# ANNEX II: USING AN LRP TO SET A RISK-AVERSE TARGET FOR EXPLOITATION - THE FMSY EXAMPLE

There may be circumstances when fisheries managers are able to specify an upper limit to fishing intensity, beyond which an extremely undesirable state of the fishery is agreed to exist. As noted, this may be referred to as a Limit Reference Point (LRP). For mathematical simplicity we assume in the following, that FMSY is a conventionally-accepted value, known without error. In the following example we further assume that the LRP used in this case is a pre-established value for the fishing mortality corresponding to MSY conditions.

The managers acknowledge that they are operating in an uncertain environment and that the current ‘status quo’ for the fishery, and the F-value during the last season ( = FNOW), was not precisely known, but that some rough estimates of its standard deviation can be made. In the hypothetical case in question, there is strong evidence that the fishing intensity last year was below FMSY , and it is assumed that if the same effort were to be exerted in the next season, we could expect the probability distribution of fishing mortality rates to remain the same. The managers feel however that it would be useful to define a target reference point in such a way that this results in a small, prespecified risk that FMSY is not exceeded.

Given this situation, the following illustrates one procedure for calculating appropriate target values for FNOW which result in a pre-specified probability of an agreed LRP being respected. In this example, the LRP is assumed to be a pre-established value for FMSY = 0.6. Although there is no unambiguous evidence in the literature as to the most appropriate distribution function to use for F, it is believed that the uncertainty in the relative position of FNOW < FMSY can reasonably be expressed by a normal distribution (see Fig 18 below), although similar calculations could readily be performed for other distribution functions.

Let us assume that the current level of the fishing mortality, F, is less than the target reference point, FMSY. For mathematical simplicity we assume that FMSY is known without uncertainty. We further assume that the uncertainty of F, is described by a lognormal distribution where, with equal probability, i.e. with equal levels of confidence, the actual value of F in the fishery may be twice or half the estimated parameter, FNOW. We seek to find a value for this limit reference point, FNOW, that lies safely below FMSY, leaving only an acceptably small probability that the ‘true’ value of the current F is, in fact, greater than FMSY.

Mathematically the suggested procedure is the following: The level of risk the fishery can safely tolerate, (quantified in the appendix figure as the shaded area on the right hand tail of the normal distribution), is equivalent to the integral of the probability that the current F exceeds our limit reference point. Referring to this chosen level of acceptable risk as P (F > FMSY), we must solve for the value of FNOW that corresponds to the target reference point that provides this margin of safety. The cumulative probability for the right hand tail of the normal distribution may then be written as:

From this we can obtain FNOW using a mathematical package (e.g. MAPLE, MATHEMATICA) designed for equation solving.

The standard deviation of F could then, in theory, be estimated from the historical record by comparing the predicted F's and the ‘true’ values obtained retrospectively from VPA, or by agreeing on a ‘likely’ margin of error given estimates of the variance in population size estimates. Furthermore, this type of historical comparison could enable fishery managers to describe more accurately the nature of the distribution in uncertainty of F.

Appendix table: Assuming FMSY= 0.6, the following gives indicative values of FNOW that could be used as TRPs for combinations of (Columns): - The acceptable proportion of the time that FNOW > FMSY , and (Rows):- standard deviations of FNOW.
[P(x)]STANDARD
σ = 0.25
DEVIATION:
= 0.5
= 1.0
P(FNOW > FMSY)
30%0.530.4750.39
20%0.50 0.42  0.33
10%0.450.3650.26