Background: The influence of farming on plant, animal and microbial biodiversity has been carefully studied and much debated. Here, we compare an isolate-based study of 196 Rhizobium strains to amplicon-based MAUI-seq analysis of rhizobia from 17,000 white clover root nodules. We use these data to investigate the influence of soil properties, geographic distance, and field management on Rhizobium nodule populations. Results: Overall, there was good agreement between the two approaches and the precise allele frequency estimates from the large-scale MAUI-seq amplicon data allowed detailed comparisons of rhizobium populations between individual plots and fields. A few specific chromosomal core-gene alleles were significantly correlated with soil clay content, and core-gene allele profiles became increasingly distinct with geographic distance. Field management was associated with striking differences in Rhizobium diversity, where organic fields showed significantly higher diversity levels than conventionally managed trials. Conclusions: Our results indicate that MAUI-seq is suitable and robust for assessing nodule Rhizobium diversity. We further observe possible profound effects of field management on microbial diversity, which could impact plant health and productivity and warrant further investigation.

Background: Sequencing and PCR errors are a major challenge when characterising genetic diversity using high-throughput amplicon sequencing (HTAS). Results: We have developed a multiplexed HTAS method, MAUI-seq, which uses unique molecular identifiers (UMIs) to improve error correction by exploiting variation among sequences associated with a single UMI. We show that two main advantages of this approach are efficient elimination of chimeric and other erroneous reads, outperforming DADA2 and UNOISE3, and the ability to confidently recognise genuine alleles that are present at low abundance or resemble chimeras. Conclusions: The method provides sensitive and flexible profiling of diversity and is readily adaptable to most HTAS applications, including microbial 16S rRNA profiling and metabarcoding of environmental DNA.