Landing data are the most basic information used to manage fisheries, although they are often unavailable or incomplete. The objective of this work was to reconstruct the national database of marine commercial landings for the Brazilian industrial and artisanal fisheries, from 1950 to 2015. Total landings increased strongly from 1950 to mid-1980s and suffered sharp decline in the early 1990s, mainly associated to the collapse of sardine fisheries. After that, another period of increasing landings was observed, but at a much lower rate. Industrial landings always surpassed artisanal landings in Brazilian waters, except for the beginning of the time series, when many industrial fleets had not started yet, and in the early 2000s, when a change in the methodology for collecting landing statistics was implemented in the state of Pará leading to an overestimation of artisanal landings. Artisanal fisheries have been declining since 2005, which is worrisome due to the social impact it may have on local income and food security. Regional differences were also observed, with industrial landings being always higher than artisanal landings in southeastern-southern Brazil, while the opposite was true for the northern-northeastern regions. Higher landings were observed in the southeastern-southern regions when both artisanal and industrial fleets were combined. Sardine and demersal fishes were the main resources landed by industrial fishers. Artisanal fishers caught more species than their industrial counterpart, featuring Xiphopenaeus kroyeri, Cynoscion acoupa, and Ucides cordatus. Although the fishing of Epinephelus itajara was banned in Brazil, it continues to be landed. Yet, catches of this species and others under some threat status are still not properly registered, including: Carcharhinus longimanus, Galeorhinus galeus, Sphyrna lewini, Sphyrna mokarran, Pristis pectinata, and Pseudobatos horkelii. Fishing resources not identified in previous landing reconstruction efforts, such as sea urchins and sea cucumbers, have now been reported. The database presented here should be continuously updated and improved. It is of paramount importance to resume the collection of landing statistics, including information on fishing effort, to assess the relative impact of fisheries and environmental factors on the main Brazilian fishing stocks.
While there have been recent improvements in reducing bycatch in many fisheries, bycatch remains a threat for numerous species around the globe. Static spatial and temporal closures are used in many places as a tool to reduce bycatch. However, their effectiveness in achieving this goal is uncertain, particularly for highly mobile species. We evaluated evidence for the effects of temporal, static, and dynamic area closures on the bycatch and target catch of 15 fisheries around the world. Assuming perfect knowledge of where the catch and bycatch occurs and a closure of 30% of the fishing area, we found that dynamic area closures could reduce bycatch by an average of 57% without sacrificing catch of target species, compared to 16% reductions in bycatch achievable by static closures. The degree of bycatch reduction achievable for a certain quantity of target catch was related to the correlation in space and time between target and bycatch species. If the correlation was high, it was harder to find an area to reduce bycatch without sacrificing catch of target species. If the goal of spatial closures is to reduce bycatch, our results suggest that dynamic management provides substantially better outcomes than classic static marine area closures. The use of dynamic ocean management might be difficult to implement and enforce in many regions. Nevertheless, dynamic approaches will be increasingly valuable as climate change drives species and fisheries into new habitats or extended ranges, altering species-fishery interactions and underscoring the need for more responsive and flexible regulatory mechanisms.
Summary:We propose a novel Bayesian hierarchical structure of state-space surplus production models that accommodate multiple catch per unit effort (CPUE) data of various fisheries exploiting the same stock. The advantage of this approach in data-limited stock assessment is the possibility of borrowing strength among different data sources to estimate reference points useful for management decisions. The model is applied to thirteen years of data from seven fisheries of the lebranche mullet (Mugil liza) southern population, distributed along the southern and southeastern shelf regions of Brazil. The results indicate that this modelling strategy is useful and has room for extensions. There are reasons for concern about the sustainability of the mullet stock, although the wide posterior credibility intervals for key reference points preclude conclusive statistical evidence at this time.Keywords: hierarchical models; MCMC; multiple fisheries; data-limited; stock assessment; Mugil liza.Modelos bayesianos espacio-temporales con datos múltiples de CPUE: el caso de una pesquería de lebranche Resumen: Proponemos una nueva estructura jerárquica bayesiana para modelos de producción excedente espacio-temporales que permite incorporar datos de captura por unidad de esfuerzo (CPUE) de diversas fuentes para varias pesquerías que explotan el mismo stock. La ventaja de este enfoque en la evaluación de stocks con datos limitados es la posibilidad de reforzar las estimaciones a partir de diferentes fuentes de datos para estimar puntos de referencia útiles para las decisiones de gestión. El modelo se aplica a trece años de datos de siete pesquerías de la población meridional de lebranche (Mugil liza), distribuidas a lo largo de las regiones sur y sudeste de Brasil. Los resultados indican que esta estrategia de modelado es útil y puede formar la base de futuras extensiones. En cuanto a la sostenibilidad del efectivo de lebranche, hay razones para preocuparse, aunque los amplios intervalos de credibilidad posterior en los puntos clave de referencia excluyen evidencia estadística concluyente en este momento.Palabras clave: modelos jerárquicos; MCMC; pesquerías múltiples; datos limitados; evaluación de stocks; Mugil liza. Citation/Como citar este artículo: Sant'Ana R., Kinas P.G., Miranda L.V., Schwingel P.R., Castello J.P., Vieira J.P. 2017. Bayesian state-space models with multiple CPUE data: the case of a mullet fishery. Sci. Mar. 81(3): 361-370. doi: http://dx
In the presente study, cluster analysis was performed to classify 1080 interviews with captains of national pelagic longline fleet (2000 to 2011), and 38 trips of the chartered fleet (2003 to 2008), in relation to the composition of species landed. For the national fleet 4 groups were identified: 1 - Albacores, 2 - Blue shark, 3 - Swordfish, and 4 - Dolphinfish. For the chartered fleet 3 groups were identified: 1 - Swordfish, 2 - Blue shark, and 3 - Albacores. The results indicated that part of the national fleet change their strategies according to the availability of the target species and market demand (internal and external). A part of the fleet from Espírito Santo state operates in the region between December and March, exclusively to capture dolphinfish. The chartered fleet differed from the national one as regards the fishing areas of each target species, mainly as regards swordfish and blue shark. Despite the great difference in the data sets, it is evident that both the national and the chartered fleets operated in accordance with their peculiarities and technological development, adopting strategies that optimize catches and net profits per trip.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.