Experiential-informed data reconstruction for fishery sustainability and policies in the Azores
Abstract Fishery analysis is critical in maintaining the long-term sustainability of species and the livelihoods of millions of people who depend on fishing for food and income. The fishing gear, or metier, is a key factor significantly impacting marine habitats, selectively targeting species and fi...
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| Format: | Article |
| Language: | English |
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Springer
2025-05-01
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| Series: | Discover Data |
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| Online Access: | https://doi.org/10.1007/s44248-025-00034-6 |
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| author | Brenda Nogueira Gui M. Menezes Rita P. Ribeiro Nuno Moniz |
| author_facet | Brenda Nogueira Gui M. Menezes Rita P. Ribeiro Nuno Moniz |
| author_sort | Brenda Nogueira |
| collection | DOAJ |
| description | Abstract Fishery analysis is critical in maintaining the long-term sustainability of species and the livelihoods of millions of people who depend on fishing for food and income. The fishing gear, or metier, is a key factor significantly impacting marine habitats, selectively targeting species and fish sizes. Analysis of commercial catches or landings by metier in fishery stock assessment and management is crucial, providing robust estimates of fishing efforts and their impact on marine ecosystems. In this paper, we focus on a unique data set from the Azores’ fishing data collection programs between 2010 and 2017, where little information on metiers is available and sparse throughout our timeline. Our main objective is to tackle the task of data set reconstruction, leveraging domain knowledge and machine learning methods to retrieve or associate metier-related information to each fish landing. We empirically validate the feasibility of this task using a diverse set of modeling approaches and demonstrate how it provides new insights into different fisheries’ behavior and the impact of metiers over time, which are essential for future fish population assessments, management, and conservation efforts. |
| format | Article |
| id | doaj-art-296e6dd9c1f643188e830cde3a0b0d7c |
| institution | DOAJ |
| issn | 2731-6955 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Data |
| spelling | doaj-art-296e6dd9c1f643188e830cde3a0b0d7c2025-08-20T03:09:19ZengSpringerDiscover Data2731-69552025-05-013111510.1007/s44248-025-00034-6Experiential-informed data reconstruction for fishery sustainability and policies in the AzoresBrenda Nogueira0Gui M. Menezes1Rita P. Ribeiro2Nuno Moniz3Lucy Family Institute for Data & Society, University of Notre DameInstitute of Marine Sciences-OKEANOS, University of the AzoresFaculty of Science, University of PortoLucy Family Institute for Data & Society, University of Notre DameAbstract Fishery analysis is critical in maintaining the long-term sustainability of species and the livelihoods of millions of people who depend on fishing for food and income. The fishing gear, or metier, is a key factor significantly impacting marine habitats, selectively targeting species and fish sizes. Analysis of commercial catches or landings by metier in fishery stock assessment and management is crucial, providing robust estimates of fishing efforts and their impact on marine ecosystems. In this paper, we focus on a unique data set from the Azores’ fishing data collection programs between 2010 and 2017, where little information on metiers is available and sparse throughout our timeline. Our main objective is to tackle the task of data set reconstruction, leveraging domain knowledge and machine learning methods to retrieve or associate metier-related information to each fish landing. We empirically validate the feasibility of this task using a diverse set of modeling approaches and demonstrate how it provides new insights into different fisheries’ behavior and the impact of metiers over time, which are essential for future fish population assessments, management, and conservation efforts.https://doi.org/10.1007/s44248-025-00034-6SustainabilityFishery dataData set reconstructionMachine learningEvidence-based policy |
| spellingShingle | Brenda Nogueira Gui M. Menezes Rita P. Ribeiro Nuno Moniz Experiential-informed data reconstruction for fishery sustainability and policies in the Azores Discover Data Sustainability Fishery data Data set reconstruction Machine learning Evidence-based policy |
| title | Experiential-informed data reconstruction for fishery sustainability and policies in the Azores |
| title_full | Experiential-informed data reconstruction for fishery sustainability and policies in the Azores |
| title_fullStr | Experiential-informed data reconstruction for fishery sustainability and policies in the Azores |
| title_full_unstemmed | Experiential-informed data reconstruction for fishery sustainability and policies in the Azores |
| title_short | Experiential-informed data reconstruction for fishery sustainability and policies in the Azores |
| title_sort | experiential informed data reconstruction for fishery sustainability and policies in the azores |
| topic | Sustainability Fishery data Data set reconstruction Machine learning Evidence-based policy |
| url | https://doi.org/10.1007/s44248-025-00034-6 |
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