@article{ ISI:000279549500001,
Author = {Vermard, Youen and Rivot, Etienne and Mahevas, Stephanie and Marchal,
   Paul and Gascuel, Didier},
Title = {{Identifying fishing trip behaviour and estimating fishing effort from
   VMS data using Bayesian Hidden Markov Models}},
Journal = {{ECOLOGICAL MODELLING}},
Year = {{2010}},
Volume = {{221}},
Number = {{15}},
Pages = {{1757-1769}},
Month = {{JUL 24}},
Abstract = {{Recent advances in technologies have lead to a vast influx of data on
   movements, based on discrete recorded position of animals or fishing
   boats, opening new horizons for future analyses. However, most of the
   potential interest of tracking data depends on the ability to develop
   suitable modelling strategies to analyze trajectories from discrete
   recorded positions. A serious modelling challenge is to infer the
   evolution of the true position and the associated spatio-temporal
   distribution of behavioural states using discrete, error-prone and
   incomplete observations. In this paper, a Bayesian Hierarchical Model
   (HBM) using Hidden Markov Process (HMP) is proposed as a template for
   analyzing fishing boats trajectories based on data available from
   satellite-based vessel monitoring systems (VMS). The analysis seeks to
   enhance the definition of the fishing pressure exerted on fish stocks,
   by discriminating between the different behavioural states of a fishing
   trip, and also by quantifying the relative importance of each of these
   states during a fishing trip. The HBM approach is tested to analyse the
   behaviour of pelagic trawlers in the Bay of Biscay. A hidden Markov
   chain with a regular discrete time step is used to model transitions
   between successive behavioural states (e.g., fishing, steaming, stopping
   (at Port or at sea)) of each vessel. The parameters of the movement
   process (speed and turning angles) are defined conditionally upon the
   behavioural states. Bayesian methods are used to integrate the available
   data (typically VMS position recorded at discrete time) and to draw
   inferences on any unknown parameters of the model. The model is first
   tested on simulated data with different parameters structures. Results
   provide insights on the potential of HBM with HMP to analyze VMS data.
   They show that if VMS positions are recorded synchronously with the
   instants at which the process switch from one behavioural state to
   another, the estimation method provides unbiased and precise inferences
   on behavioural states and on associated movement parameters. However, if
   the observations are not gathered with a sufficiently high frequency,
   the performance of the estimation method could be drastically impacted
   when the discrete observations are not synchronous with the switching
   instants. The model is then applied to real pathways to estimate
   variables of interest such as the number of operations per trip, time
   and distance spent fishing or travelling. (C) 2010 Elsevier B.V. All
   rights reserved.}},
Publisher = {{ELSEVIER SCIENCE BV}},
Address = {{PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Vermard, Y (Reprint Author), 150 Quai Gambetta,BP 699, F-62321 Boulogne S Mer, France.
   Vermard, Youen; Marchal, Paul, IFREMER, Channel \& N Sea Fisheries Dept, F-62321 Boulogne S Mer, France.
   Vermard, Youen; Rivot, Etienne; Gascuel, Didier, AGROCAMPUS OUEST, UMR Ecol \& Sante Ecosyst 985, F-35042 Rennes, France.
