Scale-dependent patterns of soil microbial abundance, diversity and function in an agricultural landscape

Background/Question/Methods

The examination of spatial patterns in ecology is needed to better understand ecosystem processes and to further model development. In soil, microorganisms drive important ecosystem processes but spatial variation of microbial processes results from biotic and abiotic properties that operate at multiple scales. Therefore, the interpretation of microbially-mediated ecosystem processes can be misleading because correlations at one scale may be obscured by contrasting correlation at other scales. The objective of this study was to quantify scale-specific patterns associated with microbial biomass, bacterial diversity (16S rRNA), and specific functional groups of microbes. To do this, we sampled a transect that ran upslope from uncultivated switchgrass to cultivated corn. Wavelet transformations were used to decompose the data into a set of wavelet coefficients that capture the variance in a single variable and correlations between two variables at defined locations and scales. We hypothesized that the strength and direction of relationships between microorganisms and soil properties is scale-dependent and that the magnitude of these relationships is unique to different phylogenetic and functional groups. We also hypothesized that scale-dependent correlations between microbes and the soil environment are not uniform across the transect, but change, reflecting differences in factors most limiting to different microbial groups.

Results/Conclusions

The largest contribution to the variance in microbial biomass was associated with the finest spatial scales (2-4 meters) and appears to be driven by dissolved organic carbon, a labile microbial substrate. This is in contrast to the relationship between microbial biomass and soil moisture, which increased in strength from fine to coarse scales and reflects larger scale constraints on the microbial environment. Significant changes in wavelet variance were also identified at the transition from switchgrass to corn, demonstrating the importance of rhizosphere inputs to the distribution of microbial biomass. Greater variation of microbial biomass in the switchgrass portion of the transect suggests that the extensive perennial root network supports a greater abundance and/or diversity of microorganisms. These data, coupled with deep sequencing of 16S rRNA, will provide insights in to scale-specific drivers of microbial biogeography across an agricultural landscape.