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.

Amazonian tree species threatened by deforestation and climate change


Deforestation is currently the major threat to Amazonian tree species but climate change may surpass it in just a few decades. Here, we show that climate and deforestation combined could cause a decline of up to 58% in Amazon tree species richness, whilst deforestation alone may cause 19–36% and climate change 31–37% by 2050. Quantification is achieved by overlaying species distribution models for current and future climate change scenarios with historical and projected deforestation. Species may lose an average of 65% of their original environmentally suitable area, and a total of 53% may be threatened according to IUCN Red List criteria; however, Amazonian protected area networks reduce these impacts. The worst-case combined scenario—assuming no substantial climate or deforestation policy progress—suggests that by 2050 the Amazonian lowland rainforest may be cut into two blocks: one continuous block with 53% of the original area and another severely fragmented block. This outlook urges rapid progress to zero deforestation, which would help to mitigate climate change and foster biodiversity conservation.

This is a preview of subscription content

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Loss by global change for E. coriacea (DC) SA Mori.
Fig. 2: Amazonian species richness (number of species per grid cell) affected by global change and deforestation.
Fig. 3: Only half of the Amazonian forest may remain in 2050 (worst-case combined scenario).

Data availability

All data used can be freely downloaded from GBIF ( and WorldClim ( and are also available from the corresponding author upon request. A full list of species used can be found in Supplementary Table 1.

Code availability

The R code used for calculations and analyses is available from the corresponding author upon request.


  1. 1.

    Crowther, T. W. et al. Mapping tree density at a global scale. Nature 525, 201–205 (2015).

    CAS  Article  Google Scholar 

  2. 2.

    Cardoso, D. et al. Amazon plant diversity revealed by a taxonomically verified species list. Proc. Natl Acad. Sci. USA 114, 10695–10700 (2017).

    CAS  Article  Google Scholar 

  3. 3.

    ter Steege, H. et al. Hyperdominance in the Amazonian tree flora. Science 342, 1243092 (2013).

    Article  Google Scholar 

  4. 4.

    Huntingford, C. et al. Towards quantifying uncertainty in predictions of Amazon ‘dieback’. Philos. Trans. R. Soc. Lond. B 363, 1857–1864 (2008).

    Article  Google Scholar 

  5. 5.

    ter Steege, H. Will tropical biodiversity survive our approach to global change? Biotropica 42, 561–562 (2010).

    Article  Google Scholar 

  6. 6.

    Hansen, M. C. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–854 (2013).

    CAS  Article  Google Scholar 

  7. 7.

    ter Steege, H. et al. Estimating the global conservation status of over 15,000 Amazonian tree species. Sci. Adv. 1, e1500936 (2015).

    Article  Google Scholar 

  8. 8.

    Lovejoy, T. E. & Nobre, C. Amazon tipping point. Sci. Adv. 4, 2340 (2018).

    Article  Google Scholar 

  9. 9.

    Bellard, C., Bertelsmeier, C., Leadley, P., Thuiller, W. & Courchamp, F. Impacts of climate change on the future of biodiversity. Ecol. Lett. 15, 365–377 (2012).

    Article  Google Scholar 

  10. 10.

    Cowling, S. A. et al. Contrasting simulated past and future responses of the Amazonian forest to atmospheric change. Philos. Trans. R. Soc. Lond. B 359, 539–547 (2004).

    CAS  Article  Google Scholar 

  11. 11.

    Feeley, K. J. & Rehm, E. M. Amazon’s vulnerability to climate change heightened by deforestation and man-made dispersal barriers. Glob. Chang. Biol. 18, 3606–3614 (2012).

    Article  Google Scholar 

  12. 12.

    Pecl, G. T. et al. Biodiversity redistribution under climate change: impacts on ecosystems and human well-being. Science 355, eaai9214 (2017).

    Article  Google Scholar 

  13. 13.

    Mayle, F. E., Burbridge, R. & Killeen, T. J. Millennial-scale dynamics of southern Amazonian rain forests. Science 290, 2291–2294 (2000).

    CAS  Article  Google Scholar 

  14. 14.

    Feeley, K. J. & Silman, M. R. Disappearing climates will limit the efficacy of Amazonian protected areas. Divers. Distrib. 22, 1081–1084 (2016).

    Article  Google Scholar 

  15. 15.

    Gomes, V. H. F. et al. Species distribution modelling: contrasting presence-only models with plot abundance data. Sci. Rep. 8, 1003 (2018).

