Published May 28, 2024 | Version v1
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Evolution in fossil time series reconciles observations in micro- and macroevolution

  • 1. University of Oslo
  • 2. National Museum of Nature and Science
  • 3. University of Toledo

Description

Extrapolating microevolutionary models does not always provide satisfactory explanations for phenotypic diversification on million-year time scales. For example, short-term evolutionary change is often modeled assuming a fixed adaptive landscape, but macroevolutionary changes are likely to involve changes in the adaptive landscape itself. A better understanding of how the adaptive landscape changes across different time intervals and how these changes cause populations to evolve has the potential to narrow the gap between micro- and macroevolution. Here, we analyze two fossil diatom time series of exceptional quality and resolution covering time intervals of a few hundred thousand years using models that account for different behaviors of the adaptive landscape. We find that one of the lineages evolves on a randomly and continuously changing landscape, whereas the other lineage evolves on a landscape that shows a rapid shift in the position of the adaptive peak of a magnitude that is typically associated with species-level differentiation. This suggests phenotypic evolution beyond generational timescales may be a consequence of both gradual and sudden repositioning of adaptive peaks. Both lineages are showing rapid and erratic evolutionary change and are constantly readapting towards the optimal trait state, observations that align with evolutionary dynamics commonly observed in contemporary populations. The inferred trait evolution over a span of a few hundred thousand years in these two lineages is therefore chimeric in the sense that it combines components of trait evolution typically observed on both short and long timescales.

Notes

Funding provided by: European Research Council
ROR ID: https://ror.org/0472cxd90
Award Number: 948465

Funding provided by: National Science Foundation
ROR ID: https://ror.org/021nxhr62
Award Number: 1625040

Funding provided by: National Science Foundation
ROR ID: https://ror.org/021nxhr62
Award Number: 1251678

Funding provided by: Grant-in-Aid for Scientific Research *
Crossref Funder Registry ID:
Award Number: 17K05695

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