bioRxivEvolutionary Biology
Host dietary specialization and neutral assembly shape gut bacterial communities of wild dragonflies
Abstract
Host-associated gut microbial communities can have large impacts on host ecology and evolution, and are typically shaped by host taxonomy and diet. Different host species often harbor distinct microbial communities, potentially because (1) host dietary specialization determines microbial colonization, (2) host-specific selection acts on diet-acquired microbiota, and (3) a combination of both processes. While the first possibility involves passive community structuring, the other two may arise from a functional association and should produce stable microbial communities. However, these alternatives have rarely been tested in wild host populations. We used 16S rRNA amplicon sequencing to characterize the gut bacterial communities of six dragonfly species collected across multiple seasons and locations. We found that variation in bacterial community composition was predominantly explained by sampling season and location, and secondarily by host species. To distinguish the role of host dietary specialization and host-imposed selection, we used insect-specific primers to identify prey in the gut contents of three focal dragonfly species. We found that these dragonflies - considered to be generalist predators - consumed distinct prey, with seasonal diet variation. Together, the patterns of host dietary specialization and spatial and temporal variation suggest a strong role of passive processes in shaping the gut bacterial community. Indeed, the abundance and distribution of ~76% of the bacterial community members were consistent with neutral community assembly. Our results contradict the pervasive expectation that host-imposed selection shapes gut microbial communities, and highlight the importance of joint analyses of variation in host diet and gut microbial communities of natural host populations.
- organism: Drosophila
- assay: bulk RNA-seq
- research focus: cell type annotation
- approach: statistical model
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