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. 2024 Aug 16;12(8):1685.
doi: 10.3390/microorganisms12081685.

The Driving Mechanism of Phytoplankton Resource Utilization Efficiency Variation on the Occurrence Risk of Cyanobacterial Blooms

Yongxin Zhang  1 , Yang Yu  2 , Jiamin Liu  1 , Yao Guo  1 , Hongxian Yu  1 , Manhong Liu  1
Affiliations

The Driving Mechanism of Phytoplankton Resource Utilization Efficiency Variation on the Occurrence Risk of Cyanobacterial Blooms

Yongxin Zhang et al. Microorganisms. .

Abstract

Algae are highly sensitive to environmental factors, especially nutrient fluctuations; excessive nutrients can lead to the proliferation of specific algae species, resulting in dominance. In this study, we aimed to reevaluate changes in algal dominance from the perspective of resource utilization efficiency (RUE). We established 80 monitoring sites across different water systems, collecting water and phytoplankton samples. Using canonical correspondence analysis (CCA) and a generalized additive model (GAM), we analyzed the correlation between phytoplankton RUE and nutrient concentrations, quantifying the corresponding relationship between algal dominance and RUE. Our results indicate a significant negative correlation between the RUE of total phosphorus (TP) and total nitrogen (TN) concentration, but a positive correlation with N:P. The RUE of TN was negatively correlated with TN concentration and N:P. We constructed GAMs with interaction terms and confirmed a nonlinear relationship between algal dominance and RUE. When the RUE of TN was low, a positive correlation was observed, while a negative correlation was observed otherwise. These findings reveal the ecological adaptability of algal communities and provide valuable insights for predicting the risk of algal bloom outbreaks.

Keywords: Cyanobacteria; generalized additive model; nutrients; phytoplankton; resource utilization efficiency.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Spatial distribution of 80 phytoplankton and water monitoring stations. (a) D1–D11. (b) S1–S23. (c) X1–X20. (d) M1–M26.
Figure 2
Figure 2
Spatial variations in nutrient concentrations, RUE, and cyanobacterial dominance (RAC). The upper edge of the box plot represents the upper quartile, the lower edge represents the lower quartile, and the middle line represents the mean; the dots represent the observed values.
Figure 3
Figure 3
RDA results of RUE and environment factors.
Figure 4
Figure 4
The response curves of cyanobacterial dominance and RUE: (a) Spring. (b) Summer. (c) Autumn.
See this image and copyright information in PMC

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