School of Earth, Environmental, and Marine Sciences Faculty Publications and Presentations
Document Type
Article
Publication Date
10-27-2021
Abstract
Conservation concerns are increasing for numerous freshwater turtle species, including Pseudemys gorzugi, which has led to a call for more research. However, traditional sampling methodologies are often time consuming, labor intensive, and invasive, restricting the amount of data that can be collected. Biases of traditional sampling methods can further impair the quality of the data collected, and these shortfalls may discourage their use. The use of unmanned aerial vehicles (UAVs, drones) for conducting wildlife surveys has recently demonstrated the potential to bridge gaps in data collection by offering a less labor intensive, minimally invasive, and more efficient process. Photographs and video can be obtained by camera attachments during a drone flight and analyzed to determine population counts, abundance, and other types of data. In this study we developed a detailed protocol to survey for large, freshwater turtle species in an arid, riverine landscape. This protocol was implemented with a DJI Matrice 600 Pro drone and a SONY ILCE α6000 digital camera to determine P. gorzugi and sympatric turtle species occurrence across 42 sites in southwestern Texas, USA. The use of a large drone and high-resolution camera resulted in high identification percentages, demonstrating the potential of drones to survey for large, freshwater turtle species. Numerous advantages to drone-based surveys were identified as well as some challenges, which were addressed with additional refinement of the protocol. Our data highlight the utility of drones for conducting freshwater turtle surveys and provide a guideline to those considering implementing drone-mounted high-resolution cameras as a survey tool.
Recommended Citation
Bogolin AP, Davis DR, Kline RJ, Rahman AF (2021) A drone-based survey for large, basking freshwater turtle species. PLoS ONE 16(10): e0257720. https://doi.org/10.1371/journal.pone.0257720
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Publication Title
PLoS ONE
DOI
10.1371/journal.pone.0257720
Comments
© 2021 Bogolin et al.