A Novel and Convenient Method for Early Warning of Algal Cell Density by Chlorophyll Fluorescence Parameters and Its Application in a Highland Lake.

The occurrence of algal blooms in drinking water sources and recreational water bodies have been increasing and causing severe environmental problems worldwide, particularly when blooms dominated by spp. Bloom prediction and early warning mechanisms are becoming increasingly important for preventing harmful algal blooms in freshwater ecosystems. Chlorophyll fluorescence parameters (CFpars) have been widely used to evaluate growth scope and photosynthetic efficiency of phytoplankton. According to our 2-year monthly monitor datasets in Lake Erhai, a simple but convenient method was established to predict blooms and algal cell densities based on a CFpar representing maximal photochemical quantum yield of Photosystems II (PSII) of algae. Generalized linear mixed models, used to identify the key factors related to the phytoplankton biomass in Lake Erhai, showed significant correlations between Chl concentration and both the light attenuation coefficient and water temperature. We fitted seasonal trends of CFpars (/ and Δ/') and algal cell densities into the trigonometric regression to predict their seasonal variations and the autocorrelation function was applied to calculate the time lag between them. We found that the time lag only existed between / from blue channel and algal cell densities even both / and Δ/' show the significant non-linear dynamics relationships with algal cell densities. The peak values of total algal cell density, cyanobacteria density and density followed the foregoing peak value of / from blue channel with a time lagged around 40 days. Therefore, we could predict the possibilities of bloom and estimate the algal cell densities in Lake Erhai ahead of 40 days based on the trends of / values from blue channel. The results from our study implies that the corresponding critical thresholds between / value and bloom occurrence, which might give new insight into prediction of cyanobacteria blooms and provide a convenient and efficient way for establishment of early warning of cyanobacteria bloom in eutrophic aquatic ecosystems.