Mapping Fishing Effort through AIS Data

RESEARCH ARTICLE Mapping Fishing Effort through AIS Data Fabrizio Natale1*, Maurizio Gibin1, Alfredo Alessandrini1, Michele Vespe1, Anton Paulrud2 1 European Commission Joint Research Centre Institute for the Protection and Security of the Citizen, Ispra, Italy, 2 Swedish Agency for Marine and Water management, Göteborg, Sweden * Abstract OPEN ACCESS Citation: Natale F, Gibin M, Alessandrini A, Vespe M, Paulrud A (2015) Mapping Fishing Effort through AIS Data. PLoS ONE 10(6): e0130746. doi:10.1371/ journal.pone.0130746 Editor: George Tserpes, Hellenic Centre for Marine Research, GREECE Received: December 15, 2014 Accepted: May 23, 2015 Several research initiatives have been undertaken to map fishing effort at high spatial resolution using the Vessel Monitoring System (VMS). An alternative to the VMS is represented by the Automatic Identification System (AIS), which in the EU became compulsory in May 2014 for all fishing vessels of length above 15 meters. The aim of this paper is to assess the uptake of the AIS in the EU fishing fleet and the feasibility of producing a map of fishing effort with high spatial and temporal resolution at European scale. After analysing a large AIS dataset for the period January-August 2014 and covering most of the EU waters, we show that AIS was adopted by around 75% of EU fishing vessels above 15 meters of length. Using the Swedish fleet as a case study, we developed a method to identify fishing activity based on the analysis of individual vessels’ speed profiles and produce a high resolution map of fishing effort based on AIS data. The method was validated using detailed logbook data and proved to be sufficiently accurate and computationally efficient to identify fishing grounds and effort in the case of trawlers, which represent the largest portion of the EU fishing fleet above 15 meters of length. Issues still to be addressed before extending the exercise to the entire EU fleet are the assessment of coverage levels of the AIS data for all EU waters and the identification of fishing activity in the case of vessels other than trawlers. Published: June 22, 2015 Copyright: © 2015 Natale et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data were obtained from third parties and are available at the following URLs: European Union Fleet Register data (http://ec.europa.eu/fisheries/fleet/index.cfm?method= Download.menu), AIS data to measure the level of uptake from the Maritime Safety and Security Information System (MSSIS) team at the US Department of Transportation’s Volpe Center (https:// mssis.volpe.dot.gov/Main/contact/), and logbook data for the Swedish case study to validate the fishing detection algorithm from the the Swedish Agency for Marine and Water Management (https://www. havochvatten.se/en/start.html). Introduction Fisheries research in the EU is heavily relying on effort, catch and fleet capacity data from the fleet register, the logbooks, the sales notes and the Vessel Monitoring System (VMS) established by the control regulation (Council Regulation (EC) No 1224/2009). While VMS data provides detailed information on the vessel tracks at high spatial temporal resolution, the logbooks include essential information on the gear used, species and volume of the catches. The availability of VMS data has been indicated as a “revolution” for fisheries research [1]. VMS information gives the opportunity to assess more precisely the impacts of fishing activity in space. In addition, VMS allows improving time precision of effort estimates moving from a resolution of 24 hours or calendar day (normally adopted when processing logbook data) to the 2 hours intervals of VMS messages which represents the typical transmission frequency for VMS. PLOS ONE | DOI:10.1371/journal.pone.0130746 June 22, 2015 1 / 16 Mapping Fishing Effort through AIS Data Funding: The authors received no specific funding for this work. Competing Interests: The authors have declared that no competing interests exist. Several studies have proved the value of using effort data at these finer time and space resolutions when evaluating environmental impacts of fishing activity [2, 3, 4]. A not yet fully developed area of application is in fisheries economic studies where only few papers have been published until now about spatially explicit bio-economic models [5, 6]. With precise spatial information on fishing behaviour there are new possibilities to disaggregate economics at the level of coastal communities [7], exploring dependencies between coastal communities and fishing grounds and derive quantitative analyses and agent based models of fishing behaviour. The use of VMS data poses a series of data management and methodological challenges linked to the treatment of large volumes of data and the need to relate tracking data to fishing activity. Two main software libraries, VMStools [8] and VMSbase [1], have been developed in the R statistical language to process and analyse VMS and logbook data. Both libraries provide functionalities for cleaning the data, interpolating between consecutive VMS messages, merging VMS and logbook data, clustering the fleet into métier, discriminating between fishing and not fishing activity and producing high resolution maps of fishing effort. Methods for the interpolation of consecutive VMS messages range from a simple straight line interpolation to more sophisticated approaches like a cubic Hermite spline interpolation [9] or incorporating specific variables which have an influence on vessel navigation such as human control and drift [10]. Most of the methods developed for the discrimination between fishing and non-fishing activity rely on the analysis of speed profiles [11], either through statistical and data mining approaches or through the reference to thresholds based on expert knowledge. The frequency distributions of the speed profiles of fishing vessels with towed gear typically show a bimodal shape with the first mode, at lower speed, corresponding to the fishing activity and the second mode, at higher speed, corresponding to steaming. The statistical methods for the analysis of speed profiles available in the package VMStools are based on a segmented regression of the cumulative frequencies of speed data of individual vessels or of groups of vessels [8]. More complex classification methods use also information on the bearing in combination with Bayesian approaches [12, 13, 14]. Despite the promising methodological developments and increasing number of applications and tools, the extensive use of VMS data for scientific purposes is hindered by the difficulty of accessing control data for scientific purposes [15], often due to confidentiality and personal data protection concerns. The ne (...truncated)