Artículo de publicación ISISome 290 species of squids comprise the order Teuthida that belongs to the molluscan Class Cephalopoda. Of these, about 30-40 squid species have substantial commercial importance around the world. Squid fisheries make a rather small contribution to world landings from capture fisheries relative to that of fish, but the proportion has increased steadily over the last decade, with some signs of recent leveling off. The present overview describes all substantial squid fisheries around the globe. The main ecological and biological features of exploited stocks, and key aspects of fisheries management are presented for each commercial species of squid worldwide. The history and fishing methods used in squid fisheries are also described. Special attention has been paid to interactions between squid fisheries and marine ecosystems including the effects of fishing gear, the role of squid in ecosystem change induced by overfishing on groundfish, and ecosystem-based fishery management
Coastal shellfish are being threatened by several drivers acting at multiple temporal and spatial scales, including fishing, climate, and globalization of markets. We evaluated largescale and long-term combined effects of fishing, climate, and economic variables on 2 congeneric clams that inhabit sandy beaches of the Pacific (Mesodesma donacium) and the Atlantic (M. mactroides) in South America. Bioeconomic and climatic variables, such as coastal sea surface temperature anomalies (SSTA) and broad-scale climatic indices (Pacific Decadal Oscillation and Atlantic Multidecadal Oscillation), were related to variations in clam populations in a differential way according to latitude and oceanographic features. For M. donacium, the nature and sign of the relationships between landings and explanatory predictors markedly differed between bioclimatic units. El Niño Southern Oscillation events negatively affected landings in Peru and northern Chile, whereas landings increased in southern Chile and showed a positive correlation with increasing SSTA, suggesting a positive effect at the southernmost edge of the species distribution. Long-term trends in the abundance of M. mactroides were related to fishing intensity and SSTA. As anticipated by basic economic theory, deficit of supply relative to demand, exacerbated by very low harvesting costs, pushed the price up and has driven these clam species to levels close to extinction (anthropogenic Allee effect). The lack of response of the stocks to long-term closures suggests that these systems exceeded critical thresholds (tipping points). Information on early warnings of tipping points is needed to help manage coastal shellfisheries that are increasingly threatened by long-lasting and large-scale stressors.
a b s t r a c tThe Peruvian Bay scallop (Argopecten purpuratus) fishery in Independencia bay (Southern Peru) is being subjected to great inter-annual variability in catch and effort. This is mainly due to the ENSO (El Niño-Southern oscillation)-caused changes in the population dynamics of the stock, which greatly proliferated during the El Niño events 1983 and 1998. As a consequence "gold rush" conditions arose and resource users profited from a multi-million dollar export business. After the El Niño booms, the system normalized and catches dropped to normal levels. This boom and bust situation has made a rational management of the resource difficult, and annual catches are considered unpredictable, just like the stochastic environment. This paper attempts to provide a catch forecast model to enable the scallop fishery to better prepare for and adapt to the ever-changing conditions of the scallop stock.The model proposes that annual catches are mainly the result of the recruitment success of the incoming new cohort, which is a function of adult spawning stock size and the number of settlers to the sea bottom. The latter is considered a function of the larval mortality rate and the temperature-dependent development time to the settlement stage, while the former is proportional to the catches taken over the spawning period (November-April). Using monthly catch and temperature data for the period 1983-2005, we constructed a regression model to predict the catch for the year after the recruitment period (July-June) as a function of (a) the catch during the spawning period (as a proxy for spawning stock biomass) and (b) the settlement factor that was derived from the mean water temperature over the spawning period, an assumed instantaneous larval mortality rate, and the relationship between temperature and larval period to settlement. The resulting multiple regression (R 2 = 0.930) proves that both factors can explain a large part of the inherent variability of the data. The model reveals that annual catches greatly depend on the spawning stock size when temperatures are low, while this factor decreases in importance with increasing temperatures, at which the settlement factor is much more influential instead. These findings are relevant for the stock management: at low temperatures, the maintenance of a large enough spawning stock over the spawning period (November-April) is decisive for the yield of the post-recruitment fishing period thereafter, while at increasing spawning temperatures, spawning stock size is of decreasing importance for determining the yield.
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.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.