Analysis of functional diversity, based on plant traits and community structure, provides a promising approach for exploration of the adaptive strategies of plants and the relationship between plant traits and ecosystem functioning. However, it is unclear how the number of plant traits included influences functional diversity, and whether or not there are quantitatively dependent traits. This information is fundamental to the correct use of functional diversity metrics. Here, we measured 34 traits of 366 plant species in nine forests from the tropical to boreal zones in China. These traits were used to calculate seven functional diversity metrics: functional richness (functional attribute diversity (FAD), modified FAD (MFAD), convex hull hypervolume (FRic)), functional evenness (FEve), and functional divergence (functional divergence (FDiv), functional dispersion (FDis), quadratic entropy (RaoQ)). Functional richness metrics increased with an increase in trait number, whereas the relationships between the trait divergence indexes (FDiv and FDis) and trait number were inconsistent. Four of the seven functional diversity indexes (FAD, MFAD, FRic, and RaoQ) were comparable with those in previous studies, showing predictable trends with a change in trait number. We verified our hypothesis that the number of traits strongly influences functional diversity. The relationships between these predictable functional diversity metrics and the number of traits facilitated the development of a standard protocol to enhance comparability across different studies. These findings can support integration of functional diversity index data from different studies at the site to the regional scale, and they focus attention on the influence of quantitative selection of traits on functional diversity analysis.

The continuous increase of livestock production in Inner Mongolia has caused severe degradation of the grassland ecosystems in recent years. Previous grazing experiments have shown a wide range of vegetation responses between the biome types on a global scale, but there is still a lack of sufficient studies to discern the relative responses of a given biome type. We conducted a meta-analysis of vegetation coverage (VC), plant density (PD), total biomass (TB), above-ground biomass (AGB), under-ground biomass (UGB) and Shannon-Weaver Index (SI) in different grassland types in Inner Mongolia obtained under conditions of different grazing intensities and durations. The results showed that grazing decreased VC, TB, AGB, UGB, and PD significantly. Compared to the global and national average values, the negative effects of grazing to steppe biomass in Inner Mongolia were higher than that on the global scale, while less pronounced than that in China. TB of the meadow steppe in Inner Mongolia increased by 40% under moderate grazing intensity and duration because of compensatory growth. SI of the desert and meadow steppe showed negative linear relationships with the grazing intensity in Inner Mongolia. The percentage changes in AGB, PD, and SI to grazing showed quadratic relationships with the mean annual temperature of the experimental year. With increasing mean annual precipitation, the negative effects of grazing on UGB and SI first decreased and then increased, with that of VC and grazing showing a cubic relationship.

The Bowen ratio (β) is used to quantify heat transfer from the land surface into the air, which is becoming a hot topic in research on the biogeophysical effects of land use and cover changes. The Three-River Headwaters (TRH), as a sensitive and fragile region, was selected as the study area. The β for 2001-2018 was estimated from the evapotranspiration product (ET_MOD16) of MODIS and the net radiation of the land surface through the albedo from GLASS. The ET_MOD16 data were evaluated against the observation data (ET_OBS) at two alpine grassland flux towers obtained from ChinaFLUX. The interannual trend of the β was analyzed by multiple linear regression (MLR) and structure model (SEM) with the multiple factors of precipitation, temperature, humidity, albedo, and normalized difference vegetation index (NDVI, MOD09Q1). The results show that the ET_MOD16 values were significantly correlated with ET_OBS, with a correlation coefficient above 0.70 (P < 0.01) for the two sites. In 2001-2018, the regional mean β was 2.52 ± 0.77 for the whole grassland, and its spatial distribution gradually increased from the eastern to western region. The interannual β showed a downward trend with a slope of -0.025 and a multiple regression coefficient (R ²) of 0.21 (P = 0.056). Most of the variability (51%) in the interannual β can be explained by the linear regression of the above multiple factors, and the temperature plays a dominant role for the whole region. The SEM analysis further shows that an increasing NDVI results in a decreasing albedo with a path coefficient of -0.57, because the albedo was negatively correlated with NDVI (R ² = 0.52, P < 0.01), which indicates a negative and indirect effect on β from vegetation restoration. An obvious warming climate was found to prompt more evapotranspiration, and restoring vegetation makes the land surface receive more radiation, which both resulted in a decreasing trend in the annual β. This study revealed the biogeophysical mechanisms of vegetation restoration under a changing climate, and demonstrated the Bowen ratio can be applied as an indicator of climate-regulating functions in ecosystem assessments.