Title
Shark detection probability from aerial drone surveys within a temperate estuary
Date Issued
01 January 2020
Access level
open access
Resource Type
journal article
Author(s)
Fodrie F.J.
Johnston D.W.
University of North Carolina at Chapel Hill
Publisher(s)
Canadian Science Publishing
Abstract
Drones are easy to operate over metres-to-kilometre scales, making them potentially useful to monitor species distributions and habitat use in shallow estuaries with widely varying environmental conditions. To investigate the utility of drones for surveying bonnethead sharks (Sphyrna tiburo) across estuarine environmental gradients, we deployed decoys, fashioned to mimic sharks, in the field. Decoys were placed in two flight areas (0.8 km2 each) in shallow (<2 m) water near Beaufort, N.C., on five days during 2015–2016. Survey flights were conducted using a fixed-wing drone (senseFly eBee) equipped with a digital camera. Images were indexed for combinations of six environmental factors across flights. Images representative of all (N = 36) observed environmental combinations were sent to a group of 15 scientists who were asked to identify sharks in each image. Non-parametric rank-sum comparisons and regression tree analysis on resultant detection probabilities highlighted depth as having the largest, statistically reliable influence on detection probabilities, with decreasing detection probabilities at increased depth. Detection probabilities were higher during midday flights, with notable effects of wind speed and cloud presence also apparent. Our study highlights depth as a first-order factor constraining the temperate estuarine habitats over which drones may reliably quantify sharks (i.e., <0.75 m).
Start page
44
End page
56
Volume
8
Issue
1
Language
English
Scopus EID
2-s2.0-85082144852
Source
Journal of Unmanned Vehicle Systems
Sponsor(s)
This research project was funded by a North Carolina Aquarium Society Conservation Grant (award No. 2015-03) and the first author was supported by a Ph.D. scholarship from the Peruvian National Council for Science, Technology and Technological Innovation (CONCYTEC). Fieldwork was conducted under Research Permit No. 7-2016 issued by the N.C. Coastal Reserve and National Estuarine Research Reserve. We thank members of the Marine Robotics and Remote Sensing Lab at the Duke University Marine Lab, especially Everette Newton and Julian Dale for UAS flight planning and logistics, as well as Austin Moore for assistance with experimental design and decoy construction. We also thank members of the Coastal Fisheries Oceanography and Ecology lab at the UNC Institute of Marine Sciences, especially Matthew Kenworthy, Shelby Ziegler, Maxwell Tice-Lewis, and Mariah Livernois for assistance with decoy deployment in the field. We thank interns Giada Bargione (Polytechnic University of Marche) and Connor Neagle (North Carolina State University) for assistance with image assessments and data analysis. We also thank our volunteer scorers (not already named): Amy Yarnall, Cori Lopazanski, Danielle Keller, Lauren Clance, Owen Mulvey-McFerron, James Morley, Omar Fais, Haley Ealey, Ryan Giannelli, Charles Bangley, Claire Pelletier, and Seth Sykora-Bodie. Finally, comments from Stephen Fegley (UNC Institute of Marine Sciences), Nathan Bacheler (NOAA Beaufort Lab), and two anonymous reviewers were most helpful in refining and revising the manuscript.
Sources of information: Directorio de Producción Científica Scopus