קידוח ביודיוורסיטה תת-קרקעית: חיזוי שימוש בלמידה עצמית ותמונות לוויינים

תקציר מקורי באנגליתarXiv:2604.09818v2 Announce Type: replace Abstract: Mycorrhizal fungi are vital to terrestrial ecosystem functioning. Yet monitoring their biodiversity at landscape scales is often unfeasible due to time and cost constraints. Current predictions suggest that 90% of mycorrhizal diversity hotspots remain unprotected, opening questions of how to broadly and effectively map underground fungal communities. We show that self-supervised learning (SSL) applied to satellite imagery can predict below-ground ectomycorrhizal fungal richness across diverse environments. Our models explain over half the variance in species richness across ~12,000 field samples spanning Europe and Asia. SSL-derived features are the most informative tested predictor group, and outperform each of the established climate, s