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.

Invasion of the Polygraphus proximus Blandford bark beetle causes catastrophic damage to forests with firs (Abies sibirica Ledeb) in Russia, especially in Central Siberia. Determining tree damage stage based on the shape, texture and colour of tree crown in unmanned aerial vehicle (UAV) images could help to assess forest health in a faster and cheaper way. However, this task is challenging since (i) fir trees at different damage stages coexist and overlap in the canopy, (ii) the distribution of fir trees in nature is irregular and hence distinguishing between different crowns is hard, even for the human eye. Motivated by the latest advances in computer vision and machine learning, this work proposes a two-stage solution: In a first stage, we built a detection strategy that finds the regions of the input UAV image that are more likely to contain a crown, in the second stage, we developed a new convolutional neural network (CNN) architecture that predicts the fir tree damage stage in each candidate region. Our experiments show that the proposed approach shows satisfactory results on UAV Red, Green, Blue (RGB) images of forest areas in the state nature reserve “Stolby” (Krasnoyarsk, Russia).

Biomass burning produces a spectrum of thermally altered materials that releases pyrogenic carbon (PyC) to terrestrial, atmospheric, and aquatic systems. Most studies focus on the refractory end of the PyC spectrum, derived from middle‐ to high‐temperature combustion. Low‐temperature PyC is produced during wildfires and has been found to be particularly labile and water soluble. Here we find that in each of the major Siberian watersheds, low‐temperature fire‐derived biomarkers are present in detectable concentrations during all flow regimes of the 2004–2006 sampling period, confirming that PyC is an intrinsic component of the dissolved organic carbon (DOC) pool mobilized by hydrologic events. Gymnosperm combustion, from the southern portions of these watersheds, is the primary source of this Py‐DOC input. Using first‐order degradation rates and transit times of water through these rivers, about half of the total estimated flux of this material may be remineralized during transport from fire source to river mouth (20–40 days), demonstrating the input of a labile source of PyC to these watersheds.

Soil CO₂ emission is one of the most important components of the global carbon cycle. This study analyzes the seasonal dynamics of soil emission for various land cover types in the middle taiga subzone of central Siberia during five growing seasons. It is shown that, throughout a vast area covered by pine forests and their derivatives formed on sandy soils, seasonal CO₂ emission values are determined primarily by the moisture conditions and only secondarily by the temperature regime and ecosystem type. The effect of the forest type is manifested only under the most favorable moisture conditions. A new approach is proposed: divide the growing season into dry and moist periods depending on the threshold soil moisture for areas with different vegetation types.