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.
Highlights
Phytoplankton are a major component of marine ecosystems and contribute approximately half of the global primary productivity (Field et al, 1998), providing organic carbon and energy for higher trophic levels in food webs (Calbet and Landry, 2004; Steinberg and Landry, 2017; Abbreviations: Daypeak, the time elapsed until the Chl-a peak occurred.Bloom Magnitude and TimingArmengol et al, 2019)
At the beginning of each bottle incubation experiment, the most dominant planktonic protist species based on carbon biomass in chronological order was the diatom Cylindrotheca closterium on May 16, the photosynthetic dinoflagellate T. furca on May 19, the tintinnid ciliate Tintinnopsis sp. on July 17 and July 22, the diatoms Pseudo-nitzschia pungens on July 24 and Chaetoceros curvisetus on August 20, and the photosynthetic ciliate Mesodinium rubrum on August 22 (Figures 3A–G)
We successfully obtained equations to predict the magnitude and timing of peak phytoplankton blooms by following several critical steps: measuring daily fluctuations in the concentrations of nutrients and Chl-a in seawater enclosures; performing linear regression analyses between pairs of diverse variables described in section “Data Analysis and Equation Development.”
Summary
Phytoplankton are a major component of marine ecosystems and contribute approximately half of the global primary productivity (Field et al, 1998), providing organic carbon and energy for higher trophic levels in food webs (Calbet and Landry, 2004; Steinberg and Landry, 2017; Abbreviations: Daypeak, the time elapsed until the Chl-a peak occurred.Bloom Magnitude and TimingArmengol et al, 2019). Prior to the present study, models for predicting blooms had been established based on correlations between phytoplankton abundance and various environmental factors such as nutrient concentrations and water temperature in natural aquatic environments (e.g., Pinckney et al, 1997; Onderka, 2007; Feki-Sahnoun et al, 2017; Lin et al, 2018; Kahru et al, 2020; Mahmudi et al, 2020) These models may not provide sufficient time for aquaculture farmers to manage their cages at sea or aqua tanks on land. To minimize losses due to harmful blooms, a model for predicting the magnitude and timing of blooms one week in advance is needed
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