Besides water relations, nutrient allocation, and stoichiometric traits are fundamental feature of shrubs. Knowledge concerning the nutrient stoichiometry of xerophytes is essential to predicting the biogeochemical cycling in desert ecosystems as well as to understanding the homoeostasis and variability of nutrient traits in desert plants. Here, we focused on the temperate desert species Reaumuria soongorica and collected samples from plant organs and soil over 28 different locations that covered a wide distributional gradient of this species. Carbon (C), nitrogen (N), and phosphorus (P) concentrations and their stoichiometry were determined and subsequently compared with geographic, climatic, and edaphic factors. The mean leaf C, N, and P concentrations and C/N, C/P, and N/P ratios were 371.6 mg g−1, 10.6 mg g−1, 0.73 mg g−1, and 59.7, 837.9, 15.7, respectively. Stem and root C concentrations were higher than leaf C, while leaf N was higher than stem and root N. Phosphorus concentration and N/P did not differ among plant organs. Significant differences were found between root C/N and leaf C/N as well as between root C/P and leaf C/P. Leaf nutrient traits respond to geographic and climatic factors, while nutrient concentrations of stems and roots are mostly affected by soil P and pH. We show that stoichiometric patterns in different plant organs had different responses to environmental variables. Studies of species-specific nutrient stoichiometry can help clarify plant–environment relationships and nutrient cycling patterns in desert ecosystems.

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With decreasing water availability as a result of climate change and human activities, analysis of the influential factors and variation trends of chlorophyll a has become important to prevent reservoir eutrophication and ensure water supply safety. In this paper, a structurally simplified hybrid model of the genetic algorithm (GA) and the support vector machine (SVM) was developed for the prediction of monthly concentration of chlorophyll a in the Miyun Reservoir of northern China over the period from 2000 to 2010. Based on the influence factor analysis, the four most relevant influence factors of chlorophyll a (i.e., total phosphorus, total nitrogen, permanganate index, and reservoir storage) were extracted using the method of feature selection with the GA, which simplified the model structure, making it more practical and efficient for environmental management. The results showed that the developed simplified GA-SVM model could solve nonlinear problems of complex system, and was suitable for the simulation and prediction of chlorophyll a with better performance in accuracy and efficiency in the Miyun Reservoir. OPEN ACCESSWater 2015, 7 1611