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On the move: Influence of animal movements on count error during drone surveys
  • +4
  • Emma Schultz,
  • Natasha Ellison,
  • Melanie Boudreau,
  • Garrett Street,
  • Landon Jones,
  • Kristine Evans,
  • Raymond B. Iglay
Emma Schultz
Mississippi State University

Corresponding Author:[email protected]

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Natasha Ellison
Mississippi State University
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Melanie Boudreau
Mississippi State University
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Garrett Street
Mississippi State University
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Landon Jones
Mississippi State University
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Kristine Evans
Mississippi State University
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Raymond B. Iglay
Mississippi State University
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Abstract

The use of remote sensing to monitor animal populations has greatly expanded during the last decade. Drones (i.e. Unoccupied Aircraft Systems or UAS) provide a cost- and time-efficient remote sensing option to survey animals in various landscapes and sampling conditions. However, drone-based surveys may also introduce counting errors, especially when monitoring mobile animals. Using an agent-based model simulation approach, we evaluated the error associated with counting a single animal across various drone flight patterns under three animal movement strategies (random, directional persistence, and biased towards a resource) among five animal speeds (2, 4, 6, 8, 10 m/s). Flight patterns represented increasing spatial independence (ranging from lawnmower pattern with image overlap to systematic point counts). Simulation results indicated that flight pattern was the most important variable influencing count accuracy, followed by the type of animal movement pattern, and then animal speed. A lawnmower pattern with 0% overlap produced the most accurate count of a solitary, moving animal on a landscape (average count of 1.1 ± 0.6) regardless of the animal’s movement pattern and speed. Image overlap flight patterns were more likely to result in multiple counts even when accounting for mosaicking. Based on our simulations, we recommend using a lawnmower pattern with 0% image overlap to minimize error and augment drone efficacy for animal surveys. Our work highlights the importance of understanding interactions between animal movements and drone survey design on count accuracy to inform the development of broad applications among diverse species and ecosystems.
05 Feb 2024Submitted to Ecology and Evolution
06 Feb 2024Submission Checks Completed
06 Feb 2024Assigned to Editor
18 Feb 2024Reviewer(s) Assigned
12 Apr 2024Editorial Decision: Revise Minor
31 May 20241st Revision Received
01 Jun 2024Submission Checks Completed
01 Jun 2024Assigned to Editor
01 Jun 2024Review(s) Completed, Editorial Evaluation Pending
20 Aug 20242nd Revision Received
21 Aug 2024Submission Checks Completed
21 Aug 2024Assigned to Editor
21 Aug 2024Review(s) Completed, Editorial Evaluation Pending
29 Aug 2024Editorial Decision: Accept