Article | Published:

Plant functional trait change across a warming tundra biome

Naturevolume 562pages5762 (2018) | Download Citation

Abstract

The tundra is warming more rapidly than any other biome on Earth, and the potential ramifications are far-reaching because of global feedback effects between vegetation and climate. A better understanding of how environmental factors shape plant structure and function is crucial for predicting the consequences of environmental change for ecosystem functioning. Here we explore the biome-wide relationships between temperature, moisture and seven key plant functional traits both across space and over three decades of warming at 117 tundra locations. Spatial temperature–trait relationships were generally strong but soil moisture had a marked influence on the strength and direction of these relationships, highlighting the potentially important influence of changes in water availability on future trait shifts in tundra plant communities. Community height increased with warming across all sites over the past three decades, but other traits lagged far behind predicted rates of change. Our findings highlight the challenge of using space-for-time substitution to predict the functional consequences of future warming and suggest that functions that are tied closely to plant height will experience the most rapid change. They also reveal the strength with which environmental factors shape biotic communities at the coldest extremes of the planet and will help to improve projections of functional changes in tundra ecosystems with climate warming.

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Data availability

Trait data. Data compiled through the Tundra Trait Team are publicly accessible50. The public TTT database includes traits not considered in this study as well as tundra species that do not occur in our vegetation survey plots, for a total of nearly 92,000 trait observations on 978 species. Additional trait data from the TRY trait database can be requested at https://www.try-db.org/.

Composition data. Most sites and years of the vegetation survey data included in this study are available in the Polar Data Catalogue (ID 10786_iso). Much of the individual site-level data has additionally been made available in the BioTIME database60 (https://synergy.st-andrews.ac.uk/biotime/biotime-database/).

