期刊
ANNUAL REVIEW OF ECOLOGY, EVOLUTION, AND SYSTEMATICS, VOL 52, 2021
卷 52, 期 -, 页码 587-609出版社
ANNUAL REVIEWS
DOI: 10.1146/annurev-ecolsys-011921-023644
关键词
speciation; extinction; genetics; phenotypes; microevolution; macroevolution
资金
- Natural Sciences and Engineering Research Council of Canada (NSERC)
- European Union's Horizon 2020 Research and Innovation Program under the Marie Sklodowska-Curie actions [785910]
- Investissement d'Avenir grants [ANR-10-LABX-2501, ANR-10-LABX-0041]
- National Science Foundation (NSF) [1832399, 1942717, 1835904]
- University of British Columbia (UBC)
- Direct For Biological Sciences
- Division Of Environmental Biology [1942717] Funding Source: National Science Foundation
- Direct For Computer & Info Scie & Enginr
- Office of Advanced Cyberinfrastructure (OAC) [1835904] Funding Source: National Science Foundation
- Marie Curie Actions (MSCA) [785910] Funding Source: Marie Curie Actions (MSCA)
Evolutionary rates are central in connecting micro- and macroevolution, with all types of rates following a similar scaling pattern with time. The highest rates measured over short time intervals suggest a large potential for variation that is underutilized by macroevolution. The ubiquitous scaling pattern of evolutionary rates may lead to unexpected results when comparing rates across different timescales.
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 rates over 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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