Published December 30, 2022 | Version v1
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Time-series analysis of zooplankton diversity in upper reaches of the Ob River

  • 1. Institute for Water and Environmental Problems, Siberian Branch of the Russian Academy of Sciences

Description

Long-term data sets on various ecosystem parameters serve as the basis for environmental monitoring. Time series analysis is used to identify the structure of dynamic series and their prediction. The demographic characteristics of zooplankton are well suited to analyze seasonal and interannual changes in ecosystems. Since the dynamics of species richness and river flow are often interdependent, we studied zooplankton biodiversity in the upper reaches of the Ob River in relation to the phases of the water regime. A six-year sampling of zooplankton was performed from surface water from the Ob River at two stations near the city of Barnaul. In total, 203 species and forms of zooplankton were detected. In all phases of the water cycle, Rotifera dominated in species number. To analyze the species diversity of zooplankton, we used 20 indices, of which 10 were not random on both coasts and could be used in monitoring. The species diversity of zooplankton in a sample, according to Margalef and Menhinick indices, was the highest during the recession of the second flood wave. The generalized measures of diversity (Williams polydominance and Shannon indices, and Fischer alpha) showed their maximum during the recession of the second wave of high water and in the summer low water period. Statistically significant declines in trends of some species diversity are evidence of small changes in the structure of the zooplankton. Time series analysis in the assessment of community biodiversity helps to select indices suitable for predicting ecosystem state, as well as to identify related changes in the community.

Notes

The study was carried out as part of State Assignment of the Institute for Water and Environmental Problems, Siberian Branch of the Russian Academy of Sciences (No. 121031200178-8) and supported by RFBR grant (No. 20-05-00528). The author has declared that there are no competing interests. The author thanks A.V. Kotovshchikov for his assistance in field work, L.A. Dolmatova for providing hydrochemical data used in correlation analysis, and L.V. Yanygina for her valuable comments in writing this article.

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