Eyes in the sky: Drone monitoring of the largest gharial and mugger populations in the East Rapti River, Chitwan National Park

Thapa, GJ, Thapa, K ORCID: 0000-0002-3838-2804, Poudel, S, Pun, DBP, Shrestha, S ORCID: 0000-0002-0594-1331, Poudel, P, Acharya, HB ORCID: 0000-0002-0974-372X, Lamsal, BK, Sada, R and Wich, SA ORCID: 0000-0003-3954-5174 (2025) Eyes in the sky: Drone monitoring of the largest gharial and mugger populations in the East Rapti River, Chitwan National Park. PLOS One, 20 (8).

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Abstract

Drone-based aerial monitoring can play a pivotal role in scaling up efforts to monitor species at risk. In this study, we assessed the population size, occupancy, and spatial interactions of gharials and muggers in the Eastern Rapti River and its tributaries within Chitwan National Park, complying with national regulations. Using a Wingtra Tail-Sitter Vertical Take-Off and Landing fixed-wing drone, we surveyed a 73-km river stretch during the species’ basking period. The drone captured 24,129 photographs across 27 flight missions, covering 702.66 km and 44.68 km², of which 153 contained dorsal images of gharials (77) and muggers (76). An experienced image analyst identified and counted 323 crocodiles (205 gharials and 118 muggers) from the images. The encounter rates were 14.33 gharials and 9.95 muggers detections per 1 hour of drone flight time. To measure habitat-use through an occupancy framework, we divided the 73-km river stretch into 809 grid cells of 0.04 km² each. The site-level probabilities of habitat-use were 0.47 for gharials and 0.24 for muggers. As anticipated, both species co-occurred spatially along the Eastern Rapti River during the winter season, with a spatial interaction factor (SIF) of 1.94. This study demonstrates the effectiveness of drones in collecting high-resolution ecological data—both spatial and temporal—for assessing population parameters and monitoring threatened crocodile species at scale. Drones offer a cost-effective and less labor-intensive (~US$ 0.61 per km) alternative to traditional ground-based surveys (~US$ 21 per km). Integrating machine learning with drone surveys for automated image analyses has significant potential to further reduce costs and increase efficiency and could strengthen conservation efforts across South Asian River system.

Item Type:	Article
Uncontrolled Keywords:	General Science & Technology
Subjects:	G Geography. Anthropology. Recreation > GE Environmental Sciences
Divisions:	Biological and Environmental Sciences (from Sep 19)
Publisher:	Public Library of Science (PLoS)
Date of acceptance:	30 July 2025
Date of first compliant Open Access:	22 August 2025
Date Deposited:	22 Aug 2025 15:19
Last Modified:	22 Aug 2025 15:30
DOI or ID number:	10.1371/journal.pone.0330350
Editors:	Sharma, B
URI:	https://researchonline.ljmu.ac.uk/id/eprint/26983

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