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close this bookA short review of precautionary reference points and some proposals for their use in data-poor situations (1998)
View the document(introduction...)
View the documentIntroduction
View the documentSome General Considerations
View the documentThe Role of RPs and Harvest Laws in Management
View the documentObjectives Underlying Formulation of Management and Reference Points
View the documentA Brief Classification of LRPs and their Applicability in Information-limited Situations
View the documentFunctioning of an LRP-based System
View the documentSpecific Problems and Approaches for Tuna Fisheries
View the documentReference Points from Size-and-age Data?
View the documentDiagnosis of Areas Where LRPs are Required
View the documentMultispecies RPs?
View the documentSpatial Information and Reference Points
View the documentRotating Closures and Gauntlet Models
View the documentDiscussion
View the documentReferences
View the documentAnnex: Alternative Closed-form Solution for FNOW

A Brief Classification of LRPs and their Applicability in Information-limited Situations

Reference Points Based on S-R Relationships

For completeness, reference points based on availability of historical S-R data are included here and are obviously those which require the most complete data sets, consisting of time series of age-structured catches and population sizes, often supplemented by regular biomass surveys. As noted, such reference points form the main basis for LRP definition in the ICES and, to a lesser extent, NAFO areas. For example, FMED (Sissenwine and Shepherd, 1987) is the fishing pressure that reduces spawning biomass of a year class over its lifetime below the corresponding value for its parents and is estimated from the slope of the line through the origin of the S-R relationship for a fishery. Others are referred to more extensively in Mace (1988), Caddy and Mahon (1995), etc., and it remains to be seen whether these are likely to have wide application for data-poor fisheries, but a brief list of common approaches to LRP formulation follows.

Reference Points Based on Y/R and SSB/R Analysis

The classical TRP for Y/R analysis is of course FMAX, which again, nowadays, tends to be regarded as an LRP, with F0.1 commonly used as the corresponding TRP, both being subject to change as a result of changes in age distribution, recruitment and selectivity changes. Hence the need for the use of F0.1 in conjunction with an LRP. In New Zealand, if an LRP cannot be estimated from a model, it is often substituted for by the relevant value of M (see later). In South Africa, F0.2 is frequently used and is a more restrictive exploitation rate reference point, which has been used as a target or a benchmark in a fisheries management procedure (Butterworth et al., 1997).

Spawning stock biomass/recruit reference points include FREP, FHIGH and FLOW and these may also be subject to variability for the same reason as mentioned for F0.1 above (as, in fact, is true for most RPs). F35% or F30% are two fishing rates based on previous data sets for stock and recruitment that calculation has shown will maintain SSB/R at safe levels: the latter is an RP used by the North Pacific Fishery Management Council (Hollowed and Megrey, 1993), which is believed to be roughly equivalent to about 75% of long-term MSY as long as spawning stock size does not fall below 20-60% of the unfished level (i.e. both fishing mortality and biomass need to be monitored).

Reference Points Based on Production Modelling

The classical RP (now regarded in some quarters as an LRP) is of course FMSY In the past, MSY has been the classical management target, to be probably replaced now by quotas set at lower levels of harvest pressure, such as F0.1 or that corresponding to 2/3 MSY, (Doubleday, 1976). Although, strictly speaking, it belongs in the previous category since in the ICES area it is usually estimated by the Shepherd approach, which depends on data for fitting S-R relationships, FCRASH In theory could be estimated by more conventional production modelling as the point of interception of the right-hand limb of the curve on the fishing effort axis. One may doubt whether this is a particularly safe LRP and certainly not one to be exceeded! More relevant here could be the economic benchmark referred to as the point of Bioeconomic Equilibrium, where costs equal revenues from fishing (e.g. Panayotou, 1988). This seems a logical LRP to use, especially in high-seas fisheries where costs and revenues should be relatively easily measured.

Using size compositions to arrive at simple estimates of total annual mortality rate may allow application of production models using mortality in place of fishing effort (e.g. Caddy and Defeo, 1996). These authors show that, given reasonable time series of annual data for Zt and Yt, it is possible to use bootstrapping to arrive at a series of estimates for a TRP such as MSY. Ranking separate estimates and plotting them cumulatively (Fig. 7), the 50% quartile of the cumulative distribution of MSY estimates might make a suitable LRP, while the 25% or more cautious percentile could be a candidate for a TRP.

Reference Points Based on Survey Data

The trend in many fisheries where commercial data is uncertain or unreliable is to rely to a progressive extent on estimates of biomass coming from surveys. While this does not seem a readily available option for most tuna fisheries, a study of a longline fishery for sablefish (Ruppert et al., 1985) suggests a general formula for using survey data in setting harvests under thresholds, namely

Catch in year t of age group a: Cat = Gt*(Sa*Nat - T)

where Gt is some function of the exploitation fraction in year t, Sa is selectivity at age, Nat is the number of age group a in year t, and T is the threshold population size below which harvesting ceases. Some variation of this approach may provide elements for constructing a harvest rule whose degree of precaution will depend largely on the value of Gt used. Other more complex procedures for calculating quotas based on RPs are of course part of fishery management procedures (Butterworth et al., 1997), and the 'Magnusson-Stefansson feedback gain rule' is similar and is described in the following section.

Reference Points Based on Past Fishery Yields

Past yields from a fishery, under certain circumstances, can provide some rough indication of future potential, as long as independent biological indications support the continuity of the fishery at historical levels. Some ideas on this have been proposed by Pope (1983), and in New Zealand (Annala, 1993) several yield-based reference points are currently used in fisheries management. These are either developing fisheries, in which case extremely precautionary criteria are used, or are fisheries with a fairly long history of exploitation, where maximum potential yields are believed to have been reached in the past but where the stock continues to produce at close to historical levels. Maximum Constant Yield (MCY) is the "maximum constant catch that is estimated to be sustainable, with an acceptable level of risk, at all probable future levels of biomass". Recognizing that this does not take into account future catastrophes, under certain constraints it nonetheless corresponds to a relatively low level of harvest and can be regarded as a precautionary target, which could be tested by stochastic simulations incorporating likely recruitment variability.


