Please use this identifier to cite or link to this item: http://dx.doi.org/10.25673/117958
Title: CoRRE Trait Data : adataset of 17 categorical and continuous traits for 4079 grassland species worldwide
Author(s): Komatsu, Kimberly J.Look up in the Integrated Authority File of the German National Library
Avolio, Meghan L.
Cubino, Josep Padullés
Schrodt, Franziska
Auge, HaraldLook up in the Integrated Authority File of the German National Library
Cavender-Bares, JeannineLook up in the Integrated Authority File of the German National Library
Clark, Adam T.
Flores-Moreno, Habacuc
Grman, Emily
Harpole, StanLook up in the Integrated Authority File of the German National Library
Issue Date: 2024
Type: Article
Language: English
Abstract: In our changing world, understanding plant community responses to global change drivers is critical for predicting future ecosystem composition and function. Plant functional traits promise to be a key predictive tool for many ecosystems, including grasslands; however, their use requires both complete plant community and functional trait data. Yet, representation of these data in global databases is sparse, particularly beyond a handful of most used traits and common species. Here we present the CoRRE Trait Data, spanning 17 traits (9 categorical, 8 continuous) anticipated to predict species’ responses to global change for 4,079 vascular plant species across 173 plant families present in 390 grassland experiments from around the world. The dataset contains complete categorical trait records for all 4,079 plant species obtained from a comprehensive literature search, as well as nearly complete coverage (99.97%) of imputed continuous trait values for a subset of 2,927 plant species. These data will shed light on mechanisms underlying population, community, and ecosystem responses to global change in grasslands worldwide.
URI: https://opendata.uni-halle.de//handle/1981185920/119918
http://dx.doi.org/10.25673/117958
Open Access: Open access publication
License: (CC BY 4.0) Creative Commons Attribution 4.0(CC BY 4.0) Creative Commons Attribution 4.0
Journal Title: Scientific data
Publisher: Nature Publ. Group
Publisher Place: London
Volume: 11
Original Publication: 10.1038/s41597-024-03637-x
Appears in Collections:Open Access Publikationen der MLU

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