Fig. 4: Movement procedure for badger agents in an ABM, illustrating two scenarios. The top panel (B and C) shows movement in a homogeneous habitat, where agents follow step lengths and turning angles from movement data without considering habitat suitability. The bottom panel (D and E) shows agents in diverse habitats, selecting the best option based on Resource Selection Function (RSF) scores, ensuring adaptive, data-driven movement.
A complementary example is migration, where both static and dynamic data play critical roles. Static input data can dictate the timing of seasonal migration based on known patterns or environmental triggers (e.g., temperature thresholds or day length) and identify suitable migration endpoints, such as areas with high NDVI or optimal elevation ranges. During migration, agents access external data to adapt their routes in response to changing environmental conditions, such as selecting paths with favourable topography. This integration of static seasonal triggers with dynamic movement decisions enhances the model’s ability to simulate realistic migratory behaviours. These examples showcase how ABMs can combine static parameters, such as telemetry-derived behaviours or seasonal triggers, with continuous access to external databases to inform agent decision-making.
Most traditional ABM procedures rely heavily on rule-based or random behaviours, which can limit the accuracy and ecological validity of model outputs (DeAngelis & Diaz, 2019; Crevier et al, 2022). By integrating dual data sources—static input data and continuously accessed external databases—this method moves beyond such limitations to enable data-driven procedures. Importantly, these parameters are drawn from distributions of values derived from field data, introducing an element of variability that mirrors real-world unpredictability. For example, step length may range from 5m to 500m, allowing agents to exhibit diverse and context-sensitive movement behaviours. This inherent variability is critical for accurately simulating animal movement, behaviour, and environmental dynamism. This unpredictability within a data-driven framework is a significant advantage, as it allows for emergent trends in model outputs that can lead to new discoveries. For instance, agents may exhibit unexpected patterns of habitat use, dispersal, or resource selection, revealing insights that are not immediately apparent from field data alone. These emergent behaviors enhance the model’s value not just as a predictive tool but as a means of exploring ecological processes under varied conditions.
Most importantly, because the procedures are grounded in field data, they are inherently more realistic and ecologically relevant than rule-based or random approaches. This foundation ensures that the trends and predictions emerging from the simulations are reflective of real-world dynamics, making the model outputs more reliable for informing management decisions and advancing ecological understanding.
Experimentation & Analysis
A well-crafted experimental design must aim to extract data at critical moments within the simulation, as well as collect cumulative end-of-model outputs, to provide a comprehensive understanding of the system under study. One of the most powerful aspects of ABMs lies in their ability to capture high-resolution data from every single event of interest—something that is simply unattainable in field ecology. For instance, in a predator-prey model designed to assess the impact of wolf density on red deer populations, the model can record detailed information for every predation event, including the pack ID, pack size, date, season, habitat, elevation, prey species, prey sex, prey biomass, and prey density at both territorial and landscape scales. In field ecology, researchers rely on representative sampling to infer patterns, but in ABMs, every event is accounted for, allowing for near-complete data on system dynamics. In addition to capturing fine-scale events, end-of-simulation reporters play an equally important role in summarizing broader ecological trends. These outputs might include overall changes in prey density, the number of natural mortalities, or metrics related to management interventions, such as the number of issued hunting licenses.
