Fitting Time Series Models to Fisheries Data to Ascertain Age
The ability of government agencies to assign accurate ages of fish is important to fisheries management. Accurate ageing allows for most reliable age-based models to be used to support sustainability and maximize economic benefit. Assigning age relies on validating putative annual marks by evaluatin...
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | Journal of Probability and Statistics |
| Online Access: | http://dx.doi.org/10.1155/2023/9991872 |
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| author | Kathleen S. Kirch Norou Diawara Cynthia M. Jones |
| author_facet | Kathleen S. Kirch Norou Diawara Cynthia M. Jones |
| author_sort | Kathleen S. Kirch |
| collection | DOAJ |
| description | The ability of government agencies to assign accurate ages of fish is important to fisheries management. Accurate ageing allows for most reliable age-based models to be used to support sustainability and maximize economic benefit. Assigning age relies on validating putative annual marks by evaluating accretional material laid down in patterns in fish ear bones, 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 key 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. We 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 component, and copula. These methods are expected to enable the identification of yearly patterns in accretion. ARIMA and unobserved components account for the dependence of observations and error, while copula incorporates a variety of marginal distributions and dependence structures. The unobserved component model produced the best results (AIC: −123.7, MSE 0.00626), followed by the time series model (AIC: −117.292, MSE: 0.0081), and then the copula model (AIC: −96.62, Kendall’s tau: −0.5503). The unobserved component model performed best due to the completeness of the dataset. In conclusion, all three models are effective tools to validate yearly accretional patterns in fish ear bones despite their differences in constraints and assumptions. |
| format | Article |
| id | doaj-art-58d8e99dff1141d59fdb717b3a7d2ab6 |
| institution | OA Journals |
| issn | 1687-9538 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Probability and Statistics |
| spelling | doaj-art-58d8e99dff1141d59fdb717b3a7d2ab62025-08-20T02:05:23ZengWileyJournal of Probability and Statistics1687-95382023-01-01202310.1155/2023/9991872Fitting Time Series Models to Fisheries Data to Ascertain AgeKathleen S. Kirch0Norou Diawara1Cynthia M. Jones2Department of Ocean & Earth SciencesDepartment of Mathematics and StatisticsDepartment of Ocean & Earth SciencesThe ability of government agencies to assign accurate ages of fish is important to fisheries management. Accurate ageing allows for most reliable age-based models to be used to support sustainability and maximize economic benefit. Assigning age relies on validating putative annual marks by evaluating accretional material laid down in patterns in fish ear bones, 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 key 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. We 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 component, and copula. These methods are expected to enable the identification of yearly patterns in accretion. ARIMA and unobserved components account for the dependence of observations and error, while copula incorporates a variety of marginal distributions and dependence structures. The unobserved component model produced the best results (AIC: −123.7, MSE 0.00626), followed by the time series model (AIC: −117.292, MSE: 0.0081), and then the copula model (AIC: −96.62, Kendall’s tau: −0.5503). The unobserved component model performed best due to the completeness of the dataset. In conclusion, all three models are effective tools to validate yearly accretional patterns in fish ear bones despite their differences in constraints and assumptions.http://dx.doi.org/10.1155/2023/9991872 |
| spellingShingle | Kathleen S. Kirch Norou Diawara Cynthia M. Jones Fitting Time Series Models to Fisheries Data to Ascertain Age Journal of Probability and Statistics |
| title | Fitting Time Series Models to Fisheries Data to Ascertain Age |
| title_full | Fitting Time Series Models to Fisheries Data to Ascertain Age |
| title_fullStr | Fitting Time Series Models to Fisheries Data to Ascertain Age |
| title_full_unstemmed | Fitting Time Series Models to Fisheries Data to Ascertain Age |
| title_short | Fitting Time Series Models to Fisheries Data to Ascertain Age |
| title_sort | fitting time series models to fisheries data to ascertain age |
| url | http://dx.doi.org/10.1155/2023/9991872 |
| work_keys_str_mv | AT kathleenskirch fittingtimeseriesmodelstofisheriesdatatoascertainage AT noroudiawara fittingtimeseriesmodelstofisheriesdatatoascertainage AT cynthiamjones fittingtimeseriesmodelstofisheriesdatatoascertainage |