The evolution, ecology, and conservation of hummingbirds and their interactions with flowering plants

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Most cited references 434

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ggplot2

Hadley Wickham (2016)

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Collinearity: a review of methods to deal with it and a simulation study evaluating their performance

Carsten F Dormann, Jane Elith, Sven Bacher … (2013)

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Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10

Marc Suchard, Philippe Lemey, Guy Baele … (2018)

Abstract The Bayesian Evolutionary Analysis by Sampling Trees (BEAST) software package has become a primary tool for Bayesian phylogenetic and phylodynamic inference from genetic sequence data. BEAST unifies molecular phylogenetic reconstruction with complex discrete and continuous trait evolution, divergence-time dating, and coalescent demographic models in an efficient statistical inference engine using Markov chain Monte Carlo integration. A convenient, cross-platform, graphical user interface allows the flexible construction of complex evolutionary analyses.

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Author and article information

Contributors

Kara G. Leimberger: (View ORCID Profile)

Bo Dalsgaard: (View ORCID Profile)

Joseph A. Tobias: (View ORCID Profile)

Christopher Wolf: (View ORCID Profile)

Matthew G. Betts: (View ORCID Profile)

Journal

Title: Biological Reviews

Abbreviated Title: Biological Reviews

Publisher: Wiley

ISSN (Print): 1464-7931

ISSN (Electronic): 1469-185X

Publication date Created: June 2022

Publication date (Electronic): January 13 2022

Publication date (Print): June 2022

Volume: 97

Issue: 3

Pages: 923-959

Affiliations

[1 ]Forest Biodiversity Research Network, Department of Forest Ecosystems and Society Oregon State University 3100 SW Jefferson Way Corvallis OR 97331 U.S.A.

[2 ]Center for Macroecology, Evolution and Climate GLOBE Institute, University of Copenhagen Universitetsparken 15 Copenhagen Ø 2100 Denmark

[3 ]Department of Life Sciences Imperial College London Silwood Park, Buckhurst Road, Ascot Berkshire SL5 7PY U.K.

Article

DOI: 10.1111/brv.12828

PubMed ID: 35029017

SO-VID: e3782fee-d6a9-4c48-84a7-c0a4a170e8e7

License:

http://onlinelibrary.wiley.com/termsAndConditions#vor

http://doi.wiley.com/10.1002/tdm_license_1.1

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