We evaluated traditional biogeographic boundaries of coastal marine regions in Southwestern Atlantic using DNA sequence data from common, rocky-shore inhabiting, marine mites of the genera Agauopsis and Rhombognathus, family Halacaridae.
We investigated geographic population genetic structure using CO1 gene sequences, estimated divergence times using a multigene dataset and absolute time-calibrated molecular clock analyses, and performed environmental niche modeling (ENM) of common marine mite species.
Agauopsis legionium has a shallow history (2.01 Ma) with four geographically differentiated groups. Two of them corresponded to the traditional Amazonian and Northeastern ecoregions, but the boundary between the two other groups was inferred at the Abrolhos Plateau, not Cabo Frio. Rhombognathus levigatoides s. lat. was represented by two cryptic species that diverged 7.22 (multilocus data) or 10.01 Ma (CO1-only analyses), with their boundary, again at the Abrolhos Plateau. ENM showed that A. legionium has suitable habitats scattered along the coast, while the two R. levigatoides cryptic species differ considerably in their niches, especially in parameters related to upwelling. This indicates that genetic isolation associated with the Abrolhos Plateau occurred in both lineages, but for the R. levigatoides species complex, ecological niche specialization was also an important factor.
Our study suggests that the major biogeographic boundary in the Southwestern Atlantic lies not at Cabo Frio but at the Abrolhos Plateau. There two biogeographically relevant factors meet (a) changes in current directions (which limit dispersal) and (b) abrupt changes in environmental parameters associated with the South Atlantic Central Waters (SACW) upwelling (offering distinct ecological niches). We suggest that our result represents a general biogeographic pattern because a barrier at the Abrolhos Plateau was found previously for the fish genus Macrodon (phylogeographic data), prosobranch mollusks, ascidians, and reef fishes (community-level data).
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