Evidence map›Paper›PMID 39955104›Full record

ArticleEvolution; international journal of organic evolution2025

Scaling the fitness effects of mutations with respect to differentially adapted Arabidopsis thaliana accessions under natural conditions.

Frank W Stearns, Juannan Zhou, Charles B Fenster

Abstract read
In one paragraph

Article in Evolution; international journal of organic evolution, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Frank W StearnsDepartment of Biology, Stevenson University, Owings Mills, MD, United States.
Juannan ZhouDepartment of Biology, University of Florida, Gainesville, FL, United States.
Charles B FensterDepartment of Biology and Microbiology, South Dakota State University, Brookings, SD, United States.

Funding

Modeling non-additive genetic mechanisms for complex traitsR35GM154908 · NIGMS · UNIVERSITY OF FLORIDA · PI Juannan Zhou · 2024 to 2026
$1.0M
National Science Foundation DEB 1257902NIGMS NIH HHS R35 GM154908NIGMS NIH HHS R35GM154908NIH HHS R35GM154908University of Florida College of Liberal Arts and Sciences
6 · The paper itself

Abstract

Mutations are the ultimate source of genetic variation for natural selection to act upon. A major question in evolutionary biology is the extent to which new mutations can generate genetic variation under natural conditions to permit adaptive evolution over ecological time scales. Here, we collected fitness data for chemically induced (ethylmethane sulfonate, EMS) mutant lines descended from two Arabidopsis thaliana ecotypes that show differential adaptation to the local environment of our common garden plot. Using a novel nonparametric Bayesian statistical approach, we found that both ecotypes accumulated substantial proportions of beneficial mutations. The poorly adapted ecotype exhibited higher mean mutational effects and higher variance in the fitness effects of mutations compared to the well-adapted ecotype. Furthermore, we predict that it takes less than 4,000 generations for the fitness space of the two ecotypes to overlap through mutation accumulation, and that a single founder, through mutation accumulation, is able to achieve the species-wide genetic variation in less than 10,000 generations. Our results provide evidence for relatively rapid local adaptation of Arabidopsis thaliana in natural conditions through new mutations, as well as the utility of a nonparametric Bayesian method for modeling the distribution of fitness effects for field-collected data.

Indexed as

Adaptation, PhysiologicalArabidopsisGenetic FitnessMutationBayes TheoremEcotypeGenetic VariationModels, GeneticSelection, Geneticadaptation geneticsArabidopsisBayesian hierarchical modelEMSmutationsnonparametric model

Identifiers

PMID39955104
PMCPMC12167596

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.