An experimental approach to evaluate the potential of drones in terrestrial mammal research: a gregarious ungulate as a study model
Open accessAbstract
Research on the use of unmanned aircraft systems (UAS) in wildlife has made remarkable progress recently. Few studies to date have experimentally evaluated the effect of UAS on animals and have usually focused primarily on aquatic fauna. In terrestrial open arid ecosystems, with relatively good visibility to detect animals but little environmental noise, there should be a trade-off between flying the UAS at high height above ground level (AGL) to limit the disturbance of animals and flying low enough to maintain count precision. In addition, body size or social aggregation of species can also affect the ability to detect animals from the air and their response to the UAS approach. To address this gap, we used a gregarious ungulate, the guanaco ( Lama guanicoe ), as a study model. Based on three types of experimental flights, we demonstrated that (i) the likelihood of miscounting guanacos in images increases with UAS height, but only for offspring and (ii) higher height AGL and lower UAS speed reduce disturbance, except for large groups, which always reacted. Our results call into question mostly indirect and observational previous evidence that terrestrial mammals are more tolerant to UAS than other species and highlight the need for experimental and species-specific studies before using UAS methods.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Flying at lower altitudes, for example lower than 150 m, can have a significant impact on group and individual behavior of mammals, although the severity of wildlife disturbance from drone deployments depends heavily on the focal species, the equipment used, and characteristics of the drone flight.
“A recent study80 showed that flying at lower altitudes (e.g., lower than 150 m) can have a significant impact on group and individual behavior of mammals, although the severity of wildlife disturbance from drone deployments will depend heavily on the focal species, the equipment used, and characteristics of the drone flight (such as approach speed and altitude)81”
From Perspectives in machine learning for wildlife conservation (2022) · cites this paper as [80]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Measuring Behaviour: An Introductory Guide
Martin, P., & Bateson, P. (2007) · doi:10.1017/cbo9780511810893 · Cited by 1,008 · see citing works
Linchant, J., Lisein, J., Ngabinzeke, J. S., Lejeune, P., & Vermeulen, C. (2015) · Mammal Review · doi:10.1111/mam.12046 · Cited by 502 · see citing works
Precision wildlife monitoring using unmanned aerial vehicles
Hodgson, J. C., Baylis, S. M., Mott, R., Herrod, A., & Clarke, R. H. (2016) · Scientific Reports · doi:10.1038/srep22574 · Cited by 476 · see citing works
Gonzalez, F., Montes, G., Puig, E., Johnson, S., Mengersen, K. L., & Gaston, K. J. (2016) · Sensors · doi:10.3390/s16010097 · Cited by 470 · see citing works
Drones count wildlife more accurately and precisely than humans
Hodgson, J. C., Mott, R., Baylis, S. M., Pham, T. T., Wotherspoon, S. J., Kilpatrick, A. D., Segaran, R. R., Reid, I. D., Terauds, A., & Koh, L. P. (2018) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.12974 · Cited by 465 · see citing works
Bears Show a Physiological but Limited Behavioral Response to Unmanned Aerial Vehicles
Ditmer, M. A., Vincent, J. B., Werden, L. K., Tanner, J. C., Laske, T. G., Iaizzo, P. A., Garshelis, D. L., & Fieberg, J. (2015) · Current Biology · doi:10.1016/j.cub.2015.07.024 · Cited by 350 · see citing works
Christie, K. S., Gilbert, S. L., Brown, C. L., Hatfield, M. C., & Hanson, L. (2016) · Frontiers in Ecology and the Environment · doi:10.1002/fee.1281 · Cited by 341 · see citing works
Unmanned aircraft systems as a new source of disturbance for wildlife: A systematic review
Mulero‐Pázmány, M., Jenni‐Eiermann, S., Strebel, N., Sattler, T. L., Negro, J. J., & Tablado, Z. (2017) · PLoS ONE · doi:10.1371/journal.pone.0178448 · Cited by 317 · see citing works
Drones for Conservation in Protected Areas: Present and Future
Jiménez, J., & Mulero‐Pázmány, M. (2019) · Drones · doi:10.3390/drones3010010 · Cited by 304 · see citing works
Approaching birds with drones: first experiments and ethical guidelines
Vas, E., Lescroël, A., Duriez, O., Boguszewski, G., & Grémillet, D. (2015) · Biology Letters · doi:10.1098/rsbl.2014.0754 · Cited by 300 · see citing works
Wildlife research and management methods in the 21st century: Where do unmanned aircraft fit in?
