Dynamic stock model for analysing semi‐intensive production of whiteleg shrimpLitopenaeus(Penaeus)vannameiaffected by the acute hepatopancreatic necrosis disease: Assessment of disease severity indicators and relationships with pond water quality parameters

Abstract

The production of Litopenaeus vannamei was analysed when affected by the acute hepatopancreatic necrosis disease using a dynamic stock model and primary data of seven production cycles from a shrimp farm in Mexico from 2013 to 2016. Significant results (p < .05) of the correlation analysis indicated that during those years mortalities by the disease were more severe when water salinity was high and productivity was low. Significant results from ANOVA showed that throughout the period, disease severity and salinity diminished while pond productivity initially declined but subsequently improved. Significant results from regression analyses conducted for each production cycle also indicated the importance of salinity and productivity on disease severity and showed that early mortality by the disease occurred in ponds with warmer water. Within the observed range of water quality parameters, increases of 1 cm in water transparency and 1 g/L in salinity resulted in increments within 0.17%–0.25% and 1.7%–3.1% in shrimp mortality by the disease. When increases of 1°C in water temperature were recorded, outbreaks occurred 0.2–1.57 weeks earlier. In conclusion, the disease strongly determines the dynamics of shrimp production, and the role of salinity, productivity and temperature is worthy of further delving.

Growth and population modelling based on density of the Pacific white shrimp intensively cultured in freshwater

“…Furthermore, N 3 was used to represent the number of individuals within the population (Table 3). In contrast to most population models used in aquaculture which assume a constant mortality rate, G 3 is a non‐linear model to population's density (Araneda et al, 2013; Estrada‐Pérez et al, 2019, 2020; Ruiz‐Velazco et al, 2010; Villanueva et al, 2013).…”

“…To represent this phenomenon, multiple regression approach has been applied to empirical data to generate growth models (Tian et al, 1993; Yu et al, 2006, 2009). Recently, Estrada‐Pérez et al (2019) predicted the harvest biomass on each culture cycle using a multilinear model and including factors such as seed stock density by following similar previous studies (Estrada‐Pérez et al, 2018; González‐Romero et al, 2018; Ruiz‐Velazco et al, 2010).…”

“…Recently, Estrada-Pérez et al (2019) predicted the harvest biomass on each culture cycle using a multilinear model and including factors such as seed stock density by following similar previous studies (Estrada-Pérez et al, 2018;González-Romero et al, 2018;Ruiz-Velazco et al, 2010).…”

EN PRENSA Predicting semi-intensive production of Penaeus vannamei using simple linear regression models: A sensitivity analysis using environmental and management variables. EN PRENSA

“…En el mismo sentido, se ha enfatizado el uso de unidades de producción con tamaño de estanque pequeño para las mejores prácticas de manejo (Hernández-Llamas & Villareal-Colmenares, 1999;Milstein et al, 2005;Magallón, 2006). Varios estudios que utilizan modelos de regresión lineal múltiple apoyan lo anterior, ya que establecen que en unidades pequeñas se obtienen menores tasas de mortalidad (Ruiz-Velazco et al, 2010b;Estrada-Pérez et al, 2020). Del mismo modo, el presente estudio también encontró una correlación negativa entre los estanques de gran tamaño y la supervivencia.…”

Quantifying the Importance of Abiotic and Biotic Factors Governing the Succession of Gut Microbiota Over Shrimp Ontogeny

“…Rearing water temperature was positively associated with the abundances of anaerobes and the anaerobic Bifidobacterium (Li et al, 2018). Considering the functional importance of gut microbiota in host health, this pattern may partially explain why a sudden change in water temperature generally causes shrimp disease (Estrada-Perez et al, 2020). Furthermore, water temperature could directly alter the bacterioplankton community (Yang et al, 2018), which in turn affects shrimp gut microbiota.…”