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Acknowledgements

This paper is an outcome of the sTundra working group supported by sDiv, the Synthesis Centre of the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig (DFG FZT 118). A.D.B. was supported by an iDiv postdoctoral fellowship and The Danish Council for Independent Research - Natural Sciences (DFF 4181-00565 to S.N.). A.D.B., I.H.M.-S., H.J.D.T. and S.A.-B. were funded by the UK Natural Environment Research Council (ShrubTundra Project NE/M016323/1 to I.H.M.-S.). S.N., A.B.O., S.S.N. and U.A.T. were supported by the Villum Foundation’s Young Investigator Programme (VKR023456 to S.N.) and the Carlsberg Foundation (2013-01-0825). N.R. was supported by the DFG-Forschungszentrum ‘German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig’ and Deutsche Forschungsgemeinschaft DFG (RU 1536/3-1). A.Buc. was supported by EU-F7P INTERACT (262693) and MOBILITY PLUS (1072/MOB/2013/0). A.B.O. was additionally supported by the Danish Council for Independent Research - Natural Sciences (DFF 4181-00565 to S.N.). J.M.A. was supported by the Carl Tryggers stiftelse för vetenskaplig forskning, A.H. by the Research Council of Norway (244557/E50), B.E. and A.Mic. by the Danish National Research Foundation (CENPERM DNRF100), B.M. by the Soil Conservation Service of Iceland and E.R.F. by the Swiss National Science Foundation (155554). B.C.F. was supported by the Academy of Finland (256991) and JPI Climate (291581). B.J.E. was supported by an NSF ATB, CAREER and Macrosystems award. C.M.I. was supported by the Office of Biological and Environmental Research in the US Department of Energy’s Office of Science as part of the Next-Generation Ecosystem Experiments in the Arctic (NGEE Arctic) project. D.B. was supported by The Swedish Research Council (2015-00465) and Marie Skłodowska Curie Actions co-funding (INCA 600398). E.W. was supported by the National Science Foundation (DEB-0415383), UWEC–ORSP and UWEC–BCDT. G.S.-S. and M.I.-G. were supported by the University of Zurich Research Priority Program on Global Change and Biodiversity. H.D.A. was supported by NSF PLR (1623764, 1304040). I.S.J. was supported by the Icelandic Research Fund (70255021) and the University of Iceland Research Fund. J.D.M.S. was supported by the Research Council of Norway (262064). J.S.P. was supported by the US Fish and Wildlife Service. J.C.O. was supported by Klimaat voor ruimte, Dutch national research program Climate Change and Spatial Planning. J.F.J., P.G., G.H.R.H., E.L., N.B.-L., K.A.H., L.S.C. and T.Z. were supported by the Natural Sciences and Engineering Research Council of Canada (NSERC). G.H.R.H., N.B.-L., E.L., L.S.C. and L.H. were supported by ArcticNet. G.H.R.H., N.B.-L., M.Tr. and L.S.C. were supported by the Northern Scientific Training Program. G.H.R.H., E.L. and N.B.-L. were additionally supported by the Polar Continental Shelf Program. N.B.-L. was additionally supported by the Fonds de recherche du Quebec: Nature et Technologies and the Centre d’études Nordiques. J.P. was supported by the European Research Council Synergy grant SyG-2013-610028 IMBALANCE-P. A.A.-R., O.G. and J.M.N. were supported by the Spanish OAPN (project 534S/2012) and European INTERACT project (262693 Transnational Access). K.D.T. was supported by NSF ANS-1418123. L.E.S. and P.A.W. were supported by the UK Natural Environment Research Council Arctic Terrestrial Ecology Special Topic Programme and Arctic Programme (NE/K000284/1 to P.A.W.). P.A.W. was additionally supported by the European Union Fourth Environment and Climate Framework Programme (Project Number ENV4-CT970586). M.W. was supported by DFG RTG 2010. R.D.H. was supported by the US National Science Foundation. M.J.S. and K.N.S. were supported by the Niwot Ridge LTER (NSF DEB-1637686). H.J.D.T. was funded by a NERC doctoral training partnership grant (NE/L002558/1). V.G.O. was supported by the Russian Science Foundation (14-50-00029). L.B. was supported by NSF ANS (1661723) and S.J.G. by NASA ABoVE (NNX15AU03A/NNX17AE44G). B.B.-L. was supported as part of the Energy Exascale Earth System Model (E3SM) project, funded by the US Department of Energy, Office of Science, Office of Biological and Environmental Research. A.E. was supported by the Academy of Finland (projects 253385 and 297191). E.K. was supported by Swedish Research Council (2015-00498), and S.Dí. was supported by CONICET, FONCyT and SECyT-UNC, Argentina. The study has been supported by the TRY initiative on plant traits (http://www.try-db.org), which is hosted at the Max Planck Institute for Biogeochemistry, Jena, Germany and is currently supported by DIVERSITAS/Future Earth and the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig. A.D.B. and S.C.E. thank the US National Science Foundation for support to receive training in Bayesian methods (grant 1145200 to N. Thompson Hobbs). We thank H. Bruelheide and J. Ramirez-Villegas for helpful input at earlier stages of this project. We acknowledge the contributions of S. Mamet, M. Jean, K. Allen, N. Young, J. Lowe, O. Eriksson and many others to trait and community composition data collection, and thank the governments, parks, field stations and local and indigenous people for the opportunity to conduct research on their land.

Reviewer information

Nature thanks G. Kunstler, F. Schrodt and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Author information

Affiliations

  1. School of GeoSciences, University of Edinburgh, Edinburgh, UK

    • Anne D. Bjorkman
    • , Isla H. Myers-Smith
    • , Damien Georges
    • , Haydn J. D. Thomas
    • , Sandra Angers-Blondin
    •  & Lorna E. Street
  2. Ecoinformatics and Biodiversity, Department of Bioscience, Aarhus University, Aarhus, Denmark

    • Anne D. Bjorkman
    • , Signe Normand
    • , Anne Blach-Overgaard
    • , Sigrid Schøler Nielsen
    •  & Urs A. Treier
  3. Senckenberg Gesellschaft für Naturforschung, Biodiversity and Climate Research Centre (BiK-F), Frankfurt, Germany

    • Anne D. Bjorkman
    •  & Peter Manning
  4. Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, CO, USA

