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How deregulation, drought and increasing fire impact Amazonian biodiversity


Biodiversity contributes to the ecological and climatic stability of the Amazon Basin1,2, but is increasingly threatened by deforestation and fire3,4. Here we quantify these impacts over the past two decades using remote-sensing estimates of fire and deforestation and comprehensive range estimates of 11,514 plant species and 3,079 vertebrate species in the Amazon. Deforestation has led to large amounts of habitat loss, and fires further exacerbate this already substantial impact on Amazonian biodiversity. Since 2001, 103,079–189,755 km2 of Amazon rainforest has been impacted by fires, potentially impacting the ranges of 77.3–85.2% of species that are listed as threatened in this region5. The impacts of fire on the ranges of species in Amazonia could be as high as 64%, and greater impacts are typically associated with species that have restricted ranges. We find close associations between forest policy, fire-impacted forest area and their potential impacts on biodiversity. In Brazil, forest policies that were initiated in the mid-2000s corresponded to reduced rates of burning. However, relaxed enforcement of these policies in 2019 has seemingly begun to reverse this trend: approximately 4,253–10,343 km2 of forest has been impacted by fire, leading to some of the most severe potential impacts on biodiversity since 2009. These results highlight the critical role of policy enforcement in the preservation of biodiversity in the Amazon.

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Fig. 1: Overview of plant and vertebrate species richness and fire-impacted forest in the Amazon Basin.
Fig. 2: Cumulative effects of fire on biodiversity in the Amazon rainforest.
Fig. 3: Newly fire-impacted forest in Brazil (based on MODIS burned area).
Fig. 4: Newly fire-impacted forest area and the impacts on plant and vertebrate species in Brazil.

Data availability

The plant occurrences from the BIEN database are accessible using the RBIEN package ( The climatic data are accessible from and the soil data are available from MODIS active fire and burned area products are available at The MODIS Vegetation Continuous Fields data are publicly available from The annual forest loss layers are available from The plant range maps are accessible at The vertebrate range maps are available from The SPEI data are available from SPEI Global Drought Monitor (

Code availability

The code to process the remote-sensing data is available at


  1. 1.

    Yachi, S. & Loreau, M. Biodiversity and ecosystem productivity in a fluctuating environment: the insurance hypothesis. Proc. Natl Acad. Sci. USA 96, 1463–1468 (1999).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  2. 2.

    Oliver, T. H. et al. Biodiversity and resilience of ecosystem functions. Trends Ecol. Evol. 30, 673–684 (2015).

    PubMed  Article  PubMed Central  Google Scholar 

  3. 3.

    Barlow, J., Berenguer, E., Carmenta, R. & França, F. Clarifying Amazonia’s burning crisis. Glob. Change Biol. 9, 1 (2019).

    Google Scholar 

  4. 4.

    Brando, P. M. et al. The gathering firestorm in southern Amazonia. Sci. Adv. 6, eaay1632 (2020).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  5. 5.

    IUCN. IUCN Red List of Threatened Species version 6.2. (2019).

  6. 6.

    Flores, M. et al. WWF’s Living Amazon Initiative (Grambs Corporación Gráfica, 2010).

  7. 7.

    Hubbell, S. P. et al. How many tree species are there in the Amazon and how many of them will go extinct? Proc. Natl Acad. Sci. USA 105 Suppl. 1, 11498–11504 (2008).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  8. 8.

    Nepstad, D. C., Stickler, C. M., Filho, B. S.- & Merry, F. Interactions among Amazon land use, forests and climate: prospects for a near-term forest tipping point. Philos. Trans. R. Soc. Lond. B 363, 1737–1746 (2008).

    Article  Google Scholar 

  9. 9.

    Rankin-de-Mérona, J. M. et al. Preliminary results of a large-scale tree inventory of upland rain forest in the Central Amazon. Acta Amazon. 22, 493–534 (1992).

    Article  Google Scholar 

  10. 10.

    Sakschewski, B. et al. Resilience of Amazon forests emerges from plant trait diversity. Nat. Clim. Change 6, 1032–1036 (2016).

    ADS  Article  Google Scholar 

  11. 11.

    Poorter, L. et al. Biomass resilience of Neotropical secondary forests. Nature 530, 211–214 (2016).

    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

  12. 12.

    Beisner, B. E., Haydon, D. T. & Cuddington, K. Alternative stable states in ecology. Front. Ecol. Environ. 1, 376–382 (2003).

    Article  Google Scholar 

  13. 13.

    Lovejoy, T. E. & Nobre, C. Amazon tipping point. Sci. Adv. 4, eaat2340 (2018).

    ADS  PubMed  PubMed Central  Article  Google Scholar 

  14. 14.

    Veldman, J. W. Clarifying the confusion: old-growth savannahs and tropical ecosystem degradation. Philos. Trans. R. Soc. Lond. B 371, (2016).

