Multivariate modelling of soil microbial variables in forest soil contaminated by heavy metals using wet chemical analyses and pyrolysis GC/MS

Microbial activity, biomass and community patterns were measured in the forest soils surrounding a large smelter emitting heavy metals and sulphur in northern Sweden. The chemical background to the high variation of the microbial variables in mor samples with a content of Cu+Zn between 100 and 1000μgg−1 organic matter was investigated. Soil respiration rate was modelled using different combinations of wet chemistry data and pyrolysis. A combination of carbohydrate data, humus fractionation data and physicochemical data explained 86% of the variance in soil respiration rate. Pyrolysis data explained less variance and was more difficult to interpret. Phospholipid fatty acid (PLFA) patterns in the soils were also analysed. A multivariate (MVP-PLS) model of basal respiration rate, substrate induced respiration (SIR), lag time after glucose addition and the scores from the first component of a principal components analysis (PCA) of 33 phospholipid fatty acids (PLFAcomp1) was made. The explained variance for basal respiration rate was 75%, for SIR 85%, for lag time 52% and for PLFAcomp1 82%. Respiration rate and SIR were negatively correlated to the amount of soil organic matter in the mor layer (SOM m−2), nitrogen content and humic acids and positively correlated to glucans and humins. The content of base cations and pH were positively correlated to respiration rate and SIR and negatively correlated to lag time. The phospholipid fatty acid patterns (PLFAcomp1) showed that fungal fatty acid patterns dominated in shallow mor layers with higher cellulose content, while gram-positive bacteria were more abundant in thicker mor layers with more humic acids and a higher nitrogen content. The variation in the microbial variables was partitioned into a heavy metal dependent and an organic matter quality dependent part, which increased the correlation between heavy metals and the microbial variables.