Butterworth and Glazer (2014) reported results from the application of a simple form of the dynamic Schaefer model to account for a large drop in sole CPUE over the period 2009-2013. The two hypotheses considered were (i) the drop in CPUE was a consequence of decreasing catchability and (ii) the drop in CPUE was a consequence of decreasing productivity. The model was applied again in 2016 (Butterworth et al., 2016) and 2017 (Glazer et al., 2017). Results from the assessment conducted in 2017 yielded more positive future projections for the most pessimistic scenario (a decrease in productivity, where effort remains at its current reduced level into the future). Recent biomasses were estimated to be higher and projected to decrease more slowly if the 2013 effort level was maintained. Given that data are now available for 2017, the assessment model has once again been updated, with results reported here. These point to yet a more positive resource situation

Butterworth, Doug S

Glazer, Jean

English

Cape Town University OpenUCT

FISHERIES/2018/OCT/SWG-DEM/65 1  An updated assessment of the Agulhas sole resource, Austroglossus pectoralis  J.P. Glazer and D.S. Butterworth  Summary Butterworth and Glazer (2014) reported results from the application of a simple form of the dynamic Schaefer model to account for a large drop in sole CPUE over the period 2009-2013. The two hypotheses considered were (i) the drop in CPUE was a consequence of decreasing catchability and (ii) the drop in CPUE was a consequence of decreasing productivity.  The model was applied again in 2016 (Butterworth et al., 2016) and 2017 (Glazer et al., 2017).  Results from the assessment conducted in 2017 yielded more positive future projections for the most pessimistic scenario (a decrease in productivity, where effort remains at its current reduced level into the future).  Recent biomasses were estimated to be higher and projected to decrease more slowly if the 2013 effort level was maintained.  Given that data are now available for 2017, the assessment model has once again been updated, with results reported here. These point to yet a more positive resource situation.  Introduction  In 2014 concern was expressed regarding the steady decline in sole CPUE since 2009.  An initial assessment of the resource was thus undertaken by applying a simple form of the dynamic Schaefer model (Butterworth and Glazer, 2014).  Data inputs to the assessment comprise annual catches and a standardized CPUE index, both of which are available since 2000. Results from the most recent assessment which includes data to 2017 are reported below.  The data  The annual catch series and CPUE index used in the assessment model are reported in Table 1 and cover the period 2000-2017.  The catches relate to the total sole catch made per annum, while the standardized CPUE index relates to that of Model Cb in Fairweather and Glazer (2018).  This reflects a CPUE index derived from data for seven sole specialist vessels in six of the nine grid blocks that comprise the sole grounds, where these data are further restricted to sole targeted fishing only.  Of note is the substantial increase in CPUE in 2017.  It should also be noted that no inshore trawl fishing took place as a result of a moratorium placed on the fishery over the period mid-December 2016 to June 2017.  