  15. 15.

    Arruda, D., Candido, H. G. & Fonseca, R. Amazon fires threaten Brazil’s agribusiness. Science 365, 1387 (2019).

    ADS  PubMed  Article  CAS  PubMed Central  Google Scholar 

  16. 16.

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

    ADS  PubMed  PubMed Central  Article  Google Scholar 

  17. 17.

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

    ADS  Article  Google Scholar 

  18. 18.

    Brando, P. et al. Amazon wildfires: scenes from a foreseeable disaster. Flora 268, 151609 (2020).

    Article  Google Scholar 

  19. 19.

    Balch, J. K. et al. The susceptibility of southeastern Amazon forests to fire: insights from a large-scale burn experiment. Bioscience 65, 893–905 (2015).

    Article  Google Scholar 

  20. 20.

    Barlow, J. et al. The critical importance of considering fire in REDD+ programs. Biol. Conserv. 154, 1–8 (2012).

    Article  Google Scholar 

  21. 21.

    Cochrane, M. A. & Schulze, M. D. Fire as a recurrent event in tropical forests of the eastern Amazon: effects on forest structure, biomass, and species composition. Biotropica 31, 2–16 (1999).

    Google Scholar 

  22. 22.

    Brando, P. M. et al. Prolonged tropical forest degradation due to compounding disturbances: Implications for CO2 and H2O fluxes. Glob. Change Biol. 25, 2855–2868 (2019).

    ADS  Article  Google Scholar 

  23. 23.

    Barlow, J. & Peres, C. A. Fire-mediated dieback and compositional cascade in an Amazonian forest. Philos. Trans. R. Soc. Lond. B 363, 1787–1794 (2008).

    Article  Google Scholar 

  24. 24.

    Cochrane, M. Tropical Fire Ecology: Climate Change, Land Use and Ecosystem Dynamics (Springer, 2010).

  25. 25.

    Uhl, C. & Kauffman, J. B. Deforestation, fire susceptibility, and potential tree responses to fire in the eastern Amazon. Ecology 71, 437–449 (1990).

    Article  Google Scholar 

  26. 26.

    Cochrane, M. A. Fire science for rainforests. Nature 421, 913–919 (2003).

    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

  27. 27.

    Cochrane, M. A. & Laurance, W. F. Synergisms among fire, land use, and climate change in the Amazon. Ambio 37, 522–527 (2008).

    PubMed  Article  PubMed Central  Google Scholar 

  28. 28.

    Nepstad, D. C. et al. Large-scale impoverishment of Amazonian forests by logging and fire. Nature 398, 505–508 (1999).

    ADS  CAS  Article  Google Scholar 

  29. 29.

    Aragão, L. E. O. C. et al. 21st Century drought-related fires counteract the decline of Amazon deforestation carbon emissions. Nat. Commun. 9, 536 (2018).

    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

  30. 30.

    Nepstad, D. et al. Slowing Amazon deforestation through public policy and interventions in beef and soy supply chains. Science 344, 1118–1123 (2014).

    ADS  CAS  PubMed  Article  Google Scholar 

  31. 31.

    Hope, M. The Brazilian development agenda driving Amazon devastation. Lancet Planet. Health 3, e409–e411 (2019).

    Article  Google Scholar 

  32. 32.

    Brown, J. H. On the relationship between abundance and distribution of species. Am. Nat. 124, 255–279 (1984).

    Article  Google Scholar 

  33. 33.

    Barnagaud, J.-Y. et al. Ecological traits influence the phylogenetic structure of bird species co-occurrences worldwide. Ecol. Lett. 17, 811–820 (2014).

    PubMed  Article  Google Scholar 

  34. 34.

    Šímová, I. et al. Spatial patterns and climate relationships of major plant traits in the New World differ between woody and herbaceous species. J. Biogeogr. 45, 895–916 (2018).

    Article  Google Scholar 

  35. 35.

    Enquist, B. J. et al. The commonness of rarity: Global and future distribution of rarity across land plants. Sci. Adv. 5, eaaz0414 (2019).

    ADS  PubMed  PubMed Central  Article  Google Scholar 

  36. 36.

    May, P. H., Gebara, M. F., de Barcellos, L. M., Rizek, M. B. & Millikan, B. The Context of REDD+ in Brazil: Drivers, Agents, and Institutions, 3rd edition, (Center for International Forestry Research, 2016).

  37. 37.

    Neves, D. M., Dexter, K. G., Baker, T. R., Coelho de Souza, F. & Oliveira-Filho, A. T. Evolutionary diversity in tropical tree communities peaks at intermediate precipitation. Sci. Rep. 10, 1188 (2020).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  38. 38.

    Cadotte, M. W., Cardinale, B. J. & Oakley, T. H. Evolutionary history and the effect of biodiversity on plant productivity. Proc. Natl Acad. Sci. USA 105, 17012–17017 (2008).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  39. 39.

