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.
| Identifer | oai:union.ndltd.org:NCSU/oai:NCSU:etd-08182009-100250 |
| Date | 06 October 2009 |
| Creators | Krachey, Matthew James |
| Contributors | Ken Pollock, Kevin Gross, Sujit Ghosh, Joseph Hightower |
| Publisher | NCSU |
| Source Sets | North Carolina State University |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://www.lib.ncsu.edu/theses/available/etd-08182009-100250/ |
| Rights | unrestricted, I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dis sertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to NC State University or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. |
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