Climate-LUCC synergy drives soil respiration dynamics in China: a biome-specific machine learning approach
文献类型：期刊论文
| 作者 | Ming, Ru1,3; Zhou, Yan4; Cui, Yaoping4; Huang, Ni2; Wang, Junbang3 |
| 刊名 | JOURNAL OF PLANT ECOLOGY
 |
| 出版日期 | 4599 |
| 卷号 | 18期号:6页码:rtaf153 |
| 关键词 | climate trends carbon cycle machine learning LUCC ecological restoration |
| ISSN号 | 1752-9921 |
| DOI | 10.1093/jpe/rtaf153 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Soil respiration (R-S) releases CO2 through autotrophic and heterotrophic respiration, representing the second largest carbon flux in terrestrial ecosystems after photosynthesis. It plays a pivotal role in global carbon cycling and climate feedback. China's climate shifted from a warming hiatus (2001-2010) to accelerated warming (2010-2019), coupled with ongoing land use/cover change (LUCC), jointly drives the spatiotemporal dynamics of R-S. However, the relative contributions and underlying mechanisms of these factors remain underexplored. In this study, biome-specific machine learning models (R-2 = 0.69-0.82) were developed to estimate R-S at a 1 km spatial resolution across China from 2001 to 2019. Results indicate that the long-term average annual R-S across China's vegetated areas is 4.24 +/- 0.02 Pg C year(-1). Interannual variability shifted from relative stability during 2001-2010 (-5.58 Tg C year(-1); -0.08 g C m(-2) year(-1), P = 0.77) to a significant increase (36.29 Tg C year(-1); 0.52 g C m(-2) year(-1), P < 0.05) during 2010-2019. Climate and LUCC together accounted for 61.7% of interannual R-S variability, with moisture as the primary driver (29.6% of variance). Large-scale ecological engineering projects, while effective in enhancing carbon sequestration, also promote R-S, potentially offsetting some carbon storage gains. The long-term time-series dataset obtained in this study not only supports research on the mechanisms influencing R-S but also provides benchmark data for improving terrestrial ecosystem carbon cycle models. These findings highlight R-S' critical role in China's carbon budget and its sensitivity to climatic and anthropogenic drivers. |
| URL标识 | 查看原文 |
| WOS关键词 | TERRESTRIAL ECOSYSTEMS ; ECOLOGICAL RESTORATION ; CARBON SEQUESTRATION ; PRECIPITATION ; RESPONSES ; WATER ; TEMPERATURE ; VARIABILITY ; VEGETATION ; NITROGEN |
| WOS研究方向 | Plant Sciences ; Environmental Sciences & Ecology ; Forestry |
| 语种 | 英语 |
| WOS记录号 | WOS:001651555100001 |
| 出版者 | OXFORD UNIV PRESS |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/219515]  |
| 专题 | 生态系统网络观测与模拟院重点实验室_外文论文 |
| 通讯作者 | Wang, Junbang |
| 作者单位 | 1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China; 2.Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing Sci, Beijing 100094, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Natl Ecosyst Sci Data Ctr, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China; 4.Henan Univ, Coll Geog Sci, Fac Geog Sci & Engn, Zhengzhou 450046, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Ming, Ru,Zhou, Yan,Cui, Yaoping,et al. Climate-LUCC synergy drives soil respiration dynamics in China: a biome-specific machine learning approach[J]. JOURNAL OF PLANT ECOLOGY,4599,18(6):rtaf153. |
| APA | Ming, Ru,Zhou, Yan,Cui, Yaoping,Huang, Ni,&Wang, Junbang.(4599).Climate-LUCC synergy drives soil respiration dynamics in China: a biome-specific machine learning approach.JOURNAL OF PLANT ECOLOGY,18(6),rtaf153. |
| MLA | Ming, Ru,et al."Climate-LUCC synergy drives soil respiration dynamics in China: a biome-specific machine learning approach".JOURNAL OF PLANT ECOLOGY 18.6(4599):rtaf153. |
入库方式： OAI收割
来源：地理科学与资源研究所
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