Preprints
https://doi.org/10.5194/essd-2022-199
https://doi.org/10.5194/essd-2022-199
 
01 Jul 2022
01 Jul 2022
Status: this preprint is currently under review for the journal ESSD.

TiP-Leaf: A dataset of leaf traits across vegetation types on the Tibetan Plateau

Yili Jin, Haoyan Wang, Jie Xia, Jian Ni, Kai Li, Ying Hou, Jing Hu, Linfeng Wei, Kai Wu, Haojun Xia, and Borui Zhou Yili Jin et al.
  • College of Chemistry and Life Sciences, Zhejiang Normal University, Jinhua 321004, China

Abstract. Functional trait databases are emerging as a crucial tool for a wide range of ecological studies including the next-generation vegetation modeling across the world. However, few large-scale studies have been reported on plant traits in the Tibetan Plateau (TP), the cradle of East Asian flora and fauna with specific alpine ecosystems, no report on plant trait databases could be found. Here an extensive dataset of 11 leaf functional traits (TiP-Leaf) for mainly herbs and shrubs and a few trees on the TP was compiled through field surveys. The TiP-Leaf dataset, compiled from 336 sites distributed mainly in the plateau surface and the northern margin of the TP across alpine and temperate vegetation regions and sampled from 2018 to 2021, contains 1692 morphological trait measurements of leaf thickness, leaf fresh weight, leaf dry weight, leaf dry-matter content, leaf water content, leaf area, specific leaf area and leaf mass per area and 1645 chemical element trait measurements of leaf carbon, nitrogen and phosphorus contents. Thus, 468 species belonging to 184 genera and 51 families were obtained and measured. In addition to leaf trait measurements, geographic coordinates, bioclimate variables, disturbance intensity and vegetation types of each site were also recorded. The dataset could provide solid data support for effectively quantifying the modern ecological features of alpine ecosystems, further evaluating the response of alpine ecosystem to climate change and human disturbances and improving the next-generation vegetation model. It could be a great contribution to the regional and global plant trait databases. The dataset is available from the National Tibetan Plateau Data Center (TPDC; Jin et al., 2022; https://doi.org/10.11888/Terre.tpdc.272516).

Yili Jin et al.

Status: open (until 14 Sep 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2022-199', Anonymous Referee #1, 09 Aug 2022 reply

Yili Jin et al.

Data sets

A dataset of leaf traits on the Tibetan Plateau (2018-2021) Jin, Y., Wang, H., Xia, J., Ni, J., Li, K., Hou, Y., Hu, J., Wei, L., Wu, K., Xia, H., Zhou, B. https://doi.org/10.11888/Terre.tpdc.272516

Yili Jin et al.

Viewed

Total article views: 217 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
169 42 6 217 2 3
  • HTML: 169
  • PDF: 42
  • XML: 6
  • Total: 217
  • BibTeX: 2
  • EndNote: 3
Views and downloads (calculated since 01 Jul 2022)
Cumulative views and downloads (calculated since 01 Jul 2022)

Viewed (geographical distribution)

Total article views: 201 (including HTML, PDF, and XML) Thereof 201 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 09 Aug 2022
Download
Short summary
The TiP-Leaf dataset was compiled from direct field measurements and included 11 leaf traits from 468 species of 1692 individuals, covering a great proportion of species and vegetation types on the highest plateau in the world. This work is the first plant trait dataset that represents all of the alpine vegetation on the TP, which is not only an update of the Chinese plant trait database but also a great contribution to the global trait database.