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Tropical forests are significant carbon sinks and their soils’ carbon storage potential is immense. However, little is known about the soil organic carbon (SOC) stocks of tropical mountain areas whose complex soil-landscape and difficult accessibility pose a challenge to spatial analysis. The choice of methodology for spatial prediction is of high importance to improve the expected poor model results in case of low predictor-response correlations. Four aspects were considered to improve model performance in predicting SOC stocks of the organic layer of a tropical mountain forest landscape: Different spatial predictor settings, predictor selection strategies, various machine learning algorithms and model tuning. Five machine learning algorithms: random forests, artificial neural networks, multivariate adaptive regression splines, boosted regression trees and support vector machines were trained and tuned to predict SOC stocks from predictors derived from a digital elevation model and satellite image. Topographical predictors were calculated with a GIS search radius of 45 to 615 m. Finally, three predictor selection strategies were applied to the total set of 236 predictors. All machine learning algorithms—including the model tuning and predictor selection—were compared via five repetitions of a tenfold cross-validation. The boosted regression tree algorithm resulted in the overall best model. SOC stocks ranged between 0.2 to 17.7 kg m-2, displaying a huge variability with diffuse insolation and curvatures of different scale guiding the spatial pattern. Predictor selection and model tuning improved the models’ predictive performance in all five machine learning algorithms. The rather low number of selected predictors favours forward compared to backward selection procedures. Choosing predictors due to their indiviual performance was vanquished by the two procedures which accounted for predictor interaction.

The reconstruction of Holocene thermokarst landform evolution is important to understand the potential impact of current global climate change on permafrost regions. A multi-proxy approach was applied to analyse the sedimentological and biogeochemical characteristics as well as pollen and lacustrine microfossils of a core profile drilled in a small pingo within a large Central Yakutian thermokarst basin (alas). Age–depth modelling with macrofossil 14C ages reveals high thermokarst deposit sedimentation rates and a complete thermokarst sequence spanning about 900 years during the mid-Holocene between ~6750 and 5870 cal. yr BP. In total, three stages of thermokarst landscape evolution have been identified. Thermokarst processes were initiated at ⩽6750 to 6500 cal. yr BP. Terrestrial conditions changed quickly to lacustrine conditions, and a thermokarst lake rapidly emerged and grew to an estimated size of 120–600 m diameter and 7.5–15 m depth during only ~150 years between ~6500 and 6350 cal. yr BP. The decline of thermokarst processes and lake decrease may have been affected by local hydrological conditions between ~6350 and 5870 cal. yr BP but ceased completely after 5870 cal. yr BP, likely due to climatic changes. Clear evidence for long-lasting and stable lacustrine conditions was not obtained. The study emphasises that short-term warming led to very active permafrost degradation and rapid but locally variable modification of alas and thermokarst evolution.