The performance of drones and artificial intelligence for monitoring sage‐grouse at leks

Abstract

Accurately monitoring sage-grouse populations is critical for conservation, yet traditional ground-based visual surveys face challenges in scalability and consistency, prompting the exploration of innovative drone-based methodologies enhanced by artificial intelligence. We evaluated the effectiveness of drone-based survey protocols combined with an AI count model relative to traditional ground-based visual surveys for counting sage-grouse at leks. Drone-induced flushing of sage-grouse from leks occurred in 16% of flight attempts. Point-of-interest (POI) flight profiles outperformed linear flight profiles in counting accuracy for both AI and manual methods. POI flights provided more images and a larger field of view, resulting in counts similar to traditional ground-based visual (GBV) lek surveys, while linear flights consistently produced undercounts. Our custom AI counter (INDECS) yielded counts of sage-grouse similar to manual counts in POI surveys, but not in linear surveys. When integrated into modified N-mixture models, drone surveys with POI profiles yielded precise estimates of detection probabilities and abundance for all survey methods that resulted in similar inference to GBV surveys. Our results suggest that AI-enhanced drone surveys, particularly with POI flight profiles, offer a promising alternative to traditional surveys with reduced bias and improved consistency in sage-grouse population monitoring.