Phytoplankton bloom dynamics in incubated natural seawater: predicting bloom magnitude and timing

Abstract

Phytoplankton blooms can cause imbalances in marine ecosystems leading to great economic losses in diverse industries. Better understanding and prediction of blooms one week in advance would help to prevent massive losses, especially in areas where aquaculture cages are concentrated. This study has aimed to develop a method to predict the magnitude and timing of phytoplankton blooms using nutrient and chlorophyll-a concentrations. We explored variations in nutrient and chlorophyll-a concentrations in incubated seawater collected from the coastal waters off Yeosu, South Korea, seven times between May and August 2019. Using the data from a total of seven bottle incubations, four different linear regressions for the magnitude of bloom peaks and four linear regressions for the timing were analyzed. To predict the bloom magnitude, the chlorophyll-a peak or peak-to-initial ratio was analyzed against the initial concentrations of NO3 or the ratio of the initial NO3 to chlorophyll-a. To predict the timing, the chlorophyll-a peak timing or the growth rate against the natural log of NO3 or the natural log of the ratio of the initial NO3 to chlorophyll-a was analyzed. These regressions were all significantly correlated. From these regressions, we developed the best-fit equations to predict the magnitude and timing of the bloom peak. The results from these equations led to the predicted bloom magnitude and timing values showing significant correlations with those of natural seawater in other regions. Therefore, this method can be applied to predict bloom magnitude and timing one week in advance and give aquaculture farmers time to harvest fish in cages early or move the cages to safer regions.