    Article  Google Scholar 

  16. 16.

    Feeley, K. J. & Silman, M. R. The data void in modeling current and future distributions of tropical species. Glob. Chang. Biol. 17, 626–630 (2010).

    Article  Google Scholar 

  17. 17.

    Pimm, S. L., Jenkins, C. N., Joppa, L. N., Roberts, D. L. & Russell, G. J. How many endangered species remain to be discovered in Brazil? Nat. Conservacao 8, 71–77 (2010).

    Article  Google Scholar 

  18. 18.

    Pos, E. T. et al. Are all species necessary to reveal ecologically important patterns? Ecol. Evol. 4, 4626–4636 (2014).

    Article  Google Scholar 

  19. 19.

    van Proosdij, A. S. J., Sosef, M. S. M., Wieringa, J. J. & Raes, N. Minimum required number of specimen records to develop accurate species distribution models. Ecography 39, 542–552 (2015).

    Article  Google Scholar 

  20. 20.

    Calabrese, J. M., Certain, G., Kraan, C. & Dormann, C. F. Stacking species distribution models and adjusting bias by linking them to macroecological models. Glob. Ecol. Biogeogr. 23, 99–112 (2014).

    Article  Google Scholar 

  21. 21.

    IPCC Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).

  22. 22.

    Peters, G. P. et al. The challenge to keep global warming below 2°C. Nat. Clim. Change 3, 4–6 (2012).

    Article  Google Scholar 

  23. 23.

    Diffenbaugh, N. S. & Field, C. B. Changes in ecologically critical terrestrial climate conditions. Science 341, 486–492 (2013).

    CAS  Article  Google Scholar 

  24. 24.

    Brodie, J., Post, E. & Laurance, W. F. Climate change and tropical biodiversity: a new focus. Trends Ecol. Evol. 27, 145–150 (2012).

    Article  Google Scholar 

  25. 25.

    Brando, P. M. et al. Abrupt increases in Amazonian tree mortality due to drought-fire interactions. Proc. Natl Acad. Sci. USA 111, 6347–6352 (2014).

    CAS  Article  Google Scholar 

  26. 26.

    Silvério, D. V. et al. Agricultural expansion dominates climate changes in southeastern Amazonia: the overlooked non-GHG forcing. Environ. Res. Lett. 10, 104015 (2015).

    Article  Google Scholar 

  27. 27.

    Taubert, F. et al. Global patterns of tropical forest fragmentation. Nature 554, 512–519 (2018).

    Article  Google Scholar 

  28. 28.

    Laurance, W. F. et al. Rapid decay of tree-community composition in Amazonian forest fragments. Proc. Natl Acad. Sci. USA 103, 19010–19014 (2006).

    CAS  Article  Google Scholar 

  29. 29.

    Bicknell, J. & Peres, C. A. Vertebrate population responses to reduced-impact logging in a neotropical forest. For. Ecol. Manage. 259, 2267–2275 (2010).

    Article  Google Scholar 

  30. 30.

    Bello, C. et al. Defaunation affects carbon storage in tropical forests. Sci. Adv. 1, e1501105 (2015).

    Article  Google Scholar 

  31. 31.

    Laurance, W. F., Delamônica, P., Laurance, S. G., Vasconcelos, H. L. & Lovejoy, T. E. Rainforest fragmentation kills big trees. Nature 404, 836 (2000).

    CAS  Article  Google Scholar 

  32. 32.

    Feldpausch, T. R., Jirka, S., Passos, C. A. M., Jasper, F. & Riha, S. J. When big trees fall: damage and carbon export by reduced impact logging in southern Amazonia. For. Ecol. Manage. 219, 199–215 (2005).

    Article  Google Scholar 

  33. 33.

    MacDicken, K. et al. Global Forest Resources Assessment 2015: How Are the World’s Forests Changing? (FAO, 2016).

  34. 34.

    Fearnside, P. M. Business as usual: a resurgence of deforestation in the Brazilian Amazon. Yale Environment 360 1–6(18 April 2017).

  35. 35.

    Tollefson, J. Forests in spotlight at Paris climate talks. Nature News (1 December 2015).

  36. 36.

    Moutinho, P., Guerra, R. & Azevedo-Ramos, C. Achieving zero deforestation in the Brazilian Amazon: what is missing? Elementa (Wash DC) 4, 000125 (2016).

    Google Scholar 

  37. 37.

