Forest soil ecosystems are associated with large pools and fluxes of carbon (C) and nitrogen (N), which could be strongly affected by variation in rainfall events under current climate change. Understanding how dry and wet cycle events might influence the metabolic state of indigenous soil microbes is crucial for predicting forest soil responses to environmental change. We used 454 pyrosequencing and quantitative PCR to address how present (DNA-based) and potentially active (RNA-based) soil bacterial communities might response to the changes in water availability across three different forest types located in two continents (Africa and Asia) under controlled drying and rewetting cycles. Sequencing of rRNA gene and transcript indicated that Proteobacteria, Actinobacteria, and Acidobacteria were the most responsive phyla to changes in water availability. We defined the ratio of rRNA transcript to rRNA gene abundance as a key indicator of potential microbial activity and we found that this ratio was increased following soil dry-down process whereas it decreased after soil rewetting. Following rewetting Crenarchaeota-like 16S rRNA gene transcript increased in some forest soils and this was linked to increases in soil nitrate levels suggesting greater nitrification rates under higher soil water availability. Changes in the relative abundance of (1) different microbial phyla and classes, and (2) 16S and amoA genes were found to be site- and taxa-specific and might have been driven by different life-strategies. Overall, we found that, after rewetting, the structure of the present and potentially active bacterial community structure as well as the abundance of bacterial (16S), archaeal (16S) and ammonia oxidizers (amoA), all returned to pre-dry-down levels. This suggests that microbial taxa have the ability to recover from desiccation, a critical response, which will contribute to maintaining microbial biodiversity in harsh ecosystems under environmental perturbations, such as significant changes in water availability.
Keywords: amoA; forest; pyrosequencing; qPCR; rDNA; rRNA; seed bank.