Data from: Evolution in fossil time series reconciles observations in micro- and macroevolution
Data files
Nov 13, 2025 version files 35.95 KB
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README.md
3.98 KB
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Supplemental_files.zip
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Abstract
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.
https://doi.org/10.5061/dryad.bk3j9kdmj
Authors: Kjetil Lysne Voje1, Megumi Saito-Kato2, Trisha L. Spanbauer3
- Natural History Museum, P.O. 1172, Blindern, 0318 Oslo, Norway
- Department of Geology and Paleontology, National Museum of Nature and Science, 4–1–1 Amakubo, Tsukuba, Ibaraki 305–0005, Japan
- Department of Environmental Science and Lake Erie Center, University of Toledo, Toledo, OH 43606, USA
Brief summary: Using two high-quality fossil diatom time series, we study phenotypic evolution over several hundred thousand years by incorporating models that consider changes in the adaptive landscape.
Responsible for data collection: MS-K, TLS
The data was first published in the following papers:
Spanbauer, T. L., Fritz, S. C. & Baker, P. A. 2018. Punctuated changes in the morphology of an endemic diatom from Lake Titicaca. Paleobiology 44, 89–100. DOI: https://doi.org/10.1017/pab.2017.27
Saito-Kato, M., Tanimura, Y., Mori, S. & Julius, M. L. 2015. Morphological evolution of Stephanodiscus (Bacillariophyta) in Lake Biwa from a 300 ka fossil record. J Micropalaeontol 34, 165–179. DOI: https://doi.org/10.1144/jmpaleo2014-015
Responsible for R code: KLV
Supplemental_files.zip
Data
The folder called Data contains all time series of phenotypic change in the two diatom lineages from the fossil record analyzed in our paper:
andinus.txt:
Contains the time series of phenotypic change in size in the diatom lineage Cyclostephanos andinus. Data originally published in Spanbauer et al. (2018).
Stephanodiscus_biwa.txt:
Contains the time series of phenotypic change in size in the diatom lineage Biwa Stephanodiscus. Data originally published in Saito-Kato et al. (2015).
Stephanodiscus_biwa_extremes.txt:
Contains the dataset comprising only the 20% largest individuals per sample in the time series of phenotypic change in size in the diatom lineage Biwa Stephanodiscus.
Explanation for the columns in the three text files in the folder data:
N = sample size; mm = trait mean (log micrometers); vv = trait variance (log micrometers squared); age = absolute age of each sample in years; interval.length.MY = the time interval of the complete time series in millions of years; MY.per.meter = NA; age.in.MY = absolute age of each sample in millions of years; dimension = trait dimension; generation.time = default setting, not used in the analysis; log = whether the trait data is on a log scale; taxa = The broad-scale taxonomic affinity of the species; species = Scientific (binomial) name of the organism studied; min_diameter = minimum diameter in a sample in log micrometers; max_diameter = maximum diameter in a sample in log micrometers, s_mean = average trait mean among the 10% smallest specimens in a sample; s_var = trait variance among the 10% smallest specimens in a sample; s_N = sample size for the 10% smallest specimens in a sample; l_mean = average trait mean among the 10% largest specimens in a sample; l_var = trait variance among the 10% largest specimens in a sample; l_N = sample size for the 10% largest specimens in a sample;
Links to other publicly accessible locations of the data: https://pets.nhm.uio.no/
R scripts
The folder called R scripts contains all R script used to conduct the analyzes in the paper:
main_analyses_andinus_biwa.R:
All analyses of the two time series of phenotypic change in the diatom lineages, except the analyses of the dataset comprising only the 20% largest individuals per sample.
extreme_size_analysis.R:
Contains the scripts to conduct the analyses of the 20% largest individuals per sample.