Predictive species distribution models (SDMs) are becoming increasingly important in ecology, in the light of rapid environmental change. The predictions of most current SDMs are specific to the habitat composition of the environments in which such models were fitted. However, species respond differently to a given habitat depending on the availability of all habitats in their environment, a phenomenon known as a functional response in resource selection. The Generalised Functional Response (GFR) framework captures this dependence by formulating the SDM coefficients as functions of habitat availability in the broader environment. The original GFR implementation used global polynomial functions of habitat availability to describe functional responses. In the present thesis, I develop several refinements of this approach and compare their explanatory and predictive performance using two simulated and three real datasets.