Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1‐km²resolution for 0–5 and 5–15 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1‐km²pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse‐grained air temperature estimates from ERA5‐Land (an atmospheric reanalysis by the European Centre for Medium‐Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (−0.7 ± 2.3°C). The observed substantial and biome‐specific offsets emphasize that the projected impacts of climate and climate change on near‐surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil‐related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications.

Species turnover is ubiquitous. However, it remains unknown whether certain types of species are consistently gained or lost across different habitats. Here, we analysed the trajectories of 1827 plant species over time intervals of up to 78 years at 141 sites across mountain summits, forests, and lowland grasslands in Europe. We found, albeit with relatively small effect sizes, displacements of smaller‐ by larger‐ranged species across habitats. Communities shifted in parallel towards more nutrient‐demanding species, with species from nutrient‐rich habitats having larger ranges. Because these species are typically strong competitors, declines of smaller‐ranged species could reflect not only abiotic drivers of global change, but also biotic pressure from increased competition. The ubiquitous component of turnover based on species range size we found here may partially reconcile findings of no net loss in local diversity with global species loss, and link community‐scale turnover to macroecological processes such as biotic homogenisation.

Questions: Long‐term programs monitoring the impact of climate change on alpine vegetation necessarily involve changing observers. We aim at quantifying observer errors and ask if the signal of alpine vegetation transformation due to climate change exceeds pseudo‐changes caused by observer errors.Location: Two mountain regions in the Alps, Schrankogel and Hochschwab (both Austria), and one in the High Tatra Mountains (Slovakia).Methods: Vascular plant species presence and cover were recorded on 10–12 1‐m² plots by 13–14 observers per site. Observer errors were calculated as species turnover, and deviations of species cover and the plot thermic vegetation indicator (which is correlated with temperature) from the mean over all observers. Observer errors in estimating species cover were split into a random and systematic part. The influence of plot and species characteristics on observer errors was investigated using (generalized) linear mixed‐effect models. Changes over time from three surveys in species turnover, cover and the thermic vegetation indicator were related to the amount of observer error using a bootstrap approach.Results: Species cover was the most influential factor affecting observer errors in recording species lists and in species cover estimation. Plot attributes and observer identity had a weak but significant influence on errors in the thermic vegetation indicator. Systematic errors in estimating species cover were ≤5%. Changes over time in estimating species cover, as well as in species turnover and the thermic vegetation indicator exceeded observer errors in all cases where the observation period was ≥10 years.Conclusions: The thermic vegetation indicator, which combines species composition and cover with species’ elevational distributions, provides a reliable estimate of warming‐related vegetation changes. Our results underline the importance of long‐term monitoring and long observation periods, which enable us to account for short‐term fluctuations and observer errors alike.

While climatic research about treeline has a long history, the climatic conditions corresponding to the upper limit of closed alpine grasslands remain poorly understood. Here, we propose a climatic definition for this limit, the ‘grassline’, in analogy to the treeline, which is based on the growing season length and the soil temperature. Eighty-seven mountain summits across ten European mountain ranges, covering three biomes (boreal, temperate, Mediterranean), were inventoried as part of the GLORIA project. Vascular plant cover was estimated visually in 326 plots of 1 × 1 m. Soil temperatures were measured in situ for 2–7 years, from which the length of the growing season and mean temperature were derived. The climatic conditions corresponding to 40% plant cover were defined as the thresholds for alpine grassland. Closed vegetation was present in locations with a mean growing season soil temperature warmer than 4.9 °C, or a minimal growing season length of 85 days, with the growing season defined as encompassing days with daily mean ≥ 1 °C. Hence, the upper limit of closed grasslands was associated with a mean soil temperature close to that previously observed at the treeline, and in accordance with physiological thresholds to growth in vascular plants. In contrast to trees, whose canopy temperature is coupled with air temperature, small-stature alpine plants benefit from the soil warmed by solar radiation and consequently, they can grow at higher elevations. Since substrate stability is necessary for grasslands to occur at their climatic limit, the grassline rarely appears as a distinct linear feature.

Landslides are the most common geodynamic phenomenon in Slovakia, and the most affected area is the northwestern part of the Kysuca River Basin, in the Western Carpathian flysch zone. In this paper, we evaluate the susceptibility of this region to landslides using logistic regression and random forest models. We selected 15 landslide conditioning factors as potential predictors of a dependent variable (landslide susceptibility). Classes of factors with too detailed divisions were reclassified into more general classes based on similarities of their characteristics. Association between the conditioning factors was measured by Cramer’s V and Spearman’s rank correlation coefficients. Models were trained on two types of datasets—balanced and stratified, and both their classification performance and probability calibration were evaluated using, among others, area under ROC curve ($AUC$), accuracy ($Acc$), and Brier score ($BS$) using 5-fold cross-validation. The random forest model outperformed the logistic regression model in all considered measures and achieved very good results on validation datasets with average values of $AU{C}_{val}=0.967$, $Ac{c}_{val}=0.928$, and $B{S}_{val}=0.079$. The logistic regression model results also indicate the importance of assessing the calibration of predicted probabilities in landslide susceptibility modelling.