Proc Natl Acad Sci U S A. 2025 Aug 5;122(31):e2410934122. doi: 10.1073/pnas.2410934122. Epub 2025 Jul 28.
ABSTRACT
Reticulate evolution has long been recognized as a key mechanism that contributes to genetic and trait diversity. With the widespread availability of genomic data, investigating historical reticulate evolution across taxa has gained significant attention, driven by the rapid development of statistical methods for detecting nontreelike patterns. Phylogenetic networks provide a biologically intuitive approach to depicting evolutionary processes such as hybrid speciation and introgressive hybridization, which result in signatures of historical gene flow. Interpreting phylogenetic networks is especially critical for groups of conservation concern that lack reference genome resources and explicit hypotheses from prior investigation, such as those based on molecular data, morphology, or species distributions. Here, we highlight recent advances in computational methods for inferring networks from genome-scale data and offer guidelines for deriving biological insights from phylogenetic networks. Particular emphasis is placed on modeling hybridization and whole-genome duplication in the context of allopolyploidization. Practical recommendations for empirical studies and the limitations of commonly used methods are discussed throughout. We anticipate that phylogenetic networks will influence conservation biology and biodiversity research, emphasizing the need for careful consideration of reticulate evolution inferred from these networks in the near future. Networks will accelerate other pressing avenues of biodiversity research, especially investigations of orphan crops and climate change resilience in natural systems. The promise of phylogenetic networks connects with broader themes in the special feature Monitoring and restoring gene flow in the increasingly fragmented ecosystems of the Anthropocene by providing an emerging probabilistic framework for inferring historical connectivity between species and populations.
PMID:40720655 | DOI:10.1073/pnas.2410934122
