title: Data-driven discovery of the spatial scales of habitat choice by elephants.
abstract: Setting conservation goals and management objectives relies on understanding animal habitat preferences. Models that predict preferences combine location data from tracked 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.
authors.affiliation: Department of Biological Sciences, Rutgers University , Newark, NJ , USA.
authors.forename: Andrew F
authors.initials: AF
authors.lastname: Mashintonio
authors.affiliation: Nicholas School of Environmental Science, Duke University , Durham, NC , USA.
authors.forename: Stuart L
authors.initials: SL
authors.lastname: Pimm
authors.affiliation: United States Fish and Wildlife Service , Albuquerque, NM , USA.
authors.forename: Grant M
authors.initials: GM
authors.lastname: Harris
authors.affiliation: Conservation Ecology Research Unit, University of Pretoria , Pretoria , South Africa.
authors.forename: Rudi J
authors.initials: RJ
authors.lastname: van Aarde
authors.affiliation: Department of Biological Sciences, Rutgers University , Newark, NJ , USA ; Department of Biological Sciences, New Jersey Institute of Technology , Newark, NJ , USA.
authors.forename: Gareth J
authors.initials: GJ
authors.lastname: Russell
keywords: Etosha National Park; Loxodonta africana; Maputo Elephant Reserve; Resource selection function; Scale-dependent preference; Smoothing kernel