Using big data to understand evolutionary patterns in Geometridae and Erebidae, the two most diverse families of Lepidoptera

Lepidoptera (moths and butterflies) are one of the most diverse groups of organisms on earth. They have conquered all the continents apart from Antarctica. The reasons of such high diversity are still not clear. One of the first steps to study the causes of such evolutionary success is to have a clear idea of their phylogenetic relationships. In this thesis I focus on the diversity of two of the most diverse families of Lepidoptera, Geometridae with over 23,000 described species and Erebidae with over 24,000 species. In the case of Geometridae I focus in obtaining a robust phylogenetic hypothesis and then study the diversification dynamics and the biogeographical history which have shaped their actual diversity and distribution. In this project I used the most complete dataset of the family in order to study their evolutionary patterns. In the case of the Erebidae family, obtaining a robust phylogenetic hypothesis was more challenging. In the most complete study of the group up to date, using a multi locus Sanger based approach, it was not possible to resolve the deep phylogeny of the family. Therefore, I used high throughput sequencing (HTS) approaches to resolve the complex deep phylogenetic history of the group. In this project I used old DNA extracts of over 10 years old to explore the possibility of using this genetic resource for genomic studies. In addition, I evaluated the accuracy and range of resolution of the mitochondrial genomes in this family. And finally, I explored the alternative possibilities which the HTS approaches offer us to study the presence of symbiotic interactions using genomic data.