Leaf multi-dimensional stoichiometry as a robust predictor of productivity on the Tibetan Plateau
文献类型：期刊论文
| 作者 | Li, Xin2,3; Zhang, Jiahui4,5; Rousk, Kathrin6,7; Zhang, Yinghua2; Jiao, Yi6,7; Yan, Pu1; He, Nianpeng4,5 |
| 刊名 | JOURNAL OF INTEGRATIVE PLANT BIOLOGY
 |
| 出版日期 | 2025-06-27 |
| 卷号 | N/A |
| 关键词 | carbon cycle climate change machine learning productivity prediction stoichiometry Tibetan Plateau |
| ISSN号 | 1672-9072 |
| DOI | 10.1111/jipb.13960 |
| 产权排序 | 1 |
| 文献子类 | Article ; Early Access |
| 英文摘要 | Accurately predicting gross primary productivity (GPP) is crucial for understanding carbon cycling; however, most studies have predominantly investigated GPP using only environmental metrics, overlooking the pivotal role of functional traits as intermediaries between the environment and GPP and the predictive potential of GPP. Therefore, this study developed a three-dimensional engine framework to predict GPP and tested it by leveraging functional traits from 2,040 plant communities on the Tibetan Plateau, incorporating environmental factors and the length of the plant-growing season. Our results highlight that while the environment exerts a dominant direct influence on GPP dynamics, the contribution of leaf density traits to GPP prediction should not be overlooked. The proposed framework achieved a prediction accuracy close to 0.92, underscoring its feasibility in GPP prediction. However, incorporating the nitrogen-to-phosphorus ratio into the framework diminished the model's predictive accuracy. Within the stoichiometric dimension alone, the prediction accuracy significantly increased with the number of input traits, indicating a substantial potential for enhancing predictive capability. In the dimension of environmental factors, incorporating more environmental factors does not significantly enhance the model's predictive ability. Our research facilitates the dynamic, continuous, and relatively accurate monitoring of GPP, contributing to a better understanding of carbon cycle dynamics and supporting informed ecosystem planning and management. |
| URL标识 | 查看原文 |
| WOS关键词 | GROSS PRIMARY PRODUCTIVITY ; PLANT ; GROWTH ; CLIMATE ; RATIOS ; CHINA |
| WOS研究方向 | Biochemistry & Molecular Biology ; Plant Sciences |
| 语种 | 英语 |
| WOS记录号 | WOS:001517622800001 |
| 出版者 | WILEY |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/215411]  |
| 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 | Zhang, Jiahui; He, Nianpeng |
| 作者单位 | 1.Georgia Inst Technol, Sch Biol Sci, Atlanta, GA 30332 USA 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China; 3.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China; 4.Northeast Forestry Univ, Key Lab Sustainable Forest Ecosyst Management, Minist Educ, Harbin 150040, Peoples R China; 5.Chinese Acad Sci, Earth Crit Zone & Flux Res Stn Xingan Mt, Daxinganling 165200, Peoples R China; 6.Univ Copenhagen, Ctr Volatile Interact VOLT, Dept Biol, DK-2100 Copenhagen, Denmark; 7.Univ Copenhagen, Terr Ecol Sect, DK-2100 Copenhagen, Denmark; |
| 推荐引用方式 GB/T 7714 | Li, Xin,Zhang, Jiahui,Rousk, Kathrin,et al. Leaf multi-dimensional stoichiometry as a robust predictor of productivity on the Tibetan Plateau[J]. JOURNAL OF INTEGRATIVE PLANT BIOLOGY,2025,N/A. |
| APA | Li, Xin.,Zhang, Jiahui.,Rousk, Kathrin.,Zhang, Yinghua.,Jiao, Yi.,...&He, Nianpeng.(2025).Leaf multi-dimensional stoichiometry as a robust predictor of productivity on the Tibetan Plateau.JOURNAL OF INTEGRATIVE PLANT BIOLOGY,N/A. |
| MLA | Li, Xin,et al."Leaf multi-dimensional stoichiometry as a robust predictor of productivity on the Tibetan Plateau".JOURNAL OF INTEGRATIVE PLANT BIOLOGY N/A(2025). |
入库方式： OAI收割
来源：地理科学与资源研究所
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