Information flow among fishing vessels modelled using a Bayesian network

Reaction of fishers is an essential source of uncertainty in implementing fishery management decisions. Provided they realistically capture fisher behaviour, models of fishing vessel dynamics provide the basis for evaluating the impact of proposed management strategies. Information flow among vessels has not been a major focus of such models however, although it might play a critical role in how a fleet responds to changes to management restrictions or levels of a resource. Such a response might then modify subsequent exploitation of the resource. In this paper, a spatially-explicit model of vessel fishing behaviour is developed for a line fishery on the Great Barrier Reef, Australia. Vessel behaviour is conditioned on past catch and effort data at a spatial resolution of 6 × 6 nautical mile grid cells. For each vessel, the probability of fishing a particular grid cell is determined from past income per unit effort experienced at that location, and the cost of steaming to it. The probability distribution across all possible grid cells represents a particular vessel’s perspective. This perspective is modified by information conveyed by other vessels using Bayesiannetwork information propagation. The information conveyed is the effort distribution of other vessels and is equivalent to a vessel ‘watching’ where other vessels fish. We compare the behaviours that vessels display when they act independently with those they display when they ‘watch’ each other, and show the effect that such information flow can have on a resource. Information flow among fishing vessels can be shown to have an effect on the dynamics and resource exploitation of a simulated fishery.