    Hopkins, M. J. G. Modelling the known and unknown plant biodiversity of the Amazon Basin. J. Biogeogr. 34, 1400–1411 (2007).

    Article  Google Scholar 

  40. 40.

    Wilson, E. O. in Biodiversity (eds Wilson E. O. & Peter F. M.) Ch. 1 (National Academies Press, 1988).

  41. 41.

    Brooks, T. M. et al. Habitat loss and extinction in the hotspots of biodiversity. Conserv. Biol. 16, 909–923 (2002).

    Article  Google Scholar 

  42. 42.

    Gibbs, H. K. et al. Brazil’s soy moratorium. Science 347, 377–378 (2015).

    ADS  CAS  PubMed  Article  Google Scholar 

  43. 43.

    Alix-Garcia, J. & Gibbs, H. K. Forest conservation effects of Brazil’s zero deforestation cattle agreements undermined by leakage. Glob. Environ. Change 47, 201–217 (2017).

    Article  Google Scholar 

  44. 44.

    Escobar, H. There’s no doubt that Brazil’s fires are linked to deforestation, scientists say. Science (2019).

  45. 45.

    Amazon fires: Brazil sends army to help tackle blazes. BBC News (24 August 2019).

  46. 46.

    Marengo, J. A., Tomasella, J., Soares, W. R., Alves, L. M. & Nobre, C. A. Extreme climatic events in the Amazon basin. Theor. Appl. Climatol. 107, 73–85 (2012).

    ADS  Article  Google Scholar 

  47. 47.

    Malhi, Y. et al. Exploring the likelihood and mechanism of a climate-change-induced dieback of the Amazon rainforest. Proc. Natl Acad. Sci. USA 106, 20610–20615 (2009).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  48. 48.

    Swann, A. L. S. et al. Continental-scale consequences of tree die-offs in North America: identifying where forest loss matters most. Environ. Res. Lett. 13, 055014 (2018).

    ADS  Article  Google Scholar 

  49. 49.

    McCoy, T. Amazon fires dropped unexpectedly in September, after summer spike. Washington Post (2 October 2019).

  50. 50.

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

    Google Scholar 

  51. 51.

    Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on Earth: A new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity. Bioscience 51, 933–938 (2001).

    Article  Google Scholar 

  52. 52.

    Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).

    ADS  CAS  Article  Google Scholar 

  53. 53.

    Giglio, L., Schroeder, W. & Justice, C. O. The collection 6 MODIS active fire detection algorithm and fire products. Remote Sens. Environ. 178, 31–41 (2016).

    ADS  PubMed  PubMed Central  Article  Google Scholar 

  54. 54.

    Giglio, L. MODIS Collection 6 Active Fire Product User’s Guide Revision A (Univ. Maryland, 2015).

  55. 55.

    Barlow, J., Lagan, B. O. & Peres, C. A. Morphological correlates of fire-induced tree mortality in a central Amazonian forest. J. Trop. Ecol. 19, 291–299 (2003).

    Article  Google Scholar 

  56. 56.

    Brando, P. M. et al. Fire-induced tree mortality in a neotropical forest: the roles of bark traits, tree size, wood density and fire behavior. Glob. Change Biol. 18, 630–641 (2012).

    ADS  Article  Google Scholar 

  57. 57.

    Gibbs, H. K. et al. Tropical forests were the primary sources of new agricultural land in the 1980s and 1990s. Proc. Natl Acad. Sci. USA 107, 16732–16737 (2010).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  58. 58.

    Barlow, J. & Peres, C. in Emerging Threats to Tropical Forests (eds. Laurance, W. F. & Peres, C. A.) 225–240 (Univ. Chicago Press, 2006).

  59. 59.

    Barlow, J. et al. Wildfires in bamboo-dominated Amazonian forest: impacts on above-ground biomass and biodiversity. PLoS ONE 7, e33373 (2012).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  60. 60.

    Gerwing, J. J. Degradation of forests through logging and fire in the eastern Brazilian Amazon. For. Ecol. Manage. 157, 131–141 (2002).

    Article  Google Scholar 

  61. 61.

    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).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  62. 62.

    Barlow, J. & Peres, C. A. Avifaunal responses to single and recurrent wildfires in Amazonian forests. Ecol. Appl. 14, 1358–1373 (2004).

    Article  Google Scholar 

  63. 63.

    Paolucci, L. N., Schoereder, J. H., Brando, P. M. & Andersen, A. N. Fire-induced forest transition to derived savannas: cascading effects on ant communities. Biol. Conserv. 214, 295–302 (2017).

    Article  Google Scholar 

  64. 64.

    Roy, D. P. & Kumar, S. S. Multi-year MODIS active fire type classification over the Brazilian Tropical Moist Forest Biome. Int. J. Digital Earth 10, 54–84 (2017).

