Abstract
Habitat‐selection analysis lacks an appropriate measure of the ecological significance of the statistical estimates—a practical interpretation of the magnitude of the selection coefficients. There is a need for a standard approach that allows relating the strength of selection to a change in habitat conditions across space, a quantification of the estimated effect size that can be compared both within and across studies. We offer a solution, based on the epidemiological risk ratio, which we term the relative selection strength (RSS). For a “used‐available” design with an exponential selection function, the RSS provides an appropriate interpretation of the magnitude of the estimated selection coefficients, conditional on all other covariates being fixed. This is similar to the interpretation of the regression coefficients in any multivariable regression analysis. Although technically correct, the conditional interpretation may be inappropriate when attempting to predict habitat use across a given landscape. Hence, we also provide a simple graphical tool that communicates both the conditional and average effect of the change in one covariate. The average‐effect plot answers the question: What is the average change in the space use probability as we change the covariate of interest, while averaging over possible values of other covariates? We illustrate an application of the average‐effect plot for the average effect of distance to road on space use for elk (Cervus elaphus) during the hunting season. We provide a list of potentially useful RSS expressions and discuss the utility of the RSS in the context of common ecological applications.
Highlights
Habitat-selection analysis (HSA) is central to many ecological studies and applications seeking to understand and/or predict the association between the probability of animal occurrence and local environmental conditions
We have described the correct interpretation of the regression coefficients in the exponential RS(P)F model that is commonly used in HSAs
All statistical analyses are based on assumptions (Sólymos & Lele, 2016)
Summary
Habitat-selection analysis (HSA) is central to many ecological studies and applications seeking to understand and/or predict the association between the probability of animal occurrence and local environmental conditions. If the HSA includes an interaction between hi and hj (hi·hj), with a corresponding selection coefficient βij, and given that hj(x1) = hj(x2), the conditional log-RSS is given by Δhi⋅(βi + βij⋅hj(x1)] (see Figure 1). Log-transformed variables mean that the relative selection strength is a function of the ratio, rather than the difference, between available habitat values.
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