Extent-dependent habitat selection in a migratory large herbivore: road avoidance across scales

Prokopenko, Christina M.; Boyce, Mark S.; Avgar, Tal

doi:10.1007/s10980-016-0451-1

Cited by 53 publications

(54 citation statements)

References 70 publications

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“…Therefore, although habitat selection patterns can be consistent across scales (Schaefer and Messier , Prokopenko et al. ), they typically are not, allowing the possibility that wolves in our study area were selecting for prey abundance at a finer scale than our analysis investigated (McPhee et al. , but see Courbin et al.…”

Section: Discussionmentioning

confidence: 91%

Landscape‐level wolf space use is correlated with prey abundance, ease of mobility, and the distribution of prey habitat

et al. 2017

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Abstract. Predator space use influences ecosystem dynamics, and a fundamental goal assumed for a foraging predator is to maximize encounter rate with prey. This can be achieved by disproportionately utilizing areas of high prey density or, where prey are mobile and therefore spatially unpredictable, utilizing patches of their prey's preferred resources. A third, potentially complementary strategy is to increase mobility by using linear features like roads and/or frozen waterways. Here, we used novel population-level predator utilization distributions (termed "localized density distributions") in a single-predator (wolf), two-prey (moose and caribou) system to evaluate these space-use hypotheses. The study was conducted in contrasting sections of a large boreal forest area in northern Ontario, Canada, with a spatial gradient of human disturbances and predator and prey densities. Our results indicated that wolves consistently used forest stands preferred by moose, their main prey species in this part of Ontario. Direct use of prey-rich areas was also significant but restricted to where there was a high local density of moose, whereas use of linear features was pronounced where local moose density was lower. These behaviors suggest that wolf foraging decisions, while consistently influenced by spatially anchored patches of prey forage resources, were also determined by local ecological conditions, specifically prey density. Wolves appeared to utilize prey-rich areas when regional preferred prey density exceeded a threshold that made this profitable, whereas they disproportionately used linear features that promoted mobility when low prey density made directly tracking prey distribution unprofitable.

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Section: Discussionmentioning

confidence: 91%

Landscape‐level wolf space use is correlated with prey abundance, ease of mobility, and the distribution of prey habitat

et al. 2017

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“…Under this assumption, habitat availability may be quantified by randomly sampling locations within the assumed geographical availability domain (e.g., the animal's home range; Johnson 1980, Beyer et al 2010. A major drawback of this approach is that the resulting inference is sensitive to the somewhat subjective definition of the availability domain (Beyer et al 2010, Prokopenko et al 2016.…”

Section: Introductionmentioning

confidence: 99%

Estimating utilization distributions from fitted step‐selection functions

2017

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Citation: Signer, J., J. Fieberg, and T. Avgar. 2017. Estimating utilization distributions from fitted step-selection functions.Ecosphere 8(4):e01771. 10.1002/ecs2.1771Abstract. Habitat-selection analyses are often used to link environmental covariates, measured within some spatial domain of assumed availability, to animal location data that are assumed to be independent.Step-selection functions (SSFs) relax this independence assumption, by using a conditional model that explicitly acknowledges the spatiotemporal dynamics of the availability domain and hence the temporal dependence among successive locations. However, it is not clear how to produce an SSF-based map of the expected utilization distribution. Here, we used SSFs to analyze virtual animal movement data generated at a fine spatiotemporal scale and then rarefied to emulate realistic telemetry data. We then compared two different approaches for generating maps from the estimated regression coefficients. First, we considered a na€ ıve approach that used the coefficients as if they were obtained by fitting an unconditional model. Second, we explored a simulation-based approach, where maps were generated using stochastic simulations of the parameterized step-selection process. We found that the simulation-based approach always outperformed the na€ ıve mapping approach and that the latter overestimated home-range size and underestimated local space-use variability. Differences between the approaches were greatest for complex landscapes and high sampling rates, suggesting that the simulation-based approach, despite its added complexity, is likely to offer significant advantages when applying SSFs to real data.

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“…The fundamental difference between HSA and SSA is their respective definitions of the availability domain—the geographical space that is deemed accessible to the animal (and hence also the habitat space deemed available) at any point in space and time. In fact, different definitions of availability are common within “global” (unconditional) HSAs, ranging from a minimum convex polygon encompassing all observed occurrences (with or without buffers), through various types of kernel estimators (with various cutoff values), and on to the “population range” or simply the “study area” (Beyer et al., ; Prokopenko, Boyce, & Avgar, and refs therein). Not only that the resulting inference is sensitive to the definition of the availability domain (Beyer et al., ; Prokopenko, Boyce, & Avgar, ), it is often sensitive to the habitat availability and configuration within this domain (a so called “functional response”; Matthiopoulos, Hebblewhite, Aarts, & Fieberg, ; Mysterud & Ims, ; Paton & Matthiopoulos, ).…”

Section: Discussionmentioning

confidence: 99%

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

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Keim³

et al. 2017

Ecology and Evolution

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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. K E Y W O R D SSSA, log odds, logistic regression, odds ratio, resource selection function, HSA, step selection function | BACKGROUNDHabitat-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. Habitat-selection studies often involve numerous habitat attributes and can be based on a variety of sampling and statisticalmodeling techniques. Consequently, appropriate interpretation and graphical presentation of the results and their ecological significance can be challenging, with consequences for effective communication of findings, as well as for the capacity to compare and synthesize across studies.Broadly speaking, HSAs include two types of models that differ in their estimations procedure and hence in the type of predictions they generate. A "resource selection probability function" (RSPF) predicts the probability of selection of any given spatial unit (given its habitat

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Extent-dependent habitat selection in a migratory large herbivore: road avoidance across scales

Cited by 53 publications

References 70 publications

Landscape‐level wolf space use is correlated with prey abundance, ease of mobility, and the distribution of prey habitat

Landscape‐level wolf space use is correlated with prey abundance, ease of mobility, and the distribution of prey habitat

Estimating utilization distributions from fitted step‐selection functions

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

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