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5. Management

There has been an increasing trend towards the incorporation of non-monetary criteria in the bioeconomic analysis of fisheries, such as conservation of marine biodiversity, protection of endangered species and other unpriced values (existence value and option demand value). This approach needs to reconcile economic and ecological criteria, which in many cases conflict (Charles, 1989; Charles & Herrera, 1994). On the one hand, a maximization of the net present value perceived by fishers is desired; on the other hand, abundance and yield of the harvested species should be sustainable over time. In this Chapter, some fisheries regulation criteria are described, notably forms of State intervention. A bioeconomic approach introducing a non-linear optimization algorithm for fisheries management with multiple criteria, is also presented.

5.1. State intervention criteria

The following fisheries management criteria have been identified (see also Panayotou, 1983; Beddington & Rettig, 1984; Lawson, 1984):

Conservation approach

This is usually based on fishing effort regulation, under the assumption that once fishing effort is regulated, changes in species abundance could be reversible in a reasonable time period. However, the lack of long-term time series means that the results of the evaluation techniques commonly used (e.g. Virtual Population Analysis: VPA) don't show enough evidence to justify the reduction of effort until overexploitation occurs. Thus, there has been a risky trend towards the exploitation of stocks at levels close to overexploitation, which in many cases determined an important reduction of fishing effort levels or even the closure of the fishery, with social and economic implications.

Economic approach

In open access fisheries, the bioeconomic equilibrium is reached with increasing economic inefficiency (Clark, 1985). When a fishery is developing, a trend of declining costs occurs due to an increasing fishing power and efficiency, together with increasing knowledge of the most important fish concentrations. However, these factors tend to (a) reduce stock abundance, (b) increase harvesting costs and (c) diminish net revenues per unit of effort. When this happens, fishing areas closest to port are gradually overexploited or even depleted (e.g., sedentary stocks) and the areas previously less attractive will tend to be progressively used. Thus, the bioeconomic equilibrium will be reached at high levels of stock overexploitation, at which point the lack of profits discourages entry of new vessels.

Policy makers rarely refer to the concept of economic efficiency; on the contrary, they respond more to high unemployment rates, fishery collapse and decline in income levels, with the consequent socio-economic repercussions (Maiolo & Orbach, 1982). Indeed, some stocks are exploited even at very low biomass levels, because increasing costs are compensated by an increase in the species price, as a result of a reduction in market supply. This constitutes an important drawback to the wholesale application of economic criteria.

A simple bioeconomic approach that does not consider variations in environmental factors will not provide a real representation of the fishery, especially for species highly sensitive to environmental variability as small pelagics (e.g. anchovy and sardine). Exogenous environmental factors affect the stock and thus the economic rent derived from the fishery. This is the reason why some fishery scientists state that the true deterministic bioeconomic equilibrium does not hold (Beddington & Rettig, 1984). Again, this precludes the application of economic criteria for fishery management.

Equity approach in resource use

Under open access conditions and decreasing stock abundance, some fishers will be more affected than others. Generally, those vessels with high autonomy and economic rent will increase harvest rates by exerting their fishing effort on distant fishing grounds. This could produce conflicts among fishers, and thus the major task in the selection of management alternatives resides in how to favor a user' group without demaging others. Economic damage is defined as the reduction in incomes or satisfaction of an individual as a result of a public decision (in this case a given management strategy). In this context, equity is an important social consideration by which to assess State intervention strategies; however it is difficult to implement, as equal opportunities do not guarantee an equal distribution of non-renewable resources.

The main postulate of economic welfare theory as applied to fisheries (Hannesson, 1978), establishes that the maximization of social welfare is the final purpose of fishery policy. The main underlying concept is Pareto optimality. A fishery management strategy is Pareto optimal if changes generated by management instruments allow improving the welfare of one or more fishers without worsening others. The necessary and sufficient conditions for a management strategy to be Pareto optimal are as follows:

  1. The technical substitution rate between any pair of inputs of fishing effort would be equal for all vessels and equal to the price ratio of the inputs.

  2. It would not be possible to increase catches and economic rent generated by a species (or group of species) without diminishing catches and economic rent that other fishers would obtain by harvesting this (these) species or others that are ecological and/or technologically interdependent.

  3. It would not be possible to redistribute catches between consumers of sea products in such a way that the welfare of a consumer could improve without diminishing the welfare of others.

    By combining conditions (ii) and (iii), it is possible to derive the general condition that relates production and consumption of exploited fishing resources:

  4. The substitution rate for any pair of marine goods will be equal for any pair of consumers, and will also be equal to the transformation rate of products of the same pair of goods for all the firms of the fishing industry, also being equal to the price ratio between the two goods.

