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Purpose: Soil microbial communities and related key ecological processes play critical roles in timberline delineation and soil carbon balance in alpine ecosystems, which are highly vulnerable to climate change. Accordingly, understanding their geographical differentiation will facilitate recognition of ecosystem functions and improve soil carbon models. In this study, we explored the biogeographic patterns of soil bacterial communities and their mechanisms in maintaining soil carbon balance in an alpine timberline ecosystem of the Sygera Mountains, Southeast Tibet. Materials and methods: Soil samples were collected from typical forest belts above and below the timberline. The abundance and composition of bacterial communities, as well as functional genes, were assessed using the gene chip technology. The relationship of key microbial taxa, functional genes, and soil carbon maintenance was investigated using random forest analysis, multi-model inference, and structural equation modeling. Results and discussion: The shrubland soil bacterial community exhibited greater diversity compared with the coniferous forest community, with higher Shannon Index and more functional genes at the taxonomic and functional levels, respectively. Bacterial community composition differed between the two forest types, with copiotrophic bacteria more abundant in shrubland, and oligotrophic bacteria more abundant in coniferous forest. The shrubland community was also more efficient at utilizing labile organic carbon, while the coniferous forest community utilized recalcitrant organic carbon more efficiently. Genes related to labile carbon degradation were more intense in shrubland, while genes related to recalcitrant carbon degradation were more concentrated in the coniferous forest. Soil temperature and C:N ratio were dominant drivers of bacterial community composition and function. Besides key soil-environment and microbial properties, certain bacterial taxa and functional genes also exerted unique roles in soil carbon variation. Conclusions: Significant differences exist in soil bacterial community composition and functions between the two forest types above and below the timberline of the Sygera Mountains. These differences may be attributed to soil temperature and soil C:N ratio. Coupling these microbial variables into the earth system model can improve the predictive power of the carbon feedback process in terrestrial ecosystems. [ABSTRACT FROM AUTHOR]

Soil biogeochemical processes may present depth‐dependent responses to climate change, due to vertical environmental gradients (e.g., thermal and moisture regimes, and the quantity and quality of soil organic matter) along soil profile. However, it is a grand challenge to distinguish such depth dependence under field conditions. Here we present an innovative, cost‐effective and simple approach of field incubation of intact soil cores to explore such depth dependence. The approach adopts field incubation of two sets of intact soil cores: one incubated right‐side up (i.e., non‐inverted), and another upside down (i.e., inverted). This inversion keeps soil intact but changes the depth of the soil layer of same depth origin. Combining reciprocal translocation experiments to generate natural climate shift, we applied this incubation approach along a 2200 m elevational mountainous transect in southeast Tibetan Plateau. We measured soil respiration (Rs) from non‐inverted and inverted cores of 1 m deep, respectively, which were exchanged among and incubated at different elevations. The results indicated that Rs responds significantly (p <.05) to translocation‐induced climate shifts, but this response is depth‐independent. As the incubation proceeds, Rs from both non‐inverted and inverted cores become more sensitive to climate shifts, indicating higher vulnerability of persistent soil organic matter (SOM) to climate change than labile components, if labile substrates are assumed to be depleted with the proceeding of incubation. These results show in situ evidence that whole‐profile SOM mineralization is sensitive to climate change regardless of the depth location. Together with measurements of vertical physiochemical conditions, the inversion experiment can serve as an experimental platform to elucidate the depth dependence of the response of soil biogeochemical processes to climate change. [ABSTRACT FROM AUTHOR]

Precipitation plays an important role in the water cycle and in matter and energy exchanges. Acquiring accurate information on precipitation over the Qinghai–Tibet Plateau, which has a limited rain gauge network, has been a great challenge. Downscaling the TRMM Multisatellite Precipitation Analysis (TMPA) 3B43 Version 7 dataset (0.25° resolution) provided an optimal approach to estimating precipitation at 1 km resolution over this highland plateau. Our downscaling assumptions were that non-stationary relationships between precipitation and land surface characteristics occur and have varying two-dimensional scale effects, and that the relationships vary in different sub-regions having differing combinations of land surface characteristics, including vegetation index, topographical factors, and land surface temperatures. We used Cubist (a spatial data mining algorithm) to implement our assumption. Cubist separated the Qinghai–Tibet Plateau into sub-regions according to geographical similarities, and selected the most effective variables over each sub-region to build models. We found that: (1) the downscaled results using this algorithm were more accurate and precise than other commonly used algorithms (e.g., geographically weighted regression) and the original TMPA data at 0.25° resolution; (2) DEM showed limited correlation with precipitation over the Qinghai–Tibet Plateau; and (3) the effects of systematic anomalies in the original TMPA data were removed in the downscaled results based on Cubist. We conclude that Cubist is a promising algorithm able to take hundreds of variables into consideration, and in this study was used to retrieve precipitation estimates at approximately 1 km resolution. [ABSTRACT FROM AUTHOR]

