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

Avgar, Tal; Lele, Subhash R.; Keim, Jonah L.; Boyce, Mark S.

doi:10.1002/ece3.3122

Cited by 176 publications

(190 citation statements)

References 34 publications

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“…Caribou are not shown due to insufficient individuals to obtain population‐level averages. The relative selection strength for anthropogenic disturbances and riparian areas was calculated as the expected tendency of moving towards a given feature type compared to away from it (Appendix S3; Avgar et al, )…”

Section: Resultsmentioning

confidence: 99%

Corridors or risk? Movement along, and use of, linear features varies predictably among large mammal predator and prey species

Dickie

McNay²,

Sutherland³

et al. 2019

Journal of Animal Ecology

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131

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Space‐use behaviour reflects trade‐offs in meeting ecological needs and can have consequences for individual survival and population demographics. The mechanisms underlying space use can be understood by simultaneously evaluating habitat selection and movement patterns, and fine‐resolution locational data are increasing our ability to do so. We use high‐resolution location data and an integrated step‐selection analysis to evaluate caribou, moose, bear, and wolf habitat selection and movement behaviour in response to anthropogenic habitat modification, though caribou data were limited. Space‐use response to anthropogenic linear features (LFs) by predators and prey is hypothesized to increase predator hunting efficiency and is thus believed to be a leading factor in woodland caribou declines in western Canada. We found that all species moved faster while on LFs. Wolves and bears were also attracted towards LFs, whereas prey species avoided them. Predators and prey responded less strongly and consistently to natural features such as streams, rivers and lakeshores. These findings are consistent with the hypothesis that LFs facilitate predator movement and increase hunting efficiency, while prey perceive such features as risky. Understanding the behavioural mechanisms underlying space‐use patterns is important in understanding how future land‐use may impact predator–prey interactions. Explicitly linking behaviour to fitness and demography will be important to fully understand the implications of management strategies.

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Section: Resultsmentioning

confidence: 99%

Corridors or risk? Movement along, and use of, linear features varies predictably among large mammal predator and prey species

Dickie

McNay²,

Sutherland³

et al. 2019

Journal of Animal Ecology

Self Cite

131

130

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“…Importance values are a key advantage of the machine‐learning approach, and for which there is no perfect analogy for model‐based statistical approaches (but see Avgar, Lele, Keim, & Boyce, ). In the RF modeling framework, importance scores reflect the degree to which each predictor variable contributes useful information for (in the case of resource selection models) discriminating between known‐use and available spatial units (Breiman, ; Strobl, Boulesteix, Kneib, Augustin, & Zeileis, ).…”

Section: Discussionmentioning

confidence: 99%

A machine‐learning approach for extending classical wildlife resource selection analyses

Shoemaker

Heffelfinger

Jackson

et al. 2018

Ecology and Evolution

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Resource selection functions (RSFs) are tremendously valuable for ecologists and resource managers because they quantify spatial patterns in resource utilization by wildlife, thereby facilitating identification of critical habitat areas and characterizing specific habitat features that are selected or avoided. RSFs discriminate between known‐use resource units (e.g., telemetry locations) and available (or randomly selected) resource units based on an array of environmental features, and in their standard form are performed using logistic regression. As generalized linear models, standard RSFs have some notable limitations, such as difficulties in accommodating nonlinear (e.g., humped or threshold) relationships and complex interactions. Increasingly, ecologists are using flexible machine‐learning methods (e.g., random forests, neural networks) to overcome these limitations. Herein, we investigate the seasonal resource selection patterns of mule deer (Odocoileus hemionus) by comparing a logistic regression framework with random forest (RF), a popular machine‐learning algorithm. Random forest (RF) models detected nonlinear relationships (e.g., optimal ranges for slope and elevation) and complex interactions which would have been very challenging to discover and characterize using standard model‐based approaches. Compared with standard RSF models, RF models exhibited improved predictive skill, provided novel insights about resource selection patterns of mule deer, and, when projected across a relevant geographic space, manifested notable differences in predicted habitat suitability. We recommend that wildlife researchers harness the strengths of machine‐learning tools like RF in addition to “classical” tools (e.g., mixed‐effects logistic regression) for evaluating resource selection, especially in cases where extensive telemetry data sets are available.

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“…Step‐selection functions were fitted using conditional logistic regression, which parameterizes an exponential function to estimate the relative likelihood of Auklets selecting specific habitat characteristics during their next step (Avgar et al. ). The movement‐independent habitat weighting function took the general form

\overset{false^}{normalw} false(X false) = \exp false(β_{1} x_{1} + β_{2} x_{2} + β_{3} x_{3} \dots β_{p} x_{p} false)

with X a vector of matched observed and available locations, and β i (for i = 1, 2, …, p) the estimated selection coefficients for environmental covariates x i (for i = 1, 2, …, p ).…”

Section: Methodsmentioning

confidence: 99%

Oceanographic drivers of winter habitat use in Cassin’s Auklets

Johns

Warzybok

Jahncke

et al. 2020

Ecological Applications

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Reduced prey abundance and severe weather can lead to a greater risk of mortality for seabirds during the non‐breeding winter months. Resource patterns in some regions are shifting and becoming more variable in relation to past conditions, potentially further impacting survival and carryover to the breeding season. As animal tracking technologies and methods to analyze movement data have advanced, it has become increasingly feasible to draw fine‐scale inference about how environmental variation affects foraging behavior and habitat use of seabirds during this critical period. Here, we used archival light‐sensing tags to evaluate how interannual variation in oceanography affected the winter distribution of Cassin's Auklets from Southeast Farallon Island, California. Thirty‐five out of 93 geolocators deployed from 2015 to 2017 were recovered and successfully recorded light‐level data, from which geographic positions were estimated. Step‐selection functions were applied to identify environmental covariates that best explained winter movement decisions and habitat use, revealing Cassin's Auklets dispersed farther from the colony during a winter with warm SST anomalies, but remained more centralized near the breeding colony during two average winters. Movement patterns were driven by avoidance of areas with higher sea surface temperatures and possible limits of dispersal from the breeding colony, and selection for areas with well‐defined mesoscale fronts and cooler surface waters. Through multiple years of tagging and the application of step‐selection functions, a robust and widely applied approach for analyzing animal movement in terrestrial species, we show how interannual differences in the movement patterns of a small seabird are driven by oceanographic variability across years. Understanding the winter habitat use of seabirds can help inform changes in population structure and measures of reproductive success, aiding managers in determining potential causes of breeding failures.

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

Cited by 176 publications

References 34 publications

Corridors or risk? Movement along, and use of, linear features varies predictably among large mammal predator and prey species

Corridors or risk? Movement along, and use of, linear features varies predictably among large mammal predator and prey species

A machine‐learning approach for extending classical wildlife resource selection analyses

Oceanographic drivers of winter habitat use in Cassin’s Auklets

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