Using hidden Markov models to infer vessel activities in the snow crab (Chionoecetes opilio) fixed gear fishery and their application to catch standardization

Abstract

Tracking vessel movements has become increasingly important in fisheries research to identify fishing grounds, monitor responses to area closures, and other actions for fishery managers. Vessel monitoring systems (VMS) have given fishery managers and researchers the ability to study vessel interactions by automated tracking of vessels throughout fishing seasons. The high spatial and temporal resolution obtained from VMS records in the Gulf of St. Lawrence snow crab (Chionoecetes opilio) fishery provides information on movement patterns and fishing locations. With the use of hidden Markov models (HMM), we inferred behaviours exhibited by the fishermen during the course of fishing trips and related these behaviours to catch rates across years with varying abundance estimates. The HMM classified three behavioural states in the VMS data that were identified with travelling, setting traps in novel locations (new sets), and retrieving previously set traps (resets). Catches within a trip were modeled by combining VMS-based estimates of these behaviours with logbook information in a generalized linear model. Our model demonstrates that behavioural variables can contribute to the standardization of catch similar to classical trip and vessel variables used in constructing abundance indices.