Evidence map›Paper›PMID 41000771›Full record

ArticlebioRxiv : the preprint server for biology2025

Ecological diversification in rapidly evolving populations.

Daniel P G H Wong, Benjamin H Good

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. 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

2 authors.

Daniel P G H WongDepartment of Applied Physics, Stanford University, Stanford, CA 94305.ORCID 0000-0002-4330-1896
Benjamin H GoodDepartment of Applied Physics, Stanford University, Stanford, CA 94305.ORCID 0000-0002-7757-3347

Funding

Quantitative approaches for mapping the real-time evolution of the gut microbiotaR35GM146949 · NIGMS · STANFORD UNIVERSITY · PI Benjamin H Good · 2022 to 2026
$2.0M
NIGMS NIH HHS R35 GM146949
6 · The paper itself

Abstract

Microbial populations have an enormous capacity for rapid evolutionary change. Some mutations increase the fitness of their lineage and compete with each other in a process known as clonal interference. Other mutations can evade competitive exclusion by diversifying into distinct ecological niches. Both processes are frequently observed in natural and experimental settings, yet little is known about how they interact in the parameter regimes most relevant for microbes. Here we address this gap by analyzing the dynamics of ecological diversification in a simple class of resource competition models, where individuals acquire mutations that alter their resource uptake rates. We focus on large adapting populations, where mutations occur so frequently that their ecological and evolutionary timescales overlap. In this regime, we show that the competition between linked mutations causes the population to self-organize into a smaller number of distinct ecotypes, driven by an emergent priority effect that favors the resident strains. We demonstrate that these priority effects bias the long-term metabolic structure of the population, producing qualitative departures from existing ecological theory. We argue that similar dynamics should arise for other rapidly evolving ecosystems, where adaptive mutations accumulate at many linked genetic loci.

Identifiers

PMID41000771
PMCPMC12458155

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.