Comparative Analysis of Tick Microbiomes in Remnant and Reconstructed Prairie Ecosystems of Central Missouri

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Abstract

The ecological processes that shape tick-associated microbial communities are not fully understood. Ticks harbor diverse microbial communities that include both symbiotic and pathogenic bacteria, and these microbes can influence tick physiology, vector competence, and pathogen transmission. We evaluated bacterial microbiome diversity and composition in two medically important tick species, Amblyomma americanum and Dermacentor variabilis , collected from remnant and reconstructed tallgrass prairies in central Missouri between 2020 and 2023. Using 16S rRNA gene amplicon sequencing, we characterized microbial communities from 200 pooled tick samples and assessed patterns in alpha diversity, beta diversity, and differential abundance across tick species and prairie types. Microbiome diversity and composition differed strongly between tick species, with D. variabilis exhibiting higher alpha diversity than A. americanum and clear species-specific clustering in beta diversity analyses. In contrast, differences between remnant and reconstructed prairies were modest. Differential abundance testing revealed Rickettsia to be enriched in A. americanum and Francisella in D. variabilis , consistent across prairie types. Species-level screening further showed that A. americanum primarily harbored low virulent Rickettsia , whereas D. variabilis more frequently carried pathogenic Rickettsia species. Prairie restoration status did not significantly predict Rickettsia type, although a higher proportion of potentially pathogenic lineages was observed in reconstructed sites. These findings indicate that tick species identity is the primary determinant of microbiome structure in prairie ecosystems, with habitat context exerting secondary influences. Our results underscore the importance of incorporating microbiome ecology into medical entomology and suggest that land management practices may subtly shape vector–microbe interactions relevant to disease risk.

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