Long-term precipitation estimates with both finer spatial resolution and better quality are vital and highly needed in various related fields. Numerous downscaling algorithms have been investigated based on the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA), to obtain precipitation data with finer resolution (~1 km). However, this research was restricted by the time span of the TMPA dataset, as the starting time of TMPA was 1998. In this study, a new methodological framework incorporating wavelet coherence and Cubist was proposed to retrospectively obtain downscaled precipitation estimates (DS) over the Tibetan Plateau (TP), based on TMPA and ground observations, in 1990s. The correlations and similarities of precipitation patterns between the target years, from 1990 to 1999, and reference years, from 2000 to 2013, were firstly determined using wavelet coherence based on ground observations. Following this, the TMPA data in the reference years were regarded as the reference in the corresponding target years, which were adopted to be downscaled using Cubist models and land surface variables, to obtain the DS in the target years. We found that the DS showed continuous trends, which corresponded well with the ground observations. Additionally, the performances of the DS were better than those of the Climate Hazards group Infrared Precipitation with Stations (CHIRPS) data over the TP. Therefore, this methodological framework has great potential for obtaining precipitation estimates for the period of the 1990s for which TMPA data is inaccessible. [ABSTRACT FROM AUTHOR]

• Microbial community diversity and composition are sensitive to temperature. • Instantaneous microbial community is a poor predictor of SOC mineralization. • Microbial community is a proxy of substrate quality and availability. • Substate quality and availability are predominant regulators of SOC mineralization. Soil microbes drive soil organic carbon (SOC) mineralization. Because microbial groups differ in metabolic efficiency and respond differently to temperature variation, it is reasonable to expect a close association of SOC mineralization and its temperature sensitivity (Q 10 which is defined as the factor of the change of soil carbon mineralization induced by 10 °C temperature increase) with microbial community diversity and composition. However, these relations have rarely been tested. Here, we conducted an incubation experiment to assess the temperature responses of microbial α diversity and the relative abundance of microbial r- and K-strategists in soils from a wide range of ecosystems across a climate gradient in the southeast Tibet. The results indicated that the instantaneous α diversity and the relative abundance of r- and K-strategists are significantly (P < 0.05) influenced by temperature, but these microbial variables are poor predictors of SOC mineralization measured at the same time. Rather, microbial community diversity and the relative abundance of r- and K-strategists of fresh soils showed consistent and significant (P < 0.05) effects on both SOC mineralization and Q 10 at different incubation stages. Importantly, path analysis indicated that microbial α diversity and r- and K-strategists exerts no independent effects on SOC mineralization and Q 10 when variation in climate, SOC chemistry, physical protection, and edaphic properties are accounted for. Together, our results suggest that while soil microbial community diversity and composition are a strong proxy of SOC quality and availability, they are not a fundamental determinant of SOC mineralization and Q 10. [ABSTRACT FROM AUTHOR]

Soil erosion by water is accelerated by a warming climate and negatively impacts water security and ecological conservation. The Tibetan Plateau (TP) has experienced warming at a rate approximately twice that observed globally, and heavy precipitation events lead to an increased risk of erosion. In this study, we assessed current erosion on the TP and predicted potential soil erosion by water in 2050. The study was conducted in three steps. During the first step, we used the Revised Universal Soil Equation (RUSLE), publicly available data, and the most recent earth observations to derive estimates of annual erosion from 2002 to 2016 on the TP at 1-km resolution. During the second step, we used a multiple linear regression (MLR) model and a set of climatic covariates to predict rainfall erosivity on the TP in 2050. The MLR was used to establish the relationship between current rainfall erosivity data and a set of current climatic and other covariates. The coefficients of the MLR were generalised with climate covariates for 2050 derived from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) models to estimate rainfall erosivity in 2050. During the third step, soil erosion by water in 2050 was predicted using rainfall erosivity in 2050 and other erosion factors. The results show that the mean annual soil erosion rate on the TP under current conditions is 2.76 t ha −1 y −1 , which is equivalent to an annual soil loss of 559.59 × 10 6 t. Our 2050 projections suggested that erosion on the TP will increase to 3.17 t ha −1 y −1 and 3.91 t ha −1 y −1 under conditions represented by RCP2.6 and RCP8.5, respectively. The current assessment and future prediction of soil erosion by water on the TP should be valuable for environment protection and soil conservation in this unique region and elsewhere. [ABSTRACT FROM AUTHOR]