SMU Data Science Review
Abstract
In 1992, the Louisiana black bear (Ursus americanus luteolus) was placed on the U.S. Endangered Species List. This was due to bear populations in Louisiana being small and isolated enough where their populations couldn’t intersect with other populations to grow. Interchange of individuals between subpopulations of bears in Louisiana is critical to maintain genetic diversity and avoid inbreeding effects. Utilizing GPS (Global Positioning System) data gathered from 31 radio-collared bears from 2010 through 2012, this research will investigate how bears traverse the landscape, which has implications for gene exchange. This paper will leverage machine learning tools to improve upon existing techniques of classifying bear movement to strengthen predictions about gene flow, and to apply our methodology to future animal studies. This includes reviewing and optimizing review the model framework used to analyze location data of Louisiana black bears and land survey data to perform a movement trajectory analysis utilizing machine learning models for purposes of prediction. A model will be developed for predicting steps based on learned behaviors, attitudinal behaviors and environmental data which can be applied to model overall movement of the species.
Recommended Citation
Clark, Daniel; Shaw, David; Vela, Armando; Weinstock, Shane; Santerre, John; and Clark, Joseph D.
(2021)
"Using Machine Learning Methods to Predict the Movement Trajectories of the Louisiana Black Bear,"
SMU Data Science Review: Vol. 5:
No.
1, Article 11.
Available at:
https://scholar.smu.edu/datasciencereview/vol5/iss1/11
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
