Abstract Microbial activity is highly dependent on climatic factors (moisture and temperature) and edaphic characteristics in temperate ecosystems. Moreover, soil microbial community composition in high mountain areas is less known when compared to plant communities. In this study we investigated the soil microbial community from a functional perspective using PLFA (phospholipid fatty acid) methods in the four aspects of four summits (2,242 – 3,012 m above sea level) in the Spanish Central Pyrenees. Soil organic carbon (C), microbial biomass and nutrient dynamics ($$N{H}_{4}^{+}$$ NH4+  + $$N{O}_{3}^{-}$$ NO3− , N mineralization and nitrification potential) were also determined. Microbial biomass C was highest in the lowermost summit and decreased by approximately 50, 14 and 12% with increasing altitude. In each summit soil $$N{H}_{4}^{+}$$ NH4+ and $$N{O}_{3}^{-}$$ NO3− concentrations differed significantly among summits and aspects. Soil nitrification potential varied significantly between the factors summit and aspects, e.g., southerly vs. northerly, easterly vs. westerly aspects. Gram negative bacteria and Actinobacteria functional groups dominated the microbial community, with almost 40% of the total PLFA. Non-metric multidimensional scale (NMS) analysis showed that most of the PLFA functional groups were present in all summits and aspects, although with specific biomarkers. A high abundance of biomarkers 16:1ω9c and 16:0 2OH (gram negative bacteria) were obtained in the lowermost summit, while the biomarkers 16.1ω7cDMA (anaerobes) and 19:3ω6c (Eukaryote) were only found in the uppermost summit. Linear mixed model (lmm) analysis was used with summit as fixed effect and aspect as random effect. In general, our results demonstrate a fundamental role for environment, principally moisture, temperature and organic matter in explaining the pattern observed for soil PLFA biomarkers. Under a global change scenario, we need to shed light on the relationships between soil microbial functional groups and soil nutrient-related variables in order to identify the associated patterns of decomposition rates and soil processes driven by microbial communities in mountain areas. The results could thus be used in global predictive models on climate change impact on C or N cycles in these environments.