The largely F-based reference points discussed in this paper reflect almost a century of development of the ideas pertaining to the dynamics and management of fishery resources. They are now technically complex (in relation to the skills of most managers), require considerable quantities of data, usually collected systematically over many years, are fraught with uncertainty, and need good judgement to apply. Due to their cost of application, they are probably unavailable as tools for managers of most small stocks, and outside the reach of managers in most developing countries, due to their technical complexity. Furthermore, even in those areas where there is abundant data, and the best technical expertise, they have not always provided an adequate basis for sustainable harvesting of fishery resources, although this may be largely because, in the final analysis, strict rules for their application have not been followed.
There are many sources of uncertainty in the estimation and application of references points in fisheries management. The estimation and communication of this uncertainty is increasingly a focus in stock assessment. The methods used in developed countries are usually complex and require extensive data. Given the wide variety of sources of uncertainty, it appears unlikely that all factors which may contribute to the probability of an undesirable event can be accounted for in estimating its probability.
For those stocks where the data and expertise are available, and where the value of the resource can support the associated costs, the current trend should lead to significant improvements in management. However, for the thousands of small/and little studied stocks, mostly in the developing world, these methods will not be applicable in the foreseeable future, and it will be necessary to take a conservative or precautionary approach in a situation of poor stock data in order to account for uncertainty and mitigate risk.
In practical terms, this will mean that many users and managers must accept that they will not have good formal estimates of risk in the near future. Consequently, in order to manage sustainably and responsibly they will have to use the best available information to define, albeit arbitrarily, an acceptable level of risk, and to agree upon often empirical target and limit reference points consistent with the reduction of risk to acceptable levels.
The common perception has been that the failure of management has been due to errors and uncertainties in models: a failure that can be remedied by more investment in technical advice. If this is true, it poses major problems for managing small resources, and for management bodies with limited funds and trained manpower. We may question, however, if the lack of technical advice is the real problem. The lack of inadequacy of the institutional framework for implementation may be as significant a contributory factor in the failure of fisheries management as it appears to be in the case of fisheries development (O'Boyle 1993, Bailey and Jentoft 1990).
What we are proposing here is that within the framework of management, some specific attempts be made to set limits to exploitation, and arrange the flow of advice and decision-making in such a way that ‘precaution’ is the key word for management action, and the setting of practical limits to exploitation, management actions should be based on information, not be decoupled from the fishery it is intended to serve.
There appears to be a variety of straightforward reference points which could be adopted in a participatory manner as conventions, were which might serve to limit exploitation to population sizes which can support sustainable yields. Many of these are based, albeit loosely, on the models which are used to derive the Reference Points in Table 4. However, the emphasis in applying them is different; it is on establishing mechanisms by which management can be implemented almost automatically, on the basis of prior agreement, once there is evidence that a limiting resource condition is in danger of arising.
From the preceding review, it is evident that fishery management reference points are ultimately set by convention. As such, where data and expertise are available and cost effective, they may be technically based, and may be considered in the context of a formal evaluation of risk and uncertainty. However, they may also be based on intuition, traditional knowledge, or just plain common sense. They must be responsible, and where necessary precautionary. Regardless of their basis, to be successfully applied, they must have a public justification and be agreed upon in advance by the participants in the fishery.
|“WHATEVER APPROACH IS ADOPTED, THERE SHOULD BE AVAILABLE TO THE PUBLIC CLEAR AND AGREED LONG-TERM OBJECTIVES AGAINST WHICH PERFORMANCE CAN BE ASSESSED IN TERMS OF WHAT THE BROADER COMMUNITY EXPECTS FROM ITS FISHERIES MANAGERS”|
To some extent, the perception that fishery management must be based entirely on technically derived reference points has provided decision makers with an excuse to avoid difficult decisions, and has excluded knowledgeable non-technical inputs to the management process. For fisheries management to move beyond its current impasse in industrialised fishing regions, and to make its rightful contribution to economic enfranchisement in developing countries, there must be a new focus on the fishery management process, at national and international levels, with the technical inputs placed correctly in a supporting role, and the flow of advice consultation, and decision-making clearly identified.
The process of applying Target and Limit Reference Points requires a formal institutional mechanism in which they are discussed, agreed upon, and which their implementation overseen. Implementation, in turn must be formally vested in the appropriate national and international agency which can apply the management tools and monitoring systems required to reach a TRP, and ensure that pre-established LRPs and their unfortunate consequences, are avoided.
The required change in advisory and decision-making mechanisms for fisheries management must not just focus on desirable targets, but must meet requirements for action based on Limit Reference Points and precautionary principles. The suggestions provided here are not intended to be peremptory, but simply to promote debate on possible structures and to bring the problem more fully into focus. The problem does not just fall within the ambit of fisheries science under classical ‘biological and bioeconomic approaches’ (Anderson 1987): a proper evaluation of risk and decision-making under a precautionary approach, cannot avoid assessing the efficiency of the management system as a whole.
