The three major axes of terrestrial ecosystem function

Mirco Migliavacca, Talie Musavi, Miguel D. Mahecha, Jacob A. Nelson, Jürgen Knauer, Dennis D. Baldocchi, Oscar Perez-Priego, Rune Christiansen, Jonas Peters, Karen Anderson, Michael Bahn, T. Andrew Black, Peter D. Blanken, Damien Bonal, Nina Buchmann, Silvia Caldararu, Arnaud Carrara, Nuno Carvalhais, Alessandro Cescatti, Jiquan ChenJamie Cleverly, Edoardo Cremonese, Ankur R. Desai, Tarek S. El-Madany, Martha M. Farella, Marcos Fernández-Martínez, Gianluca Filippa, Matthias Forkel, Marta Galvagno, Ulisse Gomarasca, Christopher M. Gough, Mathias Göckede, Andreas Ibrom, Hiroki Ikawa, Ivan A. Janssens, Martin Jung, Jens Kattge, Trevor F. Keenan, Alexander Knohl, Hideki Kobayashi, Guido Kraemer, Beverly E. Law, Michael J. Liddell, Xuanlong Ma, Ivan Mammarella, David Martini, Craig Macfarlane, Giorgio Matteucci, Leonardo Montagnani, Daniel E. Pabon-Moreno, Cinzia Panigada, Dario Papale, Elise Pendall, Josep Penuelas, Richard P. Phillips, Peter B. Reich, Micol Rossini, Eyal Rotenberg, Russell L. Scott, Clement Stahl, Ulrich Weber, Georg Wohlfahrt, Sebastian Wolf, Ian J. Wright, Dan Yakir, Sönke Zaehle, Markus Reichstein

Research output: Contribution to journalArticlepeer-review

Abstract

The leaf economics spectrum1,2 and the global spectrum of plant forms and functions3 revealed fundamental axes of variation in plant traits, which represent different ecological strategies that are shaped by the evolutionary development of plant species2. Ecosystem functions depend on environmental conditions and the traits of species that comprise the ecological communities4. However, the axes of variation of ecosystem functions are largely unknown, which limits our understanding of how ecosystems respond as a whole to anthropogenic drivers, climate and environmental variability4,5. Here we derive a set of ecosystem functions6 from a dataset of surface gas exchange measurements across major terrestrial biomes. We find that most of the variability within ecosystem functions (71.8%) is captured by three key axes. The first axis reflects maximum ecosystem productivity and is mostly explained by vegetation structure. The second axis reflects ecosystem water-use strategies and is jointly explained by variation in vegetation height and climate. The third axis, which represents ecosystem carbon-use efficiency, features a gradient related to aridity, and is explained primarily by variation in vegetation structure. We show that two state-of-the-art land surface models reproduce the first and most important axis of ecosystem functions. However, the models tend to simulate more strongly correlated functions than those observed, which limits their ability to accurately predict the full range of responses to environmental changes in carbon, water and energy cycling in terrestrial ecosystems7,8.

Original languageEnglish (US)
Pages (from-to)468-472
Number of pages5
JournalNature
Volume598
Issue number7881
DOIs
StatePublished - Oct 2021

Bibliographical note

Funding Information:
Funding Open access funding provided by Max Planck Society.

Funding Information:
Acknowledgements This work has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 721995. M.M. and M. Reichstein acknowledge the Alexander Von Humboldt Foundation for funding with the Max Planck Research Prize 2013 to M. Reichstein. This work used eddy covariance data acquired and shared by the FLUXNET community, including these networks: AmeriFlux, AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada, GreenGrass, ICOS, KoFlux, LBA, NECC, OzFlux-TERN, Swiss FluxNet, TCOS-Siberia and USCCC. The ERA-Interim reanalysis data were provided by ECMWF and processed by LSCE. The FLUXNET eddy covariance data processing and harmonization was carried out by the European Fluxes Database Cluster, the AmeriFlux Management Project and the Fluxdata project of FLUXNET, with the support of the CDIAC and the ICOS Ecosystem Thematic Center, and the OzFlux, ChinaFlux and AsiaFlux offices. R.C. and J. Peters were supported by the VILLUM FONDEN (18968) and J. Peters in addition by the Carlsberg Foundation; P.R. acknowledges funding support from the US National Science Foundation (NSF) Long-Term Ecological Research (DEB-1831944) and Biological Integration Institutes (NSF-DBI-2021898); N.B. acknowledges funding from various SNF projects, including ICOS-CH (20FI21_148992, 20FI20_173691), the ETH Board and ETH Zurich (TH-1006-02); and T.F.K. acknowledges support from the Reducing Uncertainties in Biogeochemical Interactions through Synthesis and Computation Scientific Focus Area (RUBISCO SFA), which is sponsored by the Regional and Global Model Analysis (RGMA) Program of the Office of Biological and Environmental Research (BER) in the US Department of Energy Office of Science. OzFlux is supported by the Australian Government’s Terrestrial Ecosystem Research Network (TERN, www.tern.org.au). We thank K. Morris and S. Paulus for comments on the draft, K. Blakeslee for English editing and G. Bohrer for sharing nitrogen data for his site.

Publisher Copyright:
© 2021, The Author(s).

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