The neoclassical welfare theory applied to natural resources (Randall, 1981; Hannesson, 1978; Schmid, 1978, 1989) considers several welfare approaches that have different distributional implications of costs and resulting benefits of alternative management strategies (Table 5.1).

Table 5.1. Welfare criteria applied to marine fisheries.

Welfare CriteriaDistributional CharacteristicImpact
Pareto EfficiencyEconomic damage is permissibleNeutral for the less efficient fishers
Pareto SafetyEconomic damage is not allowedNeutral for all fishers
Maximum value of the social productPermissible economic damage is allowed if the ∑ welfare > 0 and maximizedNeutral
Proportional partsAbsolute or relative economic damage between fishers is not allowedProportional
Maximum social welfareEconomic damage is allowed. Assumes the existence of a social indifference curveNeutral

Pareto efficiency. This approach consists in eliminating the inefficient management solutions. It should be mentioned however, that more than one efficient solution could exist since any point of the Grand Utility Frontier (GUF) curve involves efficiency in: (a) resource allocation, (b) production, and (c) consumption (Randall, 1981: 111–118). Each point of the GUF curve is Pareto efficient, because it does not consider:

  1. The combinations of inputs that do not remain in the production possibilities curve.

  2. The distribution of goods that don't remain in the consumption efficiency curve.

  3. All the points that don't remain in the GUF curve.

The concept of Pareto Efficiency is therefore defined as a situation in which it is impossible to improve a fisher without simultaneously damaging another. In order to achieve this goal, all the possibilities of voluntary exchange that allow reallocating resources or redistributing goods in a more efficient manner, have been exhausted. However, inherent to each point of the GUF curve is an initial distribution of wealth. This indicates that the selection of a given fishery management strategy will involve value judgements by policy makers, since they will implicitly consider a given initial distribution of wealth. The efficiency approach permits economic damage: as total efficiency must be reached through exchange, an initial distribution of rights will affect the distribution of benefits and costs.

Pareto safety. This approach searches for improvements in the fishing sector, with the condition that the selected management strategy must increase the income of a group of fishers without reducing the income of others in the fishery. In welfare theory, this concept is defined as Paretian improvement, where economic damage is not permitted. However, a relative economic damage could exist, since although the reduction in the income of any fisher is not allowable, the relative distribution of the wealth could change.

Maximum value of social welfare. This approach allows for real economic damage inside the limits defined by the social indifference curve, since the optimum is located at its tangent point with the GUF. Even though there has been academic discussion concerning the impossibility of estimating a social indifference curve (Arrow, 1976), the general concept could be applied to fisheries in a qualitative way. Thus, economic damage is permitted only when there is Social agreement in that the selected management strategy represents an improvement in the social welfare of the fishing sector as a whole.

Maximum value of the social product. This management approach is the basis of benefit-cost analysis, i.e., the management strategy with the greatest present value of the social product is selected. Economic damage is permissible when the sum of the economic rents obtained by the winners is highest and exceeds the losses of fishers that did not obtain economic rent. The analytic principles are similar to those of the previous approach, but in this case the indifference curve is assumed as a straight line with slope-1. The optimum solution, i.e., the management strategy to be selected, is the tangent of the indifference curve with the GUF curve. A slope-1 in the indifference curve means the absence of distributional weights for society.

Proportional constant parts. This approach defines a management strategy as an improvement that results in proportional increments in fishers incomes. Therefore,all fishers are benefited from the new management strategy in proportion to their initial income. Ecomomic damage is not allowed. However, the distribution of the absolute amount of rent generated by the fishery is not the same for each fisher, but is in proportion to the relative rent previous to the implementation of the new management strategy.

The above mentioned approaches could be combined in management plans, and policy-makers should make explicit the distributional value judgements inherent to each approach.

Intergenerational equity approach

The concept of intergenerational equity means that future generations have the same opportunities as current ones of using (with or without consumption) fishing resources. Thus, exploitation rates will determine the availability of a given stock for future generations. If this approach is considered relevant for the policy maker, a dynamic analysis will be needed to determine which will be the magnitude of the spatio-temporal allocation of fishing effort that should allow to sustain resource availability over time.

Other management approaches

Generation of employment and foreign exchange, as well as the contribution of food to coastal countries, could constitute relevant approaches for fisheries management. Although the generation of employment could be inefficient in economic terms, it could be important in order to achieve political and social stability.

