Jens Schumacher

Land-use changes are the second largest source of human-induced greenhouse gas emission, mainly due to deforestation in the tropics and subtropics. CO2 emissions result from biomass and soil organic carbon (SOC) losses and may be offset with afforestation programs. However, the effect of land-use changes on SOC is poorly quantified due to insufficient data quality (only SOC concentrations and no SOC stocks, shallow sampling depth) and representativeness. In a global meta-analysis, 385 studies on land-use change in the tropics were explored to estimate the SOC stock changes for all major land-use change types. The highest SOC losses were caused by conversion of primary forest into cropland (−25%) and perennial crops (−30%) but forest conversion into grassland also reduced SOC stocks by 12%. Secondary forests stored less SOC than primary forests (−9%) underlining the importance of primary forests for C stores. SOC losses are partly reversible if agricultural land is afforested (+29%) or under cropland fallow (+32%) and with cropland conversion into grassland (+26%). Data on soil bulk density are critical in order to estimate SOC stock changes because (i) the bulk density changes with land-use and needs to be accounted for when calculating SOC stocks and (ii) soil sample mass has to be corrected for bulk density changes in order to compare land-use types on the same basis of soil mass. Without soil mass correction, land-use change effects would have been underestimated by 28%. Land-use change impact on SOC was not restricted to the surface soil, but relative changes were equally high in the subsoil, stressing the importance of sufficiently deep sampling.

Land-use change (LUC) is a major driving factor for the balance of soil organic carbon (SOC) stocks and the global carbon cycle. The temporal dynamic of SOC after LUC is especially important in temperate systems with a long reaction time. On the basis of 95 compiled studies covering 322 sites in the temperate zone, carbon response functions (CRFs) were derived to model the temporal dynamic of SOC after five different LUC types (mean soil depth of 30±6 cm). Grassland establishment caused a long lasting carbon sink with a relative stock change of 128±23% and afforestation on former cropland a sink of 116±54%, 100 years after LUC (mean±95% confidence interval). No new equilibrium was reached within 120 years. In contrast, there was no SOC sink following afforestation of grasslands and 75% of all observations showed SOC losses, even after 100 years. Only in the forest floor, there was carbon accumulation of 0.38±0.04 Mg ha−1 yr−1 in afforestations adding up to 38±4 Mg ha−1 labile carbon after 100 years. Carbon loss after deforestation (−32±20%) and grassland conversion to cropland (−36±5%), was rapid with a new SOC equilibrium being reached after 23 and 17 years, respectively. The change rate of SOC increased with temperature and precipitation but decreased with soil depth and clay content. Subsoil SOC changes followed the trend of the topsoil SOC changes but were smaller (25±5% of the total SOC changes) and with a high uncertainty due to a limited number of datasets. As a simple and robust model approach, the developed CRFs provide an easily applicable tool to estimate SOC stock changes after LUC to improve greenhouse gas reporting in the framework of UNFCCC.

Predicting the response of communities to climate change is a major challenge for ecology. Communities may well not respond as entities but be disrupted, particularly if trophic levels respond differently, but as yet there is no evidence for differential responses from natural systems. We therefore analyzed unusually detailed plant and animal data collected over 20 years from two grassland communities to determine whether functional group climate sensitivity differed between trophic levels. We found that sensitivity increases significantly with increasing trophic level. This differential sensitivity would lead to community destabilization under climate change, not simple geographical shifts, and consequently must be incorporated in predictive ecological climate models.