Multilevel selection theory informs context-dependent mycorrhizal functioning

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.

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Publication Details

Journal
Frontiers in Microbiomes
Published
2026-01-05
DOI
https://doi.org/10.3389/frmbi.2025.1676639
Primary Topic
Mycorrhizal Fungi and Plant Interactions
Type
article
Field-Weighted Citation Impact
0.00

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article

Multilevel selection theory informs context-dependent mycorrhizal functioning

Michelle Afkhami, Anne M Katula, Nancy Collins Johnson, V. Bala Chaudhary
Frontiers in Microbiomes
Mycorrhizal Fungi and Plant Interactions
article

Multilevel selection theory informs context-dependent mycorrhizal functioning

Michelle Afkhami, Anne M Katula, Nancy Collins Johnson, V. Bala Chaudhary
article en

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.

Frontiers in MicrobiomesVol. 4
Dartmouth College (US), University of Miami (US), Northern Arizona University (US), Cold Regions Research and Engineering Laboratory (US)
National Science Foundation, U.S. Department of Defense
Openalex Percentile: Top 97%
Mycorrhizal Fungi and Plant Interactions
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Multilevel selection theory informs context-dependent mycorrhizal functioning — Michelle Afkhami, Anne M Katula, et al. · Frontiers in Microbiomes (2026) | TGRS Research Map | TGRS