Uma análise de evidências totais dos morcegos loncofilideos nectarívoros (Lonchophyllinae: Phyllostomidae), comparando dados morfológicos, moleculares e morfométricos.

• Autor(a) principal: Leon-Alvarado, Omar Daniel
• Data de Publicação: 2021
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Biblioteca Digital de Teses e Dissertações do UFSM
• Texto Completo: http://repositorio.ufsm.br/handle/1/23042
• topic: Lonchophyllinae; Morfometria geométrica; Filogenia; Geometric morphometrics; Phylogeny; CNPQ::CIENCIAS BIOLOGICAS

Lonchophyllinae is a subfamily of phyllostomid bats encompassing four genera and 20 species. Although it is one of the two nectar-feeding bats subfamilies, it is a poorly studied group, especially regarding its evolutive aspects. Catalano et al., (2010) and Goloboff and Catalano (2011) presented a technique that allows phylogenetic analyses based on landmark data. As many species of Lonchophyllinae lack molecular data but have available specimens in museum collections, this technique and other data open the possibility to infer the relationships among its members, including new taxa. We photographed the skulls and jaws of 246 specimens of 19 species. We created five landmark configurations for the skull and two for the jaw. Using a General Procrustes Analysis, we generated a consensus configuration for each species. Then, we performed a multiple species superimposition. Also, we compiled morphological and molecular data available from previous works. We ran a Maximum Parsimony analysis with Implied Weighting as TNT v1.5 for each type of data. We managed to recover both tribes as monophyletic using discrete and molecular characters. Furthermore, among all topologies recovered, the species Platalina genovensium, Xeronycteris vieirai, and Lionycteris spurrelli were placed within the Lonchophylla genus leaving it as paraphyletic. Overall, we demonstrated that even though the landmark data is not enough for phylogenetic analysis, it can be advantageous when used with the rest of the data in a total evidence approach.