EVALUATING ENVIRONMENTAL VARIABLES THAT INFLUENCE POND DISSOLVED OXYGEN TO INFORM PREDICTION MODEL DEVELOPMENT

Pond aquaculture accounts 65% of global finfish production. A major factor limiting pond aquaculture productivity is fluctuating oxygen levels, which are heavily influenced by atmospheric conditions and primary productivity. Being able to predict DO concentrations by measuring environmental parameters would be beneficial to improving the industry’s efficiencies. The data collected included pond DO, water temperature, air temperature, atmospheric pressure, wind speed/direction, solar irradiance, rainfall, pond Chl-a concentrations as well as water color images. Pearson’s correlations and stepwise regressions were used to determine the variables’ connection to DO and their potential usefulness for a prediction model. It was determined that sunlight levels play a crucial role in DO fluctuations and crashes because of its influence on pond heating, primary productivity, and pond stratification. It was also found that image data did have correlations to certain weather variables and helped improve prediction strength.