Éditeur(s) : Elsevier Science Bv
Résumé : 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.
Ecological Modelling (0304-3800) (Elsevier Science Bv), 2010-07 , Vol. 221 , N. 15 , P. 1757-1769

Droits : 2010 Elsevier B.V. All rights reserved.
http://archimer.ifremer.fr/doc/00009/11993/9342.pdf
DOI:10.1016/j.ecolmodel.2010.04.005
http://archimer.ifremer.fr/doc/00009/11993/

Éditeur(s) : HAL CCSD
Résumé : International audience
In many situations it is important to be able to propose N independent real- izations of a given distribution law. We propose a strategy for making N parallel Monte Carlo Markov Chains (MCMC) interact in order to get an approximation of an indepen- dent N-sample of a given target law. In this method each individual chain proposes can- didates for all other chains. We prove that the set of interacting chains is itself a MCMC method for the product of N target measures. Compared to independent parallel chains this method is more time consuming, but we show through examples that it possesses many advantages. This approach is applied to a biomass evolution model.
International Conference on Applied Statistics for Development in Africa Sada'07
Cotonou, Benin

inria-00506398
https://hal.inria.fr/inria-00506398
https://hal.inria.fr/inria-00506398/document
https://hal.inria.fr/inria-00506398/file/campillo2007b.pdf

Éditeur(s) : HAL CCSD INRIA
Résumé : International audience
Markov chain Monte Carlo (MCMC) methods together with hidden Markov models are extensively used in the Bayesian inference for many scientific fields like environment and ecology. Through simulated examples we show that the speed of convergence of these methods can be very low. In order to improve the convergence properties, we propose a method to make parallel chains interact. We apply this method to a biomass evolution model for fisheries.
Les méthodes de Monte Carlo par chaînes de Markov (MCMC) couplées à des modèles de Markov cachés sont utilisées dans de nombreux domaines, notamment en environnement et en écologie. Sur des exemples simples, nous montrons que la vitesse de convergence de ces méthodes peut être très faible. Nous proposons de mettre en interaction plusieurs algorithmes MCMC pour accélérer cette convergence. Nous appliquons ces méthodes à un modèle d'évolution de la biomasse d'une pêcherie.
ISSN: 1638-5713

inria-00506386
https://hal.inria.fr/inria-00506386
https://hal.inria.fr/inria-00506386/document
https://hal.inria.fr/inria-00506386/file/campillo2008d.pdf