    ADS  Article  Google Scholar 

  65. 65.

    Giglio, L., Schroeder, W., Hall, J. V. & Justice, C. O. MODIS Collection 6 Active Fire Product User’s Guide Revision B (Univ. Maryland, 2018).

  66. 66.

    Barriopedro, D., Fischer, E. M., Luterbacher, J., Trigo, R. M. & García-Herrera, R. The hot summer of 2010: redrawing the temperature record map of Europe. Science 332, 220–224 (2011).

    ADS  CAS  PubMed  Article  Google Scholar 

  67. 67.

    Chen, Y. et al. Forecasting fire season severity in South America using sea surface temperature anomalies. Science 334, 787–791 (2011).

    ADS  CAS  PubMed  Article  Google Scholar 

  68. 68.

    Giglio, L. et al. Assessing variability and long-term trends in burned area by merging multiple satellite fire products. Biogeosciences 7, 1171–1186 (2010).

    ADS  Article  Google Scholar 

  69. 69.

    Justice, C. O. et al. The MODIS fire products. Remote Sens. Environ. 83, 244–262 (2002).

    ADS  Article  Google Scholar 

  70. 70.

    Giglio, L., Boschetti, L., Roy, D. P., Humber, M. L. & Justice, C. O. The Collection 6 MODIS burned area mapping algorithm and product. Remote Sens. Environ. 217, 72–85 (2018).

    ADS  PubMed  PubMed Central  Article  Google Scholar 

  71. 71.

    Nóbrega, C. C., Brando, P. M., Silvério, D. V., Maracahipes, L. & de Marco, P. Effects of experimental fires on the phylogenetic and functional diversity of woody species in a neotropical forest. For. Ecol. Manage. 450, 117497 (2019).

    Article  Google Scholar 

  72. 72.

    Alencar, A., Nepstad, D. & Diaz, M. C. V. Forest understory fire in the Brazilian Amazon in ENSO and Non-ENSO years: area burned and committed carbon emissions. Earth Interact. 10, 1–17 (2006).

    Article  Google Scholar 

  73. 73.

    Siegert, F., Ruecker, G., Hinrichs, A. & Hoffmann, A. A. Increased damage from fires in logged forests during droughts caused by El Niño. Nature 414, 437–440 (2001).

    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

  74. 74.

    Cochrane, M. A. & Laurance, W. F. Fire as a large-scale edge effect in Amazonian forests. J. Trop. Ecol. 18, 311–325 (2002).

    Article  Google Scholar 

  75. 75.

    Ray, D., Nepstad, D. & Moutinho, P. Micrometeorological and canopy controls of fire susceptibility in a forested Amazon landscape. Ecol. Appl. 15, 1664–1678 (2005).

    Article  Google Scholar 

  76. 76.

    Silvério, D. V. et al. Fire, fragmentation, and windstorms: a recipe for tropical forest degradation. J. Ecol. 107, 656–667 (2019).

    Article  Google Scholar 

  77. 77.

    Guisan, A. & Zimmermann, N. E. Predictive habitat distribution models in ecology. Ecol. Modell. 135, 147–186 (2000).

    Article  Google Scholar 

  78. 78.

    Fegraus, E. Tropical Ecology Assessment and Monitoring Network (TEAM Network). Biodivers. Ecol. 4, 287–287 (2012).

    Article  Google Scholar 

  79. 79.

    Peet, R. K., Lee, M. T., Jennings, M. D. & Faber-Langendoen, D. VegBank: a permanent, open-access archive for vegetation plot data. Biodivers. Ecol. 4, 233–241 (2012).

    Article  Google Scholar 

  80. 80.

    DeWalt, S. J., Bourdy, G., Chavez de Michel, L. R. & Quenevo, C. Ethnobotany of the Tacana: quantitative inventories of two permanent plots of Northwestern Bolivia. Econ. Bot. 53, 237–260 (1999).

    Article  Google Scholar 

  81. 81.

    USDA Forest Service. Forest Inventory and Analysis National Program, (2013).

  82. 82.

    Wiser, S. K., Bellingham, P. J. & Burrows, L. E. Managing biodiversity information: development of New Zealand’s National Vegetation Survey databank. N. Z. J. Ecol. 25, 1–17 (2001).

    Google Scholar 

  83. 83.

    Anderson-Teixeira, K. J. et al. CTFS-ForestGEO: a worldwide network monitoring forests in an era of global change. Glob. Change Biol. 21, 528–549 (2015).

    ADS  Article  Google Scholar 

  84. 84.

    Enquist, B. & Boyle, B. SALVIAS – the SALVIAS vegetation inventory database. Biodivers. Ecol. 4, 288 (2012).

    Article  Google Scholar 

  85. 85. GBIF Occurrence Download (2018).

  86. 86.

    Dauby, G. et al. RAINBIO: a mega-database of tropical African vascular plants distributions. PhytoKeys 74, 1–18 (2016).

