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Analysis of biodiversity data suggests that mammal species are hidden in predictable places.
Parsons, Danielle J; Pelletier, Tara A; Wieringa, Jamin G; Duckett, Drew J; Carstens, Bryan C.
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  • Parsons DJ; Museum of Biological Diversity, The Ohio State University, Columbus, OH 43212.
  • Pelletier TA; Department of Evolution, Ecology, and Organismal Biology, The Ohio State University, Columbus, OH 43212.
  • Wieringa JG; Department of Biology, Radford University, Radford, VA 24142.
  • Duckett DJ; Department of Evolution, Ecology, and Organismal Biology, The Ohio State University, Columbus, OH 43212.
  • Carstens BC; Department of Evolution, Ecology, and Organismal Biology, School of Environment and Natural Resources, Ohio Biodiversity Conservation Partnership, The Ohio State University, Columbus, OH 43210.
Proc Natl Acad Sci U S A ; 119(14): e2103400119, 2022 04 05.
Article en En | MEDLINE | ID: mdl-35344422
SignificanceOnly an estimated 1 to 10% of Earth's species have been formally described. This discrepancy between the number of species with a formal taxonomic description and actual number of species (i.e., the Linnean shortfall) hampers research across the biological sciences. To explore whether the Linnean shortfall results from poor taxonomic practice or not enough taxonomic effort, we applied machine-learning techniques to build a predictive model to identify named species that are likely to contain hidden diversity. Results indicate that small-bodied species with large, climatically variable ranges are most likely to contain hidden species. These attributes generally match those identified in the taxonomic literature, indicating that the Linnean shortfall is caused by societal underinvestment in taxonomy rather than poor taxonomic practice.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Biodiversidad / Mamíferos Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Animals Idioma: En Revista: Proc Natl Acad Sci U S A Año: 2022 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Biodiversidad / Mamíferos Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Animals Idioma: En Revista: Proc Natl Acad Sci U S A Año: 2022 Tipo del documento: Article Pais de publicación: Estados Unidos