Tab B, No. 5a SEDAR 47: Presentation to the Gulf of Mexico Fishery Management Council 17Oct2016 Joe O’Hop and Joseph Munyandorero Fish and Wildlife Research Institute, St. Petersburg, FL
Adult Goliath Grouper aggregating at the MG111 barge wreck off of Jupiter, FL in 65 feet of water. Photo by Mr. Walt Stearns, Underwater Journal (http://www.waltstearns.com/underwaterjournal.html )
Assessment efforts • SEDAR 3 (2003) – Data workshop concluded that data were insufficient to conduct a quantitative stock assessment, but survey data were subsequently discovered leading to the Review Panel recommending that an assessment should be attempted. • SEDAR 6 (2006) – Review workshop only to consider Goliath Grouper and Hogfish assessments. – First use of the “catch - free” model and relative benchmarks
Assessment efforts • SEDAR 23 (2010) – rejected by Review Panel – Data, Assessment, and Review Workshops, Catch-free model used – Review Panel rejected the assessment, among other reasons, because it could not provide absolute benchmarks (TORs) • FWC update (2015) – Revised and updated indices for the Catch-free model. – Primarily designed to inform the FWC commissioners on current trends in the population since SEDAR 47 was already being planned.
Assessment efforts • In planning the analyses for SEDAR 47, data sources were considered to determine whether new types of data suitable for the assessment had become available. – Research studies had been conducted on estuarine and offshore portions of the population • Good information on sizes of individuals, movements, site fidelity, genetics, potential for nursery habitat identification, mercury levels, and other aspects of its life history. • Some potential information on age composition of offshore fish available, but still undergoing evaluation and was not available for the SEDAR 47. – Without new data suitable for the assessment, we did not hold data or assessment workshops • We used the recommendations from SEDAR 23 to structure data inputs for this assessment.
Since SEDAR 23: • Length measurements – Underwater – Capture, episodic mortality events • Dorsal fin rays – Genetics (kinship analyses in progress) – Ages – fin rays still being evaluated • Mark-recapture – Movements – Site fidelity – Potential estimate of total mortality – (depends on ages) • Refinements to model inputs – new structure for MRFSS/MRIP index – recreational data re-estimated – two models: • Catch-free (Porch et al. 2004) • Stochastic Stock Reduction Analysis (Martell et al. 2008)
Everglades National Park - gamma fit to 1.0 350 ENP Mangrove and other habitats Length- lengths from Koenig et al. (2007) (Brusher and Schull 2009), 300 and Brusher and Schull (2007) stochastic ageing 0.8 [ L max =32.7, cv=0.288, n=2199) 250 frequencies, Frequency Proportion 0.6 200 Goliath grouper observed stochastic 150 0.4 predicted 100 0.2 ageing 50 0.0 0 0 50 100 150 200 250 0 1 2 3 4 5 6 7 8 9 10 L max Age (years) Length Class (cm) Everglades National Park Angler Creel Survey, 120 lengths measured 1974-1990, stochastic ageing 100 Estuarine 80 Frequency Goliath grouper vs. 60 40 Offshore 20 0 0 1 2 3 4 5 6 7 8 9 10 Age (years) 0.14 Estimated proportion at age Collins WFL sites 0.12 stochastic ageing from length composition 0.10 0.08 0.06 Goliath Grouper 0.04 0.02 0.00 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 Age (years)
Non-lethal ageing techniques Dorsal spine cross-section Dorsal fin ray cross-section (Brusher and Schull 2009) (Murie et al. 2009) Brusher, J. H., and J. Schull. 2009. Non-lethal age determination for juvenile goliath grouper ( Epinephelus itajara ) from southwest Florida. Endangered Species Research 7:205-212. Murie, D., D. Parkyn, C. C. Koenig, F. C. Coleman, J. Schull, and S. Frias-Torres. 2009. Evaluation of finrays as a non-lethal ageing method for protected goliath grouper Epinephelus itajara in Florida Endangered Species Research 7:213-220.
