Tim Appelhans

TL;DR: The study reveals that climate can modulate the effects of land use on biodiversity and ecosystem functioning, and points to a lowered resistance of ecosystems in climatically challenging environments to ongoing land-use changes in tropical mountainous regions.

TL;DR: This work quantifies cross-taxon consensus in diversity gradients and evaluates predictors of diversity from single taxa to a multi-taxa community level and points to the importance of temperature for diversification and species coexistence in plant and animal communities.

TL;DR: In this article, the authors investigated the potential of the random forests ensemble classification and regression technique to improve rainfall rate assignment during day, night and twilight (resulting in 24-hour precipitation estimates) based on cloud physical properties retrieved from Meteosat Second Generation (MSG) Spinning Enhanced Visible and InfraRed Imager (SEVIRI) data.

TL;DR: A combined Cubist and residual kriging approach can be considered the best solution for predicting spatial temperature patterns based on a network of temperature observation plots across the southern slopes of Mt. Kilimanjaro.

TL;DR: The performance of a simple linear regression model to predict T a i r from LST was compared to the performance of three machine learning algorithms: Random Forest, generalized boosted regression models (GBM) and Cubist and auxiliary predictor variables were tested in these models.