   Mahevas, Stephanie, IFREMER, Fisheries \& Ecol Modeling Dept, F-44311 Nantes 03, France.}},
DOI = {{10.1016/j.ecolmodel.2010.04.005}},
ISSN = {{0304-3800}},
Keywords = {{Bayesian Hierarchical Models; Hidden Markov Model; State-space model;
   VMS; Fleet behaviour; Fishing effort}},
Keywords-Plus = {{ANCHOVY ENGRAULIS-RINGENS; STATE-SPACE MODELS; SPATIAL-DISTRIBUTION;
   ISIS-FISH; MANAGEMENT STRATEGIES; ANIMAL MOVEMENT; FISHERIES; FLEET;
   IMPACT; DYNAMICS}},
Research-Areas = {{Environmental Sciences \& Ecology}},
Web-of-Science-Categories  = {{Ecology}},
Author-Email = {{Youen.Vermard@ifremer.fr}},
ResearcherID-Numbers = {{Gascuel, Didier/C-1439-2011
   martel, celine/M-9779-2014
   martel, celine/O-6651-2016
   }},
ORCID-Numbers = {{Gascuel, Didier/0000-0001-5447-6977
   martel, celine/0000-0002-1800-4558
   martel, celine/0000-0002-1800-4558
   Marchal, Paul/0000-0003-2047-4599
   Vermard, Youen/0000-0003-2828-2815}},
Funding-Acknowledgement = {{European Union {[}022644]; Region Bretagne}},
Funding-Text = {{The work was funded through the CAFE project of the European Union
   (DG-Fish, contract no. 022644) and the Region Bretagne, for which
   support we are very grateful. We are also indebted to fishers, who
   kindly provided their VMS data on a voluntary basis and people from the
   French Fisheries Information System at IFREMER. The authors thank
   Marie-Pierre Etienne, AgroParis Tech, ENGREF, Paris, and Emily Walker
   and Nicolas Bez (IRD Sete) for helpful comments and discussions and the
   two anonymous referees for their relevant comments that have greatly
   improved the paper.}},
Number-of-Cited-References = {{39}},
Times-Cited = {{37}},
Usage-Count-Last-180-days = {{4}},
Usage-Count-Since-2013 = {{23}},
Journal-ISO = {{Ecol. Model.}},
Doc-Delivery-Number = {{621BU}},
Unique-ID = {{ISI:000279549500001}},
OA = {{No}},
DA = {{2017-08-17}},
}

{"_id":"HhHN7iyMFWdSWuYQC","bibbaseid":"vermard-rivot-mahevas-marchal-gascuel-identifyingfishingtripbehaviourandestimatingfishingeffortfromvmsdatausingbayesianhiddenmarkovmodels-2010","downloads":0,"creationDate":"2017-08-17T14:08:07.620Z","title":"Identifying fishing trip behaviour and estimating fishing effort from VMS data using Bayesian Hidden Markov Models","author_short":["Vermard, Y.","Rivot, E.","Mahevas, S.","Marchal, P.","Gascuel, D."],"year":2010,"bibtype":"article","biburl":"http://flr-project.org/flr.bib","bibdata":{"bibtype":"article","type":"Article","author":[{"propositions":[],"lastnames":["Vermard"],"firstnames":["Youen"],"suffixes":[]},{"propositions":[],"lastnames":["Rivot"],"firstnames":["Etienne"],"suffixes":[]},{"propositions":[],"lastnames":["Mahevas"],"firstnames":["Stephanie"],"suffixes":[]},{"propositions":[],"lastnames":["Marchal"],"firstnames":["Paul"],"suffixes":[]},{"propositions":[],"lastnames":["Gascuel"],"firstnames":["Didier"],"suffixes":[]}],"title":"Identifying fishing trip behaviour and estimating fishing effort from VMS data using Bayesian Hidden Markov Models","journal":"ECOLOGICAL MODELLING","year":"2010","volume":"221","number":"15","pages":"1757-1769","month":"JUL 24","abstract":"Recent advances in technologies have lead to a vast influx of data on movements, based on discrete recorded position of animals or fishing boats, opening new horizons for future analyses. However, most of the potential interest of tracking data depends on the ability to develop suitable modelling strategies to analyze trajectories from discrete recorded positions. A serious modelling challenge is to infer the evolution of the true position and the associated spatio-temporal distribution of behavioural states using discrete, error-prone and incomplete observations. In this paper, a Bayesian Hierarchical Model (HBM) using Hidden Markov Process (HMP) is proposed as a template for analyzing fishing boats trajectories based on data available from satellite-based vessel monitoring systems (VMS). The analysis seeks to enhance the definition of the fishing pressure exerted on fish stocks, by discriminating between the different behavioural states of a fishing trip, and also by quantifying the relative importance of each of these states during a fishing trip. The HBM approach is tested to analyse the behaviour of pelagic trawlers in the Bay of Biscay. A hidden Markov chain with a regular discrete time step is used to model transitions between successive behavioural