Climate change and bird extinctions in the Amazon

59. R CORE TEAM. R: A language and environment for statistical computing. R J Version 3.4.0 [software]. Vienna, Austria. 2017. [accessed September 16, 2019]. Available in: https://www.R-project.org/. 60. IPCC. Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, R.K. Pachauri and L.A. Meyer (eds.)]. IPCC, Geneva, Switzerland, 2014, 151 pp. 61. Rocchini D, Hortal J, Lengyel S, Lobo JM, Jime´ nez-Valverde A, Ricotta C, et al. Accounting for uncer- tainty when mapping species distributions: The need for maps of ignorance. Prog Phys Geogr. 2011; 35: 211–226. https://doi.org/10.1177/0309133311399491 62. Phillips SJ, Dudı´k M. Modeling of species distributions with Maxent: new extensions and a comprehen- sive evaluation. Ecography (Cop). 2008; 31: 161–175. https://doi.org/10.1111/j.2007.0906–7590. 05203.x 63. Tax DMJ, Duin RPW. Support Vector Data Description. Machine Learning. 2004; 54: 45–66. 64. Breiman L. Random forests. Machine Learning. 2001; 45:5–32. 65. Zaniewski AE, Lehmann A, Overton JMC. Predicting species spatial distributions using presence-only data: A case study of native New Zealand ferns. Ecol Modell. 2002; 157: 261–280. https://doi.org/10. 1016/S0304-3800(02)00199-0 66. Buckland ST, Elston DA. Empirical Models for the Spatial Distribution of Wildlife. J Appl Ecol. 1993; 30: 478. https://doi.org/10.2307/2404188 67. Andrade AFA de, Velazco SJE, De Marco Ju´nior P. ENMTML: An R package for a straightforward con-

struction of complex ecological niche models. Environ Model Softw. 2020; 125: 104615. https://doi. org/10.1016/j.envsoft.2019.104615 68. Sørensen T. A Method of Establishing Groups of Equal Amplitude in Plant Sociology Based on Similar- ity of Species Content and its Application to Analyses of the Vegetation on Danish Commons. Det K Danske Vidensk Selsk Biol Skr. 1948; 5: 1–34. Available: http://www.tandfonline.com/doi/full/10. 3109/00016924809131205 69. Leroy B, Delsol R, Hugueny B, Meynard CN, Barhoumi C, Barbet-Massin M, et al. Without quality presence–absence data, discrimination metrics such as TSS can be misleading measures of model performance. J Biogeogr. 2018; 45: 1994–2002. https://doi.org/10.1111/jbi.13402 70. Allouche O, Steinitz O, Rotem D, Rosenfeld A, Kadmon R. Incorporating distance constraints into spe- cies distribution models. J Appl Ecol. 2008; 45: 599–609. https://doi.org/10.1111/j.1365-2664.2007. 01445.x 71. De Marco P, No´brega CC. Evaluating collinearity effects on species distribution models: An approach based on virtual species simulation. PLoS One. 2018; 13: 020240. https://doi.org/10.1371/journal. pone.0202403 PMID: 30204749 72. Davis JC. Statistics and Data Analysis in Geology, 2nd edn. John Wiley and Sons, New York, NY, 1986 73. Neme´sio A, Silva DP, Nabout JC, Varela S. Effects of climate change and habitat loss on a forest- dependent bee species in a tropical fragmented landscape. Insect Conserv Divers. 2016; 9: 149–160. https://doi.org/10.1111/icad.12154

74. Guisan A, Theurillat JP. Equilibrium modeling of alpine plant distribution: How far can we go? Phyto- coenologia. 2000; 30: 353–384. https://doi.org/10.1127/phyto/30/2000/353 75. Wisz MS, Hijmans RJ, Li J, Peterson AT, Graham CH, Guisan A, et al. Effects of sample size on the performance of species distribution models. Divers Distrib. 2008; 14: 763–773. https://doi.org/10. 1111/j.1472-4642.2008.00482.x 76. Distler T, Schuetz JG, Vela´squez-Tibata´ J, Langham GM. Stacked species distribution models and macroecological models provide congruent projections of avian species richness under climate change. J Biogeogr. 2015; 42: 976–988. https://doi.org/10.1111/jbi.12479 77. ICMBio. Aplicac¸ão de Crite´ rios e Categorias da UICN na Avaliac¸ão da Fauna Brasileira. Version2.0. Brası´lia, DF–Ministe´rio do Meio Ambiente. 2013. 78. Bland LM, Keith DA, Miller RM, Murray NJ, Rodrı´guez J.P. (eds.). Guidelines for the application of IUCN Red List of Ecosystems Categories and Criteria, Version 1.0. Gland, Switzerland: IUCN. 2016. ix + 94pp. 79. Powers RP, Jetz W. Global habitat loss and extinction risk of terrestrial vertebrates under future land- use-change scenarios. Nat Clim Chang. 2019; 9:323–9. https://doi.org/10.1038/s41558-019-0406-z 80. Hanski I. Habitat Loss, the Dynamics of Biodiversity, and a Perspective on Conservation. Ambio. 2011; 40: 248–255. https://doi.org/10.1007/s13280-011-0147-3 PMID: 21644453 81. Keil P, Storch D, Jetz W. On the decline of biodiversity due to area loss. Nat Commun. 2015; 6: 1–11. https://doi.org/10.1038/ncomms9837 PMID: 26575347 PLOS ONE

Bird extinctions in the Amazon PLOS ONE | https://doi.org/10.1371/journal.pone.0236103 July 17, 2020 16 / 18