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      Relative Selection Strength: Quantifying effect size in habitat‐ and step‐selection inference


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          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.

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          Accounting for animal movement in estimation of resource selection functions: sampling and data analysis.

          Patterns of resource selection by animal populations emerge as a result of the behavior of many individuals. Statistical models that describe these population-level patterns of habitat use can miss important interactions between individual animals and characteristics of their local environment; however, identifying these interactions is difficult. One approach to this problem is to incorporate models of individual movement into resource selection models. To do this, we propose a model for step selection functions (SSF) that is composed of a resource-independent movement kernel and a resource selection function (RSF). We show that standard case-control logistic regression may be used to fit the SSF; however, the sampling scheme used to generate control points (i.e., the definition of availability) must be accommodated. We used three sampling schemes to analyze simulated movement data and found that ignoring sampling and the resource-independent movement kernel yielded biased estimates of selection. The level of bias depended on the method used to generate control locations, the strength of selection, and the spatial scale of the resource map. Using empirical or parametric methods to sample control locations produced biased estimates under stronger selection; however, we show that the addition of a distance function to the analysis substantially reduced that bias. Assuming a uniform availability within a fixed buffer yielded strongly biased selection estimates that could be corrected by including the distance function but remained inefficient relative to the empirical and parametric sampling methods. As a case study, we used location data collected from elk in Yellowstone National Park, USA, to show that selection and bias may be temporally variable. Because under constant selection the amount of bias depends on the scale at which a resource is distributed in the landscape, we suggest that distance always be included as a covariate in SSF analyses. This approach to modeling resource selection is easily implemented using common statistical tools and promises to provide deeper insight into the movement ecology of animals.
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            Integrated step selection analysis: bridging the gap between resource selection and animal movement

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              The interpretation of habitat preference metrics under use-availability designs.

              Models of habitat preference are widely used to quantify animal-habitat relationships, to describe and predict differential space use by animals, and to identify habitat that is important to an animal (i.e. that is assumed to influence fitness). Quantifying habitat preference involves the statistical comparison of samples of habitat use and availability. Preference is therefore contingent upon both of these samples. The inferences that can be made from use versus availability designs are influenced by subjectivity in defining what is available to the animal, the problem of quantifying the accessibility of available resources and the framework in which preference is modelled. Here, we describe these issues, document the conditional nature of preference and establish the limits of inferences that can be drawn from these analyses. We argue that preference is not interpretable as reflecting the intrinsic behavioural motivations of the animal, that estimates of preference are not directly comparable among different samples of availability and that preference is not necessarily correlated with the value of habitat to the animal. We also suggest that preference is context-dependent and that functional responses in preference resulting from changing availability are expected. We conclude by describing advances in analytical methods that begin to resolve these issues.

                Author and article information

                Ecol Evol
                Ecol Evol
                Ecology and Evolution
                John Wiley and Sons Inc. (Hoboken )
                14 June 2017
                July 2017
                : 7
                : 14 ( doiID: 10.1002/ece3.2017.7.issue-14 )
                : 5322-5330
                [ 1 ] Department of Biological Sciences University of Alberta Edmonton AB Canada
                [ 2 ] Department of Mathematical and Statistical Sciences University of Alberta Edmonton AB Canada
                [ 3 ] Trove Predictive Data Science Edmonton AB Canada
                Author notes
                [*] [* ] Correspondence

                Tal Avgar, Department of Integrative Biology, University of Guelph, Guelph, ON, Canada.

                Email: avgart@ 123456uoguelph.ca

                © 2017 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.

                This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                Page count
                Figures: 4, Tables: 2, Pages: 9, Words: 7543
                Funded by: Banting Postdoctoral Fellowship for TA
                Original Research
                Original Research
                Custom metadata
                July 2017
                Converter:WILEY_ML3GV2_TO_NLMPMC version:5.1.4 mode:remove_FC converted:26.07.2017

                Evolutionary Biology
                ssa,log odds,logistic regression,odds ratio,resource selection function,hsa,step selection function


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