Integrating multiple plant functional traits to predict ecosystem productivity
文献类型：期刊论文
| 作者 | Yan, Pu7,8; He, Nianpeng6,8; Yu, Kailiang4,5; Xu, Li3,8; Van Meerbeek, Koenraad2,7 |
| 刊名 | COMMUNICATIONS BIOLOGY
 |
| 出版日期 | 2023-03-03 |
| 卷号 | 6期号:1 |
| ISSN号 | 2399-3642 |
| DOI | 10.1038/s42003-023-04626-3 |
| 文献子类 | Article |
| 英文摘要 | Quantifying and predicting variation in gross primary productivity (GPP) is important for accurate assessment of the ecosystem carbon budget under global change. Scaling traits to community scales for predicting ecosystem functions (i.e., GPP) remain challenging, while it is promising and well appreciated with the rapid development of trait-based ecology. In this study, we aim to integrate multiple plant traits with the recently developed trait-based productivity (TBP) theory, verify it via Bayesian structural equation modeling (SEM) and complementary independent effect analysis. We further distinguish the relative importance of different traits in explaining the variation in GPP. We apply the TBP theory based on plant community traits to a multi-trait dataset containing more than 13,000 measurements of approximately 2,500 species in Chinese forest and grassland systems. Remarkably, our SEM accurately predicts variation in annual and monthly GPP across China (R-2 values of 0.87 and 0.73, respectively). Plant community traits play a key role. This study shows that integrating multiple plant functional traits into the TBP theory strengthens the quantification of ecosystem primary productivity variability and further advances understanding of the trait-productivity relationship. Our findings facilitate integration of the growing plant trait data into future ecological models. Integration of plant functional traits into trait-based productivity theory helps quantify primary productivity in terrestrial ecosystems. |
| WOS关键词 | GROSS PRIMARY PRODUCTIVITY ; R PACKAGE ; TEMPERATURE ; FRAMEWORK ; COVARIATION ; DRIVERS ; CAPTURE ; FORESTS ; CLIMATE ; PATTERN |
| WOS研究方向 | Life Sciences & Biomedicine - Other Topics ; Science & Technology - Other Topics |
| 出版者 | NATURE PORTFOLIO |
| WOS记录号 | WOS:000943534900001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/190251]  |
| 专题 | 生态系统网络观测与模拟院重点实验室_外文论文 |
| 作者单位 | 1.Katholieke Univ Leuven, KU Leuven Plant Inst, Leuven, Belgium 2.Chinese Acad Sci, Earth Crit Zone & Flux Res Stn Xingan Mt, Daxinganling 165200, Peoples R China 3.Princeton Univ, High Meadows Environm Inst, Princeton, NJ USA 4.Princeton Univ, Dept Ecol & Evolutionary Biol, Princeton, NJ USA 5.Northeast Forestry Univ, Ctr Ecol Res, Harbin 150040, Peoples R China 6.Katholieke Univ Leuven, Dept Earth & Environm Sci, Div Forest Nat & Landscape, Leuven, Belgium 7.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 8.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yan, Pu,He, Nianpeng,Yu, Kailiang,et al. Integrating multiple plant functional traits to predict ecosystem productivity[J]. COMMUNICATIONS BIOLOGY,2023,6(1). |
| APA | Yan, Pu,He, Nianpeng,Yu, Kailiang,Xu, Li,&Van Meerbeek, Koenraad.(2023).Integrating multiple plant functional traits to predict ecosystem productivity.COMMUNICATIONS BIOLOGY,6(1). |
| MLA | Yan, Pu,et al."Integrating multiple plant functional traits to predict ecosystem productivity".COMMUNICATIONS BIOLOGY 6.1(2023). |
入库方式： OAI收割
来源：地理科学与资源研究所
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