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Elevation alters ecosystem properties across temperate treelines globally


Temperature is a primary driver of the distribution of biodiversity as well as of ecosystem boundaries1,2. Declining temperature with increasing elevation in montane systems has long been recognized as a major factor shaping plant community biodiversity, metabolic processes, and ecosystem dynamics3,4. Elevational gradients, as thermoclines, also enable prediction of long-term ecological responses to climate warming5,6,7. One of the most striking manifestations of increasing elevation is the abrupt transitions from forest to treeless alpine tundra8. However, whether there are globally consistent above- and belowground responses to these transitions remains an open question4. To disentangle the direct and indirect effects of temperature on ecosystem properties, here we evaluate replicate treeline ecotones in seven temperate regions of the world. We find that declining temperatures with increasing elevation did not affect tree leaf nutrient concentrations, but did reduce ground-layer community-weighted plant nitrogen, leading to the strong stoichiometric convergence of ground-layer plant community nitrogen to phosphorus ratios across all regions. Further, elevation-driven changes in plant nutrients were associated with changes in soil organic matter content and quality (carbon to nitrogen ratios) and microbial properties. Combined, our identification of direct and indirect temperature controls over plant communities and soil properties in seven contrasting regions suggests that future warming may disrupt the functional properties of montane ecosystems, particularly where plant community reorganization outpaces treeline advance.

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Figure 1: Ecosystem N/P from seven temperate regions in relation to elevation from treeline.
Figure 2: Ecosystem patterns among specific plant and soil N and P pools along the elevational gradients.
Figure 3: Representations of cascading relationships among above- and belowground ecosystem components to elevation-associated temperature effects.
Figure 4: Decomposition of the total variability in cover-weighted foliar N and P concentrations (cwN, cwP) (mass basis) for the ground-layer plant community.

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  1. von Humboldt, A. Aspects of Nature, in Different Lands and Different Climates; with Scientific Elucidations, Vol. II (Longman, Brown, Green, Longmans, John Murray, 1849)

  2. Whittaker, R. H. Vegetation of the Great Smokey Mountains. Ecol. Monogr. 26, 1–80 (1956)

    Article  Google Scholar 

  3. Graham, C. H. et al. The origin and maintenance of montane diversity: integrating evolutionary and ecological processes. Ecography 37, 711–719 (2014)

    Article  Google Scholar 

  4. Sundqvist, M. K., Sanders, N. J. & Wardle, D. A. Community and ecosystem responses to elevational gradients: processes, mechanisms, and insights for global change. Annu. Rev. Ecol. Syst. 44, 261–280 (2013)

    Article  Google Scholar 

  5. Dunne, J. A., Saleska, S. R., Fischer, M. L. & Harte, J. Integrating experimental and gradient methods in ecological climate change research. Ecology 85, 904–916 (2004)

    Article  Google Scholar 

  6. Asner, G. P. et al. Amazonian functional diversity from forest canopy chemical assembly. Proc. Natl Acad. Sci. USA 111, 5604–5609 (2014)

    Article  CAS  ADS  Google Scholar 

  7. Pepin, N. et al. Elevation-dependent warming in mountain regions of the world. Nature Clim. Chang. 5, 424–430 (2015)

    Article  ADS  Google Scholar 

  8. Körner, C. A re-assessment of high elevation treeline positions and their explanation. Oecologia 115, 445–459 (1998)

    Article  ADS  Google Scholar 

  9. Körner, C. Alpine Treelines: Functional Ecology of the Global High Elevation Tree Limits (Springer, 2012)

  10. Hoch, G. & Körner, C. Global patterns of mobile carbon stores in trees at the high-elevation tree line. Glob. Ecol. Biogeogr. 21, 861–871 (2012)

    Article  Google Scholar 

  11. Loomis, P. F., Ruess, R. W., Sveinbjörnsson, B. & Kielland, K. Nitrogen cycling at treeline: latitudinal and elevational patterns across a boreal landscape. Ecoscience 13, 544–556 (2006)

