A Statistical Approach to Understand the Evolution of Exotic Butterfly Species

Abstract: The alarming rate at which we see the decline in biodiversity due to human activity has raised concerns about the well-being of our planet. Butterflies which serve as pollinators are an essential part of many ecosystems and sensitive indicators of environmental changes and can provide valuable insight into how ecosystems function and evolve. This thesis aims to create phylogenetic trees based on DNA sequences from butterflies and compare different nucleotide substitution models and methods in order to better understand butterflies' evolution and genetic relationships. Our approach was to use Markov theory to investigate how the four nucleotides are evolving. In regard to the comparison of models, the General Time Reversible model with more degrees of freedom was found to be better than the K80 model. Although the Maximum Likelihood and Pairwise Distance methods were found to have different transition rate matrices, the tree reconstructions had no registered differences. Interestingly, the Q matrix was found to be similar across butterfly families. These findings can suggest that it is possible to have a standard Q matrix when estimating or inferring evolutionary relationships among butterflies, and probably other animal groups. This should improve the accuracy of estimations within phylogenetics when dealing with small data sets. The information helps with reconstructing evolutionary relationships and species, therefore contributing to preserving biodiversity and thereby the ecosystems to whom they belong - and in addition humankind.