Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Widespread but heterogeneous responses of Andean forests to climate change

An Author Correction to this article was published on 15 January 2019

This article has been updated


Global warming is forcing many species to shift their distributions upward, causing consequent changes in the compositions of species that occur at specific locations. This prediction remains largely untested for tropical trees. Here we show, using a database of nearly 200 Andean forest plot inventories spread across more than 33.5° latitude (from 26.8° S to 7.1° N) and 3,000-m elevation (from 360 to 3,360 m above sea level), that tropical and subtropical tree communities are experiencing directional shifts in composition towards having greater relative abundances of species from lower, warmer elevations. Although this phenomenon of ‘thermophilization’ is widespread throughout the Andes, the rates of compositional change are not uniform across elevations. The observed heterogeneity in thermophilization rates is probably because of different warming rates and/or the presence of specialized tree communities at ecotones (that is, at the transitions between distinct habitats, such as at the timberline or at the base of the cloud forest). Understanding the factors that determine the directions and rates of compositional changes will enable us to better predict, and potentially mitigate, the effects of climate change on tropical forests.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Map of Andean forest plot locations.
Fig. 2: Thermophilization rates of repeatedly censused plots.
Fig. 3: Thermophilization rates of Andean forest plots.
Fig. 4: Mean warming rate at plots.

Similar content being viewed by others

Data availability

The plot data that support the findings of this study are available from the Red de Bosques ( upon reasonable request. The list of species included in the analysis with their number of GBIF records after filtering and their estimated thermal optima is available in Supplementary Table 2.

Change history

  • 15 January 2019

    In Fig. 2 of this Article, the positive part of the y axis scale should read 0, 0.02, 0.04 instead of 0, 0.04, 0.02. This has been corrected online.


  1. Thuiller, W. Climate change and the ecologist. Nature 448, 550–552 (2007).

    Article  ADS  CAS  Google Scholar 

  2. Lenoir, J. & Svenning, J. C. Climate-related range shifts - a global multidimensional synthesis and new research directions. Ecography 38, 15–28 (2015).

    Article  Google Scholar 

  3. Feeley, K. J., Rehm, E. M. & Machovina, B. The responses of tropical forest species to global climate change: acclimate, adapt, migrate, or go extinct? Front. Biogeogr. 4, 69–84 (2012).

    Article  Google Scholar 

  4. Chen, I. C., Hill, J. K., Ohlemüller, R., Roy, D. B. & Thomas, C. D. Rapid range shifts of species associated with high levels of climate warming. Science 333, 1024–1026 (2011).

    Article  ADS  CAS  Google Scholar 

  5. Steinbauer, M. J. et al. Accelerated increase in plant species richness on mountain summits is linked to warming. Nature 556, 231–234 (2018).

    Article  ADS  CAS  Google Scholar 

  6. Feeley, K. J., Silman, M. R. & Duque, A. Where are the tropical plants? A call for better inclusion of tropical plants in studies investigating and predicting the effects of climate change. Front. Biogeogr. 7, 174–176 (2015).

    Google Scholar 

  7. Perez, T. M., Stroud, J. T. & Feeley, K. J. Thermal trouble in the tropics. Science 351, 1392–1393 (2016).

    Article  ADS  CAS  Google Scholar 

  8. Colwell, R. K., Brehm, G., Cardelús, C. L., Gilman, A. C. & Longino, J. T. Global warming, elevational range shifts, and lowland biotic attrition in the wet tropics. Science 322, 258–261 (2008).

    Article  ADS  CAS  Google Scholar 

  9. Telwala, Y., Brook, B. W., Manish, K. & Pandit, M. K. Climate-induced elevational range shifts and increase in plant species richness in a Himalayan biodiversity epicentre. PLoS ONE 8, e57103 (2013).

    Article  ADS  CAS  Google Scholar 

  10. Jump, A. S., Huang, T.-J. & Chou, C.-H. Rapid altitudinal migration of mountain plants in Taiwan and its implications for high altitude biodiversity. Ecography 35, 204–210 (2012).

    Article  Google Scholar 

  11. Angelo, C. L. & Daehler, C. C. Upward expansion of fire-adapted grasses along a warming tropical elevation gradient. Ecography 36, 551–559 (2013).

