Aquaculture 2025

March 6 - 10, 2025

New Orleans, Louisiana USA

Add To Calendar 08/03/2025 15:45:0008/03/2025 16:05:00America/ChicagoAquaculture 2025ASSESSMENT OF ENVIRONMENTAL AND CLIMATIC FACTORS FOR SPATIAL PLANNING IN AQUACULTURE PRODUCTIONSalon DThe World Aquaculture Societyjohnc@was.orgfalseDD/MM/YYYYanrl65yqlzh3g1q0dme13067

ASSESSMENT OF ENVIRONMENTAL AND CLIMATIC FACTORS FOR SPATIAL PLANNING IN AQUACULTURE PRODUCTION

Rabi Elabor¹, Andongma Wanduku Tende², Charles Jagoe¹, Benjamin M Mwashote¹, Veera L D Badisa¹, Tolulope Fiola¹, Victor Ibeanusi¹

 

  1. School of the Environment, Florida Agricultural and Mechanical University, 1601 S. Martin Luther King Jr Blvd, Tallahassee, FL 32307, USA
  2. Department of Geology, Faculty of Earth and Environmental Sciences, Aliko Dangote

University of Science and Technology, Wudil, Nigeria.

 



Geography information System (GIS) play a crucial role in environmental analysis and can be systematically applied in spatial planning for aquaculture studies. This study analyzes spatial data such as bathymetry, slope, curvature, chlorophyll, temperature, humidity, windspeed, and precipitation to determine the best locations for aquaculture farms in Apalachicola Bay, which is part of the Gulf of Mexico. Data redundancy among variables was examined using the Pearson correlation matrix analysis, while the predictive power of the evidentiary data was evaluated using prediction plot analysis. The Additive Ratio Assessment (ARAS) and Evaluation Based on Distance from Average (EDAS) statistical models were employed for data integration. The prediction accuracy of these spatial models was evaluated using Area Under the Curve and Receiver Operating Characteristics curve (ROC/AUC) analysis. Pearson correlation analysis results indicated a generally low negative correlation and varying positive correlations. Prediction plot analysis revealed a strong correlation (≥ 0.7) between oyster beds and bathymetry (0.83), humidity (0.7), and chlorophyll (0.7). Spatial data integration using the ARAS and EDAS models identified the most ideal zones for aquaculture in the eastern, middle, and western sectors of the study region, accounting for 30.6 percent (ARAS model) and 22.97 percent (EDAS model) of the study site. Validation of prediction accuracy revealed high efficiency for both the ARAS and EDAS models in mapping natural oyster beds, with accuracy scores of 77.0 percent and 77.0 percent, respectively. Basically, the study serves as a valuable environmental and climatic proxy, facilitating integration into oyster aquaculture management programs, promoting informed decision-making, and encouraging sustainable practices. The application of GIS models, integrating climatic, environmental, and geomorphological variables, proves highly effective in identifying optimal sites for aquaculture practices. We recommend implementing these models in other coastal zones with similar geospatial parameters and models.

Keywords: Aquaculture, Oysters. Geospatial mapping, Apalachicola Bay, Multi Criteria