Data-driven analysis of fine-scale badger movement in the UK

Understanding animal movements at different spatial scales presents a significant challenge as their patterns can vary widely from daily foraging behaviours to broader migration or territorial movements. This challenge is of general interest because it impacts the ability to manage wildlife populations effectively. In this study, we conduct diffusion analysis based on European badger (Meles meles) movement data obtained from three different regions in the UK (Gloucestershire, Cornwall, and Northern Ireland) and fit a generalised linear mixed-effects model to examine the relationship between variables. We also feature a novel application of extended dynamic mode decomposition (EDMD) to uncover patterns relating to badger social organisation. By applying our approach to these different populations, we were able to assess its performance across a range of badger densities. A key result was that in some areas, EDMD clusters matched observed group home ranges, whilst in others, discrepancies likely arose because of population management interventions, such as badger culling. The methods presented offer a promising approach for studying territoriality and the impacts of management strategies on animal movement behaviour.Author summary: Wild animals move for many reasons, such as searching for food, finding mates, or maintaining territories, and these movements can be affected by changes in their environment. In this comparative study, we focused on European badgers, a social species whose movements are important for understanding behaviour and disease spread. Using GPS data from different locations around the UK, we explore how badger movement patterns vary both from day to day and over longer periods, revealing differences by sex, season, and region. The core contribution of this study lies in the novel application of extended dynamic mode decomposition (EDMD) alongside a more established generalised linear mixed-effects model (GLMM), together capturing movement dynamics across multiple timescales. These tools offer a new way to interpret animal behaviour including territoriality, and can be adapted to other species and ecological contexts. While dependent on tracking data, this approach lays the groundwork for future tools that may inform wildlife management and policy in conservation and disease control.