Murray State Theses and Dissertations

Abstract

Capture-recapture models are essential tools for estimating population dynamics in ecological studies. A fundamental component of these models is the capture history matrix, which records individual detection over time and serves as the basis for estimating survival and capture probabilities. This presentation explores three statistical approaches to these estimations: the Cormack-Jolly-Seber (CJS) model, the Hidden Markov Model (HMM) for CJS, and the Bayesian CJS model. The CJS model provides a likelihood-based framework for estimation, and the HMM CJS incorporates latent states into the model to account for uncertainty in detection. The Bayesian CJS extends this same analysis by integrating prior knowledge using Markov Chain Monte Carlo (MCMC) methods. Applying these models to the capture data of the Arizona tiger salamander, I will compare the assumptions, estimation techniques, and results of these methods while also discussing their strengths and limitations in ecological research.

Year manuscript completed

2025

Year degree awarded

2025

Author's Keywords

Capture-Recapture Models, Cormack-Jolly-Seber, Hidden Markov Model CJS, Bayesian CJS, Arizona Tiger Salamander

Degree Awarded

Master of Science

Department

Mathematics & Statistics

College/School

Jesse D. Jones College of Science, Engineering and Technology

Dissertation Committee Chair

Christopher Mecklin

Thesis Advisor

Christopher Mecklin

Committee Chair

Christopher Mecklin

Committee Member

Manoj Pathak

Committee Member

Howard Whiteman

Document Type

Thesis

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