    Article  Google Scholar 

  12. Vitousek, P. M., Matson, P. A. & Turner, D. R. Elevational and age gradients in Hawaiian montane rainforest: foliar and soil nutrients. Oecologia 77, 565–570 (1988)

    Article  ADS  Google Scholar 

  13. Thébault, A. et al. Nitrogen limitation and microbial diversity at the treeline. Oikos 123, 729–740 (2014)

    Article  Google Scholar 

  14. Davis, J., Schober, A., Bahn, M. & Sveinbjörnsson, B. Soil carbon and nitrogen turnover at and below the elevational treeline in northern Fennoscandia. Arct. Alp. Res. 23, 279–286 (1991)

    Article  Google Scholar 

  15. Güsewell, S. N: P ratios in terrestrial plants: variation and functional significance. New Phytol. 164, 243–266 (2004)

    Article  Google Scholar 

  16. Fajardo, A. & Piper, F. I. An experimental approach to explain the southern Andes elevational treeline. Am. J. Bot. 101, 788–795 (2014)

    Article  Google Scholar 

  17. 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)

    Article  CAS  ADS  Google Scholar 

  18. Wright, I. J. et al. Modulation of leaf economic traits and trait relationships by climate. Glob. Ecol. Biogeogr. 14, 411–421 (2005)

    Article  Google Scholar 

  19. Woods, H. A. et al. Temperature and the chemical composition of poikilothermic organisms. Funct. Ecol. 17, 237–245 (2003)

    Article  Google Scholar 

  20. Elser, J. J., Fagan, W. F., Kerkhoff, A. J., Swenson, N. G. & Enquist, B. J. Biological stoichiometry of plant production: metabolism, scaling and ecological response to global change. New Phytol. 186, 593–608 (2010)

    Article  CAS  Google Scholar 

  21. Yuan, Z. Y. & Chen, H. Y. H. Decoupling of nitrogen and phosphorus in terrestrial plants associated with global changes. Nature Clim. Chang. 5, 465–469 (2015)

    Article  CAS  ADS  Google Scholar 

  22. Harsch, M. A., Hulme, P. E., McGlone, M. S. & Duncan, R. P. Are treelines advancing? A global meta-analysis of treeline response to climate warming. Ecol. Lett. 12, 1040–1049 (2009)

    Article  Google Scholar 

  23. Greenwood, S. & Jump, A. S. Consequences of treeline shifts for the diversity and function of high altitude ecosystems. Arct. Antarct. Alp. Res. 46, 829–840 (2014)

    Article  Google Scholar 

  24. Sistla, S. A. & Schimel, J. P. Stoichiometric flexibility as a regulator of carbon and nutrient cycling in terrestrial ecosystems under change. New Phytol. 196, 68–78 (2012)

    Article  CAS  Google Scholar 

  25. Yu, Q. et al. Stoichiometric homeostasis predicts plant species dominance, temporal stability, and responses to global change. Ecology 96, 2328–2335 (2015)

    Article  Google Scholar 

  26. Gottfried, M. et al. Continent-wide response of mountain vegetation to climate change. Nature Clim. Chang. 2, 111–115 (2012)

    Article  ADS  Google Scholar 

  27. Ernakovich, J. G. et al. Predicted responses of arctic and alpine ecosystems to altered seasonality under climate change. Glob. Change Biol. 20, 3256–3269 (2014)

    Article  ADS  Google Scholar 

  28. Svenning, J.-C. & Sandel, B. Disequilibrium vegetation dynamics under future climate change. Am. J. Bot. 100, 1266–1286 (2013)

    Article  Google Scholar 

  29. Peñuelas, J. et al. Human-induced nitrogen-phosphorus imbalances alter natural and managed ecosystems across the globe. Nature Commun. 4, 2934 (2013)

    Article  ADS  Google Scholar 

  30. Wookey, P. A. et al. Ecosystem feedbacks and cascade processes: understanding their role in the responses of Arctic and alpine ecosystems to environmental change. Glob. Change Biol. 15, 1153–1172 (2009)

    Article  ADS  Google Scholar 

  31. Chadwick, O. A., Derry, L. A., Vitousek, P. M., Huebert, B. J. & Hedin, L. O. Changing sources of nutrients during four million years of ecosystem development. Nature 397, 491–497 (1999)

