Evidence map›Paper›PMID 41852392›Full record

ArticleFrontiers in microbiomes2025

Multilevel selection theory informs context-dependent mycorrhizal functioning.

Anne M Katula, Nancy Collins Johnson, V Bala Chaudhary, Michelle E Afkhami

Abstract read
In one paragraph

Article in Frontiers in microbiomes, 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

4 authors.

Anne M KatulaDepartment of Biology, University of Miami, Coral Gables, FL, United States.
Nancy Collins JohnsonDepartment of Biological Sciences, Northern Arizona University, Flagstaff, AZ, United States.
V Bala ChaudharyDepartment of Environmental Studies, Dartmouth College, Hanover, NH, United States.
Michelle E AfkhamiDepartment of Biology, University of Miami, Coral Gables, FL, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Arbuscular mycorrhizal (AM) fungi form widespread, ancient, and critically important symbioses with plants, but their functioning and beneficial effects are highly context-dependent. This variability stems from eco-evolutionary dynamics operating across multiple levels of biological organization (e.g., genes to holobionts), making generalizable predictions about mycorrhizal outcomes challenging. Multilevel selection theory (MLST), which posits that selection acts simultaneously on multiple levels of biological organization including in opposite directions, can serve as a powerful framework for interpreting this variability in mycorrhizal functional phenotypes. Here, we outline the key principles of MLST and explore how its application to AM fungal symbioses can improve our understanding of this ubiquitous symbiosis. We highlight how four levels of biological organization important to AM symbioses - genes, nuclei, spores, and holobionts - can serve as one or more units of selection under a tripartite framework for the units of selection. We then examine how ecological contexts, such as stress, spatial structure, and community composition, can modulate the balance of selective forces across levels, ultimately shaping the degree of cooperation among symbiotic partners. We conclude by proposing future research directions using MLST to generate deeper insights into the complexity and adaptability of this globally important symbiosis.

Indexed as

arbuscular mycorrhizal fungicontext-dependencycooperationholobiontmultilevel selection theory

Identifiers

PMID41852392
PMCPMC12993660

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.