The assessment model  The dynamic Schaefer model (chosen for use here because of its simplicity) is of the form:  𝐵𝑦+1 = 𝐵𝑦 + 𝑟𝐵𝑦[1 −𝐵𝑦𝐾] − 𝐶𝑦         (1)  where  𝐵𝑦 is the biomass estimated in year y, with the starting biomass B2000 assumed to be at the MSY level K/2,  FISHERIES/2018/OCT/SWG-DEM/65 2  r is an estimable parameter (the intrinsic rate of population growth), which for realism was constrained to lie in the range [0.4; 0.7] in past analyses.  K is pristine biomass set at 800/(r/4), i.e. the MSY is assumed to be 800 tons (an amount landed regularly in the past), and  𝐶𝑦 is the annual catch.  The likelihood is calculated assuming that the abundance index (CPUE) is log-normally distributed about its expected values:   𝐼𝑦 = 𝑞𝑦𝐵𝑦𝑒 𝑦           (2)  where 𝐼𝑦 is the abundance index for year y, 𝑞𝑦𝐵𝑦 is the corresponding model estimate (𝑞𝑦 being the estimated year-dependent catchability coefficients), and 𝑦 is the observation error, ~𝑁(0, 𝜎𝑐𝑝𝑢𝑒2 ), in year y.  The contribution of the abundance index to the negative log-likelihood function (after the removal of constants) is given by:  −ℓ𝑛𝐿 = 𝑛ℓ𝑛(?̂?𝑐𝑝𝑢𝑒) +𝑛2         (3)  In assessing the status of the sole resource two hypotheses have been considered in an attempt explain the decline in CPUE experienced over the period 2009-2013.  These are as follows:  • Hypothesis 1: assumes that catchability decreased over the period 2009-2013.  For the assessment conducted in 2017 the following assumptions were made for the year-dependent 𝑞𝑦 parameters:  𝑞𝑦 is defined as 𝑞𝑍𝑦, where:  ▪ 𝑍𝑦=1 for y≤2010, ▪ 𝑍2011 = 1 − 𝜇, ▪ 𝑍2012 = 1 − 2𝜇, ▪ 𝑍2013 = 1 − 4𝜇, ▪ 𝑍2014 = 1 − 3𝜇, ▪ 𝑍2015 = 1 − 2.5𝜇, and ▪ 𝑍2016+ = 1 − 3.5𝜇  Given additional data, 𝑍2016 was maintained at 1 − 3.5𝜇 and 𝑍2017+ was set at 1 − 1.5𝜇.  𝜇 is assumed to be 0.2.  • Hypothesis 2: assumes that productivity decreased over the period 2007-2013.  For the assessment conducted in 2017the following assumptions were made for the year-dependent 𝑟𝑦 and 𝐾𝑦 parameters:  𝑟𝑦 and 𝐾𝑦 are defined as 𝑟𝑈𝑦  and 𝐾𝑈𝑦, where: ▪ 𝑈𝑦=1 for y≤2007, FISHERIES/2018/OCT/SWG-DEM/65 3  ▪ 𝑈2008+ = 𝑒−𝛿, ▪ 𝑈2009 = 𝑒−2𝛿, ▪ 𝑈2010 = 𝑒−3𝛿, ▪ 𝑈2011 = 𝑒−4𝛿 ▪ 𝑈2012 = 𝑒−5𝛿, ▪ 𝑈2013 = 𝑒−6𝛿, ▪ 𝑈2014 = 𝑒−5𝛿 ▪ 𝑈2015 = 𝑒−4𝛿, and ▪ 𝑈2016+ = 𝑒−3𝛿  𝛿 is assumed to be 0.3.  Given the substantial CPUE increase in 2017, the 𝑈𝑦 values assumed previously (shown above) required re-evaluation since they no longer provided an adequate fit to the CPUE data.  The turn-around point of 𝑈𝑦 is now set at 2011 instead of 2013 as per the 2017 assessment.  Additionally, 𝑈2017 is set at 𝑒−𝑊𝛿 , where W and 𝛿 were initially included as estimable parameters with a linear relationship assumed between 𝑈2011 and 𝑈2017.  Attempts to estimate both W and 𝛿 (for a fixed r =0.4) proved unsuccessful.  Model fits were thus evaluated for a combination of fixed W and 𝛿 values (and r=0.4).  The resulting −𝑙𝑛𝐿 values indicated that a W of zero was preferred by the data.  r and W were thus fixed at 0.4 and 0 respectively and 𝛿 was estimated across the range [0.01, 1] in units of 0.01.  The best fit was achieved for 𝛿=0.36.  The model was thus re-run, fixing W and 𝛿 and estimating r, with r=0.39 providing the best fit to the data.  The model fits to the CPUE index and the resultant biomass indices for the two hypotheses are shown in Figure 1.  