Evidence map›Paper›PMID 42361316›Full record

ArticleG3 (Bethesda, Md.)2026

Deconstructing empirical fitness seascapes across scales of granularity.

Swathi Nachiar Manivannan, C Brandon Ogbunugafor

Abstract read
In one paragraph

Article in G3 (Bethesda, Md.), 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
–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

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

2 authors.

Swathi Nachiar ManivannanDepartment of Ecology and Evolutionary Biology,Yale University, 165 Prospect Street, New Haven, CT 06511, United States.
C Brandon OgbunugaforDepartment of Ecology and Evolutionary Biology,Yale University, 165 Prospect Street, New Haven, CT 06511, United States.ORCID 0000-0002-1581-8345

Funding

Defining the Interplay Between Viral Adaptation and Host ProteostasisR01AI168166 · NIAID · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI Matthew Donald Shoulders · 2022 to 2026
$2.9M
A*STAR National Science Scholarship, SingaporeNational Science Foundation's Division of Environmental Biology 2142720NIAID NIH HHS R01 AI168166NIH HHS R01AI168166
6 · The paper itself

Abstract

The fitness landscape metaphor remains resonant in evolutionary theory and has facilitated the birth of newer concepts, like the fitness seascape, that consider the role of environmental context in shaping the dynamics of evolution. Since its emergence, the seascape has appeared in numerous studies examining how different and fluctuating environments shape evolutionary outcomes. Despite growing interest, we lack comprehensive examinations of how environmental context shapes features of fitness seascapes. In this study, we address this gap by deconstructing empirical fitness seascapes across scales of granularity: loci, locus interactions (epistasis), alleles, trajectories, and entire seascapes. For each, we examine how environmental context influences qualitative and quantitative aspects of seascapes, and find that they change appreciably, with patterns specific to individual systems of study. We also quantify how much each scale varies across environments, and find that certain scales tend to be more sensitive to context than others. In summary, we reflect on the implications of the seascape metaphor for the incorporation of environmental effects into theoretical population genetics, for understanding how the environment shapes evolution in disease systems, and for contemporary bioengineering efforts.

Indexed as

Genetic FitnessModels, GeneticAllelesBiological EvolutionEnvironmentEpistasis, GeneticGene-Environment InteractionGenetics, Populationadaptive trajectoriesenvironmental epistasisfitness landscapesfitness seascapeslandscape topographylocus robustnessPEQG2026reaction norm

Identifiers

PMID42361316
PMCPMC13535436

What OpenQuestion holds

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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.