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С.Г. Михалап, Е.М. Воробьева, Д.Н. Судницына, В.В. Борисов.
Использование многомерных методов анализа при изучении динамики биомассы планктонных сине-зеленых, диатомовых и зеленых водорослей в южной части Чудско-Псковского озера
// Труды КарНЦ РАН. No 6. Сер. Лимнология и океанология. 2022. C. 133–141
S.G. Mikhalap, E.M. Vorob’eva, D.N. Sudnitsyna, V.V. Borisov. Application of multidimensional analysis methods in studying the dynamics of planktonic cyanobacteria, diatoms and chlorophyta biomass in the southern part of Lake Peipus // Transactions of Karelian Research Centre of Russian Academy of Science. No 6. Limnology and oceanology. 2022. P. 133–141
Keywords: Lake Peipus; phytoplankton; factor analysis; biomass
The influence of hydrological and hydrochemical conditions on the long-term biomass dynamics is studied using three taxonomic groups of microalgae in Lake Peipus (Cyanobacteria, Chlorophyta and Diatoma). Cluster analysis based on long-term phytoplankton
biomass dynamics in the lake showed that blue-green algae stood apart, while diatoms and green algae were quite similar to each other. The rationale for the application of principal components analysis is that this procedure allows reducing the data set and creating independent components, making it much easier to describe patterns in the systems. Before the analysis, data were checked for multicollinearity using correlation analysis. Analysis revealed five principal components which determined the patterns of change in phytoplankton biomass. It is shown that Cyanobacteria depend the most on the lake temperature regime and the content of dissolved mineral and organic compounds in the water, which is in full agreement with the data reported by other authors. Chlorophyta, compared with blue-green algae, are more sensitive to the acidbase balance of the water. As for diatoms, only the water level and the duration of the ice-covered period matter.
biomass dynamics in the lake showed that blue-green algae stood apart, while diatoms and green algae were quite similar to each other. The rationale for the application of principal components analysis is that this procedure allows reducing the data set and creating independent components, making it much easier to describe patterns in the systems. Before the analysis, data were checked for multicollinearity using correlation analysis. Analysis revealed five principal components which determined the patterns of change in phytoplankton biomass. It is shown that Cyanobacteria depend the most on the lake temperature regime and the content of dissolved mineral and organic compounds in the water, which is in full agreement with the data reported by other authors. Chlorophyta, compared with blue-green algae, are more sensitive to the acidbase balance of the water. As for diatoms, only the water level and the duration of the ice-covered period matter.
DOI: 10.17076/lim1626
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Last modified: November 7, 2022