Higher level classification of Phyllostomid bats with a summary of DNA synapomorphies
ABSTRACT: The family Phyllostomidae is recognized as representing the most extensive radiation known in any mammalian family. Creating a Linnaean classification for this clade has been difficult and controversial. In two companion papers, we here propose a revised classification drawing on the stren...
- Autores:
-
Solari Torres, Sergio
Baker, Robert J.
Cirranello, Andrea
Simmons, Nancy B.
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2016
- Institución:
- Universidad de Antioquia
- Repositorio:
- Repositorio UdeA
- Idioma:
- eng
- OAI Identifier:
- oai:bibliotecadigital.udea.edu.co:10495/37301
- Acceso en línea:
- https://hdl.handle.net/10495/37301
- Palabra clave:
- Bases de Datos de Ácidos Nucleicos
Databases, Nucleic Acid
Murciélagos
Bats
Phyllostomidae
higher-level classification
- Rights
- openAccess
- License
- https://creativecommons.org/licenses/by-nc-nd/4.0/
| Summary: | ABSTRACT: The family Phyllostomidae is recognized as representing the most extensive radiation known in any mammalian family. Creating a Linnaean classification for this clade has been difficult and controversial. In two companion papers, we here propose a revised classification drawing on the strengths of genetic and morphological data and reflecting current ideas regarding phylogenetic relationships within this monophyletic clade. We recognize 11 subfamilies (Macrotinae, Micronycterinae, Desmodontinae, Phyllostominae, Glossophaginae, Lonchorhininae, Lonchophyllinae, Glyphonycterinae, Carolliinae, Rhinophyllinae, and Stenodermatinae), 12 tribes (Diphyllini, Desmodontini, Macrophyllini, Phyllostomini, Vampyrini, Glossophagnini, Brachyphyllini, Choeronycterini, Lonchophyllini, Hsunycterini, Sturnirini, and Stenodermatini), and nine subtribes (Brachyphyllina, Phyllonycterina, Anourina, Choeronycterina, Vampyressina, Enchisthenina, Ectophyllina, Artibeina, and Stenodermatina). The proposed arrangement avoids non-monophyletic associations, only keeping those detected based on analyses of DNA sequence data. We propose that a classification based on the strengths of the most complete morphological and genetic data sets will provide the most robust classification for multiple uses by science and society. |
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