J.C. Seijo 87
The complexities of managing a marine fishery involving high exclusion costs, and high information and enforcement costs, are presented together with a set of mitigating strategies to deal with them. Designing intelligent management plans for marine fisheries require assessing fishery performance over time through the use of sustainability indicators; limit reference points (LRPs) and the corresponding control law to generate proactively management strategies when estimated risks are perceived as too high by the decision-maker. To account for natural variability and other sources of uncertainty, estimates of risks of exceeding LRPs are needed to re-evaluate periodically the fishery and establish new reference points and corresponding management strategies. This paper concentrates on this aspect of the precautionary management process through the use of a simple framework to deal with unsustainability factors affecting fishery performance over time. It presents a classification of bio-ecologic, economic/social and institutional indicators of critical unsustainability factors previously identified. The major steps used in the application of the Monte Carlo method to estimate the risks of exceeding limit reference points are introduced. The need for considering data collection, advanced modelling and risk analysis for the use of sustainability indicators and reference points within the ecosystem based fishery management approach (EBFM), is also discussed.
The complexities of managing a marine fisheries, involving a high exclusion cost good (sensu Schmid, 2004), and high information and enforcement costs are presented together with a set of mitigating strategies to deal with them. In addition, following the recognition that “unsustainability occurs in a fishery system when it is agreed that there is an unacceptable high risk that the fishery system is currently or will be in some predefined undesired state. The risk may be aggravated by natural variability. Desirable states, in terms of both human and ecosystem well-being, are defined by society and may change over time” (Swan and Gréboval, 2004:p.18). This paper concentrates in providing a simple framework for specifying sustainability indicators and corresponding reference points of fishery subsystems as an aid to decision-making of management efforts aiming at mitigating unsustainability of marine fisheries. It is furthered argued that to become useful for management purpose, efforts should be made to estimate current values of indicators and the corresponding probabilities of exceeding their limit reference points, i.e. the risks of exceeding pre-determined LRPs.
To facilitate this communication, factors affecting sustainability can be looked at by following the four components of the framework suggested by Charles (2001): (i) bio-ecological, (ii) social, (iii) economic, and (iv) institutional dimensions of fishery systems.
In order to estimate the risks of exceeding unacceptable levels in any of the above mentioned components in the paper a simple structure of sustainability dimension, indicators, reference points, and risks of exceeding them is proposed.
In the Bangkok and Mauritius meetings organized by FAO in 2002 and 2003, the unsustainability of fisheries was attributed to six main factors (Swan and Gréboval, 2004):
This paper concentrates on factors i), ii) and iii).
2. LACK OF GOVERNANCE STRUCTURES: HIGH INFORMATION, ENFORCEMENT AND EXCLUSION COSTS
In this section the major socio-economic factors affecting fishing capacity are discussed together with the difficulties of controlling access to domestic and international fleets. The current schemes of property right allocation are presented with a brief description of the corresponding strengths and weaknesses. The types of subsidies present in many fisheries today are discussed with respect to their impacts in fostering effort expansion over time and resource depletion. A set of factors mitigating the expansion of fishing capacity are also discussed.
To further understand management constraints, we now discuss the basic economic assumptions underlying the optimal allocation of natural resources, and the inherent characteristics of fisheries that prevent markets, under unrestricted access, from optimally allocating fishery resources. Although current literature invokes the allocation of property rights as a solution, even in fisheries where rights have been allocated, the un-sustainability syndrome tends to remain. This leads us to ask what conditions are not being met which are not allowing the market to optimally allocate fish resources once individual property rights have been established?
2.1 Some basic assumptions underlying sustainable allocation of fish resources
It is generally agreed that to ensure optimal allocation of natural resources, non-attenuated property rights should be in place. Those rights must be (Randall, 1981, Schmid, 1978; Seijo et al. 1998):
In fisheries, the basic assumptions of the neoclassic market model mentioned above are usually violated. As we saw earlier in this document, overexploitation, overcapacity, and unsustainability are a syndrome common to many important fisheries. Fishery resources in fact have inherent characteristics that distinguish them from other natural renewable resources and require further discussion in order to understand the importance of short and long-term exploitation patterns (Seijo et al. 1998).
2.2 Obstacles to property right allocation
The violation of the basic assumptions of exclusivity and low information and enforcement costs, are serious obstacles to effective property rights allocation. The inherently high exclusion and transaction costs characteristic of fish resources require us to look beyond the simple solution of providing for “proper allocation of individual rights”. Self-policing and questions related to numbers of fishers are discussed below as ways of mitigating these obstacles.
