Document Type

Article

Publication Title

Global Ecology and Biogeography

Publication Date

2010

Abstract

Aim Although patterns are emerging for macroorganisms, we have limited under- standing of the factors determining soil microbial community composition and productivity at large spatial extents. The overall objective of this study was to discern the drivers of microbial community composition at the extent of biogeo- graphical provinces and regions. We hypothesized that factors associated with land use and climate would drive soil microbial community composition and biomass. Location Great Basin Province, Desert Province and California Floristic Province, California, USA. Methods Using phospholipid fatty acid analysis, we compared microbial com- munities across eight land-use types sampled throughout the State of California, USA (n = 1117). Results The main factor driving composition and microbial biomass was land- use type, especially as related to water availability and disturbance. Dry soils were more enriched in Gram-negative bacteria and fungi, and wetter soils were more enriched in Gram-positive, anaerobic and sulphate-reducing bacteria. Microbial biomass was lowest in ecosystems with the wettest and driest soils. Disturbed soils had less fungal and more Gram-positive bacterial biomass than wildland soils. However, some factors known to influence microbial communities, such as soil pH and specific plant taxa, were not important here. Main conclusions Distinct microbial communities were associated with land- use types and disturbance at the regional extent. Overall, soil water availability was an important determinant of soil microbial community composition. However, because of the inclusion of managed and irrigated agricultural ecosystems, the effect of precipitation was not significant. Effects of environmental and manage- ment factors, such as flooding, tillage and irrigation, suggest that agricultural man- agement can have larger effects on soil microbial communities than elevation and precipitation gradients.

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