Abstract Controlled greenhouse studies have shown the numerous ways that soil microbes can impact plant growth and development. However, natural soil communities are highly complex, and plants interact with many bacterial and fungal taxa simultaneously. Due to logistical challenges associated with manipulating more complex microbiome communities, how microbial communities impact emergent patterns of plant growth therefore remains poorly understood. For instance, do the interactions between bacteria and fungi generally yield additive (i.e. sum of their parts) or nonadditive, higher order plant growth responses? Without this information, our ability to accurately predict plant responses to microbial inoculants is weakened. To address these issues, we conducted a meta-analysis to determine the type (additive or higher-order, nonadditive interactions), frequency, direction (positive or negative), and strength that bacteria and mycorrhizal fungi (arbuscular and ectomycorrhizal) have on six phenotypic plant growth responses. Our results demonstrate that co-inoculations of bacteria and mycorrhizal fungi tend to have positive additive effects on many commonly reported plant responses. However, ectomycorrhizal plant shoot height responds positively and nonadditively to co-inoculations of bacteria and ectomycorrhizal fungi, and the strength of additive effects also differs between mycorrhizae type. These findings suggest that inferences from greenhouse studies likely scale to more complex field settings and that inoculating plants with diverse, beneficial microbes is a sound strategy to support plant growth.
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Spatiotemporal Heterogeneity and Intragenus Variability in Rhizobacterial Associations with Brassica rapa Growth
The rhizosphere, the zone of soil surrounding plant roots, is a hot spot for microbial activity, hosting bacteria capable of promoting plant growth in ways like increasing nutrient availability or fighting plant pathogens. This microbial system is highly diverse and most bacteria are unculturable, so to identify specific bacteria associated with plant growth, we used culture-independent community DNA sequencing combined with machine learning techniques.
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- Award ID(s):
- 1655726
- PAR ID:
- 10472514
- Editor(s):
- Anderton, Christopher R.
- Publisher / Repository:
- American Society for Microbiology
- Date Published:
- Journal Name:
- mSystems
- Volume:
- 7
- Issue:
- 3
- ISSN:
- 2379-5077
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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