Fig. 2: Comparison between simple and hyper-real environments in ABMs. (A) The Simple Environment represents a grid-based, homogeneous landscape with abstract interactions. (B) The Hyper-Real Environment shows the Bohemian Forest ecosystem, incorporating spatially explicit data layers such as administrative boundaries, habitats, elevation, and species territories for more realistic simulations.
Implementing these parameters into the model requires careful sensitivity analysis. For instance, directly applying mortality rates derived from monitoring data may lead to unrealistic outcomes, such as population crashes in simulations. By iteratively testing parameter values, sensitivity analysis ensures that the model aligns with ecological expectations, such as population stability under known conditions. For example, while monitoring data may suggest a 1% annual mortality rate, sensitivity testing might reveal that a slight adjustment (e.g., 0.8%) ensures population stability in the model, reflecting real-world dynamics. This process is critical to ensure that parameters remain ecologically sound and do not introduce unintended biases into the simulation.
By leveraging these tools and processes, ABMs achieve a balance between data-driven realism and adaptive flexibility. The capacity to parameterise agents and environments using empirical data enhances model accuracy and relevance while maintaining the unpredictability essential for simulating emergent ecological trends. This, combined with rigorous sensitivity testing and collaborative data preparation, provides a robust foundation for answering complex ecological and management questions.
Parameterisation
Agent decision-making should be informed by all available relevant input data and parameter databases (Fig. 3), encompassing both static input data and continuous access to external parameter databases. For instance, the movement procedure illustrated in Fig. 4 highlights how agents can use dynamic data access to make habitat-informed decisions. In this example, an agent (e.g., a badger) selects a movement step length and turning angle from a telemetry-derived parameter database. Once a step is initiated, the agent evaluates the habitats within its radius, accessing an external resource selection function (RSF) database to determine the relative suitability of each habitat type. If multiple habitats are available, the agent moves toward the one with the highest RSF value. Conversely, if only one habitat is present, the agent moves stochastically while adhering to data-driven parameters such as step length and turning angle. This dynamic integration ensures that movement is both realistic and adaptive, replicating observed patterns while allowing emergent behaviours.