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.
This is a preview of subscription content, access via your institution
Open Access articles citing this article.
Respiratory loss during late-growing season determines the net carbon dioxide sink in northern permafrost regions
Nature Communications Open Access 26 September 2022
Nature Communications Open Access 21 September 2022
BMC Plant Biology Open Access 14 September 2022
Subscribe to Nature+
Get immediate online access to Nature and 55 other Nature journal
Subscribe to Journal
Get full journal access for 1 year
only $3.90 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Get time limited or full article access on ReadCube.
All prices are NET prices.
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/).
Post, E. et al. Ecological dynamics across the Arctic associated with recent climate change. Science 325, 1355–1358 (2009).
Elmendorf, S. C. et al. Plot-scale evidence of tundra vegetation change and links to recent summer warming. Nat. Clim. Change 2, 453–457 (2012).
Sistla, S. A. et al. Long-term warming restructures Arctic tundra without changing net soil carbon storage. Nature 497, 615–618 (2013).
Crowther, T. W. et al. Quantifying global soil carbon losses in response to warming. Nature 540, 104–108 (2016).
Cornelissen, J. H. C. et al. Global negative vegetation feedback to climate warming responses of leaf litter decomposition rates in cold biomes. Ecol. Lett. 10, 619–627 (2007).
Lavorel, S. & Garnier, E. Predicting changes in community composition and ecosystem functioning from plant traits: revisiting the Holy Grail. Funct. Ecol. 16, 545–556 (2002).
Pearson, R. G. et al. Shifts in Arctic vegetation and associated feedbacks under climate change. Nat. Clim. Change 3, 673–677 (2013).
Wright, I. J. et al. The worldwide leaf economics spectrum. Nature 428, 821–827 (2004).
Díaz, S. et al. The plant traits that drive ecosystems: evidence from three continents. J. Veg. Sci. 15, 295–304 (2004).
Cornwell, W. K. et al. Plant species traits are the predominant control on litter decomposition rates within biomes worldwide. Ecol. Lett. 11, 1065–1071 (2008).
Myers-Smith, I. H. et al. Shrub expansion in tundra ecosystems: dynamics, impacts and research priorities. Environ. Res. Lett. 6, 045509 (2011).
Sturm, M. & Douglas, T. Changing snow and shrub conditions affect albedo with global implications. J. Geophys. Res. 110, G01004 (2005).
Callaghan, T. V. et al. Effects on the function of Arctic ecosystems in the short- and long-term perspectives. Ambio 33, 448–458 (2004).
Moles, A. T. et al. Global patterns in plant height. J. Ecol. 97, 923–932 (2009).
Moles, A. T. et al. Global patterns in seed size. Glob. Ecol. Biogeogr. 16, 109–116 (2007).
Reich, P. B. & Oleksyn, J. Global patterns of plant leaf N and P in relation to temperature and latitude. Proc. Natl Acad. Sci. USA 101, 11001–11006 (2004).
Díaz, S. et al. The global spectrum of plant form and function. Nature 529, 167–171 (2016).
Siefert, A. et al. A global meta-analysis of the relative extent of intraspecific trait variation in plant communities. Ecol. Lett. 18, 1406–1419 (2015).
McMahon, S. M. et al. Improving assessment and modelling of climate change impacts on global terrestrial biodiversity. Trends Ecol. Evol. 26, 249–259 (2011).
Elmendorf, S. C. et al. Experiment, monitoring, and gradient methods used to infer climate change effects on plant communities yield consistent patterns. Proc. Natl Acad. Sci. USA 112, 448–452 (2015).
De Frenne, P. et al. Latitudinal gradients as natural laboratories to infer species’ responses to temperature. J. Ecol. 101, 784–795 (2013).
Sandel, B. et al. Contrasting trait responses in plant communities to experimental and geographic variation in precipitation. New Phytol. 188, 565–575 (2010).
Ackerman, D., Griffin, D., Hobbie, S. E. & Finlay, J. C. Arctic shrub growth trajectories differ across soil moisture levels. Glob. Change Biol. 23, 4294–4302 (2017).
Wright, I. J. et al. Global climatic drivers of leaf size. Science 357, 917–921 (2017).
Wrona, F. J. et al. Transitions in Arctic ecosystems: ecological implications of a changing hydrological regime. J. Geophys. Res. Biogeosci. 121, 650–674 (2016).