The allocation of resources between stakeholders is the problem area where progress is most urgently required, both nationally and internationally. Deliberate and unwitting free rider behaviour is discussed, defined as the participation on the harvest, without participation in the costs and constraints imposed by management of the stock. The roles of information on fish conservation and self-policing are presented as mitigating factors.
2.3 High exclusion costs in fisheries
An inherent characteristic of exploited fish stocks is the high cost of excluding unauthorised fishers from exploiting the resource, and enforcing regulatory compliance on those authorised to fish. The mobility and migratory nature of most fish resources, combined with high uncertainty as to stock magnitude, means that an individual fisher is unlikely to benefit from postponing capture of a fish with the expectation of taking it at a larger and more valuable size later, since others are likely to have caught it in the meantime; that is, unless all or most fishers also agree to abstain (Eckert, 1979). Consequently, each fisher tends to maintain a high rate of harvesting, and thus generates high exclusion costs to the other fishermen who tend to behave likewise.
Mitigating factors. Traditional approaches to avoiding high exclusion costs involve institutional structures such as: (i) resource privatisation through allocation of individual transferable quotas (ITQs); (ii) State intervention to regulate size and age composition of the catch, and the level of fishing effort, (iii) implementation of community-based management systems (Berkes, 1989) or (iv) mixed strategies based on a combination of the above schemes (Seijo, 1993; Castilla and Defeo 2001).
2.4 The social trap and free rider behaviour in fisheries
Without an agreement to limit catches, the main result of a single fisher’s reduced catch rate is to lower the extraction cost of other fishers without necessarily increasing his own benefits. Using Shelling’s (1978) terminology, this constitutes a social trap, because the micro-motives of an individual fisher in the short-run are not consistent with the macro-results he and other fishers desire in the long run. The short-run micro-motives consist of catching as many fish as possible in order to increase individual marginal benefits, while the long-run desired macro-results may involve achieving the maximum economic yield. Uncertainty as to future stock availability in the face of the unsustainability of resources we were discussing earlier, determines that long-run results are usually dominated by short-run marginal benefits. Allowing for temporal fluctuations in resource productivity and preferences of resource use, a sustainable yield from a fishery will be an attainable only when the number of fishers is limited, and they act in concert to implement some form of effort regulation. However, if the group is large, a fisher may be an unintentional free rider or non-contributing user. This type of individual usually occurs when there is no voluntary collective action by the majority of community members to prevent resource depletion, and also when uncertainty exists as to stock abundance (which is the usual case).
Mitigating factor. The size of the fisher community exploiting a resource is relevant to avoiding this social trap. When the group is small, exclusion costs are not necessarily lower, but the non-contributing user can be more easily identified (Olson, 1965; Schmid, 2004).
2.5 High transaction costs
Marine fisheries involve high transaction costs, which also diminish the efficiency of resource allocation over time. Transactions costs in most fisheries involve costs of information, enforcement or policing.
High Information costs. The complexity of fishery management is increased by the major uncertainties inherent in natural systems, as well as by a range of other biological, social, political and economic factors requiring a precautionary approach to fisheries management (Hilborn and Peterman, 1996; Defeo and Seijo, 1998). These increase the probability of non-contributing users emerging, and also deplete stocks and dissipate economic rent. Efficient fisheries management implies high information costs, but interdisciplinary research in biology, ecology, statistics and socio-economics is hampered by academic fragmentation.
Unfortunately, an overall increase in fishing intensity is not typically accompanied by a corresponding increase in scientific and fishery information. To the contrary, as Maximum Sustainable Yield conditions are approached, the need for more accurate and real time information increases, precisely at a time when system variance is increasing due to less regular recruitment and a higher probability of ecosystem change. Thus effort overshoot, increases in harvesting costs, and the elimination of economic rent from the fishery, are almost inevitable consequences of fishing near MSY conditions.
High Enforcement costs. Fisheries management involves high enforcement or policing costs if management schemes are implemented and property rights allocated and policed. For oceanic (and many shelf fisheries) the areas to be policed are extensive and conventional patrol vessel operations are ineffective and costly. Under these circumstances, a non-enforceable right becomes an empty right.
Mitigating factors. Some strategies for mitigating the effects of high exclusion costs and high information and enforcement costs are summarized in Table 1. Strategies are differentiated for varying degrees of resource mobility.