    Article  Google Scholar 

  87. 87.

    Arellano, G. et al. A standard protocol for woody plant inventories and soil characterisation using temporary 0.1-ha plots in tropical forests. J. Trop. For. Sci. 28, 508–516 (2016).

    Google Scholar 

  88. 88.

    O’Connell, B. M. et al. The Forest Inventory and Analysis Database: Database Description and User Guide for Phase 2 (version 6.1), (USDA Forest Service, 2016).

  89. 89.

    Oliveira-Filho, A. T. NeoTropTree, Flora arbórea da Região Neotropical: Um Banco de Dados Envolvendo Biogeografia, Diversidade e Conservação, (Univ. Federal de Minas Gerais, 2017).

  90. 90.

    Peet, R. K., Lee, M. T., Jennings, M. D. & Faber-Langendoen, D. VegBank: The Vegetation Plot Archive of the Ecological Society of America, (accessed 2013).

  91. 91.

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

    Article  Google Scholar 

  92. 92.

    Goldsmith, G. R. et al. Plant-O-Matic: a dynamic and mobile guide to all plants of the Americas. Methods Ecol. Evol. 7, 960–965 (2016).

    Article  Google Scholar 

  93. 93.

    McFadden, I. R. et al. Temperature shapes opposing latitudinal gradients of plant taxonomic and phylogenetic β diversity. Ecol. Lett. 22, 1126–1135 (2019).

    PubMed  Article  PubMed Central  Google Scholar 

  94. 94.

    Enquist, B. J., Condit, R., Peet, R. K., Schildhauer, M. & Thiers, B. M. Cyberinfrastructure for an integrated botanical information network to investigate the ecological impacts of global climate change on plant biodiversity. Preprint at (2016).

  95. 95.

    Maitner, B. S. et al. The BIEN R package: A tool to access the Botanical Information and Ecology Network (BIEN) database. Methods Ecol. Evol. 9, 373–379 (2017).

    Article  Google Scholar 

  96. 96.

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

    Article  Google Scholar 

  97. 97.

    Merow, C. & Silander, J. A. A comparison of Maxlike and Maxent for modelling species distributions. Methods Ecol. Evol. 5, 215–225 (2014).

    Article  Google Scholar 

  98. 98.

    Aiello-Lammens, M. E., Boria, R. A., Radosavljevic, A., Vilela, B. & Anderson, R. P. spThin: an R package for spatial thinning of species occurrence records for use in ecological niche models. Ecography 38, 541–545 (2015).

    Article  Google Scholar 

  99. 99.

    Grubbs, F. E. Sample criteria for testing outlying observations. Ann. Math. Statist. 21, 27–58 (1950).

    MathSciNet  MATH  Article  Google Scholar 

  100. 100.

    Komsta, L. outliers: Tests for outliers. R package v.0.14 (2011).

  101. 101.

    Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).

    Article  Google Scholar 

  102. 102.

    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 

  103. 103.

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

  104. 104.

    Friedman, J., Hastie, T. & Tibshirani, R. glmnet: Lasso and elastic-net regularized generalized linear models. R package v.4.0-2 (2020).

  105. 105.

    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 

  106. 106.

    Drake, J. M. Range bagging: a new method for ecological niche modelling from presence-only data. J. R. Soc. Interface 12, 20150086 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  107. 107.

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  108. 108.

    Warton, D. I. & Shepherd, L. C. Poisson point process models solve the “pseudo-absence problem” for presence-only data in ecology. Ann. Appl. Stat. 4, 1383–1402 (2010).

    MathSciNet  MATH  Google Scholar 

  109. 109.

    Renner, I. W. et al. Point process models for presence-only analysis. Methods Ecol. Evol. 6, 366–379 (2015).

    Article  Google Scholar 

  110. 110.

    Dinerstein, E. et al. An ecoregion-based approach to protecting half the terrestrial realm. Bioscience 67, 534–545 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  111. 111.

    Roberts, D. R. et al. Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecography 40, 913–929 (2016).

    Article  Google Scholar 

  112. 112.

    Phillips, S. J. Transferability, sample selection bias and background data in presence-only modelling: a response to Peterson et al. (2007). Ecography 31, 272–278 (2008).

    Article  Google Scholar 

  113. 113.

    Merow, C., Smith, M. J. & Silander, J. A. Jr. 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 

  114. 114.

    Qiao, H. et al. An evaluation of transferability of ecological niche models. Ecography 42, 521–534 (2019).

    Article  Google Scholar 

  115. 115.

    Peterson, A. T., Papeş, M. & Soberón, J. Rethinking receiver operating characteristic analysis applications in ecological niche modeling. Ecol. Modell. 213, 63–72 (2008).

    Article  Google Scholar 

  116. 116.

    Allouche, O., Tsoar, A. & Kadmon, R. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 43, 1223–1232 (2006).

