—this framework ensures a maximised return on investment by
continuously linking field data collection and simulation modeling. The
methodological framework presented in this paper establishes a circular
data-economy, creating a closed-loop system that continuously improves
the quality of field data collection and simulation modeling in an
iterative process. In this cycle, high-quality data collected in the
field is used to parameterise and validate the model, while the model
generates outputs that highlight gaps and priorities for further field
data collection. This iterative process ensures that both the simulation
and real-world monitoring efforts evolve in tandem, continually
improving the understanding and management of ecological systems. By
embedding this feedback loop into the workflow, the framework not only
optimises the value of data but also promotes good data practices, such
as standardized collection, centralization, and accessibility. This
approach prevents the loss of investment value through long-tail science
and dark data, ensuring that datasets remain active contributors to
scientific and management advancements (Hampton et al. 2013). The case
study demonstrates the unique capacity of ABMs to uncover unexpected
trends that challenge conventional thinking, providing actionable
insights for management and policy. Two notable patterns emerged from
the case study model, offering fresh perspectives on disease
transmission risk and wildlife management strategies in Ireland (Murphy
et al. 2024).
First, the spatial configuration of dairy farms emerged as a previously
unrecognised driver of disease risk. Traditionally, research and policy
efforts have focused on the health, longevity, and husbandry of dairy
cattle as possible reasons for increased transmission risk (Broughan et
al. 2016). However, the ABM revealed that the spatial arrangement and
size of dairy farms within the landscape significantly influence
wildlife-host interactions, thereby increasing the risk of bovine
tuberculosis (bTB) exposure. This finding shifts the focus toward
understanding the role of landscape structure in disease dynamics,
opening new avenues for policy interventions, such as spatial planning
or buffer zones, and targeted research programs aimed at mitigating risk
through landscape-level management. Second, the model highlighted an
unexpected relationship between farm density, habitat heterogeneity, and
disease risk. The case study model found the opposite: areas with lower
farm density posed a higher risk of disease transmission. This
counterintuitive result suggests that wildlife hosts, such as badgers,
may exhibit greater movement and contact with farms in less fragmented
landscapes, where natural habitats are more contiguous. This finding
underscores the need to monitor wildlife activity in low farm density
areas, challenging traditional assumptions and guiding new research and
monitoring programs to address these overlooked interfaces. These
emergent trends demonstrate the power of ABMs to go beyond static
analyses and identify nuanced, system-specific patterns. By highlighting
these novel dynamics, the model provides an evidence base to inform
targeted interventions, improve monitoring strategies, and refine
existing policies.
ABMs for applied ecological scenarios offer immense potential, but their
implementation comes with significant challenges that must be carefully
considered to ensure their success. One of the foremost challenges is
the intensive data requirements. High-quality, high-resolution data is
crucial for parameterising agents and their environments, as this
framework relies on empirical evidence to achieve realism. However, such
data is not always readily available, particularly in regions or systems
where monitoring efforts have been minimal. This issue contrasts with
traditional ABMs, where questions can often be explored without
requiring fieldwork (Murphy et al. 2020). For applied scenarios, the
absence of adequate data can be a significant barrier but also presents
an opportunity. Identifying these data gaps provides a compelling
argument for establishing long-term monitoring programs (Ariño et al.
2016). In this sense, discovering a lack of suitable data for model
parameterisation can itself be considered a valuable result, as it
highlights areas where investment is needed to inform evidence-based
management and policy decisions.
Another major consideration is the computational intensity required to
run realistic, large-scale ABMs. These models often involve thousands of
agents interacting across millions of patches in dynamic environments,
which can demand significant computational resources (Murphy et al.
2020). For example, while NetLogo, a commonly used ABM platform,
theoretically has no fixed model size limits, memory usage can quickly
escalate. For larger models, such as those involving millions of patches
or tens of thousands of agents, up to 50 GB of RAM or more may be
required to achieve smooth performance. This poses a barrier for
researchers with limited access to high-performance computing
facilities. While NetLogo offers configuration options to optimise RAM
allocation, running large-scale simulations or BehaviourSpace
experiments (which multiply memory usage by running parallel
simulations) can still push the limits of standard computational setups.
As ABMs become more complex and data-driven, access to powerful
computers and appropriate configuration of computational environments
will become increasingly critical.
Another challenge is the time and expertise required to build and
validate models. Constructing an ABM involves not only technical
proficiency in coding and data integration but also a deep understanding
of the ecological system being simulated. Collaborating with domain
experts and stakeholders adds value but also increases the complexity of
project coordination. Similarly, external validation requires robust
datasets that are entirely independent of the input data—a requirement
that can be difficult to meet, particularly for novel systems or
species. Lastly, stakeholder engagement can be both a strength and a
challenge (Silvy, 2020). While this framework promotes transparency and
collaboration, stakeholders may have differing priorities, levels of
trust in the model, or expectations regarding its outputs. For example,
stakeholders may favour management actions (e.g., culling vs.
vaccination) that the model does not support, creating potential
conflicts. Clear communication, education about the model’s
capabilities, and iterative collaboration are essential to mitigate such
challenges and foster trust in the model’s outputs. Despite these
hurdles, the challenges are not insurmountable. In fact, many of
them—such as identifying data gaps or engaging with stakeholders—can
lead to positive outcomes, such as improved monitoring programs or
better-aligned management priorities. Addressing these challenges
head-on ensures that ABMs remain a powerful tool for applied ecological
research and decision-making.