Abstract Reticulate processes such as hybridization, introgression, and horizontal gene transfer cannot be fully represented by a bifurcating tree. Enter phylogenetic networks: first as split graphs to visualize tree discordance, then as explicit probabilistic models that capture biological phenomena. Here, we describe the broad taxonomy of network representations, distinguishing the principal classes of explicit networks, their biological interpretability and our ability to accurately estimate them from empirical data. We also trace the evolution of the main network inferential methods from hybrid detection tests, distance- and subgraph-based amalgamation methods, probabilistic approaches under the multispecies network coalescent, composite-likelihood and divide-and-conquer frameworks, while highlighting the selective pressures of statistical identifiability and computational scalability that have shaped this evolution. As we move towards a network thinking paradigm, previously isolated methodological lineages from population genetics, phylogenomics, and mathematical network theory are now introgressing, uniting diverse network models into a shared framework that can integrate sequence- and species-level reticulate processes, increase robustness to systematic errors, and refine algorithms for genome-scale data, expanding the tree of life into a richer, more entangled yet clearer picture of evolution.
Solís-Lemus Claudia (Wed,) studied this question.