Identifying fishing grounds from vessel tracks: model-based inference for small scale fisheries
Recent technological developments facilitate the collection of location data from fishing vessels at an increasing rate. The development of low-cost electronic systems allows tracking of small-scale fishing vessels, a sector of fishing fleets typically characterized by many, relatively small vessels...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
The Royal Society
2019-10-01
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| Series: | Royal Society Open Science |
| Subjects: | |
| Online Access: | https://royalsocietypublishing.org/doi/pdf/10.1098/rsos.191161 |
| _version_ | 1818996395154079744 |
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| author | Tania Mendo Sophie Smout Theoni Photopoulou Mark James |
| author_facet | Tania Mendo Sophie Smout Theoni Photopoulou Mark James |
| author_sort | Tania Mendo |
| collection | DOAJ |
| description | Recent technological developments facilitate the collection of location data from fishing vessels at an increasing rate. The development of low-cost electronic systems allows tracking of small-scale fishing vessels, a sector of fishing fleets typically characterized by many, relatively small vessels. The imminent production of large spatial datasets for this previously data-poor sector creates a challenge in terms of data analysis. Several methods have been used to infer the spatial distribution of fishing activities from positional data. Here, we compare five approaches using either vessel speed, or speed and turning angle, to infer fishing activity in the Scottish inshore fleet. We assess the performance of each approach using observational records of true vessel activity. Although results are similar across methods, a trip-based Gaussian mixture model provides the best overall performance and highest computational efficiency for our use-case, allowing accurate estimation of the spatial distribution of active fishing (97% of true area captured). When vessel movement data can be validated, we recommend assessing the performance of different methods. These results illustrate the feasibility of designing a monitoring system to efficiently generate information on fishing grounds, fishing intensity, or monitoring of compliance to regulations at a nationwide scale in near-real-time. |
| first_indexed | 2024-12-20T21:29:03Z |
| format | Article |
| id | doaj.art-190c67065953491aac6f21d7b612e3cd |
| institution | Directory Open Access Journal |
| issn | 2054-5703 |
| language | English |
| last_indexed | 2024-12-20T21:29:03Z |
| publishDate | 2019-10-01 |
| publisher | The Royal Society |
| record_format | Article |
| series | Royal Society Open Science |
| spelling | doaj.art-190c67065953491aac6f21d7b612e3cd2022-12-21T19:26:05ZengThe Royal SocietyRoyal Society Open Science2054-57032019-10-0161010.1098/rsos.191161191161Identifying fishing grounds from vessel tracks: model-based inference for small scale fisheriesTania MendoSophie SmoutTheoni PhotopoulouMark JamesRecent technological developments facilitate the collection of location data from fishing vessels at an increasing rate. The development of low-cost electronic systems allows tracking of small-scale fishing vessels, a sector of fishing fleets typically characterized by many, relatively small vessels. The imminent production of large spatial datasets for this previously data-poor sector creates a challenge in terms of data analysis. Several methods have been used to infer the spatial distribution of fishing activities from positional data. Here, we compare five approaches using either vessel speed, or speed and turning angle, to infer fishing activity in the Scottish inshore fleet. We assess the performance of each approach using observational records of true vessel activity. Although results are similar across methods, a trip-based Gaussian mixture model provides the best overall performance and highest computational efficiency for our use-case, allowing accurate estimation of the spatial distribution of active fishing (97% of true area captured). When vessel movement data can be validated, we recommend assessing the performance of different methods. These results illustrate the feasibility of designing a monitoring system to efficiently generate information on fishing grounds, fishing intensity, or monitoring of compliance to regulations at a nationwide scale in near-real-time.https://royalsocietypublishing.org/doi/pdf/10.1098/rsos.191161fishing activitiesspatial distributionsmall-scale fisherygaussian mixture modelhidden markov model |
| spellingShingle | Tania Mendo Sophie Smout Theoni Photopoulou Mark James Identifying fishing grounds from vessel tracks: model-based inference for small scale fisheries Royal Society Open Science fishing activities spatial distribution small-scale fishery gaussian mixture model hidden markov model |
| title | Identifying fishing grounds from vessel tracks: model-based inference for small scale fisheries |
| title_full | Identifying fishing grounds from vessel tracks: model-based inference for small scale fisheries |
| title_fullStr | Identifying fishing grounds from vessel tracks: model-based inference for small scale fisheries |
| title_full_unstemmed | Identifying fishing grounds from vessel tracks: model-based inference for small scale fisheries |
| title_short | Identifying fishing grounds from vessel tracks: model-based inference for small scale fisheries |
| title_sort | identifying fishing grounds from vessel tracks model based inference for small scale fisheries |
| topic | fishing activities spatial distribution small-scale fishery gaussian mixture model hidden markov model |
| url | https://royalsocietypublishing.org/doi/pdf/10.1098/rsos.191161 |
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