Stock assessment

6.4.5.1 Final assessment with AMCI

The key settings and data for the final blue whiting assessment in 2003 are shown in the table below. The key settings of the final assessment in 2002 are also shown for comparison. Some of the settings are described in more detail after the table.

1 Three of these were split into two periods.

2 The youngest age group in the Spanish CPUE tuning fleet was down-weighted.

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Settings/options for the AMCI run 2002 2003

AMCI version 2.1 2.2³

Age range for the analysis 0-10+ 1-10+

Last age a plus-group? Yes Yes

Age at recruitment (from Jan 1 in the year of spawning) 0.5 1 Recruitment in the terminal year Fixed Fixed Recruitment in the terminal year-1 Estimated Fixed Catch data

Weights for the partial objective functions for the catch fleet

Log sum of squares of catches-at-age 1 1 Log sum of squares of yearly yields 1 1 Weights of catch-at-age, age 0 and 1 years 0.1, 0.5 n.a., 0.5 Constant selection pattern for the catch fleet? Almost Almost Selectivity for age 10 equals average of selectivity at age 8-9? No Yes Age-structured tuning time-series

Norwegian acoustic survey on the spawning grounds, ages 2-8 1981-2002 1981-2003 Flat selectivity for ages 6-8? Yes No Weight in tuning for the partial objective function 1 1

Russian acoustic survey on the spawning grounds, ages 3-8, 1982-1996 1982-1996 Flat selectivity for ages 7-8? Yes No Weight in tuning for the partial objective function 1 1

Norwegian Sea acoustic survey, ages 1-7 1981-2001 1981-2001 Flat selectivity for ages 5-7? Yes No Weight in tuning for the partial objective function 1 1

CPUE time-series from Spanish pair trawlers, ages 1-6⁴ 1983-2001 not used Flat selectivity for ages 5-6? Yes n.a.

Weight in tuning for the partial objective function 1 n.a.

Piece-wise constant selection pattern for the tuning fleets? Yes Yes

Biomass tuning time-series 0 0

Survey data used in tuning are shown in Table 6.4.5.1.1. As in previous years, the three acoustic surveys were split into two time periods reflecting a likely change in catchability caused by a change in acoustic equipment (Simrad EK-500).

From 2002 onwards the splitting of these time-series has technically been obtained by estimating age-specific catchability separately for the two periods. Survey indices are treated as relative abundance indices.

Fishing mortality was modelled as separable, but with an allowance for a gradual change in the selection from year to year. The gain factor for change in selection was 0.2 for age 1, and 0.1 for the older ages. This implies that the selection at age 1 is allowed to vary more according to the year-to-year variation in the catches than the selection at the older ages. The selection at age 10+ was fixed to the average of ages 8-9 years.

Recruitment in 2001 was set to 30x10⁹ individuals at age 1. Recruitment in 2002 was set to the geometric mean recruitment in the period 1981-2000, 11.5x10⁹.

3 AMCI 2.3 was used for scanning over terminal F’s.

4

Catch-at-age data are input at yearly resolution (Table 6.3.2.4). However, AMCI operates internally on a quarterly basis. The spawning stock is derived from the mean stock numbers in the first quarter, and the survey indices are related to the mean values in the survey season (Table 6.4.5.1.1.). The yearly fishing mortality was split on quarters assuming that the proportion 0.35 of the total annual fishing mortality occurs in the first and in the second quarter, 0.2 in the third quarter, and 0.1 in the fourth quarter.

The model was run until 2005. The results for 2003 and onwards, except the SSB in 2003, are predicted values assuming the same fishing mortality as in 2002 and constant recruitment. The key results are presented in Tables 6.4.5.1.2–6.4.5.1.4 and summarized in Figure 6.4.5.1.1. Residuals of the model fit are shown in Figure 6.4.5.1.2. Some cohort effects are visible in the catch residuals for the early cohorts, and year effects occur throughout the survey time-series. The minimum SSQ (between the fitted model and the observed data) is not well defined. A range of terminal F’s can give SSQ that are close to the global minimum (Figure 6.4.5.1.3). Thus, the data do not allow unique characterisation of the stock in the most recent years. Selection pattern in terms of age- and year-specific F’s is shown in Figure 6.4.5.1.4.

The assessment (Table 6.4.5.1.4) indicates that fishing mortality has increased sharply in recent years. The exploitation pattern has been relatively stable with the major exploitation being on adults. SSB has increased compared to the period from the early 1980s to the late 1990s. However, it has been declining since 1999 and is expected to continue doing so if fishing mortality remains at F=0.48 yr^-1 estimated for 2002 and assumed recruitment. Recruitment of the 1996 year class is the highest in the time-series. All following year classes are also large, indicating a possible change in recruitment dynamics of the stock. Even the weakest year class born after 1996, that of 1998, is much stronger than was typical in the previous period.