Read, Q. D., Moorhead, L. C., Swenson, N. G., Bailey, J. K. & Sanders, N. J. Convergent effects of elevation on functional leaf traits within and among species. Funct. Ecol. 28, 37–45 (2014).
Albert, C. H., Grassein, F., Schurr, F. M., Vieilledent, G. & Violle, C. When and how should intraspecific variability be considered in trait-based plant ecology? Perspect. Plant Ecol. Evol. Syst. 13, 217–225 (2011).
Elmendorf, S. C. et al. Global assessment of experimental climate warming on tundra vegetation: heterogeneity over space and time. Ecol. Lett. 15, 164–175 (2012).
Gottfried, M. et al. Continent-wide response of mountain vegetation to climate change. Nat. Clim. Change 2, 111–115 (2012).
Blok, D. et al. Shrub expansion may reduce summer permafrost thaw in Siberian tundra. Glob. Change Biol. 16, 1296–1305 (2010).
Blok, D., Elberling, B. & Michelsen, A. Initial stages of tundra shrub litter decomposition may be accelerated by deeper winter snow but slowed down by spring warming. Ecosystems 19, 155–169 (2016).
Cahoon, S. M. P. et al. Interactions among shrub cover and the soil microclimate may determine future Arctic carbon budgets. Ecol. Lett. 15, 1415–1422 (2012).
Lawrence, D. M. & Swenson, S. C. Permafrost response to increasing Arctic shrub abundance depends on the relative influence of shrubs on local soil cooling versus large-scale climate warming. Environ. Res. Lett. 6, 045504 (2011).
Christiansen, C. T. et al. Enhanced summer warming reduces fungal decomposer diversity and litter mass loss more strongly in dry than in wet tundra. Glob. Change Biol. 23, 406–420 (2017).
Kaarlejärvi, E., Eskelinen, A. & Olofsson, J. Herbivores rescue diversity in warming tundra by modulating trait-dependent species losses and gains. Nat Commun. 8, 419 (2017).
Bjorkman, A. D., Vellend, M., Frei, E. R. & Henry, G. H. R. Climate adaptation is not enough: warming does not facilitate success of southern tundra plant populations in the high Arctic. Glob. Change Biol. 23, 1540–1551 (2017).
Reich, P. B. The world-wide ‘fast–slow’ plant economics spectrum: a traits manifesto. J. Ecol. 102, 275–301 (2014).
Wullschleger, S. D. et al. Plant functional types in Earth system models: past experiences and future directions for application of dynamic vegetation models in high-latitude ecosystems. Ann. Bot. 114, 1–16 (2014).
Butler, E. E. et al. Mapping local and global variability in plant trait distributions. Proc. Natl Acad. Sci. USA 114, E10937–E10946 (2017).
Reich, P. B., Rich, R. L., Lu, X., Wang, Y.-P. & Oleksyn, J. Biogeographic variation in evergreen conifer needle longevity and impacts on boreal forest carbon cycle projections. Proc. Natl Acad. Sci. USA 111, 13703–13708 (2014).
Harris, I., Jones, P. D., Osborn, T. J. & Lister, D. H. Updated high-resolution grids of monthly climatic observations – the CRU TS3.10 Dataset. Int. J. Climatol. 34, 623–642 (2014).
Blok, D. et al. The cooling capacity of mosses: controls on water and energy fluxes in a Siberian tundra site. Ecosystems 14, 1055–1065 (2011).
Soudzilovskaia, N. A., van Bodegom, P. M. & Cornelissen, J. H. C. Dominant bryophyte control over high-latitude soil temperature fluctuations predicted by heat transfer traits, field moisture regime and laws of thermal insulation. Funct. Ecol. 27, 1442–1454 (2013).
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, J. L. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).
Myers-Smith, I. H. et al. Climate sensitivity of shrub growth across the tundra biome. Nat. Clim. Change 5, 887–891 (2015).
Willmott, C. J. & Robeson, S. M. Climatologically aided interpolation (CAI) of terrestrial air temperature. Int. J. Climatol. 15, 221–229 (1995).
Sperna Weiland, F. C., Vrugt, J. A., van Beek, R. (L.) P. H., Weerts, A. H. & Bierkens, M. F. P. Significant uncertainty in global scale hydrological modeling from precipitation data errors. J. Hydrol. 529, 1095–1115 (2015).