    Article  Google Scholar 

  12. Morueta-Holme, N. et al. Strong upslope shifts in Chimborazo’s vegetation over two centuries since Humboldt. Proc. Natl Acad. Sci. USA 112, 12741–12745 (2015).

    Article  ADS  CAS  Google Scholar 

  13. Feeley, K. J. & Silman, M. R. Keep collecting: accurate species distribution modelling requires more collections than previously thought. Divers. Distrib. 17, 1132–1140 (2011).

    Article  Google Scholar 

  14. Stuart-Smith, R. D., Edgar, G. J., Barrett, N. S., Kininmonth, S. J. & Bates, A. E. Thermal biases and vulnerability to warming in the world’s marine fauna. Nature 528, 88–92 (2015).

    ADS  CAS  PubMed  Google Scholar 

  15. Devictor, V. et al. Differences in the climatic debts of birds and butterflies at a continental scale. Nat. Clim. Change 2, 121–124 (2012).

    Article  ADS  Google Scholar 

  16. Bertrand, R. et al. Changes in plant community composition lag behind climate warming in lowland forests. Nature 479, 517–520 (2011).

    Article  ADS  CAS  Google Scholar 

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

    Article  ADS  Google Scholar 

  18. Bush, M. B., Silman, M. R. & Urrego, D. H. 48,000 years of climate and forest change in a biodiversity hot spot. Science 303, 827–829 (2004).

    Article  ADS  CAS  Google Scholar 

  19. Feeley, K. J. et al. Upslope migration of Andean trees. J. Biogeogr. 38, 783–791 (2011).

    Article  Google Scholar 

  20. Feeley, K. J., Hurtado, J., Saatchi, S., Silman, M. R. & Clark, D. B. Compositional shifts in Costa Rican forests due to climate-driven species migrations. Glob. Change Biol. 19, 3472–3480 (2013).

    Google Scholar 

  21. Duque, A., Stevenson, P. R. & Feeley, K. J. Thermophilization of adult and juvenile tree communities in the northern tropical Andes. Proc. Natl Acad. Sci. USA 112, 10744–10749 (2015).

    Article  ADS  CAS  Google Scholar 

  22. Báez, S. et al. Large-scale patterns of turnover and basal area change in Andean forests. PLoS ONE 10, e0126594 (2015).

    Article  Google Scholar 

  23. Karger, D. N. et al. Climatologies at high resolution for the earth’s land surface areas. Sci. Data 4, 170122 (2017).

    Article  Google Scholar 

  24. Russell, A. M., Gnanadesikan, A. & Zaitchik, B. Are the Central Andes mountains a warming hot spot? J. Clim. 30, 3589–3608 (2017).

    Article  ADS  Google Scholar 

  25. Vuille, M., Franquist, E., Garreaud, R., Lavado Casimiro, W. S. & Cáceres, B. Impact of the global warming hiatus on Andean temperature. J. Geophys. Res. Atmos. 120, 3745–3757 (2015).

    Article  ADS  Google Scholar 

  26. Rapp, J. M. & Silman, M. R. Diurnal, seasonal, and altitudinal trends in microclimate across a tropical montane cloud forest. Clim. Res. 55, 17–32 (2012).

    Article  Google Scholar 

  27. Palin, O. F. et al. Termite diversity along an Amazon–Andes elevation gradient, Peru. Biotropica 43, 100–107 (2011).

    Article  Google Scholar 

  28. Fyllas, N. M. et al. Solar radiation and functional traits explain the decline of forest primary productivity along a tropical elevation gradient. Ecol. Lett. 20, 730–740 (2017).

    Article  Google Scholar 

  29. Alexander, J. M., Diez, J. M. & Levine, J. M. Novel competitors shape species’ responses to climate change. Nature 525, 515–518 (2015).

    Article  ADS  CAS  Google Scholar 

  30. Rehm, E. & Feeley, K. J. Many species risk mountain top extinction long before they reach the top. Front. Biogeogr. 8, e27788 (2016).

    Article  Google Scholar 

  31. Crimmins, S. M., Dobrowski, S. Z., Greenberg, J. A., Abatzoglou, J. T. & Mynsberge, A. R. Changes in climatic water balance drive downhill shifts in plant species’ optimum elevations. Science 331, 324–327 (2011).

