The conservation of wildlife is closely linked with its habitat. Knowing the preferred habitat is crucial for most managers and researchers to conserve wildlife effectively. Here we present an software approach to 'presence only' (wildlife location) data in order to derive habitat preferences in an automated but flexible fashion. The approach is based on presence and random locations, e.g. derived from a telemetry and GIS study, and uses for instance a GLM (Generalized Linear Model) to develop Resource Sdection Functions (RSF). The presented approach is very flexible and computationally powerful and can easily be adjusted for various applications. We show how this approach is used for conservation applications by using endangered and charismatic wildlife species as examples. In addition, we give an oudook of future applications for the presented method.

Wildlife habitat preferences, GIS, wildlife telemetry, GLM (Generalized Linear Models), Resource Selection Functions (RSF), SPLUS software tool