With the advent of new technologies, animal locations are being collected
at ever finer spatiotemporal scales. We review analytical methods
for dealing with correlated data in the context of resource selection,
including post hoc variance inflation techniques, 'two-stage' approaches
based on models fit to each individual, generalized estimating equations
and hierarchical mixed-effects models. These methods are applicable
to a wide range of correlated data problems, but can be difficult
to apply and remain especially challenging for use-availability sampling
designs because the correlation structure for combinations of used
and available points are not likely to follow common parametric forms.
We also review emerging approaches to studying habitat selection
that use finescale temporal data to arrive at biologically based
definitions of available habitat, while naturally accounting for
autocorrelation by modelling animal movement between telemetry locations.
Sophisticated analyses that explicitly model correlation rather than
consider it a nuisance, like mixed effects and state-space models,
offer potentially novel insights into the process of resource selection,
but additional work is needed to make them more generally applicable
to large datasets based on the use-availability designs. Until then,
variance inflation techniques and two-stage approaches should offer
pragmatic and flexible approaches to modelling correlated data. ©
2010 The Royal Society.
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