Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloModeling landings profiles of fishing vessels: An application of Self-Organizing Maps to VMS and logbook data
Anno di pubblicazione2016
FormatoCartaceo
Autore/iRusso T.; Carpentieri P.; Fiorentino F.; Arneri E.; Scardi M.; Cioffi A.; Cataudella S.
Affiliazioni autoriLaboratory of Experimental Ecology and Aquaculture, Department of Biology, University of Rome Tor Vergata, Rome, , Italy; CONISMA CONSORTIUM, Piazzale Flaminio, Roma, 9-00196, , Italy; CNR IAMC-National Research Council, Institute for Coastal Marine Environment, Mazara del Vallo, , , Italy; CNR IAMC-National Research Council, Institute for Coastal Marine Environment, Mazara del Vallo, , , Italy; CNR ISMAR-National Research Council of Italy, Marine Sciences Institute, Largo Fiera della pesca 2, Ancona, 60125, , Italy; FAO ADRIAMED and MEDSUDMED Projects, Via delle Terme di Caracalla, Rome, 00153, , Italy; ITAFISHSTAT CONSORTIUM, Via Irno, Salerno, 11-84135, , Italy
Autori CNR e affiliazioni
  • ENRICO ARNERI
  • FABIO FIORENTINO
Lingua/e
  • inglese
AbstractLogbook data constitute a key element within the electronic recording and reporting system of the European Fisheries Control Technologies Framework and are used to record, report, process, store and send information about fishing operations, including landings and fishing gear. A relevant application of logbook data is to account for the heterogeneity of fishing practices (e.g., by gear or métier), which is a key aspect of the Common Fishery Policy. However, despite their importance, few published studies have explored the potential and pitfalls of logbook data, even in combination with other powerful data sources such as the Vessel Monitoring System (VMS). Here, a new approach to characterizing the composition of landings for the different types of gear based on the use of Self-Organizing Maps (SOMs - a particular type of Artificial Neural Network) is applied to the Italian fleet logbook dataset. The SOM is trained on the landings composition and the resulting patterns are interpreted using some measures obtained from the analysis of the corresponding VMS data. Namely, the mean sea bottom depth and the area of activity are obtained for each fishing trip. Moreover, the ability of the trained SOM to predict gear from landings is tested using a new dataset. The trained SOM classifies logbook records according to the ecological, taxonomical, and trophic characteristics of the species caught, and the depth of fishing activities plays an important role in diversifying the landings associated with certain widely used fishing gear such as the bottom otter trawl. The clustering of SOM units allows the identification of a set of 12 groups, which are strongly related to the types of gear used by the Italian fleet. Furthermore, the trained SOM shows a high ability to recognize gear from logbook data, thus confirming the robustness of the landings profiles detected.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da34
Pagine a47
Pagine totali-
RivistaFisheries research
Attiva dal 1981
Editore: Elsevier - Amsterdam
Paese di pubblicazione: Paesi Bassi
Lingua: inglese
ISSN: 0165-7836
Titolo chiave: Fisheries research
Titolo proprio: Fisheries research.
Titolo abbreviato: Fish. res.
Numero volume della rivista181
Fascicolo della rivista-
DOI10.1016/j.fishres.2016.04.005
Verificato da refereeSì: Internazionale
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)
  • Scopus (Codice:2-s2.0-84962921142)
Parole chiaveMétier, Neural networks, Self-Organizing Maps, VMS
Link (URL, URI)http://www.scopus.com/inward/record.url?eid=2-s2.0-84962921142&partnerID=q2rCbXpz
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Strutture CNR
  • IRBIM — Istituto per le Risorse Biologiche e le Biotecnologie Marine
Moduli/Attività/Sottoprogetti CNR-
Progetti Europei-
Allegati