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Leaf-level coordination principles propagate to the ecosystem scale

Authors :
Ulisse Gomarasca
Mirco Migliavacca
Jens Kattge
Jacob A. Nelson
Ülo Niinemets
Christian Wirth
Alessandro Cescatti
Michael Bahn
Richard Nair
Alicia T. R. Acosta
M. Altaf Arain
Mirela Beloiu
T. Andrew Black
Hans Henrik Bruun
Solveig Franziska Bucher
Nina Buchmann
Chaeho Byun
Arnaud Carrara
Adriano Conte
Ana C. da Silva
Gregory Duveiller
Silvano Fares
Andreas Ibrom
Alexander Knohl
Benjamin Komac
Jean-Marc Limousin
Christopher H. Lusk
Miguel D. Mahecha
David Martini
Vanessa Minden
Leonardo Montagnani
Akira S. Mori
Yusuke Onoda
Josep Peñuelas
Oscar Perez-Priego
Peter Poschlod
Thomas L. Powell
Peter B. Reich
Ladislav Šigut
Peter M. van Bodegom
Sophia Walther
Georg Wohlfahrt
Ian J. Wright
Markus Reichstein
Source :
Nature Communications, Vol 14, Iss 1, Pp 1-11 (2023)
Publication Year :
2023
Publisher :
Nature Portfolio, 2023.

Abstract

Abstract Fundamental axes of variation in plant traits result from trade-offs between costs and benefits of resource-use strategies at the leaf scale. However, it is unclear whether similar trade-offs propagate to the ecosystem level. Here, we test whether trait correlation patterns predicted by three well-known leaf- and plant-level coordination theories – the leaf economics spectrum, the global spectrum of plant form and function, and the least-cost hypothesis – are also observed between community mean traits and ecosystem processes. We combined ecosystem functional properties from FLUXNET sites, vegetation properties, and community mean plant traits into three corresponding principal component analyses. We find that the leaf economics spectrum (90 sites), the global spectrum of plant form and function (89 sites), and the least-cost hypothesis (82 sites) all propagate at the ecosystem level. However, we also find evidence of additional scale-emergent properties. Evaluating the coordination of ecosystem functional properties may aid the development of more realistic global dynamic vegetation models with critical empirical data, reducing the uncertainty of climate change projections.

Subjects

Subjects :
Science

Details

Language :
English
ISSN :
20411723
Volume :
14
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Nature Communications
Publication Type :
Academic Journal
Accession number :
edsdoj.34eec95839f4cc1bc7fcc5dad3031d2
Document Type :
article
Full Text :
https://doi.org/10.1038/s41467-023-39572-5