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Fishery Bulletin 101(4) 



important to assess the sensitivity of the results to the 

 quahty of the data. The row "deterministic data" in Table 

 5 provides results for a trial in which the survey biomass 

 index, the catch-rate index, and the age-composition data 

 are known without error. The results from this trial provide 

 an upper bound on the impact of improved data quality 

 on the assessment results.^ Somewhat surprisingly, the 

 results for this trial are not notably better than for the 

 baseline trial — the most notable difference between the 

 baseline trial and the "deterministic data" trial being the 

 higher values for the "5%D" statistics for the latter trial. 

 The lack of major improvement in performance arises 

 because, even with perfect information on spawning 

 output and recruitment, it is still not possible to estimate 

 Bq exactly by multiplying average recruitment for the first 

 10 years of the assessment period by spawning output-per- 

 recruit in the absence of fishing (hence the value of 0.84 

 for Pjed'- Furthermore, the rebuilding analyses are still 

 based on generating future recruitment by using spawn- 

 ing output and recruitment data for only 20 years, which is 

 clearly a major source of variability in the predictions from 

 the rebuilding analysis. 



Decreasing the catch-at-age sample size from 200 to 50 

 has relatively little impact on the values for the perfor- 

 mance statistics (the AAV statistic is marginally higher 

 and the average catch, particularly for the 20-year pro- 

 jection horizon, is lower). Decreasing the precision of the 

 catch-rate data has a rather larger impact. This is most 

 evident in the value for the "5%D" statistic which is 0.2 

 rather than 0.25, as is the case for the baseline trial. The 



" The assessment still ignores interannual changes in selectivity; 

 therefore the assessment results will not be exactly the same as 

 the true values. 



"5-yr update frequency" scenario in Table 5 examines the 

 implications conducting assessments every fifth rather 

 than every third year. The results are not markedly sensi- 

 tive to the interassessment period although the lower val- 

 ues for the "5%D" statistics are perhaps noteworthy. 



General remarks 



The framework developed in this paper provides an objec- 

 tive basis for contrasting different management procedures 

 and evaluating their sensitivity to uncertainty. Given such 

 a framework, it becomes possible to compare variants of 

 one class of management procedure (e.g. Table 4) and to 

 compare variants among different classes of management 

 procedure. 



The management procedure options presented in this 

 paper are but a small subset of those possible. In particular, 

 it should be possible to improve performance by modifying 

 the approach used to generate future recruitment when 

 conducting rebuilding analyses to make use of some form 

 of stock-recruitment relationship. One reason for expected 

 improved performance is that it may then be feasible to 

 estimate the fishing mortality rate corresponding to 0.4Bg 

 rather than having to set it to the default value of F^j,^, or 

 basing it on F . Other possible management procedure 

 options include 1) not increasing the rebuilding fishing 

 mortality rate selected when the rebuilding analysis was 

 first conducted if a stock is recovering faster than initially 

 anticipated; 2) not decreasing the rebuilding fishing mor- 

 tality rate as long of the probability of recovery by T^^^^ is 

 at least 0.5; and 3) smoothing the discontinuity that arises 

 when a stock changes status from being under a rebuild- 

 ing plan to being managed with the 40-10 rule when the 



