Date of Award
12-2025
Document Type
Thesis
Degree Name
Master of Arts in Mathematics
Department
Mathematics
First Reader/Committee Chair
Hector Banos Cervantes
Abstract
Inferring phylogenies in the presence of hybridization remains a difficult problem. As a result, many current methods for reconstructing phylogenetic networks are restricted to a simple class of networks known as level-1. This restriction arises from theoretical considerations rather than empirical evidence, with real data possibly arising from complex networks. In this work, we evaluate the robustness of two level-1 network inference methods, SNaQ and NANUQ+, through a simulation study, when the input data originates from a more complex network. Specifically, we investigate whether these methods can accurately recover important features of the true species network, such as the circular order (the arrangement of taxa around a network), taxa of hybrid origin (taxa that arose from a hybridization event), and other structural properties. Our results show that while both methods are accurate in recovering the circular order of the network, they lack in other network features such as determining which taxa are product a hybridization event. These results highlight the strengths and limitations of current level-1 methods under model misspecification, guiding the interpretation of their outputs in practice.
Recommended Citation
Ceja, Roberto, "Robustness of Network Inference Algorithms under Network Class Misspecification" (2025). Electronic Theses, Projects, and Dissertations. 2336.
https://scholarworks.lib.csusb.edu/etd/2336