Bridging Satellite Productivity and Global Biodiversity: Unveiling Insights through Dynamic Habitat Indices

Conservation of global biodiversity requires scalable tools to monitor species richness patterns, and satellite remote sensing offers a promising avenue. However, a great challenge lies in identifying how best to translate satellite data into ecologically meaningful biodiversity metrics. This study examines the effectiveness of dynamic habitat indices (DHIs) derived from satellite vegetation products, including gross primary productivity (GPP), fraction of absorbed photosynthetically active radiation, leaf area index, normalized difference vegetation index, enhanced vegetation index, and solar-induced chlorophyll fluorescence, in capturing global species richness across amphibians, birds, mammals, and reptiles. The DHIs consist of 3 subindices, with each representing an important productivity–species richness hypothesis, namely, annual cumulative productivity (DHI Cum, available energy hypothesis), annual minimum productivity (DHI Min, environmental stress hypothesis), and coefficient of variation of productivity (DHI CV, environmental stability hypothesis). Results showed that DHIs derived from satellite GPP data explain a large proportion of the variance in species richness globally ( R 2 = 0.70 for amphibians, R 2 = 0.78 for birds, R 2 = 0.77 for mammals, R 2 = 0.77 for reptiles, and R 2 = 0.82 when all taxa combined), outperforming other satellite vegetation products. Validation with in situ DHIs calculated from tower-measured GPP at 124 globally FLUXNET sites demonstrated strong agreement with satellite DHIs, supporting the reliability of the satellite GPP-based DHIs. Furthermore, the relatively higher uncertainty of satellite DHIs at low-productivity sites also urges further development of satellite GPP algorithms. Globally, protected areas showed significantly higher DHI Cum and Min and lower DHI CV ( P < 0.0001), underscoring their superior habitat quality for biodiversity conservation. These findings highlight the potential of DHIs as a powerful and scalable tool for linking satellite observations to global biodiversity patterns, thus bridging the gap between remote sensing and biodiversity conservation community.