Additional Participants

Samuel Truesdell, Ph.D. Student, School of Marine Sciences, University of Maine
Dvora Hart, Population Dynamics Branch, Northeast Fisheries Science

Project Period

June 1, 2010-May 31, 2014

Level of Access

Open-Access Report

Grant Number

NA10OAR4170237

Submission Date

8-25-2014

Abstract

Most fisheries stock assessments assume that the spatial distribution of fish and/or fishing effort is random (Hilborn and Walters 1992), even though this is rarely the case (Paloheimo and Dickie 1964, Caddy 1975, Hilborn and Walters 1992, Tilzey 1994, Hutchings 1996, Chen et al. 1998, Hart 2001). The target stock is often aggregated and the distribution of fishing effort reflects this spatial pattern, along with other factors such as management restrictions, distance to port, vessel size, and the experience and habits of individual fishers. This often results in high spatial variation in fishing effort and mortality.

Ignoring this spatial variation can lead to serious biases in estimates of fishing mortality and yield (Hart 2001). Additionally, the non-random spatial distribution of fish and fishing effort makes the interpretation of commercial catch rate (i.e, catch-per-unit-effort or CPUE) difficult (Paloheimo and Dickie 1964, Cooke and Beddington, 1984, NRC 1999). These indices are often used as an abundance index in stock assessment and used as an index in monitoring fish stocks. The targeted deployment of fishing effort often makes the observed CPUE unchanged and even increasing even if the stock size decreases until a point when the stock is at very low level (Hilborn and Walters 1992, Rose and Kulka 1999). Misinterpretation of CPUE indices has been a factor in the collapse of a number of fish stocks, most prominently northern cod (Hilborn and Walters 1992, Hutchings 1996, Walters and Maguire 1996, Rose and Kulka 1999). For these reasons, understanding spatial dynamics of a fishery is an important issue in fisheries management; management that does not consider the spatial dynamics of a fishery may be less successful in optimizing harvest and building an understanding of the interactions between the fishery and other environmental variables (Caddy 1975, Walters and Maguire 1996, Atkinson et al. 1997, NRC 1999, Hart 2001).

Most stock assessments lack a spatial component due, in part, to limited spatially-explicit information regarding the distribution of fishing effort. However, vessel monitoring systems (VMS) that give detailed and accurate information about the positions of fishing vessels are increasingly being used as a fishery enforcement tool. These same data can be employed by stock assessment scientists to characterize the spatial structure of a fishery. Such information can be used as a basis for spatially explicit models for stock assessment and management.

Manuscript Number

MS584_2014_CHE_Spatial

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