    • Sarah C. Elmendorf
    •  & Katharine N. Suding
  5. National Ecological Observatory Network, Boulder, CO, USA

    • Sarah C. Elmendorf
  6. Institute of Arctic and Alpine Research, University of Colorado, Boulder, CO, USA

    • Sarah C. Elmendorf
  7. Arctic Research Center, Department of Bioscience, Aarhus University, Aarhus, Denmark

    • Signe Normand
    •  & Urs A. Treier
  8. Center for Biodiversity Dynamics in a Changing World (BIOCHANGE), Department of Bioscience, Aarhus University, Aarhus, Denmark

    • Signe Normand
    • , Anne Blach-Overgaard
    •  & Urs A. Treier
  9. German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany

    • Nadja Rüger
    • , Jens Kattge
    •  & Anu Eskelinen
  10. Smithsonian Tropical Research Institute, Balboa, Panama

    • Nadja Rüger
  11. European Commission, Joint Research Centre, Directorate D — Sustainable Resources, Bio-Economy Unit, Ispra, Italy

    • Pieter S. A. Beck
  12. Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden

    • Daan Blok
  13. Systems Ecology, Department of Ecological Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

    • J. Hans C. Cornelissen
  14. Arctic Centre, University of Lapland, Rovaniemi, Finland

    • Bruce C. Forbes
  15. International Agency for Research in Cancer, Lyon, France

    • Damien Georges
  16. School of Informatics, Computing and Cyber Systems, Northern Arizona University, Flagstaff, AZ, USA

    • Scott J. Goetz
    •  & Logan Berner
  17. Bigelow Laboratory for Ocean Sciences, East Boothbay, ME, USA

    • Kevin C. Guay
  18. Department of Geography, University of British Columbia, Vancouver, British Columbia, Canada

    • Gregory H. R. Henry
    • , Esther R. Frei
    •  & Noémie Boulanger-Lapointe
  19. Biology Department, University of Washington, Seattle, WA, USA

    • Janneke HilleRisLambers
  20. Biology Department, Grand Valley State University, Allendale, MI, USA

    • Robert D. Hollister
  21. Swiss Federal Research Institute WSL, Birmensdorf, Switzerland

    • Dirk N. Karger
    •  & Esther R. Frei
  22. Max Planck Institute for Biogeochemistry, Jena, Germany

    • Jens Kattge
  23. WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland

    • Janet S. Prevéy
    • , Christian Rixen
    • , Sonja Wipf
    • , Francesca Jaroszynska
    •  & Aino Kulonen
  24. Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland

    • Gabriela Schaepman-Strub
    • , Maitane Iturrate-Garcia
    •  & Chelsea J. Little
  25. Département de biologie, Université de Sherbrooke, Sherbrooke, Québec, Canada

    • Mark Vellend
  26. Institute of Botany and Landscape Ecology, Greifswald University, Greifswald, Germany

    • Martin Wilmking
    • , Alba Anadon-Rosell
    •  & Rohan Shetti
  27. Department of Chemistry, Life Sciences and Environmental Sustainability, University of Parma, Parma, Italy

    • Michele Carbognani
    • , Alessandro Petraglia
    •  & Marcello Tomaselli
  28. Department of Biology, Memorial University, St. John’s, Newfoundland and Labrador, Canada

    • Luise Hermanutz
    • , Laura Siegwart Collier
    •  & Andrew Trant
  29. Département des Sciences de l’environnement et Centre d’études nordiques, Université du Québec à Trois-Rivières, Trois-Rivières, Québec, Canada

    • Esther Lévesque
    • , Laurent J. Lamarque
    •  & Maxime Tremblay
  30. Department of Biological and Environmental Sciences, University of Gothenburg, Gothenburg, Sweden

    • Ulf Molau
  31. Environmental Biology Department, Institute of Environmental Sciences, Leiden University, Leiden, The Netherlands

    • Nadejda A. Soudzilovskaia
  32. Department of Evolution, Ecology and Organismal Biology, University of California Riverside, Riverside, CA, USA