    Article  CAS  ADS  Google Scholar 

  32. Cieraad, E., McGlone, M. S. & Huntley, B. Southern Hemisphere temperate tree lines are not climatically depressed. J. Biogeogr. 41, 1456–1466 (2014)

    Article  Google Scholar 

  33. Körner, C. & Paulsen, J. A worldwide study of high altitude treeline temperatures. J. Biogeogr. 31, 713–732 (2004)

    Article  Google Scholar 

  34. Beychok, M. Atmospheric lapse rate. The Encyclopedia of Earth (2013)

  35. Minder, J. R., Mote, P. W. & Lundquist, J. D. Surface temperature lapse rates over complex terrain: lessons from the Cascade Mountains. J. Geophys. Res. 115, D14122 (2010)

    Article  ADS  Google Scholar 

  36. Holtmeier, F.-K. & Broll, G. Treeline advance – driving processes and adverse factors. Landscape Online 1, 1–32 (2007)

    Article  Google Scholar 

  37. Harsch, M. A. & Bader, M. Y. Treeline form – a potential key to understanding treeline dynamics. Glob. Ecol. Biogeogr. 20, 582–596 (2011)

    Article  Google Scholar 

  38. Walker, T. W. & Adams, A. F. R. Studies on soil organic matter: I. Influence of phosphorus content of parent materials on accumulations of carbon, nitrogen, sulfur and organic phosphorus in grassland soils. Soil Sci. 85, 307–318 (1958)

    Article  CAS  ADS  Google Scholar 

  39. Bray, R. H. & Kurtz, L. T. Determination of total, organic, and available forms of phosphorus in soils. Soil Sci. 59, 39–46 (1945)

    Article  CAS  ADS  Google Scholar 

  40. Anderson, J. P. E. & Domsch, K. H. A physiologically active method for the quantification of microbial biomass in soil. Soil Biol. Biochem. 10, 215–221 (1978)

    Article  CAS  Google Scholar 

  41. Wardle, D. A. Changes in the microbial biomass and metabolic quotient during leaf litter succession in some New Zealand forest and scrubland ecosystems. Funct. Ecol. 7, 346–355 (1993)

    Article  Google Scholar 

  42. Tunlid, A., Hoitink, H. A. J., Low, C. & White, D. C. Characterization of bacteria that suppress rhizoctonia damping-off in bark compost media by analysis of fatty acid biomarkers. Appl. Environ. Microbiol. 55, 1368–1374 (1989)

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Frostegård, Å. & Bååth, E. The use of phospholipid fatty acid analysis to estimate bacterial and fungal biomass in soil. Biol. Fertil. Soils 22, 59–65 (1996)

    Article  Google Scholar 

  44. Kroppenstedt, R. M. in Chemical Methods in Bacterial Systematics (eds Googfellow, M. & Minnikin, D. E. ) 173–199 (Academic, 1985)

  45. Markesteijn, L., Poorter, L. & Bongers, F. Light-dependent leaf trait variation in 43 tropical dry forest tree species. Am. J. Bot. 94, 515–525 (2007)

    Article  Google Scholar 

  46. Mueller-Dombois, D. & Ellenberg, H. Aims and Methods of Vegetation Ecology (John Wiley, 1974)

  47. Kichenin, E., Wardle, D. A., Peltzer, D. A., Morse, C. W. & Freschet, G. T. Contrasting effects of plant inter- and intraspecific variation on community-level trait measures along an environmental gradient. Funct. Ecol. 27, 1254–1261 (2013)

    Article  Google Scholar 

  48. Garnier, E. et al. Plant functional markers capture ecosystem properties during secondary succession. Ecology 85, 2630–2637 (2004)

    Article  Google Scholar 

  49. Lepš, J., De Bello, F., Smilauer, P. & Dolezal, J. Community trait response to environment: disentangling species turnover vs intraspecific trait variability effects. Ecography 34, 856–863 (2011)