A bootstrap run (Figure 6.4.5.1.5) gives an indication of the uncertainty in the assessment. Even though the bootstrap replicates reproduce similar temporal patterns in recruitment, spawning stock biomass and fishing mortality as the final assessment, uncertainty in the absolute level of these metrics during the recent years is clearly visible. Moreover, the development of SSB and fishing mortality after 1999 remain highly uncertain.

Retrospective analysis (Figure 6.4.5.1.6) shows that the assessments with 2001 or 2002 as the terminal year are very consistent. However, 1999 or 2000 as the terminal year yield assessments suggesting lower SSB and recruitment and higher F.

6.4.5.2 Final assessment with ISVPA

For the final run the model was run with the same settings as for exploratory runs, but only signals from age-structured auxiliary information were used in addition to signals from catch-at-age. The results of the stock assessment are given in the Tables 6.4.5.2.1–6.4.5.2.3.

Comparison of theoretical catches and reported ones for age groups, included into analysis, are shown in Figure 6.4.5.2.1.

Residuals for catch-at-age and each survey data (in the minimum of the ISVPA loss function) are shown in Figure 6.4.5.2.2. ISVPA residuals in logarithmic catch-at-age residuals have apparent cohort structure. This structure may be dealt with by a large period of separability constraint, used to increase the stability of estimates of selection factors. The cohort peculiarities in the matrix of residuals may be considered as a negative feature, but, as it was mentioned in the Report of the Working Group on Methods (ICES 2003/D:03), the 1992 year class has a strong anomaly in its catch dynamics for young ages and it may not be unreasonable to retain high residuals for this year class, which means that the estimates of the model parameters are less based on this cohort. The structure of residuals for indices is less certain and includes some elements of both cohort and year effects.

The selection pattern in terms of age- and year-specific F’s is shown in Figure 6.4.5.2.3.

In the retrospective analysis the same settings were retained even though some of the indices might become uninformative because of the shortening of the year range. This may be the reason for the instability of the results for 1998-2000. The results of the retrospective analysis are shown in Figure 6.4.5.2.4.

Results of the bootstrap estimation of confidence intervals are shown in Figure 6.4.5.2.5. For effort-controlled version of the model, used for blue whiting, the procedure consists in application of conditional parametric bootstrap (assuming lognormal distribution of residuals) with variance, estimated in the basic run; a lognormally-distributed random noise with variance = 0.3 for each survey was added to the age-structured indices.

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6.4.5.3 Comparison between AMCI and ISVPA assessment

The results of the assessments carried out with the AMCI and ISVPA model are compared in Figure 6.4.5.3.1.

SSB: The models show reasonable agreement in the historical estimates of SSB except for the most recent years. Both models indicate a significant increase in SSB in the late 1990’s to about 4 million t. In the period thereafter, SSB continues to increase to a historic high (about 6 million t) in 2002 in the ISVPA assessment but has decreased slightly in the AMCI assessment to about 3.5 million t.

Fishing mortality: Trends in fishing mortality estimated by both models are similar in the historical period. However, historical ISVPA estimates are consistently higher than those by AMCI. Both models indicate a sharp decrease in F from 1990 to 1991. The reasons for this decrease are unclear and may reflect a shift in the fishery to other components of the stock. Also, both models indicate an increase in F after 1994, which continues to increase to a historic high in 2002 in the AMCI assessment. However, ISVPA shows a decrease in F after 1998.

Recruitment: Both models indicate that recruitment has increased after 1995. The 1996 year class was estimated as the strongest in the time-series. The estimate by both models is almost the same. The estimates of more recent year classes by ISVPA are consistently higher than those by AMCI.

The models give a different interpretation of the large increase in catches by the fishery since 1998. ISVPA explains the increase of the catches almost entirely by the increase in biomass resulting from the large recruitment. The model supports this interpretation unambiguously, in a sense that the minimum SSE is well defined (Figure 6.4.4.3.2). AMCI, on the other hand, explains the increase of the catches partly by a large increase in fishing mortality and partly by an increase in biomass. However, the SSE surface around the minimum is rather flat (Figure 6.4.5.1.3), indicating that solutions with somewhat higher or lower F would fit the data almost as well.

In the absence of quantitative data demonstrating changes in the fishery the WG felt it difficult to make a choice between the two models. Nevertheless, the increase in F estimated by AMCI is in line with the exploratory analysis of catch curves and survey data (Section 6.4.4).