Abstract
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 Microcystis 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 Microcystis 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 a concentration and both the light attenuation coefficient and water temperature. We fitted seasonal trends of CFpars (Fv/Fm and ΔF/Fm′) 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 Fv/Fm from blue channel and algal cell densities even both Fv/Fm and ΔF/Fm′ show the significant non-linear dynamics relationships with algal cell densities. The peak values of total algal cell density, cyanobacteria density and Microcystis density followed the foregoing peak value of Fv/Fm from blue channel with a time lagged around 40 days. Therefore, we could predict the possibilities of Microcystis bloom and estimate the algal cell densities in Lake Erhai ahead of 40 days based on the trends of Fv/Fm values from blue channel. The results from our study implies that the corresponding critical thresholds between Fv/Fm 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.
Highlights
Harmful algal blooms (HABs) in freshwater ecosystems are subject of serious concern for ecosystems and human health because they reduce the quality and quantity of habitat for plants and animals, disrupt food web dynamics, create hypoxic zones, and produce toxins (Paerl et al, 2001; Miller et al, 2017)
Data presented in this study correspond to a 2-year (June 2013–May 2015) field survey conducted in Lake Erhai (25◦36 – 25◦58 N, 100◦05 –100◦17 E), the second largest high-altitude freshwater lake of the Yunnan Highlands in China with the normal elevation is 1974 m, to trace algal dynamics and Microcystis bloom
Cyanophyta was the major phylum of phytoplankton during the whole year with 50% of total phytoplankton cell density, and Microcystis was the overwhelming dominant genus during the periods of cyanobacterial blooms with 78% of total cyanobacterial cell density (Figure 3)
Summary
Harmful algal blooms (HABs) in freshwater ecosystems are subject of serious concern for ecosystems and human health because they reduce the quality and quantity of habitat for plants and animals, disrupt food web dynamics, create hypoxic zones, and produce toxins (Paerl et al, 2001; Miller et al, 2017). Artificial neural networks (ANNs) provide an alternative to parametric forecast models, where several environmental factors act as input variables to estimate the evolution of algal bloom and predict cell densities of freshwater phytoplankton species (Recknagel et al, 1997; Lee et al, 2003; Muttil and Chau, 2006) Statistical methods such as cross-correlation (Trimbee and Prepas, 1987), and generalized additive model (Lamon et al, 1996; Tao et al, 2012), as well as the development of satellite remote sensing forecasting techniques (Stumpf, 2001; Kutser, 2004), are other possible options for predicting the occurrence of HAB. Previous studies have highlighted the need for simple, rapid, and geographically non-restricted approaches to predict algae blooms
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