Evaluating methods for setting catch limits for gag grouper: data- - - PowerPoint PPT Presentation

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

Skyler R. Sagarese1,2, John F. Walter III2, Meaghan D. Bryan2, and Thomas R. Carruthers3

1 Cooperative Institute for Marine and Atmospheric

Studies, RSMAS, Univ. of Miami; 2 Southeast Fisheries Science Center; 3 Univ. of British Columbia

30th Lowell Wakefield Fisheries Symposium May 13, 2015 Anchorage, Alaska

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

Biodiversity in the southeast US

• High biodiversity

Table 1 from Fautin et al. (2010)

Image: http://www.nmfs.noaa.gov /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. 2010. An overview of marine biodiversity in United States waters. PLoS One 5:e11914. 10.1371/ journal.pone.0011914

Biodiversity in the southeast US

• High biodiversity

Table 1 from Fautin et al. (2010)

Image: http://www.nmfs.noaa.gov /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. 2010. An overview of marine biodiversity in United States waters. PLoS One 5:e11914. 10.1371/ journal.pone.0011914

Biodiversity in the southeast US

• High biodiversity

Table 1 from Fautin et al. (2010)

Image: http://www.nmfs.noaa.gov /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. 2010. An overview of marine biodiversity in United States waters. PLoS One 5:e11914. 10.1371/ journal.pone.0011914

Biodiversity in the southeast US

• High biodiversity

Table 1 from Fautin et al. (2010)

Image: http://www.nmfs.noaa.gov /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. 2010. An overview of marine biodiversity in United States waters. PLoS One 5:e11914. 10.1371/ journal.pone.0011914

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

Shallow-water groupers (SWG)

• Managed

as a species complex

• Assessed
• n a

species basis

Complex Species

Model

Shallow-water gag Mycteroperca microlepis

Integrated analysis

black M. bonaci

Catch-at-age

scamp M. phenax

−

yellowfin M. venenosa

−

yellowmouth M. interstitialis

−

red Epinephelus morio

Integrated analysis

red hind E. guttatus

Data-limited

rock hind E. adscensionis

−

Goliath

• E. itajara

Catch-Free

Nassau

• E. striatus

−

Images: http://safmc.net/fish-id-and-regs/regulations-species

SWG landings

1000 2000 3000 4000 5000 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Landings (t) Red Grouper Gag Black Grouper Scamp Other SWG Data source: http://www.st.nmfs.noaa.gov/commercial-fisheries/commercial-landings/annual-landings/

Objective

• Could we have

achieved a similar assessment result for GOM gag with less data or with computationally less-intensive methods on aggregated data?

Figure: Data inputs from Stock Synthesis model

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: http://nft.nefsc.noaa.gov/Stock_Synthesis_3.htm

Methods – data-limited (DLM)

• ‘DLMtool’ package in R (Carruthers et al. 2014)

‘DLMtool’ available from: http://cran.r-project.org/web/packages/DLMtool/index.html

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 FMSY to M ratio (Fratio) 40% depletion, 40-10, ML Beddington and Kirkwood Life-History Analysis (BK) CC (Catch Curve Estimation), ML Delay-Difference (DD) 40-10 Demographic FMSY (Fdem) CC, ML Surplus Production MSY (SPMSY)

• Surplus Production Stock Reduction

Analysis (SPSRA) ML Yield Per Recruit (YPR) CC, ML

Model evaluation

• Compared OFL distributions between SS

(“truth”) and DLMs using relative absolute error (RAE) (Dick and MacCall 2011):

• Sensitivity analysis to determine which data

inputs strongly influence quota recommendations

𝑆𝐵𝐹= ¡​|𝑛𝑓𝑒𝑗𝑏𝑜(𝑃𝐺𝑀)− ¡​𝑃𝐺𝑀↓𝑏𝑡𝑡𝑓𝑡𝑡𝑛𝑓𝑜𝑢 |/​𝑃𝐺𝑀↓𝑏𝑡𝑡𝑓𝑡𝑡𝑛𝑓𝑜𝑢

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
• verfishing, yield, and probability of the

biomass dropping below BMSY

Results – quota comparison with SS

Overfishing Limit (OFL, t) Methods Min Median Max RAE GB_slope 2,164 3,060 3,246 0.04 DCAC_ML 1,148 2,950 17,504 0.08 Fdem_ML 293 2,854 22,205 0.11 SPSRA 453 3,565 1,439,942 0.12

SS: 3,192 t ± 204 SD

Results - Sensitivity

• OFL quotas
• Sensitive to:
• Natural mortality
• Catch
• Abundance
• Depletion
• Steepness
• Insensitive to:
• Von Bertalanffy t0
• Von Bertalanffy Linf
• Weight length a

Data Inputs Method Mort AM vbt0 vbK vbLinf wla wlb steep MaxAge Cat AvC LFC LFS CAA CAL FMSY_M BMSY_B0 Cref Bref Ind Dt Dep Abun 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

• - - - -
• -

Results - MSE

• Best performance:
• Depletion Fratio, Fratio4010
• < 20% POF
• Intermediate yield (55 – 60 t)
• SS-matching methods:
• SPSRA
• < 40% of biomass dropping

below BMSY

• Intermediate yield (~50 t)
• GB_slope
• Low yield

Probability of

• verfishing (POF)

B < BMSY

Results - MSE

• Best performance:
• Depletion Fratio, Fratio4010
• < 20% POF
• Intermediate yield (55 – 60 t)
• SS-matching methods:
• SPSRA
• < 40% of biomass dropping

below BMSY

• Intermediate yield (~50 t)
• GB_slope
• Low yield

B < BMSY Probability of

• verfishing (POF)

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

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

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?

• 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

Acknowledgments