Table 1. Some strategies for mitigating the effects of high exclusion, information and enforcement costs in fisheries targeting at stocks with different degrees of mobility
(Caddy and Seijo, in press)
|Stock mobility||Exclusion costs||Information costs||Enforcement costs|
|Sedentary or low mobility||•||Establish Individual transferable grounds (ITGs,) or leases (Seijo, 1993).||Costs of stock assessment and bio-economic analysis are shared between those deriving resource rent and the government.||Emphasis on self policing|
|Community managed MCS|
|•||Assess the effectiveness of using individual transferable quotas (ITQs)||Co-management with Government|
|Mobile (Transboundary or shared stocks)||Limited entry agreed bilaterally or multilaterally with allocation of a shared TACs||Bilateral/multilateral cooperation between parties and standardized data collection and stock assessment is essential, and coordinated MCS functions||Bilateral/multilateral cooperation in management and enforcement of common or harmonized regulations|
|Highly migratory (High seas)||•||Harvest quotas are established by the Commission||Data collection and stock assessment are organized by the Commission. Use of satellite tracking schemes allows location of vessel fishing areas. Remote telemetry of fishing operations allows for more efficient MCS operations||Resource Commission members share enforcement costs proportional to annual harvest by individual countries|
|•||Members of the Commission establish rules for entry to the fishery, and arranges negotiations on resource allocations|
In addition to the above, market distortions are present in most world fisheries, and may foster the overcapacity problem. Principal among these distortions worth mentioning is the presence of subsidies.
2.6 Imbalances caused by subsidies
In addition to the factors mentioned above which underlie the overcapacity problem, there is a growing awareness by governments and industry of the negative influence of subsidies on international trade, the environment and sustainable development, (Milazzo 2000).
Among subsidies fostering increases in fishing capacity the following seem to be critical (Seijo 2001):
The impact of subsidies on sustainability acts mostly through the dynamics of fleet capacity and fishing effort and it is therefore fundamentally important to estimate impacts on cost-reduction and vessel profit margins, recognizing that at the margin, a subsidy allows profitable operation at lower stock levels than without subsidies.
Mitigating strategies: The fact that subsidies artificially inflate profits of artisanal and industrial fleets at low stock sizes has its serious conservation implications, and efforts should be made to eliminate them. Two scenarios occur: (i) vessel owners were granted subsidies at the start of the fishery to promote development, and (ii) subsidies arrived later to “alleviate” short-run crises in fisheries sectors. The rationale for the first scenario has now ceased, since close to 70 percent of global stocks are fully or overexploited (FAO, 2000). If the second scenario applies, government is effectively perpetuating a social trap by artificially encouraging capacity to remain in the fishery, even though harvest returns cannot pay for variable costs of fishing, such as fuel, ice, etc. The elimination of subsidies would, to many fisheries, result in negative net revenues leading, in some cases, to substantial reductions in short-run effort for those vessels which are not covering their trip or daily variable costs.
2.7 Stock fluctuations due to natural causes
Concerning the last identified factor of unsustainability, it should be mentioned that still it is not always taken into account that apart from fishing, stocks also fluctuate in the short and long run due to natural causes. As pointed out in Caddy and Seijo (In press), for pelagic resources, major stock fluctuations occurred even prior to human exploitation (Soutar and Isaacs 1974). These fluctuations have been best documented in relation to the ENSO climatic phenomenon, especially as it affects production of small pelagics in the eastern Pacific (e.g. Lluch-Belda et al. 1989) but occurs for other resources, and elsewhere (Cushing 1982). Similar climatic forcing factors have been affecting marine production systems on the global level (Kawasaki 1992, Klyashtorin 2001), and long-term fluctuations will be reinforced by climate change (Kelly 1983). Thus, although ‘decadal’periodicities are frequently mentioned in the fisheries literature (e.g. Zwanenberg et al. 2002), Klystron (2001) suggests that natural cycles in productivity of around 50–60 years duration are likely to be dominant.
Coastal fishery resources are also vulnerable to other humanactivities that may affect critical habitats and/or biological processes (e.g. De Leila Moreno et al. 2000). In fact, the role of environmental change has become more evident in recent years as fisheries data series of all but the longest-established fisheries exceed a half century in duration, but our ability to discriminate between natural environmental changes, the effects of fishing, and other human activities seems to remain poor.
Considering the above mentioned human and natural factors of stock variability, proactive fisheries management should then take into account the probabilities of exceeding biologic, economic and social limit reference points with alternative management strategies under consideration and decide accordingly.