Figure 8. Relationships among Maximum Yield (MCY), Current Annual Yield (CAY) and Maximum Average Yield (MAY) (from Annala, 1993)

Current Annual Yield (CAY) is a dynamic version of MCY (Fig. 8), in which a constant exploitation strategy at FREF is applied. This F-based reference point is "the level of instantaneous fishing mortality which, if applied each year, would, with an acceptable level of risk, maximize the average catch from the fishery" and, again, would be established from simulation. According to Annala, CAY is calculated as:

CAY = [FREF/(FREF + M)]*[1-exp - (FREF + M)]*BBEG

where BBEG is the stock biomass at the beginning of the fishing year.

The mean value of historical annual values of CAYs is another measure, the Maximum Average Yield (MAY) (Fig. 8), which is considered to be close to MSY and is often interpreted as such by New Zealand's fishing industry (Annala, 1993).

One other useful approach to setting quotas when only commercial or survey indices are available has been referred to as the 'Magnusson-Stefansson feedback gain rule' (NAFO, 1998 draft report) and is reported as particularly useful for restoring a depleted fishery to productive condition which has been gradually declining in stock size over time. The rule is:

Yt = Yt-1*(1+g(Bt-1 - Bt-2)/Bt-2)

where Y is catch and B is biomass (or commercial CPUE index) in year t, and g, referred to as the 'feedback gain', reflects the degree of proportionality between changes in biomass observed between the last and current year and, hence, the extent to which it will be reflected in the coming quota. Values for g of 1 and over seemed to contribute to precautionary approaches in simulations, but higher values of g were found to do so by leading to progressively more frequent closures.

Other Empirical Approaches

The concept of an RP as a 'conventional' value that is accepted by industry is one idea that may have to be considered if available data does not allow calculation of a 'scientific' or model-based index (even performance of these latter is inevitably uncertain until they have been used in a management system, for reasons mentioned earlier). One example here was the use of a spawning escapement (40% of the biomass) for squid stocks in the Falkland-Malvinas region, which was in a sense used as an LRP. Such a 'conventionalized' approach to setting LRPs appears more feasible, if one considers that, even with the best of analyses, a system of LRP-based management will depend for its feasibility on the degree of implementation (Fig. 1).

A further generalization that may be useful is to consider F-based RPs in terms of the natural mortality rates that apply to a stock, since these are, after all, the natural level of risk the stock faced before fishing begins. For tunas, these values probably range from around 0.5 - 0.8+ annually for small tunas to 0.1 or lower for adult bluefin tuna. Several generalizations, beginning with Gulland, have examined the possibility of defining the MSY reference point in terms of natural mortality rate and biomass. Gulland (1971) proposed that M is approximately equal to M at MSY and hence, for the logistic model,

MSY = 0.5*M*B0 (1)

Several authors have since questioned this equation, (e.g. Garcia et al., 1989) and hence there is also a concern that F (MSY) = P*M, for P = 1 would be too optimistic as a means of guessing at the order of magnitude for a safe fishing mortality corresponding to MSY, especially for short-lived animals. In general, various authors suggested that P should be lower for shorter-lived species. Patterson (1992) proposed for small pelagics that a value of P = 0.5 (FMSY = 0.5M) would be appropriate. Objective data on this point are difficult to find, but one solution proposed here is that P declines linearly with M, so that F(LIM) is smaller relative to M for short-lived than for long-lived species. Based on the preceding account, a linear decline in P with natural mortality rate between two sets of extreme values was postulated, based on the following two data sets:


Apical predator

Small pelagic fish

M =

0.1

1.25

P =

0.9

0.50

Solving for these values (and admittedly other sets of 'seed' values could have been used), gives the empirical relationship below:

FLIM = 0.981M-0.194M2 (2)

A range of values for FLIM using this approach are given in Table 5.

Table 5. Predicted values for FLIM from equation 2

M

P

FLIM

0.1

0.836

0.084

0.2

0.803

0.161

0.3

0.771

0.231

0.4

0.739

0.296

0.5

0.707

0.353

0.6

0.674

0.405

0.7

0.642

0.449

0.8

0.610

0.488

0.9

0.577

0.520

1

0.545

0.545

1.1

0.513

0.564

1.2

0.480

0.576

1.3

0.448

0.583

1.4

0.416

0.582

1.5

0.384

0.575


It is postulated that an F-based LRP determined in this way (Fig. 9) might be tested against other estimators, and could be used to determine a cautious level of target fishing intensity using the methods given in Caddy and McGarvey or the simple exponential relationship given by ICES in Table 2.

A number of variations of equation (1) have been used, e.g. substituting x for 0.5. Annala (1993) notes that in New Zealand, values for

MCY = 0.25*F0.1*B0

have been used in new fisheries to set a conservative TRP, and

MCY = 0.5.F0.1*Bav

has been used in 'mature' fisheries, both regarded as relatively conservative. The quantity Bav in the last equation is the average historic recruited biomass. Simulation results (Mace, 1988) are reported to have shown that MCY may be as low as 60% of the deterministic MSY. Does this make MCY a possible candidate as a TRP to accompany MSY (used as an LRP) in a management system?


Figure 9. Empirical relationship proposed between natural mortality rate and the limiting value for fishing mortality rate (see text).