Fig. 1: Schematic representation of key parameters used to parameterise badger agents in an agent-based model (ABM). Central to the model is telemetry data from GPS-collared badgers, informing various behavioural and ecological parameters. Moving clockwise from the top: activity scheduling, habitat selection, step length and turning angle, movement speed, social networks, behaviour, migration and dispersal, and home range. These parameters enable realistic, data-driven agent behaviours in simulations.
To ensure the accuracy of these analyses, data must be carefully screened. This involves removing erroneous fixes, such as points with unrealistically large step lengths, which can distort movement metrics. Tools such as the dplyr package (Wickham et al., 2014) can facilitate filtering and cleaning of datasets. Stakeholder engagement is essential during this process, as their local expertise often aids in identifying and correcting errors. Additionally, for species exhibiting temporal activity patterns, such as badgers, transforming movement data into bursts (e.g., nightly tracks) allows for accurate computation of daily movement metrics. This burst-level analysis is crucial for parameterising time-dependent behaviours and ensuring high-resolution temporal alignment with the model’s objectives.
Spatial and environmental data analysis provides critical site-specific and dynamic inputs that enhance the resolution and realism of the modelled environment (Fig. 2). Land-use maps, habitat quality indices, and environmental datasets (e.g., NDVI, temperature, precipitation) can be analysed using the sf package (Pebesma, 2018) for spatial data handling. For dynamic environmental parameters, raster data such as NDVI or climate models can be processed using the raster (Hijmans, 2015) or terra (Hijmans et al., 2022) packages. Temporal changes, such as seasonal shifts in resource availability or extreme weather impacts, can then be incorporated into the model to represent dynamic landscapes. For example, combining flood risk maps with weather data can simulate habitat changes due to flooding events, affecting agent movement, resource distribution, and habitat suitability.