    Tollefson, J. Stopping deforestation: battle for the Amazon. Nature 520, 20–23 (2015).

    CAS  Article  Google Scholar 

  38. 38.

    PRODES Projeto. Mapeamento do desmatamento da Amazônia com Imagens de Satélite (Instituto Nacional de Pesquisas Espaciais, 2018).

  39. 39.

    Christopher, J. US withdrawal from the COP21 Paris Climate Change Agreement, and its possible implications. Sci. Prog. 100, 411–419 (2017).

    Article  Google Scholar 

  40. 40.

    Bockmann, F. A. et al. Brazil’s government attacks biodiversity. Science 360, 865–865 (2018).

    CAS  Google Scholar 

  41. 41.

    Armenteras, D., Schneider, L. & Dávalos, L. M. Fires in protected areas reveal unforeseen costs of Colombian peace. Nat. Ecol. Evol. 3, 20–23 (2018).

    Article  Google Scholar 

  42. 42.

    Dávalos, L. M. in The Origins of Cocaine: Colonization and Failed Development in the Amazon Andes 1st edn (eds Gootenberg, P. & Dávalos, L. M.) 19–52 (Routledge, 2018).

  43. 43.

    Hanauer, M. & Canavire Bacarreza, G. Civil Conflict Reduced the Impact of Colombia’s Protected Areas (Inter-American Development Bank, 2018).

  44. 44.

    Soares-Filho, B. et al. Role of Brazilian Amazon protected areas in climate change mitigation. Proc. Natl Acad. Sci. USA 107, 10821–10826 (2010).

    CAS  Article  Google Scholar 

  45. 45.

    Adeney, J. M., Christensen, N. L. & Pimm, S. L. Reserves protect against deforestation fires in the Amazon. PLoS ONE 4, e5014 (2009).

    Article  Google Scholar 

  46. 46.

    Watson, J. E. M., Dudley, N., Segan, D. B. & Hockings, M. The performance and potential of protected areas. Nature 515, 67–73 (2014).

    CAS  Article  Google Scholar 

  47. 47.

    Houghton, R. A., Byers, B. & Nassikas, A. A. A role for tropical forests in stabilizing atmospheric CO2. Nat. Clim. Change 5, 1022–1023 (2015).

    Article  Google Scholar 

  48. 48.

    Bonan, G. B. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320, 1444–1449 (2008).

    CAS  Article  Google Scholar 

  49. 49.

    Gibson, L. et al. Primary forests are irreplaceable for sustaining tropical biodiversity. Nature 478, 378–381 (2011).

    CAS  Article  Google Scholar 

  50. 50.

    Carrasco, L. R., Le Nghiem, T. P., Chen, Z. & Barbier, E. B. Unsustainable development pathways caused by tropical deforestation. Sci. Adv. 3, 1–10 (2017).

    Article  Google Scholar 

  51. 51.

    Dean, W. With Broadax and Firebrand: The Destruction of the Brazilian Atlantic Forest (Univ. of California Press, 1997).

  52. 52.

    Ribeiro, M. C., Metzger, J. P., Martensen, A. C., Ponzoni, F. J. & Hirota, M. M. The Brazilian Atlantic Forest: how much is left, and how is the remaining forest distributed? Implications for conservation. Biol. Conserv. 142, 1141–1153 (2009).

    Article  Google Scholar 

  53. 53.

    Soares-Filho, B. S. et al. Modelling conservation in the Amazon Basin. Nature 440, 520–523 (2006).

    CAS  Article  Google Scholar 

  54. 54.

    Soares-Filho, B. S. et al. LBA-ECO LC-14 Modeled Deforestation Scenarios, Amazon Basin: 2002–2050 (Oak Ridge National Laboratory Distributed Active Archive Center, 2013).

  55. 55.

    R: a Language and Environment for Statistical Computing v3.4.3 (R Foundation, 2018).

  56. 56.

    ter Steege, H. et al. Towards a dynamic list of Amazonian tree species. Sci. Rep. 9, 3501 (2019).

    Article  Google Scholar 

  57. 57.

    Raes, N. Partial versus full species distribution models. Nat. Conserv. 10, 127–138 (2012).

    Article  Google Scholar 

  58. 58.

    Zizka, A. & Antonelli, A. Species geocoder: an R package for linking species occurrences, user-defined regions and phylogenetic trees for biogeography, ecology and evolution. Preprint at (2015).

  59. 59.

