Theses and Dissertations

Issuing Body

Mississippi State University

Advisor

Street, Garrett M.

Committee Member

Strickland, Bronson K.

Committee Member

Borger, Luca

Date of Degree

5-3-2019

Document Type

Graduate Thesis - Open Access

Major

Wildlife, Fisheries, and Aquaculture

Degree Name

Master of Science

College

College of Agriculture and Life Sciences

Department

Department of Wildlife, Fisheries and Aquaculture

Abstract

Studies of animal spatial distributions typically use prior knowledge of animal habitat requirements and behavioral ecology to deduce the most likely explanations of observed habitat use. Animal-borne accelerometers can be used to distinguish behaviors which allows us to incorporate in situ behavior into our understanding of spatial distributions. Past research has focused on using supervised machine-learning, which requires a priori specification of behavior to identify signals whereas unsupervised approaches allow the model to identify as many signal types as permitted by the data. The following framework couples direct observation to behavioral clusters identified from unsupervised machine learning on a large accelerometry dataset. A behavioral profile was constructed to describe the proportion of behaviors observed per cluster and the framework was applied to an acceleration dataset collected from wild pigs (Sus scrofa). Although, most clusters represented combinations of behaviors, a leave-p-out validation procedure indicated this classification system accurately predicted new data.

URI

https://hdl.handle.net/11668/20971

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