Abstract
Mycorrhizal fungi are ecosystem engineers that sustain plant life and help regulate Earth’s biogeochemical cycles1–3. However, in contrast to plants and animals, the global distribution of mycorrhizal fungal biodiversity is largely unknown, which limits our ability to monitor and protect key underground ecosystems4,5. Here we trained machine-learning algorithms on a global dataset of 25,000 geolocated soil samples comprising >2.8 billion fungal DNA sequences. We predicted arbuscular mycorrhizal and ectomycorrhizal fungal richness and rarity across terrestrial ecosystems. On the basis of these predictions, we generated high-resolution, global-scale maps and identified key reservoirs of highly diverse and endemic mycorrhizal communities. Intersecting protected areas with mycorrhizal hotspots indicated that less than 10% of predicted mycorrhizal richness hotspots currently exist in protected areas. Our results describe a largely hidden component of Earth’s underground ecosystems and can help identify conservation priorities, set monitoring benchmarks and create specific restoration plans and land-management strategies. Machine-learning algorithms trained on 25,000 geolocated soil samples are used to create high-resolution global maps of mycorrhizal fungi, revealing that less than 10% of their biodiversity hotspots are in protected areas.