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Stable carbon isotope signatures of cellulose in macrophytes collected from three eutrophic lakes in China
Institution:1. State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China;2. University of Chinese Academy of Sciences, Beijing 100049, China
Abstract:Aquatic plants are essential for maintaining the diversity and stability of a lake ecosystem. Stable carbon isotopes (δ13C) of macrophytes have been widely used as a powerful tool to study ecological processes and paleoenvironmental evolution in lakes. Varying results are obtained when using the δ13C of macrophytes to study the changes in the lake environment at different spatio-temporal scales. Thus, sample preparation and subsequent laboratory analyses are crucial for studying environmental changes using the isotopic signal retained in the macrophytes, and are essential for the interpretation of isotope-environment relationships. This study analyzed the δ13C of different tissue components of macrophytes in three lakes of the lower Yangtze River basin, and a correlation analysis was performed on aquatic environments influencing the δ13C values in the different tissue components of macrophytes. The test results showed the difference between the δ13C values of the whole sample and cellulose. Relative analyses indicated that the major factors contributing to the δ13C variability in macrophytes were pH and the concentration of dissolved inorganic carbon (DIC). The δ13C of α-cellulose (δ13CAC) is more sensitive to environmental variables than that of the whole sample (δ13CW) and holocellulose (δ13CHC). The results of this study imply that extraction of α-cellulose is a prerequisite for research on the changes in lake environment using δ13C of macrophytes. This study aims to provide theoretical and data basis for further research on the environmental and ecological change using stable carbon isotopes of aquatic plants.
Keywords:Macrophytes  Stable carbon isotopes  α-Cellulose  Whole sample  Influencing factors
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