    • Marko J. Spasojevic
  33. Department of Earth Sciences, University of Gothenburg, Gothenburg, Sweden

    • Tage Vowles
    •  & Robert G. Björk
  34. Department of Biological and Environmental Sciences, Qatar University, Doha, Qatar

    • Juha M. Alatalo
  35. Department of Forestry, Forest and Wildlife Research Center, Mississippi State University, Mississippi State, MS, USA

    • Heather D. Alexander
  36. Department of Evolutionary Biology, Ecology and Environmental Sciences, University of Barcelona, Barcelona, Spain

    • Alba Anadon-Rosell
    •  & Josep M. Ninot
  37. Biodiversity Research Institute, University of Barcelona, Barcelona, Spain

    • Alba Anadon-Rosell
    •  & Josep M. Ninot
  38. Department of Ecology and Environmental Science, Umeå University, Umeå, Sweden

    • Mariska te Beest
    • , Elina Kaarlejärvi
    •  & Johan Olofsson
  39. Environmental Sciences, Copernicus Institute of Sustainable Development, Utrecht University, Utrecht, The Netherlands

    • Mariska te Beest
  40. Gothenburg Global Biodiversity Centre, Göteborg, Sweden

    • Robert G. Björk
  41. Institute of Geoecology and Geoinformation, Adam Mickiewicz University, Poznan, Poland

    • Agata Buchwal
  42. Department of Biological Sciences, University of Alaska, Anchorage, Anchorage, AK, USA

    • Agata Buchwal
  43. Forest Ecology and Forest Management, Wageningen University and Research, Wageningen, The Netherlands

    • Allan Buras
  44. The Alaska Department of Fish and Game, Anchorage, AK, USA

    • Katherine Christie
  45. Department of Arctic and Marine Biology, Faculty of Biosciences, Fisheries and Economics, UiT—The Arctic University of Norway, Tromsø, Norway

    • Elisabeth J. Cooper
    •  & Philipp Semenchuk
  46. Department of Botany and Biodiversity Research, University of Vienna, Vienna, Austria

    • Stefan Dullinger
    • , Karl Hülber
    • , Sabine B. Rumpf
    •  & Philipp Semenchuk
  47. Center for Permafrost (CENPERM), Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark

    • Bo Elberling
    •  & Anders Michelsen
  48. Department of Physiological Diversity, Helmholtz Centre for Environmental Research—UFZ, Leipzig, Germany

    • Anu Eskelinen
  49. Department of Ecology and Genetics, University of Oulu, Oulu, Finland

    • Anu Eskelinen
  50. Global Ecology Unit, CREAF-CSIC-UAB, Cerdanyola del Vallès, Spain

    • Oriol Grau
  51. CREAF, Cerdanyola del Vallès, Spain

    • Oriol Grau
    •  & Josep Penuelas
  52. Department of Biology, Queen’s University, Kingston, Ontario, Canada

    • Paul Grogan
    •  & Tara Zamin
  53. Biology Department, Swedish Agricultural University (SLU), Uppsala, Sweden

    • Martin Hallinger
  54. Biology Department, Saint Mary’s University, Halifax, Nova Scotia, Canada

    • Karen A. Harper
  55. Plant Ecology and Nature Conservation Group, Wageningen University and Research, Wageningen, The Netherlands

    • Monique M. P. D. Heijmans
  56. British Columbia Public Service, Surrey, British Columbia, Canada

    • James Hudson
  57. Climate Change Science Institute and Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA

    • Colleen M. Iversen
  58. Institute of Biological and Environmental Sciences, University of Aberdeen, Aberdeen, UK

    • Francesca Jaroszynska
  59. Department of Biology, University of Saskatchewan, Saskatoon, Saskatchewan, Canada

    • Jill F. Johnstone
    •  & Sara Kuleza
  60. Forest and Landscape College, Department of Geosciences and Natural Resource Management, University of Copenhagen, Nødebo, Denmark

    • Rasmus Halfdan Jørgensen
  61. Department of Biology, Vrije Universiteit Brussel (VUB), Brussels, Belgium