    Article  Google Scholar 

  50. R Core Team. R: a language and environment for statistical computing (R Foundation for Statistical Computing, 2014)

  51. Bates, D., Maechler, M., Bolker, B. & Walker, S. lme4: linear mixed-effects models using Eigen S4. R package version 1.1-7. (2014)

  52. Burnham, K. P. & Anderson, D. R. Multimodel inference. Sociol. Methods Res. 33, 261–304 (2004)

    Article  MathSciNet  Google Scholar 

  53. Gelman, A. Scaling regression inputs by dividing by two standard deviations. Stat. Med. 27, 2865–2873 (2008)

    Article  MathSciNet  Google Scholar 

  54. Gelman, A. & Hill, J. Data Analysis Using Regression and Multilevel/Hierarchical Models (Cambridge Univ. Press, 2007)

  55. Engqvist, L. The mistreatment of covariate interaction terms in linear model analyses of behavioural and evolutionary ecology studies. Anim. Behav. 70, 967–971 (2005)

    Article  Google Scholar 

  56. Koller, M. robustlmm: Robust Linear Mixed Effects Models. R package version 1.6. (2014)

  57. Kuznetsova, A., Brockhoff, P. B. & Christianson, R. H. B. lmerTest: tests for random and fixed effects for linear mixed effect models. R package version 2.0-11. (2014)

  58. Barton´, K. MuMIn: Multi-model inference. R package version 1.10.5. (2014)

  59. Nakagawa, S. & Schielzeth, H. A general and simple method for obtaining R2 from generalized linear mixed-effect models. Methods Ecol. Evol. 4, 133–142 (2013)

    Article  Google Scholar 

  60. Chamberlain, S. brranching: fetch ‘phylogenies’ from many sources. R package version 0.1.0. (2015)

  61. The Angiosperm Phylogeny Group. An update of the Angiosperm Phylogeny Group classification for the orders and families of flowering plants: APG III. Bot. J. Linn. Soc. 161, 105–121 (2009)

  62. Zanne, A. E. et al. Three keys to the radiation of angiosperms into freezing environments. Nature 506, 89–92 (2014)

    Article  CAS  ADS  Google Scholar 

  63. Webb, C. O. & Donoghue, M. J. Phylomatic: tree assembly for applied phylogenetics. Mol. Ecol. Notes 5, 181–183 (2005)

    Article  Google Scholar 

  64. Paradis, E., Claude, J. & Strimmer, K. APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289–290 (2004)

    Article  CAS  Google Scholar 

  65. Oksanen, J. et al. Vegan: community ecology package. (2016)

  66. Kembel, S. W. et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics 26, 1463–1464 (2010)

    Article  CAS  Google Scholar 

  67. Wickham, H. & Francois, R. dplyr: a grammar of data manipulation. R package version 0.4.3. (2015)

  68. Rosseel, Y. lavaan: an R package for structural equation modeling. J. Stat. Softw. 48 (2), 1–36 (2012)

    Article  Google Scholar 

  69. Oberski, D. lavaan.survey: an R package for complex survey analysis of structural equation models. J. Stat. Softw. 57 (1), 1–27 (2014)

    Article  Google Scholar 

  70. Quinn, G. P. & Keough, M. J. Experimental Design and Data Analysis for Biologists (Cambridge Univ. Press, 2005)

  71. Grace, J. B., Anderson, T. M., Olff, H. & Scheiner, S. M. On the specification of structural equation models for ecological systems. Ecol. Monogr. 80, 67–87 (2010)

    Article  Google Scholar 

  72. Preacher, K. J. & Hayes, A. F. Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behav. Res. Methods 40, 879–891 (2008)

    Article  Google Scholar 

  73. Rao, J. N. & Wu, C. F. J. Resampling inference with complex survey data. J. Am. Stat. Assoc. 83, 231–241 (1988)

    Article  MathSciNet  Google Scholar 

  74. Bollen, K. A. Total, direct, and indirect effects in structural equation models. Soc. Method. 17, 37–69 (1987)

    Article  Google Scholar 

  75. Hill, M. O. & Gauch, H. G. J. Detrended correspondence analysis: an improved ordination technique. Vegetatio 42, 47–58 (1980)

