Is bacterial moisture niche a good predictor of shifts in community composition under long‐term drought?

Abstract

Both biogeographical and rainfall manipulation studies show that soil water content can be a strong driver of microbial community composition. However, we do not yet know if these patterns emerge because certain bacterial taxa are better able to survive at dry soil moisture regimes or if they are due to other drought-sensitive ecosystem properties indirectly affecting microbial community composition. In this study, we evaluated (1) whether bacterial community composition changed under an 11-year drought manipulation and (2) whether shifts under drought could be explained by variation in the moisture sensitivity of growth among bacterial taxa (moisture niche partitioning). Using 454 pyrosequencing of 16S rRNA, we observed shifts in bacterial community composition under drought, coincident with changes in other soil properties. We wet-up dry soils from drought plots to five moisture levels, and measured respiration and the composition of actively growing communities using bromodeoxyuridine (BrdU) labeling of DNA. The field drought experiment affected the composition of the active community when incubated at different moisture levels in the laboratory, as well as short-term (36-hour) respiration rates. Independent of history, bacterial communities also displayed strong niche partitioning across the wet-up moisture gradient. Although this indicates that moisture has the potential to drive bacterial community composition under long-term drought, species distributions predicted by response to moisture did not reflect the community composition of plots that were subjected to long-term drought. Bacterial community structure was likely more strongly driven by other environmental factors that changed under long-term drought, or not shaped by response to water level upon wet-up. The approach that we present here for linking niches to community composition could be adapted for other environmental variables to aid in predicting microbial species distributions and community responses to environmental change.