Effects of different soil and water conservation measures on plant diversity and productivity in Loess Plateau.

Many soil and water conservation measures (SWCM) have been implemented in the Loess Plateau of China, and they have an impact on ecosystems all levels and involve complicated mechanisms. Previously, studies typically focused on a single factor's effect on diversity or productivity. With this background, the current investigation embarked on an extensive study, with vegetation survey conducted in the no measure plots (NM), vegetation measure plots (VM) and engineering measure plots (EM) in the Loess Plateau of China. We used structural equation models (SEM) to explain the mechanism by which SWCM affects plant productivity and diversity. VM have direct effects on plant diversity, and EM have direct effects on soil properties and community structure. The two measures also had indirect effects on plant functional traits and community structure. The results show that the changes in plant functional traits and community structure by SWCM decreased plant diversity, whereas the increase of productivity was primarily dominated by improvements in community structure, and we conclude that variability in plant diversity and productivity across different measures on the Loess Plateau was primarily due to the responses of different plants to variable soil properties and the community responses. It was also emphasized that vegetation measures were beneficial to the increase of biomass per plant, while engineering measures were more beneficial to the growth of dominant species. These findings provide a theoretical foundation for vegetation management and restoration after the application of different SWCM.

Agents Affecting the Plant Functional Traits in National Soil and Water Conservation Demonstration Park (China).

Plant functional traits (PFTs) can reflect the response of plants to environment, objectively expressing the adaptability of plants to the external environment. In previous studies, various relationships between various abiotic factors and PFTs have been reported. However, how these factors work together to influence PFTs is not clear. This study attempted to quantify the effects of topographic conditions, soil factors and vegetation structure on PFTs. Four categories of variables were represented using 29 variables collected from 171 herb plots of 57 sites (from different topographic and various herb types) in Xindian SWDP. The partial least squares structural equation modeling showed that the topographic conditions and soil properties also have a direct effect on plant functional traits. Among the topographic conditions, slope (SLO) has the biggest weight of 0.629, indicating that SLO contributed the most to plant functional traits and vegetation structure. Among soil properties, maximum water capacity (MWC) contributes the most and is followed by soil water content (SWC), weighted at 0.588 and 0.416, respectively. In a word, the research provides new points into the quantification of the correlation between different drivers that may be important for understanding the mechanisms of resource utilization, competition and adaptation to the environment during plant recovery.

[Inversion of aboveground biomass of Pinus tabuliformis plantations based on GF-2 data]. / 基于GF-2的油松人工林地上生物量反演.

Pinus tabuliformis is an important afforestation species in the Loess Plateau. Quick and accurate estimation of aboveground biomass (AGB) of P. tabuliformis plantations plays an important role in monitoring regional forest resources. Here, we used multi-spectral remote sensing data of domestic satellite GF-2 and the field data to estimate the aboveground biomass of P. tabuliformis plantations in Shibao forest farm of Huanglong Mountain in Shaanxi Province. We calculated eight texture features and five vegetation indices, and then built models based four texture windows (3×3, 5×5, 7×7, 9×9) by using five regression methods including normal regression, stepwise regression, ridge regression, Lasso regression and principal component regression. We used the leave-one-out cross validation (LOOCV) to test the estimation accuracy of each model. We found serious multi-collinearity relationships between the extracted remote sensing factors. Most of the remote sensing factors had significant correlations with aboveground biomass of P. tabuliformis plantations. GF-2 data could achieve higher accuracy in the inversion of aboveground biomass of P. tabuliformis plantations in the Shibao forest farm. The best estimation result was the principal component regression model using 9×9 texture window, and the worst one was the normal regression model using 3×3 texture window. Inversion of aboveground biomass of P. tabuliformis plantation using domestic high-resolution satellite imagery could provide a scientific basis for forestry biomass monitoring, resource management, and sustainable management in the forestry departments of northwest China.