    • Elina Kaarlejärvi
  62. Department of Forest Resources Management, Faculty of Forestry, University of British Columbia, Vancouver, British Columbia, Canada

    • Rebecca Klady
  63. School of Environmental Studies, University of Victoria, Victoria, British Columbia, Canada

    • Trevor Lantz
  64. Department of Aquatic Ecology, Eawag: Swiss Federal Institute of Aquatic Science and Technology, Dubendorf, Switzerland

    • Chelsea J. Little
  65. NTNU University Museum, Norwegian University of Science and Technology, Trondheim, Norway

    • James D. M. Speed
  66. Department of Biology, University of Copenhagen, Copenhagen, Denmark

    • Anders Michelsen
  67. Research Institute for Nature and Forest (INBO), Brussels, Belgium

    • Ann Milbau
  68. Department of Bioscience, Aarhus University, Roskilde, Denmark

    • Jacob Nabe-Nielsen
  69. Department of Biological Sciences, Florida International University, Miami, FL, USA

    • Steven F. Oberbauer
  70. Department of Geobotany, Lomonosov Moscow State University, Moscow, Russia

    • Vladimir G. Onipchenko
  71. Institute of Northern Engineering, University of Alaska Fairbanks, Fairbanks, AK, USA

    • Ken D. Tape
  72. School of Environment, Resources and Sustainability, University of Waterloo, Waterloo, Ontario, Canada

    • Andrew Trant
  73. Département de biologie, Centre d’études nordiques and Centre d’étude de la forêt, Université Laval, Quebec City, Québec, Canada

    • Jean-Pierre Tremblay
  74. Centre for Integrative Ecology, School of Life and Environmental Sciences, Deakin University, Burwood, Victoria, Australia

    • Susanna Venn
  75. Department of Geography, University of Bonn, Bonn, Germany

    • Stef Weijers
  76. USDA Forest Service International Institute of Tropical Forestry, Río Piedras, Puerto Rico

    • William A. Gould
  77. Department of Biological Sciences, Simon Fraser University, Burnaby, British Columbia, Canada

    • David S. Hik
  78. Norwegian Institute for Nature Research, Trondheim, Norway

    • Annika Hofgaard
  79. Faculty of Life and Environmental Sciences, University of Iceland, Reykjavík, Iceland

    • Ingibjörg S. Jónsdóttir
  80. University Centre in Svalbard, Longyearbyen, Norway

    • Ingibjörg S. Jónsdóttir
  81. Arctic National Wildlife Refuge, US Fish and Wildlife Service, Fairbanks, AK, USA

    • Janet Jorgenson
  82. Department of Ecosystem Science and Sustainability, Colorado State University, Fort Collins, CO, USA

    • Julia Klein
  83. Icelandic Institute of Natural History, Gardabaer, Iceland

    • Borgthor Magnusson
  84. University of Texas at El Paso, El Paso, TX, USA

    • Craig Tweedie
  85. Biology and Environmental Sciences, Faculty of Natural Sciences, University of Stirling, Stirling, UK

    • Philip A. Wookey
  86. Institute of Ecology, University of Innsbruck, Innsbruck, Austria

    • Michael Bahn
  87. Environmental Change Institute, School of Geography and the Environment, University of Oxford, Oxford, UK

    • Benjamin Blonder
  88. Rocky Mountain Biological Laboratory, Crested Butte, CO, USA

    • Benjamin Blonder
  89. Institute of Environmental Sciences, Leiden University, Leiden, The Netherlands

    • Peter M. van Bodegom
  90. Joint Global Change Research Institute, Pacific Northwest National Laboratory, College Park, MD, USA

    • Benjamin Bond-Lamberty
  91. School of Biosciences and Veterinary Medicine, Plant Diversity and Ecosystems Management Unit, University of Camerino, Camerino, Italy

    • Giandiego Campetella
  92. DiSTA, University of Insubria, Varese, Italy

    • Bruno E. L. Cerabolini
  93. Institute of Arctic Biology, University of Alaska Fairbanks, Fairbanks, AK, USA

