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Plant trait and vegetation data along a 1314 m elevation gradient with fire history in Puna grasslands, Perú.

Authors :
Halbritter AH
Vandvik V
Cotner SH
Farfan-Rios W
Maitner BS
Michaletz ST
Oliveras Menor I
Telford RJ
Ccahuana A
Cruz R
Sallo-Bravo J
Santos-Andrade PE
Vilca-Bustamante LL
Castorena M
Chacón-Labella J
Christiansen CT
Duran SM
Egelkraut DD
Gya R
Haugum SV
Seltzer L
Silman MR
Strydom T
Spiegel MP
Barros A
Birkeli K
Boakye M
Chiappero F
Chmurzynski A
Garen JC
Gaudard J
Gauthier TJ
Geange SR
Gonzales FN
Henn JJ
Hošková K
Isaksen A
Jessup LH
Johnson W
Kusch E
Lepley K
Lift M
Martyn TE
Muñoz Mazon M
Middleton SL
Quinteros Casaverde NL
Navarro J
Zepeda V
Ocampo-Zuleta K
Palomino-Cardenas AC
Pastor Ploskonka S
Pierfederici ME
Pinelli V
Rickenback J
Roos RE
Rui HS
Sanchez Diaz E
Sánchez-Tapia A
Smith A
Urquiaga-Flores E
von Oppen J
Enquist BJ
Source :
Scientific data [Sci Data] 2024 Feb 21; Vol. 11 (1), pp. 225. Date of Electronic Publication: 2024 Feb 21.
Publication Year :
2024

Abstract

Alpine grassland vegetation supports globally important biodiversity and ecosystems that are increasingly threatened by climate warming and other environmental changes. Trait-based approaches can support understanding of vegetation responses to global change drivers and consequences for ecosystem functioning. In six sites along a 1314 m elevational gradient in Puna grasslands in the Peruvian Andes, we collected datasets on vascular plant composition, plant functional traits, biomass, ecosystem fluxes, and climate data over three years. The data were collected in the wet and dry season and from plots with different fire histories. We selected traits associated with plant resource use, growth, and life history strategies (leaf area, leaf dry/wet mass, leaf thickness, specific leaf area, leaf dry matter content, leaf C, N, P content, C and N isotopes). The trait dataset contains 3,665 plant records from 145 taxa, 54,036 trait measurements (increasing the trait data coverage of the regional flora by 420%) covering 14 traits and 121 plant taxa (ca. 40% of which have no previous publicly available trait data) across 33 families.<br /> (© 2024. The Author(s).)

Details

Language :
English
ISSN :
2052-4463
Volume :
11
Issue :
1
Database :
MEDLINE
Journal :
Scientific data
Publication Type :
Academic Journal
Accession number :
38383609
Full Text :
https://doi.org/10.1038/s41597-024-02980-3