Methodological Framework – Building
Applied
ABMs
Effective ecological management requires tools that can adapt to the
complexity and variability of real-world systems. To achieve this, we
propose a framework for building applied agent-based models (ABMs) that
integrate data collection, modeling, and decision-making into an
adaptive feedback loop. This approach ensures that ABMs remain relevant
and actionable as new data becomes available, enhancing their practical
application for conservation and management. The framework is structured
to guide the iterative development and use of ABMs in applied ecology.
It begins with identifying specific management or policy goals, followed
by parameterising the model using diverse field data to reflect
real-world systems. The model outputs actionable insights for management
decisions and highlights areas where additional data could improve
future iterations. This continuous refinement process helps to reduce
uncertainty and improve the reliability of model-based recommendations
over time.
Each component of this framework is detailed in the following sections,
including practical guidelines for parameterisation, validation, and
application. To illustrate the framework’s utility, we refer to a
previously published case study (Murphy et al. 2024) that applies this
methodology to explore wildlife hosts’ role in zoonotic disease
transmission within a disturbance-driven landscape. While the case study
itself is a work apart from this paper, it demonstrates how the
framework can be effectively applied in real-world scenarios.Objective ABMs can only be effectively assessed and refined if they are guided
by clear, measurable objectives that align with the desired management
or policy outcomes (Richardson, 2020). This requires a clear
understanding of the purpose the model is intended to serve and
tailoring it to provide actionable insights for stakeholders. Setting
objectives begins with actively engaging policymakers, wildlife
managers, conservation practitioners, and other stakeholders to
understand their needs, the context of their decisions, and the type of
outputs required (Decker et al. 2012). A key consideration when refining
the objective is identifying the available data and aligning it with the
desired outputs. For example, an ABM designed to guide predator-prey
management might focus on providing year-end prey population indices to
set optimal hunting quotas in multi-predator landscapes. By focusing on
outputs that directly address the questions at hand, the model remains
streamlined, avoiding unnecessary complexity while ensuring relevance to
practical decision-making (Murphy et al. 2020). A fundamental objective
of these ABMs should not only be to provide immediate management and
policy recommendations but to guide future data collection by
identifying gaps or uncertainties, such as gaps in the available field
data. This feedback loop between modeling and field data ensures that
each iteration of the model provides validated recommendations to
improve species management within the system while also identifying data
limitations that can be addressed in future monitoring efforts.
Data Collection
Data collection to provide high-resolution input data is the
foundation of building effective applied ABMs. The quality and
resolution of input data determines the model’s ability to emulate
complex systems. A well-designed data collection strategy ensures that
all available datasets, as outlined in Table 1, are integrated to
achieve the model’s specific objectives while maintaining parsimony
(Murphy et al. 2020). Collaboration with stakeholders and policymakers
during this process ensures the inclusion of all relevant data needed to
accurately emulate the system which can foster increased trust in the
model’s outputs. Remote sensing data provides critical environmental
inputs, enabling the characterisation of landscapes and environmental
processes, such as land cover, vegetation indices, and anthropogenic
impacts (Skidmore et al. 2021). Telemetry data from GPS and VHF collars
captures wildlife movement and behaviour, ensuring realistic agent
procedures represented in the model (Hebblewhite and Haydon, 2010).
Site-specific spatial monitoring data enhances spatial accuracy, while
administrative data incorporates human activity and jurisdictional
boundaries, critical for addressing human-wildlife interactions and
accurately implementing realistic management and policy interventions in
the model. Historical records and grid-based environmental data further
enrich the model by allowing for the creation of dynamic patch based
sub-models (e.g. population change) which alter agent behaviour and
allow for emergent trends as the model progresses forward in time. These
datasets collectively allow the model to simulate realistic and dynamic
systems, as summarized in Table 1.
A crucial component of applied ABMs is obtaining external validation
data, which evaluates the model’s performance. Unlike other inputs,
validation data must be independent of the datasets used to parameterise
the model. However, obtaining such data can be challenging, particularly
in regions or for species with limited monitoring efforts. In these
cases, proxies be useful alternative data to assess model performance
(Murphy et al. 2022). The identification of validation dataset gaps also
highlights opportunities for future field data collection, creating a
feedback loop between site and simulation that improves our
understanding of both systems.
Parameter Databases
The creation of parameter databases bridges the gap between raw input
data and the realism of agents and environments in applied ABMs. These
analyses transform observational data into distributions of behaviours
(e.g., habitat selection, movement speed), enabling agents and
environments to access real-world field data to inform their behaviours
such that they exhibit data-driven, realistic actions within the model.
This approach replaces rule-based procedures with behaviours grounded in
empirical data while still allowing for emergent dynamics by drawing
parameter values from distributions.
Telemetry data from GPS or VHF collars frequently serves as the
foundation for these databases (Fig. 1). Using the amt package
(Signer et al., 2019), key parameters such as movement speed, turning
angles, and step lengths can be calculated to reflect realistic movement
patterns. Advanced analyses, such as Kernel Density Estimation (KDE) to
estimate home ranges, can be conducted using the adehabitatHR package (Calenge, 2023). Net squared displacement, a metric for
quantifying movements such as dispersal or migration, provides
additional insights into animal movement at specific points of the year.
For habitat selection analysis, Resource Selection Functions (RSFs) can
be implemented by correlating GPS points with the availability of
environmental data using the glmm function in the lme4 package (Bates, 2010). RSFs compute relative selection probabilities for
habitats within the model, which parameterises agents to assess their
environment and select the optimum habitat and path in a data-driven
way.