Forward Projections  Hypothesis 1 was projected deterministically 20 years into the future for the following two scenarios: Scenario A: project forward from the same levels as for 𝑍𝑦 as estimated for 2017; and Scenario B: project forward, allowing 𝑍𝑦 to increase back to 1 by 2019. The above two scenarios are depicted graphically in Figure 2.  Hypothesis 2 was also projected deterministically 20 years into the future for a scenario where 𝑈𝑦 increases back to 1 by 2017. This is depicted graphically in Figure 3.  The following future effort levels (𝐸𝑦) were applied in the projections:  𝐸2018 = 𝐸2013, and 𝐸2019+ = (1 − 𝑎)𝐸2013  Values of 𝑎 for which results have been reported in the past are 0 (i.e. no phase down from the 2013 level), 0.2 and 0.3.  For this updated assessment one further 𝑎 value has been considered; namely α=-0.1 (i.e. 𝐸2019+is 10% higher than 𝐸2013). This was added because of the improved resource situation indicated by the updated data.  FISHERIES/2018/OCT/SWG-DEM/65 4  Results and Discussion  Results from the analyses for projection scenarios A and B for Hypothesis 1 (related to a change in catchability) are shown in Figure 4 while the projection scenario for Hypothesis 2 (related to a change in productivity) is shown in Figure 5 for the each of the future effort level options specified.  A comparison across Hypothesis/Scenario combinations for the each of the 𝑎 values considered is shown in Figure 6.  Figure 4 indicates that if the data reflect a catchability effect, and whether future levels of catchability remain low or increase, this would not be a cause for concern given that the projected biomass remains at high levels irrespective of the level of future effort (amongst the options considered) that is applied.  It is further notable that allowing an increase in effort for the 2019+ period (i.e. α=-0.1) would result in greater catches with only a slight reduction in biomass levels when compared against the α=0 scenario.  Figure 5 indicates that if the data reflect a productivity effect, then biomass, catches and CPUE increase over the projection period for each of value of α tested.  Allowing an increase in effort for the 2019+ period (i.e. α=-0.1) would result in greater catches but lower biomass levels when compared against the α=0 scenario.  Comparisons of the Hypothesis/Scenario combinations for each of the future effort levels tested are shown in Figure 6.  Across the 𝑎 options, 𝑎=0.3 would be best to ensure biomass recovery if biomass is indeed low, but this is at the expense of future catches. If slower recovery can be accepted for the reduced productivity hypothesis, then and effort increase of up to 10% (i.e. α=-0.1) might be considered.  References Butterworth, DS and Glazer, JP.  2014.  A preliminary assessment of the South African east coast sole resource, Austroglossus pectoralis.  Unpublished DAFF working Group document: Fisheries/2018/OCT/SWG-DEM/63. 12pp.  Butterworth, DS, Glazer, JP, Fairweather, TP and Durholtz, MD.  2016.  An updated assessment of the Agulhas sole resource, Austroglossus pectoralis.  Unpublished DAFF Working Group document: Fisheries/2016/SEP/SWG-DEM/53.  12pp.  Fairweather, TP and Glazer, JP.  2018.  Agulhas sole 2018 update of input data: catch & CPUE.  Unpublished DAFF Working Group document: Fisheries/2018/OCT/SWG-DEM/56.  3pp. Glazer JP, and Fairweather, TP.  2017.  An updated assessment of the Agulhas sole resource, Austroglossus pectoralis.  Unpublished DAFF Working Group document: Fisheries/2017/SEP/SWG-DEM/25.  12pp.   