The genetic and phenotype architecture of speciation and diversification

• Grant number: 15/26619-2
• Support Opportunities: Scholarships abroad - Research Internship - Post-doctor
• Start date: April 30, 2016
• End date: October 29, 2016
• Field of knowledge: Physical Sciences and Mathematics - Physics
• Principal Investigator: Marcus Aloizio Martinez de Aguiar
• Grantee: Ayana de Brito Martins
• Supervisor: Carlos Javier Melian Peñate
• Host Institution: Instituto de Física Gleb Wataghin (IFGW). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
• Institution abroad: EAWAG - Swiss Federal Institute for Aquatic Science and Technology, Kastanienbaum, Switzerland
• Associated to the scholarship: 14/10470-7 - The effect of the genetic architecture of reproductive isolation on speciation, BP.PD

Abstract

The origin of new species has long been a central topic in evolutionary biology. Recently, due to technical advances, the genetic basis of the traits involved in reproductive isolation is being uncovered and the research on speciation has entered a new era. This development calls for new speciation models that deal explicitly with the relationship between the phenotypes that are important to speciation and their genotypes. Here we propose a generalization of the model already being developed during the candidate's postdoctoral project. Taking advantage of the empirical research being carried out in the research center where the proposed internship should take place, we will focus on investigating how speciation is affected by the genetic architecture of traits acting as cues for mating by phenotype matching. In this context, we will explore both genotype-phenotype associations that incorporate patterns commonly found in nature and that relate to well-known evolutionary models. As a result, our approach should be relevant to 1) synthesize different modeling approaches into a general and testable theory, 2) build novel models informed by new experimental and analytical data, and 3) infer from the models the mechanisms that best predict the observed data.