    • F. Stuart Chapin III
  94. School of Biological, Earth and Environmental Sciences, Ecology and Evolution Research Centre, UNSW Sydney, Sydney, New South Wales, Australia

    • William K. Cornwell
  95. Jonah Ventures, Boulder, CO, USA

    • Joseph Craine
  96. Institute for Alpine Environment, Eurac Research, Bolzano, Italy

    • Matteo Dainese
  97. School of Earth and Environmental Sciences, The University of Manchester, Manchester, UK

    • Franciska T. de Vries
  98. Instituto Multidisciplinario de Biología Vegetal (IMBIV), CONICET and FCEFyN, Universidad Nacional de Córdoba, Córdoba, Argentina

    • Sandra Díaz
  99. Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, USA

    • Brian J. Enquist
  100. The Santa Fe Institute, Santa Fe, NM, USA

    • Brian J. Enquist
  101. Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA

    • Walton Green
  102. Área de Biodiversidad y Conservación. Departamento de Biología, Geología, Física y Química Inorgánica, Universidad Rey Juan Carlos, Madrid, Spain

    • Ruben Milla
  103. Estonian University of Life Sciences, Tartu, Estonia

    • Ülo Niinemets
  104. Graduate School of Agriculture, Kyoto University, Kyoto, Japan

    • Yusuke Onoda
  105. World Agroforestry Centre — Latin America, Lima, Peru

    • Jenny C. Ordoñez
  106. Team Vegetation, Forest and Landscape Ecology, Wageningen Environmental Research (Alterra), Wageningen, The Netherlands

    • Wim A. Ozinga
  107. Institute for Water and Wetland Research, Radboud University Nijmegen, Nijmegen, The Netherlands

    • Wim A. Ozinga
  108. Global Ecology Unit CREAF-CSIC-UAB, Consejo Superior de Investigaciones Cientificas, Bellaterra, Spain

    • Josep Penuelas
  109. Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, Jülich, Germany

    • Hendrik Poorter
  110. Department of Biological Sciences, Macquarie University, North Ryde, New South Wales, Australia

    • Hendrik Poorter
  111. Ecology and Conservation Biology, Institute of Plant Sciences, University of Regensburg, Regensburg, Germany

    • Peter Poschlod
  112. Department of Forest Resources, University of Minnesota, St. Paul, MN, USA

    • Peter B. Reich
  113. Hawkesbury Institute for the Environment, Western Sydney University, Penrith, New South Wales, Australia

    • Peter B. Reich
  114. Department of Biology, Santa Clara University, Santa Clara, CA, USA

    • Brody Sandel
  115. Department of Biology, Algoma University, Sault Ste. Marie, Ontario, Canada

    • Brandon Schamp
  116. Komarov Botanical Institute, St Petersburg, Russia

    • Serge Sheremetev
  117. Department of Biology, University of Wisconsin — Eau Claire, Eau Claire, WI, USA

    • Evan Weiher

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Contributions

A.D.B., I.H.M.-S. and S.C.E. conceived the study, with input from the sTundra working group (S.N., N.R., P.S.A.B., A.B.-O., D.B., J.H.C.C., W.C., B.C.F., D.G., S.J.G., K.G., G.H.R.H., R.D.H., J.K., J.S.P., J.H.R.L., C.R., G.S.-S., H.J.D.T., M.V., M.W. and S.Wi.). A.D.B. performed the analyses, with input from I.H.M.-S., N.R., S.C.E. and S.N. D.N.K. made the maps of temperature, moisture and trait change. A.D.B. wrote the manuscript, with input from I.H.M.-S., S.C.E., S.N., N.R. and contributions from all authors. A.D.B. compiled the Tundra Trait Team database, with assistance from I.H.M.-S., H.J.D.T. and S.A.-B. Authorship order was determined as follows: (1) core authors; (2) sTundra participants (alphabetical) and other major contributors; (3) authors contributing both trait (Tundra Trait Team) and community composition (for example, ITEX) data (alphabetical); (4) Tundra Trait Team contributors (alphabetical); (5) contributors who provided community composition data only (alphabetical) and (6) contributors who provided TRY trait data (alphabetical).

