Date of Award
Spring 2024
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Ocean & Earth Sciences
Program/Concentration
Oceanography
Committee Director
John Klinck
Committee Member
Cynthia Jones
Committee Member
Norou Diawara
Committee Member
Kent Carpenter
Abstract
The ability to assign accurate ages of fish is important to fisheries management. Accurate ageing allows for the most reliable age-based models to be used to support sustainability and maximize economic benefit. Structures used to age include bones, scales, and most commonly ear bones (otoliths). Assigning age relies on validating putative annual marks by evaluating accretional material laid down in patterns in fish otoliths, typically by marginal increment analysis. These patterns often take the shape of a sawtooth wave with an abrupt drop in accretion yearly to form an annual band and are typically validated qualitatively. Researchers have shown keen interest in modeling marginal increments to verify the marks do, in fact, occur yearly. However, it has been challenging in finding the best model to predict this sawtooth wave pattern. I propose three new applications of time series models to validate the existence of the yearly sawtooth wave patterned data: autoregressive integrated moving average (ARIMA), unobserved components model (UCM) and copula. These methods are expected to enable the identification of yearly patterns in accretion. ARIMA and UCM account for the dependence of observations and error, while copula incorporates a variety of marginal distributions and dependence structure. Results indicate that all three models are valid to predict annuli formation. ARIMA works best with a sharp, distinct sawtooth wave while copula is best for data without the sharp drop
Rights
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DOI
10.25777/xtd6-g229
ISBN
9798382770826
Recommended Citation
Kirch, Kathleen S..
"Time Series Modeling to Ascertain Age in Fisheries Management"
(2024). Doctor of Philosophy (PhD), Dissertation, Ocean & Earth Sciences, Old Dominion University, DOI: 10.25777/xtd6-g229
https://digitalcommons.odu.edu/oeas_etds/393
ORCID
0009-0009-1484-5390
Included in
Aquaculture and Fisheries Commons, Environmental Sciences Commons, Marine Biology Commons