OpenLB
Open Library of Bioscience

The environmental factors contributing to the Microcystis aeruginosa bloom (hereafter referred to as Microcystis bloom) are still debatable as they vary with season and geographic settings. We examined the environmental factors that triggered Microcystis bloom outbreak in India's largest brackish water coastal lagoon, Chilika. The warmer water temperature (25.31-32.48 °C), higher dissolved inorganic nitrogen (DIN) loading (10.15-13.53 μmol L), strong P-limitation (N:P ratio 138.47-246.86), higher water transparency (46.62-73.38 cm), and low-salinity (5.45-9.15) exerted a strong positive influence on blooming process. During the bloom outbreak, M. aeruginosa proliferated, replaced diatoms, and constituted 70-88% of the total phytoplankton population. The abundances of M. aeruginosa increased from 0.89 × 10 cells L in September to 1.85 × 10 cells L in November and reduced drastically during bloom collapse (6.22 × 10 cells L) by the late November of year 2017. The decrease in M. aeruginosa during bloom collapse was associated with a decline in DIN loading (2.97 μmol L) and N:P ratio (73.95). Sentinel-3 OLCI-based satellite monitoring corroborated the field observations showing Cyanophyta Index (CI) > 0.01 in September, indicative of intense bloom and CI < 0.0001 during late November, suggesting bloom collapse. The presence of M. aeruginosa altered the phytoplankton community composition. Furthermore, co-occurrence network indicated that bloom resulted in a less stable community with low diversity, inter-connectedness, and prominence of a negative association between phytoplankton taxa. Variance partitioning analysis revealed that TSM (16.63%), salinity (6.99%), DIN (5.21%), and transparency (5.15%) were the most influential environmental factors controlling the phytoplankton composition. This study provides new insight into the phytoplankton co-occurrences and combination of environmental factors triggering the rapid onset of Microcystis bloom and influencing the phytoplankton composition dynamics of a large coastal lagoon. These findings would be valuable for future bloom forecast modeling and aid in the management of the lagoon.