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A machine-learning approach for extending classical wildlife resource selection analyses.
Kevin T Shoemaker1, Levi J Heffelfinger1, Nathan J Jackson1
1Department of Natural Resources and Environmental Science University of Nevada, Reno Reno NV USA.
Ecology and Evolution
|April 3, 2018
Summary
Random forest models offer superior insights into mule deer (Odocoileus hemionus) habitat selection compared to traditional logistic regression. These machine-learning approaches reveal complex relationships, improving predictions of habitat suitability.
Area of Science:
- Ecology
- Wildlife Management
- Spatial Analysis
Background:
- Resource selection functions (RSFs) are crucial for understanding wildlife habitat use and identifying critical areas.
- Standard RSFs, typically using logistic regression, struggle with nonlinear relationships and complex interactions.
Purpose of the Study:
- To compare the efficacy of random forest (RF) models against traditional logistic regression for analyzing seasonal resource selection in mule deer (Odocoileus hemionus).
- To identify nonlinear relationships and complex interactions in habitat selection patterns.
Main Methods:
- Applied logistic regression and random forest (RF) models to analyze mule deer telemetry data and environmental features.
- Evaluated model performance and predictive skill.
Main Results:
- Random forest models successfully detected nonlinear relationships (e.g., optimal slope and elevation ranges) and complex interactions.
- RF models demonstrated improved predictive accuracy over standard RSF models.
- Projecting RF models revealed significant differences in predicted habitat suitability compared to traditional methods.
Conclusions:
- Machine-learning tools like RF provide enhanced insights into wildlife resource selection patterns.
- Researchers should consider integrating RF with traditional methods for robust habitat selection analyses, especially with large datasets.
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