Soil organic carbon in an old-growth temperate forest: Spatial pattern, determinants and bias in its quantification

Although there exists a consensus regarding the spatial variation of Soil Organic Carbon (SOC) are important inputs in the models being used to understand the present and future C cycle and to predict the global climate change, neither the way to quantify the relative contribution of factors such as topography or canopy composition on SOC variation nor how soil sampling intensity affects the estimated fraction is completely clear. In this study, we propose the use of variation partitioning with environmental factors (topographic and soil variables), canopy composition and spatial structure, as a powerful tool for partitioning spatial variation in SOC. Furthermore, we address the importance of sampling density of observations that are required to characterize the spatial variations in SOC. Our results indicated that SOC variation was mainly determined by soil factors like moisture and pH, but the topography and canopy composition also contributed significantly. The spatial pattern of SOC was weaker along trajectories of sparser sampling density when compared with the reference data (n = 967). SOC is spatially structured, partially due to the soil conditions that determine decomposition rates of the organic matter, but also due to the sink-source balance of the canopy structure and composition, and to the different conditions created by the topographic heterogeneity. Moreover, these factors are interrelated because topographic conditions can influence soil variations. The estimation of SOC variation is strongly dependent on sampling density, and, thus, to draw strong conclusions about local patterns, an exhaustive and intensive sampling effort is needed.

► Estimate the spatial variation of SOC using 967 soil samples and an unaligned grid design.
► Characterize the spatial pattern, determinants of SOC in an old growth forest.
► The using of variation partitioning is a powerful tool to partitioning spatial variations in SOC.
► Address the importance of sampling density to exhaustively represent spatial variations in SOC.