Considering ecological dynamics in resource selection functions

Abstract

Summary 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.

Seasonal resource selection of woodland caribou (Rangifer tarandus caribou) across a gradient of anthropogenic disturbance

“…RSFs describe landscapes using probabilistic functions, permitting a spatially explicit examination of resource availability and selection, which is an improvement from past methods that provided categorical or binary representations of habitat and matrix (Manly 2002;Chetkiewicz et al 2006). RSFs can be used to project the probability of a species' occurrence on the landscape or be used to assess the cumulative impacts of habitat alteration due to development or climate change (Manly 2002;Chetkiewicz et al 2006;McLoughlin et al 2010). Boreal woodland caribou (herein caribou) are listed as "threatened" under Canada's Species at Risk Act (Government of Canada 2004) and Ontario's Endangered Species Act (Government of Ontario 2007).…”

Avoidance of roads and selection for recent cutovers by threatened caribou: fitness-rewarding or maladaptive behaviour?

“…Habitat selection is a fundamental mechanism by which animals achieve their distribution, and as such, it is central to a broad range of ecological fields, such as landscape ecology, wildlife conservation and management, and evolutionary ecology [1][2][3][4]. Habitat selection decisions can vary broadly among members of a population [5 -7], and this behavioural plasticity can yield different fitness payoffs [8].…”

“…Nevertheless, most habitat selection studies do not account for the complexity of the selection process (i.e. no multivariate assessment), or when they do, the fitness consequences of selection decisions are generally not assessed [3,8].…”

Effects of a marine fishery reserve and habitat characteristics in the abundance and demography of the red hind grouper,Epinephelus guttatus

“…Multilevel linear models allow slope coefficients to vary among cross-shelf reef strata, which could reveal disparate strata-level responses to habitat covariates. Terrestrial applications of multilevel models have been demonstrably beneficial in revealing how habitat use patterns can vary between geographic regions and times of year (Boyce et al 2002;Gillies et al 2006;McLoughlin et al 2010). In revealing whether and how habitat relationships vary spatially, previously unforeseen variance structures can in turn inform stratification schemes that generate cost-effective allocations of sampling effort in future surveys (Xu et al 2015).…”