To develop a climate-based, probabilistic framework that maps global biomes as climatic domains and explicitly quantifies the spatial distribution and intensity of biome transition zones.
Global terrestrial biomes.
We trained a probabilistic machine-learning classifier linking biomes to multivariate climate space. We quantified biome ambiguity for each grid cell using Shannon entropy, identifying ecotones as regions where climate supports multiple biome outcomes, that is, high entropy. We applied the framework to present-day climate and mid-century climate projections to evaluate potential biome reorganization and ecotone shifts under climate change.
The model reproduced the broad geographic distribution of major biomes, although spatially blocked validation revealed variation in geographic transferability among biome classes. High-entropy zones form coherent transition belts that coincide with well-known ecotones, including forest–savanna boundaries, desert margins, temperate forest–grassland transitions, and the boreal–tundra interface. Biome core regions show low entropy, whereas ecotones exhibit diffuse probability structure and heightened sensitivity to climatic variation. Projections of the model under mid-century climatic conditions (2041–2060) identify spatially structured patterns of biome transitions, particularly shifts from tundra to boreal forest and from boreal to temperate systems, while also highlighting regions where uncertainty and model disagreement are greatest.
Mapping biomes in climate-probability space reframes biome boundaries as spatially extended, permeable zones shaped by overlapping climatic regimes. By combining probabilistic classification with entropy-based uncertainty quantification, our framework complements traditional biome maps by revealing the gradient structure and stability of biome transitions and by identifying where climatic change is most likely to drive ecological reorganization. The resulting transition and uncertainty surfaces offer globally comparable spatial layers for macroecology, biogeography, and evolutionary research, including studies linking ecotones to biodiversity patterns, trait turnover, and lineage diversification.
Soil communities are highly diverse. Due to a lack of spatially attributed data, our understanding of the geographical distribution of soil biodiversity across Europe and the factors driving this distribution remains limited. The NemEu database aims to fill this gap by providing nematode community data at genus or family level across Europe. This level of taxonomic resolution facilitates the discovery of nematode distribution patterns beyond feeding-group level, as well as the calculation of nematode-based indices of soil food-web dynamics.
The dataset contains nematode abundance data classified by taxon, along with associated metadata: geographic coordinates, sampling date and season, soil depth, land use type, and site descriptors.
The NemEu database encompasses 1567 unique European locations, primarily concentrated in western Europe, spanning latitudes from southern Portugal to the Ural Mountains. The precision of the coordinates varies from approximately 11 km to 0.01 m.
Sampling spans from 1975 to 2020, where 90% of locations were sampled between 1999 and 2020. The season of sampling is recorded for all locations, and the year for all but two. Sampling month and day are available for 76% and 60% of locations, respectively.
A total of 276 nematode taxa were recorded, of which 56 families and 220 genera.
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