2.8 Proactive management: indicators, reference points and risks
The development of management plans for marine living resources require systematic integration of aspects of the resource biology and ecology with the economic and social factors that determine fishers' behaviour over time (Anderson, 1981; Seiko et al. 1998; Cochrane, 2002).
An indicator, as suggested by García (1996b), “is a variable, a pointer, an index of a complex phenomena. Its fluctuations reveal the variations in components of the ecosystem, the resource or the sector. When consider together, the position and trend of the indicator in relation to the criteria indicate the present state and dynamics of the system”. Fisheries indicators are taken here to be variables derived from monitoring a fishery, which can assume discrete values conveying information believed to be relevant to the proper management of exploitation of the resource. Reference points are considered to be discrete values of these indicators, which have been agreed to represent situations calling for pre-negotiated management action. A set of fishery indicators and the reference points they can assume should be assembled into a control law which forms a feedback loop between incoming information on the fishery and the corresponding management response.
Fishery indicators should be able to provide information for assessing the biologic, economic and social performance of the fishery, and as an element of the management plan they should become an input for establishing, over time, new reference points and corresponding management strategies to achieve them. Indicators can be quite simple in conception, and can be based on semi-quantitative or even qualitative information. They may need to be tuned in the light of events. They have to be integrated fully into the management system. They should be sensitive indicators with the capacity of measuring dynamic change.
It should be pointed out that earlier reference points proposed by scientists have been used primarily as Target Reference Points (TRPs), but owing to problems caused by overshooting TRPs, there has been a perceived need for reference points that help to avoid situations that are dangerous to the resource, and hence, to fishery sustainability. These have been referred to as Limit Reference Points (LRPs), and represent fishery sustainability threshold reference points (See Caddy and Mahon (1995), Die and Caddy (1997) and Seijo and Caddy (2000)).
The use of limit reference points as constraints for resource administration represents an important step in the management process. Indicators for fishery performance are an integral part of fisheries management plans providing dynamic signs of the relative position of such indicators with respect to the predetermined reference points. It has also been recognised that wise use of fish resources over time should incorporate the inherent risk and uncertainty of fishery systems (García, 1996a; Hilborn and Peterman 1996; FAO 1996).
Using the level, change and structure framework proposed by García (1996a) and Seijo and Caddy (2000), a set of sustainability indicators are suggested in Table 2.
Table 2. Level, change, and structure indicators of fishery sustainability dimensions. Adapted from Seijo and Caddy (2000)
|Sustainability dimension||Level indicators||Change indicators||Structure indicators|
Current level of Zt
|Recruitment trends (inter-annual)|
Trend in Bt/B∞ratio
Trends in Bs,t/Bt ratio
Changes in Zt
Changes in biodiversity index
|Spatial distribution of recruitment, biomass, spawners and juveniles. Age and space specific total mortality|
Spatial distribution of community structure
|Economic and social|
|Current fishery investment rate|
Catchability & selectivity
| Trends in fishery investment (vessel, engine, fishing gear, navigation technology)|
Changes in selectivity
| Age composition of the fleets|
Fleet specific fishing power
|Fishing days||Trends in allocation of fishing days to the target species||Fleet specific effort indices of spatial effort concentration|
|Yield||Tons of harvest||Yield trends||Size composition of harvest|
|Unit cost of effort Variable costs over catch rate|
Transfer costs over total costs
|Changes in costs of fishing|
Changes in transfer costs from port to fishing grounds
|Fleet specific unit cost of effort.|
|Revenues per unit of effort.||Changes in real prices of species and sizes.||Fleet specific revenues per unit of effort.|
|Rent per unit of effort.MEY|
Quasi-rent of the variable costs.
|Present value of rent with different prices of time||Fishery rent distribution between fleets.|
|Direct and indirect fishery employment||Trends in direct and indirect fishery employment.||Spatial distribution of employment in coastal area.|
|Percentage of harvest that remains in coastal area.|
Per capita non-export harvest.
|Trends in harvest remaining in coastal area.|
Trends in per capita consumption of seafood.
|Geographic distribution of seafood in coastal communities|
Coastal-inland distribution of per capita seafood consumption
|Current rate of Infringements.|
Annual seizures and prosecutions.
Number of active fishermen.
Rate of community member exclusion.
| Trends of compliance Trends of self-policing|
Rate of increase in the number of fishermen
Rate of increase in the number of free riders i.e. non-contributing users.
|Distribution of costs and benefits of fishery management|
Changes in resource use rules within the fishermen community
Once the fishery sustainability indicators have been established with their corresponding limit reference points, the next step involves estimating the probabilities of exceeding these LRPs.
