Abstract:
Setting conservation goals and management objectives relies on understanding
animal habitat preferences. Models that predict preferences combine location data
fromtracked animals with environmental information, usually at a spatial resolution
determined by the available data. This resolution may be biologically irrelevant for
the species in question. Individuals likely integrate environmental characteristics
over varying distances when evaluating their surroundings; we call this the scale of
selection. Even a single characteristic might be viewed differently at different scales;
for example, a preference for sheltering under trees does not necessarily imply a
fondness for continuous forest. Multi-scale preference is likely to be particularly
evident for animals that occupy coarsely heterogeneous landscapes like savannahs.
We designed a method to identify scales at which species respond to resources and
used these scales to build preference models. We represented different scales of
selection by locally averaging, or smoothing, the environmental data using kernels
of increasing radii. First, we examined each environmental variable separately across
a spectrum of selection scales and found peaks of fit. These ‘candidate’ scales then
determined the environmental data layers entering a multivariable conditional
logistic model. We used model selection via AIC to determine the important
predictors out of this set. We demonstrate this method using savannah elephants (Loxodonta africana) inhabiting two parks in southern Africa. The multi-scale
models were more parsimonious than models using environmental data at only
the source resolution. Maps describing habitat preferences also improved when
multiple scales were included, as elephants were more often in places predicted to
have high neighborhood quality.We conclude that elephants select habitat based on
environmental qualities at multiple scales. For them, and likely many other species,
biologists should include multiple scales in models of habitat selection. Species
environmental preferences and their geospatial projections will be more accurately
represented, improving management decisions and conservation planning.