states (e.g., fishing, steaming, stopping (at Port or at sea)) of each vessel. The parameters of the movement process (speed and turning angles) are defined conditionally upon the behavioural states. Bayesian methods are used to integrate the available data (typically VMS position recorded at discrete time) and to draw inferences on any unknown parameters of the model. The model is first tested on simulated data with different parameters structures. Results provide insights on the potential of HBM with HMP to analyze VMS data. They show that if VMS positions are recorded synchronously with the instants at which the process switch from one behavioural state to another, the estimation method provides unbiased and precise inferences on behavioural states and on associated movement parameters. However, if the observations are not gathered with a sufficiently high frequency, the performance of the estimation method could be drastically impacted when the discrete observations are not synchronous with the switching instants. The model is then applied to real pathways to estimate variables of interest such as the number of operations per trip, time and distance spent fishing or travelling. (C) 2010 Elsevier B.V. All rights reserved.","publisher":"ELSEVIER SCIENCE BV","address":"PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS","language":"English","affiliation":"Vermard, Y (Reprint Author), 150 Quai Gambetta,BP 699, F-62321 Boulogne S Mer, France. Vermard, Youen; Marchal, Paul, IFREMER, Channel & N Sea Fisheries Dept, F-62321 Boulogne S Mer, France. Vermard, Youen; Rivot, Etienne; Gascuel, Didier, AGROCAMPUS OUEST, UMR Ecol & Sante Ecosyst 985, F-35042 Rennes, France. Mahevas, Stephanie, IFREMER, Fisheries & Ecol Modeling Dept, F-44311 Nantes 03, France.","doi":"10.1016/j.ecolmodel.2010.04.005","issn":"0304-3800","keywords":"Bayesian Hierarchical Models; Hidden Markov Model; State-space model; VMS; Fleet behaviour; Fishing effort","keywords-plus":"ANCHOVY ENGRAULIS-RINGENS; STATE-SPACE MODELS; SPATIAL-DISTRIBUTION; ISIS-FISH; MANAGEMENT STRATEGIES; ANIMAL MOVEMENT; FISHERIES; FLEET; IMPACT; DYNAMICS","research-areas":"Environmental Sciences & Ecology","web-of-science-categories":"Ecology","author-email":"Youen.Vermard@ifremer.fr","researcherid-numbers":"Gascuel, Didier/C-1439-2011 martel, celine/M-9779-2014 martel, celine/O-6651-2016 ","orcid-numbers":"Gascuel, Didier/0000-0001-5447-6977 martel, celine/0000-0002-1800-4558 martel, celine/0000-0002-1800-4558 Marchal, Paul/0000-0003-2047-4599 Vermard, Youen/0000-0003-2828-2815","funding-acknowledgement":"European Union [022644]; Region Bretagne","funding-text":"The work was funded through the CAFE project of the European Union (DG-Fish, contract no. 022644) and the Region Bretagne, for which support we are very grateful. We are also indebted to fishers, who kindly provided their VMS data on a voluntary basis and people from the French Fisheries Information System at IFREMER. The authors thank Marie-Pierre Etienne, AgroParis Tech, ENGREF, Paris, and Emily Walker and Nicolas Bez (IRD Sete) for helpful comments and discussions and the two anonymous referees for their relevant comments that have greatly improved the paper.","number-of-cited-references":"39","times-cited":"37","usage-count-last-180-days":"4","usage-count-since-2013":"23","journal-iso":"Ecol. Model.","doc-delivery-number":"621BU","unique-id":"ISI:000279549500001","oa":"No","da":"2017-08-17","bibtex":"@article{ ISI:000279549500001,\nAuthor = {Vermard, Youen and Rivot, Etienne and Mahevas, Stephanie and Marchal,\n Paul and Gascuel, Didier},\nTitle = {{Identifying fishing trip behaviour and estimating fishing effort from\n VMS data using Bayesian Hidden Markov Models}},\nJournal = {{ECOLOGICAL MODELLING}},\nYear = {{2010}},\nVolume = {{221}},\nNumber = {{15}},\nPages = {{1757-1769}},\nMonth = {{JUL 24}},\nAbstract = {{Recent advances in technologies have lead to a vast influx of data on\n movements, based on discrete recorded position of animals or fishing\n boats, opening new horizons for future analyses. However, most of the\n potential interest of tracking data depends on the ability to develop\n suitable modelling strategies to analyze trajectories from discrete\n recorded positions. A serious modelling challenge is to infer the\n evolution of the true position and the associated spatio-temporal\n distribution of