Grazing Affects the Ecological Stoichiometry of the Plant–Soil–Microbe System on the Hulunber Steppe, China

Effects of grazing intensity on diversity and composition of rhizosphere and non-rhizosphere microbial communities in a desert grassland

Overgrazing-induced grassland degradation has become a serious ecological problem worldwide. The diversity and composition of soil microbial communities are sensitive to grazing disturbances. However, our understanding is limited with respect to the effects of grazing intensity on bacterial and fungal communities, especially in plant rhizosphere. Using a long-term grazing experiment, we evaluated the diversity and composition of microbial communities in both rhizosphere and non-rhizosphere soils under three grazing intensities (light, moderate, and heavy grazing) in a desert grassland and examined the relative roles of grazing-induced changes in some abiotic and biotic factors in affecting the diversity and composition of microbial communities. Our results showed that soil bacteria differed greatly in diversity and composition between rhizosphere and non-rhizosphere zones, and so did soil fungi. Moderate and heavy grazing significantly reduced the rhizosphere bacterial diversity. Grazing intensity substantially altered the bacterial composition and the fungal composition in both zones but with different mechanisms. While root nitrogen and soil nitrogen played an important role in shaping the rhizosphere bacterial composition, soil-available phosphorus greatly affected the non-rhizosphere bacterial composition and the fungal composition in both soils. This study provides direct experimental evidence that the diversity and composition of microbial communities were severely altered by heavy grazing on a desert grassland. Thus, to restore the grazing-induced, degraded grasslands, we should pay more attention to the conservation of soil microbes in addition to vegetation recovery.

A consumer-driven recycling theory for the impact of large herbivores on terrestrial ecosystem stoichiometry

Biological control of nutrient cycles is well documented in aquatic ecosystems, where consumer-driven recycling by herbivores can significantly impact ecosystem stoichiometry. In contrast, little is known in terrestrial ecosystems, where there is evidence that herbivores can also impact ecosystem stoichiometry. I studied a stoichiometric model of the soil-plant-herbivore system. The model shows that herbivores influence the ecosystem stoichiometry mainly through the direct and indirect controls of ecosystem inputs and losses, in a more complex way than predicted by the classic consumer-driven recycling theory. Overall, it shows that herbivores affect nutrient ratios in terrestrial ecosystems mostly independently of their own stoichiometric ratios, and that their impact may be different in forest versus grassland. The results highlight the sensitivity of terrestrial ecosystems to elusive actors, negligible in biomass but capable of modifying nutrient loss rates with major impacts on nutrient cycles and ecosystem stoichiometry.

Using UAV LiDAR to Extract Vegetation Parameters of Inner Mongolian Grassland

The accurate estimation of grassland vegetation parameters at a high spatial resolution is important for the sustainable management of grassland areas. Unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) sensors with a single laser beam emission capability can rapidly detect grassland vegetation parameters, such as canopy height, fractional vegetation coverage (FVC) and aboveground biomass (AGB). However, there have been few reports on the ability to detect grassland vegetation parameters based on RIEGL VUX-1 UAV LiDAR (Riegl VUX-1) systems. In this paper, we investigated the ability of Riegl VUX-1 to model the AGB at a 0.1 m pixel resolution in the Hulun Buir grazing platform under different grazing intensities. The LiDAR-derived minimum, mean, and maximum canopy heights and FVC were used to estimate the AGB across the entire grazing platform. The flight height of the LiDAR-derived vegetation parameters was also analyzed. The following results were determined: (1) The Riegl VUX-1-derived AGB was predicted to range from 29 g/m2 to 563 g/m2 under different grazing conditions. (2) The LiDAR-derived maximum canopy height and FVC were the best predictors of grassland AGB (R2 = 0.54, root-mean-square error (RMSE) = 64.76 g/m2). (3) For different UAV flight altitudes from 40 m to 110 m, different flight heights showed no major effect on the derived canopy height. The LiDAR-derived canopy height decreased from 9.19 cm to 8.17 cm, and the standard deviation of the LiDAR-derived canopy height decreased from 3.31 cm to 2.35 cm with increasing UAV flight altitudes. These conclusions could be useful for estimating grasslands in smaller areas and serving as references for other remote sensing datasets for estimating grasslands in larger areas.