    Article  Google Scholar 

  117. 117.

    Jung, M. et al. Areas of global importance for terrestrial biodiversity, carbon, and water. Preprint at (2020).

  118. 118.

    Carlson, C. J. et al. Climate change will drive novel cross-species viral transmission. Preprint at (2020).

  119. 119.

    BirdLife International. IUCN Red List for Birds (2019).

  120. 120.

    Brooks, T. M. et al. Measuring terrestrial area of habitat (AOH) and its utility for the IUCN Red List. Trends Ecol. Evol. 34, 977–986 (2019).

    PubMed  Article  PubMed Central  Google Scholar 

  121. 121.

    de Area Leão Pereira, E. J., de Santana Ribeiro, L. C., da Silva Freitas, L. F. & de Barros Pereira, H. B. Brazilian policy and agribusiness damage the Amazon rainforest. Land Use Policy 92, 104491 (2020).

    Article  Google Scholar 

  122. 122.

    Garcia, R. T. After Brazil’s summer of fire, the militarization of the Amazon remains. Foreign Policy (19 November 2019).

  123. 123.

    Vicente-Serrano, S. M., Beguería, S. & López-Moreno, J. I. A multiscalar drought index sensitive to global warming: The Standardized Precipitation Evapotranspiration Index. J. Clim. 23, 1696–1718 (2010).

    ADS  Article  Google Scholar 

  124. 124.

    Feldpausch, T. R. et al. Amazon forest response to repeated droughts. Global Biogeochem. Cycles 30, 964–982 (2016).

    ADS  CAS  Article  Google Scholar 

  125. 125.

    Marin, P.-G., Julio, C. J., Arturo, R.-T. D. & Jose, V.-N. D. Drought and spatiotemporal variability of forest fires across Mexico. Chin. Geogr. Sci. 28, 25–37 (2018).

    Article  Google Scholar 

  126. 126.

    Adams, H. D. et al. Temperature sensitivity of drought-induced tree mortality portends increased regional die-off under global-change-type drought. Proc. Natl Acad. Sci. USA 106, 7063–7066 (2009).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

