• Energy Research

AbstractBelowground plant traits play important roles in plant diversity loss driven by atmospheric nitrogen (N) deposition. However, the way N enrichment shapes plant microhabitats by patterning belowground traits and finally determines aboveground responses is poorly understood. Here, we investigated the rhizosheath trait of 74 plant species in seven N‐addition simulation experiments across multiple grassland ecosystems in China. We found that rhizosheath formation differed among plant functional groups and contributed to changes in plant community composition induced by N enrichment. Compared with forb species, grass and sedge species exhibited distinct rhizosheaths; moreover, grasses and sedges expanded their rhizosheaths with increasing N‐addition rate which allowed them to colonize belowground habitats. Grasses also shaped a different microenvironment around their roots compared with forbs by affecting the physicochemical, biological, and stress‐avoiding properties of their rhizosphere soil. Rhizosheaths act as a “biofilm‐like shield” by the accumulation of protective compounds, carboxylic anions and polysaccharides, determined by both plants and microorganisms. This enhanced the tolerance of grasses and sedges to stresses induced by N enrichment. Conversely, forbs lacked the protective rhizosheaths which renders their roots sensitive to stresses induced by N enrichment, thus contributing to their disappearance under N‐enriched conditions. This study uncovers the processes by which belowground facilitation and trait matching affect aboveground responses under conditions of N enrichment, which advances our mechanistic understanding of the contribution of competitive exclusion and environmental tolerance to plant diversity loss caused by N deposition.

AbstractBelowground plant traits play important roles in plant diversity loss driven by atmospheric nitrogen (N) deposition. However, the way N enrichment shapes plant microhabitats by patterning belowground traits and finally determines aboveground responses is poorly understood. Here, we investigated the rhizosheath trait of 74 plant species in seven N‐addition simulation experiments across multiple grassland ecosystems in China. We found that rhizosheath formation differed among plant functional groups and contributed to changes in plant community composition induced by N enrichment. Compared with forb species, grass and sedge species exhibited distinct rhizosheaths; moreover, grasses and sedges expanded their rhizosheaths with increasing N‐addition rate which allowed them to colonize belowground habitats. Grasses also shaped a different microenvironment around their roots compared with forbs by affecting the physicochemical, biological, and stress‐avoiding properties of their rhizosphere soil. Rhizosheaths act as a “biofilm‐like shield” by the accumulation of protective compounds, carboxylic anions and polysaccharides, determined by both plants and microorganisms. This enhanced the tolerance of grasses and sedges to stresses induced by N enrichment. Conversely, forbs lacked the protective rhizosheaths which renders their roots sensitive to stresses induced by N enrichment, thus contributing to their disappearance under N‐enriched conditions. This study uncovers the processes by which belowground facilitation and trait matching affect aboveground responses under conditions of N enrichment, which advances our mechanistic understanding of the contribution of competitive exclusion and environmental tolerance to plant diversity loss caused by N deposition.

AbstractUnderstanding the large-scale pattern of soil microbial carbon use efficiency (CUE) and its temperature sensitivity (CUET) is critical for understanding soil carbon–climate feedback. We used the18O-H2O tracer method to quantify CUE and CUETalong a north-south forest transect. Climate was the primary factor that affected CUE and CUET, predominantly through direct pathways, then by altering soil properties, carbon fractions, microbial structure and functions. Negative CUET(CUE decreases with measuring temperature) in cold forests (mean annual temperature lower than 10 °C) and positive CUET(CUE increases with measuring temperature) in warm forests (mean annual temperature greater than 10 °C) suggest that microbial CUE optimally operates at their adapted temperature. Overall, the plasticity of microbial CUE and its temperature sensitivity alter the feedback of soil carbon to climate warming; that is, a climate-adaptive microbial community has the capacity to reduce carbon loss from soil matrices under corresponding favorable climate conditions.

AbstractUnderstanding the large-scale pattern of soil microbial carbon use efficiency (CUE) and its temperature sensitivity (CUET) is critical for understanding soil carbon–climate feedback. We used the18O-H2O tracer method to quantify CUE and CUETalong a north-south forest transect. Climate was the primary factor that affected CUE and CUET, predominantly through direct pathways, then by altering soil properties, carbon fractions, microbial structure and functions. Negative CUET(CUE decreases with measuring temperature) in cold forests (mean annual temperature lower than 10 °C) and positive CUET(CUE increases with measuring temperature) in warm forests (mean annual temperature greater than 10 °C) suggest that microbial CUE optimally operates at their adapted temperature. Overall, the plasticity of microbial CUE and its temperature sensitivity alter the feedback of soil carbon to climate warming; that is, a climate-adaptive microbial community has the capacity to reduce carbon loss from soil matrices under corresponding favorable climate conditions.