2.9 Monte Carlo analysis: risks of exceeding limit reference points
Given a probability density function with known parameters, Monte Carlo analysis allows introducing the uncertainty associated with natural variations and imperfect knowledge about the fishery system being assessed through sustainability indicators. The process consists of an iterative calculation of the performance variables, where in each trial a new value for the unknown parameter is generated with the specified probability density function. Naturally, we will get as many outputs as trials to be intended. The major steps used in the application of the Monte Carlo method to estimate the risks of exceeding reference points are the following:
This last step of the process is in essence the estimation of the risk of exceeding the specified limit reference point. The results are obtained for as many LRPs as specified in the Monte Carlo analysis.
In Table 3, a simple illustration of bio-ecologic, economic and social sustainability indicators and limit reference points are presented with the corresponding risks of exceeding LRPs under different management decisions. It should be pointed out that target reference points (TRPs) could also be included if considered appropriate. Nevertheless, for the purpose of this paper, namely the estimation of risks of exceeding limit reference points in fishery systems, only the former was considered.
In this simple illustration the fishery decision maker is able to identify the possible changes in risks of exceeding limit reference points of sustainability indicators in an artisanal fishery with two alternative number of boat licences to be authorized for the fishery: D1 = 610, and D2 = 525.
By choosing D2, the risks of exceeding the LRPs of the biologic and economic sustainability indicators are substantially reduced. The risk of falling bellow the LRP for fishery direct employment increases from 14 percent to 18 percent .Nevertheless, it should be pointed out that the decision-maker choice will also be a function of his/her attitude towards risk (Shotton, 1995; Shotton and Francis, 1997).
Each fishery should identify the bio-ecologic, socio-economic, and institutional indicators that are critical for the species being produced and the ecosystem in which they live. The desired (TRPs) and undesired (LRPs) discrete values of these indicators should therefore be defined by decision –makers with the aid of specialists in the specific marine species.
Table 3. A simple illustration of fishery sustainability indicators and corresponding estimation of risks of exceeding limit reference points with alternative management strategies.
|Indicators of Fishery dimension||Limit reference point (LRP)||Risk of exceeding LRP with decision D1= 610 boats||Risk of exceeding LRP with decision D2= 525 boats|
|Biologic||LRP = 0.3|
|Current over initial biomass of target species: Bt/B∞|
|Economic||LRP = 2500(US$/boat/year)|
|Profits per unit of effort: π/f|
|Social||LRP = 2000 (fishers)|
|Direct fishery employment|
2.10 Some suggestions for the use of indicators and limit reference points to aid management
Given the inherent uncertainty and non-linearity of the fishery system, direct application of control theory using one or two indicators and their appropriate reference points, is unlikely to be fully effective outside developed country situations. Indicators used for fisheries management should be multiple, robust and easily understood, ideally with an adequate dynamic range and reproducibility (Seijo and Caddy, 2000). Some suggestions for the use of indicators and reference points are presented as follows:
They can be simple in conception, and perhaps assembled into composite indices through use of questionnaires.
They should have a degree of redundancy but, where possible, not all depend on the same basic information source.
A 'traffic light' approach has been proposed by Caddy (1998) whereby a series of simple indicators are used in combination with reference points defined to 'fire' at roughly the same level of exploitation or population risk. The number of indicators moving from 'green' (safe) to ‘red’(dangerous) conditions can be used to trigger management responses aimed at reducing effort/catches in a manner progressive with the degree of perceived risk to the system. Such a system is easy to comprehend, provides a basis for a control system if it can be agreed to by stake holders, and is easy for them to perceive and use in the same manner as forest fire warning indicators.
They should where possible reflect events in all sectors, ecological to socio-economic judgements should be made as to the critical (TRP and LRP) values for such indicators where the system changes from Red to Green or vice versa, in the traffic-light analogy.
These LRP/TRP values will need tuning in the light of experience so that to the extent possible, they respond at roughly similar stock levels, but may be sensitive to different types of risk.
These indicators and their LRPs will need to be built into a management control system so that a response to alternative management strategies.
There may have to be three sets of composite indices to facilitate interpretation and management response: (i) a basic set of indices that measure the state of the resource, (ii) a set of indices that reflect socio-economic changes, which is, however, not allowed to override the resource system when resource indices show a level of high risk, and finally, (iii) environmental indicators that represents the effect of natural or anthropogenic changes on the ecosystem affecting the resource and its users.