    Maldonado, C. et al. Estimating species diversity and distribution in the era of Big Data: to what extent can we trust public databases? Glob. Ecol. Biogeogr. 24, 973–984 (2015).

    Article  Google Scholar 

  60. 60.

    Boyle, B. et al. The taxonomic name resolution service: an online tool for automated standardization of plant names. BMC Bioinforma. 13, 14–16 (2013).

    Google Scholar 

  61. 61.

    IUCN Standards and Petitions Subcommittee. Guidelines for Using the IUCN Red List Categories and Criteria v.13 (IUCN, 2017).

  62. 62.

    The Global Database on Protected Areas Management Effectiveness (UNEP-WCMC, IUCN, 2018);

  63. 63.

    Amazonia socioambiental - Protected areas and indigenous territories (Rede Amazônica de Informação Socioambiental Georreferenciada, 2017);

  64. 64.

    Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Modell. 190, 231–259 (2006).

    Article  Google Scholar 

  65. 65.

    Phillips, S. J., Dudík, M. & Schapire, R. E. A maximum entropy approach to species distribution modeling. In Proc. 21st Int. Conf. Machine Learning (eds Carla Brodley) 83 (ACM Press, 2004).

  66. 66.

    Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).

    Article  Google Scholar 

  67. 67.

    Hijmans, R. J. & van Etten, J. raster: geographic data analysis and modeling v2.5-8 (CRAN, 2016);

  68. 68.

    van Vuuren, D. P., Eickhout, B., Lucas, P. L. & den Elzen, M. G. J. Long-term multi-gas scenarios to stabilise radiative forcing: exploring costs and benefits within an integrated assessment framework. Energy J. 27, 201–233 (2006).

    Google Scholar 

  69. 69.

    van Vuuren, D. P. et al. Stabilizing greenhouse gas concentrations at low levels: an assessment of reduction strategies and costs. Clim. Change 81, 119–159 (2007).

    CAS  Article  Google Scholar 

  70. 70.

    Riahi, K., Grübler, A. & Nakicenovic, N. Scenarios of long-term socio-economic and environmental development under climate stabilization. Technol. Forecast. Soc. Change 74, 887–935 (2007).

    Article  Google Scholar 

  71. 71.

    Xiao-Ge, X., Tong-Wen, W. & Jie, Z. Introduction of CMIP5 experiments carried out with the climate system models of Beijing Climate Center. Adv. Clim. Chang. Res. 4, 41–49 (2013).

    Article  Google Scholar 

  72. 72.

    Yeager, S., Karspeck, A., Danabasoglu, G., Tribbia, J. & Teng, H. A decadal prediction case study: late twentieth-century north Atlantic Ocean heat content. J. Clim. 25, 5173–5189 (2012).

    Article  Google Scholar 

  73. 73.

    Jones, C. D. et al. The HadGEM2-ES implementation of CMIP5 centennial simulations. Geosci. Model Dev. 4, 543–570 (2011).

    Article  Google Scholar 

  74. 74.

    Swingedouw, D., Mignot, J., Labetoulle, S., Guilyardi, É. & Madec, G. Initialisation and predictability of the AMOC over the last 50 years in a climate model. Clim. Dyn. 40, 2381–2399 (2013).

    Article  Google Scholar 

  75. 75.

    Watanabe, S. et al. MIROC-ESM 2010: model description and basic results of CMIP5-20c3m experiments. Geosci. Model Dev. 4, 845–872 (2011).

    Article  Google Scholar 

  76. 76.

    Giorgetta, M. A. et al. Climate and carbon cycle changes from 1850 to 2100 in MPI-ESM simulations for the Coupled Model Intercomparison Project phase 5. J. Adv. Model. Earth Syst. 5, 572–597 (2013).

    Article  Google Scholar 

  77. 77.

    Tatebe, H. et al. The initialization of the MIROC climate models with hydrographic data assimilation for decadal prediction. J. Meteorol. Soc. Jpn 90, 275–294 (2012).

    Article  Google Scholar 

  78. 78.

    van Vuuren, D. P. et al. The representative concentration pathways: an overview. Clim. Change 109, 5–31 (2011).

    Article  Google Scholar 

  79. 79.

    IPCC Climate Change 2014: Mitigation of Climate Change (eds Edenhofer, O. et al.) 413–510 (Cambridge Univ. Press, 2014).

  80. 80.

