Hierarchical Bayesian application to instantaneous rates tag-return models

Bayesian;Tag-return models

Krachey, Matthew James ; Ken Pollock, Committee Chair,Kevin Gross, Committee Member,Sujit Ghosh, Committee Member,Joseph Hightower, Committee Co-Chair,Krachey, Matthew James ; Ken Pollock ; Committee Chair ; Kevin Gross ; Committee Member ; Sujit Ghosh ; Committee Member ; Joseph Hightower ; Committee Co-Chair

University:North Carolina State University

关键词: Bayesian;    Tag-return models;   

Others  :  https://repository.lib.ncsu.edu/bitstream/handle/1840.16/4299/etd.pdf?sequence=1&isAllowed=y

美国|英语

来源: null

【 摘 要 】

Natural mortality has always been a challenging quantity to estimate in harvested populations. The most common approaches to estimation include a regression model based on life history parameters and more recently tag-return models. In recent years, Bayesian methods have been increasingly implemented in ecological models due to their ability to handle increased model complexity and auxiliary datasets. In this dissertation, I explore the implementation of Bayesian methods to analyze tag-return data focusing on natural mortality. Chapter 1 is focused on the addition of two components to the tag-return model framework: random effects and auxiliary data. Auxiliary information on the instantaneous rate of natural mortality is provided through Hoenig's equation relating lifespan to natural mortality, and also implemented through a hierarchical prior. A simulation study validates the performance of the model while an analysis of the classic Cayuga Lake trout dataset demonstrates its use. Chapter 2 adds a change-point allowing for the estimation of two levels of natural mortality and the timing of the discrete-time shift in mortality. Analysis is focused on a Chesapeake Bay striped bass tagging dataset of fish tagged at six years of age and older from 1991-2002. Results show the ability to account for shift in timing. Contrasting with Jiang et al.'s study on the same striped bass dataset, the timing of the change-point was different between the two studies, likely because the Jiang study assumed a fixed tag-reporting probability of 0.43 whereas estimates seem to indicate it may be closer to 0.3. Chapter 3 introduces a change-point allowing for a shift in the tag-reporting probability while assuming a constant natural mortality rate. High reward tags are included in a subset of the data time-series to improve estimation. A factorial simulation design was used to investigate the model performance with different reporting rate and high reward tag scenarios. In general, the model performed very well with little bias except in the case of no high-reward tags. The model performed surprisingly well in a six year study. The results suggest the importance of high reporting rates and/ or auxiliary data sources such as high reward tags.