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Life History Demographic Parameter Synthesis for Exploited Florida and Caribbean Coral Reef Fishes

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Life History Demographic Parameter Synthesis for Exploited Florida and Caribbean Coral Reef Fishes Please do not remove this page Life history demographic parameter synthesis for exploited Florida and Caribbean coral reef fishes Stevens, Molly H; Smith, Steven Glen; Ault, Jerald Stephen https://scholarship.miami.edu/discovery/delivery/01UOML_INST:ResearchRepository/12378179400002976?l#13378179390002976 Stevens, M. H., Smith, S. G., & Ault, J. S. (2019). Life history demographic parameter synthesis for exploited Florida and Caribbean coral reef fishes. Fish and Fisheries (Oxford, England), 20(6), 1196–1217. https://doi.org/10.1111/faf.12405 Published Version: https://doi.org/10.1111/faf.12405 Downloaded On 2021/09/28 21:22:59 -0400 Please do not remove this page Received: 11 April 2019 | Revised: 31 July 2019 | Accepted: 14 August 2019 DOI: 10.1111/faf.12405 ORIGINAL ARTICLE Life history demographic parameter synthesis for exploited Florida and Caribbean coral reef fishes Molly H. Stevens | Steven G. Smith | Jerald S. Ault Rosenstiel School of Marine and Atmospheric Science, University of Miami, Abstract Miami, FL, USA Age‐ or length‐structured stock assessments require reliable life history demo‐ Correspondence graphic parameters (growth, mortality, reproduction) to model population dynamics, Molly H. Stevens, Rosenstiel School of potential yields and stock sustainability. This study synthesized life history informa‐ Marine and Atmospheric Science, University of Miami, 4600 Rickenbacker Causeway, tion for 84 commercially exploited tropical reef fish species from Florida and the Miami, FL 33149, USA. U.S. Caribbean (Puerto Rico and the U.S. Virgin Islands). We attempted to identify a Email: [email protected] useable set of life history parameters for each species that included lifespan, length Funding information at age, weight at length and maturity at length. Key aspects of the life history syn‐ Biscayne National Park, Grant/Award Number: P15AC01746; Dry Tortugas thesis were development of: (a) a database that characterized study details including National Park, Grant/Award Number: sampling region, biological and statistical methods, length range of sampled individu‐ P15AC012441 and P16AC01758; Florida RESTORE Act Center of Excellence, Grant/ als, sample size, capture gears and sampling time frame; (b) reproducible procedural Award Number: FIO‐4710112600B; criteria for parameter identification for a given species; and (c) a reliability metric National Park Service Natural Resource Conservation Assessment Program, for each parameter type. Complete life history parameter sets were available for 46 Grant/Award Number: H5000105040, species analysed. Of these, only 16 species had parameter sets meeting the highest PA15AC01547 and PA16AC01758; NOAA Southeast Fisheries Science Center & Coral standards for reliability, highlighting future research needs. Reef Conservation Program, Grant/Award Number: NA15OAR4320064‐SUB36 KEYWORDS data‐limited fisheries, fish population dynamics, growth, lifespan, maturity, stock assessment 1 | INTRODUCTION involving scientists, managers, commercial and recreational fishers, and other stakeholders was designed to increase transparency and Tropical reef fish populations have been commercially fished in reliability in assessments, and better inform management strategies Florida and the U.S. Caribbean (i.e. Puerto Rico, U.S. Virgin Islands) for state governments and regional fishery management councils for nearly two centuries (GMFMC, 1981). In the early 1980s, declining (i.e. Gulf of Mexico, South Atlantic and Caribbean). The lack of quan‐ catch rates and overfishing prompted the first regional management titative assessments has been mainly due to inherent limitations in actions that included minimum size limits and gear restrictions for resources for sampling and processing catch, effort and life history several snapper and grouper species (NOAA, 1983). Additional regu‐ data needed to conduct assessments (Newman, Berkson, & Suatoni, lations implemented over the past 30 years have included increased 2015). Life history demography defining lifespan, growth and re‐ minimum size limits, bag limits, gear restrictions, seasonal and spatial productive maturity are critical inputs to both conventional stock closures, and annual catch limits for an increasing number of reef assessments (e.g. biomass dynamics, statistical catch‐at‐age mod‐ fishes (GMFMC, 2008; SAFMC, 2018). The reef ecosystem supports els) and data‐limited approaches (e.g. mean length estimator) (Ault, more than 50 exploited fish species from many families (snappers, Bohnsack, & Meester, 1998; Ault et al., 2019; Haddon, 2011; Quinn groupers, grunts, porgies, triggerfishes, parrotfishes, etc.), yet only a & Deriso, 1999). In addition, life history information contributes to small portion have undergone formal quantitative stock assessments other analyses focused on assessing productivity and susceptibility via the federal SEDAR (SouthEast Data, Assessment, and Review) (Patrick et al., 2009), which can be used to guide management deci‐ process (e.g. SEDAR, 2014, 2015b, 2016a). This cooperative review sions or stock assessment prioritization. 