Administrative and politic feasibility. Fishery regulations need administrative feasibility, because their implementation is time consuming (resource monitoring nd control). However, the most important issue relates to enforcement costs (see Chapter 1), and thus fishers should participate, understand and share the regulatory measures imposed. The activity of the fishery also needsto be monitored, since different regulations will imply different strategies for monitoring and data collection from primary and secondary sources of information.

When a management strategy is evaluated, policy makers usually consider its political feasibility, because of the possible repercussions on their own political credibility. They could weight the repercussions of different management measures in a bio-socio-economic framework, including the risk of stock overexploitation, potential conflicts between different groups of fishers, or even international conflicts with coastal contiguous States in case of shared resources. The recognition of political costs is a factor that could influence a policy maker when considering adopting a management measure.

5.2. Management strategies

A fishery management plan can be carried out through allocation of property rights, regulation of catch composition, regulation of the catch size and the adoption of extension fishing programs (Anderson, 1977; Pearse, 1980; Seijo, 1986).

Property regimes and allocation of property rights

Four different property regimes (state, private, common and open access) were discussed in Chapter 1 and thus will not considered in detail here. However, some specific aspects deserve more consideration in the context of the present Chapter.

Under open access, many developed fisheries of the world were overexploited, overcapitalized, and generated externalities. As a result, coastal States implemented different approaches to allocating property rights as mentioned in Chapter 1. Under state property conditions, the management agency establishes the norms of use and access to the fish stock. Rights allocation includes the regulation of the amount and composition of the catch. Under a common property regime, exclusive rights are given to groups of fishers (having the right to exclude others), which we generally organized into cooperatives or fishing communities (Berkes, 1989; Ostrom, 1990; Seijo, 1993). Finally, the allocation of private property rights has been explored since the 80's by setting Individual Transferable Quotas (ITQ's: see Morgan, 1997).

Fishing rights could be allocated to fishers in a “community-based” context, using species or fishing areas as criteria for setting quotas/privileges (Defeo, 1989; Castilla, 1994). This implies the decision to exclude others, and thus the explicit definition of differential access to a specific fishing area or stock. These individuals or organizations acquire responsibility, although not exclusive, over the exploitation rates of the resource, defining their own norms of use (Acheson & Reidman, 1982; Seijo & Fuentes, 1989). The number of vessels and fishers should be fixed not only as a function of the perceived economic benefits, but also considering sustainable biomass and production levels. This management regime could be efficiently applied to sedentary species in a regional context. Defeo (1989, 1996) and Castilla (1994) documented successful community-based examples in Uruguayan and Chilean small-scale benthic shellfisheries. The execution of large-scale experiments in connection with artisanal fishers communities allowed specific hypotheses to be listed about natural re-stocking of overexploited invertebrates, including the economical viability of this operation. Even though fishermen's communities took care of their traditional fishing grounds by preventing illegal fishing, and allowed invertebrate stocks to rebuild, in both cases the State implemented an effective monitoring system to evaluate resource status over time.

Each form of right allocation has inherent value judgments that should be explicitly made, also recognizing the corresponding advantages and limitations (Pearse, 1980; Young, 1981; Schmid, 1987).

Regulation of catch composition

Regulation of catch composition is generally directed at protecting a critical phase of the life cycle of a species. This strategy could be carried out by: (a) closed seasons, mainly in spawning periods; (b) closing nursery areas to protect the spawning stock, or grounds with important occurrence of juveniles; (c) controlling gear selectivity; (d) restricting certain fishing gears, explosives and poisons; and (e) setting a minimum harvestable size. The response of the stock to alternative regulations will depend on the biological characteristics of the stock.

Regulation of the amount of catch

Restricting fishing effort through the following criteria could regulate the amount of catch: (a) number of vessels; (b) fishing power; (c) spatial distribution of fishing intensity; and (d) effective fishing time. The main regulation instruments are: (1) vessels quotas by fishing gear; (2) catch quotas by season and/or locality; (3) taxes and subsidies; (4) closed areas, marine refugia or marine protected areas; (5) changes in the duration of a closed season; and (6) spatio-temporal restrictions in the use of a fishing gear.

The standard procedure for limiting fishing effort has been to define catch quotas based on a reference point, the total allowable catch, according to abundance estimates. This could be difficult in practice, due to population fluctuations, catch flows and fishing power variations over time. It is also difficult to define how to allocate catches among fishers or even between countries (shared resource).

