Abstract
Ecological stoichiometry of terrestrial ecosystems has been a hot issue in current research, with intense focus on the proportional relationships of nutritional elements within plants and between plants and their environment. To clarify these relationships along continuous environmental gradients is essential for a more comprehensive understanding how plants adapt to a changing environment. In arid regions, the varying plant and soil types along altitude gradients offer a unique opportunity to examine the vertical spectrum of plant and soil ecological stoichiometry. In this study, the northern slope of the Tianshan Mountains was selected as the study area to explore the carbon (C), nitrogen (N), and phosphorus (P) ecological stoichiometric characteristics of herbaceous plants along 900-m-3,500-m altitude gradients. We also investigated the variation of ecological stoichiometric characteristics among different grassland types. The results indicated that the mean C, N, and P in leaf of grassland were 342.95 g·kg-1-557.73 g·kg-1, 6.02 g·kg-1-20.97 g·kg-1, and 0.71 g·kg-1-3.14 g·kg-1, respectively. There was no significant change in leaf carbon content along the elevation gradient, and the highest and lowest leaf C concentrations were in the upland meadow and the semidesert grasslands. Both N and P concentrations obtained their highest value in the meadow steppe. The P concentration gradually increased in desert and semidesert grasslands and reached the highest value in the meadow steppe, and then decreased to the lowest value in the upland meadow and subsequently increased in the alpine meadow. The ranges of the C:N ratio, C:P ratio, and N:P ratio were 16.36-155.53, 109.36-786.52, and 2.58-17.34, respectively. Due to fluctuations in the P concentration, the C:P ratio and N:P ratio reached the lowest value in the meadow steppe and obtained their highest value in the upland meadow. Redundancy analysis showed that temperature was the dominant factor affecting the C, N, and P ecological stoichiometry of herbaceous plants, followed by soil organic carbon, mean annual precipitation, soil pH, and soil electrical conductivity. Corresponding results could enhance predictive models of nutrient cycling and ecosystem responses to climate change, particularly in arid and semiarid regions.
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