Discordant phylogeographic patterns in ecologically similar sympatric sister species: Revisiting the null hypothesis of comparative phylogeography

Unravelling the complex factors underlying the current geographic patterns of biodiversity is a fundamental goal of biogeography and phylogeography. Comparative phylogeographic studies of co-distributed species have often addressed this issue by attributing observed differences in population structures to differences in interpretable traits between species. However, this approach implicitly relies on the largely untested assumption that species sharing similar ecological, spatial, and phylogenetic contexts should exhibit similar population structures. Herein, we revisited this null hypothesis of phylogeography by comparing the population structures of two sympatric sister species with high ecological similarity using high-throughput genomic data. With extensive sampling and advanced genomic analyses, we revealed the fine-scale population structure of Chaenogobius gulosus, enabling a direct comparison with the well-documented phylogeography of C. annularis. Our findings indicate that the origin of intraspecific lineages in C. gulosus is estimated to be four to five times younger than in C. annularis, with a distinct number of intraspecific lineages. These findings demonstrate that the phylogeographic origin can differ even between sympatric sister species with high ecological similarity, providing a counterexample to the null phylogeographic hypothesis. Our results highlight the role of subtle ecological differences or stochasticity in shaping geographical patterns of biodiversity.

Methods
We obtained mitochondiral sequence data (154 samples from 26 locations) and ddRAD-seq data (88 samples from 21 locations) for Chaenogobius gulosus, a coastal goby species inhabiting the Japanese Archipelago. For comparison, we also used the published data for C. annularis as necessary. Our analyses included generation of the pseudo-reference sequence for C. gulosus, population genetic analysis, and Bayesian demographic inference, and demographic modeling.