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Anne D. Bjorkman.

Extended data figures and tables

  1. Extended Data Fig. 1 Overview of trait data and analyses.

    a, Count of traits per latitude (rounded to the nearest degree) for all georeferenced observations in TRY and TTT that correspond to species in the vegetation survey dataset. b, Work flow and analyses of temperature–trait relationships. Intraspecific temperature–trait relationships over space were used to estimate the potential contribution of ITV to overall temperature–trait relationships over space and time (CWM + ITV) as trait measurements for individual plants over time are not available.

  2. Extended Data Fig. 2 All temperature–trait relationships.

    Slope of temperature–trait relationships over space (within-species (ITV) and across communities (CWM)) and with interannual variation in temperature (community temperature sensitivity). Spatial - ITV, spatial relationship between ITV and temperature; spatial-CWM, spatial relationship between CWM and summer temperature; temporal sensitivity-CWM, temperature sensitivity of CWM (that is, correspondence between interannual variation in CWM values with interannual variation in temperature). Error bars represent 95% credible intervals on the slope estimate. We used five-year mean temperatures (temperature of the survey year and four previous years) to estimate temperature sensitivity, because this interval has been shown to explain vegetation change in tundra20 and alpine29 plant communities. All slope estimates are in transformed units (height = log(cm), LDMC = logit(g g−1), leaf area = log(cm2), leaf nitrogen = log(mg g−1), SLA = log(mm2 mg−1)). Community (CWM) temperature–trait relationships are estimated across all 117 sites; intraspecific temperature–trait relationships are estimated as the mean of 108 and 109 species for SLA, 80 and 86 species for plant height, 74 and 72 species for leaf nitrogen, 85 and 76 species for leaf area, and 43 and 52 species for LDMC, for summer and winter temperature, respectively (see Methods for details).

  3. Extended Data Fig. 3 Community woodiness and evergreenness over space and time.

    a, b, Variation in community woodiness (a) and evergreenness (b) across space with summer temperature and soil moisture. Community woodiness is the abundance-weighted proportion of woody species versus all other plant species in the community. Community evergreenness is the abundance-weighted proportion of evergreen shrubs versus all shrub species (deciduous and evergreen). The evergreen model was generated using a reduced number of sites (98 instead of 117), because some sites did not have any woody species (and it was thus not possible to calculate a proportion of evergreen species). Both temperature and moisture were important predictors of community woodiness and evergreenness. The 95% credible interval for a temperature × moisture interaction term overlapped zero in both models (−0.100 to 0.114 and −0.201 to 0.069 for woodiness and evergreenness, respectively). c, d, There was no change over time in woodiness (c) or evergreenness (d). Thin lines represent slopes per site (woodiness, n = 117 sites; evergreenness, n = 98 sites). In all panels, bold lines indicate overall model predictions and shaded ribbons designate 95% credible intervals on these model predictions.

  4. Extended Data Fig. 4 Range in species mean values of each trait by summer temperature.

    Black dashed lines represent quantile regression estimates for 1% and 99% quantiles. Species mean values are estimated from intercept-only Bayesian models using the estimation technique described in the Methods (see ‘Calculation of CWM values’). Species locations are based on species in the 117 vegetation survey sites. All values are back-transformed into their original units (height (cm), LDMC (g g−1), leaf area (cm2), leaf nitrogen (mg g−1), SLA (mm2 mg−1).

  5. Extended Data Fig. 5 The rate of community trait change is not related to the rate of temperature change or soil moisture for any trait.

    a, b, Rate of CWM change over time per site (n = 117 sites) related to temperature change and long-term mean soil moisture (a) or soil moisture change (b) at a site. Points represent mean trait change values for each site, lines represent the predicted relationship between trait change, temperature change and soil moisture or soil moisture change, and transparent ribbons are the 95% credible intervals on these predictions. Both mean soil moisture and soil moisture change were modelled as a continuous variables, but are shown as predictions for minimum and maximum values or rates of change. Trait change estimates are in transformed units (log for height, leaf area, leaf nitrogen and SLA, and logit for LDMC). Soil moisture change was estimated from downscaled ERA-Interim data and may not accurately represent local changes in moisture availability at each site.