Absolute dominance in decision making by a non-technical senior committee is not necessarily compatible with precautionary management especially when the resource is in danger. A system of checks and balances involving two groups with different membership, and specific, complementary skills and powers offers the possibility of a flexible precautionary response to unplanned technological changes and other ‘surprises’ that fisheries are subject to. It also offers the possibility of placing fisheries within the broader context of coastal zone management. The dominance might even be allowed to shift between the two groups depending on the state of the stock.
It thus seems advisable for the State(s) concerned to provide for not only a body responsible for routine assessment and management, but also a review body whose role is specifically to limit or override management measures or errors that may lead to resource collapse and/or associated negative effects on national ecosystems. This function could be incorporated within national structures which are already in place for reviewing the health of aquatic ecosystems and their living resources. Such a system of mutual constraints by government bodies should help ensure that long-term natural resource constraints are not subordinate to short-term economic expediency.
|% of Fmay||10||20||30||40||50||60||70||80||90|
|% of MSY||19||36||51||64||75||84||91||96||99|
|% of CPUE at MSY (+ ve values)||190||180||170||160||150||140||130||120||110|
|AGE CLASS||MATURITY||SET I||SET II|
|F||Quota (t)||F||Quota (t)|
|Survival to maturity at Age 5||13.5%||13.5%|
|RP||THEORETICAL BASIS||DATA NEEDS||ADVANTAGES||DISADVANTAGES||USE AS:||REFERENCE|
|Fmcy||Production model||Annual series for Y & calibrated f for all removals||Estimates and historical Y,f series often available||High danger of overfishing as TRP||N||Y Y||N||Hilborn & Walters (1992)|
|Fmcy||Simulation from Annual recruitment series||Popn. parameters and probability distribution of annual recruitment||In theory, allows constant low quota||Data-intensive (info on recruitment variability)||Y||N||Y||Sissenwine(1978)|
|2/3Fmcy||Production model||A production model is assumed fitted||Simply calculated if production model exists||Empirical;needs historical data (Y;f/F)||Y||N |
|Fmax||Y/R calculations||Y/R model fitted||Simply calculated||Needs growth/mortality info||N||Y||N||Beverton & Holt (1957)|
|F0.1||Y/R calculation and current state of popn.||Popn.parameters (M size at age,size at first recruitment||well studied:simple to calculate from popn. parameters||Varies with fishing strategy: sensitive to recruitment level||Y||N||Y |
|Gulland & Boerema (1973)|
|Zmbp||Production model||Annual data series of standard catch rate and Z||Incorporates predation; requires simple historical data on CPUE; Z||In present form assumes Schaefer model||Y||N||Y |
|Caddy and Csirke (1983)|
|Z°||Simulate overall Z and mean size caught||Popn.parameters;mean death rate/size in popn. & catch||Simply calculated from basic parameters||Needs unbiased data on size frequency of catch||N||Y||N||Die & Caddy (in press)|
|Fmcr||Theoretically attractive if stock/recruit data exist||Both best Y and F will vary annually||Y||N||Y|
|Flow||Estimate F giving 90% of years with stock replacement||Assumes data for fitting stock recruitment (usually from cohort analysis)||Reflects past probability of recruitment||Needs historical data on stock/recruitment||Y |
|N||Y||ICES(1984); Jakobsen (1992)|
|Fmde||Estimate F giving 50% replacement||Assumes data for fitting stock recruitment (usually from cohort analysis)||Reflects past probability of recruitment||Needs historical data on stock/recruitment||Y||N||N||ICES (1984); Jakobsen (1992)|
|Fhigh||Estimate F giving 10% replacement||Assumes data for fitting stock recruitment (usually from cohort analysis)||Reflects past probability of recruitment||Needs historical data on stock/recruitment||N||Y||N||ICES (1984); Jakobsen (1992)|
|F% SPR||Analytic model of Biomass/recruit||Popn. parameters and maturity-at-age data||Simple to calculate and flexible (depends on O/O)||Sensitive to life-history parameters, must be generalised cautiously||Y||Y||Y||e.g.Clark (1991)|
|F>=M||Empirical (for top predators)||M and sustainable Fs for similar resources||Top predators Low data needs (estimate of M)||M often guessed. An empirical approach||Y||N||N||Fisheries literature|
|FM||As above (for small pelagics)||M and sustainable Fs for similar resources||Small pelagics. Low data needs (estimate of M)||M often guessed.An empirical approach||Y||N||N||Patterson (1992)|
|Fmey||Econometric modelling||Historical data on Yield/effort/cost and earnings||Can use production model fit plus cost/revenue data||Hard to define for multiple fleets & varying economic systems/indicators||Y||N||N||Clarke (1976) Panayotou (1988) Gordon (1954)|
