A machine-learning approach for extending classical wildlife resource selection analyses
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Authors
Shoemaker, Kevin T.
Heffelfinger, Levi J.
Jackson, Nathan J.
Blum, Marcus E.
Wasley, Tony
Stewart, Kelley M.
Issue Date
2018
Type
Article
Language
Keywords
habitat suitability , logistic regression , machine learning , Odocoileus hemionus , random forest , resource selection function
Alternative Title
Abstract
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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Citation
Shoemaker, K. T., Heffelfinger, L. J., Jackson, N. J., Blum, M. E., Wasley, T., & Stewart, K. M. (2018). A machine-learning approach for extending classical wildlife resource selection analyses. Ecology and Evolution, 8(6), 3556�"3569. doi:10.1002/ece3.3936
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Creative Commons Attribution 4.0 International
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PubMed ID
ISSN
2045-7758