Beguería, S., Vicente Serrano, S. M., Tomás Burguera, M. & Maneta, M. Bias in the variance of gridded data sets leads to misleading conclusions about changes in climate variability. Int. J. Climatol. 36, 3413–3422 (2016).
Kattge, J. et al. TRY—a global database of plant traits. Glob. Change Biol. 17, 2905–2935 (2011).
Bjorkman, A. D. et al. Tundra Trait Team: a database of plant traits spanning the tundra biome. Glob. Ecol. Biogeogr. https://doi.org/10.1111/geb.12821 (2018).
Cayuela, L., Granzow-de la Cerda, Í., Albuquerque, F. S. & Golicher, D. J. taxonstand: an R package for species names standardisation in vegetation databases. Methods Ecol. Evol. 3, 1078–1083 (2012).
Plummer, M. rjags: Bayesian graphical models using MCMC. R package version 4.6 https://CRAN.R-project.org/package=rjags (2016).
Stan Development Team. RStan: the R interface to Stan. R package version 2.14.1 http://mc-stan.org/ (2016).
Gelman, A. & Rubin, D. B. Inference from iterative simulation using multiple sequences. Stat. Sci. 7, 457–472 (1992).
Messier, J., McGill, B. J. & Lechowicz, M. J. How do traits vary across ecological scales? A case for trait-based ecology. Ecol. Lett. 13, 838–848 (2010).
Violle, C. et al. The return of the variance: intraspecific variability in community ecology. Trends Ecol. Evol. 27, 244–252 (2012).
Bintanja, R. & Selten, F. M. Future increases in Arctic precipitation linked to local evaporation and sea-ice retreat. Nature 509, 479–482 (2014).
AMAP. Snow, Water, Ice and Permafrost in the Arctic (SWIPA) 2017. https://www.amap.no (Arctic Monitoring and Assessment Programme, 2017).
Oksanen, J., Blanchet, F., Kindt, R. & Legendre, P. vegan: Community Ecology Package. R package version 2.4.6 https://CRAN.R-project.org/package=vegan (2011).
Dornelas, M. et al. BioTIME: A database of biodiversity time series for the Anthropocene. Glob. Ecol. Biogeogr. 27, 760–786 (2018).
Chapin, F. S. III, BretHarte, M. S., Hobbie, S. E. & Zhong, H. L. Plant functional types as predictors of transient responses of arctic vegetation to global change. J. Veg. Sci. 7, 347–358 (1996).
Weiher, E. et al. Challenging Theophrastus: a common core list of plant traits for functional ecology. J. Veg. Sci. 10, 609–620 (1999).
Violle, C. et al. Let the concept of trait be functional! Oikos 116, 882–892 (2007).
Hudson, J. M. G. & Henry, G. H. R. Increased plant biomass in a high Arctic heath community from 1981 to 2008. Ecology 90, 2657–2663 (2009).
De Deyn, G. B., Cornelissen, J. H. C. & Bardgett, R. D. Plant functional traits and soil carbon sequestration in contrasting biomes. Ecol. Lett. 11, 516–531 (2008).
Kunstler, G. et al. Plant functional traits have globally consistent effects on competition. Nature 529, 204–207 (2016).
Gaudet, C. L. & Keddy, P. A. A comparative approach to predicting competitive ability from plant traits. Nature 334, 242–243 (1988).
Westoby, M., Falster, D. S., Moldes, A. T., Vesk, P. A. & Wright, I. J. Plant ecological strategies: some leading dimensions of variation between species. Annu. Rev. Ecol. Syst. 33, 125–159 (2002).
Moles, A. T. & Leishman, M. R. Seedling Ecology and Evolution (Cambridge Univ. Press, Cambridge, 2008).
Sturm, M. et al. Snow–shrub interactions in Arctic tundra: a hypothesis with climatic implications. J. Clim. 14, 336–344 (2001).
Loranty, M. M., Berner, L. T., Goetz, S. J., Jin, Y. & Randerson, J. T. Vegetation controls on northern high latitude snow–albedo feedback: observations and CMIP5 model simulations. Glob. Change Biol. 20, 594–606 (2014).
Myers-Smith, I. H. & Hik, D. S. Shrub canopies influence soil temperatures but not nutrient dynamics: an experimental test of tundra snow–shrub interactions. Ecol. Evol. 3, 3683–3700 (2013).
DeMarco, J., Mack, M. C. & Bret-Harte, M. S. Effects of arctic shrub expansion on biophysical vs. biogeochemical drivers of litter decomposition. Ecology 95, 1861–1875 (2014).
