Comparative phylogeography reveals the demographic patterns of neotropical ancient mountain species

Molecular Ecology - Tập 32 Số 12 - Trang 3165-3181 - 2023
Marcos Vinícius Dantas-Queiroz1,2, Fernanda Hurbath3, Fernanda Maria de Russo Godoy4, Flávia M. Lanna5, Leonardo M. Versieux6, Clarisse Palma‐Silva7,7
1Department of Evolutionary Plant Biology Institute of Botany of the Czech Academy of Sciences Zámek 1 Průhonice Czech Republic
2Programa de Pós‐Graduação em Biologia Vegetal Universidade Estadual Paulista (UNESP) São Paulo Brazil
3Universidade do Estado de Minas Gerais – Unidade Passos, Av. Juca Stockler 1130, bairro Belo Horizonte Passos Brazil
4Programa de Pós‐Graduação em Biotecnologia e Biodiversidade, Faculdade de Ciências Farmacêuticas, Alimentos e Nutrição Universidade Federal de Mato Grosso do Sul Campo Grande Brazil
5Department of Evolution, Ecology and Organismal Biology. Museum of Biological Diversity, The Ohio State University, Columbus, Ohio, USA
6Departamento de Botânica e Zoologia, Centro de Biociências, Universidade Federal do Rio Grande do Norte, Natal, Brazil
7Departamento de Biologia Vegetal, Instituto de Biologia, Universidade Estadual de Campinas (UNICAMP), Campinas, Brazil

Tóm tắt

AbstractMountains are renowned for their bountiful biodiversity. Explanations on the origin of such abundant life are usually regarded to their orogenic history. However, ancient mountain systems with geological stability also exhibit astounding levels of number of species and endemism, as illustrated by the Brazilian Quartzitic Mountains (BQM) in Eastern South America. Thus, cycles of climatic changes over the last couple million years are usually assumed to play an important role in the origin of mountainous biota. These climatic oscillations potentially isolated and reconnected adjacent populations, a phenomenon known as flickering connectivity, accelerating speciation events due to range fragmentation, dispersion, secondary contact, and hybridization. To evaluate the role of the climatic fluctuations on the diversification of the BQM biota, we estimated the ancient demography of distinct endemic species of animals and plants using hierarchical approximate Bayesian computation analysis and Ecological Niche Modelling. Additionally, we evaluated if climatic oscillations have driven a genetic spatial congruence in the genetic structure of codistributed species from the Espinhaço Range, one of the main BQM areas. Our results show that the majority of plant lineages underwent a synchronous expansion over the Last Glacial Maximum (LGM, c. 21 thousand years ago), although we could not obtain a clear demographic pattern for the animal lineages. We also obtained a signal of a congruent phylogeographic break between lineages endemic to the Espinhaço Range, suggesting how ancient climatic oscillations might have driven the evolutionary history of the Espinhaço's biota.

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Tài liệu tham khảo

10.1111/j.1365‐2664.2006.01214.x

10.1093/molbev/msl170

10.7717/peerj.5644

10.1038/s41561‐018‐0236‐z

10.1073/pnas.0811421106

10.1111/j.1095‐8312.2010.01438.x

10.1111/j.1365-294X.2005.02655.x

10.1016/S0895‐9811(00)00022‐5

10.1007/978-3-319-29808-5_22

10.1093/botlinnean/boy080

10.1146/annurev‐ecolsys‐110617‐062431

10.1016/j.flora.2015.11.001

10.1016/j.ppees.2022.125700

10.1111/mec.16210

10.1111/mec.12780

10.1111/2041‐210X.13901

10.1023/A:1010933404324

10.1007/s12229‐019‐09216‐9

10.1111/jbi.14035

10.1111/j.1365‐2699.2007.01870.x

10.1007/BF00051966

10.1093/molbev/msu187

10.1080/14772000.2014.972477

Chaves M. L. S. C., 2004, Depósitos superficiais diamantíferos da região de Diamantina, Serra do Espinhaço (Minas Gerais), Geosciences, 23, 31

