Interactive effects of landscape position and soil diversity drive the spatial variability of soil organic carbon concentration in subalpine soils of Switzerland

Subalpine soils store a significant amount of soil organic carbon (SOC), yet the factors driving its landscape-scale variability remain poorly constrained. Although topography, soil type, soil texture, and moisture are recognised as key drivers of SOC concentration, their interactive effects in subalpine environments remain largely unexplored. In particular, the extent to which soil type shapes landscape-scale SOC patterns remains unclear. In this study, we assessed SOC concentration, soil type, soil moisture, soil texture and elemental composition across 100 plots in two subalpine sites in the Swiss Alps. Using factor analysis, we generated three standardised continuous predictors from our dataset, namely, mineral content, sedimentary rock influence and texture index. We investigated the joint influence of these continuous predictors on SOC variability at the landscape scale in a linear mixed-effects model with topographic position (plain vs. slope areas), soil depth and soil type as additional categorical predictors. We found that soil type diversity was greater on the plain than on the slope, reflecting a greater hydrological heterogeneity. Plains supported a broad spectrum of soils, ranging from organic-rich Histosols to mineral-rich Fluvisols, while Cambisols dominated slopes. Although plains exhibited higher moisture and SOC concentration, and slopes were characterised by higher clay fractions and lithogenous elements, topographic position was not a significant predictor for the spatial distribution of SOC. Our linear mixed-effect model identified mineral content as the strongest predictor of SOC concentration, followed by the texture index and sedimentary rock influence. SOC concentration decreased as mineral content and sedimentary rock influence increased, and as the texture index shifted towards a lower proportion of clay-sized particles. Crucially, while mineral content integrates much of the variability described by the categorical variables included in the model, soil type diversity effectively captures the range of mineral content that drives SOC concentration across our sampling locations and soil depth. Our findings suggest that relying strictly on topographic position to assess SOC spatial variability overlooks important nuances in soil property distribution. We therefore suggest combining topographic position and soil type assessment into a robust framework for predicting landscape-scale SOC variability in subalpine environments.