FN Archimer Export Format
PT J
TI Identifying fishing trip behaviour and estimating fishing effort from VMS data using Bayesian Hidden Markov Models
BT 
AF VERMARD, Youen
   RIVOT, Etienne
   MAHEVAS, Stephanie
   MARCHAL, Paul
   GASCUEL, Didier
AS 1:1,2;2:2;3:3;4:1;5:2;
FF 1:PDG-DOP-DCMMN-HMMN-RHBL;2:PDG-DOP-DCM-HMT-RHGUYANE;3:PDG-DOP-DCN-EMH;4:PDG-DOP-DCMMN-HMMN-RHBL;5:;
C1 IFREMER, Channel & N Sea Fisheries Dept, F-62321 Boulogne S Mer, France.
   AGROCAMPUS QUEST, UMR Ecol & Sante Ecosyst 985, F-35042 Rennes, France.
   IFREMER, Fisheries & Ecol Modeling Dept, F-44311 Nantes, France.
C2 IFREMER, FRANCE
   AGROCAMPUS OUEST, FRANCE
   IFREMER, FRANCE
SI BOULOGNE
   GUYANE
   NANTES
SE PDG-DOP-DCMMN-HMMN-RHBL
   PDG-DOP-DCM-HMT-RHGUYANE
   PDG-DOP-DCN-EMH
IN WOS Ifremer jusqu'en 2018
   copubli-france
IF 1.769
TC 75
UR https://archimer.ifremer.fr/doc/00009/11993/9342.pdf
LA English
DT Article
DE ;Bayesian Hierarchical Models;Hidden Markov Model;State-space model;VMS;Fleet behaviour;Fishing effort
AB 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.
PY 2010
PD JUN
SO Ecological Modelling
SN 0304-3800
PU Elsevier Science Bv
VL 221
IS 15
UT 000279549500001
BP 1757
EP 1769
DI 10.1016/j.ecolmodel.2010.04.005
ID 11993
ER

EF