Critical Zone (CZ) research investigates the chemical, physical, and biological processes that modulate the Earth’s surface. Here, we advance 12 hypotheses that must be tested to improve our understanding of the CZ: (1) Solar-to-chemical conversion of energy by plants regulates flows of carbon, water, and nutrients through plant-microbe soil networks, thereby controlling the location and extent of biological weathering. (2) Biological stoichiometry drives changes in mineral stoichiometry and distribution through weathering. (3) On landscapes experiencing little erosion, biology drives weathering during initial succession, whereas weathering drives biology over the long term.(4) In eroding landscapes, weathering-front advance at depth is coupled to surface denudation via biotic processes.(5) Biology shapes the topography of the Critical Zone.(6) The impact of climate forcing on denudation rates in natural systems can be predicted from models incorporating biogeochemical reaction rates and geomorphological transport laws.(7) Rising global temperatures will increase carbon losses from the Critical Zone.(8) Rising atmospheric PCO2 will increase rates and extents of mineral weathering in soils.(9) Riverine solute fluxes will respond to changes in climate primarily due to changes in water fluxes and secondarily through changes in biologically mediated weathering.(10) Land use change will impact Critical Zone processes and exports more than climate change. (11) In many severely altered settings, restoration of hydrological processes is possible in decades or less, whereas restoration of biodiversity and biogeochemical processes requires longer timescales.(12) Biogeochemical properties impart thresholds or tipping points beyond which rapid and irreversible losses of ecosystem health, function, and services can occur.

We used geo-spatial statistical techniques to examine the spatial variation and relationship of soil organic carbon (SOC) and soil moisture (SM) in the Luquillo Experimental Forest (LEF), Puerto Rico, in order to test the hypothesis that mountainous terrain introduces spatial autocorrelation and crosscorrelation in ecosystem and soil properties. Soil samples (n = 100) were collected from the LEF in the summer of 1998 and analyzed for SOC, SM, and bulk density (BD). A global positioning system was used to georeference the location of each sampling site. At each site, elevation, slope and aspect were recorded. We calculated the isotropic and anisotropic semivariograms of soil and topographic properties, as well as the cross-variograms between SOC and SM, and between SOC and elevation. Then we used four models (random, linear, spherical and wave/hole) to test the semivariances of SOC, SM, BD, elevation, slope and aspect for spatial dependence. Our results indicate that all the studied properties except slope angle exhibit spatial dependence within the scale of sampling (200 – 1000 m sampling interval). The spatially structured variance (the variance due to the location of sampling sites) accounted for a large proportion of the sample variance for elevation (99%), BD (90%), SOC (68%), aspect (56%) and SM (44%). The ranges of spatial dependence (the distances within which parameters are spatially dependent) for aspect, SOC, elevation, SM, and BD were 9810 m, 3070 m, 1120 m, 930 m and 430 m, respectively. Cross correlograms indicate that SOC varies closely with elevation and SM depending on the distances between samples. The correlation can shift from positive to negative as the separation distance increases. Larger ranges of spatial dependence of SOC, aspect and elevation indicate that the distribution of SOC in the LEF is determined by a combination of biotic (e.g., litterfall) and abiotic factors (e.g., microclimate and topographic features) related to elevation and aspect. This demonstrates the importance of both elevation and topographic gradients in controlling climate, vegetation distribution and soil properties as well as the associated biogeochemical processes in the LEF.

Topography may affect soil microbial processes, however, the use of topographic data to model and predict the spatial distribution of soil microbial properties has not been widely reported. We studied the effect of topography on the activity of denitrifiers under different hydrologic conditions in a typical agroecosystem of the northern grasslands of North America using digital terrain modelling (DTM). Three data sets were used: (1) digital models of nine topographic attributes, such as elevation, slope gradient and aspect, horizontal, vertical, and mean land surface curvatures, specific catchment area, topographic, and stream power indices; (2) two soil environmental attributes (soil gravimetric moisture and soil bulk density); and (3) six attributes of soil microbial activity (most probable number of denitrifiers, microbial biomass carbon content, denitrifier enzyme activity, nitrous oxide flux, denitrification rate, and microbial respiration rate). Linear multiple correlation, rank correlation, circular–linear correlation, circular rank correlation, and multiple regression were used as statistical analyses. In wetter soil conditions, topographically controlled and gravity-driven supply of nutritive materials to microbiota increased the denitrification rate. Spatial differentiation of the denitrification rate and amount of denitrifying enzyme in the soil was mostly effected by redistribution and accumulation of soil moisture and soil organic matter down the slope according to the relative position of a point in the landscape. The N2O emission was effected by differentiation and gain of soil moisture and organic matter due to the local geometry of a slope. The microbial biomass, number of denitrifiers, and microbial respiration depended on both the local geometry of a slope and relative position of a point in the landscape. In drier soil conditions, although denitrification persisted, it was reduced and did not depend on the spatial distribution of soil moisture and thus land surface morphology. This may result from a reduction in soil moisture content below a critical level sufficient for transient induction of denitrification but not sufficient to preserve spatial patterns of the denitrification according to relief. Digital terrain models can be used to predict the spatial distribution of the microbial biomass and amount of denitrifying enzyme in the soil. The study demonstrated a feasibility of applying digital terrain modelling to investigate relations of other groups of soil microbiota with topography and the system ‘topography–soil microbiota’ as a whole.