    IPCC Climate Change 2014: Mitigation of Climate Change (eds Edenhofer, O. et al.) 3 (Cambridge Univ. Press, 2014).

  81. 81.

    Dormann, C. F. et al. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 36, 27–46 (2013).

    Article  Google Scholar 

  82. 82.

    Phillips, S. J. & Dudík, M. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31, 161–175 (2008).

    Article  Google Scholar 

  83. 83.

    Boucher-Lalonde, V., Morin, A. & Currie, D. J. How are tree species distributed in climatic space? A simple and general pattern. Glob. Ecol. Biogeogr. 21, 1157–1166 (2012).

    Article  Google Scholar 

  84. 84.

    Merow, C., Smith, M. J. & Silander, J. A. A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter. Ecography 36, 1058–1069 (2013).

    Article  Google Scholar 

  85. 85.

    Syfert, M. M. et al. Using species distribution models to inform IUCN Red List assessments. Biol. Conserv. 177, 174–184 (2014).

    Article  Google Scholar 

  86. 86.

    Algar, A. C., Kharouba, H. M., Young, E. R. & Kerr, J. T. Predicting the future of species diversity: macroecological theory, climate change, and direct tests of alternative forecasting methods. Ecography 32, 22–33 (2009).

    Article  Google Scholar 

  87. 87.

    Distler, T., Schuetz, J. G., Velásquez-Tibatá, J. & Langham, G. M. Stacked species distribution models and macroecological models provide congruent projections of avian species richness under climate change. J. Biogeogr. 42, 976–988 (2015).

    Article  Google Scholar 

  88. 88.

    Raes, N. & ter Steege, H. A null-model for significance testing of presence-only species distribution models. Ecography 30, 727–736 (2007).

    Article  Google Scholar 

  89. 89.

    Hijmans, R. J., Phillips, S., Leathwick, J. & Elith, J. dismo: species distribution modeling v1.1-4 (CRAN, 2016);

  90. 90.

    Pebesma, E. & Heuvelink, G. Spatio-temporal interpolation using gstat. RFID J. 8, 204–218 (2016).

    Google Scholar 

  91. 91.

    Bivand, R. & Lewin-Koh, N. maptools: tools for reading and handling spatial objects v0.9-2 (CRAN, 2017);

  92. 92.

    Bivand, R., Keitt, T. & Rowlingson, B. rgdal: bindings for the ‘geospatial’ data abstraction library v1.2-16 (CRAN, 2017);

  93. 93.

    Bivand, R. & Rundel, C. rgeos: interface to geometry engine v0.3-26 (CRAN, 2017);

  94. 94.

    Urbanek, S. rJava: low-level R to Java interface v0.9-9 (CRAN, 2017);

  95. 95.

    Zizka, A. speciesgeocodeR: prepare species distributions for the use in phylogenetic analyses v1.0-4 (CRAN, 2015);

Download references


V.H.F.G, H.t.S. and R.P.S. were supported by Conselho Nacional de Desenvolvimento Científico e Tecnológico grant no. 407232/2013-3—PVE—MEC/MCTI/CAPES/CNPq/FAPs. R.P.S. is also supported by Programa Professor Visitante Nacional Sênior na Amazônia—CAPES. I.C.G.V. is supported by Conselho Nacional de Desenvolvimento Científico e Tecnológico grant no. 308778/2017-0—CNPq. This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal Nível Superior—Brazil (CAPES)—Finance Code 001. We thank S. Mota de Oliveira for constructive comments on the manuscript.

Author information




V.H.F.G. conceived the study. V.H.F.G., I.C.G.V. and H.t.S. designed the study. V.H.F.G. carried out the GBIF data collection. H.t.S checked the species list. V.H.F.G. carried out the analyses and wrote the R scripts. V.H.F.G. and H.t.S wrote the manuscript. I.C.G.V and R.P.S provided comments and feedback.

Corresponding author

Correspondence to Vitor H. F. Gomes.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information: Nature Climate Change thanks Luke Gibson and other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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

Supplementary information

Supplementary Information

Supplementary Text, Supplementary Figures 1–9, legends for Supplementary Tables 1–7 and Supplementary References.

Reporting Summary

Supplementary Tables

Supplementary Tables 1–7.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Gomes, V.H.F., Vieira, I.C.G., Salomão, R.P. et al. Amazonian tree species threatened by deforestation and climate change. Nat. Clim. Chang. 9, 547–553 (2019).

Download citation

Further reading


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