Unfavourable changes in the resource biology and ecology, which is expressed in a composite index of resource health, can be modulated upwards or downwards by the environmental module within precautionary limits. While the economic and social subsystem(e.g. yield, rent, employment, contribution to food security, etc.) will only be allowed to modify the resource module towards higher exploitation if the biomass, spawning stock size or physical/biotic environment is suitable for a temporarily more risk-prone management strategy. Feedback mechanisms are expected to occur between the resource module and socio-economic module where fishers, as resource users, react to resource abundance in space and time. We believe all three subsystems can be accommodated into a more formal system such as those mentioned above.
2.11 Ecosystem dimension of fishery indicators: some recent suggestions
In order for the fishery indicators to be meaningful they should explicitly account for the ecosystem in which they occur. The concept of ecosystem based fishery management (EBFM) has the overall objective of sustaining healthy marine ecosystems and the fisheries they support and therefore calls for reversing the order of management priorities starting from the ecosystem and then moving to target species (Sinclair and Valdimarsson, 2003). In their recent contribution in Science (Pikitch et al., 2004) suggest that EBFM “…should (i) avoid degradation of ecosystems, as measured by indicators of environmental quality and system status; (ii) minimize the risk of irreversible change to natural assemblages of species and ecosystem processes; (iii) obtain and maintain long-term socio-economic benefits without compromising the ecosystem, and (iv) generate knowledge of ecosystem processes sufficient to understand the likely consequences of human actions”. Two of the major recommendations provided by the 14 distinguished authors of the mentioned above paper, indicate that:
“…we need to develop community and system level standards, reference points and control rules similar to single species decision criteria”.
“New analytical models and management tools will be needed as well. Multispecies and eco-trophic models must be refined and expanded to better account for system-level uncertainties, to derive system-level reference points, and to evaluate the ecosystem-level consequences of proposed EBFM actions”.
Before specifying ecosystem indicators and reference points, as pointed out by Sainsbury and Sumalia (2003), there are two basic questions to answer: (i) Is there a need for explicit reference points for the ecosystem, such as food web dynamics, ecological community structure and biodiversity, or are species-based reference points sufficient? (ii) If ecosystem reference points are needed, should they be based on properties of the undisturbed coastal ecosystem? There seems to be an additional question: How to proceed in the absence of baseline studies of early stages of coastal development? Again, the use of advanced dynamic models and techniques for their parameter estimation in data limited situations seem to be a future research priority in this field. Because of the inherent uncertainty of the “original status” of ecosystem habitat and community structure, these modelling efforts should be stochastic in nature. The potential and associated complexities of conducting risk analysis for ecosystem base management are discussed by Butterworth and Punt (2003).
2.12 Spatial dimension in fisheries indicators
Fishers respond spatially with different degrees of correlation to resource distribution when allocating over space and time their fishing intensity. Different fleets (e.g. artisanal and industrial) have different friction of distance and consequently their response to the spatially generated quasi-rent in previous fishing trips could result in non-proportional allocation of effort in subsequent fishing days (Seijo et al. 1994). Changes in transfer costs from different ports of origin to alternative fishing grounds are an important spatial indicator of changes in resource abundance in space and time. This aspect should be accounted for when assessing fisheries targeting short-lived species where seasonality in the spatial distribution of resource and fishing intensity are relevant. The same consideration holds when targeting sedentary resources with heterogeneous patchy distribution over space. In this respect, bio-economic indicators should be disaggregated over space and time to provide meaningful information to decision-makers (Caddy & Seijo, 1998; Seijo et al., 2004). Concerning ecosystem based fishery management, Pikitch et al. (2004), suggest that advanced models for EBFM should incorporate spatial structure and dynamic environmental processes, to properly account for changes in habitat and ecosystem function in the context dynamic fluctuations.
Anderson, L.G. 1981. Economic Analysis of Fisheries Management Plans. Ann Arbor Science Publishers, Michigan.
Berkes, F. (1989). Common Property Resources : Ecology and Community-Based Sustainable Development. Belhaven Press, London.
Butterworth, D.S. & Punt, A.E. 2003. The role of harvest control laws, risk and uncertainty and the precautionary approach in ecosystem-based management. In: Sinclair & Valdimarsson (eds.). Responsible Fisheries in The Marine Ecosystem. FAO and CABI Publishing. 426p.
Caddy, J.F. 1998. Deciding on precautionary management measures for stock and appropriate limit reference points (LRPs) as a basis for a multi-LRP harvest law. Paper presented in NAFO Scientific Council Meeting, March 1998.
