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The breakdown of plant material fuels soil functioning and biodiversity. Currently, process understanding of global decomposition patterns and the drivers of such patterns are hampered by the lack of coherent large‐scale datasets. We buried 36,000 individual litterbags (tea bags) worldwide and found an overall negative correlation between initial mass‐loss rates and stabilization factors of plant‐derived carbon, using the Tea Bag Index (TBI). The stabilization factor quantifies the degree to which easy‐to‐degrade components accumulate during early‐stage decomposition (e.g. by environmental limitations). However, agriculture and an interaction between moisture and temperature led to a decoupling between initial mass‐loss rates and stabilization, notably in colder locations. Using TBI improved mass‐loss estimates of natural litter compared to models that ignored stabilization. Ignoring the transformation of dead plant material to more recalcitrant substances during early‐stage decomposition, and the environmental control of this transformation, could overestimate carbon losses during early decomposition in carbon cycle models. [ABSTRACT FROM AUTHOR]

Over the past decade, the use of future climate projections from the coupled model intercomparison project (CMIP) has become central in biodiversity science. Pre‐packaged datasets containing future projections of the widely used bioclimatic variables, for different times and socio‐economic pathways, have contributed immensely to the study of climate change implications for biodiversity. However, these datasets lack the flexibility to obtain projections to other target years, and the use of raw data requires coding and spatial information systems expertise. The Python tool, 'chelsa‐cmip6', developed by Karger et al., provides the flexibility needed by allowing users to generate bioclimatic variables for the time of their choice provided the selected general circulation model and socioeconomic pathway combination exists. This is a fantastic step forward in bringing flexibility to the use of climate datasets in biodiversity and will allow for more widespread use of data provided by CMIP6. We hope it also will prompt the development of more user‐friendly tools for the study of the effects of climate change on biodiversity. [ABSTRACT FROM AUTHOR]

Microclimate research gained renewed interest over the last decade and its importance for many ecological processes is increasingly being recognized. Consequently, the call for high‐resolution microclimatic temperature grids across broad spatial extents is becoming more pressing to improve ecological models. Here, we provide a new set of open‐access bioclimatic variables for microclimate temperatures of European forests at 25 × 25 m2 resolution. [ABSTRACT FROM AUTHOR]

Ecological networks have classically been studied at site and landscape scales, yet recent efforts have been made to collate these data into global repositories. This offers an opportunity to integrate and upscale knowledge about ecological interactions from local to global scales to gain enhanced insights from the mechanistic information provided by these data. By drawing on existing research investigating patterns in ecological interactions at continental to global scales, we show how data on ecological networks, collected at appropriate scales, can be used to generate an improved understanding of many aspects of ecology and biogeography—for example, species distribution modelling, restoration ecology and conservation. We argue that by understanding the patterns in the structure and function of ecological networks across scales, it is possible to enhance our understanding of the natural world. [ABSTRACT FROM AUTHOR]

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‐km2 resolution 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‐km2 pixels (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. [ABSTRACT FROM AUTHOR]

Land use is a key factor driving changes in soil carbon (C) cycle and contents worldwide. The priming effect (PE)—CO2 emissions from changed soil organic matter decomposition in response to fresh C inputs—is one of the most unpredictable phenomena associated with C cycling and related nutrient mobilization. Yet, we know very little about the influence of land use on soil PE across contrasting environments. Here, we conducted a continental‐scale study to (i) determine the PE induced by 13C‐glucose additions to 126 cropland and seminatural (forests and grasslands) soils from 22 European countries; (ii) compare PE magnitude in soils under various crop types (i.e., cereals, nonpermanent industrial crops, and orchards); and (iii) model the environmental factors influencing PE. On average, PEs were negative in seminatural (with values ranging between −60 and 26 µg C g−1 soil after 35 days of incubation; median = −11) and cropland (from −55 to 27 µC g−1 soil; median = −4.3) soils, meaning that microbial communities preferentially switched from soil organic C decomposition to glucose mineralization. PE was significantly less negative in croplands compared with seminatural ecosystems and not influenced by the crop type. PE was driven by soil basal respiration (reflecting microbial activity), microbial biomass C, and soil organic C, which were all higher in seminatural ecosystems compared with croplands. This cross European experimental and modeling study elucidated that PE intensity is dependent on land use and allowed to clarify the factors regulating this important C cycling process. [ABSTRACT FROM AUTHOR]

Ecological research heavily relies on coarse‐gridded climate data based on standardized temperature measurements recorded at 2 m height in open landscapes. However, many organisms experience environmental conditions that differ substantially from those captured by these macroclimatic (i.e. free air) temperature grids. In forests, the tree canopy functions as a thermal insulator and buffers sub‐canopy microclimatic conditions, thereby affecting biological and ecological processes. To improve the assessment of climatic conditions and climate‐change‐related impacts on forest‐floor biodiversity and functioning, high‐resolution temperature grids reflecting forest microclimates are thus urgently needed. Combining more than 1200 time series of in situ near‐surface forest temperature with topographical, biological and macroclimatic variables in a machine learning model, we predicted the mean monthly offset between sub‐canopy temperature at 15 cm above the surface and free‐air temperature over the period 2000–2020 at a spatial resolution of 25 m across Europe. This offset was used to evaluate the difference between microclimate and macroclimate across space and seasons and finally enabled us to calculate mean annual and monthly temperatures for European forest understories. We found that sub‐canopy air temperatures differ substantially from free‐air temperatures, being on average 2.1°C (standard deviation ± 1.6°C) lower in summer and 2.0°C higher (±0.7°C) in winter across Europe. Additionally, our high‐resolution maps expose considerable microclimatic variation within landscapes, not captured by the gridded macroclimatic products. The provided forest sub‐canopy temperature maps will enable future research to model below‐canopy biological processes and patterns, as well as species distributions more accurately. [ABSTRACT FROM AUTHOR]