Allelopathy is a common and important stressor that shapes plant communities and can alter soil microbiomes, yet little is known about the direct effects of allelochemical addition on bacterial and fungal communities or the potential for allelochemical‐selected microbiomes to mediate plant performance responses, especially in habitats naturally structured by allelopathy. Here, we present the first community‐wide investigation of microbial mediation of allelochemical effects on plant performance by testing how allelopathy affects soil microbiome structure and how these microbial changes impact germination and productivity across 13 plant species. The soil microbiome exhibited significant changes to ‘core’ bacterial and fungal taxa, bacterial composition, abundance of functionally important bacterial and fungal taxa, and predicted bacterial functional genes after the addition of the dominant allelochemical native to this habitat. Furthermore, plant performance was mediated by the allelochemical‐selected microbiome, with allelopathic inhibition of plant productivity moderately mitigated by the microbiome. Through our findings, we present a potential framework to understand the strength of plant–microbial interactions in the presence of environmental stressors, in which frequency of the ecological stress may be a key predictor of microbiome‐mediation strength.
Water and nutrient acquisition are key drivers of plant health and ecosystem function. These factors impact plant physiology directly as well as indirectly through soil‐ and root‐associated microbial responses, but how they in turn affect aboveground plant–microbe interactions are not known. Through experimental manipulations in the field and growth chamber, we examine the interacting effects of water stress, soil fertility, and arbuscular mycorrhizal fungi on bacterial and fungal communities of the tomato ( Both water stress and mycorrhizal disruption reduced leaf bacterial richness, homogenized bacterial community composition among plants, and reduced the relative abundance of dominant fungal taxa. We observed striking parallelism in the individual microbial taxa in the phyllosphere affected by irrigation and mycorrhizal associations. Our results show that soil conditions and belowground interactions can shape aboveground microbial communities, with important potential implications for plant health and sustainable agriculture.
- NSF-PAR ID:
- 10367526
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- New Phytologist
- Volume:
- 234
- Issue:
- 6
- ISSN:
- 0028-646X
- Page Range / eLocation ID:
- p. 2018-2031
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Summary Habitat restoration may depend on the recovery of plant microbial symbionts such as arbuscular mycorrhizal (AM) fungi, but this requires a better understanding of the rules that govern their community assembly.
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Our results provide evidence that restored and remnant forests harbour the same core fungal symbionts, while rare host‐specific taxa differ, and that geography, host identity and taxonomic resolution strongly affect the observed distribution patterns of these fungi. We suggest that host‐specific interactions with AM fungi, as well as spatial processes, should be explicitly considered to effectively re‐establish target host and symbiont communities.
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Abstract In recent times, interest has grown in understanding how microbiomes – the collection of microorganisms in a specific environment – influence the survivability or fitness of their plant and animal hosts. The profound diversity of bacterial and fungal species found in certain environments, such as soil, provides a large pool of potential microbial partners that can interact in ways that reveal patterns of associations linking host–microbiome traits developed over time. However, most microbiome sequence data are reported as a community fingerprint, without analysis of interaction networks across microbial taxa through time.
To address this knowledge gap, more robust tools are needed to account for microbiome dynamics that could signal a beneficial change to a plant or animal host. In this paper, we discuss applying mathematical tools, such as dynamic network modelling, which involves the use of longitudinal data to study system dynamics and microbiomes that identify potential alterations in microbial communities over time in response to an environmental change. In addition, we discuss the potential challenges and pitfalls of these methodologies, such as handling large amounts of sequencing data and accounting for random processes that influence community dynamics, as well as potential ways to address them.
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Plain Language Summary can be found within the Supporting Information of this article. -
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