Publications
Refined gap analysis for biodiversity conservation under climate change
SDM-based gap analysis is widely used to evaluate protected-area (PA) effectiveness under future climate scenarios, yet binary thresholding can cause information loss and limits multi-species prioritization. We developed FuzzyGap, a fuzzy logic–based framework that couples machine-learning SDMs with multi-species groups, dispersal scenarios, and uncertainty assessment. By treating suitability and protection as continuous surfaces, FuzzyGap improves prioritization, supports realistic range-shift planning, and helps identify potential climate refugia.