Assessment of Six Machine Learning Methods for Predicting Gross Primary Productivity in Grassland by Hao Wang, Wei Shao, Yunfeng Hu, Wei Cao, Yunzhi Zhang

“…Grassland gross primary productivity (GPP) is an important part of global terrestrial carbon flux, and its accurate simulation and future prediction play an important role in understanding the ecosystem carbon cycle. …”
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Quantifying impacts of climate and human activities on the grassland in the Three-River Headwater Region after two phases of Ecological Project by Xiaogang Ning, Ning Zhu, Yafei Liu, Hao Wang

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Spatio-Temporal Changes and Contribution of Human and Meteorological Factors to Grassland Net Primary Productivity in the Three-Rivers Headwater Region from 2000 to 2019 by Yang Song, Tian Liang, Linbo Zhang, Chaozhi Hao, Hao Wang

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Long‐term collar deployment leads to bias in soil respiration measurements by Xiaoliang Ma, Shengjing Jiang, Zhiqi Zhang, Hao Wang, Chao Song, Jin‐Sheng He

Subjects: “…alpine grassland…”
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Sheepfolds induce significant increase of seasonal CO2, CH4 and N2O emissions in temperate steppes of Inner Mongolia by Xinyu Wang, Xudong Guo, Di Zhou, Hao Wang, Haoxin Li, Frank Yonghong Li

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Research progress of reduced amino acid alphabets in protein analysis and prediction by Yuchao Liang, Siqi Yang, Lei Zheng, Hao Wang, Jian Zhou, Shenghui Huang, Lei Yang, Yongchun Zuo

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A cost-effective machine learning-based method for preeclampsia risk assessment and driver genes discovery by Hao Wang, Zhaoyue Zhang, Haicheng Li, Jinzhao Li, Hanshuang Li, Mingzhu Liu, Pengfei Liang, Qilemuge Xi, Yongqiang Xing, Lei Yang, Yongchun Zuo

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Carbon fluxes and soil carbon dynamics along a gradient of biogeomorphic succession in alpine wetlands of Tibetan Plateau by Hao Wang, Lingfei Yu, Litong Chen, Zhenhua Zhang, Xuefei Li, Naishen Liang, Changhui Peng, Jin-Sheng He

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Response of fungal communities to afforestation and its indication for forest restoration by Kaichuan Huang, Zhenli Guo, Wen Zhao, Changge Song, Hao Wang, Junning Li, Reyila Mumin, Yifei Sun, Baokai Cui

“…This finding emphasizes that soil pH has a strong effect on the transition of fungal communities and functional taxa from grassland to plantation, providing a novel indicator for forest restoration.…”
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Experimental warming causes mismatches in alpine plant-microbe-fauna phenology by Rui Yin, Wenkuan Qin, Xudong Wang, Dong Xie, Hao Wang, Hongyang Zhao, Zhenhua Zhang, Jin-Sheng He, Martin Schädler, Paul Kardol, Nico Eisenhauer, Biao Zhu

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Response of summer maize growth to drought-flood abrupt alternation by Wuxia Bi, Wuxia Bi, Wuxia Bi, Baisha Weng, Baisha Weng, Denghua Yan, Denghua Yan, Dawei Zhang, Cuishan Liu, Xiaoliang Shi, Xiaoliang Shi, Lanshu Jing, Lanshu Jing, Siying Yan, Hao Wang

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Pollinator probing preference and switching mode-mediated self-interference within a monoecious plant significantly reduced reproductive fitness by Bi-Xian Wu, Li-Na Ma, Nan Xia, Hao Wang, Guo-Xing Cao

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Consistency and Accuracy of Four High-Resolution LULC Datasets—Indochina Peninsula Case Study by Hao Wang, Huimin Yan, Yunfeng Hu, Yue Xi, Yichen Yang

“…The accuracy of cropland, forest, water area, and built-up land is generally high (above 85%); the accuracy of grassland, shrubland, and bare land is low (below 60%). …”
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Analysis on Land-Use Change and Its Driving Mechanism in Xilingol, China, during 2000–2020 Using the Google Earth Engine by Junzhi Ye, Yunfeng Hu, Lin Zhen, Hao Wang, Yuxin Zhang

“…The main findings are summarized as follows. (1) The RF classification algorithm supported by the GEE platform enables fast and accurate acquisition of the LULC dataset, and the overall accuracy is 0.88 ± 0.01. (2) The ecological condition across Xilingol has improved significantly in the last 20 years (2000–2020), and the area of vegetation (grassland and woodland) has increased. Specifically, the area of high-coverage grass and woodland increases (+13.26%, +1.19%), while the area of water and moderate- and low-coverage grass decreases (−15.96%, −7.23%, and −3.27%). …”
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Phylogenomic Analysis Reconstructed the Order Matoniales from Paleopolyploidy Veil by Jiang-Ping Shu, Hao Wang, Hui Shen, Rui-Jiang Wang, Qiang Fu, Yong-Dong Wang, Yuan-Nian Jiao, Yue-Hong Yan

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Afforestation-Induced Shifts in Soil Bacterial Diversity and Community Structure in the Saihanba Region by Kai-Chuan Huang, Wen Zhao, Jun-Ning Li, Reyila Mumin, Chang-Ge Song, Hao Wang, Yi-Fei Sun, Bao-Kai Cui

“…<i>mongolica</i>, on soil bacterial diversity and community structure in comparison to grassland. Sixty soil samples were collected at a 20 cm depth, and high-throughput sequencing was employed to identify bacterial communities and assess their interactions with environmental factors. …”
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Ellagic acid Alleviates hepatic ischemia–reperfusion injury in C57 mice via the Caspase-1-GSDMD pathway by Hao Wang, Fujun Miao, Delu Ning, Chunlan Shan

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Study on Dynamic Changes of Soil Erosion in the North and South Mountains of Lanzhou by Hua Zhang, Jinping Lei, Hao Wang, Cungang Xu, Yuxin Yin

“…Under different environmental factors, the soil erosion modulus increased with elevation and then decreased; the soil erosion modulus increased with a slope; the average soil erosion modulus of grassland was the largest, followed by forest land, cultivated land, unused land, construction land, and it was the smallest for water; except for bare land, the average soil erosion modulus decreases with the increase of vegetation cover; Soil erosion modulus was the greatest in the pedocal of the North and South Mountains, and the least in the alpine soil.…”
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Comparative Analysis and Comprehensive Trade-Off of Four Spatiotemporal Fusion Models for NDVI Generation by Yunfeng Hu, Hao Wang, Xiaoyu Niu, Wei Shao, Yichen Yang

“…In this study, four spatiotemporal fusion models (STARFM, ESTARFM, FSDAF, and GF-SG) were selected to carry out NDVI image fusion in grassland, forest, and farmland test areas, and three indicators of root mean square error (RMSE), average difference (AD), and edge feature richness difference (EFRD) were used. …”
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Fusion and Analysis of Land Use/Cover Datasets Based on Bayesian-Fuzzy Probability Prediction: A Case Study of the Indochina Peninsula by Hao Wang, Yunfeng Hu, Zhiming Feng

“…For the land types with poor original accuracy (grassland, shrubland, wetland, and bare land), the accuracy of the fusion result improved more, and the F1 score improved by at least 4.02–5.82%, and at most 14.41–48.35%. …”
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