Download references


We acknowledge the herbaria that contributed data to this work: HA, FCO, MFU, UNEX, VDB, ASDM, BPI, BRI, CLF, L, LPB, AD, TAES, FEN, FHO, A, ANSM, BCMEX, RB, TRH, AAH, ACOR, AJOU, UI, AK, ALCB, AKPM, EA, AAU, ALU, AMES, AMNH, AMO, ANA, GH, ARAN, ARM, AS, CICY, ASU, BAI, AUT, B, BA, BAA, BAB, BACP, BAF, BAL, COCA, BARC, BBS, BC, BCN, BCRU, BEREA, BG, BH, BIO, BISH, SEV, BLA, BM, MJG, BOL, CVRD, BOLV, BONN, BOUM, BR, BREM, BRLU, BSB, BUT, C, CAMU, CAN, CANB, CAS, CAY, CBG, CBM, CEN, CEPEC, CESJ, CHR, ENCB, CHRB, CIIDIR, CIMI, CLEMS, COA, COAH, COFC, CP, COL, COLO, CONC, CORD, CPAP, CPUN, CR, CRAI, FURB, CU, CRP, CS, CSU, CTES, CTESN, CUZ, DAO, HB, DAV, DLF, DNA, DS, DUKE, DUSS, E, HUA, EAC, ECU, EIF, EIU, GI, GLM, GMNHJ, K, GOET, GUA, EKY, EMMA, HUAZ, ERA, ESA, F, FAA, FAU, UVIC, FI, GZU, H, FLAS, FLOR, HCIB, FR, FTG, FUEL, G, GB, GDA, HPL, GENT, GEO, HUAA, HUJ, CGE, HAL, HAM, IAC, HAMAB, HAS, HAST, IB, HASU, HBG, IBUG, HBR, IEB, HGI, HIP, IBGE, ICEL, ICN, ILL, SF, NWOSU, HO, HRCB, HRP, HSS, HU, HUAL, HUEFS, HUEM, HUSA, HUT, IAA, HYO, IAN, ILLS, IPRN, FCQ, ABH, BAFC, BBB, INPA, IPA, BO, NAS, INB, INEGI, INM, MW, EAN, IZTA, ISKW, ISC, GAT, IBSC, UCSB, ISU, IZAC, JBAG, JE, SD, JUA, JYV, KIEL, ECON, TOYA, MPN, USF, TALL, RELC, CATA, AQP, KMN, KMNH, KOR, KPM, KSTC, LAGU, UESC, GRA, IBK, KTU, KU, PSU, KYO, LA, LOMA, SUU, UNITEC, NAC, IEA, LAE, LAF, GMDRC, LCR, LD, LE, LEB, LI, LIL, LINN, AV, HUCP, MBML, FAUC, CNH, MACF, CATIE, LTB, LISI, LISU, MEXU, LL, LOJA, LP, LPAG, MGC, LPD, LPS, IRVC, MICH, JOTR, LSU, LBG, WOLL, LTR, MNHN, CDBI, LYJB, LISC, MOL, DBG, AWH, NH, HSC, LMS, MELU, NZFRI, M, MA, UU, UBT, CSUSB, MAF, MAK, MB, KUN, MARY, MASS, MBK, MBM, UCSC, UCS, JBGP, OBI, BESA, LSUM, FULD, MCNS, ICESI, MEL, MEN, TUB, MERL, CGMS, FSU, MG, HIB, TRT, BABY, ETH, YAMA, SCFS, SACT, ER, JCT, JROH, SBBG, SAV, PDD, MIN, SJSU, MISS, PAMP, MNHM, SDSU, BOTU, MPU, MSB, MSC, CANU, SFV, RSA, CNS, JEPS, BKF, MSUN, CIB, VIT, MU, MUB, MVFA, SLPM, MVFQ, PGM, MVJB, MVM, MY, PASA, N, HGM, TAM, BOON, MHA, MARS, COI, CMM, NA, NCSC, ND, NU, NE, NHM, NHMC, NHT, UFMA, NLH, UFRJ, UFRN, UFS, ULS, UNL, US, NMNL, USP, NMR, NMSU, XAL, NSW, ZMT, BRIT, MO, NCU, NY, TEX, U, UNCC, NUM, O, OCLA, CHSC, LINC, CHAS, ODU, OKL, OKLA, CDA, OS, OSA, OSC, OSH, OULU, OXF, P, PACA, PAR, UPS, PE, PEL, SGO, PEUFR, PH, PKDC, SI, PMA, POM, PORT, PR, PRC, TRA, PRE, PY, QMEX, QCA, TROM, QCNE, QRS, UH, R, REG, RFA, RIOC, RM, RNG, RYU, S, SALA, SANT, SAPS, SASK, SBT, SEL, SING, SIU, SJRP, SMDB, SNM, SOM, SP, SRFA, SPF, STL, STU, SUVA, SVG, SZU, TAI, TAIF, TAMU, TAN, TEF, TENN, TEPB, TI, TKPM, TNS, TO, TU, TULS, UADY, UAM, UAS, UB, UC, UCR, UEC, UFG, UFMT, UFP, UGDA, UJAT, ULM, UME, UMO, UNA, UNM, UNR, UNSL, UPCB, UPNA, USAS, USJ, USM, USNC, USZ, UT, UTC, UTEP, UV, VAL, VEN, VMSL, VT, W, WAG, WII, WELT, WIS, WMNH, WS, WTU, WU, Z, ZSS, ZT, CUVC, AAS, AFS, BHCB, CHAM, FM, PERTH and SAN. X.F., D.S.P., E.A.N., A.L. and J.R.B. were supported by the University of Arizona Bridging Biodiversity and Conservation Science program. Z.L. was supported by NSFC (41922006) and K. C. Wong Education Foundation. The BIEN working group was supported by the National Center for Ecological Analysis and Synthesis, a centre funded by NSF EF-0553768 at the University of California, Santa Barbara, and the State of California. Additional support for the BIEN working group was provided by iPlant/Cyverse via NSF DBI-0735191. B.J.E., B.M. and C.M. were supported by NSF ABI-1565118. B.J.E. and C.M. were supported by NSF ABI-1565118 and NSF HDR-1934790. B.J.E., L.H. and P.R.R. were supported by the Global Environment Facility SPARC project grant (GEF-5810). D.D.B. was supported in part by NSF DEB-1824796 and NSF DEB-1550686. S.R.S. was supported by NSF DEB-1754803. X.F. and A.L. were partly supported by NSF DEB-1824796. B.J.E. and D.M.N. were supported by NSF DEB-1556651. M.M.P. is supported by the São Paulo Research Foundation (FAPESP), grant 2019/25478-7. D.M.N. was supported by Instituto Serrapilheira/Brazil (Serra-1912-32082). E.I.N. was supported by NSF HDR-1934712. We thank L. López-Hoffman and L. Baldwin for constructive comments.

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X.F. conceived the idea, which was refined by discussion with D.S.P., C.M., B.M., P.R.R., E.A.N., B.L.B., A.L., J.R.B., D.D.B., J.R.S., K.C.E. and B.J.E.; X.F. and Z.L. processed the remote-sensing data; C.M., X.F., B.M., B.L.B., D.S.P. and B.J.E. conducted the analyses of plant data; P.R.R., C.M., B.M., X.F. and D.S.P. conducted the analyses of vertebrate data; X.F., C.M., S.R.S. and E.A.N. processed the drought data; D.S.P., X.F., C.M., P.R.R. and B.M. designed the illustrations with help from B.J.E., D.D.B., K.C.E. and E.A.N.; E.A.N., X.F., and D.S.P. conducted the statistical analyses with help from B.J.E.; X.F., B.J.E., B.M., A.L., J.R.B., D.S.P., C.M., E.A.N., Z.L. and P.R.R. wrote the original draft; all authors contributed to interpreting the results and the editing of manuscript drafts. B.J.E., C.M., K.C.E. and D.D.B. led to the acquisition of the financial support for the project. X.F., C.M., B.M., D.S.P., P.R.R., Z.L., E.A.N. and B.J.E. contributed equally to data, analyses and writing.