10.1002/ece3.236

10.1093/aob/mcp157

10.3389/fgene.2015.00031

10.1111/jbi.13585

10.1007/978-3-319-29808-5_6

10.1002/jqs.3459

10.1600/036364422X16512564801696

10.1111/j.2041‐210X.2011.00179.x

10.1111/jbi.14154

10.1038/nmeth.2109

10.1016/j.ympev.2021.107113

10.1111/j.1365‐2699.2007.01732.x

10.1016/j.ympev.2008.09.009

10.1186/1471‐2148‐7‐214

10.1016/j.jtbi.2019.110087

10.1016/j.flora.2011.04.003

10.1093/gbe/evab176

10.1016/B978-0-08-100524-8.00003-8

10.1007/s10682‐008‐9286‐9

10.1016/S0304‐3800(02)00327‐7

10.1111/evo.13476

10.1111/j.1759‐6831.2009.00046.x

10.1093/botlinnean/boz051

Flantua S. G. A., 2018, Mountains, climate and biodiversity, 171

10.1111/jbi.13607

10.1111/1755‐0998.13427

10.1093/sysbio/syt033

GBIF.org. (2022).GBIF Home Page. Retrieved March 1 2020 fromhttps://www.gbif.org

10.1111/mec.14239

Giulietti A. M. &Pirani J. R.(1987).Patterns of geographic distribution of some plant species from the Espinhaço range Minas Gerais and Bahia. In. P. E. Vanzolini & W. R. Heyer (Eds) Proceedings of a workshop on neotropical distribution patterns 1.

10.1111/j.1420‐9101.2009.01718.x

Hijmans R. J.(2022).Raster: Geographic data analysis and modeling. Retrieved fromhttps://CRAN.R‐project.org/package=raster

Hijmans R. J. Phillips S. Leathwick J. &Elith J.(2022).dismo: Species Distribution Modeling. Retrieved fromhttps://CRAN.R‐project.org/package=dismo

10.1002/jqs.3209

10.1016/j.palaeo.2015.07.027

Hornik K., 2004, kernlab—An S4 package for kernel methods in R, Journal of Statistical Software, 11, 1

10.1111/nph.13230

10.1038/sdata.2017.122

10.1046/j.1365‐294X.2002.01410.x

10.1111/jbi.12860

10.1007/s00035‐016‐0182‐6

10.1111/nph.13920

10.1016/j.tree.2013.09.009

10.1093/bioinformatics/btq539

10.1111/boj.12437

10.1080/07352689.2016.1254494

10.1038/s41576‐021‐00394‐0

10.1073/pnas.1513062113

10.1186/s12862‐018‐1236‐8

Liaw A., 2002, Classification and regression by randomForest, R News, 2, 18

10.1093/biolinnean/blaa142

10.1145/2016741.2016785

10.1038/s41437‐020‐0342‐8

10.1111/plb.12909

10.3389/fpls.2019.00195

10.1111/jbi.13715

10.1371/journal.pone.0206732

10.1016/j.flora.2017.03.011

Nix H. A., 1986, A biogeographic analysis of Australian elapid snakes, Atlas of Elapid Snakes of Australia, 7, 4

10.1111/jbi.13500

10.1371/journal.pone.0056283

10.1111/mec.15548

10.1111/jbi.14320

10.1126/science.1120808

10.1038/hdy.2009.116

10.1111/j.1365‐294X.2011.05143.x

10.1111/j.0014‐3820.2003.tb00307.x

10.1073/pnas.1601069113

10.1111/mec.12722

10.1038/hdy.2016.17

10.1111/1755‐0998.13534

10.1111/jbi.13731

10.1111/mec.14156

10.1080/07352689.2018.1471565

10.1073/pnas.1601063113

QGIS Development Team. (2022).QGIS Geographic Information System. Open Source Geospatial Foundation. Retrieved fromhttp://qgis.org

R Core Team. (2019).R: A language and environment for statistical computing. R Foundation for Statistical Computing. Retrieved fromhttps://www.R‐project.org/

10.1093/sysbio/syy032

10.1093/biolinnean/blaa179

10.1111/mec.15165

10.1186/1471‐2148‐12‐196

Revelle W., 2022, Psych: Procedures for psychological, psychometric, and personality research

10.12705/636.16

10.1111/j.1365‐2699.2005.01252.x

10.1016/B978-0-12-815591-2.00002-1

10.3389/fpls.2022.881879

10.1111/evo.12924

10.1007/978-3-319-29808-5_3

10.1111/nph.13234

10.1111/j.1095‐8312.2011.01656.x

10.1016/j.jsames.2020.102728

10.1007/s11104‐015‐2637‐8

10.1371/journal.pone.0025628

10.1111/j.1365‐294X.2006.03061.x

10.1023/B:MACH.0000008084.60811.49

10.1016/j.ympev.2020.106812

10.1073/pnas.1601064113

10.1111/mec.12164

10.1098/rspb.2019.2933

10.21425/F5FBG45377

10.3389/fpls.2017.02141