Management for the White Shrimp (Litopenaeus vannamei) from the Southeastern Gulf of California through Biomass Models Analysis

Abstract

Samples taken during the closed fishing seasons from 1992 to 2010 were analyzed at sea. These data along with the landing records for the fishing periods from 1992-1993 to 2009-2010 were used to allow the situation of Litopenaeus van-namei from the coasts of Sinaloa and Nayarit to be analyzed by means of stochastic models and by a graphic approach for the surplus biomass. Using the catch from 1993-1994 as a reference point and comparing this to the 2008-2009 catch revealed a stock decrease of about 65%. By taking into account the percentage contribution to total shrimp land-ings, these changes showed a decrease from 76% to 12%. There were changes between 2000 and 2001 when the fleet grew by 50%. Considering a 3600 t maximum sustained yield (MSY) in the series 1992-2010, 50% of the reports are lower. It is necessary to recover the stock

Bioeconomics of technological interdependencies in a sequential shrimp fishery: optimal size of the industrial fleet in southern Gulf of California

“…The bioeconomic model shows for the analyzed 2014-2015 fishing season a L. vannamei stock decrease of 73% with respect to the calculated virgin biomass (X max ). This problem is recurrent and had already been reported for the species and area showing similar values in the population reduction of 76% (Pérez-Vivar, 2003) and 65% (Madrid-Vera et al, 2012), so historically it has not been possible to achieve an adequate fishing management. Moreover, this status of overexploitation was also observed in the white shrimp stock of the Gulf of Tehuantepec (Cervantes-Hernández et al, 2006;Cervantes-Hernández et al, 2008;Ramos-Cruz, 2013).…”

“…Reported stock assessments for the Pacific penaeid stock indicates a fully exploited fishery (National Fishing Chart, DOF 11/06/2018). However, there are local areas reporting overfishing status and deteriorated shrimp stocks in Sonora, Sinaloa and Tehuatepec (Madrid-Vera et al, 2012;INAPESCA, 2016;Cervantes-Hernández et al, 2008;Rivera-Velázquez et al, 2009;Ramos-Cruz, 2013). In the case of the overexploited white shrimp, the main conflicts are caused by: a) illegal fishing during closure; b) overcapitalization of both fleets; c) weak governance; and d) limitations on fishery surveillance and enforcement for regulation, especially for the small-scale fleet.…”

“…Unfortunately, most Mexican shrimp fishery models do not address the fundamental structure of sequential fishery analysis: technological interdependencies and/or age-structure models. Their main focus is on industrial fleet fishing assessment using dynamic biomass models under constant natural mortality and/or catchability assumptions (Industrial fleet: Morales-Bojórquez et al, 2001;Medina & Soto, 2003;Cervantes-Hernández et al, 2006;García-Juárez et al, 2009;Meráz-Sánchez et al, 2013;García-Juárez et al, 2014;Small scale fleet: Rivera-Velazquez et al, 2009;Madrid-Vera et al, 2012). BIOECONOMICS OF A MEXICAN SHRIMP SEQUENTIAL FISHERY As such, this study aims to represent the seasonal bioeconomic behavior of the white shrimp sequential fishery in southern Sinaloa for the 2014-2015 season using an age-structured bioeconomic model with two heterogenous fleets competing for the same resource and harvesting different age components of the stock (i.e.…”

An Approach to Assessment to Population of the Brown Shrimp, <i>Farfantepenaeus californiensis</i> (Holmes, 1900), as a Management Fisheries Tool in the Southeastern Gulf of California

“…These models are an alternative to age-structured estimation approaches, such as virtual population analysis, when the information on age structure of the catch is not available. Biomass dynamic models are the most commonly used stock assessment techniques for shrimp, and have become a powerful analysis tool [6][7][8][9][10][11].…”

“…For shortlived species such as brown shrimp, the value of parameter approaches 1.0 [16]. Morales-Bojorquez et al [7] estimated values of 2.50 for the same species with observation error, and 4.06 with process error; Madrid-Vera et al [11] estimated values of 0.34 for Litopenaeus vannamei using observation error; and García-Juárez et al [28] estimated values of 1.18 for Litopenaeus styilirostris using observation error. Thus our best estimate for this parameter is well within the expected value for the species, and below a value which will theoretically generate deterministic chaos in population trajectories, a situation which is not supported by observations [6].…”