bioRxivMicrobiology
Environments and host genetics influence the geographic distribution of plant microbiome structure
Abstract
O_LITo understand how microbiota influence plant populations in nature, it is important to examine the geographic distribution of plant-associated microbiomes and the underlying mechanisms. However, we currently lack a fundamental understanding of the biogeography of plant microbiomes and the environmental and host genetic factors that shape their distribution. C_LIO_LILeveraging the broad distribution and extensive genetic variation in duckweeds (the Lemna species complex), we identified the key factors that influenced the geographic distribution of plant microbiome diversity and compositional variation. C_LIO_LIIn line with the pattern observed in microbial biogeography based on free-living environmental microbiomes, we observed higher bacterial richness in temperate regions relative to lower latitudes in duckweed microbiomes (with 10% higher in temperate populations). Our analyses revealed that temperature and sodium concentration in aquatic environments had a negative impact on duckweed bacterial richness, whereas temperature, precipitation, pH, and concentrations of phosphorus and calcium, along with duckweed genetic variation, influenced the geographic variation of duckweed bacterial community composition. C_LIO_LIThe findings add significantly to our understanding of host-associated microbial biogeography and provide insights into the relative impact of different ecological processes, such as selection by environments and host genetics, dispersal, and chance, on plant microbiome assembly. These insights have important implications for predicting plant microbiome vulnerability and resilience under changing climates and intensifying anthropogenic activities. C_LI
- organism: plant (other)
- assay: metagenomics
- research focus: ecology & biodiversity
- research focus: chromatin & epigenetic regulation
- research focus: development & morphogenesis
- research focus: host-pathogen & immune response
- approach: statistical model
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