  6. Extended Data Fig. 6 Increasing community height is driven by the immigration of taller species, not the loss of shorter ones.

    Probability that a species newly arrived in a site (gained) or disappeared from a site (lost) as a function of its traits (n = 117 sites). Lines and ribbons represent overall model predictions and the 95% credible intervals on these predictions, respectively. Dark ribbons and solid lines represent species gains whereas pale ribbons and dashed lines represent species losses. Only for plant height was the trait–probability relationship different for gains and losses.

  7. Extended Data Fig. 7 Comparison of actual, expected and projected CWM trait change over time.

    Actual, expected and projected CWM trait changes are shown as solid coloured, solid black, and dashed or dotted lines, respectively. The expected trait change is calculated using the observed spatial temperature–trait relationship and the average rate of recent summer warming across all sites. Note that these projections assume no change in soil moisture conditions. The dotted and dashed black lines after 2015 show the projected trait change for the maximum (RCP8.5) and minimum (RCP2.6) IPCC carbon emission scenarios, respectively, from the HadGEM2 AO Global Circulation Model, given the expected temperature change associated with those scenarios. Points along the left axis of each panel show the distribution of present-day CWM per site (n = 117 sites) to better demonstrate the magnitude of projected change. Values are in original units (height (cm), LDMC (g g−1), leaf area (cm2), leaf nitrogen (mg g−1) and SLA (mm2 mg−1)).

  8. Extended Data Fig. 8 Community trait co-variation is structured by temperature and moisture.

    a, PCA of plot-level community-weighted traits for seven key functional traits demonstrating how communities vary in multidimensional trait space. Trait correlations are highest between SLA and leaf nitrogen, and evergreenness and woodiness. Variation in SLA, leaf nitrogen, evergreenness and woodiness (principal component (PC)1) are orthogonal to variation in height (PC2). Variation in leaf area and LDMC are explained by both PC1 and PC2. The colour of the points indicates the soil moisture status of each plot at the site-level. b, c, Plot scores along PC1, related to plant resource economy, vary with summer temperature, soil moisture and their interaction (b), whereas plot scores along PC2 vary only with soil moisture (c). The colour of the points indicates the soil moisture of each site. Because not all plots and sites had woody species (and thus proportion evergreen could not be calculated), this analysis was conducted on a subset of 1,098 (out of 1,520) plots at 98 (out of 117) different sites.

  9. Extended Data Fig. 9 Temperature–trait relationships by growth form and site elevation.

    a, Mean (±s.d.) intraspecific temperature–height relationships (n = 80 species) per functional group. Dwarf shrubs are defined as those shrubs that do not grow above 30 cm in height (as estimated by regional floras, such as Flora of North America, USDA or the Royal Horticultural Society) and are generally genetically limited in their ability to grow upright. There are no differences among functional groups in the magnitude of mean intraspecific temperature–height relationships. b, Relationship between community-weighted trait values, summer temperature and soil moisture across biogeographical gradients, as in Fig. 2a. Points represent mean estimates per site (n = 117 sites) and are sized by the elevation of the site (larger circles indicate higher elevation). Ribbons represent the overall trait–temperature–moisture relationship (95% credible intervals on predictions at minimum and maximum soil moisture) across all sites.

  10. Extended Data Table 1 Ecosystem functions influenced by each of the seven plant traits

Supplementary information

  1. Supplementary Information

    This file contains Supplementary Tables 1-10, STAN code for the model of CWM trait - temperature relationships over space, STAN code for the model of CWM trait - change over time and TRY trait data references.

  2. Reporting Summary

About this article

Publication history

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Published

DOI

https://doi.org/10.1038/s41586-018-0563-7

Article notes

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