Behaviour-time budget and functional habitat use of a free-ranging European badger(Meles meles)

Abstract

Background:The European badger (Meles meles) is involved in the maintenance of bovine tuberculosis infection and onward spread to cattle. However, little is known about how transmission occurs. One possible route could be through direct contact between infected badgers and cattle. It is also possible that indirect contact between cattle and infected badger excretory products such as faeces or urine may occur either on pasture or within and around farm buildings. A better understanding of behaviour patterns in wild badgers may help to develop biosecurity measures to minimise direct and indirect contact between badgers and cattle. However, monitoring the behaviour of free-ranging badgers can be logistically challenging and labour intensive due to their nocturnal and semi-fossorial nature. We trialled a GPS and tri-axial accelerometer-equipped collar on a free-ranging badger to assess its potential value to elucidate behaviour-time budgets and functional habitat use. Results: During the recording period between 16:00 and 08:00 on a single night, resting was the most commonly identified behaviour (67.4%) followed by walking (20.9%), snuffling (9.5%) and trotting (2.3%). When examining accelerometer data associated with each GPS fix and habitat type (occurring 2 min 30 s before and after), walking was the most common behaviour in woodland (40.3%) and arable habitats (53.8%), while snuffling was the most common behaviour in pasture (61.9%). Several nocturnal resting periods were also observed. The total distance travelled was 2.28 km. Conclusions:In the present report, we demonstrate proof of principle in the application of a combined GPS and accelerometer device to collect detailed quantitative data on wild badger behaviour. Behaviour-time budgets allow us to investigate how badgers allocate energy to different activities and how this might change with disease status. Such information could be useful in the development of measures to reduce opportunities for onward transmission of bovine tuberculosis from badgers to cattle.

Effect of culling on individual badger Meles meles behaviour: Potential implications for bovine tuberculosis transmission

“…Alongside changes in ranging behaviour, culling might be associated with changes in individual nightly activity patterns. Badger populations are largely regulated by food availability (Kruuk & Parish, 1982) and badgers spend a large proportion of their aboveground time foraging (McClune, Marks, Delahay, Montgomery, & Scantlebury, 2015). As culling reduces the population size, behaviour to cull-based TB control strategies and furthermore, they highlight the negative impacts culling can have on integrated disease control strategies.…”

Using tri-axial accelerometers to identify wild polar bear behaviors

“…Additionally, we evaluated the effectiveness of using accelerometer data from captive polar and brown bears U. arctos to predict behaviors of wild polar bears. Though it is generally assumed that accelerometer signatures of captives or surrogates are similar to those of their instrumented wild counterparts (Williams et al 2014, McClune et al 2015, Wang et al 2015, Hammond et al 2016, this has rarely been tested. Captive individuals may exhibit different behaviors and/or kinematics than wild counterparts (McPhee & Carlstead 2010), which could potentially influence accelerometer signatures.…”

Animal-borne behaviour classification for sheep (Dohne Merino) and Rhinoceros (Ceratotherium simum and Diceros bicornis)

“…These classifiers can then automatically categorise large datasets according to the chosen behaviours. Automatic behaviour classification has already been applied to a range of animals using various statistical classification and machine learning techniques, including artificial neural networks [9], decision trees [8][9][10][11][12][13], discriminant function analysis [14], hidden Markov models [15], k-nearest neighbours [8,10,16], linear discriminant analysis [9], moving averages with thresholds [17], quadratic discriminant analysis [18], random forests [9,19] and support vector machines [9,15,20]. These systems have accomplished automatic behaviour classification with high accuracy, as listed in Table 1.…”