FISHERIES/2018/OCT/SWG-DEM/65 5  Table 1: Catch and standardized CPUE input to the dynamic Schaefer assessment model.  Year Catch (t) CPUE (kg/min) 2000 1060 0.31 2001 850 0.32 2002 702 0.22 2003 754 0.22 2004 612 0.23 2005 485 0.20 2006 428 0.22 2007 331 0.20 2008 448 0.20 2009 568 0.30 2010 570 0.25 2011 436 0.24 2012 338 0.17 2013 127 0.09 2014 208 0.13 2015 258 0.15 2016 125 0.105 2017 113 0.269    FISHERIES/2018/OCT/SWG-DEM/65 6       Figure 1: Fits to the CPUE data (top panel) and the biomass trends (lower panel) for Hypothesis 1 (a reduction in catchability) and Hypothesis 2 (a reduction in productivity) respectively.   Catchability: Hypothesis 100.050.10.150.20.250.30.35200020012002200320042005200620072008200920102011201220132014201520162017CPUE (kg/min)Nominal CPUE Estimated CPUE0100020003000400050006000700080002000200120022003200420052006200720082009201020112012201320142015201620172018Biomass (t)Productivity: Hypothesis 200.050.10.150.20.250.30.35200020012002200320042005200620072008200920102011201220132014201520162017CPUE (kg/min)Nominal CPUE Estimated CPUE0100020003000400050006000200120022003200420052006200720082009201020112012201320142015201620172018Biomass (t)FISHERIES/2018/OCT/SWG-DEM/65 7    Figure 2:  Projection Scenarios A and B related to Hypothesis 1: catchability has decreased by 80% over 2010-2013, it fluctuates until 2017, and then remains at the same level as for 2017 or increases back to a relative 𝒁𝒚 of 1 by 2019.   Figure 3:  Projection Scenario related to Hypothesis 2: productivity has dropped by 76% over 2007-2011 and then increases back to a relative 𝑼𝒚 of 1 by 2017.  Linear interpolation was applied to obtain the 𝑼𝒚 values between 𝑼𝟐𝟎𝟏𝟏 and 𝑼𝟐𝟎𝟏𝟕.   00.20.40.60.811.220002002200420062008201020122014201620182020202220242026Z(y)Z(y) Scenario A Scenario B00.20.40.60.811.220002002200420062008201020122014201620182020202220242026U(y)U(y) Projection ScenarioFISHERIES/2018/OCT/SWG-DEM/65 8    Figure 4: Projected biomass, catch and effort for Hypothesis 1: catchability has decreased by 80% over 2010-2013 for Scenario A (no change from 2017 value – left side plots) and Scenario B (back to normal by 2019 – right side plots) for different future effort levels.  Catchability Hypothesis: Scenario A (remain at 2017 level) Catchability Hypothesis: Scenario A (Z increases back to a relative value of 1 by 2019)01000200030004000500060007000800020002002200420062008201020122014201620182020202220242026202820302032203420362038Biomass (t)Biomass a=-0.1 a=0 a=0.2 a=0.30200400600800100012002000200220042006200820102012201420162018202020222024202620282030203220342036Catch (t)catch a=-0.1 a=0 a=0.2 a=0.300.050.10.150.20.250.30.350.40.452000200220042006200820102012201420162018202020222024202620282030203220342036CPUE (kg/min)nominal CPUE a=-0.1 a=0 a=0.2 a=0.301000200030004000500060007000800020002002200420062008201020122014201620182020202220242026202820302032203420362038Biomass (t)Biomass a=-0.1 a=0 a=0.2 a=0.30200400600800100012002000200220042006200820102012201420162018202020222024202620282030203220342036Catch (t)catch a=-0.1 a=0 a=0.2 a=0.300.050.10.150.20.250.30.350.40.452000200220042006200820102012201420162018202020222024202620282030203220342036CPUE (kg/min)nominal CPUE a=-0.1 a=0 a=0.2 a=0.3050010001500200025003000350040002000200220042006200820102012201420162018202020222024202620282030203220342036Effort a=-0.1 a=0 a=0.2 a=0.3050010001500200025003000350040002000200220042006200820102012201420162018202020222024202620282030203220342036Effort a=-0.1 a=0 a=0.2 a=0.3FISHERIES/2018/OCT/SWG-DEM/65 9   Figure 5: Projected biomass, catch and effort for Hypothesis 2: productivity has dropped by 76% over 2007-2011.  