    Article  ADS  CAS  Google Scholar 

  32. Rehm, E. M. & Feeley, K. J. Freezing temperatures as a limit to forest recruitment above tropical Andean treelines. Ecology 96, 1856–1865 (2015).

    Article  Google Scholar 

  33. Bowler, D. & Böhning-Gaese, K. Improving the community-temperature index as a climate change indicator. PLoS ONE 12, e0184275 (2017).

    Article  Google Scholar 

  34. Malizia, A., Easdale, T. A. & Grau, H. R. Rapid structural and compositional change in an old-growth subtropical forest: using plant traits to identify probable drivers. PLoS ONE 8, e73546 (2013).

    Article  ADS  CAS  Google Scholar 

  35. Carilla, J. & Grau, R. Tendencias sucesionales de los bosques montanos subtropicales del noroeste argentino. Bosque (Valdivia) 32, 97–111 (2011).

    Article  Google Scholar 

  36. McCain, C. M. & Colwell, R. K. Assessing the threat to montane biodiversity from discordant shifts in temperature and precipitation in a changing climate. Ecol. Lett. 14, 1236–1245 (2011).

    Article  Google Scholar 

  37. Myers, N., Mittermeier, R. A., Mittermeier, C. G., da Fonseca, G. A. B. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).

    Article  ADS  CAS  Google Scholar 

  38. Raxworthy, C. J. et al. Extinction vulnerability of tropical montane endemism from warming and upslope displacement: a preliminary appraisal for the highest massif in Madagascar. Glob. Change Biol. 14, 1703–1720 (2008).

    Article  ADS  CAS  Google Scholar 

  39. Chen, I.-C. et al. Elevation increases in moth assemblages over 42 years on a tropical mountain. Proc. Natl Acad. Sci. USA 106, 1479–1483 (2009).

    Article  ADS  CAS  Google Scholar 

  40. Forero-Medina, G., Terborgh, J., Socolar, S. J. & Pimm, S. L. Elevational ranges of birds on a tropical montane gradient lag behind warming temperatures. PLoS ONE 6, e28535 (2011).

    Article  ADS  CAS  Google Scholar 

  41. Feeley, K. J. Moving forward with species distributions. Am. J. Bot. 102, 173–175 (2015).

    Article  Google Scholar 

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

    Article  Google Scholar 

  43. Menéndez, R. et al. Species richness changes lag behind climate change. Proc. R. Soc. B 273, 1465–1470 (2006).

    Article  Google Scholar 

  44. Bertrand, R. et al. Ecological constraints increase the climatic debt in forests. Nat. Commun. 7, 12643 (2016).

    Article  ADS  CAS  Google Scholar 

  45. Bivand, R. & Yu, D. spgwr: geographically weighted regression. R package version 0.6-32 (2017).

  46. Dobrowski, S. Z. A climatic basis for microrefugia: the influence of terrain on climate. Glob. Change Biol. 17, 1022–1035 (2011).

    Article  ADS  Google Scholar 

  47. Hijmans, R. J. & van Etten, J. raster: geographic analysis and modeling with raster data. R package version 2.0-12 (2012).

  48. The Plant List. The plant list: a working list of all plant species. version 1.1 (2013).

  49. Flann, C. Global Compositae Checklist. (2009).

  50. White, R. ILDIS: The International Legume Database and Information Service. (accessed 13 May 2016).

  51. Missouri Botanical Garden. Tropicos. (accessed 13 May 2016).

  52. USDA NRCS. The PLANTS Database. (accessed 13 May 2016).

  53. Feeley, K. J. & Silman, M. R. Biotic attrition from tropical forests correcting for truncated temperature niches. Glob. Change Biol. 16, 1830–1836 (2010).

    Article  ADS  Google Scholar 

Download references


We thank the many individuals and institutions (including the Red de Bosques Andinos, CODESAN, APECO, CONICET and RAINFOR) who are working to protect and understand Andean forests; GBIF and contributing institutions for making collection data publicly available and E. Ortíz for creating the map of plot locations. B.F. and K.J.F. were supported by the US NSF (DEB-1350125) and the Swiss Agency for Development and Cooperation. M.R.S. and W.F.R. were supported by the US NSF (DEB-1754647, DEB-1258112, and EAR-1338694). J.H. was supported by DFG Grants HO3296/2 and HO3296/4. Peruvian plot monitoring was supported by the Blue Moon Fund and the Gordon and Betty Moore Foundation’s Andes to Amazon Program and RAINFOR grant 1656 (coordinated by O. Phillips). A complete list of acknowledgments and funding sources can be found in the Supplementary Information.

