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.

Photosynthetic leaf traits were determined for savanna and forest ecosystems in West Africa, spanning a large range in precipitation. Standardized major axis fits revealed important differences between our data and reported global relationships. Especially for sites in the drier areas, plants showed higher photosynthetic rates for a given N or P when compared with relationships from the global data set. The best multiple regression for the pooled data set estimated V_cmax and J_max from N_DW and S. However, the best regression for different vegetation types varied, suggesting that the scaling of photosynthesis with leaf traits changed with vegetation types. A new model is presented representing independent constraints by N and P on photosynthesis, which can be evaluated with or without interactions with S. It assumes that limitation of photosynthesis will result from the least abundant nutrient, thereby being less sensitive to the allocation of the non‐limiting nutrient to non‐photosynthetic pools. The model predicts an optimum proportionality for N and P, which is distinct for V_cmax and J_max and inversely proportional to S. Initial tests showed the model to predict V_cmax and J_max successfully for other tropical forests characterized by a range of different foliar N and P concentrations.

Background: There is no generally agreed classification scheme for the many different vegetation formation types occurring in the tropics. This hinders cross-continental comparisons and causes confusion as words such as ‘forest’ and ‘savanna’ have different meanings to different people. Tropical vegetation formations are therefore usually imprecisely and/or ambiguously defined in modelling, remote sensing and ecological studies. Aims: To integrate observed variations in tropical vegetation structure and floristic composition into a single classification scheme. Methods: Using structural and floristic measurements made on three continents, discrete tropical vegetation groupings were defined on the basis of overstorey and understorey structure and species compositions by using clustering techniques. Results: Twelve structural groupings were identified based on height and canopy cover of the dominant upper stratum and the extent of lower-strata woody shrub cover and grass cover. Structural classifications did not, however, always agree with those based on floristic composition, especially for plots located in the forest–savanna transition zone. This duality is incorporated into a new tropical vegetation classification scheme. Conclusions: Both floristics and stand structure are important criteria for the meaningful delineation of tropical vegetation formations, especially in the forest/savanna transition zone. A new tropical vegetation classification scheme incorporating this information has been developed.