Evidence map›Paper›PMID 41247087›Full record

ArticleJournal of evolutionary biology2026

Does genetic variation in controlled experiments predict phenology of wild plants?

Victoria L DeLeo, David L Des Marais, Claire M Lorts, Thomas E Juenger, Jesse R Lasky

Abstract read
In one paragraph

Article in Journal of evolutionary biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Victoria L DeLeoDepartment of Biology, Pennsylvania State University, University Park, United States.
David L Des MaraisDepartment of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, United States.ORCID 0000-0002-6772-6987
Claire M LortsDepartment of Biology, Pennsylvania State University, University Park, United States.
Thomas E JuengerDepartment of Integrative Biology, University of Texas at Austin, Austin, United States.
Jesse R LaskyDepartment of Biology, Pennsylvania State University, University Park, United States.ORCID 0000-0001-7688-5296

Funding

Evolutionary genetics of genotype-by-environment interactions for complex traitsR35GM138300 · NIGMS · PENNSYLVANIA STATE UNIVERSITY, THE · PI LASKY, JESSE · 2020 to 2024
$1.8M
NIGMS NIH HHS R35 GM138300NIH HHS R35 GM138300
6 · The paper itself

Abstract

Phenology and the timing of development are often under selection. However, the relative contributions of genotype, environment, and prior developmental transitions to variance in the phenology of wild plants is largely unknown. Individual components of phenology (e.g., germination) might be loosely related with the timing of maturation due to variation in prior developmental transitions. Given widespread evidence that genetic variation in life history is adaptive, we investigated to what degree experimentally measured genetic variation in Arabidopsis phenology predicts phenology of plants in the wild. As a proxy of phenology, we obtained collection dates from nature of 227 naturally inbred Arabidopsis thaliana accessions from across Eurasia. We compared this phenology in nature with experimental data on the descendant inbred lines that we synthesized from two new and 155 published controlled experiments. We tested whether the genetic variation in flowering and germination timing from experiments predicted the phenology of the same lines in nature. We found that genetic variation in phenology from controlled experiments significantly predicts day of collection from wild individuals, as a proxy for date of flowering, across Eurasia. However, local variation in collection dates within a region was not explained by genetic variance in phenology in experiments, suggesting high plasticity across small-scale environmental gradients or complex interactions between the timing of different developmental transitions. While experiments have shown phenology is under selection, understanding the subtle environmental and stochastic effects on phenology may help to clarify the heritability and evolution of phenological traits in nature.

Indexed as

ArabidopsisGenetic VariationFlowersGerminationArabidopsisArabidopsis thalianageneticphenologyplantswild

Identifiers

PMID41247087
PMCPMC13333257

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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.