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.
Type
Journal articleSubject
Etosha National ParkLoxodonta africana
Maputo Elephant Reserve
Resource selection function
Scale-dependent preference
Smoothing kernel
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https://hdl.handle.net/10161/23563Published Version (Please cite this version)
10.7717/peerj.504Publication Info
Mashintonio, Andrew F; Pimm, Stuart L; Harris, Grant M; van Aarde, Rudi J; & Russell,
Gareth J (2014). Data-driven discovery of the spatial scales of habitat choice by elephants. PeerJ, 2(1). pp. e504. 10.7717/peerj.504. Retrieved from https://hdl.handle.net/10161/23563.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Stuart L. Pimm
Doris Duke Distinguished Professor of Conservation Ecology in the Nicholas School
of the Environment and Earth Sciences
Stuart Pimm is a world leader in the study of present-day extinctions and what can
be done to prevent them. His research covers the reasons why species become extinct,
how fast they do so, the global patterns of habitat loss and species extinction and,
importantly, the management consequences of this research. Pimm received his BSc degree
from Oxford University in 1971 and his Ph.D. from New Mexico State University in 1974.
Pimm is the author of over 350 scientific papers and five books. He i

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