pH: A core node of interaction networks among soil organo-mineral fractions

Mineral-associated organic matter (MAOM) is the largest soil organic carbon (OC) pool with the longest turnover. MAOM is expected to have relatively little sensitivity to climate change due to mineral protection, but its persistence involves several organo-mineral fractions. The uncertainty in the response of specific organo-mineral fractions to climate change hampers the reliability of predictions of MAOM preservation in the future. Here, we applied a sequential chemical fractionation method integrated with network analysis to investigate MAOM stabilization mechanisms across five alpine ecosystems: alpine desert, alpine steppe, alpine meadow, alpine wetland, and alpine forest. Hierarchical cluster analysis revealed grouping of seven extractable OM fractions in MAOM into three OM clusters: a cluster with weak bondings consisting of water-soluble OM (WSOM) and weakly adsorbed fractions (2.1–21.3% of total OC); a cluster with metal-bound complexes comprising Ca-OM complexes and Fe/Al-OM complexes (3.8–12.2% of total OC); and a cluster with strong bonding composed of Al oxyhydroxides, carbonates and Fe oxyhydroxides (12.2–33.5% of total OC). The relative percentages of OM from soils of the five ecosystems in the three clusters exhibited distinct pH dependence patterns. With the increase in pH, the cluster with weak bondings decreased, and that with strong bondings increased, while the one with metal-bound complexes showed a maximum at weakly acidic pH. Organo-mineral fractions and metal cations in MAOM constructed a complex network with pH as the central node. Results suggest that precipitation does not only alter vegetation type and microbial biomass but also regulate soil pH, which is balanced by specific metal cations, thus resulting in particular pH preference of specific OM clusters. These findings demonstrate that soil pH plays a central role in unveiling MAOM dynamics and can serve as a good predictor of soil organo-mineral fractions across alpine ecosystems

GUPNet++: Geometry Uncertainty Propagation Network for Monocular 3D Object Detection

Geometry plays a significant role in monocular 3D object detection. It can be used to estimate object depth by using the perspective projection between object's physical size and 2D projection in the image plane, which can introduce mathematical priors into deep models. However, this projection process also introduces error amplification, where the error of the estimated height is amplified and reflected into the projected depth. It leads to unreliable depth inferences and also impairs training stability. To tackle this problem, we propose a novel Geometry Uncertainty Propagation Network (GUPNet++) by modeling geometry projection in a probabilistic manner. This ensures depth predictions are well-bounded and associated with a reasonable uncertainty. The significance of introducing such geometric uncertainty is two-fold: (1). It models the uncertainty propagation relationship of the geometry projection during training, improving the stability and efficiency of the end-to-end model learning. (2). It can be derived to a highly reliable confidence to indicate the quality of the 3D detection result, enabling more reliable detection inference. Experiments show that the proposed approach not only obtains (state-of-the-art) SOTA performance in image-based monocular 3D detection but also demonstrates superiority in efficacy with a simplified framework.Comment: 18 pages, 9 figure

Multilevel Nitrogen Additions Alter Chemical Composition and Turnover of the Labile Fraction Soil Organic Matter via Effects on Vegetation and Microorganisms

Global nitrogen (N) deposition greatly impacts soil carbon sequestration. A 2- yr multiple N addition (0, 10, 20, 40, 80, and 160 kg N·ha- 1·yr- 1) experiment was conducted in alpine grassland to illustrate the mechanisms underlying the observed soil organic matter (SOM) dynamics on the Qinghai- Tibet Plateau (QTP). Labile fraction SOM (LF- SOM) fingerprints were characterized by pyrolysis- gas chromatography/tandem- mass spectrometry, and microbial functional genes (GeoChip 4.6) were analyzed in conjunction with LF- SOM fingerprints to decipher the responses of LF- SOM transformation to N additions. The significant correlations between LF- SOM and microbial biomass, between organic compounds in LF- SOM and compound degradation- related genes, and between LF- SOM and net ecosystem exchange implied LF- SOM were the main fraction utilized by microorganisms and the most sensitive fraction to N additions. The LF- SOM increased at the lowest N addition levels (10 and 20 kg N·ha- 1·yr- 1) and decreased at higher N addition levels (40 to 160 kg N·ha- 1·yr- 1), but the decrease of LF- SOM was weakened at 160 kg N·ha- 1·yr- 1 addition. The nonlinear response of LF- SOM to N additions was due to the mass balance between plant inputs and microbial degradation. Plant- derived compounds in LF- SOM were more sensitive to N addition than microbial- derived and aromatic compounds. It is predicted that when the N deposition rate increased by 10 kg N·ha- 1·yr- 1 on the QTP, carbon sequestration in the labile fraction may increase by nearly 170% compared with that under the current N deposition rate. These findings provide insight into future N deposition impacts on LF- SOM preservation on the QTP.Key PointsThe LF- SOM quantity increased at the lowest N additions (N10 and N20) and decreased from N40 to N160, but the decrease was weakened at the highest N addition (N160)Plant- derived compounds in LF- SOM were more sensitive to N addition than microbial- derived and aromatic compoundsThe organic compounds in LF- SOM were significantly correlated with compound degradation- related genesPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154963/1/jgrg21637_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154963/2/jgrg21637.pd