Enquist, B. J., Brown, J. H. & West, G. B. Allometric scaling of plant energetics and population density. Nature 395, 163–165 (1998).
Street, L. E., Shaver, G. R., Williams, M. & van Wijk, M. T. What is the relationship between changes in canopy leaf area and changes in photosynthetic CO2 flux in arctic ecosystems? J. Ecol. 95, 139–150 (2007).
Poorter, H. et al. Biomass allocation to leaves, stems and roots: meta-analyses of interspecific variation and environmental control. New Phytol. 193, 30–50 (2012).
Greaves, H. E. et al. Estimating aboveground biomass and leaf area of low-stature Arctic shrubs with terrestrial LiDAR. Remote Sens. Environ. 164, 26–35 (2015).
Westoby, M. & Wright, I. J. Land-plant ecology on the basis of functional traits. Trends Ecol. Evol. 21, 261–268 (2006).
Niinemets, Ü. A review of light interception in plant stands from leaf to canopy in different plant functional types and in species with varying shade tolerance. Ecol. Res. 25, 693–714 (2010).
Freschet, G. T., Aerts, R. & Cornelissen, J. H. C. A plant economics spectrum of litter decomposability. Funct. Ecol. 26, 56–65 (2012).
Manning, P. et al. Simple measures of climate, soil properties and plant traits predict national-scale grassland soil carbon stocks. J. Appl. Ecol. 52, 1188–1196 (2015).
Iida, Y. et al. Wood density explains architectural differentiation across 145 co-occurring tropical tree species. Funct. Ecol. 26, 274–282 (2012).
Ménard, C. B., Essery, R., Pomeroy, J., Marsh, P. & Clark, D. B. A shrub bending model to calculate the albedo of shrub-tundra. Hydrol. Processes 28, 341–351 (2014).
Nauta, A. L. et al. Permafrost collapse after shrub removal shifts tundra ecosystem to a methane source. Nat. Clim. Change 5, 67–70 (2015).
Hobbie, S. E. Temperature and plant species control over litter decomposition in Alaskan tundra. Ecol. Monogr. 66, 503–522 (1996).
Weedon, J. T. et al. Global meta-analysis of wood decomposition rates: a role for trait variation among tree species? Ecol. Lett. 12, 45–56 (2009).
Dorrepaal, E., Cornelissen, J., Aerts, R., Wallen, B. & van Logtestijn, R. Are growth forms consistent predictors of leaf litter quality and decomposability across peatlands along a latitudinal gradient? J. Ecol. 93, 817–828 (2005).
Larsen, K. S., Michelsen, A., Jonasson, S., Beier, C. & Grogan, P. Nitrogen uptake during fall, winter and spring differs among plant functional groups in a subarctic heath ecosystem. Ecosystems 15, 927–939 (2012).
Chapin, F. S. III, Shaver, G. R., Giblin, A. E., Nadelhoffer, K. J. & Laundre, J. A. Responses of arctic tundra to experimental and observed changes in climate. Ecology 76, 694–711 (1995).
Reich, P. B., Walters, M. B. & Ellsworth, D. S. From tropics to tundra: global convergence in plant functioning. Proc. Natl Acad. Sci. USA 94, 13730–13734 (1997).
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.
Nature thanks G. Kunstler, F. Schrodt and the other anonymous reviewer(s) for their contribution to the peer review of this work.
The authors declare no competing interests.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
A list of authors and their affiliations appears online.
Extended data figures and tables
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.
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).
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.
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).
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.
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.
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)).
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.
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.
About this article
Cite this article
Bjorkman, A.D., Myers-Smith, I.H., Elmendorf, S.C. et al. Plant functional trait change across a warming tundra biome. Nature 562, 57–62 (2018). https://doi.org/10.1038/s41586-018-0563-7
- Trait Changes
- Heights Community
- Leaf Dry Matter Content (LDMC)
- Static Soil Moisture Estimates
- Intraspecific Trait Variation (ITV)
This article is cited by
BMC Plant Biology (2022)
Nature Reviews Earth & Environment (2022)
Respiratory loss during late-growing season determines the net carbon dioxide sink in northern permafrost regions
Nature Communications (2022)
Alpine shrub growth follows bimodal seasonal patterns across biomes – unexpected environmental controls
Communications Biology (2022)
Nature Communications (2022)