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ZENODO
Dataset . 2020
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2020
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2020
License: CC BY
Data sources: ZENODO
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Leaf traits data from Gongga Mountain, China

Authors: Huiying Xu; Han Wang; I. Colin Prentice; Sandy P. Harrison;

Leaf traits data from Gongga Mountain, China

Abstract

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.

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Keywords

leaf trait, photosynthesis

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selected citations
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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).
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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.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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