Vocalização e plumagem elucidam a distribuição e contestam a zona de intergradação proposta para dois Oscines irmãos : Turdus hauxwelli e T. fumigatus (Passeriformes : Turdidae)

Abstract

The diagnosis of cryptic species has always been a challenge in ornithology, making some species only come to be recognized in the last decades, with the support of vocal and molecular data. A classic case occurs in the Amazon, within the Turdidae family, with the pair of sister-species Turdus hauxwelli and Turdus fumigatus. Studies based on plumage led to the suggestion of an intergradation zone in the south of Central Amazonia and Mato Grosso. In addition to the plumage, both have similar songs, often being misidentified. Therefore, our proposal was to evaluate whether there is a vocal diagnosis that allows refining the geographic limits of both T. hauxwelli and T. fumigatus, especially in Central Amazonia and Mato Grosso, and, with the re-identification of the available specimens, to evaluate for differences in plumage. We analyzed recordings of each species and performed a multivariate analysis of vocal characters, as well as a morphometric analysis of specimens and visual comparisons of plumage patterns. In our vocal analyses we found two types of calls in the repertoire of both species, a one-note call and a multiple-note call, with each species using one of them as its main call. The analyzes showed that both species are fully diagnosable based on the calls. Our data indicates that the recordings from Mato Grosso belong exclusively to T. hauxwelli, questioning the literature information T. fumigatus occurs in the state and dismissing the intergradation zone previously proposed for the two species. On the other hand, morphometry and plumage patterns did not allow delimiting the two species, given that the plumage of T. hauxwelli in Mato Grosso is inseparable from that of T. fumigatus, which could explain the misinterpretation in the literature. The vocal analyzes showed that the calls of thrushes carry a taxonomic signal for species recognition and reinforce the importance of analyzing new datasets for a better understanding of the identity and distribution of species.