Relationships among soil carbon distributions and biophysical factors at nested spatial scales in rain forests of northeastern Costa Rica

Forested soils in the tropics contain a large carbon pool that may respond to global environmental changes such as climate warming and land-use change. A better understanding of the distribution of tropical soil carbon (C) pools is necessary in order to manage soil C as well as to predict its potential responses to global change. The goals of this study were to quantify the relationships among soil C and environmental variables for 35 forest plots in a 140,000-ha landscape in northeastern Costa Rica, and to identify variables that can predict soil C storage at unsampled sites. The biophysical variables included indices of net primary productivity (forest floor mass, root biomass, and an index of productivity derived from satellite imagery), soil particle-size distribution and mineralogy, elevation, and slope. Soil carbon storage in these volcanic soils was relatively high, ranging from 51.1 to 138.6 Mg C ha−1 in the top 30 cm of mineral soil. The relationships among forest soil C and biotic and abiotic variables were different for low-elevation (<120 m) and high-elevation (120–800 m) sites, and elevation explained much of the variability in soil C concentrations. Soil particle-size distribution and mineralogical variables are correlated in this landscape and co-vary in predictable ways along the elevation gradient. Thus, elevation represents a weathering gradient with younger, allophanic soils at higher elevations and older soils with gibbsite, goethite, and kaolinite as dominant clay minerals in the lowlands. We propose two mechanisms of C stabilization: soil C concentrations and contents are positively correlated to the amount of noncrystalline clays (e.g. allophane, imogolite, and ferrihydrite) in the high elevation soils, and positively correlated to aluminum in organo-metal complexes in the low elevation sites. The strong correlations among soil C concentrations, contents, and elevation (mediated through effects on soil mineralogy) indicate that it is possible to predict soil C in this landscape using variables that are easily mapped in a geographic information system.