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This dataset reports on leaf traits of plants and climate data used in the paper: Xu et al. (under review in Tree Physiology) at 18 sites from 1143 to 4361 m in Gongga Mountain region (29° 22' to 29° 55' N and 101° 1' to 102° 9' E). The trait data were collected during the active growing season in 2018 and 2019, as part of a Chinese research project. The vegetation type changes from deciduous broad-leaved forest dominated by Betulaceae, Urticaceae, Caprifoliaceae and Rosaceae, to evergreen needle-leaved forest and deciduous shrubland dominated by Pinaceae and/or Rosaceae and Ericaceae with increasing elevation. Relevant dataset information can be found: https://github.com/Huiying-Xu/PTG
Grants: National Natural Science Foundation of China (no. 91837312, 31971495); Tsinghua University Initiative Scientific Research Program (no. 2019Z07L01001). The High-End Foreign Expert program of the China State Administration of Foreign Expert Affairs; European Research Council (694481 to S.P.H., REALM 787203 to I.C.P.); AXA Chair Programme in Biosphere and Climate Impacts; Imperial College initiative on Grand Challenges in Ecosystems and the Environment.
leaf trait, photosynthesis
leaf trait, photosynthesis
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
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| downloads | 16 |

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