Smart fishing and stock conservation. On the limits of improved fish finding capacity and its contribution to resource conservation

Friday Seminar by Arne Eide

Abstract

Increased knowledge on the spatial distribution of targeted species makes it possible for the fishers to actively approaching fish dense areas, targeting fish stock characterised by non-uniform distribution. Improved fish finding technology and biological knowledge about fish behaviour and migration pattern, contribute in enhancing clever fishing. This paper investigates the limits of smart fishing by presenting a harvest model which includes a smartness parameter, representing the ability to identify the most fish dense areas of the total fish distribution area. The harvest model interacts with a fish population model where growth and distribution is represented by simple cellular automata rules. The finding indicates that smart fishing may contribute to improve stock conservation when the smartness parameter becomes sufficiently high, while a uniform distribution of fishing effort may cause stock collapse and depletion at relatively low fishing efforts. Efficient fish finding techniques could therefore be a possible tool in fish conservation, reducing the fishing mortality in the less fish dense areas, rather than a threat to the resource, since the fishing activity always will be constrained by the economic condition wherein it takes place. A 2D cellular automata model for the spatial distribution and growth of a fish population is presented. An open access fishery, targeting the species, is assumed to take place in the area and all harvest is landed in a single port within the defined area. The fishery is only restricted by biological and economic constraints representing population dynamics, prices and cost of fishing. The latter also includes an increasing cost due to increasing distance between the fishing area and the port. The cellular automata model of biological growth and distribution presented in this paper is inspired of some features of the Northeast Arctic Cod stock fishery, but the modelling exercise does not claim to be a reflect this fishery, rather presenting a possible implementation of cellular automata techniques in seasonal fisheries on migrating stocks. Numerical examples are provided to indicate how fishers behaviour and fish finding ability affect economic results and biological processes in open access fisheries.

The corresponding paper is found here (pdf). I will link the presentation of the paper to climate change issues (changes in spatial distribution, increased distance costs of fishing) and the use of scenario models, in this case the cellular automata model.

Arne Eide
Associate Professor at the Norwegian College of Fishery Science at the University of Tromsø
http://arneeide.maremacentre.com

Published Apr. 12, 2012 2:53 PM - Last modified May 11, 2012 11:31 AM