Back

Soil viral and prokaryotic communities shifted significantly after wildfire in chaparral and woodland habitats

Geonczy, S.; ter Horst, A. M.; Emerson, J. B.

2024-11-16 microbiology
10.1101/2024.11.15.623834 bioRxiv
Show abstract

Increased wildfire activity warrants more research into fire-driven biotic changes in soil, given that soil microbes contribute to biogeochemical processes by way of organic matter decomposition, nutrient cycling, and promoting plant growth. Viruses of prokaryotes apply pressure to microbial communities, making their responses to fire also important for understanding post-fire ecology. Leveraging viromes and 16S rRNA gene amplicon sequencing, here we studied viral and prokaryotic community responses to wildfire in woodland and chaparral soils at five timepoints over one year following the California LNU Complex wildfire. We also compared post-fire samples to unburned controls at the final three timepoints, beginning five months after the fire. Viromic DNA yields were low-to-undetectable (indicative of low viral particle abundances), particularly for the first post-fire timepoint, and comparisons to controls suggest a return to baseline viral particle abundances within five months of the wildfire. Viral and prokaryotic community composition and soil chemistry differed significantly in burned samples compared to controls from both habitats. Compared to controls, a greater proportion of viral species (vOTUs) from a burned conifer forest were detected in both burned habitats here, suggesting fire-associated habitat filtering. Published viromes collected from the same sites nine months pre-fire were more similar to controls than to post-fire viromes. Together, these results indicate significant changes in soil viral and prokaryotic communities due to wildfire.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.