A workflow for standardizing the analysis of highly resolved vessel tracking data

Author:

Mendo T1ORCID,Mujal-Colilles A2ORCID,Stounberg J3,Glemarec G3ORCID,Egekvist J3ORCID,Mugerza E4ORCID,Rufino M56ORCID,Swift R7,James M7ORCID

Affiliation:

1. School of Geography and Sustainable Development, University of St. Andrews , KY16 9AL St. Andrews , UK

2. Barcelona School of Nautical Studies, Universitat Politècnica de Catalunya , 08003 Barcelona , Catalunya

3. National Institute of Aquatic Resources, Technical University of Denmark , Kemitorvet, DK-2800 Kgs. Lyngby , Denmark

4. AZTI, Sustainable Fisheries Management, Basque Research and Technology Alliance (BRTA) , Txatxarramendi Ugartea z/g, 48395 Sukarrieta, Bizkaia (Basque Country) , Spain

5. Portuguese Institute for the Sea and the Atmosphere (IPMA), Division of Modelling and Management of Fisheries Resources , Av. Dr. Alfredo Magalhães Ramalho, 6, 1495-165 Lisboa , Portugal

6. Centre of Statistics and its Applications (CEAUL), Faculty of Sciences, University of Lisbon , 1649-004 Lisboa , Portugal

7. Scottish Oceans Institute, University of St Andrews , East SandsFife KY16 8LB , UK

Abstract

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.

Funder

Interreg

European Regional Development Fund

Generalitat de Catalunya

Publisher

Oxford University Press (OUP)

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