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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.

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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).

  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).

  3. 3.

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

  4. 4.

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

  5. 5.

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

  6. 6.

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

  7. 7.

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

  8. 8.

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

  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).

  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).

  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).

  12. 12.

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

  13. 13.

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

  14. 14.

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

  15. 15.

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

  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).

  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).

  18. 18.

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

  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).

  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).

  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).

  23. 23.

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

  24. 24.

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

  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).

  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).

  27. 27.

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

  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).

  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).

  30. 30.

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

  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).

  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).

  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).

  37. 37.

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

  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).

  40. 40.

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

  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).

  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).

  45. 45.

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

  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).

  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).

  48. 48.

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

  49. 49.

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

  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).

  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).

  53. 53.

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

  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).

  57. 57.

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

  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).

  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).

  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).

  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).

  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).

  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).

  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).

  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).

  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).

  73. 73.

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

  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).

  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).

  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).

  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).

  78. 78.

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

  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).

  82. 82.

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

  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).

  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).

  85. 85.

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

  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).

  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).

  88. 88.

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

  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).

  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);

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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.

Correspondence to Vitor H. F. Gomes.

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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).