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

ICES Journal of Marine Science, 2024, Vol. 81, Issue 2, 390–401 https://doi.org/10.1093/icesjms/fsad209 Received: 18 August 2023; revised: 30 November 2023; accepted: 1 December 2023 Advance access publication date: 11 January 2024 Original Article A workflow for standardizing the analysis of highly resolved vessel tracking data T. Mendo 1 ,* , A. Mujal-Colilles 2 ,* , J. Stounberg3 , G. Glemarec E. Mugerza 4 , M. Rufino 5,6 , R Swift7 , M. James 7 3 , J. Egekvist 3 , 1 School of Geography and Sustainable Development, University of St. Andrews, KY16 9AL St. Andrews, UK 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 2 First authorship and author’s correspondence shared between these authors. ; 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. Keywords: small-scale fisheries; geospatial data; fisheries management; marine spatial planning Introduction Ecosystem-based fisheries management has revealed the need for much more detailed spatial information about fish distribution and fishing effort at the vessel level, to enable the implementation of fine-scale spatial management (Wilen 2004, Stelzenmuller et al. 2008, Parnell et al. 2010). This need has become exacerbated in the last few decades due to the increased pressure from human activities in the marine environment. Indeed, within the ‘blue economy’ agenda, coastal and marine regions are seen as grounds for new economic opportunities, such as energy generation, mining, tourism, aquaculture, and fisheries, increasing the pressure exerted on the marine environment (Bennett et al. 2019). The designation of marine protected areas also represents a spatial constraint and the pressure to expand these designations is increasing. 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 user of the marine space, namely small-scale fishers (Trouillet 2019). Technological developments have enabled the collection of vessel’s tracking data to represent fisher’s activities in space. Among the systems used to track vessels, Vessel Monitoring Systems (VMS) are mandatory for all EU vessels >12 m [EU Fisheries Control Regulation (EC 1224/2009)] and transmit the location of the vessel every 2 h. Automated Identification Systems (AIS), Electronic Monitoring (EM) systems, and other high-resolution vessel-tracking systems report the position of a vessel at intervals from seconds to minutes (Lee et al. 2010, Gerritsen and Lordan 2011, Burgos et al. 2013, Natale et al. 2015, James et al. 2018, Behivoke et al. 2021, Mujal-Colilles et al. 2022, Navarrete Forero et al. 2017). These higher-frequency data allow prediction of vessel activity with much higher precision than using VMS. This is especially important for fisheries that display complex fishing patterns (Muench et al. 2018), in areas where spatial constraints are dense or in small-scale fisheries (SSF), where fishing operations are relatively short in distance and/or duration (Katara and Silva 2017, Mendo et al. 2019a). Tracking systems can produce a significant amount of data, creating challenges for data transmission, processing, and analysis. For VMS data, standardized methods and tools have been developed to process, analyse, and visualize vessel location (Hintzen et al. 2012, Russo et al. 2014). However, for highly resolved data, only ad-hoc tools (developed as needed) have been used thus far, making it difficult to replicate analyses. Moreover, most of the methods currently in use focus on large-scale fishing vessels (e.g. Global Fishing Watch), and © The Author(s) 2024. Published by Oxford University Press on behalf of International Council for the Exploration of the Sea. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. ∗ 391 A workflow for standardizing the analysis of highly resolved vessel tracking data Table 1. Description of the user-input variables. INDIVIDUAL TRIPS Chronological steps IDENTIFY Level of analysis Data preparation Parameter Units Definition Duplicates cum_dist metres crs_wgs84 crs_utm EPSG EPSG d_interp metres dist_travelled lat_max, lat_min lon_max, lon_min n_obs kilometres decimal degrees decimal degrees number port_buffer metres speed_filter knots time_travelled t_interp hour minutes Cumulative distance from trip extreme points EPSG code for WGS84 CRS EPSG code for UTM projected CRS Minimum distance between points to interpolate Minimum length of a trip Latitude boundaries Longitude boundaries Minimum observations for a valid trip Distance within the port where points will be discarded Maximum allowed sp (...truncated)