By integrating both fine-scale event data and cumulative end-of-simulation outputs, researchers can analyse system dynamics across multiple scales, offering a nuanced understanding of the relationships between agents, their behaviours, and their environments. Once simulation data is generated, robust statistical modeling is necessary to draw meaningful conclusions. Comprehensive event-level data collection enhances the capacity of statistical models derived from ABMs, achieving nearly 100% deviance explained (R² = 1) within the modelled system. However, it is crucial to note that this does not imply that the entire real-world system is captured by the model; instead, it reflects the ability to accurately analyse relationships and patterns within the model itself, offering valuable insights tailored to specific management or policy objectives. White et al. (2013) caution against over-reliance on simple linear analyses, as these can oversimplify complex systems by focusing only on direct relationships. Multivariate approaches, particularly flexible statistical models such as Generalized Additive Models (GAMs), provide a more powerful framework for interpreting simulation outputs. GAMs allow researchers to incorporate nonlinear relationships and complex interactions, capturing the full range of dynamics present in ecological systems. For instance, in the predator-prey example, prey density might not change linearly with wolf density due to interactions with other variables such as habitat type, seasonality, or prey behaviour. By using a multivariate framework, researchers can disentangle these effects and assess the relative importance of different predictors. This ensures that the insights derived from the model are ecologically realistic and biologically meaningful, rather than constrained by the limitations of simpler statistical approaches. Such modeling approaches also help to identify indirect or emergent effects that might otherwise go unnoticed, providing a richer understanding of system-level processes. By capturing every event and modeling it comprehensively, ABMs provide a more detailed and reliable foundation for policy and management recommendations.
Validation
Validation is a critical final step in this framework, ensuring that the model outputs align with real-world data and are robust enough to inform decision-making. Validation relies on independent datasets that were not used during model parameterisation or calibration. For instance, in a predator-prey model, independent telemetry data from wolves or field surveys of prey populations could be used to assess the accuracy of the model’s predictions. By comparing model outputs to observed data, researchers can identify discrepancies and refine the model as necessary. Validation provides a mechanism for building trust in the model, both within the scientific community and among stakeholders. A well-validated model gives confidence that its predictions are not only plausible but also actionable, supporting its application in management and policy contexts. Ultimately, experimental design, statistical modeling, and validation form an iterative process that drives the scientific and practical value of ABMs. Data collected during simulations, when analysed using sophisticated statistical approaches, can reveal patterns and dynamics that enhance our understanding of ecological systems. When validated against real-world data, these insights gain the credibility needed to inform policy, guide management strategies, and advance ecological knowledge. By combining event-level data, multivariate modeling, and rigorous validation, ABMs become powerful tools for addressing complex ecological challenges in a systematic and data-driven way.
Evaluation & Feedback
The final and arguably most important step in applied ABM workflow is the integration of model outputs into a feedback loop that connects simulation insights with real-world data collection and policy refinement. All simulation outputs and statistical analyses should be carefully reviewed with stakeholders and policymakers to ensure they align with their understanding of the system and address practical management needs. This collaborative evaluation process is essential for validating the utility of the model, identifying areas for improvement, and fostering trust in its recommendations. Unexpected results (for example, if in a wolf-deer predator-prey model it is found that wolf packs near wide rivers have a disproportionately greater impact on local red deer density) can serve as valuable starting points for targeted field studies or adjustments to annual monitoring programs to ground truth functional aspects of the model and investigate these emergent trends in a real-world scenario. Testing these findings in real-world sites not only provides an opportunity to verify simulation outputs but also enhances the precision and reliability of future simulations by addressing data gaps or uncertainties identified in earlier iterations.
This continuous feedback loop transforms the simulation into a dynamic ”living lab,” where model predictions directly inform field data collection, and field observations, in turn, refine the model. By incorporating unexpected trends and emergent dynamics into subsequent monitoring efforts, researchers and managers can systematically test and validate both the model’s predictions and the ecological processes it represents. This iterative process allows the model to evolve alongside the system it simulates, creating a synergistic relationship between simulation and reality. Over time, this approach increases the granularity of understanding, reduces uncertainty, and produces increasingly precise and trusted models. Moreover, the feedback loop significantly enhances the return on investment for research funding by doubling the outputs and insights generated across the study system. Rather than functioning as a standalone tool, the model becomes part of a broader adaptive management framework, driving both theoretical knowledge and practical application. The ability to iteratively test management interventions and policy decisions in both the field and the simulation provides a unique advantage, offering a deeper and more nuanced understanding of the study system. Most importantly, as the model becomes better parameterised with each iteration, its credibility among stakeholders grows, paving the way for more informed and impactful management and policy outcomes. In this way, the continuous exchange of data between the simulation environment and the real-world system ensures that ABMs remain robust, relevant, and valuable tools for ecological research and decision-making.