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Integrating the US Caribbean reef fish visual census into fishery stock assessments
The United States Caribbean reef fish visual census (RVC) is a fishery-independent survey of the coral reef fish community, comprised of hundreds of species, designed to provide quantitative information for data-poor fisheries to support regional stock assessments. The Caribbean RVC, implemented in 2014 as part of the National Coral Reef Monitoring Program (NCRMP), uses a probabilistic stratified random sampling design capitalizing on the strong mean-variance relationship of population abundance dependent on hardbottom habitats and depths. Here we highlight RVC data for queen triggerfish in Puerto Rico, where survey precision improved from CVs of 20.6% in 2014 to 12.6% in 2021, which produced accurate and cost-effective estimates of key assessment metrics like size-structured relative population abundance and biomass. We estimated a relatively stable exploited-phase average population abundance for 2014â2021 at 2.3 million queen triggerfish with a biomass of 1246 metric tons. To support stock assessments using âstatistical catch at ageâ models, we generated a 2001â2021 time series of several population metrics by a statistical conversion of spatially-restricted pre-NCRMP data to the RVC frame. To cross-check assessment modeling, we combined 2014â2021 RVC results with life history demographics in an alternative length-based risk analysis (LBRA) model to evaluate stock sustainability consequences using two lifetime growth scenarios. LBRA models indicated low overfishing risks. We concluded that only a few quality fishery independent surveys are needed to obtain understanding of stock sustainability status, and that RVC not only provides reliable data for single-species stock assessments, but also for design evaluation of marine protected areas and ecosystem-based fishery management
Population Structure and Evolutionary History of Southern Flounder in the Gulf of Mexico and Western Atlantic Ocean
An Updated Description of the Benefits and Consequences of Red Snapper Shrimp Trawl Bycatch Management Actions in the Gulf of Mexico
Red Snapper Distribution on Natural Habitats and Artificial Structures in the Northern Gulf of Mexico
<p>In 2011, an intensive, multiple-gear, fishery-independent survey was carried out in the northern Gulf of Mexico (GOM) to collect comprehensive age and length information on Red Snapper <i>Lutjanus campechanus</i>. Based on this synoptic survey, we produced a spatial map of Red Snapper relative abundance that integrates both gear selectivity effects and ontogenetically varying habitat usage. Our methodology generated a spatial map of Red Snapper at a 10-km<sup>2</sup> grid resolution that is consistent with existing knowledge of the species: Red Snapper occurred in relatively high abundances at depths of 50â90 m along the coasts of Texas and Louisiana and in smaller, patchy âhot spotsâ at a variety of depths along the Alabama coast and the west Florida shelf. Red Snapper biomass and fecundity estimates were higher for the northwestern GOM than for the northeastern GOM, as the latter area contained mostly smaller, younger individuals. The existence of similar surveys on petroleum platforms and artificial reefs also enabled us to calculate their relative contribution to Red Snapper distribution compared with that of natural habitats. We estimated that for the youngest age-classes, catch rates were approximately 20 times higher on artificial structures than on natural reefs. Despite the high catch rates observed on artificial structures, they represent only a small fraction of the total area in the northern GOM; thus, we estimated that they held less than 14% of Red Snapper abundance. Because artificial structuresâparticularly petroleum platformsâattract mostly the youngest individuals, their contribution was even lower in terms of total population biomass (7.8%) or spawning potential (6.4%). Our estimates of Red Snapper relative abundance, biomass, and spawning potential can be used to design spatial management strategies or as inputs to spatial modeling techniques.</p> <p>Received January 1, 2016; accepted October 28, 2016</p