Community Characteristics and Leaf Stoichiometric Traits of Desert Ecosystems Regulated by Precipitation and Soil in an Arid Area of China.

Precipitation is a key environmental factor determining plant community structure and function. Knowledge of how community characteristics and leaf stoichiometric traits respond to variation in precipitation is crucial for assessing the effects of global changes on terrestrial ecosystems. In this study, we measured community characteristics, leaf stoichiometric traits, and soil properties along a precipitation gradient (35-209 mm) in a desert ecosystem of Northwest China to explore the drivers of these factors. With increasing precipitation, species richness, aboveground biomass, community coverage, foliage projective cover (FPC), and leaf area index (LAI) all significantly increased, while community height decreased. The hyperarid desert plants were characterized by lower leaf carbon (C) and nitrogen/phosphorus (N/P) levels, and stable N and P, and these parameters did not change significantly with precipitation. The growth of desert plants was limited more by N than P. Soil properties, rather than precipitation, were the main drivers of desert plant leaf stoichiometric traits, whereas precipitation made the biggest contribution to vegetation structure and function. These results test the importance of precipitation in regulating plant community structure and composition together with soil properties, and provide further insights into the adaptive strategy of communities at regional scale in response to global climate change.

Soil TPH concentration estimation using vegetation indices in an oil polluted area of eastern China.

Assessing oil pollution using traditional field-based methods over large areas is difficult and expensive. Remote sensing technologies with good spatial and temporal coverage might provide an alternative for monitoring oil pollution by recording the spectral signals of plants growing in polluted soils. Total petroleum hydrocarbon concentrations of soils and the hyperspectral canopy reflectance were measured in wetlands dominated by reeds (Phragmites australis) around oil wells that have been producing oil for approximately 10 years in the Yellow River Delta, eastern China to evaluate the potential of vegetation indices and red edge parameters to estimate soil oil pollution. The detrimental effect of oil pollution on reed communities was confirmed by the evidence that the aboveground biomass decreased from 1076.5 g m(-2) to 5.3 g m(-2) with increasing total petroleum hydrocarbon concentrations ranging from 9.45 mg kg(-1) to 652 mg kg(-1). The modified chlorophyll absorption ratio index (MCARI) best estimated soil TPH concentration among 20 vegetation indices. The linear model involving MCARI had the highest coefficient of determination (R(2) = 0.73) and accuracy of prediction (RMSE = 104.2 mg kg(-1)). For other vegetation indices and red edge parameters, the R(2) and RMSE values ranged from 0.64 to 0.71 and from 120.2 mg kg(-1) to 106.8 mg kg(-1) respectively. The traditional broadband normalized difference vegetation index (NDVI), one of the broadband multispectral vegetation indices (BMVIs), produced a prediction (R(2) = 0.70 and RMSE = 110.1 mg kg(-1)) similar to that of MCARI. These results corroborated the potential of remote sensing for assessing soil oil pollution in large areas. Traditional BMVIs are still of great value in monitoring soil oil pollution when hyperspectral data are unavailable.