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Causes and Consequences of Apparent Timescaling Across All Estimated Evolutionary Rates
- Luke J. Harmon1, Matthew W. Pennell2, L. Francisco Henao-Diaz2, Jonathan Rolland3, Breanna N. Sipley4, and Josef C. Uyeda5
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View Affiliations Hide AffiliationsAffiliations: 1Institute for Bioinformatics and Evolutionary Studies (IBEST) and Department of Biological Sciences, University of Idaho, Moscow, Idaho 83844, USA; email: [email protected] 2Department of Zoology and Biodiversity Research Centre, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada 3Laboratoire Evolution et Diversité Biologique, CNRS, UMR5174, Université Toulouse III–Paul Sabatier, 31062 Toulouse, France 4Program for Bioinformatics and Computational Biology, University of Idaho, Moscow, Idaho 83844, USA 5Department of Biological Sciences, Virginia Tech University, Blacksburg, Virginia 24061, USA
- Vol. 52:587-609 (Volume publication date November 2021) https://doi.org/10.1146/annurev-ecolsys-011921-023644
- First published as a Review in Advance on September 08, 2021
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Copyright © 2021 by Annual Reviews. All rights reserved
Abstract
Evolutionary rates play a central role in connecting micro- and macroevolution. All evolutionary rate estimates, including rates of molecular evolution, trait evolution, and lineage diversification, share a similar scaling pattern with time: The highest rates are those measured over the shortest time interval. This creates a disconnect between micro- and macroevolution, although the pattern is the opposite of what some might expect: Patterns of change over short timescales predict that evolution has tremendous potential to create variation and that potential is barely tapped by macroevolution. In this review, we discuss this shared scaling pattern across evolutionary rates. We break down possible explanations for scaling into two categories, estimation error and model misspecification, and discuss how both apply to each type of rate. We also discuss the consequences of this ubiquitous pattern, which can lead to unexpected results when comparing ratesover different timescales. Finally, after addressing purely statistical concerns, we explore a few possibilities for a shared unifying explanation across the three types of rates that results from a failure to fully understand and account for how biological processes scale over time.
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