|VARIABLE||DATA SOURCE||CV VALUES (ROUGH) RANGE)||REMARKS|
|Annual catch||Commercial statistics||> 10%||Significant bias (discards/misreporting)|
|Commercial Catch rate||"||around 10%||" "|
|Catch-at-age||"||around 10%||Subject to ageing errors|
|Survey for biomass||trawling||35–40%||Improves with repetition (more stations sampled)|
|" "||acoustic (small pelagic fish)||25–35%||" " "|
|Fishing mortality rate (F)||cohort analysis etc.||10–30%|
|Natural mortality rate (M)||catch curves etc.||(Usually indefinite)||Most assessments employ values developed for other stocks|
|* There is an 85% chance that the variable lies within 1 standard deviation of its mean value. Thus,if the CV = 30%, and the mean is 100 t, there is an 85% chance that the mean lies between 70and 130t.|
|OBJECTIVE AND DEPENDENCE OF RECRUITMENT ON STOCK SIZE||OPTIMUM F||CATCH (t)|
|Maximum physical yield|
- Moderately density-dependent
- Strongly density dependent
|Maximum Economic Yield (for given costs and prices)|
- Moderately density-dependent
- Strongly density dependent
|Habitat||a rate of destruction of habitat ≤ rate of regeneration|
|Biodiversity||a rate of harvesting allowing sustainable reproduction of all (including by catch) species|
|Associated prey species||stock size expressed as (e.g. 70%)virgin biomass||stock size expressed as (e.g. 50%) of virgin biomass|
|Bycatch sp.||e.g.less than 5% of annual catch||less than 10% of annual catch|
|Size of target sp. Mean L||> size of 1st maturity plus 10%||> size at 1st maturity|
|F of target sp.||Fnow < 0.3||Fnow = Fmsy = 0.6|
|Catch rates of target sp.||≥ 60% of catch rates from virgin stock||≥ 40% of catch rates from virgin stock|
|Committee on Limits and Standards (CLS)||Standard Fisheries Management Authority (SFMA)|
|Resource biologists and ecologists from government, NGOs and universities||National or regional resource managers and socioeconomists|
|Representatives of the public||Fisher's representatives|
|Economists||Control and surveillance officers|
|Quantitative experts including analysts*||Fish stock assessment experts*|
|* in an advisory capacity|
|The Committee on Limits and Standards (CLS)||The Standard Fisheries Management Authority (SFMA)|
|AUTHORITY: Overrides or constrains decisions of SFMA when LRPs are in danger of being exceeded.||Decides exploitation in relation to established TRPs when the stock is in a healthy condition, and LRPs are not exceeded.|
|FREQUENCY: May meet annually, or at intervals of several years, unless there is an unexpected problem.||Meets frequently as required.|
|TERMS OF REFERENCE: Sets limits to exploitation such that both a productive ecosystem, ecosystem diversity, and intergenerational equity are conserved.||Establishes targets for fishing in the current year that comply with the constraints set by the CLS|
1) Sets levels and modalities of catch sampling for a given stock, and standards for control and surveillance. Sees that these standards are being met in practice.
|1) Reports to CLS on attainment of agreed levels of sampling, surveys, and control and surveillance of the fishery.|
|2) Reviews performance of the fishery in relation to the measures set by the SFMA. Sets and periodically revises acceptable limits to the rate of fishery exploitation based on acceptable risks.||2) Analyses fishery and survey data to estimate current stock size, recruitment and fishing mortality, and forecasts the values for the coming year within catch/fleet constraints.|
|3) Considers the historical effects of exploitation in relation to the attainment of long-term management objectives||3) Sets targets (for catches/effort/area/ season of fishing) for the coming year such that the annual F and/or residual biomass remain within the limits specified by the CLS.|
|4) Seeks improved institutional and management measures, and suggests limits and criteria for the control of catches/access||4) Allocates TACs and/or access rights among the different stakeholders|
|5) Evaluates the impact of different gears, of by-catch and of discards.||5) Suggests practical means of dealing with fishery and species interactions.|
|6) Considers impacts of ecosystem and environment changes, and of other fisheries, on the resource and its long-term sustainability||6) Adjusts seasons, areas fished and technological criteria to minimize incidental catches that lead to catch overruns and discarding.|
|7) Takes into account the impact of the fishery on by-catch species; sets limits for bycatch and discarding||7) Develops practical measures to attain discarding and bycatch targets.|
|8) Makes recommendations for fundamental research in support of fishery assessment||8) Develops plans for, and coordinates research on, issues raised by the CLS.|
|9) Suggests targets and time frames for stock rebuilding||9) Develops and implements stock rebuilding strategies that meet CLS requirements.|