Estuarine and offshore vulnerability curves (SEDAR 47) estimated for Goliath Grouper – aged specimens 40 ENP Mangrove and other habitats 7 Koenig et al. (2013) 35 Koenig et al. (2007) and 6 offshore specimens Brusher and Schull (2009) 30 Frequency (%) Frequency (%) (finray ages) at 5 25 spawning sites, off of 4 Jupiter, FL 20 3 15 2 10 1 5 0 0 0 5 10 15 20 25 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Age (years) Age (years) 1.0 observed age proportions 1.0 Everglades National Park - gamma fit to predicted Cumulative proportion Aged specimens from Koenig et al. (2007) 0.8 0.8 and Brusher and Schull (2009) Proportion [a 100 =1.79, cv=0.495) a 50 Logistic fit to observed ages 0.6 0.6 of fish in spawning areas off Palm Beach, 2012 0.4 [a 50 =9.59, slope=1.342) 0.4 observed 0.2 0.2 predicted 0.0 0.0 0 5 10 15 20 25 0 2 4 6 8 10 12 14 Age (years) Age (years) a 100 Estuarine Fish Offshore Fish Figs. 3.3.7, 3.3.8, 3.3.9
REEF (Reef Environmental Education Foundation) open blue circles: sites without Goliaths yellow dots: sites with Goliaths at least once in a year.
REEF 1993-2014 open blue circles: sites without Goliaths yellow dots: sites with Goliaths at least once in a year.
REEF Index
Estuarine Indices ENP 5 Index Scaled to Mean The MRFSS/MRIP Estuarine 4 indices 3 scaled 2 to their 1 means 0 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 Year 3 Offshore Indices REEF FL Index Scaled to Mean MRFSS/MRIP Offshore 2 1 0 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 Year Fig. 4.4.1 a,b
The Catch-free and SSRA models are types of Age- structured Surplus Production Models (ASPM) • Replaces estimation of production model parameters through incorporation of a stock-recruitment relationship dependent on spawning stock size • Attempts to account for age structure of the population through time • Projects population forward through time through age-structured simulations, accounting for time lags, fleet selectivities, and age schedules for biological parameters (e.g., growth, maturity, fecundity, etc., most often fixed rather than model-estimated) • Tuned with age-aggregated or age-structured abundance indices, each with its own unique age-selection • Typically, ASPMs do not directly incorporate age or size composition of catches, and age schedules are specified by the user (estimated externally to the model) • The Catch-free model is unique among this class of models in that it does not use any information on fishery removals for its estimates.
Typical Age-structured Surplus Production Model inputs Biological parameters (growth, maturity, fecundity, weight-at-age) Harvests (removals) 250,000 Reported Landings + Indices of abundance Recreational Harvests (landings + dead releases (5%) Commercial Landings and 200,000 Adjusted Landings + 5% dead releases), kg dead releases (5%) ENP 5 150,000 Index Scaled to Mean MRFSS/MRIP Estuarine 4 100,000 3 50,000 2 0 1950 1970 1990 2010 Year 1 Vulnerabilities/selectivities 0 1.0 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 0.9 0.8 Year Fishery block1: 1950-1989 0.7 Selectivity 0.6 3 Fishery block2: 1990-2014 0.5 0.4 REEF FL Index Scaled to Mean 0.3 0.2 MRFSS/MRIP Offshore 0.1 2 0.0 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 Age 1.0 1 0.9 0.8 0.7 Selectivity MRFSS/MRIP Offshore 0.6 ENP Juvenile & MRFSS Shore indices 0.5 Dive Reef Index 0.4 0 0.3 0.2 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 0.1 0.0 Year 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36
M=0.18 per year Relative stock status under two different levels of natural mortality, “ F current ” for projections M=0.12 per year
Assessment efforts • SEDAR 47 (2016) – Further revisions and updates to indices – Models: Catch-free and stochastic stock reduction analysis – Analyses rejected by Review Panel. • Did not feel the reconstruction of fishery removals was sufficiently vetted. • Did not accept the indices of abundance as presented. • Did not accept the proxies we used for age structure for fishery catches or indices. • Expressed concern that Data and Assessment Workshops were not held for this SEDAR. • Made recommendations for a designed fishery-independent survey which would provide more acceptable data to examine changes in population abundance and distribution for this species.
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