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Data from: Dispersal patterns in a medium-density Irish badger population: implications for understanding the dynamics of tuberculosis transmission.


Gaughran, Aoibheann et al. (2020), Data from: Dispersal patterns in a medium-density Irish badger population: implications for understanding the dynamics of tuberculosis transmission., Dryad, Dataset,


European badgers (Meles meles) are group-living mustelids implicated in the spread of bovine tuberculosis (TB) to cattle and act as a wildlife reservoir for the disease. In badgers, only a minority of individuals disperse from their natal social group. However, dispersal may be extremely important for the spread of TB, as dispersers could act as hubs for disease transmission. We monitored a population of 139 wild badgers over seven years in a medium-density population (1.8 individuals/ km2). GPS-tracking collars were applied to 80 different individuals. Of these, we identified 25 dispersers, 14 of which were wearing collars as they dispersed. This allowed us to record the process of dispersal in much greater detail than ever before. We show that dispersal is an extremely complex process, and measurements of straight-line distance between old and new social groups can severely underestimate how far dispersers travel. Assumptions of straight-line travel can also underestimate direct and indirect interactions and the potential for disease transmission. For example, one female disperser which eventually settled 1.5 km from her natal territory travelled 308 km and passed through 22 different territories during dispersal. Knowledge of badgers’ranging behaviour during dispersal is crucial to understanding the dynamics of TB transmission, and for designing appropriate interventions, such as vaccination.


Badgers were tracked using Tellus Light GPS collars that weighed 240 g (Followit Wildlife, Lindsberg, Sweden). Data collection began in April 2010 and continued until October 2016, when all collars were removed. We aimed to capture as many badgers as possible within each social group. Collars were programmed to record four GPS locations a night, at 21:00, 23:00, 01:00 and 02:00, for each collared badger (following MacWhite et al. 2013; Gaughran et al. 2018). Data were visualised in ArcMap (ArcGIS version 10.4.1).

Badgers were sexed at each trapping event. Age was determined by dentition (Hancox 1988; da Silva and Macdonald 1989) and general appearance. Age cohorts were defined as follows: cub (a badger in its first year); yearling (a badger in its second year), young adult (a 2- or a 3-year old), older adult (a 4- or a 5-year old) and aged adult (badger >5 years old). A social group was defined as the group of badgers which were regularly trapped at the same main sett and whose home ranges overlapped during the time period in question (following (Macdonald et al. 2008; Woodroffe et al. 2016). Thus, badgers were assigned to a social group based on their most frequent trapping location and, if collared, their GPS tracking data.

Dispersers were defined, and confirmed retrospectively, as those badgers that were in the process of moving permanently from one social group to another social group. The majority of dispersers were identified using GPS (SI Fig. 1) and/or trapping records. Four dispersers were identified using genetic data (SI Text 1). Where we had GPS records for dispersal events, trajectory maps, which show the sequence of GPS locations, were made using the Point to Line tool in ArcMap (SI Figs. 2.1-2.13). When a badger was not wearing a collar during the dispersal event itself, maps were made for pre- and post-dispersal periods, or of exploratory forays (SI Figs. 2.14-2.16). Social group territory boundaries were mapped in ArcMap (Gaughran 2018) and the centroids were used to estimate the straight-line distance moved by dispersers. We estimated the number of social groups between the new and old territories by counting how many social groups were intersected by the straight line. If a badger moved to an adjacent social group, this parameter was recorded as zero. Some collared badgers dispersed outside the study area, so the total number of social groups crossed was unknown. Accordingly, the straight-line distance between the centroid of their original territory and the centroid of the polygon encompassing their GPS locations in their new location was calculated. We divided the straight-line distance by 1313 m as this was the mean distance between main setts in the study area (SD ± 455 m), to estimate the number of social groups crossed by the badger.

A second analysis was also conducted to describe the trajectory each badger took during dispersal, considering each day’s travel rather than just the start and end points of the dispersal event. The creation of trajectories using the GPS locations of badgers allowed us to estimate much more accurately the distances travelled during the process of dispersal. Although these distances are underestimates, as they are derived from the straight-line distances between only four GPS locations a night, they are less negatively biased than straight-line distances between social groups. We counted the number of territories crossed by these trajectories. Where trajectories fell outside the known study area, we applied a 1.3 km2 grid (based on the mean distance between main setts) to the map and used the grid squares crossed by trajectories as a proxy for estimating the number of territories crossed by the dispersing badgers.


Department of Agriculture, Food and the Marine, Award: 12774