    Article  Google Scholar 

  76. Lepš, J. & Šmilauer, P. Multivariate Analysis of Ecological Data using CANOCO (Cambridge Univ. Press, 2003)

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We thank Y. Amagai, B. Andersson, C. Arnoldi, P. Bellingham, Å. Boily, B. Case, G. Crutsinger, M. Dawes, W. Gilliam, K. Gundale, N. Hendershot, H. Hall, M. Hotter, J. Lundholm, P. Manning, C. McClure, Q. Read, B. Roskilly, A. Shimokawabe, D. Stöhr, and B. Turner for laboratory, logistical, or field assistance. This work was made possible by a Wallenberg Scholars Award to D.A.W.; regional support from Fondecyt 1120171 to A.F.; a National Science Foundation Dimensions of Biodiversity grant (NSF-1136703), a grant from the Carlsberg Fund, and support from the Danish National Research Foundation to the Center for Macroecology, Evolution, and Climate to N.J.S.; a US Department of Energy, Office of Science, Office of Biological and Environmental Research, Terrestrial Ecosystem Sciences Program Award (DE-SC0010562) to A.T.C.; support from the UK Natural Environment Research Council to R.D.B.; support from the BiodivERsA project REGARDS (ANR-12-EBID-004-01) to J.-C.C., S.L., K.G. and REGARDS (FWF-I-1056) to M.B. support from the Netherlands Organization for Scientific Research (VENI 451-14-017) to D.L.O.; and, support from the Natural Sciences and Engineering Research Council of Canada to Z.G.

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Authors and Affiliations



R.D.B., A.T.C., N.J.S., S.L., J.R.M., M.K.S., and D.A.W. designed the study; D.A.W. acquired the funding needed to initiate the study; J.-C.C., D.L.O., C.C., and M.K.S. provided specialized laboratory or statistical assistance; J.R.M. oversaw field work in each region with R.D.B., M.B., A.T.C., E.C., A.F., K.G., Z.G., G.K., S.L., N.J.S., M.K.S., and D.A.W. contributing to subsets of field sampling; J.R.M. wrote the first draft of the manuscript in close consultation with D.A.W., and all authors contributed to manuscript completion and revision.

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Correspondence to Jordan R. Mayor.

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Reviewer Information Nature thanks M. Macias-Fauria and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Extended data figures and tables

Extended Data Figure 1 Response of forest structure and tree nutrient concentrations to 150 m of increasing elevation, terminating at treeline.

Smoothing curves graphically illustrate trends for each of the seven regions; symbols represent individual plots (n = 101), P values (F statistics in brackets) represent overall effects of elevation from treeline, n.s., non-significant. a, Regionally significant decline in tree canopy height and (b) tree basal area. c, Regionally significant increases of stem density, particularly for southern hemisphere forests dominated by members of the Nothofagaceae. d, Regionally significant increases in basal area weighted tree foliar N (mass basis). e, Regionally non-significant responses of basal area weighted tree foliar P (mass basis). f, Regionally non-significant responses of basal area weighted tree foliar N/P. R2m is defined as being conditional on only the fixed effects, and R2c is defined as conditional on both the random effects of transect nested within region and the fixed effects. Test statistics from linear mixed effect models for stand characteristics and nutrient concentrations of all regions are given in Supplementary Tables 3 and 4.

Extended Data Figure 2 Representative photographs of treelines along elevational gradient transects in each of the seven regions sampled in this study.

Details of each region are given in Supplementary Table 1.

Extended Data Figure 3 Coefficient of variation (s.d./mean × 100 across regions) of cover-weighted ground-layer plant foliar N/P and total root N/P in relation to elevational groups relative to treeline position.

Blue markers are means (±1 s.e.m.), grey bars are 95% confidence intervals. a, Significant decline of ground-layer plant N/P is evidence of stoichiometric convergence of community foliar N/P across regions in the colder alpine environments (slope of solid regression line = −4.15; R2 = 0.79, P = 0.0176, F = 15.18; R2 = 0.92 for dotted regression line in alpine). b, Root N/P, by contrast, was greatest just above the treeline and did not significantly decline with increasing elevation except in alpine (R2 = 0.95 for dotted regression line in alpine).