Allocation of quotas by type of vessel is also complicated when heterogeneous fishing fleets operate over the same stock. In these cases, we require to know the corresponding fishing power, the frequency distribution of fishing days, the effective fishing time per day and the fishing gear(s) used. Effort standardization is difficult in cases where different fishing methods or types of vessels are used to exploit a single or multiple stock. Moreover, since many fisheries have shown increasing fishing power in the absence of major changes in equipment, the skill of the skipper and the crew must be integrated to analyze and standardize the fishing power of individual vessels. In this context, the generalized linear model provides a powerful method for examining the effects of vessels, areas, gears and fishers's skill on the fishing power (Hilborn & Walters, 1992).

By mid 80's, the use of privatization management schemes began. In order to promote an optimal use of marine resources, some coastal countries like New Zealand, United States, Norway, and Chile, allocated property rights to individual fishers through Individual Transferable Quotas ((ITQ's) (Morgan, 1997). Under an ITQ system, individual fishers, fishing companies and/or processing plants, have exclusive rights over a percentage of the annual catch quota. The owners of ITQ's carry out commercial transactions between them, adjusting their production flows to satisfy the total quota. Equilibrium rents and prices are generated, and the benefits of quota exchange are permitted to accumulate to users. As a result, the fishing industry is reorganized on a basis of efficiency, since the most efficient firms buy ITQ's from firms with greater costs. However, Squires et al (1995) stated that: (i) the potential rents and improvements in surveillance efficiency should be enough to guarantee an ITQ's program, and (ii) the possibility of concentration of the fishing structure exists, thus affecting distribution of wealth and promoting monopolies (see also Anderson 1994, 1995). Hannesson (1993) pointed out that ITQ's could lead to efficient utilization of fish stocks. He discussed different ways of allocating quotas (e.g., as fixed quantities or shares in the total allowable catch), and also the socio—economic implications derived from ITQ's at two levels: (a) when individual quotas are allocated; and (b) when individual quotas are exchanged. Even though he defined ITQ's as “particularly promising as an incentive-compatible and cost-efficient system of management”, he highlighted that high control and enforcement costs are the major drawbacks that could make ITQ's an ineffective management method (see Hannesson, 1993 for details).

Restriction of licenses will limit vessel entry to the fishery. As in the previous case, the problem arises in defining to whom the licenses are granted and how many types of licenses are necessary when different types of vessels exist. Moreover, the limitation of the number of licenses must be based on a continuous monitoring of catches and of the state of the resource, which implies high information costs. Surveillance costs will also tend to increase, since fishers to whom the licenses were not granted could try to fish illegally.

Another approach to limiting the amount of fishing effort is through taxes and subsidies, which will affect fixed and variable costs. Subsidies could be used in low-income communities, thus increasing the amount of effort exerted. Subsidies stimulate the participation in the fishing activity and their suppression could imply effort reduction and unemployment, which is not desirable from a normative, political and social point of view (Panayotou, 1983). On the other hand, taxes tend to discourage users to introduce new vessels to the fishery.

The introduction of new technologies will increase fishing power. Competition among fishers could stimulate the introduction of technologies, which increases catch efficiency and the risks of stock overexploitation. Thus, some regulation schemes prohibit the use of gears with a strong impact on stocks (Anderson, 1980).

Extension programs and environmental education

The previous measures need complementary extension programs regarding the intelligent use of marine resources. For this reason, research result must be transferred to fisheries communities through education programs, technical bulletins and audio-visual material.

5.3. Multiple criteria optimization approach for fisheries management

Management of marine fisheries is significantly complicated by the fact that usually there are more than one set of criteria of relevance in evaluating fishery performance. The problem is not just one of maximizing the economic rent generated by fishery (given certain inter-temporal preferences), but also of sustaining biomass of target and incidental (usually threatened) species above certain levels, and perhaps of maximizing food production, coastal employment, and export earnings. In this type of problem, decision-makers must make trade-offs between conflicting criteria. Then, the goal of the optimization process must be to assist in ensuring that the best possible trade-offs are made for a given fishery management problem. This involves seeking so-called “Pareto optimal solutions” to multiple criterion optimization problems. A Pareto optimal solution in this context, has the property that one decision-making criterion cannot be improved by a decision without making the fishery performance worse off with respect to at least one other resource management criterion (Hannesson, 1978; Mendelssohn, 1979).

