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Uncertainty in divergence time estimation

Abstract:
Understanding and representing uncertainty is crucial in academic research, because it enables studies to build on the conclusions of previous studies, leading to robust advances in a particular field. Here, we evaluate the nature of uncertainty and the manner by which it is represented in divergence time estimation, a field that is fundamental to many aspects of macroevolutionary research, and where there is evidence that uncertainty has been seriously underestimated. We address this issue in the context of methods used in divergence time estimation, and with respect to the manner by which time-calibrated phylogenies are interpreted. With respect to methods, we discuss how the assumptions underlying different methods may not adequately reflect uncertainty about molecular evolution, the fossil record, or diversification rates. Therefore, divergence time estimates may not adequately reflect uncertainty, and may be directly contradicted by subsequent findings. For the interpretation of time-calibrated phylogenies, we discuss how the use of time-calibrated phylogenies for reconstructing general evolutionary timescales leads to inferences about macroevolution that are highly sensitive to methodological limitations in how uncertainty is accounted for. By contrast, we discuss how the use of time-calibrated phylogenies to test specific hypotheses leads to inferences about macroevolution that are less sensitive to methodological limitations. Given that many biologists wish to use time-calibrated phylogenies to reconstruct general evolutionary timescales, we conclude that the development of methods of divergence time estimation that adequately account for uncertainty is necessary.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1093/sysbio/syaa096

Authors

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Institution:
University of Oxford
Division:
MPLS
Department:
Plant Sciences
Role:
Author


Publisher:
Oxford University Press
Journal:
Systematic Biology More from this journal
Volume:
70
Issue:
4
Pages:
855–861
Publication date:
2020-12-12
Acceptance date:
2020-12-02
DOI:
EISSN:
1076-836X
ISSN:
1063-5157
Pmid:
33313882


Language:
English
Keywords:
Pubs id:
1150058
Local pid:
pubs:1150058
Deposit date:
2021-01-19
ARK identifier:

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