Refined gap analysis for biodiversity conservation under climate change

Abstract

In concert with climate change, our planet faces unprecedented biodiversity loss, with half of all species at risk of extinction. Despite global conservation efforts, the biodiversity crisis continues to outpace these actions. The Global Biodiversity Framework seeks to halt this trend by expanding protected areas (PAs) to cover 30 % of terrestrial and aquatic environments by 2030. Conservation gap analysis, based on species distribution models (SDMs), is vital for assessing the effectiveness of PAs under future climate scenarios. However, traditional gap analysis often relies on binary predictions, leading to critical information loss and failing to target multiple species groups simultaneously or address dynamic species distributions. To overcome these limitations, we propose a refined gap analysis method using a fuzzy approach with machine learning models. Our method incorporates multiple species groups, dispersal scenarios, and uncertainty assessments, offering improved conservation planning. We applied this approach to amphibians—a taxon highly vulnerable to climate change—and evaluated PA effectiveness and potential refugia under various future scenarios. Our findings show that while approximately 60 % of amphibians currently protected by PAs may continue to find refuge, their average habitat suitability is expected to decline significantly under future conditions, indicating potential losses in PA effectiveness. Our refined fuzzy gap analysis captures a continuous spectrum of habitat suitability, facilitates species comparability, and integrates multiple conservation targets. This approach provides a robust tool to guide biodiversity strategies, ensuring that conservation efforts are more adaptive, resilient, and precise in the face of climate change uncertainties.