Considering ecological dynamics in resource selection functions.
1. Describing distribution and abundance is requisite to exploring interactions between organisms and their environment. Recently, the resource selection function (RSF) has emerged to replace many of the statistical procedures used to quantify resource selection by animals. 2. A RSF is defined by characteristics measured on resource units such that its value for a unit is proportional to the probability of that unit being used by an organism. It is solved using a variety of techniques, particularly the binomial generalized linear model. 3. Observing dynamics in a RSF - obtaining substantially different functions at different times or places for the same species - alerts us to the varying ecological processes that underlie resource selection. 4. We believe that there is a need for us to reacquaint ourselves with ecological theory when interpreting RSF models. We outline a suite of factors likely to govern ecologically based variation in a RSF. In particular, we draw attention to competition and density-dependent habitat selection, the role of predation, longitudinal changes in resource availability and functional responses in resource use. 5. How best to incorporate governing factors in a RSF is currently in a state of development; however, we see promise in the inclusion of random as well as fixed effects in resource selection models, and matched case-control logistic regression. 6. Investigating the basis of ecological dynamics in a RSF will allow us to develop more robust models when applied to forecasting the spatial distribution of animals. It may also further our understanding of the relative importance of ecological interactions on the distribution and abundance of species.
McLoughlin
Philip D
PD
Department of Biology, University of Saskatchewan, 112 Science Place, Saskatoon, SK S7N 5E2, Canada. philip.mcloughlin@usask.ca
Morris
Douglas W
DW
Fortin
Daniel
D
Vander Wal
Eric
E
Contasti
Adrienne L
AL
D000818:Animals; D017753:Ecosystem; D005247:Feeding Behavior; D008954:Models, Biological