The grassland of the Tibetan Plateau forms a globally significant biome, which represents 6% of the world’s grasslands and 44% of China’s grasslands. However, large uncertainties remain concerning the vegetation carbon storage and turnover time in this biome. In this study, we quantified the pool size of both the aboveground and belowground biomass and turnover time of belowground biomass across the Tibetan Plateau by combining systematic measurements taken from a substantial number of surveys (i.e. 1689 sites for aboveground biomass, 174 sites for belowground biomass) with a machine learning technique (i.e. random forest, RF). Our study demonstrated that the RF model is effective tool for upscaling local biomass observations to the regional scale, and for producing continuous biomass estimates of the Tibetan Plateau. On average, the models estimated 46.57 Tg (1 Tg = 10 ^12 g) C of aboveground biomass and 363.71 Tg C of belowground biomass in the Tibetan grasslands covering an area of 1.32 × 10 ^6 km ^2 . The turnover time of belowground biomass demonstrated large spatial heterogeneity, with a median turnover time of 4.25 years. Our results also demonstrated large differences in the biomass simulations among the major ecosystem models used for the Tibetan Plateau, largely because of inadequate model parameterization and validation. This study provides a spatially continuous measure of vegetation carbon storage and turnover time, and provides useful information for advancing ecosystem models and improving their performance.

The grassland of the Tibetan Plateau forms a globally significant biome, which represents 6% of the world’s grasslands and 44% of China’s grasslands. However, large uncertainties remain concerning the vegetation carbon storage and turnover time in this biome. In this study, we quantified the pool size of both the aboveground and belowground biomass and turnover time of belowground biomass across the Tibetan Plateau by combining systematic measurements taken from a substantial number of surveys (i.e. 1689 sites for aboveground biomass, 174 sites for belowground biomass) with a machine learning technique (i.e. random forest, RF). Our study demonstrated that the RF model is effective tool for upscaling local biomass observations to the regional scale, and for producing continuous biomass estimates of the Tibetan Plateau. On average, the models estimated 46.57 Tg (1 Tg = 10 ^12 g) C of aboveground biomass and 363.71 Tg C of belowground biomass in the Tibetan grasslands covering an area of 1.32 × 10 ^6 km ^2 . The turnover time of belowground biomass demonstrated large spatial heterogeneity, with a median turnover time of 4.25 years. Our results also demonstrated large differences in the biomass simulations among the major ecosystem models used for the Tibetan Plateau, largely because of inadequate model parameterization and validation. This study provides a spatially continuous measure of vegetation carbon storage and turnover time, and provides useful information for advancing ecosystem models and improving their performance.

Abstract Our knowledge on the responses of permafrost ecosystems to climate warming is critical for assessing the direction and magnitude of permafrost carbon‐climate feedback. However, most of the previous experiments have only been able to warm the air and surface soil, with limited effects on the permafrost temperature. Consequently, it remains challenging to realistically simulate permafrost thawing in terms of increased active layer (a layer freezing and thawing seasonally above permafrost) thickness under climate warming scenarios. Here, we presented the experimental design and warming performance of a novel experiment, Simulate Warming at Mountain Permafrost (SWAMP), the first one to successfully simulate permafrost warming and the subsequent active layer deepening in a swamp meadow situated on the Tibetan Plateau. Infrared heating was employed as above‐ground warming to elevate the temperature of the air and surface soil, and heating rods were inserted vertically in the soil to provide below‐ground warming for transmitting heat to the deep active layer and even to permafrost deposits. In 3 m diameter warmed circular plots, the air and the entire soil profile (from surface soil to 120 cm) was effectively heated, with an increase of approximately 2°C in the upper 60 cm, which progressively weakened with soil depth. Warming increased soil moisture across the growing season by inducing an earlier thawing of the soil. Values varied from 1.8 ± 1.8 to 12.3 ± 2.3% according to the soil depth. Moreover, during the growing season, the warmed plots had greater thaw depths and a deeper active layer thickness of 12.6 ± 0.8 cm. In addition, soil thawing duration was prolonged by the warming, ranging from 22.8 ± 3.3 to 49.3 ± 4.5 days depending on the soil depth. The establishment of SWAMP provides a more realistic simulation of warming‐induced permafrost thaw, which can then be used to explore the effect of climate warming on permafrost ecosystems and the potential permafrost carbon‐climate feedback. Notably, our experiment is more advantageous for investigating how deep soil processes respond to climate warming and active layer deepening, compare with experiments which use passive warming techniques such as open top chambers (OTCs).