CHARACTERIZATION OF PHYTOPLANKTON COMMUNITIES USING IMAGING FLOW CYTOMETRY ALONG THE SALINITY GRADIENT IN TENGIZ-KORGALZHYN AND ARAL LAKE SYSTEMS OF KAZAKHSTAN

Abstract

Two Kazakhstani saline lake systems were studied in the thesis: the Tengiz-Korgalzhyn Lake System and the Aral Lake System. The Tengiz-Korgalzhyn wetlands, lying on the main migratory routes of transitional birds, where more than a hundred protected and endemic species inhabit, are one of the important wetlands in Kazakhstan and Central Asia. In 1976, it was included in the Ramsar Convention's list and 2008 in the UNESCO World Heritage List. The second system is the Aral Lake System, initially the Aral Sea, which saw a significant shrinkage due to environmental and anthropogenic (water withdrawal, increase of agriculture) reasons in the last 60 years, which led to segregation and disappearance of some parts of it. Both systems provide strong salinity gradients, which are represented in biotic communities, specifically phytoplankton. Planktonic indicators are important for lakes ecosystem health evaluation. To accomplish this, the water samples were collected from both lake systems with the instant measurements of physico-chemical parameters. The samples were then analyzed using imaging flow cytometry (IFC) for further phytoplankton classification and counting. The phytoplankton metrics were obtained and statistically analyzed, and diversity index calculations, and canonical correspondence analysis (CCA) were performed. According to the IFC results, Large and Small Tengiz Lake, which had a narrow range of all environmental parameters, was characterized by a homogenous community composition in all the sites with similar phytoplankton FlowCam-based group proportions. In contrast, significant trends might be seen in changes in phytoplankton groups in small lakes of the Tengiz-Korgalzhyn Lake System that showed a wide range of salinity. In the Tengiz-Korgalzhyn Lake System, the highest phytoplankton abundance was observed mainly in the less saline waters (oligo- and mesosaline sites) and the most hypersaline areas, with the decline in total abundance in the in-between salinity zones. However, the number of phytoplankton groups saw a decline as the salinity rose. The same trend for overall phytoplankton abundance was observed in the Aral Lake System, which has a strong salinity gradient as well. In addition, the trends of phytoplankton at different salinity levels that promote the dominance of various phytoplankton groups in the lake systems were described. Moreover, it was found that along salinity gradients, most phytoplankton groups differed in response to environmental parameters between these lake systems. The significance of this research is the contribution to phytoplankton studies in two Kazakhstani lake systems and the development of classification systems for imaging analysis of phytoplanktpn suitable for IFC. These classification systems can serve as a basis for further seasonal studies of the lakes and for the development of artificial intelligence algorithms. In addition, this study may also contribute to the knowledge of algae in inland saline lakes.