[1] Resource Selection Functions require two types of data: location information for the wildlife in question, and data on the resources available across the study area.
A variety of methods are used for modeling RSFs, with logistic regression being commonly used.
[2] RSFs can be fit to data where animal presence is known, but absence is not, such as for species where several individuals within a study area are fitted with a GPS collar, but some individuals may be present without collars.
When this is the case, buffers of various distances are generated around known presence points, with a number of available points generated within each buffer, which represent areas where the animal could have been, but it is unknown whether they actually were.
Resource selection functions can be modeled at a variety of spatial scales, depending on the species and the scientific question being studied.