Zhen Liu , Yeping Han

Institute of Life Science, Jiyang College of Zhejiang A&F University, Zhuji, 311800, Zhejiang, China
Author    Correspondence author
International Journal of Marine Science, 2026, Vol. 16, No. 4   
Received: 29 Jun., 2026    Accepted: 05 Aug., 2026    Published: 17 Aug., 2026

This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Shrimp aquaculture is highly dependent on environmental stability, and fluctuations in water quality parameters can significantly affect growth performance, physiological health, and survival rates. This review aims to summarize the application of statistical approaches for evaluating environmental effects on shrimp production and to establish a quantitative framework for understanding environmental-growth-survival relationships. Key environmental factors, including temperature, dissolved oxygen, salinity, pH, ammonia, and nitrite, are discussed in relation to their impacts on shrimp metabolism, immune responses, feeding behavior, and mortality risk. Statistical methods such as correlation analysis, regression models, mixed-effects models, survival analysis, and machine learning algorithms are evaluated for their effectiveness in identifying critical environmental drivers and predicting shrimp performance. A case study based on Pacific white shrimp (Litopenaeus vannamei) production demonstrates how environmental monitoring data can be integrated with predictive models to determine optimal culture conditions and develop early-warning systems. Furthermore, the integration of statistical modeling with sensor-based monitoring and precision aquaculture technologies provides new opportunities for improving production efficiency and environmental management. Overall, statistical analysis serves as an essential tool for transforming environmental data into actionable strategies, supporting sustainable shrimp farming under increasing environmental variability and climate-related challenges.

Shrimp aquaculture; Environmental factors; Statistical modeling; Growth performance; Survival analysis