Habitats as predictors in species distribution models: Shall we use continuous or binary data?

The representation of a land cover type (i.e., habitat) within an area is
often used as an explanatory variable in species distribution models.
However, it is possible that a simple binary presence/absence of the
suitable habitat might be the most important determinant of the
presence/absence of some species and, thus, be a better predictor of
species occurrence than the continuous parameter (area). We hypothesize
that the binary predictor is more suitable for relatively rare habitats
(e.g., wetlands) while for common habitats (e.g., forests) the amount of
the focal habitat is a better predictor. We used the Third Atlas of
Breeding Birds in the Czech Republic as the source of species distribution
data and CORINE Land Cover inventory as the source of the landcover
information. To test our hypothesis, we fitted generalized linear models
of 32 water and 32 forest bird species. Our results show that for water
bird species, models using binary predictors (presence/absence of the
habitat) performed better than models with continuous predictors (i.e.,
the amount of the habitat); for forest species, however, we observed the
opposite. Thus, future studies using habitats as predictors of species
occurrences should consider the prevalence of the habitat in the
landscape, and the biological role of the habitat type in the particular
species’ life history. In addition, performing a preliminary comparison of
the performance of the binary and continuous versions of habitat
predictors (e.g., using information criteria) prior to modelling, during
variable selection, can be beneficial. These are simple steps that will
improve explanatory and predictive performance of models of species
distributions in biogeography, community ecology, macroecology, and
ecological conservation.