Thông tin tài nguyên

New records and modelling the impacts of climate change on the black-tailed marmosets

pmc-PMC8423304.pdf · Phiên bản 1 · 14 trang

18
Bản gốc
Trang 13 · Đoạn 32Vị trí nguồn

20. Prieto-Torres DA, Nori J, Rojas-Soto OR. Identifying priority conservation areas for birds associated to endangered Neotropical dry forests. Biol Conserv. 2018; 228: 205–214. https://doi.org/10.1016/j. biocon.2018.10.025 21. Martı´nez-Meyer E, Peterson AT, Servı´n JI, Kiff LF. Ecological niche modelling and prioritizing areas for species reintroductions. Oryx. 2006; 40: 411–418. https://doi.org/10.1017/S0030605306001360 22. Projeto RadamBrasil. Geologia, geomorfologia, pedologia, vegetac¸ão e uso potencial da terra. Depar- tamento Nacional de Produc¸ão Mineral. Brası´lia, Distrito Federal. 1978; 1–34. 23. Alvares CA, Stape JL, Sentelhas PC, Gonc¸alves JLM. Sparovek G. 2014. Ko¨ppen’s climate classifica- tion map for Brazil. Meteorol. Z. 2014; 22:711–728. https://doi.org/10.1127/0941-2948/2013/0507 24. Ferrari SF, Sena L, Schneider MPC, Silva-Ju´ nior JS. Rondon’s marmoset, Mico rondoni sp. n., from Southwestern Brazilian Amazonia. International Journal of Primatology. 2010; 31:693–714. https://doi. org/10.1007/s10764-010-9422-6 25. Gusmão AC, Souza LS, Costa TM, Velten T, Santos-Filho M, Ferronato M. Formac¸ão de grupo misto entre Mico rondoni (Ferrari et al., 2010), Saguinus weddelli (Devile, 1849) e M. melanurus (E´ Geoffroy, 1812) na Resex do Rio Cauta´rio. Rondoˆnia, Brasil. Neotropical Primates. 2015; 22(1):50–52. 26. Ferrari S. F. A fauna de mamı´feros não voadores da Reserva Biolo´ gica Trac¸adal-RO. Relato´ rio Te´ c- nico não publicado, Governo do estado de Rondoˆnia. Porto Velho, DC. 2001.

Trang 13 · Đoạn 33Vị trí nguồn

27. Oliveira R. Beta diversidade de mamı´feros na bacia do rio Guapore´ , Amazoˆ nia, sudoeste de Mato Grosso, Brasil. Dissertac¸ão de mestrado. Universidade do Estado de Mato Grosso. Ca´ceres, Mato Grosso, Brasil. 2013. 28. Garbino GST. Terra. The taxonomic status of Mico marcai (Alperin 1993) and Mico manicorensis (van Roosmalen et al. 2000) (Cebidae, Callitrichinae) from southwestern Brazilian Amazonia. International Journal of Primatology, 2014, 35.2: 529–546. https://doi.org/10.1007/s10764-014-9766-4 29. Barve N, Barve V, Jime´nez-Valverde A, Lira-Noriega A, Maher SP, Peterson AT, et al. The crucial role of the accessible area in ecological niche modeling and species distribution modeling. Ecol Modell. 2011; 222: 1810–1819. https://doi.org/10.1016/j.ecolmodel.2011.02.011 30. Cooper JC, Sobero´n J. Creating individual accessible area hypotheses improves stacked species distri- bution model performance. Glob Ecol Biogeogr. 2018; 27: 156–165. https://doi.org/10.1111/geb.12678 31. Olson DM, Dinerstein E, Wikramanayake ED, Burgess ND, Powell GVN, Underwood EC, et al. Terres- trial Ecoregions of the World: A New Map of Life on Earth. Bioscience. 2001; 51: 933. https://doi.org/10. 1641/0006-3568(2001)051[0933:TEOTWA]2.0.CO;2 32. Fick SE, Hijmans RJ. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int J Climatol. 2017; 37: 4302–4315. https://doi.org/10.1002/joc.5086 33. Barve N, Barve V. ENMGadgets: Tools for Pre and Post Processing in ENM Workflow. 2018. Zwiener

Trang 13 · Đoạn 34Vị trí nguồn

VP, Lira-Noriega A, Grady CJ, Padial AA, Vitule JR. Climate change as a driver of biotic homogeniza- tion of woody plants in the Atlantic Forest. Glob Ecol Biogeogr. 2018; 27:298–309. https://doi.org/10. 1111/geb.12695 34. Resende G, Sobral-Souza T., Culot L. Integrating climate and landscape models to prioritize areas and conservation strategies for an endangered arboreal. A. J. Primatology. 2020;1–9. https://doi.org/10. 1002/ajp.23202 PMID: 33040387 35. Zuquim G, Costa FRC, Tuomisto H, Moulatlet GM. The importance of soils in predicting the future of plant habitat suitability in a tropical forest. Plant Soil. 2019;1–20. https://doi.org/10.1007/s11104-018-03915-9 36. Velazco SJE, Galvão F, Villalobos F, De Marco Ju´ nior P. Using worldwide edaphic data to model plant species niches: An assessment at a continental extent. PLoS One. 2017; 12.10: e0186025. https://doi. org/10.1371/journal.pone.0186025 PMID: 29049298 37. Figueiredo FOG, Zuquim G, Tuomisto H, Balslev H, Costa FR. Beyond climate control on species range: The importance of soil data to predict distribution of Amazonian plant species. J Biogeogr. 2018; 45:190–200. https://doi.org/10.1111/jbi.13104 38. Thuiller W, Georges D, Engler R, Breiner F. biomod2: Ensemble Platform for Species Distribution Modeling. 2019. 39. Allouche O, Tsoar A, Kadmon R. Assessing the accuracy of species distribution models: Prevalence, kappa and the true skill statistic (TSS). J Appl Ecol. 2006; 43:1223–1232. https://doi.org/10.1111/j. 1365-2664.2006.01214.x

Trang 13 · Đoạn 35Vị trí nguồn

40. Pearson RG, Raxworthy C, Nakamura M, Peterson AT. Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. J. of Bioeography. 2007; 34(1):102–117. https://doi.org/10.1111/j.1365-2699.2006.01594.x 41. Barbet-Massin M, Jiguet F, Albert CH, Thuiller W. Selecting pseudo-absences for species distribution models: How, where and how many? Methods Ecol Evol. 2012; 3:327–338. https://doi.org/10.1111/j. 2041-210X.2011.00172.x PLOS ONE Modeling of Mico nigriceps PLOS ONE | https://doi.org/10.1371/journal.pone.0256270 September 7, 2021 13 / 14