Keywords: time series, bacterioplankton, moving average method, seasonal component, correlation and regression analysis, multiple regression model, lake Baikal
Estimation of bacterioplankton abundance fluctuations in the vertical water column of Lake Baikal over a multi-year period
UDC 519.8:51-76
DOI: 10.26102/2310-6018/2024.44.1.013
The paper proposes two approaches to analyzing time series of bacterioplankton abundance in three different layers of the water column in Lake Baikal. In the first approach, the values of the seasonal component of the series are calculated using the moving average method, and additive and multiplicative models are constructed, from which the best models are selected on the basis of the calculated reliability coefficients. The seasonal component values in each of them are estimated. In the second one, correlation and regression analysis of joint changes in bacterioplankton abundance, temperature and lake water level is performed. Statistical hypotheses about the significance of correlation coefficients between the considered factors are put forward and tested. A mathematical model of multiple regression with inclusion of dummy variables describing the influence of seasonal fluctuations on changes in bacterioplankton abundance is constructed. Statistical assessment of the significance of the model and the factors included in the model is calculated. The results of correlation-regression analysis are interpreted in relation to the subject area under study. The findings can be used in predicting the amount of bacterioplankton in different periods of time, in making an ecological substantiation of the state of the lake, as well as in forecasting its microbiological state.
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Keywords: time series, bacterioplankton, moving average method, seasonal component, correlation and regression analysis, multiple regression model, lake Baikal
For citation: Burdukovskaya A.V., Belykh T.I., Rodionov A.V. Estimation of bacterioplankton abundance fluctuations in the vertical water column of Lake Baikal over a multi-year period. Modeling, Optimization and Information Technology. 2024;12(1). URL: https://moitvivt.ru/ru/journal/pdf?id=1507 DOI: 10.26102/2310-6018/2024.44.1.013 (In Russ).
Received 19.01.2024
Revised 12.02.2024
Accepted 21.02.2024
Published 31.03.2024