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Data from: geo-genomic predictors of genetree heterogeneity explain phylogeographic and introgression history: a case study in an Amazonian bird (Thamnophilus aethiops)
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Data from: geo-genomic predictors of genetree heterogeneity explain phylogeographic and introgression history: a case study in an Amazonian bird (Thamnophilus aethiops)

Lukas Musher
02 Nov 2023
DOI: https://doi.org/10.5061/dryad.tht76hf3b
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https://doi.org/10.5061/dryad.tht76hf3bView
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Abstract

FOS: Biological sciences
Can knowledge about genome architecture inform biogeographic and phylogenetic inference? Selection, drift, recombination, and gene flow interact to produce a genomic landscape of divergence wherein patterns of differentiation and genealogy vary nonrandomly across the genomes of diverging populations. For instance, genealogical patterns that arise due to gene flow should be more likely to occur on smaller chromosomes, which experience high recombination, whereas those tracking histories of geographic isolation (reduced gene flow caused by a barrier) and divergence should be more likely to occur on larger and sex chromosomes. In Amazonia, populations of many bird species diverge and introgress across rivers, resulting in reticulated genomic signals. Herein, we used reduced representation genomic data to disentangle the biogeographic history of four populations of an Amazonian antbird, Thamnophilus aethiops, whose biogeographic history was associated with the dynamic evolution of the Madeira River Basin. Specifically, we evaluate whether a large river capture event ca. 200 kya, gave rise to reticulated genealogies in the genome by making spatially explicit predictions about isolation and gene flow based on knowledge about genomic processes. We first estimated chromosome-level phylogenies and recovered two primary topologies across the genome. The first topology (T1) was most consistent with predictions about population divergence, and was recovered for the Z chromosome. The second (T2), was consistent with predictions about gene flow upon secondary contact. To evaluate support for these topologies, we trained a convolutional neural network to classify our data into alternative diversification models and estimate demographic parameters. The best-fit model was concordant with T1 and included gene flow between non-sister taxa. Finally, we modeled levels of divergence and introgression as functions of chromosome length, and found that smaller chromosomes experienced higher gene flow. Given that (1) gene-trees supporting T2 were more likely to occur on smaller chromosomes and (2) we found lower levels of introgression on larger chromosomes (and especially the Z-chromosome), we argue that T1 represents the history of population divergence across rivers and T2 the history of secondary contact due to barrier loss. Our results suggest that a significant portion of genomic heterogeneity arises due to extrinsic biogeographic processes such as river capture interacting with intrinsic processes associated with genome architecture. Future biogeographic studies would benefit from accounting for genomic processes, as different parts of the genome reveal contrasting, albeit complementary histories, all of which are relevant for disentangling the intricate biogeographic mechanisms of biotic diversification.

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