Fishing trip cost modeling using generalized linear model and machine learning methods - A case study with longline fisheries in the Pacific and an application in Regulatory Impact Analysis.
Fishing trip cost is an important element in evaluating economic performance of fisheries, assessing economic effects from fisheries management alternatives, and serving as input for ecosystem and bioeconomic modeling. However, many fisheries have limited trip-level data due to low observer coverage...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2021-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0257027 |
| _version_ | 1830199564527730688 |
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| author | Hing Ling Chan Minling Pan |
| author_facet | Hing Ling Chan Minling Pan |
| author_sort | Hing Ling Chan |
| collection | DOAJ |
| description | Fishing trip cost is an important element in evaluating economic performance of fisheries, assessing economic effects from fisheries management alternatives, and serving as input for ecosystem and bioeconomic modeling. However, many fisheries have limited trip-level data due to low observer coverage. This article introduces a generalized linear model (GLM) utilizing machine learning (ML) techniques to develop a modeling approach to estimate the functional forms and predict the fishing trip costs of unsampled trips. GLM with Lasso regularization and ML cross-validation of model are done simultaneously for predictor selection and evaluation of the predictive power of a model. This modeling approach is applied to estimate the trip-level fishing costs using the empirical sampled trip costs and the associated trip-level fishing operational data and vessel characteristics in the Hawaii and American Samoa longline fisheries. Using this approach to build models is particularly important when there is no strong theoretical guideline on predictor selection. Also, the modeling approach addresses the issue of skewed trip cost data and provides predictive power measurement, compared with the previous modeling efforts in trip cost estimation for the Hawaii longline fishery. As a result, fishing trip costs for all trips in the fishery can be estimated. Lastly, this study applies the estimated trip cost model to conduct an empirical analysis to evaluate the impacts on trip costs due to spatial regulations in the Hawaii longline fishery. The results show that closing the Western and Central Pacific Ocean (WCPO) could induce an average 14% increase in fishing trip costs, while the trip cost impacts of the Eastern Pacific Ocean (EPO) closures could be lower. |
| first_indexed | 2024-12-18T02:16:28Z |
| format | Article |
| id | doaj.art-c5a0f2e2145d4d4da44aeb35c9cf37ac |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-18T02:16:28Z |
| publishDate | 2021-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-c5a0f2e2145d4d4da44aeb35c9cf37ac2022-12-21T21:24:22ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-01169e025702710.1371/journal.pone.0257027Fishing trip cost modeling using generalized linear model and machine learning methods - A case study with longline fisheries in the Pacific and an application in Regulatory Impact Analysis.Hing Ling ChanMinling PanFishing trip cost is an important element in evaluating economic performance of fisheries, assessing economic effects from fisheries management alternatives, and serving as input for ecosystem and bioeconomic modeling. However, many fisheries have limited trip-level data due to low observer coverage. This article introduces a generalized linear model (GLM) utilizing machine learning (ML) techniques to develop a modeling approach to estimate the functional forms and predict the fishing trip costs of unsampled trips. GLM with Lasso regularization and ML cross-validation of model are done simultaneously for predictor selection and evaluation of the predictive power of a model. This modeling approach is applied to estimate the trip-level fishing costs using the empirical sampled trip costs and the associated trip-level fishing operational data and vessel characteristics in the Hawaii and American Samoa longline fisheries. Using this approach to build models is particularly important when there is no strong theoretical guideline on predictor selection. Also, the modeling approach addresses the issue of skewed trip cost data and provides predictive power measurement, compared with the previous modeling efforts in trip cost estimation for the Hawaii longline fishery. As a result, fishing trip costs for all trips in the fishery can be estimated. Lastly, this study applies the estimated trip cost model to conduct an empirical analysis to evaluate the impacts on trip costs due to spatial regulations in the Hawaii longline fishery. The results show that closing the Western and Central Pacific Ocean (WCPO) could induce an average 14% increase in fishing trip costs, while the trip cost impacts of the Eastern Pacific Ocean (EPO) closures could be lower.https://doi.org/10.1371/journal.pone.0257027 |
| spellingShingle | Hing Ling Chan Minling Pan Fishing trip cost modeling using generalized linear model and machine learning methods - A case study with longline fisheries in the Pacific and an application in Regulatory Impact Analysis. PLoS ONE |
| title | Fishing trip cost modeling using generalized linear model and machine learning methods - A case study with longline fisheries in the Pacific and an application in Regulatory Impact Analysis. |
| title_full | Fishing trip cost modeling using generalized linear model and machine learning methods - A case study with longline fisheries in the Pacific and an application in Regulatory Impact Analysis. |
| title_fullStr | Fishing trip cost modeling using generalized linear model and machine learning methods - A case study with longline fisheries in the Pacific and an application in Regulatory Impact Analysis. |
| title_full_unstemmed | Fishing trip cost modeling using generalized linear model and machine learning methods - A case study with longline fisheries in the Pacific and an application in Regulatory Impact Analysis. |
| title_short | Fishing trip cost modeling using generalized linear model and machine learning methods - A case study with longline fisheries in the Pacific and an application in Regulatory Impact Analysis. |
| title_sort | fishing trip cost modeling using generalized linear model and machine learning methods a case study with longline fisheries in the pacific and an application in regulatory impact analysis |
| url | https://doi.org/10.1371/journal.pone.0257027 |
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