The routine generation of species distribution data \textit{at scale} remains a challenge. We used aquatic environmental DNA metabarcoding to sample vertebrate species across the 30,000 sqkm Gaoligongshan region along the China-Myanmar border. In just 56 calendar days (33 researcher-field-days + 69 researcher-lab-days), we detected 389 vertebrate species, of which 35 are Red-Listed. We introduce the ‘eDNA-aware’ OccPlus occupancy model, which accounts for false-negative and false-positive error in the field and lab. OccPlus leverages the taxonomic breadth of eDNA datasets by using ordination to estimate species occupancies. We recover known biogeographic patterns and find that native terrestrial and fish species have higher occupancies inside protected areas while domesticated species and non-native fishes have higher occupancies outside them. Our study demonstrates how eDNA metabarcoding can obtain high-quality, granular, timely, trustworthy, and efficient species distribution data to facilitate nature conservation and restoration.

The Kunming-Montreal Global Biodiversity Framework needs copious data on species distributions to achieve its targets, but generating such data {at scale} remains challenging. We used aquatic eDNA (environmental DNA) metabarcoding to sample vertebrate species across the 30,000 km\textsuperscript{2} Gaoligongshan protected-area complex along the China-Myanmar border. In just 33 researcher-days, we detected 397 vertebrate species, including 35 Red-Listed species. We introduce the ‘eDNA-aware’ OccPlus occupancy model, which accounts for false-negative and false-positive error at two stages of the eDNA pipeline, field and lab. OccPlus leverages the taxonomic breadth of eDNA datasets by using ordination to estimate species occupancies, even for low-detection species. We recover known biogeographic patterns and find that native terrestrial and fish species have higher occupancies inside protected areas while domesticated species and non-native fishes have higher occupancies outside them. Our study demonstrates how eDNA metabarcoding provides a scalable method for obtaining high-quality, granular, timely, and trustworthy biodiversity data.