Estimates from data putting management into practice

The role of the ecologist in the management of a natural resource is in stock assessment: making quantitative predictions about the response of the biological population to alternative management choices and addressing questions like whether a given fishing intensity will lead to a decline in the size of the stock, whether nets of a given mesh size will allow the recruitment rate of a stock to recover, and so on. In the past, it has often been assumed that this can be done simply by careful monitoring. For example, as effort and yield increase in an expanding fishery, both are monitored, and the relationship between the two is plotted until it seems that the top of a curve like that in Figure 15.7 has been reached or just exceeded, identifying the MSY. This approach, however, is deeply flawed, as can be seen from Figure 15.17. In 1975, the International Commission for the Conservation of Atlantic Tunas (ICCAT) used the available data (1964-73) to plot the yield-effort relationship for the yellowfin tuna (Thunnus albacares) in the eastern Atlantic. They felt that they

Figure 15.17 Estimated yield-effort relationships for the eastern Atlantic yellowfin tuna (Thunnus albacares) on the basis of the data for 1964-73 (ICCAT, 1975) and 1964-83 (ICCAT, 1985). (After Hunter et al., 1986; Hilborn & Walters, 1992.)

harvester as predator: human behavior monitoring effort and yield: the difficulties of 'finding the top of the curve'

Figure 15.17 Estimated yield-effort relationships for the eastern Atlantic yellowfin tuna (Thunnus albacares) on the basis of the data for 1964-73 (ICCAT, 1975) and 1964-83 (ICCAT, 1985). (After Hunter et al., 1986; Hilborn & Walters, 1992.)

had reached the top of the curve: a sustainable yield of around 50,000 tons (5.1 X 107 kg) and an optimum effort of about 60,000 fishing days. However, ICCAT were unable to prevent effort (and yield) rising further, and it soon became clear that the top of the curve had not been reached. A reanalysis using data up to 1983 suggested a sustainable yield of around 110,000 tons (1.1 X 108 kg) and an effort of 240,000 fishing days.

This illustrates what Hilborn and Walters (1992) describe as another principle: 'You cannot determine the potential yield from a fish stock without overexploiting it'. At least part of the reason for this is the tendency, already noted, for the variability in yield to increase as an MSY is approached. Furthermore, if we also recall the previously described difficulty in reducing fishing pressure, it is clear that in practice, managers are likely to have to wrestle with the combined challenge of estimation difficulties, ecological relationships (here, between yield and predictability) and socioeconomic factors (here, concerning the regulation and reduction in effort). We have moved a long way from the simple fixed-effort models of Section 15.3.3.

The practical difficulties of parameter estimation are further illustrated in Figure 15.18, which displays the time series for total catch, fishing effort and catch per unit effort (CPUE) between 1969 and 1982 for yellowfin tuna for the whole Atlantic Ocean. As effort increased, CPUE declined - presumably, a reflection of a diminishing stock of fish. On the other hand, the catch continued to rise over this period, suggesting that perhaps the stock was not yet being overfished (i.e. the MSY had not yet been reached). These, then, are the data, and they come in probably the most commonly available form - a so-called 'one-way trip' time series. But can they suggest an MSY and can they suggest the effort required to achieve that MSY? Certainly, methods exist for performing the necessary calculations, but these methods require assumptions to be made about the underlying dynamics of the population.

The most frequently used assumption describes the dynamics of the stock biomass, B, by:

estimates from catch and effort data: applying the Schaefer model dß

(Schaefer, 1954), which is simply the logistic equation of Chapter 5 (intrinsic rate of increase, r, carrying capacity, K) with a harvesting rate incorporated. The latter may itself be given, following Equation 15.1 (see Section 15.3.3), by H = qEB, where q is harvesting efficiency and E the harvesting effort. By definition:

Figure 15.18 Changes in total catch, fishing effort and catch per unit effort (CPUE) between 1969 and 1982 for the yellowfin tuna (Thussus albacares) in the Atlantic Ocean. Also shown are three separate curves fitted to the CPUE time series by methods outlined in the text, the parameters of which are given in Table 15.4. (After Hilborn & Walters, 1992.)

