Search

The most important fisheries are recording catches below their historical averages despite increased effort. This level of overfishing is worrying and requires the establishment of feasible and precise measures to prevent a continuing decrease in biomass. Determining the factors that lead to changes in the abundance and distribution of overfished resources would allow us to identify the strengths and weaknesses of management schemes; this approach would also make it possible to estimate more accurate parameters for their evaluation. We hypothesize that environmental, temporal, spatial, and operational components contribute to the variation in the relative abundance. Thus, we analyzed the red grouper fishery, the most important demersal fishery in the southeastern Gulf of Mexico (SGM); it is locally known as escama. We employed the catch per unit effort (CPUE) as an index of relative abundance recorded by the semi-industrial fleet (kilogram per effective fishing day) and the small-scale fleet (kilogram per effective fishing hour) during the overexploitation phase (from 1996 to 2019). We fitted several variables of the components using generalized additive models (GAM) and used multi-model inference to determine the best GAM for each fleet. For both fleets, the operational and temporal components (fishing gear and year) have had a greater impact on the distribution and abundance of red grouper in the SGM than the spatial and environmental components (the place of origin and sea surface temperature). These findings encourage the exploration of métier schemes for more efficient fishery management. In addition, we have identified several strategies that would support the recovery of the resource, such as restricting fishing in the quadrants located to the northeast or regulating scuba diving. We recommend that in the future, researchers use the indices we have generated in the present study to evaluate the red grouper fishery.

To be as accurate as possible, stock assessments should account for discard mortality in fisheries if it occurs. Three common approaches to modeling discards in assessments are to lump dead discards with landings, treat dead discards as their own fleet, or link them conversely with landings through use of a retention function. The first approach (lumping) implicitly assumes that the selectivity of landings applies also to discards. In many cases, that assumption is false, for example, if discards comprise smaller fish than do landings. The latter two approaches avoid the assumption by modeling discards explicitly with their own selectivity pattern. Here, we examine these approaches to modeling discards. Using a simulation study, we demonstrate that the two approaches to modeling discards explicitly can provide identical results under both static and time-varying conditions. Then, using a stock assessment case study of red grouper Epinephelus morio in the U.S. Gulf of Mexico, we demonstrate that in practice the approaches to modeling discards can provide different outcomes, with implications for the resultant management advice. We conclude by comparing and contrasting the different approaches, calling for more research to elucidate which approach is most suitable under various sources of error typically encountered in discard data.

The spatial dynamics of the red grouper Epinephelus morio plays a fundamental role in the fishery management on the Campeche Bank, where it is overfished. The objective of this study was to characterize the spatial dynamics of this population. We used a dynamic model of spatial distribution to estimate population movement rates between adjacent areas. The model was applied using information on catch per unit effort and length-frequency distributions for the Mexican mid-sized fleet that operated in the area in the period 1973-1977. These data were chosen to ensure that the information would reflect the condition of the resource before overfishing. It was possible to quantify the spatial dynamics of the red grouper and the results were consistent with previously known movement patterns. The results showed different movement rates and temporal patterns of movement for different population sectors. The spawning aggregations and the seasonal upwelling occurring in the eastern part of the Bank were considered as key factors influencing the pattern of population movements.