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Leaf-level coordination principles propagate to the ecosystem scale

2023; Nature Portfolio; Volume: 14; Issue: 1 Linguagem: Inglês

10.1038/s41467-023-39572-5

2041-1723

Ulisse Gomarasca, Mirco Migliavacca, Jens Kattge, Jacob A. Nelson, Ülo Niinemets, Christian Wirth, Alessandro Cescatti, Michael Bahn, Richard Nair, Alicia Teresa Rosario Acosta, M. Altaf Arain, Mirela Beloiu, T. Andrew Black, Hans Henrik Bruun, Solveig Franziska Bucher, Nina Buchmann, Chaeho Byun, Arnaud Carrara, Adriano Mosca Conte, Ana Carolina da Silva, Grégory Duveiller, Silvano Fares, Andreas Ibrom, Alexander Knohl, Benjamín Komac, Jean‐Marc Limousin, Christopher H. Lusk, Miguel D. Mahecha, David Martini, Vanessa Minden, Leonardo Montagnani, Akira Mori, Yusuke Onoda, Josep Peñuelas, Óscar Pérez‐Priego, Peter Poschlod, Thomas L. Powell, Peter B. Reich, Ladislav Šigut, Peter M. van Bodegom, Sophia Walther, Georg Wohlfahrt, Ian J. Wright, Markus Reichstein,

Forest ecology and management

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.