Figure 15.18 Changes in total catch, fishing effort and catch per unit effort (CPUE) between 1969 and 1982 for the yellowfin tuna (Thussus albacares) in the Atlantic Ocean. Also shown are three separate curves fitted to the CPUE time series by methods outlined in the text, the parameters of which are given in Table 15.4. (After Hilborn & Walters, 1992.)

and Equation 15.2 can be rewritten in terms of CPUE with either H or E as variables, and with r, q and K as parameters. For this model, the MSY is given by rK/4 and the effort required to achieve this by r/2 q.

There are a number of methods of obtaining estimates of these parameters from field data, perhaps the best of which is the fitting of curves to time series (Hilborn & Walters, 1992).

However, when the time series is a one-way trip, as we have noted it often is, there is no unique 'best' set of parameter values. Table 15.4, for instance, shows the parameters for three separate curves fitted to the data in Figure 15.18, providing equally good fits (the same sum of squares), but with widely differing parameter values. There are, in effect, a large number of equally good alternative explanations for the data in Figure 15.18, in some of which, for example, the population has a low carrying capacity but a high intrinsic rate of increase and is being harvested time series analysis is best - but answers are still equivocal

Table 15.4 Parameter estimates from three fits to the catch per unit effort (CPUE) time series for yellowfin tuna shown in Figure 15.18. r is the intrinsic rate of increase, K is the carrying capacity (equilibrium abundance in the absence of harvesting) and q is the harvesting efficiency. Effort is measured in fishing days; K and maximum sustainable yield (MSY) in tons. (After Hilborn & Walters, 1992.)

Fit number r K (x 1000) q (x 10~7) MSY (x 1000) Effort at MSY (x 1000) Sum of squares

efficiently, whereas in others it has a high carrying capacity, a low rate of increase and is being harvested less efficiently. In the first case, the MSY had probably already been reached in 1980; in the second, catches could probably be doubled with impunity. Moreover, in each of these cases, the population is assumed to be behaving in conformity with Equation 15.2, which may itself be wide of the mark.

It is clear, therefore, even from this limited range of rather arbitrarily chosen examples, that there are immense limitations placed on stock assessments and management plans by inadequacies in both the available data and the means of analyzing them. This is not meant, though, to be a council of despair. Management decisions must be made, and the best possible stock assessments must form the basis - although not the sole basis - for these decisions. It is regrettable that we do not know more, but the problem would be compounded if we pretended that we did. Moreover, the ecological, economic and human behavioral analyses are important - as all analyses are - for identifying what we do not know, since, armed with this knowledge, we can set about obtaining whatever information is most useful. This has been formalized, in fact, in an 'adaptive management' approach, where, in an 'actively adaptive' strategy, a policy is sought which offers some balance between, on the one hand, probing for information (directed experimentation), and on the other, exercising caution about losses in short-term yield and long-term overfishing (Hilborn & Walters, 1992). Indeed, there is a strong argument that says that the inadequacies in data and theory make the need for ecologists all the more profound: who else can appreciate the uncertainties and provide appropriately enlightened interpretations?

However, to be realistic, managing most marine fisheries to achieve optimum yields will be very difficult to achieve. There are generally too few researchers to do the work and, in many parts of the world, no researchers at all. In these situations, a precautionary approach to fisheries management might involve locking away a proportion of a coastal or coral community in marine protected areas (Hall, 1998). The term dataless management has been applied to situations where local villagers follow simple prescriptions to make sustainability more likely. For example locals on the Pacific island of Vanuatu were provided with some simple principles of management for their trochus (Tectus niloticus) shellfishery (stocks should be harvested every 3 years and left unfished in between) with an apparently successful outcome (Johannes, 1998).

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