Abstract

Habitat use and resource selection studies based on presence-only data often use simulated locations as a benchmark for comparison with locations where a species has been observed. Such designs, termed "use-versus-availability", are commonly analyzed with logistic regression, resulting in resource selection functions (RSFs). Statistical ecologists have recently expressed concerns about the appropriateness of case-control logistic regression for the calculation of RSFs, based on the claim that given enough time available locations may be used by the target species, thus creating "false negatives" and violating the assumption that the two outcomes, use and availability, are mutually exclusive. Accordingly, statistical ecologists propose alternative methods to address the latter concern. We argue that simulated absence data can be interpreted as true absences, hence amenable to case-control logistic regression, when absence data are considered as paired instantaneously in time with presence-only data.

Highlights

  • Habitat selection is a process that results in disproportionate use by individual organisms of available habitats (Johnson 1980)

  • Resource Selection Functions (RSF) were introduced in the early 1990s as a statistical means to evaluate the relative probability of occurrence in specific types of habitat by a set of animals in a given study region (Manly et al 2002)

  • A RSF is the result of a logistic regression examining two outcomes, “used” versus “unused” or “used” versus “available” locations against a set of habitat covariates measured at each location

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Summary

Introduction

Habitat selection is a process that results in disproportionate use by individual organisms of available habitats (Johnson 1980). Resource Selection Functions (RSF) were introduced in the early 1990s as a statistical means to evaluate the relative probability of occurrence in specific types of habitat by a set of animals in a given study region (Manly et al 2002).

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