On Managing Variable Marine Fisheries

Abstract

e e t~, W. M., IS. 6. Francis, and G. k. Swartzman. 1987. On managing variable marine fisheries. Can. I. Fish. Aquat. Sci. 44: 1370-1 375.Marine fisheries are difficult to manage because they are highly variable; yet the stock must be protected from over exploitation and possible collapse. Here we demonstrate that the most important source of uncertainty in estimating long-term productivity sf a Fishery is the degree of density dependence in the stock-recruitment relationship and not the environmental factors that i nfluence annual recruitment rates. We evaluate the performance sf three harvest policies in managing dynamically contrasting fisheries and conclude that close to maximum expected sustainable yield can be obtained by policies that have very different implications for short-term yields. In stochastic fisheries, the variability inherent in the stock can be ignored in determining optimal effort levels or it can be transferred into the fishery by allowing yield and effort levels to vary considerably. Thus it is relatively easy for the manager to ensure long-term maximization of yield. The hard problem is finding a suitable compromise between short-term stability of the fishery and long-term viability of the stock.La forte variabilite des stocks marins rend ceux-ci difficiles a gerer, bien qu'il soit necessaire de les proteger de la surexploitatian et d'un eventuel efbndrement. Les auteurs montrent qkse la plus importante source d'incertitude de I'estimation de la prodksctivit6 A Bong terme d'une p@che a trait la dependance envers la densite de la relation stock-recrutement et nsn aux facteurs environnernentaux influant sur les taux de recrutement annuels. Ils evaluent le rendement de trois politiques de recolte appliquees 21 la gestisn de peches de dynamiques tres diffkrentes et csncluent qu'il est possible d'obtenir un rendement soutenu approchant la valeur maximale prevue en appliquant des paiitiques qui peuvent avoir des effets tres differents sur les rendements a court terme. Bans B e cas des @ches stschastiques, la variabilite inherente au stock peut @tre iignorke lors de la determination des niveaux d'effort sptimaux, OLB integree 21 la p@che si Ifon autorise une forte variation du rendement et de Ikffort. li est donc relativement facile pour le gestiannaire d'obtenir une maximisation 5 long terme du rendement. Le probI&me le plus difficile A resoudre a trait a la d6termination d'un compromis acceptable entre la stabilite 3 court terme de la p@che et la viabilite 2I long terrne du stock.(J87.58 8 he primary goal of agencies that regulate the operation of marine fisheries is to ensure the continuing productivity of the resource. Beyond this, regulations can be devised to improve the efficiency of exploitation, especially in highly valuable fisheries where competition between fishermen leads to biological overfishing (Gordon 1954;Clark 1985). Regulations are typically designed to limit the annual catch to a particular value referred to as the catch "'quota." Determining an appropriate set of regulation...

A review of harvest policies: Understanding relative performance of control rules

“…Maximum constant yield (MCY) is one example of a catch level conceptually similar to MSY, but considers random fluctuations in production, as opposed to assuming deterministic dynamics following a Schaefer surplus production model (Sissenwine, 1978;Murawski and Idoine, 1989). A critical feature of MCY is that as variation (and possibly autocorrelation) in production increases, given stock size, MCY decreases below MSY (Sissenwine, 1978;Getz et al, 1987). Sissenwine (1978), however, warns against using estimates of MCY as target levels because the fishing mortality rate associated with that level of catch can be high, and cause declines in spawning stock biomass and subsequent recruitment.…”

“…Catch levels MSY Clark (1973), Beddington and May (1977), Larkin (1977), Sissenwine (1978), Sissenwine and Shepherd (1987), Hilborn and Walters (1992), Mahon (1995) MAY Sissenwine (1978), Getz et al (1987), Murawski and Idoine (1989), Annala (1993) MSY proxies Beddington and Cooke (1983), Annala (1993) Fishing mortality levels Fmsy Larkin (1977), Koonce and Shuter (1987), Hilborn and Walters (1992), Overholtz (1999), Mace (2001), Collie and Gislason (2001), Gibson and Myers (2004), Brodziak and Legault (2005) Alverson and Pereyra (1969), Francis (1974), Deriso (1982) Fmax and F 0.1 Ricker (1975), Sissenwine and Shepherd (1987), Deriso (1982), Deriso (1987), Clark (1991), Jakobsen (1992), Goodyear (1993), Leaman (1993), Helser and Brodziak (1998), Collie and Gislason (2001), Campana et al (2002), Rahikainen and Stephenson (2004) Fx% Sissenwine and Shepherd (1987), Gabriel et al (1989), Clark (1991), Goodyear (1993), Jakobsen (1993), Mace and Sissenwine (1993), …”

A new role for MSY in single‐species and ecosystem approaches to fisheries stock assessment and management

“…Some of these harvesting strategies may be inferior to others in theory, in the sense that they are only approximations to the ‘global’ MAY, but they may have compensatory practical advantages. Numerous theoretical modelling studies have demonstrated that, of the first three strategies, constant escapement tends to give the largest MAY, but at the expense of high interannual variability in yields (Getz et al . 1987).…”

“…Some of these harvesting strategies may be inferior to others in theory, in the sense that they are only approximations to the`global' MAY, but they may have compensatory practical advantages. Numerous theoretical modelling studies have demonstrated that, of the first three strategies, constant escapement tends to give the largest MAY, but at the expense of high interannual variability in yields (Getz et al 1987). While constant fishing mortality strategies may not achieve the`global' MAY, modelling results suggest that they often come close to this level, that they result in relatively low interannual variability in yields, and that they are relatively robust to estimation errors (Walters and Parma 1996).…”

Renewable resource management with environmental prediction

“…In this paper we build on a prior literature in which the optimal harvesting of renewable resources with stochastic growth is analysed~Reed 1974, 1978, 1979Beddington and May 1977;Clark and Kirkwood 1986;Getz, Francis, and Swartzman 1987!. We define a simple stochastic renewable resource model in section 2. As was done in the prior literature, we model the resource stock in discrete time periods as a stochastic process where shocks are independent and identically distributed.…”