Identifying fishing trip behaviour and estimating fishing effort from VMS data using Bayesian Hidden Markov Models

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

Comparative analysis of methods for inferring successful foraging areas from Argos and GPS tracking data

“…When analysing noisy tracking data, an HMM that infers the animal's behavioural modes can be nested within a statespace model for the estimation of the animal's locations (Jonsen et al 2005, Jonsen et al 2007). For filtered Argos locations or very low-error GPS data, HMM assign behavioural modes directly from inferences on movement data (Franke et al 2004, Morales et al 2004, Vermard et al 2010, Walker & Bez 2010. The use of environmental covariates in the HMM inference can help in understanding how predators adapt their foraging behaviour to local environmental conditions (Morales et al 2004).…”

Functional response of fishers in the Isle of Man scallop fishery

“…For example, improved estimates of fishing time may be achieved by adopting methods to reconstruct trawl tracks. Hintzen et al (2010) used a spline interpolation technique to model fishing tracks from VMS data while Vermard et al (2010) identified different fishing behaviour during fishing trips, using Bayesian hierarchical models. Moreover, the collection of additional data such as individual tow length and catches per tow would allow more detailed analysis of how fishers respond to changing prey availability.…”

Towards the implementation of an integrated approach to fisheries resources management in Ifremer, France

“…More safely it can be improved by looking for experience in other countries ( [47] Marchal et al, p. 483) identifying best practices. It is enhanced by the use of simulation models and the development of scenarios [44,49,[55][56][57][58]. Finally, the perceptions of the fishers themselves should not be underestimated and mechanisms involving them directly in the decision-making process may also help improving the capacity of governance to look ahead.…”

“…Developing participatory research-and-management processes [86] will help to better understand fleet dynamics and fishing strategies [43], to optimize time and space allocation of effort [45], or to assess the potential effect of new management strategies such as the introduction of fishing rights ( [53] Marchal et al, p. 463; [54] Hamon et al, p. 549). More studies of fishers' behaviour and motivations are needed [55,56] to improve foresight. There is a need for joint analysis of environmental and economic drivers and this requires new approaches and models including behavioural models [44,49,[55][56][57][58].…”