Evaluating methods for setting catch limits for gag grouper: datarich versus data-limited

Transcription

1 Evaluating methods for setting catch limits for gag grouper: datarich versus data-limited Skyler R. Sagarese 1,2, John F. Walter III 2, Meaghan D. Bryan 2, and Thomas R. Carruthers 3 1 Cooperative Institute for Marine and Atmospheric Studies, RSMAS, Univ. of Miami; 2 Southeast Fisheries Science Center; 3 Univ. of British Columbia 30 th Lowell Wakefield Fisheries Symposium May 13, 2015 Anchorage, Alaska

2 Outline Brief introduction Stock assessments in the southeast US Study species: shallow-water groupers Objective Methods Data-rich Data-limited Results Applicability to data-limited grouper stocks

3 Biodiversity in the southeast US High biodiversity Table 1 from Fautin et al. (2010) Image: /sfa / management/councils/ Reference: Fautin, D., P. Dalton, L.S. Incze, J.-A.C. Leong, C. Pautzke, A. Rosenberg, P. Sandifer, G. Sedberry, J.W. Tunnell Jr, and I. Abbott An overview of marine biodiversity in United States waters. PLoS One 5:e / journal.pone

4 Biodiversity in the southeast US High biodiversity Table 1 from Fautin et al. (2010) Image: /sfa / management/councils/ Reference: Fautin, D., P. Dalton, L.S. Incze, J.-A.C. Leong, C. Pautzke, A. Rosenberg, P. Sandifer, G. Sedberry, J.W. Tunnell Jr, and I. Abbott An overview of marine biodiversity in United States waters. PLoS One 5:e / journal.pone

5 Biodiversity in the southeast US High biodiversity Table 1 from Fautin et al. (2010) Image: /sfa / management/councils/ Reference: Fautin, D., P. Dalton, L.S. Incze, J.-A.C. Leong, C. Pautzke, A. Rosenberg, P. Sandifer, G. Sedberry, J.W. Tunnell Jr, and I. Abbott An overview of marine biodiversity in United States waters. PLoS One 5:e / journal.pone

6 Biodiversity in the southeast US High biodiversity Table 1 from Fautin et al. (2010) Image: /sfa / management/councils/ Reference: Fautin, D., P. Dalton, L.S. Incze, J.-A.C. Leong, C. Pautzke, A. Rosenberg, P. Sandifer, G. Sedberry, J.W. Tunnell Jr, and I. Abbott An overview of marine biodiversity in United States waters. PLoS One 5:e / journal.pone

7 Assessments in the southeast US Most OFLs/ABCs set using data-poor methods (Newman et al. 2015) GOM 74% South Atlantic 77% Caribbean 100% Atlantic HMS 92% OFLs are set with only minor scientific input

8 Shallow-water groupers (SWG) Managed as a species complex Complex Species Model Shallow-water gag Mycteroperca microlepis Integrated analysis black M. bonaci scamp M. phenax Catch-at-age yellowfin M. venenosa Assessed on a species basis yellowmouth M. interstitialis red Epinephelus morio red hind E. guttatus rock hind E. adscensionis Integrated analysis Data-limited Goliath E. itajara Catch-Free Nassau E. striatus Images:

9 SWG landings Red Grouper Gag Black Grouper Scamp Other SWG Landings (t) Data source:

10 Objective Figure: Data inputs from Stock Synthesis model Could we have achieved a similar assessment result for GOM gag with less data or with computationally less-intensive methods on aggregated data?

11 Methods Stock Synthesis data-rich Integrated analysis (Wetzel and Methot 2013) 1. Population sub-model Statistical catch-at-age model 2. Observational sub-model Uses a wide range of data types to calibrate model 3. Statistical sub-model Quantifies the goodness of fit statistic between values expected and observed Highly flexible model structure SS3 freely available at:

12 Methods data-limited (DLM) DLMtool package in R (Carruthers et al. 2014) Method / Management Procedure Harvest Control Rules or Extensions Tested Catch Scalars - Geromont-Butterworth (GB) Constant catch, CPUE gradient rule Simple Bluefin Tuna (SBT1) - Depletion Fratio (DepF) - Depletion-Based Stock Reduction Analysis (DBSRA) 40% depletion, 40-10, Mean Length Estimation (ML) Depletion-Corrected Average Catch (DCAC) 40% depletion, 40-10, ML F MSY to M ratio (Fratio) 40% depletion, 40-10, ML Beddington and Kirkwood Life-History Analysis (BK) CC (Catch Curve Estimation), ML Delay-Difference (DD) Demographic FMSY (Fdem) CC, ML Surplus Production MSY (SPMSY) - Surplus Production Stock Reduction Analysis (SPSRA) ML Yield Per Recruit (YPR) CC, ML DLMtool available from:

13 Model evaluation Compared OFL distributions between SS ( truth ) and DLMs using relative absolute error (RAE) (Dick and MacCall 2011): RAE= median(ofl) OFL assessment / OFL assessment Sensitivity analysis to determine which data inputs strongly influence quota recommendations

14 Management strategy evaluation Explored the relative performance among potentially applicable DLM for gag grouper Simulations: 200 Repetitions: 100 Duration: 30 years Assessment interval: 5 years Compared trade-offs between probability of overfishing, yield, and probability of the biomass dropping below B MSY

15 Results quota comparison with SS Overfishing Limit (OFL, t) Methods Min Median Max RAE GB_slope 2,164 3,060 3, DCAC_ML 1,148 2,950 17, Fdem_ML 293 2,854 22, SPSRA 453 3,565 1,439, SS: 3,192 t ± 204 SD

16 Results - Sensitivity OFL quotas Sensitive to: Natural mortality Catch Abundance Depletion Steepness Insensitive to: Von Bertalanffy t0 Von Bertalanffy Linf Weight length a Mort Data Inputs AM vbt0 vbk vblinf wla wlb steep MaxAge Cat AvC LFC LFS CAA CAL FMSY_M BMSY_B0 Cref Bref Ind Dt Dep Abun Method AverC X MCTen MCThree GB_CC X X GB_slope X X SBT1 X DepF X X X X DBSRA X X X X X 40 X X X X 4010 X X X X ML X X X X DCAC X X X X X 40 X X X X 4010 X X X X X ML X X X X X X Fratio X X X CC X X X 4010 X X X X ML X X X X X BK X X X CC X X X X ML X X X X X DD X X X X 4010 X X X X X Fdem X X X X CC X X X X X ML X X X X X X SPMSY X SPSRA X X X X ML X X YPR X X X X X CC X X X X ML

17 Results - MSE Best performance: Depletion Fratio, Fratio4010 < 20% POF Intermediate yield (55 60 t) SS-matching methods: SPSRA < 40% of biomass dropping below B MSY Intermediate yield (~50 t) GB_slope Low yield Probability of overfishing B < B MSY (POF)

18 Results - MSE Best performance: Depletion Fratio, Fratio4010 < 20% POF Intermediate yield (55 60 t) SS-matching methods: SPSRA < 40% of biomass dropping below B MSY Intermediate yield (~50 t) GB_slope Low yield Probability of overfishing B < B MSY (POF)

19 Summary Most DLMs provide lower estimates of OFL compared to SS Due to dome-shaped selectivity and cryptic biomass DLMs assume asymptotic selectivity Expect higher F Could lead to precautionary advice

20 Summary Quota recommendations influenced by data inputs M, steepness, catch, current abundance, depletion Setting management objectives Sexes combined versus female only SSB models DLM methods appear to produce similar results to sexes combined SS model Methods producing results similar to SS: DCAC_ML, Fdem_ML, GB_slope, SPSRA

21 Applicability to other SWG Use depletion estimates for gag Robin Hood approach (Punt et al. 2011) DCAC_ML and Fdem_ML If catch-at-length data is available Easy to collect Estimate F, can estimate current abundance or depletion Importance of selectivity Way to scale OFL based on degree of doming in selectivity?

22 Acknowledgments Assessment analysts J. Tetzlaff, A. Rios, S. Cass-Calay, C. Porch, M. Schirripa, M. Karnauskas, N. Cummings Fisheries Statistics & Sustainable Fisheries Divisions SEDAR participants Funding: Gulf of Mexico IFQ cost-recovery program