Evidence map›Paper›PMID 42039578›Full record

ArticlebioRxiv : the preprint server for biology2026

Emergent frequency-dependent selection predicts mutation outcomes in complex ecological communities.

Shing Yan Li, Zhijie Feng, Akshit Goyal, Pankaj Mehta

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for 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
–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

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

4 authors.

Shing Yan LiMIT Center for Theoretical Physics - a Leinweber Institute, Cambridge, MA 02139, USA.ORCID 0000-0002-1211-8220
Zhijie FengDepartment of Physics, Boston University, Boston, MA 02215, USA.ORCID 0000-0002-6244-0015
Akshit GoyalInternational Centre for Theoretical Sciences, Tata Institute of Fundamental Research, Bengaluru 560089, India.ORCID 0000-0002-9425-8269
Pankaj MehtaDepartment of Physics, Boston University, Boston, MA 02215, USA.ORCID 0000-0003-1290-5897

Funding

MODELING EMERGENT BEHAVIORS IN SYSTEMS BIOLOGY: A BIOLOGICAL PHYSICS APPROACHR35GM119461 · NIGMS · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · PI Pankaj Mehta · 2016 to 2026
$3.6M
NIGMS NIH HHS R35 GM119461
6 · The paper itself

Abstract

Ecological interactions can dramatically alter evolutionary outcomes in complex communities. Yet, the framework of population genetics largely neglects interactions from a species-rich community. Here, we bridge this gap by using dynamical mean-field theory to integrate community ecology into classical population genetics models. We show that ecological interactions result in emergent frequency-dependent selection between parents and mutants, characterized by a single parameter measuring the strength of ecological feedbacks. This result generalizes classical population genetics models to highly diverse communities and enables predictions of mutation outcomes in these eco-evolutionary settings. We derive an analytic expression for fixation probability that extends Kimura's formula and reveals that ecological interactions strongly suppress the fixation of moderately beneficial mutations. This suppression arises because frequency-dependent selection leads to prolonged coexistence between parent and mutant lineages, which acts as a barrier to fixation. The strength of these effects increases with effective population size and the number of open niches in the ecosystem. Our study establishes a framework for integrating ecological interactions into population genetics, showing that evolutionary outcomes can be predicted using simple models even in the presence of complex community feedbacks.

Identifiers

PMID42039578
PMCPMC13104891

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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.