Extended Data Figure 4 The coupled relationship between [N] and [P] pools (mass basis) in foliage of community-weighted ground-layer plants and surface roots across all regions.

a, Foliar versus root N. b, Foliar versus root P. Statistical P values (F values) are given for mixed-effect linear regressions where foliar N and P is predicted by root N or P. Most informative models also included region, vegetative community (forest versus alpine), and the random effect of transect. Inclusion of vegetative community substantially increased model fits (that is, Akaike information criteria values decreased by ≥19), highlighting the large influence of tree roots on the resulting relationships. Comparable relationships with elevation from treeline provided for reference.

Extended Data Figure 5 Abundance-weighted net relatedness index values of understorey plant communities across elevations, grouped by region.

The net relatedness index does not increase systematically with elevation (F = 1.64, P = 0.25; best-fit linear mixed-effect model with region specified as a random effect), as would be expected if plant communities were phylogenetically clustered at higher elevations.

Extended Data Figure 6 Relationship between dominance (the inverse of evenness) and elevation.

Dominance does not systematically vary with elevation (F = 1.18, P = 0.29; best-fit linear mixed-effect model with region specified as a random effect), as would be expected if plant communities became composed of more dominant species at higher elevations; n = 6 mean values for each elevational level apart from Australia (n = 5).

Extended Data Figure 7 Non-metric multidimensional scaling analysis of phylogenetic community structure of all ground-layer plants across sites.

Stress = 0.15. Sites are displayed and dashed ellipses depict standard errors of all points grouped by region. PERMANOVA results of the Bray–Curtis dissimilarities for taxonomic (data not shown) and phylogenetic community composition yielded a significant difference between vegetative categories (alpine versus forest) (pseudo-F = 1.44, P = 0.001 and pseudo-F = 1.32, P = 0.003, respectively). However, multivariate dispersion of above versus below treeline communities did not differ significantly for taxonomic (F = 0.074, P = 0.79) or phylogenetic (F = 1.35, P = 0.25) composition overall, and comparisons of above versus below treeline communities within each region were found to be non-significant apart from differences in phylogenetic dispersion in Colorado and Patagonia, where communities showed greater divergence above treeline than below (P < 0.01); n = 6 mean values for each elevational level apart from Australia (n = 5).

Extended Data Figure 8 Regional responses of selected microbial and soil properties to elevational gradients.

The seven regions are arranged from low to high concentrations of total PLFAs. Smoothing curves graphically illustrate covariance of soil properties within each of the seven regions. a, Total microbial PLFA. b, Bacterial PLFA. c, Fungal PLFA. d, Actinomycete PLFA. e, Microbial SIR. f, Soil pH. g, SOM. Significant fixed effects of elevation from treeline (E), vegetative community (V, forest or alpine), or their interactions based on a linear mixed-effect models are given. •P ≤ 0.10, *P < 0.050, **P < 0.001, ***P < 0.0001.

Extended Data Figure 9 RDA of PLFA biomarker-based microbial community composition in relation to soil environmental properties across all regions.

Black, biomarker identity; blue, soil properties. Fungi are represented by 18:2ω6,9, actinomycetes are represented by10Me17:0 and 10Me18:0, and all remaining PLFAs represent bacteria. a, Resulting RDA axes (soil pH dropped in stepwise model reduction) explains 85.9% (axis 1) and 93.4% (axes 1 + 2), respectively, of the total variation in all species–environment relationships. b, When SOM is incorporated as a covariate to evaluate how remaining variance in PLFA biomarkers responds to the remaining statistically significant soil variables, 64.0% (axis 1) and 87.2% (axes 1 + 2) of the total variation is explained in all species–environment relationships. TotP, total soil P on a volumetric basis; N/Pmin, soil mineral N to Bray-extractable P ratios; C/N, total soil carbon to nitrogen ratios.

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Mayor, J., Sanders, N., Classen, A. et al. Elevation alters ecosystem properties across temperate treelines globally. Nature 542, 91–95 (2017).

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