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Model Selection Performance in Phylogenetic Comparative Methods Under Multivariate Ornstein–Uhlenbeck Models of Trait Evolution
Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Arts and Sciences.ORCID iD: 0000-0002-5816-4345
Department of Biological Sciences, Florida International University, Miami, Fl 33199, USA.
IntiQuan GmbH, Basel, Switzerland.
Department of Biological Sciences and the Institute of Environment, Florida International University, Miami, Fl 33199, USA.
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2023 (English)In: Systematic Biology, ISSN 1063-5157, E-ISSN 1076-836X, Vol. 72, no 2, p. 275-293Article in journal (Refereed) Published
Abstract [en]

The advent of fast computational algorithms for phylogenetic comparative methods allows for considering multiple hypotheses concerning the co-adaptation of traits and also for studying if it is possible to distinguish between such models based on contemporary species measurements. Here we demonstrate how one can perform a study with multiple competing hypotheses using mvSLOUCH by analyzing two data sets, one concerning feeding styles and oral morphology in ungulates, and the other concerning fruit evolution in Ferula (Apiaceae). We also perform simulations to determine if it is possible to distinguish between various adaptive hypotheses. We find that Akaikes information criterion corrected for small sample size has the ability to distinguish between most pairs of considered models. However, in some cases there seems to be bias towards Brownian motion or simpler Ornstein-Uhlenbeck models. We also find that measurement error and forcing the sign of the diagonal of the drift matrix for an Ornstein-Uhlenbeck process influences identifiability capabilities. It is a cliche that some models, despite being imperfect, are more useful than others. Nonetheless, having a much larger repertoire of models will surely lead to a better understanding of the natural world, as it will allow for dissecting in what ways they are wrong. [Adaptation; AICc; model selection; multivariate Ornstein-Uhlenbeck process; multivariate phylogenetic comparative methods; mvSLOUCH.]

Place, publisher, year, edition, pages
OXFORD UNIV PRESS , 2023. Vol. 72, no 2, p. 275-293
National Category
Probability Theory and Statistics Evolutionary Biology Bioinformatics (Computational Biology)
Identifiers
URN: urn:nbn:se:liu:diva-193077DOI: 10.1093/sysbio/syac079ISI: 001012520200003PubMedID: 36575879OAI: oai:DiVA.org:liu-193077DiVA, id: diva2:1750309
Funder
Swedish Research Council, 2017–04951ELLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications, Call C
Note

Funding: Swedish Research Council (Vetenskapsradet) [2017-04951]; ELLIIT Call C grant; Stiftelsen for Vetenskaplig Forskning och Utbildning i Matematik (Foundation for Scientific Research and Education in Mathematics); National Science Foundation [2225683]; National Science Center [2015/18/E/NZ8/00716]; ERC-2020-STG [948465]; Swedish Research Council [2017-04951] Funding Source: Swedish Research Council

Available from: 2023-04-12 Created: 2023-04-12 Last updated: 2023-11-24

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Bartoszek, Krzysztof
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Probability Theory and StatisticsEvolutionary BiologyBioinformatics (Computational Biology)
Bartoszek, K., Fuentes Gonzalez, J., Mitov, V., Pienaar, J., Piwczyński, M., Puchałka, R., . . . Voje, K. (2023). Fast mvSLOUCH: Model comparison for multivariate Ornstein--Uhlenbeck-based models of trait evolution on large phylogenies. Dryad

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