Pedogenic Threshold in Acidity Explains Context-Dependent Tree Species Effects on Soil Carbon

Despite the general agreement that maximizing carbon storage and its persistence in forest soils are top priorities in the context of climate change mitigation, our knowledge on how to steer soil organic carbon (SOC) through forest management remains limited. For some soils, tree species selection based on litter quality has been shown a powerful measure to boost SOC stocks and stability, whereas on other locations similar efforts result in insignificant or even opposite effects. A better understanding of which mechanisms underpin such context-dependency is needed in order to focus and prioritize management efforts for carbon sequestration. Here we discuss the key role of acid buffering mechanisms in belowground ecosystem functioning and how threshold behavior in soil pH mediates tree species effects on carbon cycling. For most forests around the world, the threshold between the exchange buffer and the aluminum buffer around a pH-H2O of 4.5 is of particular relevance. When a shift between these buffer domains occurs, it triggers changes in multiple compartments in the soil, ultimately altering the way carbon is incorporated and transformed. Moreover, the impact of such a shift can be amplified by feedback loops between tree species, soil biota and cation exchange capacity (CEC). Hence, taking into account non-linearities related to acidity will allow more accurate predictions on the size and direction of the effect of litter quality changes on the way soil organic carbon is stored in forest soils. Consequently, this will allow developing more efficient, context-explicit management strategies to optimize SOC stocks and their stability.

Detecting Tree Species Effects on Forest Canopy Temperatures with Thermal Remote Sensing: The Role of Spatial Resolution

Canopy temperatures are important for understanding tree physiology, ecology, and their cooling potential, which provides a valuable ecosystem service, especially in urban environments. Linkages between tree species composition in forest stands and air temperatures remain challenging to quantify, as the establishment and maintenance of onsite sensor networks is time-consuming and costly. Remotely-sensed land surface temperature (LST) observations can potentially acquire spatially distributed crown temperature data more efficiently. We analyzed how tree species modify canopy air temperature at an urban floodplain forest (Leipzig, Germany) site equipped with a detailed onsite sensor network, and explored whether mono-temporal thermal remote sensing observations (August, 2016) at different spatial scales could be used to model air temperatures at the tree crown level. Based on the sensor-network data, we found interspecific differences in summer air temperature to vary temporally and spatially, with mean differences between coldest and warmest tree species of 1 °C, and reaching maxima of up to 4 °C for the upper and lower canopy region. The detectability of species-specific differences in canopy surface temperature was found to be similarly feasible when comparing high-resolution airborne LST data to the airborne LST data aggregated to 30 m pixel size. To realize a spatial resolution of 30 m with regularly acquired data, we found the downscaling of Landsat 8 thermal data to be a valid alternative to airborne data, although detected between-species differences in surface temperature were less expressed. For the modeling of canopy air temperatures, all LST data up to the 30 m level were similarly appropriate. We thus conclude that satellite-derived LST products could be recommended for operational use to detect and monitor tree species effects on temperature regulation at the crown scale.