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

The performance of drones and artificial intelligence for monitoring sage-grouse at leks | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL Wildlife Biology This is a preprint and has not been peer reviewed. Data may be preliminary. 24 February 2025 V1 Latest version Share on The performance of drones and artificial intelligence for monitoring sage-grouse at leks Authors : Lance B. McNew 0000-0003-0006-7304 [email protected] , Jason Hanlon , and Ilya Buzytsky Authors Info & Affiliations https://doi.org/10.22541/au.174040933.37798201/v1 Published Wildlife Biology Version of record Peer review timeline 437 views 244 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract 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. Supplementary Material File (mcnew et al. main wb.docx) Download 589.19 KB Information & Authors Information Version history V1 Version 1 24 February 2025 Peer review timeline Published Wildlife Biology Version of Record 4 Jun 2025 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Collection Wildlife Biology Keywords artificial intelligence (ai) bayesian analysis machine-learning n-mixture model population monitoring unoccupied aerial system (uas) Authors Affiliations Lance B. McNew 0000-0003-0006-7304 [email protected] Montana State University View all articles by this author Jason Hanlon The Nature Conservancy View all articles by this author Ilya Buzytsky Bias Intelligence, Inc. View all articles by this author Metrics & Citations Metrics Article Usage 437 views 244 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Lance B. McNew, Jason Hanlon, Ilya Buzytsky. 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