Quantification of ammonia-oxidising bacteria in limed and non-limed acidic coniferous forest soil using real-time PCR

• Ammonia-oxidising bacteria (AOB) in limed and non-limed acidic coniferous forest soil were investigated using real-time PCR. Two sites in southern Sweden were studied, 244 Åled and Oxafällan. The primers and probe used earlier appeared to be specific to the 16S rRNA gene of AOB belonging to the β-subgroup of the Proteobacteria [Appl. Environ. Microbiol. 67 (2001) 972]. Plots treated with two different doses of lime, 3 or 6 t ha-1, were compared with non-limed control plots on two occasions during a single growing season. Three different soil depths were analysed to elucidate possible differences in the density of their AOB communities. The only clear effect of liming on the AOB was recorded in the beginning of the growing season at 244 Åled. In samples taken in April from this site, the numbers of AOB were higher in the limed plots than in the control plots. At the end of the growing season the AOB communities were all of a similar size in the different plots at both sites, irrespective of liming. The number of AOB, determined using real-time PCR, ranged between 6×106 and 1×109 cells g-1 soil (dw) at the two sites, and generally decreased with increasing soil depth. The results showed no correlation between community density and potential nitrification. This may indicate a partly inactive AOB community. Furthermore, more than 107 cells g-1 soil (dw) were recorded using real-time PCR in the control plot at 244 Åled, although Bäckman et al. [Soil Biol. Biochem. 35 (2003) 1337] detected no AOB like sequences in the same plots using PCR followed by DGGE. Taken together our results strongly suggest that the primers and probe set used are not well suited for quantifying AOB in acidic forest soils, which is probably due to an insufficient specificity. This shows that it is extremely important to re-evaluate any primers and probe set when used in a new environment. Consideration should be given to the specificity and sensitivity, both empirically and using bioinformatic tools.

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