Effects of Water Availability on the Relationships Between Hydraulic and Economic Traits in the Quercus wutaishanica Forests

Leaf traits divergence and correlations of woody plants among the three plant functional types on the eastern Qinghai-Tibetan Plateau, China

Leaf traits are important indicators of plant life history and may vary according to plant functional type (PFT) and environmental conditions. In this study, we sampled woody plants from three PFTs (e.g., needle-leaved evergreens, NE; broad-leaved evergreens, BE; broad-leaved deciduous, BD) on the eastern Qinghai-Tibetan Plateau, and 110 species were collected across 50 sites. Here, the divergence and correlations of leaf traits in three PFTs and relationships between leaf traits and environment were studied. The results showed significant differences in leaf traits among three PFTs, with NE plants showed higher values than BE plants and BD plants for leaf thickness (LT), leaf dry matter content (LDMC), leaf dry mass per area (LMA), carbon: nitrogen ratio (C/N), and nitrogen content per unit area (N_area), except for nitrogen content per unit mass (N_mass). Although the correlations between leaf traits were similar across three PFTs, NE plants differed from BE plants and BD plants in the relationship between C/N and N_area. Compared with the mean annual precipitation (MAP), the mean annual temperature (MAT) was the main environmental factor that caused the difference in leaf traits among three PFTs. NE plants had a more conservative approach to survival compared to BE plants and BD plants. This study shed light on the regional-scale variation in leaf traits and the relationships among leaf traits, PFT, and environment. These findings have important implications for the development of regional-scale dynamic vegetation models and for understanding how plants respond and adapt to environmental change.

Soil temperature, microbial biomass and enzyme activity are the critical factors affecting soil respiration in different soil layers in Ziwuling Mountains, China

Soil microorganisms are critical biological indicators for evaluating soil health and play a vital role in carbon (C)-climate feedback. In recent years, the accuracy of models in terms of predicting soil C pools has been improved by considering the involvement of microbes in the decomposition process in ecosystem models, but the parameter values of these models have been assumed by researchers without combining observed data with the models and without calibrating the microbial decomposition models. Here, we conducted an observational experiment from April 2021 to July 2022 in the Ziwuling Mountains, Loess Plateau, China, to explore the main influencing factors of soil respiration (R_S) and determine which parameters can be incorporated into microbial decomposition models. The results showed that the R_S rate is significantly correlated with soil temperature (T_S) and moisture (M_S), indicating that T_S increases soil C loss. We attributed the non-significant correlation between R_S and soil microbial biomass carbon (MBC) to variations in microbial use efficiency, which mitigated ecosystem C loss by reducing the ability of microorganisms to decompose organic resources at high temperatures. The structural equation modeling (SEM) results demonstrated that T_S, microbial biomass, and enzyme activity are crucial factors affecting soil microbial activity. Our study revealed the relations between T_S, microbial biomass, enzyme activity, and R_S, which had important scientific implications for constructing microbial decomposition models that predict soil microbial activity under climate change in the future. To better understand the relationship between soil dynamics and C emissions, it will be necessary to incorporate climate data as well as R_S and microbial parameters into microbial decomposition models, which will be important for soil conservation and reducing soil C loss in the Loess Plateau.