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A novel aggregate cyanobacterial biomass proportion index for estimating cyanobacteria succession in early eutrophic Lake Erhai, China
Shangbo Yang * 1 , Xiaoling Chen 2 , Jianzhong Lu * 3
1  Doctorial Candidate
2  Professor
3  Associate Professor


Erhai Lake, located on the Yungui plateau in southwest China, has been considered to be in a transition period of ecological process, posing an urgent need for understanding its historical succession of cyanobacteria and further detecting the early signals of cyanobacteria accumulation for developing management strategies in advance. For this reason, an aggregate cyanobacterial biomass proportion index (ACBPI) was introduced as bio-indicator for reflecting increased accumulation of cyanobacteria, through targeting cyanobacteria-associated indexes derived from satellite remote sensing using principal component analysis. Thresholds for ranking the cyanobacteria abundance state were then determined through in situ phytoplankton composition data and the entire ACBPI time series. The results showed that the ACBPI correlated with cyanobacteria biomass proportion with an accuracy level of 66% and cyanobacteria biovolume proportion with coefficient of determination 80%. Dense bloom appeared primarily in northern regions, with 5.5% occurring in 2003, 9.1% in 2006, and 6.7% in 2008. The frequency of moderate bloom in northern lake made up a higher share (14.1±16.0%) across the whole periods, with 6.2±10.7% in central lake and 2.5±4.0% in southern lake. Apparent mitigation of cyanobacterial dominance condition was observed in 2016-2019 in contrast to 2003-2011 with obvious reduction occurring in 2018, probably resulting from series of strict protection initiatives implemented in recent years. However, moderate bloom in northern bays occurred again in 2019, indicating that strict nutrient reduction especially phosphorus pollution should be strengthened under global warming and wind speed decreasing scenario.

Keywords: cyanobacterial biomass proportion; cyanobacterial dominance level; Spatio-temporal variability; early signals