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Phylogenetic occupancy models integrate imperfect detection and phylogenetic signal to analyze community structure.
Frishkoff, Luke O; de Valpine, Perry; M'Gonigle, Leithen K.
Afiliação
  • Frishkoff LO; Department of Biology, Stanford University, 371 Serra Mall, Stanford, California, 94305, USA.
  • de Valpine P; Department of Ecology and Evolutionary Biology, University of Toronto, 25 Wilcocks, Toronto, Ontario, M5S 3B2, Canada.
  • M'Gonigle LK; Department of Environmental Science, Policy and Management, University of California, 130 Mulford Hall, Berkeley, California, 94720, USA.
Ecology ; 98(1): 198-210, 2017 Jan.
Article em En | MEDLINE | ID: mdl-28052384
ABSTRACT
Biological communities are structured phylogenetically-closely related species are typically more likely to be found at the same sites. This may be, in part, because they respond similarly to environmental gradients. Accurately surveying biological communities is, however, made difficult by the fact that detection of species is not perfect. In recent years, numerous statistical methods have been developed that aim to overcome deficiencies in the species detection process. However, these methods do not allow investigators to assess phylogenetic community structure. Here, we introduce the phylogenetic occupancy model (POM), which accounts for imperfect species detection while assessing phylogenetic patterns in community structure. Using simulated data sets we show that the POM grants less biased estimates of phylogenetic structure than models without imperfect detection, and can correctly ascertain the effects of species traits on community composition while accounting for evolutionary non-independence of taxa. Integrating phylogenetic methods into widely used occupancy models will help clarify how evolutionary history influences modern day communities.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Filogenia / Ecossistema / Modelos Teóricos Tipo de estudo: Diagnostic_studies / Prognostic_studies / Qualitative_research Idioma: En Ano de publicação: 2017 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Filogenia / Ecossistema / Modelos Teóricos Tipo de estudo: Diagnostic_studies / Prognostic_studies / Qualitative_research Idioma: En Ano de publicação: 2017 Tipo de documento: Article