behavioural states using discrete, error-prone and\n incomplete observations. In this paper, a Bayesian Hierarchical Model\n (HBM) using Hidden Markov Process (HMP) is proposed as a template for\n analyzing fishing boats trajectories based on data available from\n satellite-based vessel monitoring systems (VMS). The analysis seeks to\n enhance the definition of the fishing pressure exerted on fish stocks,\n by discriminating between the different behavioural states of a fishing\n trip, and also by quantifying the relative importance of each of these\n states during a fishing trip. The HBM approach is tested to analyse the\n behaviour of pelagic trawlers in the Bay of Biscay. A hidden Markov\n chain with a regular discrete time step is used to model transitions\n between successive behavioural states (e.g., fishing, steaming, stopping\n (at Port or at sea)) of each vessel. The parameters of the movement\n process (speed and turning angles) are defined conditionally upon the\n behavioural states. Bayesian methods are used to integrate the available\n data (typically VMS position recorded at discrete time) and to draw\n inferences on any unknown parameters of the model. The model is first\n tested on simulated data with different parameters structures. Results\n provide insights on the potential of HBM with HMP to analyze VMS data.\n They show that if VMS positions are recorded synchronously with the\n instants at which the process switch from one behavioural state to\n another, the estimation method provides unbiased and precise inferences\n on behavioural states and on associated movement parameters. However, if\n the observations are not gathered with a sufficiently high frequency,\n the performance of the estimation method could be drastically impacted\n when the discrete observations are not synchronous with the switching\n instants. The model is then applied to real pathways to estimate\n variables of interest such as the number of operations per trip, time\n and distance spent fishing or travelling. (C) 2010 Elsevier B.V. All\n rights reserved.}},\nPublisher = {{ELSEVIER SCIENCE BV}},\nAddress = {{PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS}},\nType = {{Article}},\nLanguage = {{English}},\nAffiliation = {{Vermard, Y (Reprint Author), 150 Quai Gambetta,BP 699, F-62321 Boulogne S Mer, France.\n Vermard, Youen; Marchal, Paul, IFREMER, Channel \\& N Sea Fisheries Dept, F-62321 Boulogne S Mer, France.\n Vermard, Youen; Rivot, Etienne; Gascuel, Didier, AGROCAMPUS OUEST, UMR Ecol \\& Sante Ecosyst 985, F-35042 Rennes, France.\n Mahevas, Stephanie, IFREMER, Fisheries \\& Ecol Modeling Dept, F-44311 Nantes 03, France.}},\nDOI = {{10.1016/j.ecolmodel.2010.04.005}},\nISSN = {{0304-3800}},\nKeywords = {{Bayesian Hierarchical Models; Hidden Markov Model; State-space model;\n VMS; Fleet behaviour; Fishing effort}},\nKeywords-Plus = {{ANCHOVY ENGRAULIS-RINGENS; STATE-SPACE MODELS; SPATIAL-DISTRIBUTION;\n ISIS-FISH; MANAGEMENT STRATEGIES; ANIMAL MOVEMENT; FISHERIES; FLEET;\n IMPACT; DYNAMICS}},\nResearch-Areas = {{Environmental Sciences \\& Ecology}},\nWeb-of-Science-Categories = {{Ecology}},\nAuthor-Email = {{Youen.Vermard@ifremer.fr}},\nResearcherID-Numbers = {{Gascuel, Didier/C-1439-2011\n martel, celine/M-9779-2014\n martel, celine/O-6651-2016\n }},\nORCID-Numbers = {{Gascuel, Didier/0000-0001-5447-6977\n martel, celine/0000-0002-1800-4558\n martel, celine/0000-0002-1800-4558\n Marchal, Paul/0000-0003-2047-4599\n Vermard, Youen/0000-0003-2828-2815}},\nFunding-Acknowledgement = {{European Union {[}022644]; Region Bretagne}},\nFunding-Text = {{The work was funded through the CAFE project of the European Union\n (DG-Fish, contract no. 022644) and the Region Bretagne, for which\n support we are very grateful. We are also indebted to fishers, who\n kindly provided their VMS data on a voluntary basis and people from the\n French Fisheries Information System at IFREMER. The authors thank\n Marie-Pierre Etienne, AgroParis Tech, ENGREF, Paris, and Emily Walker\n and Nicolas Bez (IRD Sete) for helpful comments and discussions and the\n two anonymous referees for their relevant comments that have greatly\n improved the paper.}},\nNumber-of-Cited-References = {{39}},\nTimes-Cited = {{37}},\nUsage-Count-Last-180-days = {{4}},\nUsage-Count-Since-2013 = {{23}},\nJournal-ISO = {{Ecol. 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