Linking above‐ and belowground traits to soil and climate variables: an integrated database on China's grassland species

Abstract

Abstract. Knowledge of plant functional traits and trait-environment interactions is important for characterizing species strategies and understanding ecological processes. However, comprehensive field data on both above-and belowground traits, together with their environmental variables are scarce. Biome-scale studies are particularly lacking. Here we present two large-scale data sets that include functional traits of leaves and fine roots and their corresponding soil and climatic variables in China's grasslands. Leaf, fine root, and soil samples were collected in three biogeographic regions: temperate grassland on the Inner Mongolia Plateau, alpine grassland on the Tibetan Plateau, and mountain grassland in the Xinjiang mountain areas. Field data were collected over two periods. The first data set collected between 2003 and 2004 includes 13 foliar traits (leaf mass per area, LMA; photosynthetic nitrogen use efficiency, PNUE; water use efficiency, WUE; stomatal conductance for water vapor, Gs; transpiration rate, TR; mass-and area-based photosynthetic capacity, Amass and Aarea; massand area-based carbon concentrations, Cmass and Carea; nitrogen concentrations, Nmass and Narea; and phosphorus concentrations, Pmass and Parea) for 170 species at 173 sites. The second data set collected between 2006 and 2007 includes six sets of analogous traits for both leaves and fine roots (C, N, and P concentrations; leaf thickness/root diameter; specific leaf area, SLA; specific root length, SRL; and tissue density) for 139 species at 82 sites, along with soil attributes (soil total and organic carbon, STC and SOC; total and available N, STN and SAN; total and available P, STP and SAP; pH, bulk density, and moisture). Moreover, associated information was also gathered, including geographical location (latitude, longitude, and altitude), climate (mean annual temperature, MAT; mean annual precipitation, MAP; growing season temperature, GST; growing season precipitation, GSP; potential evapotranspiration, PET; and actual evapotranspiration, AET) and site descriptions (vegetation and soil types). The data sets are unique because they integrate plant above-and belowground traits, climate, and soil factors over broad regional, elevational, and taxonomic ranges in understudied regions (e.g., the Tibetan Plateau). This is the only database on China's grassland species for unrestricted global access. These data sets will make a valuable contribution to future large-scale traitbased ecological studies.

Large‐Scale Distribution of Molecular Components in Chinese Grassland Soils: The Influence of Input and Decomposition Processes

“…The higher OC/N values in the surface soils of alpine versus temperate grasslands may be attributed to an inhibited degradation of SOM in the cold region (Baumann et al, 2009;Fang et al, 2010;Liu et al, 2012), since the OC/N ratio typically decreases with the degradation of plant-derived organic matter and shows values of approximately 10 in well-developed grasslands (Cleveland & Liptzin, 2007;Tian et al, 2010). Alternatively, although both roots and leaves of the investigated five dominant plants had comparable OC/N ratios between the alpine and temperate grasslands (Table 2), higher OC/N ratios were previously reported in plant fine roots (but not leaves) in the alpine (49.01 ± 2.36) than temperate grasslands (39.47 ± 1.16) (Geng et al, 2017). Hence, the higher OC/N ratios of alpine grassland soils may be partly attributed to the higher OC/N ratio of alpine versus temperate plant roots as well.…”

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

“…We selected two plant functional traits [SLA (m 2 /kg) and N area (g/m 2 )] and one structural trait of plant communities (LAI) in this study. From 2003 to 2013, we collected 1,192 functional trait observations from 580 species across 218 sites (Geng et al, 2017; Wang et al, 2018), which occupied the main climate space in China (Figure 1 and Supplementary Table S2) and the dominate species in the sampling sites were selected. The sampling sites could be classified into seven main regions.…”

“…Sites from Changbai to Inner Mongolia were distributed across an aridity gradient (Prentice et al, 2011). Sites from Inner Mongolia were distributed along the 400 mm equivalent precipitation line (Geng et al, 2017). The northwestern sites were distributed in Mount Altai and Mount Tian in the Xinjiang Autonomous Region.…”

“…In this paper, we examined a suite of leaf traits using co-located measurements and quantified the contributions of climate to predict the vegetation distribution in China. Our analysis was based on an extensive data set (Geng et al, 2017; Wang et al, 2018). We focused on three leaf traits, i.e., leaf area index (LAI), specific leaf area (SLA), and leaf nitrogen per unit area ( N area ), which together capture many functions of plants, such as carbon investment, photosynthetic ability, and sustaining the leaf temperature (Wright et al, 2004, 2017).…”

Gas exchange characteristics and their influencing factors for halophytic plant communities on west coast of Bohai Sea

“…However, the photosynthetic rate is not enough to elucidate the gas exchange capacity of a plant. In many studies, the leaf functional traits (i.e., P n , T r , G s and VPD leaf ) and leaf structural traits (i.e., LMA, leaf N and P) are generally used to evaluate the carbon assimilation patterns of different ecosystems [39][40][41]. The current study's results indicated that P. australis had higher P n , T r and G s than S. salsa and T. chinensis (Fig 1) in both sites, which may have been affected by the genera and the environmental conditions.…”