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Data-driven discovery of the spatial scales of habitat choice by elephants.

dc.contributor.author Mashintonio, Andrew F
dc.contributor.author Pimm, Stuart L
dc.contributor.author Harris, Grant M
dc.contributor.author van Aarde, Rudi J
dc.contributor.author Russell, Gareth J
dc.date.accessioned 2021-08-02T18:56:14Z
dc.date.available 2021-08-02T18:56:14Z
dc.date.issued 2014-01
dc.identifier 504
dc.identifier.issn 2167-8359
dc.identifier.issn 2167-8359
dc.identifier.uri https://hdl.handle.net/10161/23563
dc.description.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.
dc.language eng
dc.publisher PeerJ
dc.relation.ispartof PeerJ
dc.relation.isversionof 10.7717/peerj.504
dc.subject Etosha National Park
dc.subject Loxodonta africana
dc.subject Maputo Elephant Reserve
dc.subject Resource selection function
dc.subject Scale-dependent preference
dc.subject Smoothing kernel
dc.title Data-driven discovery of the spatial scales of habitat choice by elephants.
dc.type Journal article
duke.contributor.id Pimm, Stuart L|0295985
dc.date.updated 2021-08-02T18:56:12Z
pubs.begin-page e504
pubs.issue 1
pubs.organisational-group Nicholas School of the Environment
pubs.organisational-group Environmental Sciences and Policy
pubs.organisational-group Duke Science & Society
pubs.organisational-group Duke
pubs.organisational-group Initiatives
pubs.organisational-group Institutes and Provost's Academic Units
pubs.publication-status Published
pubs.volume 2
duke.contributor.orcid Pimm, Stuart L|0000-0003-4206-2456


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