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) : Wiley-blackwell Publishing, Inc
Résumé : Primary production must constrain the amount of fish and invertebrates available to expanding fisheries; however the degree of limitation has only been demonstrated at regional scales to date. Here we show that phytoplanktonic primary production, estimated from an ocean-colour satellite (SeaWiFS), is related to global fisheries catches at the scale of Large Marine Ecosystems, while accounting for temperature and ecological factors such as ecosystem size and type, species richness, animal body size, and the degree and nature of fisheries exploitation. Indeed we show that global fisheries catches since 1950 have been increasingly constrained by the amount of primary production. The primary production appropriated by current global fisheries is 17-112% higher than that appropriated by sustainable fisheries. Global primary production appears to be declining, in some part due to climate variability and change, with consequences for the near future fisheries catches.
Ecology Letters (1461-023X) (Wiley-blackwell Publishing, Inc), 2010-04 , Vol. 13 , N. 4 , P. 495-505

Droits : 2010 Blackwell Publishing Ltd/CNRS
http://archimer.ifremer.fr/doc/00002/11294/7836.pdf
DOI:10.1111/j.1461-0248.2010.01443.x
http://archimer.ifremer.fr/doc/00002/11294/