INFRAESTRUCTURA CRITERIOS DE SELECCIÓN

Sensitivity analyses were conducted to investigate structural uncertainty of the base model by investigating how changes to the model affected the estimated values and derived quantities. The sensitivities include the following:

1. Update the maturity ogive with recently collected data from 2009, 2012 and 2013. 2. Remove the 2012 survey data and index from the assessment to look at the effects of the

annual surveys since 2011.

3. Increase the standard deviation on the time-varying selectivity parameters. 4. Estimate time-varying selectivity from 1975 to present.

5. Estimate fishery and survey selectivity to age 10.

6. Use a 2013 acoustic survey biomass estimate without extrapolation off of CA.

An update of the maturity ogive (Figure 16) results in very similar parameter estimates and derived quantities when compared to the base model (Figure 46 and Table 24). The base model in this assessment does not show large changes with the new maturity-at-age ogive, but because the new ogive estimates a larger proportion of young fish being mature, the model is most sensitive when large year classes are moving through the young ages (as seen in recent estimates of depletion in Figure 46).

Removal of the 2012 survey data and index from the assessment results in little difference in most parameter estimates from the model (Table 24). The depletion time series is slightly affected in the

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1980’s, but the largest changes are in the recruitment estimates for the 2008 and 2010 cohorts, especially

with regard to uncertainty (Figure 46). This increase in uncertainty is expected because a critical year with observations of the 2008 and 2010 year classes when they were young has been removed. The estimates of the 2008 and 2010 year classes increased when removing the 2012 survey, which was a result of the fitting the 2013 index better. The closer fit to the 2013 index resulted in a larger increase in

predicted biomass from the 2011 index to the2013 index which produced a higher value for depletion (Table 24).

Increasing the standard deviation on the time-varying selectivity parameters to 0.2 has a small effect on the depletion trajectory, with only a slight departure from the base in the early years and a more

significant departure in recent years (Figure 47). This recent reduction in biomass is a result of a reduced estimate of the 2010 year class, due to the model interpreting the large proportion of the 2010 year class observed in the fishery data as changes in selectivity (Figure 47). With more observations of this year class, especially from the survey, the size of it should become more certain.

Estimating time-varying selectivity from 1975 to 2013 instead of 1991 to 2013 as in the base model, had little effect on the results. The estimates of selectivity were nearly identical to the base model for the 1991-2013 period, and from 1975-1990 the estimated selectivities showed little change from one year to the next (Figure 48.

Bayesian posterior distributions were estimated to compare additional sensitivities related to selectivity. These are 1) estimating non-parametric selectivity for both the fishery and acoustic survey to age-10 with selectivity deviations on each estimated age for the fishery, and 2) forcing fishery selectivity to be time- invariant and mimicking the base model from 2013 (JTC 2013). A comparison of the estimated selectivity at age and year is shown in Figure 49. When extending the estimates of selectivity-at-age to age 10, the acoustic survey begins to show large variability and unrealistic patterns past age 6 and the medians for fishery selectivity nearly linearly increase to age 11 (Figure 50). The stock is more depleted in the early years of the assessment, and then similar until recently when the stock is estimated to be less depleted, but wth greater uncertainty (Figure 51). This is mainly due to estimates of recruitment with larger estimates in recent years (Figure 51 and Table 25). Interestingly, the uncertainty in historical recruitment estimates is less prior to about 1980, and greater in recent years. This suggests that the historical age-structure is greatly influencing the estimates of selectivity-at-older ages.

Mimicking the base model from the 2013 assessment and not estimating time-varying selectivity resulted in little difference to the estimates of depletion except in recent years, which is a result of larger estimates for 2008 and 2010 recruitment (Table 25). Uncertainty was also slightly greater with time-invariant selectivity.

The 2013 acoustic survey biomass estimate of 2.42 million mt was comprised of at least 650,000 mt of extrapolated biomass in areas that were not surveyed, mostly off of northern California and southern Oregon. Therefore, a sensitivity run was done with a 2013 estimate of 1.8 million mt to investigate the effect of this value. The age compositions were not changed for this sensitivity, although it is likely that they would be affected. The model predicted a more depleted stock in 2015 with the lower 2013 survey estimate, resulting in a 12% reduction in the default harvest catch for 2014.

These sensitivities reflect current investigations into the Pacific Hake stock. The removal of the 2012 acoustic survey index and age composition data suggests that the estimation of recruitment of recent year- classes is more uncertain with a biennial survey than it would be with an annual survey. The relaxation of the standard deviation on the selectivity parameters has a pronounced effect on those parameters, but not on the overall results. Research into alternative parameterizations for time-varying selectivity would be useful to provide a more flexible framework, and investigating fisheries cohort targeting may lead to a

23 better understanding of time-varying selectivity parameterization for future models.