Effects of different soil and water conservation measures on plant functional traits in the Loess Plateau.

Soil and water conservation measures (SWCM) have wide-ranging effects on vegetation and soil, and their effects on the ecosystem are multifaceted, with complex mechanisms. While numerous studies have focused on the impact of such measures on soil, the improvement of plant functional traits is a major factor in the ecological recovery of the Loess Plateau. This survey extensively investigated no measure plots, vegetation measure plots, and engineering measure plots in the Loess Plateau. The impact of SWCM on plant functional traits was investigated using structural equation modeling. We examined six plant functional traits-leaf dry weight (LD), specific leaf area (SLA), leaf tissue density (LTD), leaf total phosphorus (LTP), leaf total nitrogen (LTN), and leaf volume (LV)-correlated with resource acquisition and allocation. In 122 plots, we explored the effects of measures, soil, diversity, and community structure on the weighted average of plant functional traits. The findings showed substantial positive correlations between LD and SLA, LD and LV, SLA and LV, SLA and LTP, and LTP and LTN. LTD has a substantial negative correlation with LD, LTD with SLA, and LTD with LV. SWCM limits diversity, and the mechanisms by which it affects plant functional traits vary. In the structural equation model (SEM) of vegetation measures, improving community structure enhances plant functional traits, but soil factors have the greatest influence on plant functional traits in SEM engineering measures. Plant functional trait differences on the Loess Plateau result are due to differential plant responses to diverse soil properties and community structure. Vegetation measures enhance the chemical properties of plant functional traits, while engineering measures improve physical properties. The study provides a theoretical foundation for vegetation restoration and management following the implementation of diverse 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.

Trait-Based Climate Change Predictions of Vegetation Sensitivity and Distribution in China.

Dynamic global vegetation models (DGVMs) suffer insufficiencies in tracking biochemical cycles and ecosystem fluxes. One important reason for these insufficiencies is that DGVMs use fixed parameters (mostly traits) to distinguish attributes and functions of plant functional types (PFTs); however, these traits vary under different climatic conditions. Therefore, it is urgent to quantify trait covariations, including those among specific leaf area (SLA), area-based leaf nitrogen (N area), and leaf area index (LAI) (in 580 species across 218 sites in this study), and explore new classification methods that can be applied to model vegetation dynamics under future climate change scenarios. We use a redundancy analysis (RDA) to derive trait-climate relationships and employ a Gaussian mixture model (GMM) to project vegetation distributions under different climate scenarios. The results show that (1) the three climatic variables, mean annual temperature (MAT), mean annual precipitation (MAP), and monthly photosynthetically active radiation (mPAR) could capture 65% of the covariations of three functional traits; (2) tropical, subtropical and temperate forest complexes expand while boreal forest, temperate steppe, temperate scrub and tundra shrink under future climate change scenarios; and (3) the GMM classification based on trait covariations should be a powerful candidate for building new generation of DGVM, especially predicting the response of vegetation to future climate changes. This study provides a promising route toward developing reliable, robust and realistic vegetation models and can address a series of limitations in current models.