Optimal control theory has been applied to fisheries to identify the level of fishing effort that maximizes the net present value of economic rent in a variety of fishery contexts (Clark, 1985; Cohen, 1987; Hilbom & Walters, 1987). Multi-criteria and/or non-linear optimization models have seldom been used in fishery management (Garrod & Shepherd, 1981; Kennedy & Watkins, 1986; Onal et al., 1991). Recently, hybrid simulation-optimization models have been developed for the estimation of optimal management strategies of a sequential fishery (Díaz de León & Seijo, 1992) and for a fishery with techno-ecological interdependencies (Seijo et al. 1994c). The approach combined multi-criteria and non-linear optimization modeling in a Pareto-optimal manner. The procedure allowed working with multiple input-outputs, time delays, non-linear functions and the natural trade—offs during the estimation process. The strategy involves minimizing with respect to a single criterion, namely, the maximum deviation of all fishery performance variables with respect to specific targets assigned for each of them (Osyczka, 1984, Manetsch, 1985). The method works at the beginning with the criteria with the largest goal deviation until it no longer has the largest target deviation. Then, it proceeds to improve the next management criterion which at that point experience the worse goal deviation, and so on. The method stops when all the fishery perfoemance criteria experiences essentially the same goal deviations. It has been shown to be a useful decision-making aid when explicit manager objectives are available.

M-OPTSIM (Manetsch, 1986) is an optimization package that uses the non-linear optimization method COMPLEX (Box, 1965). It can be applied to resolve highly complex optimization problems (e.g. optimization of management strategies in multispecific fisheries) with difficult-to-find global optimal solutions. The Box method has proven to be an effective way to find the neighborhood of the global optimum.

The Multi-Criteria Objective Function

Given a dynamic model built for a fishery (as the one described in the previous Chapters), the block diagram for the multi-criteria optimization problem is based on:

Ēμ(T)= (μ,T)      (5.1)


Ēμ (T) is the vector of values for the multi-criteria function corresponding to the management option μ, assessed in the time horizon (O, T) selected to evaluate alternative management options; and μ is a decision parameter vector corresponding to the management option μ. The dynamic model is then operated in optimization mode (Figure 5.1).

Figure 5.1

Figure 5.1 Conceptual block diagram for multi-criteria optimization of fisheries.

In Figure 5.1, Ωμφ could be expressed as follows:

Ωμφ =MIN[MAX[Ωμφ ]]          (5.2)

where Ωβμφ is the normalized deviation of the criterion value β with regard to the management option μ and goal set φ, and is expressed as follows:

Where gβφ is the target for the criterion Β of the goal set φ; Eβμ is the value of the criterion β for the management strategy μ, and bβ is the base run value of Β.

Finally, μ* in Fig 5.1 is the parameter vector for optimal control of the fishery, i.e., the vector that specifies the optimal combination of fishery management instruments; and Ēμ* is the vector of optimal values for the multiple criteria function used to evaluate fishery performance. The sgn () function changes the sign of Ωβμφ for those goals which are less than the “base” values. In this way, Ωβμφ values related to different variables, strategies and goal sets could be compared.

The iterative procedure finds the largest deviation and minimizes it, operating on all criteria until no significant improvement can be made in the one with the largest goal deviation. For this reason, the “min-max” method tends to find optimal Pareto solutions to the multi-criteria optimization problem. The problem with this approach is the possibility of obtaining multiple solutions, some Pareto inferior to others. To cope with this, it is necessary to use a random search procedure, such as the one employed in the “Complex” method of Box (1965), that allows one to find the global optimum solution.

The “Complex” optimization method for non-linear functions with multiple criteria

This method was developed by Box (1965) and applied in an enhanced manner to multi-criteria problems by Manetsch (1985, 1986), in order to find the maximum or minimum of a non-linear multivariate function subject to non-linear inequality constraints. It can be represented by the following objective function:

Max f (X1, X2, X3,…, Xn)          (5.4)

subject to:

LLi≤Xi≤ULi for i = 1,2,3,…,n…,k          (5.5)

The implicit variables Xn+1,…,Xk are dependent functions of the independent explicit variables X1, X2, X3,…, Xn, and the superior and inferior restrictions LLi and ULi could be either constants or independent variable functions. The method involves a sequential search procedure that attempts to find global optimum solutions, starting from an initial group of points randomly distributed within the feasible region. Each group of data corresponds to a vertex of a geometrical figure or “Complex” generated in the n dimension searching space. Associated with each vertex is a function value (calculated from a simulation model). The procedure converges toward a global optimum (Manetsch, 1985).

This method has been applied in marine fisheries to contend with management problems in which the resulting dynamic of the technological and ecological interdependencies means that the consideration of multiple criteria in the regulation process is a highly complex task (Diaz de León & Seijo, 1992; Seijo et al., 1994c).

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