Aridity indices predict organic matter decomposition and comminution processes at landscape scale

- Aridity indices are related to decomposition and comminution rates at landscape scale.
- Both above- and below-ground processes are related to aridity.
- Spatially-explicit hydro-ecological models can be improved through aridity indices.

Microbial decomposition and invertebrate comminution of a particular organic substrate are largely regulated by temperature and water availability. Numerous metrics have been used to model decay processes at large regional-to-global scales. However, their use at smaller landscape scales might not be practical or feasible. Aridity, generally defined as the balance between long term annual precipitation (P) and potential evapotranspiration (PET), is a metric that synthesizes the major climatic drivers regulating ecosystem processes including the activity of microbes and invertebrates on the forest floor. Thus, aridity indices (AIs) can theoretically represent suitable predictors of decomposition and comminution processes at landscape scale.We investigated our hypothesis in a sclerophyll forest in south-east Australia, where decomposition and comminution rates of Eucalyptus globulus leaf litter were measured in eight sites positioned along an aridity gradient caused by variable exposure to solar radiation. Four sites were also instrumented to continuously monitor air, litter and soil microclimatic variables.We found that AIs were strongly related to above- and below-ground microbial decomposition rates, as well as above-ground comminution rates. Some microclimatic variables, such as shortwave radiation, air relative humidity and litter temperature were also significantly related to above-ground processes, but not below-ground decomposition. Among the AIs tested, the index calculated using the Priestley-Taylor equation for PET had consistently higher coefficients of determination with decomposition and comminution rates. Our case study suggests that AIs can represent robust predictors of both decomposition and comminution processes at landscape scale and useful surrogates for more expensive microclimatic predictors collected at site level. AIs could also be used across spatial scales (from local to continental) to improve biogeochemical and hydrological models by incorporating a spatially-explicit representation of decay processes.