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Correspondence to Xiao Feng.

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Extended data figures and tables

Extended Data Fig. 1 Fire-impacted forest and forest loss in the Amazon Basin.

ah, Visualization of fire-impacted forest (a, b), forest loss without fire (c, d), fire-impacted forest with forest loss (e, f), and fire-impacted forest without forest loss (g, h) in the Amazon Basin based on MODIS burned area (left panels) and active fire (right panels). Data in ad are resampled from the 500m (MODIS burned area) or 1 km (MODIS active fire) to 10 km resolution using mean function and thresholded at 0.01 to illustrate the temporal dynamics. Black represents non-forested areas masked out from this study. The cumulative fire-impacted forest is classified into two categories: fire-impacted forest with forest loss (e, f) and fire-impacted forest without forest loss (g, h). Data in eh are resampled to 10 km using mean function to illustrate the cumulative percentages of impacts.

Extended Data Fig. 2 Scatter plot of species’ range impacted by fire.

Scatter plot of species’ range size in Amazon forest (x-axis) and percentage of total range impacted by fire (red) and forest loss without fire (black) up to 2019 for plants (left panel) and vertebrates (right panel).

Extended Data Fig. 3 Density plot of species’ cumulative range impacted by fire.

Density plot of species’ cumulative range impacted by fire. The different colours represent years 2001-2019. The x-axis is log10 transformed.

Extended Data Fig. 4 Summary of forest impacts in the Amazon Basin.

Areas of forest impact in the Amazon Basin estimated from MODIS burned area (top) and MODIS active fire (bottom).

Extended Data Fig. 5 Cumulative impacts on biodiversity in the Amazon Basin.

Cumulative effects of forest loss without fire on biodiversity in the Amazon rainforest. In the left panels, the black and grey shading represent the cumulative forest loss without fire based on MODIS burned area and MODIS active fire, respectively. Coloured areas represent the lower and upper bounds of cumulative numbers of a, plant and c, vertebrate species’ ranges impacted. Right panels depict the relationships between the cumulative forest loss without fire (based on MODIS burned area) and cumulative number of b, plant and d, vertebrate species. Coloured lines represent predicted values of an ordinary least squares linear regression and grey bands define the two-sided 95% confidence interval (two-sided, p values = 0.00). The silhouette of the tree is from; silhouette of the monkey is courtesy of Mathias M. Pires.

Extended Data Fig. 6 Fire-impacted forest in Brazil.

Newly fire-impacted forest in Brazil (based on MODIS active fire). a shows the area of fire-impacted forest not explained by drought conditions. Different colours represent years from different policy regimes: pre-regulations in light red (mean value in dark red), regulation in grey (mean value in black dashed line), and 2019 in blue. The y-axis represents the difference between actual area and area predicted by drought conditions calibrated by data from regulation years (Methods). A positive value on the y-axis represents more area than expected, using the regulation years as a baseline. b shows a scatter plot of newly fire-impacted forest in Brazil and drought conditions (SPEI); The lines represent the ordinary least squares linear regression between fire-impacted forest and drought conditions for pre-regulation (red) and regulation (black) respectively.

Extended Data Fig. 7 Fire-impacted forest in different countries.

The contribution (0–1) of different countries to the newly fire-impacted forest each year based on MODIS active fire (top) and MODIS burned area (bottom).

Extended Data Figure 8 Impacts of fire on forest and biodiversity in Brazil.

a, Newly fire-impacted forest, b, new range impact on plants and c, new range impacts on vertebrate species in Brazil each year (based on MODIS active fire) that are not predicted by drought conditions. The colours represent three policy regimes: pre-regulation in red, regulation in grey and 2019 in blue. The y-axis represents the difference between actual value (area or range impacted by fire) and the values predicted by drought conditions calibrated by data from regulation years (Methods). A positive value on the y-axis represents more area or range impacted by fire than the expectation using the regulation years as a baseline. The dotted lines represent a smooth curve fitted to the values based on the loess method.

Extended Data Table 1 Summary of fire-impacted forest
Extended Data Table 2 Summary of regression analyses

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Feng, X., Merow, C., Liu, Z. et al. How deregulation, drought and increasing fire impact Amazonian biodiversity. Nature 597, 516–521 (2021).

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