Productivity Hypothesis Scenario Biomass (t)Catch (t)CPUE (kg/min)0100020003000400050006000700080002000200220042006200820102012201420162018202020222024202620282030203220342036Biomass a=-0.1 a=0 a=0.2 a=0.30200400600800100012002000200220042006200820102012201420162018202020222024202620282030203220342036catch a=-0.1 a=0 a=0.2 a=0.300.050.10.150.20.250.30.350.40.452000200220042006200820102012201420162018202020222024202620282030203220342036nominal CPUE a=-0.1 a=0 a=0.2 a=0.3050010001500200025003000350040002000200220042006200820102012201420162018202020222024202620282030203220342036Effort a=0 a=0.2 a=0.3 a=-0.1FISHERIES/2018/OCT/SWG-DEM/65 10   Figure 6: Comparisons across 𝒂=-0.1, 𝒂=0, 𝒂=0.2, and 𝒂=0.3 (different future effort levels) for the two hypotheses. “cA” and “cB” refer to the catchability hypothesis where for the future projections levels remain at those of 2017 (Ca) or increase to a relative value of 1 by 2019 (cB). “p” refers to the productivity hypothesis where 𝑼𝒚 returns to a relative value of 1 by 2017. α=-0.1 α=0 α=0.2 α=0.3Biomass (t)Catch (t)CPUE (kg/min)01000200030004000500060007000800020002002200420062008201020122014201620182020202220242026202820302032203420362038biomass (c) biomass (p) a=-0.1(cA)a=-0.1(cB) a=-0.1(p)0200400600800100012002000200220042006200820102012201420162018202020222024202620282030203220342036Observed catch a=-0.1(cA) a=-0.1(cA) a=-0.1(cB)00.050.10.150.20.250.30.350.40.452000200220042006200820102012201420162018202020222024202620282030203220342036Observed CPUE a=-0.1(cA) a=-0.1(cB) a=-0.1(p)01000200030004000500060007000800020002002200420062008201020122014201620182020202220242026202820302032203420362038biomass (c) biomass (p) a0(cA) a0(cB) a0(p)0200400600800100012002000200220042006200820102012201420162018202020222024202620282030203220342036Observed catch a0(cA) a0(cB) a0(p)00.10.20.30.40.52000200220042006200820102012201420162018202020222024202620282030203220342036Observed CPUE a0(cA) a0(cB) a0(p)01000200030004000500060007000800020002002200420062008201020122014201620182020202220242026202820302032203420362038biomass (c) biomass (p) a0.2(cA)a0.2(cB) a0.2(p)0200400600800100012002000200220042006200820102012201420162018202020222024202620282030203220342036Observed catch a0.2(cA) a0.2(cB) a0.2(p)00.10.20.30.40.52000200220042006200820102012201420162018202020222024202620282030203220342036Observed CPUE a0.2(cA) a0.2(cB) a0.2(p)01000200030004000500060007000800020002002200420062008201020122014201620182020202220242026202820302032203420362038biomass (c) biomass (p) a0.3(cA)a0.3(cB) a0.3(p)0200400600800100012002000200220042006200820102012201420162018202020222024202620282030203220342036Observed catch a0(cA) a0.3(cA) a0.3(cB)00.10.20.30.40.52000200220042006200820102012201420162018202020222024202620282030203220342036Observed CPUE a0.3(cA) a0.3(cB) a0.3(p)

An updated assessment of the Agulhas sole resource, Austroglossus pectoralis.

https://open.uct.ac.za/bitstream/11427/30611/1/FISHERIES_2018_OCT_SWG-DEM_65%20Sole%20assessment%20document%202018%20FINAL.pdf

An updated assessment of the Agulhas sole resource, Austroglossus pectoralis.

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