Project description:Soils are a huge reservoir of organic C, and the efflux of CO2 from soils is one of the largest fluxes in the global C cycle. Out of all natural environments, soils probably contain the greatest microbial biomass and diversity, which classifies them as one of the most challenging habitats for microbiologists (Mocali and Benedetti, 2010). Until today, it is not well understood how soil microorganisms will respond to a warmer climate. Warming may give competitive advantage to species adapted to higher temperatures (Rinnan et al., 2009). The mechanisms behind temperature adaptations of soil microbes could be shifts within the microbial community. How microbial communities will ultimately respond to climate change, however, is still a matter of speculation. As a post-genomic approach in nature, metaproteomics allows the simultaneous examination of various protein functions and responses, and therefore is perfectly suited to investigate the complex interplay between respiration dynamics, microbial community architecture, and ecosystem functioning in a changing environment (Bastida et al., 2012). Thereby we will gain new insights into responses to climate change from a microbial perspective. Our study site was located at 910 m a.s.l. in the North Tyrolean Limestone Alps, near Achenkirch, Austria The 130 year-old mountain forests consist of Norway spruce (Picea abies) with inter-spread of European beech (Fagus sylvatica) and silver fir (Abies alba). Three experimental plots with 2 × 2 m warmed- and control- subplots were installed in 2004. The temperature difference between control and warmed plots was set to 4 °C at 5 cm soil depth. Soil was warmed during snow-free seasons. In order to extract proteins from forest soil samples, the SDS–phenol method was adopted as previously described by Keiblinger et al. (2012). Protein extractions were performed from each subplot soil samples. The abundance of protein-assigned microbial phylogenetic and functional groups, were calculated based on the normalized spectral abundance factor (NSAF, Zybailov et al., 2006).
