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Abstract Gene flow often obscures phylogenetic relationships, but the evolutionary significance of introgressed variants is unclear. Here, we examine the Australasian long-tailed parrots (Psittaculinae: Polytelini) in which an unexpected sister relationship between Polytelis alexandrae and the genus Aprosmictus, and not the other Polytelis species, has been observed. Using whole genomes, we tested whether this relationship was due to ancient introgression. We found that the majority of gene trees had Ap. erythropterus and P. alexandrae as sister taxa, whereas network analysis indicated monophyly of Polytelis, 48% of gene trees being in phylogenetic conflict due to introgression from Ap. erythropterus into P. alexandrae. Further analyses confidently confirmed that 4–8% of the genome of P. alexandrae was introgressed from Ap. erythropterus with signals of gene flow occurring throughout the genome. These findings indicate that topologies with P. alexandrae and the genus Ap. erythropterus as sister taxa were biased by gene flow and affirm that Polytelis is monophyletic. Next, we assessed the evolutionary outcomes for introgressed variants and found that, among introgressed protein-coding genes, only two (0.8%) were under positive selection, in comparison to 99 (1.7%) of non-introgressed genes. Our results indicate that, despite the ubiquity of detectable introgression in phylogenies, many genetic variants flowing between species may play a minor role in molecular adaptations. 

Vocalizations in animals, particularly birds, are critically important behaviors that influence their reproductive fitness. While recordings of bioacoustic data have been captured and stored in collections for decades, the automated extraction of data from these recordings has only recently been facilitated by artificial intelligence methods. These have yet to be evaluated with respect to accuracy of different automation strategies and features. Here, we use a recently published machine learning framework to extract syllables from ten bird species ranging in their phylogenetic relatedness from 1 to 85 million years, to compare how phylogenetic relatedness influences accuracy. We also evaluate the utility of applying trained models to novel species. Our results indicate that model performance is best on conspecifics, with accuracy progressively decreasing as phylogenetic distance increases between taxa. However, we also find that the application of models trained on multiple distantly related species can improve the overall accuracy to levels near that of training and analyzing a model on the same species. When planning big-data bioacoustics studies, care must be taken in sample design to maximize sample size and minimize human labor without sacrificing accuracy. 

Spatial models show that genetic differentiation between populations can be explained by factors ranging from geographic distance to environmental resistance across the landscape. However, genomes exhibit a landscape of differentiation, indicating that multiple processes may mediate divergence in different portions of the genome. We tested this idea by comparing alternative geographic predctors of differentiation in ten bird species that co-occur in Sonoran and Chihuahuan Deserts of North America. Using population-level genomic data, we described the genomic landscapes across species and modeled conditions that represented historical and contemporary mechanisms. The characteristics of genomic landscapes differed across species, influenced by varying levels of population structuring and admixture between deserts, and the best-fit models contrasted between the whole genome and partitions along the genome. Both historical and contemporary mechanisms were important in explaining genetic distance, but particularly past and current environments, suggesting that genomic evolution was modulated by climate and habitat There were also different best-fit models across genomic partitions of the data, indicating that these regions capture different evolutionary histories. These results show that the genomic landscape of differentiation can be associated with alternative geographic factors operating on different portions of the genome, which reflect how heterogeneous patterns of genetic differentiation can evolve across species and genomes.