Caddy, J.C. & Seijo, J.C. In press. This is more difficult than we thought! –the responsibility of scientists, managers and stakeholders to mitigate the unsustainability of marine fisheries. The Royal Society, London, UK. 34p.
Caddy, J.F. & Seijo, J.C. 2002. Reproductive contributions forgone with harvesting: a conceptual framework. Fisheries Research, Elsevier Science, 1378:1–14.
Caddy, J.F. & Seijo, J.C. 1998. Application of a spatial model to explore rotating harvest strategies for sedentary species. Can. Spec. Publ. Fish. Aquat. Sci. 125: pp. 359–365.
Caddy, J.F., & Mahon, R. 1995. Reference points for fisheries management. FAO Fish.eries Technical . Paper. No. 347. Rome, FAO. 83p.
Castilla, J. C. & Defeo, O. (2001). Latin American shellfisheries : emphasis on co-management and experimental practices. Rev. Fish Biol. Fish. 11, pp. 1–30.
Charles A. 2001. Sustainable fishery systems. Blackwell Science.
Cochrane, K. 2003. A fishery manager´s guidebook: management measures and their application. FAO Fisheries Technical Paper No. 424. Rome, FAO. 231p.
Cushing, D.H. 1982. Climate and fisheries. Academic Press.
Defeo, O. & Seijo, J.C. 1998. Yield-mortality models: a precautionary bio-economic approach. Fisheries Research, 840: pp. 1–10.
De Leiva Moreno, J.I.; Agostini, V.N.; Caddy, J.F. & Carocci, F. 2000. Is the pelagic-demersal ratio from fisheries landings a useful proxy for nutrient availability? A preliminary data exploration for the semi-enclosed seas around Europe. ICES Journal Mar. Sci. 57: pp. 1091–1102.
Die, D.J. & Caddy, J.F. 1997. Sustainable yield indicators from biomass: are there appropriate reference points for use in trpocal fisheries? Fish. Res., 32: pp. 69–79.
Eckerd, R. 1979. The Enclosure of Ocean resources. Hoover Institution Press, Stanford, California ckert, 1979.
FAO. 2002. The state of world fisheries and aquaculture. Rome, FAO. 150p.
FAO. 1996. Precautionary approach to fisheries. FAO Fisheries Technical Paper. 350(1): pp. 1–47.
García, S. M. 1996a. The precautionary approach to fisheries and its implication to fisheries research, technology and management: an updated review. In: FAO (1996): precautionary approach to fisheries. Part 2. Scientific papers. FAO Fisheries Technical . Paper No. 350/2): pp. 1/76.
García, S. M. 1996b. Indicators for sustainable development in fisheries. Paper presented at the
2nd World Fisheries Congress. Workshop on Fisheries Sustainability Indicators. Brisbane, Australia, August 1996. 28p.
Hilborn, R. & Peterman, R. 1996. The development of scientific advice with incomplete information in the context of the precautionary approach. In: Precautionary approach to fisheries. Part 2. Scientific papers. FAO Fisheries Tecnical. Paper No. . (350/2): pp. 77–101.
Kawasaki, T. 1992. Mechanisms governing fluctuations in pelagic fish populations. In Paine, A. et al. (ed) Benguela trophic functioning.S. Afr. J. Mar. Sci. 12: pp. 321–333.
Kelly, P.M. (1983). Climatic change: past lessons and future prospects. In FAO Fisheries Report No. 291, (FIRM/R291), Vol 3, pp. 557–1224.
Klyashtorin, L.B. (2001). Climate change and long-term fluctuations of commercial catches. FAO Technical Paper No. 410. rome, FAO.86p.
Lluch-Belda, D.; Crawford, R.; Kawasaki, T.; MacCall, A.; Parrish, R.; Schwartzlose, R.; Smith, P. (1989). World-wide fluctuations of sardine and anchovy stock. The regime problem. S. Afr. J. Mar. Sci. 8: pp. 195–205.
Milazzo, M. (2000). The international debate on fish subsidies. Paper presented at a conference at the University of Akureyri on the competitiveness of the Icelandic fishing industry. Iceland, April 6–7, 2000. 52p.
Olson, M. 1965. The Logic of Collective Action. Cambridge, Mass., Harvard University Press.
