Red grouper - Southeastern Atlantic coast of USA|
Fact Sheet Title Fact Sheet |
| | Red grouper - Southeastern Atlantic coast of USA |
| Data Ownership | This document provided, maintained and owned by Food and Agriculture Organization (FAO) , is part of WECAFC Stock Status Reports data collection. |
| ident Block | ident Block![tree map display tree map](/fi/figis/assets/images/factsheets/addinfo.gif) | | Species List: | Species Ref: en - Red grouper, fr - Mérou rouge, es - Mero americano |
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| ident Block Red grouper - Southeastern Atlantic coast of USA
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Large Marine Ecosystem Areas (LME) |
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6 | Southeast U.S. Continental Shelf |
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| Aq Res | Biological Stock: Yes
Value: National Management unit: Yes
Reference year: 2015
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Considered a management unit: An aquatic resource or fishery is
declared as [Fishery] Management Unit if it is
effectively the focus for the application of selected
management methods and measures, within the broader
framework of a management system. According to the FAO
Glossary for Responsible Fishing, "a Fishery Management
Unit (FMU) is a fishery or a portion of a fishery
identified in a Fishery Management Plan (FMP) relevant
to the FMP's management objectives." FMU's may be
organised around fisheries biological, geographic,
economic, technical, social or ecological dimensions ,
and the makeup and attribute of a fishery management
unit depends mainly on the FMP's management
objectives. |
Jurisdictional distribution: Jurisdictional qualifier (e.g.
"shared", "shared - highly migratory") of the aquatic
resource related with its spatial distribution. |
Environmental group: Classification of the aquatic
resource according to the environmental group (e.g.
pelagic invertebrate, or demersal fish) to which the
species belong. |
Reference Year: The Reference Year is the last year considered in the stock assessment and/or fishery status. |
| | | | Aq Res State Trend For Red grouper (Epinephelus morio) the most recent stock assessment indicates overexploitation and overfishing in the USA Southeastern Atlantic coast (SEDAR, 2017a). Habitat Bio Climatic Zone: Tropical. Vertical Dist: Demersal. Water Area Overview ![](/fi/figis/assets/images/addinfo_black.gif) | Water Area Overview Red grouper - Southeastern Atlantic coast of USA
Large Marine Ecosystem Areas (LME) | 6: Southeast U.S. Continental Shelf |
| | | | Water Area Overview
| | | | Water Area Overview |
Water Area OverviewRed grouper - Southeastern Atlantic coast of USA Aq Res Struct Biological Stock: Yes Bio Assess Uncertainty: High Data Data up to 2015. Landings of red grouper from the US southeast Atlantic in 2016 represent less than 1% of red grouper landings in FAO Area 31. The catch-age model included data from four fleets that caught red grouper in southeastern U.S. waters: commercial lines (handline and longline), commercial other (pots, traps, diving, trawl, miscellaneous), recreational headboat, and general recreational. The model was fitted to data on annual landings (in numbers for the recreational fleet, in whole weight for commercial fleets); annual discards (in numbers, with a 0.2 release mortality rate applied); annual length compositions of landings, discards, and the SERFS; annual age compositions of landings and the SERFS; two fishery dependent indices of abundance (commercial handline, headboat); and one fishery independent index of abundance (SERFS combined chevron trap and video gears). Assess Models Type: Age-structured Beaufort Assessment Model (BAM) The primary model used in SEDAR19 – and updated here – was the Beaufort Assessment Model (BAM), a statistical catch-age formulation. A base run of BAM was configured to provide point estimates of key management quantities, such as stock and fishery status. The model is similar in structure to Stock Synthesis. Results Measures of Overall Model Fit - In general, the Beaufort Assessment Model (BAM) fit well to the available data. Predicted length compositions from each fishery were reasonably close to observed data in most years, as were predicted age compositions (Figure 4). The model was configured to fit observed commercial and recreational removals closely (Figures 5–11). Fits to indices of abundance generally captured the observed trends but not all annual fluctuations (Figures 12–14). - Stock Abundance and Recruitment - In general, estimated abundance at age shows a structure that has been relatively consistent through time, reflecting effects of year-class strength and annual fishing mortality, but without severe age truncation (Figure 15; Table 6). Total estimated abundance decreased until 1990, then increased to a peak in 2004, then decreased again until reaching its lowest values at the end of the assessment period 2013-2015. The uptick in 2016 should be interpreted with caution, as it is primarily based on the forecast of recruitment (age-1 fish), which is uniformed by data on year-class strength. Instead, the forecasted 2016 value is that expected from the spawner-recruit curve precisely, which is larger than any in the last decade. Annual number of recruits is shown in Table 6 (age-1 column) and in Figure 16. The highest recruitment values were predicted to have occurred in 2003–2004. Since then, recruitment has been below expectation. - Total and Spawning Biomass - Estimated biomass at age followed a similar pattern as abundance at age (Figure 17; Table 7). Total biomass and spawning biomass showed similar trends—general decline until 1990, increase until the mid-2000s, and decrease since (Figure 18; Table 8). - Status of the Stock and Fishery - Estimated time series of stock status (SSB/MSST and SSB/SSBMSY) showed general decline throughout the beginning of the assessment period, increase starting about 1990, and then