CASE STUDY
ScenarioClearfell forestry operations are a common land-use practice in Ireland, creating significant disturbances in agroforestry mosaics. These disturbances can alter the behaviour and movement patterns of wildlife, particularly European badgers, which are a known wildlife host for bovine tuberculosis (bTB). Badger movement into farms increases the risk of bTB transmission to cattle, posing challenges for disease management. Understanding how forestry disturbances influence badger behaviour is crucial for developing evidence-based strategies to mitigate disease risk. This case study applies the ABM framework to simulate badger responses to clearfell forestry and identify management interventions that could reduce bTB transmission. See Fig. 5 for an infographic of the framework. Full details of the study are available in Murphy et al. (2024).
ObjectiveThe aim of this study was to simulate the behavioural response of European badgers to clearfell forestry and assess its role in influencing bovine tuberculosis (bTB) transmission to cattle. By understanding how forestry disturbances alter badger movements and interactions with farms, the model sought to inform evidence-based management strategies to mitigate disease risk in agroforestry mosaics across Ireland.
Data CollectionHigh-resolution data were gathered from multiple sources to construct a realistic and comprehensive model environment. GPS telemetry data from 202 badgers tracked over seven years provided detailed movement information, while sett survey data contributed fine-scale information on real-world nesting sites and population estimates. Environmental layers, such as CORINE land-use classifications and clearfell records, detailed the spatial and temporal dynamics of habitat and human-induced disturbances. Administrative data, including farm boundaries and forestry management zones, delineated areas of human-wildlife interaction. These diverse datasets ensured that the parameterisation process was informed by real-world observations, enhancing the model’s ability to reflect actual badger behaviours in response to clearfell forestry.
Parameter DatabasesTelemetry data were analysed to derive movement metrics, including step lengths, turning angles, and net squared displacement, which captured the variability in badger movement patterns. Kernel Density Estimation (KDE) was used to calculate home ranges, defining the spatial extent of badger territories. Resource Selection Functions (RSFs) quantified habitat preferences, standardizing GPS fixes by habitat availability to identify favoured land-use types across different seasons. These parameter databases informed agent behaviour within the model, ensuring that simulated badger movements closely mirrored observed patterns.
ParameterisationAgents were parameterised with realistic movement and behavioural rules derived from the telemetry and RSF analyses. Sex- and age-specific parameters allowed for differences in movement dynamics, while habitat preferences informed decision-making during foraging and dispersal. The environment was parameterised using spatial layers that integrated farm boundaries, forestry operations, and habitat patches. Clearfell sites were dynamically updated in the model to simulate post-harvest regeneration, influencing badger resource availability and movement patterns. This parameterisation ensured that agents adapted their behaviours based on real-world changes in the landscape.
Experiment & AnalysisThe ABM was implemented across 100 clearfell sites selected randomly across Ireland, thus allowing the model to be used across a national landscape. Simulations ran for one year post-clearfell, with 25 iterations per site, capturing spatial and temporal dynamics of badger-farm interactions. Output data included detailed records of “alien encounters” (badgers entering farms outside their home range as a proxy for increased disease transmission risk), habitat use, and clearfell attributes. The model revealed that clearfell size, habitat diversity, and badger density were significant predictors of alien encounters. Statistical analysis employed Generalized Additive Mixed Models (GAMMs) to evaluate the influence of these predictors, capturing non-linear relationships and providing nuanced insights into the drivers of badger behaviour. The GAMMs achieved 94% deviance explained, demonstrating the model’s effectiveness in replicating real-world dynamics.
ValidationIndependent datasets were used to validate model outputs, ensuring their applicability to real-world scenarios. Farm-level bTB breakdown data from 12,000 farms over a four-year period provided an external benchmark for model predictions. A Pearson correlation between predicted encounter rates and observed bTB incidence confirmed the model’s accuracy in identifying risk hotspots, thereby demonstrating its reliability for informing management decisions. Validation highlighted both the strengths and limitations of the model, identifying areas where further refinement was needed.
Evaluation & FeedbackThe results were reviewed collaboratively with stakeholders and policymakers to ensure their practical relevance. The model confirmed expected trends, such as the influence of badger density and herd type on bTB risk, but it also uncovered previously overlooked factors. For example, farm density and landscape heterogeneity were found to play a critical role in facilitating badger movement, insights that had not been fully considered by policymakers. These unexpected findings highlighted gaps in field data, such as limited sett survey coverage and a shortage of telemetry data outside of two specific regions. By identifying these data gaps, the model provided a roadmap for future monitoring efforts, guiding targeted field surveys to improve data coverage and refine future simulations. This feedback loop between site and simulation enhanced the model’s accuracy and ensured its continued relevance in informing policy decisions.