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Phylogeny, biogeography and trait evolution of Melocactus link & Otto and Discocactus Pfeiff. genera (Cactaceae)

Grant number: 19/11233-2
Support type:Scholarships in Brazil - Doctorate (Direct)
Effective date (Start): November 01, 2019
Effective date (End): February 28, 2023
Field of knowledge:Biological Sciences - Genetics - Plant Genetics
Principal Investigator:Evandro Marsola de Moraes
Grantee:Milena Cardoso Telhe
Home Institution: Centro de Ciências Humanas e Biológicas (CCHB). Universidade Federal de São Carlos (UFSCAR). Sorocaba , SP, Brazil
Associated research grant:18/03428-5 - Dimensions US-BIOTA-Sao Paulo: traits as predictors of adaptive diversification along the Brazilian Dry Diagonal, AP.BTA.TEM

Abstract

The Neotropical region is well known due to its species richness, although how this species richness was established and is maintained is poorly understood. Biotics interchanges were identified in large numbers in the Neotropical regions, indicating a key role for the establishment of regional biota. However, studies about biotic interchange changing the diversification rate are scare in the Neotropical region, specially between dry and open environments. Recent studies identified dispersal and establishment of taxa between Caatinga, Cerrado and Chaco biomes, which comprises the Dry Diagonal of South America. Some hypotheses have been proposed to explain the evolutionary consequences of the connection between these biomes, however few studies were dedicated to test them. The aim of this project is to investigate the biogeographic and demographic events of the genus Melocactus and Discocactus. Because these genera occurs predominantly in Caatinga and Cerrado, these analyses will contribute to the understanding of the events that drove the diversification of the Dry Diagonal of South America. Numerous orthologous regions will be obtained using the sequence capture method and will be used to make phylogenetic, biogeographic and diversification inferences. The results obtained in these analyses will be used, with biotic and abiotic variables, to test the association of theses variables and the diversification rate changes within the clades, using the machine learning methods. (AU)