Fig 1.
Map of the study area.
Table 1.
Characteristics of water bodies investigated in this study.
Table 2.
Primers used for qPCR to amplify 16S rDNA of total cyanobacteria, Synechococcus spp., Microcystis aeruginosa, and Dolichospermum spp.
Table 3.
Environmental parameters in the water bodies (mean ± SD) during the study period as measured at the sampling stations.
Water body abbreviations are given in Table 1.
Table 4.
Mean (SD) nutrient concentrations at the sampling stations in water bodies during the study period.
Water body abbreviations are given in Table 1.
Fig 2.
Principal components analysis.
Bi-plot of physicochemical variables measured in all water bodies during the entire study period. Water body abbreviations are given in Table 1.
Fig 3.
Percent composition by (A) rDNA copy numbers for Synechococcus spp., Microcystis aeruginosa, Dolichospermum spp., and other cyanobacteria, and (B) biovolume for the three identified algal taxa. Water bodies are listed from left to right in order of increasing chl a concentration (Table 3).
Fig 4.
Seasonal dynamics of cyanobacteria.
16S rDNA densities during the study period for (A) Synechococcus spp., (B) Microcystis aeruginosa, and (C) Dolichospermum spp. Shaded areas represent the wet season (May–October) as defined in text. Dashed horizontal lines represent detection limits.
Fig 5.
Regression models for the three algal taxa.
16S rDNA densities of (A) Synechococcus spp., (B) Microcystis aeruginosa, and (C) Dolichospermum spp. plotted against a respective significant predictor, with separate regression lines for different water bodies, constituting another significant predictor, water. PAR, photosynthetically active radiation; TN, total nitrogen.
Table 5.
Estimates of coefficients included in multiple regression models, and their significance, for Synechococcus spp., Microcystis aeruginosa, and Dolichospermum spp.
Fig 6.
Secchi depth plotted against its significant predictor, water depth.
Separate regression lines are shown for each water body to represent another significant predictor, water.