Soil texture and microorganisms dominantly determine the subsoil carbonate content in the permafrost-affected area of the Tibetan Plateau

Under climate warming conditions, storage and conversion of soil inorganic carbon (SIC) play an important role in regulating soil carbon (C) dynamics and atmospheric CO2 content in arid and semi-arid areas. Carbonate formation in alkaline soil can fix a large amount of C in the form of inorganic C, resulting in soil C sink and potentially slowing global warming trends. Therefore, understanding the driving factors affecting carbonate mineral formation can help better predict future climate change. Till date, most studies have focused on abiotic drivers (climate and soil), whereas a few examined the effects of biotic drivers on carbonate formation and SIC stock. In this study, SIC, calcite content, and soil microbial communities were analyzed in three soil layers (0–5 cm, 20–30 cm, and 50–60 cm) on the Beiluhe Basin of Tibetan Plateau. Results revealed that in arid and semi-arid areas, SIC and soil calcite content did not exhibit significant differences among the three soil layers; however, the main factors affecting the calcite content in different soil layers are different. In the topsoil (0–5 cm), the most important predictor of calcite content was soil water content. In the subsoil layers 20–30 cm and 50–60 cm, the ratio of bacterial biomass to fungal biomass (B/F) and soil silt content, respectively, had larger contributions to the variation of calcite content than the other factors. Plagioclase provided a site for microbial colonization, whereas Ca2+ contributed in bacteria-mediated calcite formation. This study aims to highlight the importance of soil microorganisms in managing soil calcite content and reveals preliminary results on bacteria-mediated conversion of organic to inorganic C

An Automatic Conflict Detection Framework for Urban Intersections Based on an Improved Time Difference to Collision Indicator

Urban road intersections are one of the key components of road networks. Due to complex and diverse traffic conditions, traffic conflicts occur frequently. Accurate traffic conflict detection allows improvement of the traffic conditions and decreases the probability of traffic accidents. Many time-based conflict indicators have been widely studied, but the sizes of the vehicles are ignored. This is a very important factor for conflict detection at urban intersections. Therefore, in this paper we propose a novel time difference conflict indicator by incorporating vehicle sizes instead of viewing vehicles as particles. Specially, we designed an automatic conflict recognition framework between vehicles at the urban intersections. The vehicle sizes are automatically extracted with the sparse recurrent convolutional neural network, and the vehicle trajectories are obtained with a fast-tracking algorithm based on the intersection-to-union ratio. Given tracking vehicles, we improved the time difference to the conflict metric by incorporating vehicle size information. We have conducted extensive experiments and demonstrated that the proposed framework can effectively recognize vehicle conflict accurately

An Automatic Conflict Detection Framework for Urban Intersections Based on an Improved Time Difference to Collision Indicator

Urban road intersections are one of the key components of road networks. Due to complex and diverse traffic conditions, traffic conflicts occur frequently. Accurate traffic conflict detection allows improvement of the traffic conditions and decreases the probability of traffic accidents. Many time-based conflict indicators have been widely studied, but the sizes of the vehicles are ignored. This is a very important factor for conflict detection at urban intersections. Therefore, in this paper we propose a novel time difference conflict indicator by incorporating vehicle sizes instead of viewing vehicles as particles. Specially, we designed an automatic conflict recognition framework between vehicles at the urban intersections. The vehicle sizes are automatically extracted with the sparse recurrent convolutional neural network, and the vehicle trajectories are obtained with a fast-tracking algorithm based on the intersection-to-union ratio. Given tracking vehicles, we improved the time difference to the conflict metric by incorporating vehicle size information. We have conducted extensive experiments and demonstrated that the proposed framework can effectively recognize vehicle conflict accurately

Increased precipitation accelerates soil organic matter turnover associated with microbial community composition in topsoil of the alpine grassland on the eastern Tibetan Plateau

Large quantities of carbon are stored in the alpine grassland of the Tibetan Plateau (TP), where is extremely sensitive to climate change. However, it remains unclear whether soil organic matter (SOM) in different layers responds to climate change analogously, and whether microbial communities play vital roles in SOM turnover of topsoil. In this study we measured and collected SOM turnover byThe accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author