A Dynamic Energy Budget Model for Kuruma Shrimp <i>Penaeus japonicus</i>: Parameterization and Application in Integrated Marine Pond Aquaculture

Individual growth models can form the basis of population dynamics assessment and ecosystem model construction. In order to provide a basic module for an ecosystem model of an integrated marine aquaculture pond, an individual growth model was constructed for kuruma shrimp (<i>Penaeus japonicus...

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Bibliographic Details
Main Authors: Shipeng Dong, Dapeng Liu, Boshan Zhu, Liye Yu, Hongwei Shan, Fang Wang
Format: Article
Language:English
Published: MDPI AG 2022-07-01
Series:Animals
Subjects:
Online Access:https://www.mdpi.com/2076-2615/12/14/1828
Description
Summary:Individual growth models can form the basis of population dynamics assessment and ecosystem model construction. In order to provide a basic module for an ecosystem model of an integrated marine aquaculture pond, an individual growth model was constructed for kuruma shrimp (<i>Penaeus japonicus</i>) based on dynamic energy budget (DEB) theory. The model was first parameterized based on a covariation method using the Add-my-Pet (AmP) procedure. The parametric estimation model underestimated the ultimate abdominal length for female shrimp, and the predicted values of other zero-variate parameters were generally consistent with observed values. The relative errors of the predicted and observed values of the univariate data set within three geographical regions showed acceptable goodness of fit. Parameter estimation achieved an overall goodness of fit with a mean relative error of 0.048 and a symmetric mean squared error of 0.066. A DEB model was constructed using the estimated parameters, and the goodness-of-fit indicators (R square, mean bias and absolute and relative root mean square error) showed that the model was able to reproduce the growth of kuruma shrimp in terms of total length and wet weight with high accuracy. The results provide data to support the subsequent development of integrated aquaculture management at the ecosystem level.
ISSN:2076-2615