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VMSbase: An R-Package for VMS and Logbook Data Management and Analysis in Fisheries Ecology

Author

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  • Tommaso Russo
  • Lorenzo D'Andrea
  • Antonio Parisi
  • Stefano Cataudella

Abstract

VMSbase is an R package devised to manage, process and visualize information about fishing vessels activity (provided by the vessel monitoring system - VMS) and catches/landings (as reported in the logbooks). VMSbase is primarily conceived to be user-friendly; to this end, a suite of state-of-the-art analyses is accessible via a graphical interface. In addition, the package uses a database platform allowing large datasets to be stored, managed and processed vey efficiently. Methodologies include data cleaning, that is removal of redundant or evidently erroneous records, and data enhancing, that is interpolation and merging with external data sources. In particular, VMSbase is able to estimate sea bottom depth for single VMS pings using an on-line connection to the National Oceanic and Atmospheric Administration (NOAA) database. It also allows VMS pings to be assigned to whatever geographic partitioning has been selected by users. Standard analyses comprise: 1) métier identification (using a modified CLARA clustering approach on Logbook data or Artificial Neural Networks on VMS data); 2) linkage between VMS and Logbook records, with the former organized into fishing trips; 3) discrimination between steaming and fishing points; 4) computation of spatial effort with respect to user-selected grids; 5) calculation of standard fishing effort indicators within Data Collection Framework; 6) a variety of mapping tools, including an interface for Google viewer; 7) estimation of trawled area. Here we report a sample workflow for the accessory sample datasets (available with the package) in order to explore the potentialities of VMSbase. In addition, the results of some performance tests on two large datasets (1×105 and 1×106 VMS signals, respectively) are reported to inform about the time required for the analyses. The results, although merely illustrative, indicate that VMSbase can represent a step forward in extracting and enhancing information from VMS/logbook data for fisheries studies.

Suggested Citation

  • Tommaso Russo & Lorenzo D'Andrea & Antonio Parisi & Stefano Cataudella, 2014. "VMSbase: An R-Package for VMS and Logbook Data Management and Analysis in Fisheries Ecology," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-18, June.
    • Handle: RePEc:plo:pone00:0100195
    DOI: 10.1371/journal.pone.0100195
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    References listed on IDEAS

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    1. Walker, E. & Bez, N., 2010. "A pioneer validation of a state-space model of vessel trajectories (VMS) with observers’ data," Ecological Modelling, Elsevier, vol. 221(17), pages 2008-2017.
    2. Chang, Shui-Kai, 2011. "Application of a vessel monitoring system to advance sustainable fisheries management--Benefits received in Taiwan," Marine Policy, Elsevier, vol. 35(2), pages 116-121, March.
    3. Chang, Shui-Kai, 2014. "Constructing logbook-like statistics for coastal fisheries using coastal surveillance radar and fish market data," Marine Policy, Elsevier, vol. 43(C), pages 338-346.
    4. Fock, Heino O., 2011. "Natura 2000 and the European Common Fisheries Policy," Marine Policy, Elsevier, vol. 35(2), pages 181-188, March.
    5. Tommaso Russo & Michele Scardi & Stefano Cataudella, 2014. "Applications of Self-Organizing Maps for Ecomorphological Investigations through Early Ontogeny of Fish," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-9, January.
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    1. Fabrizio Natale & Maurizio Gibin & Alfredo Alessandrini & Michele Vespe & Anton Paulrud, 2015. "Mapping Fishing Effort through AIS Data," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-16, June.
    2. Pascal Thoya & Joseph Maina & Christian Möllmann & Kerstin S. Schiele, 2021. "AIS and VMS Ensemble Can Address Data Gaps on Fisheries for Marine Spatial Planning," Sustainability, MDPI, vol. 13(7), pages 1-12, March.
    3. Angelini, Silvia & Hillary, Richard & Morello, Elisabetta B. & Plagányi, Éva E. & Martinelli, Michela & Manfredi, Chiara & Isajlović, Igor & Santojanni, Alberto, 2016. "An Ecosystem Model of Intermediate Complexity to test management options for fisheries: A case study," Ecological Modelling, Elsevier, vol. 319(C), pages 218-232.

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