decrease since 2007 (Figure 34, Table 8). Base-run estimates of spawning biomass have remained below the threshold (MSST) for most of the assessment period. Current stock status was estimated in the base run to be SSB2015/MSST = 0.38 and SSB2015/SSBMSY = 0.29 (Table 18), indicating that the stock has not yet recovered to SSBMSY. Median values from the MCB analysis indicated similar results (SSB2015/MSST = 0.37 and SSB2015/SSBMSY = 0.27). The uncertainty analysis suggested that the terminal estimate of stock status is robust (Figures 35, 36). Of the MCB runs, 100% indicated that the stock in 2015 was below SSBMSY, and 97.7% that the stock was below MSST. Age structure estimated by the base run generally showed fewer fish of all ages than the (equilibrium) age structure expected at MSY (Figure 37). The 2015 age structure showed more old fish than in previous years, reflecting the strong recruitment pulse in the early 2000’s, and it showed fewer young fish, reflecting the poor recruitment in recent years. The estimated time series of F/FMSY suggests that overfishing has occurred throughout most of the assessment period (Table 8), but with some uncertainty in terminal years demonstrated by the MCB analysis (Figure 34). Current fishery status in the terminal year, with current F represented by the geometric mean from the period 2013–2015, was estimated by the base run to be F2013−2015/FMSY = 1.54, and the median value was F2013−2015/FMSY = 1.54 (Table 18). The fishery status was less robust than the stock status (Figures 35, 36). Of the MCB runs, approximately 89.1% agreed with the base run that the stock is currently experiencing overfishing. Sci Advice Research Recommendations - • Further develop methods to combine SERFS chevron trap and video gears for creating indices of abundance. • Evaluate sample size cutoffs for using age and length compositions. • It appears that the sampling intensity for fish comprising age and length compositions has diminished, particularly for the commercial sector in 2015. Why? • In stock assessment, various likelihood formulations have been used for fitting age and length composition data. The multinomial distribution and its robust versions have been the most widely applied. However, more recently the Dirichlet-multinomial and logistic-normal have attracted attention. A simulation study could shed light on the performance of these various likelihood formulations under sampling conditions realistic in the southeast U.S. • The assessment indicated that recruitment has been lower than expected since 2005. Why? Can environmental or ecological drivers of recruitment be identified? What are the mechanisms? • Red grouper were modeled in this assessment as a unit stock off the southeastern U.S. For any stock, variation in exploitation and life-history characteristics might be expected at finer geographic scales. Modeling such substock structure would require more data, such as information on the movements and migrations of adults and juveniles, as well as spatial patterns of larval dispersal and recruitment. Even when fine-scale spatial structure exists, incorporating it into a model may or may not lead to better assessment results (e.g., greater precision, less bias). Spatial structure in a red grouper assessment model might range from the very broad (e.g., a single Atlantic stock) to the very narrow (e.g., a connected network of meta-populations living on individual reefs). What is the optimal level of spatial structure to model in an assessment of snapper-grouper species such as red grouper? Are there well defined zoogeographic breaks (e.g., Florida keys, Cape Hatteras) that should define stock structure? How much connectivity exists between the Gulf of Mexico and Atlantic stocks? • Protogynous life history: 1) Investigate possible effects of hermaphroditism on the steepness parameter; 2) Investigate the sexual transition for temporal patterns, considering possible mechanistic explanations if any patterns are identified; 3) Investigate methods for incorporating the dynamics of sexual transition in assessment models. • In this assessment, the number of spawning events per mature female per year was implicitly assumed to be constant. The underlying assumptions are that spawning frequency and spawning season duration do not change with age or size. Research is needed to address whether these assumptions for red grouper are valid. Age or size dependence in spawning frequency and/or spawning season duration would have implications for estimating spawning potential as it relates to age structure in the stock assessment. Management Management unit: Yes Sources FAO. Western Central Atlantic Fishery Commission. 2019. Review of the state of fisheries and fisheries resources in the WECAFC region. Meeting document WECAFC/SAG/IX/2018/3 of the ninth session of the Scientific Advisory Group, Christ Church, Barbados, 19-20 November 2018. http://www.fao.org/fi/static-media/MeetingDocuments/WECAFC/SAG2018/3e.pdfSEDAR. 2017a. SEDAR 53 – South Atlantic Red Grouper Assessment Report. SEDAR, North Charleston SC. 159 pp. http://sedarweb.org/docs/sar/S53_SA_RG_SAR_2.22.2017_0.pdf Bibliography FAO. Western Central Atlantic Fishery Commission/FAO Commission des pêches pour l’Atlantique Centre-Ouest/FAO Comisión Central de Pesca para el Atlántico CentroOccidental. 2019. Report of the ninth session of the Scientific Advisory Group, Christ Church, Barbados, 19-20 November 2018. Rapport de la neuvième session du Groupe scientifique consultatif, Christ Church, Barbade, 19-20 Novembre 2018. Informe de la octava sesión del Grupo Asesor Científico, Christ Church, Barbados, 19-20 de Noviembre de 2018. FAO Fisheries and Aquaculture Report/Rapport sur les pêches et l’aquaculture/Informe de Pesca y Acuicultura. No. 1266. Bridgetown, 156 pp. http://www.fao.org/3/ca4776t/ca4776t.pdfAll references to figures, tables and bibliography in the text are found within the source of information. |
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