1196 | © 2019 John Wiley & Sons Ltd wileyonlinelibrary.com/journal/faf Fish and Fisheries. 2019;20:1196–1217. STEVENS et AL. | 1197 Reef fish life history demographic research in the tropical central western Atlantic began in earnest in the 1980s, well after the effects of intensive exploitation by commercial and recreational fisheries 1. INTRODUCTION 1196 were evident. This can be highly problematic for obtaining accurate 2. METHODS 1197 population dynamics required for stock assessments. A central com‐ 2.1. Life history parameters 1197 ponent of cohort‐structured assessment models is a function that 2.2. Commercial fleet sampling database, 1198 describes average lifetime growth of an individual fish, such as the 1983–2016 von Bertalanffy (1938) function that models average length from 2.3. Literature synthesis and database for life 1199 age 0 to the biological maximum age (defined as ‘lifespan’). The usual history studies data for developing lifetime growth functions are paired length‐age 2.4. Units and conversions 1199 observations for fish sampled from the population. Increasing rates 3. RESULTS 1202 of fishery exploitation lead to decreasing probabilities that individual 3.1. Exploited species and maximum lengths 1202 fish will live to their biological maximum age or grow to their max‐ 3.2. Life history synthesis and parameter 1203 imum sizes, making a population younger, smaller and less fecund selection (i.e. juvenescence) compared to its unfished state (Ault et al., 1998; 3.3. Best available life history parameters 1206 Harris, Wyanski, White, & Moore, 2002; McBride & Richardson, 4. DISCUSSION 1209 2007). Developing lifetime growth functions from specimens ob‐ ACKNOWLEDGEMENTS 1213 tained from fishery‐truncated length (and thus age) distributions REFERENCES 1213 may give the perception that a species is faster‐growing and shorter‐ lived, when in fact it is a much slower‐growing and longer‐lived spe‐ cies. This may in turn lead to the perception that exploitation rates are sustainable when they are actually too high and not sustainable, where L∞ is mean asymptotic length, K is the Brody growth coeffi‐ because faster‐growing, shorter‐lived species are generally more re‐ cient, and a0 is theoretical age at length zero. Observed maximum silient to fishing compared to slower‐growing, longer‐lived species age a was used to estimate the mean length at oldest age L from (Beverton & Holt, 1957; Ricker, 1954). We reviewed several hundred scientific and technical publications TABLE 1 Life history demographic parameters compiled for exploited reef fishes and inventoried all available life history demographic parameters for exploited reef fishes in Florida and the U.S. Caribbean. Characteristics Parameter Definition Units of the various investigations, including time and location of specimen a Maximum observed age years collection, biological and statistical methods, length ranges and sample L Length at maximum age mm fork length sizes, were assessed to guide selection of the best available parameters (FL) for age, growth and maturity for each species. Potential problems in L ∞ Asymptotic length mm FL lifetime growth functions arising from fishery truncation were evalu‐ K Brody's growth coefficient per year ated by comparing length distributions from length‐age studies with a 0 Theoretical age at length 0 years those from fishery‐dependent sampling data with high temporal and Weight‐length scalar kg∕mmβ spatial resolution. Analyses of biological and statistical study charac‐ Weight‐length power dimensionless teristics were used to develop a species‐level reliability metric for the L selected life history parameters, providing guidance for use in stock m Length at 50% maturity mm FL a assessments and for focusing future life history research. m Age at 50% maturity years L 99 99th percentile of commercial mm FL lengths 2 | METHODS Lmin Minimum length sampled mm FL Lmax Maximum length sampled mm FL 2.1 | Life history parameters Ld Desired length units mm The life history synthesis was designed to obtain reliable demo‐ Wd Desired weight units kg L graphic parameters describing lifetime growth, survivorship and 1 Original length units cm, in, etc. reproductive maturity required for size‐age cohort‐structured stock W 1 Original weight units g,lb,etc. assessments (Table 1; c.f., Ault et al., 1998; Quinn & Deriso, 1999). u Weight conversion factor for kg∕ (g, lb, etc.) Lifetime growth was described
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