Abstract
The purpose of the current study is to establish a comprehensive framework for modelling the fine-scale movement behaviours of European badgers (<em>Meles meles</em>) and their implications for ecological and epidemiological dynamics. It is widely understood that badgers play a crucial role in the transmission of bovine tuberculosis (bTB), but the exact role is still not clear. An approach to modelling animal movement is using energy potentials, primarily seen in physics to describe particle motion, and geometric Brownian motion. Such an approach has been successful in describing the movements of free ranging Mountain elk (<em>Cervus elaphus</em>) and their avoidance of vehicles and humans, but has yet to be applied to the study of badgers. </p><p></p><p>A range of modelling strategies, including kernel density estimation and Gaussian mixture models, were developed to derive energy potentials from GPS tracking data, enabling the simulation of badger movement patterns within an energy-based framework. The study integrates stochastic differential equations to quantify movement under stochastic dynamics, bridging empirical data with theoretical modelling. </p><p></p><p>Additionally, the research introduces an infection model to explore disease spread, such as bovine tuberculosis, within badger populations. The results highlight the importance of movement behaviours in shaping disease transmission, and the potential for applying various management strategies, including vaccination and culling, to control disease outbreaks. The findings also suggest that future work could enhance these models by incorporating sex- and season-specific behaviours, refining analytical techniques, and exploring real-time field applications. </p><p></p><p>By integrating ecological data with advanced mathematical methods, this work lays the groundwork for improved wildlife management practices and advances the application of movement ecology and epidemiological modelling in conservation efforts.