Pickitch, E.K; Santora, C.; Babcock, E.A.; Bakun; A.; Bonfil, R.; Conover, D.O.; Dayton, P.; Doukakis, P.; Fluharty, D.; Heneman, B.; Houde, E.D.; Link, J.; Livingston, P.A.; Mangel, M.; McAllister, M.K.; Pope, J.; Sainsbury, K.J. 2004. Ecosystem-based fishery management. Science, 305: pp. 346–347.
Randall, A. (1981). Resource Economics: An Economic Approach to Natural resources and Environmental Policy. Grid Publishing Inc. Columbus, Ohio.
Sainsbury, K. & Sumalia, U.R. 2003. Incorporating ecosystem objectives into managemnt of sustainable marine fisheries, including ‘best practice’reference points and use of marine protected areas. In: Sinclair & Valdimarsson (eds.). Responsible Fisheries in The Marine Ecosystem. FAO and CABI Publishing. 426p.
Schmid, A.A. Conflict and cooperation: institutional and behavioural economics. Blackwell Publishing, U.K.
Seijo, J.C.; Pérez E. & Caddy, J.F. 2004. A simple approach for dealing with dynamics and uncertainty in fisheries with heterogeneous resource and effort distribution. Marine and Freshwater Research (CSIRO Publishing) 55: pp. 249–256.
Seijo, J.C.; Defeo, O. & Salas, S. 1998. Fisheries bioeconomics: theory, modelling and management. FAO Fisheries Technical Paper No. 368. Rome, FAO. 108p.
Seijo, J.C. & Caddy, J.F. (2000).Uncertainty in bio-economic reference points and indicators of marine fisheries. Marine and Freshwater Research, 51: pp. 477–83.
Seijo, J.C. 2001. Estimating fisheries subsidies and their impact on sustainability of fish resources: dealing with dynamics, risk and uncertainty. Paper presented in the Expert Consultation on Economic Incentives and Responsible Fisheries, Rome Italy, Nov.28–Dec.1.,2000. FAO: FI:EIRF, 4: 21p.
Seijo, J.C.; Caddy, J.F. & Euán, J. 1994. SPATIAL: space-time dynamics in marine fisheries, a bio-economic software package for sedentary species. FAO Comp. Inf. Ser. Fish. (6): 116p.
Seijo, J.C.; Defeo, O. & Salas, S. 1998. Fisheries bio-economics: theory, modelling and management. FAO Fisheries. Technical Paper No. 368. Rome, FAO. 108p.
Seijo, J.C. 1993. Individual transferable grounds in a community managed artisanal fishery. Mar. Res. Econ. 8: pp.78–81.
Shelling, T.C. 1978. Micromotives and macrobehaviour. W.W. Norton and Company, New York.
Shotton, R. 1995. Attitudes to risk relative to decisions on levels of fish harvest. In: Precautionary approach to fisheries. Part 2. Scientific papers. FAO Fisheries Technical Paper No. (350/2).
Shotton, R. & Francis, R.I.C.C. 1997. "Risk" in fisheries management: a review. Can. J. Fish. Aquat. Sci. 54: pp. 1699–1715.
Sinclair, M & Valdimarsson, G. 2003. Responsible fisheries in the marine ecosystem. FAO and CABI Publishing. 426p.
Soutar, A. & Isaacs, J.D. 1974. Abundance of pelagic fish during the 19thand 20thcenturies as recorded in anaerobic sediment off the Californias. Fish. Bull. (U.S.) 72, pp 257–273.
Swan, J. & Gréboval, D. 2004. Report and Documentation of the International Workshop on the Implementation of International Fisheries Instruments and Factors of Unsustainability and Overexploitation in Fisheries. Mauritius, 3–7 February2003. FAO Fisheries Report No. 700. Rome, FAO. 305p.
Zwanenberg, K.C.T.; Bowen, D.; Bundy, A.; Drinkwater, K.; Frank, K.; O’Boyle, R.; Sameoto, D. & Sinclair, M. (2002). Decadal changes in the Scotian Shelf Large Marine Ecosystem. In: Sherman, K. & H.R. Skjoldal (eds). Large Marine Ecosystems of the North Atlantic. Elsevier Science, B.V., pp. 105–150.
87The views expressed in this paper are solely those of the author, J.C. Seijo, Universidad Marista de Mérida, Periférico Norte Tablaje 13941 Carretera Mérida-Progreso, Mérida 97300, Yucatán, México, firstname.lastname@example.org.
88Keywords : Precautionary management, unsustainability factors in fisheries, limit and target reference points, bio-ecologic, socio-economic, and institutional indicators, risk, uncertainty.