Reviewer information

Nature thanks A. M. Latimer, J. Lenoir, H. Pauli and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Author information

Authors and Affiliations



B.F. and K.J.F. designed the study, carried out the analysis and wrote the Article. S.B., A.D., A.M., C.B., J.C., O.O.-A., L.M., M.S., W.F.-R., Y.M., K.R.Y., F.C.C., J.H., M.P., E.P., O.J., N.A. and Z.A. provided data and assistance with writing.

Corresponding author

Correspondence to Kenneth J. Feeley.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 CTI, MAT and elevation of study plots.

a, The relationship between the mean CTI for each of the Andean forest plots (averaged across all censuses) and the MAT at the plot locations. n = 186, slope = 0.71, R = 0.92, 95% confidence interval = 0.88–0.93, P < 0.001. b, The relationship between the mean plot CTI and plot elevation. n = 186, R = −0.77, 95% confidence interval = −0.82 to −0.7, P < 0.001. c, The relationship between plot MAT and plot elevation. n = 186, R = −0.92, 95% confidence interval = −0.93 to −0.88, P < 0.001. All analyses are two-sided Spearman correlations.

Extended Data Fig. 2 Regression-based thermophilization rates of repeatedly censused plots.

TRplot was compared to the MAT for the Andean forest plots with multiple censuses (n = 64). Each point represents one plot and the size of the point is proportional to the number of censuses. Error bars are 95% confidence intervals based on the linear least-square regressions of the CTI versus census year of each plot. Grey points represent plots with non-significant TRplot values and filled, coloured points represent plots with significant TRplot values; hollow points are plots with only two censuses and for which the significance of the TRplot could therefore not be determined. Positive and negative TRplot are coloured red and blue, respectively.

Extended Data Fig. 3 Thermophilization rates for areas with different warming rates.

The thermophilization rates in areas with different warming rates (TRwarm; the annualized change in the mean CTI of all plots within a band of equitable warming rate) were compared to the warming rate. n = 283 plot censuses, assigned to 20 warming bands. The dashed line indicates the mean TRwarm and the coloured shaded area indicates the 95% confidence interval of TRwarm. Positive and negative TRwarm is coloured red and blue, respectively.

Extended Data Fig. 4 Species richness of repeatedly censused 1-ha plots.

Species richness versus MAT in the 1-ha Andean forest plots with multiple censuses. n = 61. Each point represents one plot and the red and blue colours indicate positive and negative TRplot values, respectively. The line shows the linear regression between MAT and species richness. R2 = 0.10, P < 0.05.

Extended Data Fig. 5 Range of the thermal optima in study plots.

The range of thermal optima of co-occurring species versus TRplot in the plots with multiple censuses. n = 64. Each point represents one plot and the red and blue colours represent positive and negative TRplot values, respectively. The line shows the linear regression between the range of the thermal optima of the plots and TRplot. R2 = 0.19, P < 0.001.

Extended Data Fig. 6 Basal area change in species composition.

Percentage change in absolute basal area per plot for more-thermophilic (species thermal optimum > plot CTI) and less-thermophilic (species thermal optimum < plot CTI) species versus MAT in plots with multiple censuses (n = 64). The more- and less-thermophilic species are coloured red and blue, respectively. Lines show loess regression fits between the percentage change in basal area and MAT, and the shaded areas represent the 95% confidence intervals around the loess regressions.

Extended Data Table 1 Results for alternative calculations of TRMAT and TRplot

Supplementary information

Supplementary Information

This file contains extended Acknowledgments.

Reporting Summary

Supplementary Tables

Supplementary Table 1 in pdf format contains detailed information of the plots included in the analysis such as coordinates, plot size, MAT and CTI. Supplementary Table 2 in pdf format shows the list of the 1720 species included in the analysis, number of GBIF records available after filtering and their estimated Thermal Optima.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fadrique, B., Báez, S., Duque, Á. et al. Widespread but heterogeneous responses of Andean forests to climate change. Nature 564, 207–212 (2018).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:


This article is cited by


By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing