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Inference of network-like evolutionary relationships between species fromgenomic data must address the interwoven signals from both gene flow andincomplete lineage sorting. The heavy computational demands of standardapproaches to this problem severely limit the size of datasets that may beanalyzed, in both the number of species and the number of genetic loci. Here weprovide a theoretical pointer to more efficient methods, by showing that logDetdistances computed from genomic-scale sequences retain sufficient informationto recover network relationships in the level-1 ultrametric case. This resultis obtained under the Network Multispecies Coalescent model combined with amixture of General Time-Reversible sequence evolution models across individualgene trees, but does not depend on partitioning sequences by genes. Thus understandard stochastic models statistically justifiable inference of networkrelationships from sequences can be accomplished without consideration ofindividual genes or gene trees.

Identifiability of species network topologies from genomic sequences using the logDet distance