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A workflow for standardizing the analysis of highly resolved vessel tracking data
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dc.contributor.author | Mendo, T | |
dc.contributor.author | Mujal-Colilles, A | |
dc.contributor.author | Stounberg, J | |
dc.contributor.author | Glemarec, G | |
dc.contributor.author | Egekvist, J | |
dc.contributor.author | Mugerza, E | |
dc.contributor.author | Rufino, M | |
dc.contributor.author | Swift, R | |
dc.contributor.author | James, M | |
dc.date.accessioned | 2024-01-19T11:30:08Z | |
dc.date.available | 2024-01-19T11:30:08Z | |
dc.date.issued | 2024-01-11 | |
dc.identifier | 298348821 | |
dc.identifier | 05c483b1-a406-412b-9207-3360b8635b65 | |
dc.identifier | 85187723133 | |
dc.identifier.citation | Mendo , T , Mujal-Colilles , A , Stounberg , J , Glemarec , G , Egekvist , J , Mugerza , E , Rufino , M , Swift , R & James , M 2024 , ' A workflow for standardizing the analysis of highly resolved vessel tracking data ' , ICES Journal of Marine Science , vol. Advance Article , fsad209 . https://doi.org/10.1093/icesjms/fsad209 | en |
dc.identifier.issn | 1054-3139 | |
dc.identifier.other | RIS: urn:EF23AC895B1401EEBECCBCBD77AB55D3 | |
dc.identifier.other | ORCID: /0000-0002-7182-1725/work/151190520 | |
dc.identifier.other | ORCID: /0000-0003-4397-2064/work/151191099 | |
dc.identifier.uri | https://hdl.handle.net/10023/29030 | |
dc.description | Funding: TM and MJ acknowledge financial support provided by the “Conserving Atlantic Biodiversity by Supporting Innovative Small-scale Fisheries Co-management” (CABFISHMAN) Project. This project is co-financed by the Interreg Atlantic Area Programme through the European Regional Development Fund. Project N◦: EAPA_134/2018”. AMC acknowledges the Spanish Minstery of Science and Innovation and the Serra Hunter programme from the Generalitat de Catalunya. | en |
dc.description.abstract | Knowledge on the spatial and temporal distribution of the activities carried out in the marine environment is key to manage available space optimally. However, frequently, little or no information is available on the distribution of the largest users of the marine space, namely fishers. Tracking devices are being increasingly used to obtain highly resolved geospatial data of fishing activities, at intervals from seconds to minutes. However, to date no standardized method is used to process and analyse these data, making it difficult to replicate analysis. We develop a workflow to identify individual vessel trips and infer fishing activities from highly resolved geospatial data, which can be applied for large-scale fisheries, but also considers nuances encountered when working with small-scale fisheries. Recognizing the highly variable nature of activities conducted by different fleets, this workflow allows the user to choose a path that best aligns with the particularities in the fishery being analysed. A new method to identify anchoring sites for small-scale fisheries is also presented. The paper provides detailed code used in each step of the workflow both in R and Python language to widen the application of the workflow in the scientific and stakeholder communities and to encourage its improvement and refinement in the future. | |
dc.format.extent | 12 | |
dc.format.extent | 1525788 | |
dc.language.iso | eng | |
dc.relation.ispartof | ICES Journal of Marine Science | en |
dc.subject | Small-scale fisheries | en |
dc.subject | Geospatial data | en |
dc.subject | Fisheries management | en |
dc.subject | Marine spatial planning | en |
dc.subject | QH301 Biology | en |
dc.subject | E-DAS | en |
dc.subject | SDG 14 - Life Below Water | en |
dc.subject.lcc | QH301 | en |
dc.title | A workflow for standardizing the analysis of highly resolved vessel tracking data | en |
dc.type | Journal article | en |
dc.contributor.institution | University of St Andrews. School of Geography & Sustainable Development | en |
dc.contributor.institution | University of St Andrews. School of Biology | en |
dc.contributor.institution | University of St Andrews. Scottish Oceans Institute | en |
dc.contributor.institution | University of St Andrews. Coastal Resources Management Group | en |
dc.contributor.institution | University of St Andrews. Institute of Behavioural and Neural Sciences | en |
dc.contributor.institution | University of St Andrews. Marine Alliance for Science & Technology Scotland | en |
dc.identifier.doi | 10.1093/icesjms/fsad209 | |
dc.description.status | Peer reviewed | en |
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