Plant traits and vegetation data from climate warming experiments along an 1100 m elevation gradient in Gongga Mountains, China

Vigdis Vandvik, Aud H. Halbritter, Yan Yang, Hai He, Li Zhang, Alexander B. Brummer, Kari Klanderud, Brian S. Maitner, Sean T. Michaletz, Xiangyang Sun, Richard J. Telford, Genxu Wang, Inge H.J. Althuizen, Jonathan J. Henn, William Fernando Erazo Garcia, Ragnhild Gya, Francesca Jaroszynska, Blake L. Joyce, Rebecca Lehman, Michelangelo Sergio MoerlandElisabeth Nesheim-Hauge, Linda Hovde Nordås, Ahui Peng, Claire Ponsac, Lorah Seltzer, Christien Steyn, Megan K. Sullivan, Jesslyn Tjendra, Yao Xiao, Xiaoxiang Zhao, Brian J. Enquist

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


Functional trait data enhance climate change research by linking climate change, biodiversity response, and ecosystem functioning, and by enabling comparison between systems sharing few taxa. Across four sites along a 3000–4130 m a.s.l. gradient spanning 5.3 °C in growing season temperature in Mt. Gongga, Sichuan, China, we collected plant functional trait and vegetation data from control plots, open top chambers (OTCs), and reciprocally transplanted vegetation turfs. Over five years, we recorded vascular plant composition in 140 experimental treatment and control plots. We collected trait data associated with plant resource use, growth, and life history strategies (leaf area, leaf thickness, specific leaf area, leaf dry matter content, leaf C, N and P content and C and N isotopes) from local populations and from experimental treatments. The database consists of 6,671 plant records and 36,743 trait measurements (increasing the trait data coverage of the regional flora by 500%) covering 11 traits and 193 plant taxa (ca. 50% of which have no previous published trait data) across 37 families.

Original languageEnglish (US)
Article number189
JournalScientific Data
Issue number1
StatePublished - Dec 1 2020

ASJC Scopus subject areas

  • Statistics and Probability
  • Information Systems
  • Education
  • Computer Science Applications
  • Statistics, Probability and Uncertainty
  • Library and Information Sciences


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