Predicting Distribution and Density of European Badger (Meles Meles) Setts in Denmark

Predictive models of the spatial distribution and abundance of species based on habitat characteristics are finding increasing use in management and conservation. The European badger attracts interest as a model species both for conservation reasons and because of the important role the species is playing in understanding carnivore sociality. We developed a statistical habitat model based on presence/absence data on badger setts. To maximise the utility of the model in management, we limited the choice of model variables to those that had a clear basis in badger ecology and that could be obtained on a nation-wide digital format. We extrapolated the habitat model to a region in Denmark and developed a threshold-independent sett distribution algorithm to estimate sett densities. The habitat model was simpler than previously published models of badger sett habitat selection, but nevertheless had a predictive ability in excess of 80% judged against independent data. The sett distribution algorithm was able to simultaneously reproduce several observed patterns of sett density and distribution over the probability gradient. It thus represents a significant improvement over threshold-dependent methods used to discriminate between suitable and unsuitable habitat predicted by presence/absence regression models. Our approach demonstrates that a model of badger sett habitat suitability with high predictive power can be obtained using easily accessible map-variables and presence/absence data. This is a prerequisite for using habitat models as predictive tools over large areas. The use of a simple sett distribution algorithm circumvents the common problem of subjectively fixing a threshold to discriminate between suitable and unsuitable habitat. In conjunction the models presented here constitute an important contribution to the management of the badger in Denmark and, upon further validation, possibly to similar regions in Northern Europe.