Chabot, D., & Bird, D. M. (2015) · Journal of Unmanned Vehicle Systems · doi:10.1139/juvs-2015-0021 · Cited by 296 · see citing works
Medicine and Surgery of Camelids
Fowler, M. E., & Bravo, P. W. (2010) · Wiley eBooks · doi:10.1002/9781118785706 · Cited by 221 · see citing works
Seymour, A. C., Dale, J., Hammill, M. O., et al. (2017) · Scientific Reports · doi:10.1038/srep45127 · Cited by 217 · see citing works
Chrétien, L., Théau, J., & Ménard, P. (2016) · Wildlife Society Bulletin · doi:10.1002/wsb.629 · Cited by 170 · see citing works
A meta-analysis and review of unmanned aircraft system (UAS) imagery for terrestrial applications
Singh, K. K., & Frazier, A. E. (2018) · International Journal of Remote Sensing · doi:10.1080/01431161.2017.1420941 · Cited by 168 · see citing works
Optimizing observing strategies for monitoring animals using drone-mounted thermal infrared cameras
Burke, C., Rashman, M., Wich, S. A., Symons, A., Theron, C., & Longmore, S. N. (2019) · International Journal of Remote Sensing · doi:10.1080/01431161.2018.1558372 · Cited by 152 · see citing works
Kays, R., Sheppard, J. K., McLean, K. A., et al. (2018) · International Journal of Remote Sensing · doi:10.1080/01431161.2018.1523580 · Cited by 150 · see citing works
Locating chimpanzee nests and identifying fruiting trees with an unmanned aerial vehicle
van Andel, A. C., Wich, S. A., Boesch, C., Koh, L. P., Robbins, M. M., Kelly, J., & Kuehl, H. (2015) · American Journal of Primatology · doi:10.1002/ajp.22446 · Cited by 125 · see citing works
Burnett, J. D., Lemos, L. S., Barlow, D. R., Wing, M. G., Chandler, T., & Torres, L. G. (2018) · Marine Mammal Science · doi:10.1111/mms.12527 · Cited by 117 · see citing works
Pomeroy, P. P., O'Connor, L., & Davies, P. (2015) · Journal of Unmanned Vehicle Systems · doi:10.1139/juvs-2015-0013 · Cited by 111 · see citing works
Automated detection of koalas using low-level aerial surveillance and machine learning
Corcoran, E. M., Denman, S., Hanger, J. J., Wilson, B., & Hamilton, G. S. (2019) · Scientific Reports · doi:10.1038/s41598-019-39917-5 · Cited by 105 · see citing works
Drones as a Threat to Wildlife: YouTube Complements Science in Providing Evidence about Their Effect
Rebolo‐Ifrán, N., Grilli, M. G., & Lambertucci, S. A. (2019) · Environmental Conservation · doi:10.1017/s0376892919000080 · Cited by 101 · see citing works
Rush, G., Clarke, L., Stone, M., & Wood, M. J. (2018) · Ecology and Evolution · doi:10.1002/ece3.4495 · Cited by 90 · see citing works
Thermal Infrared Imaging from Drones Offers a Major Advance for Spider Monkey Surveys
Spaan, D., Burke, C., McAree, O., Aureli, F., Rangel-Rivera, C. E., Hutschenreiter, A., Longmore, S. N., McWhirter, P. R., & Wich, S. A. (2019) · Drones · doi:10.3390/drones3020034 · Cited by 89 · see citing works
Terrestrial mammalian wildlife responses to Unmanned Aerial Systems approaches
Bennitt, E., Bartlam-Brooks, H. L. A., Hubel, T. Y., & Wilson, A. M. (2019) · Scientific Reports · doi:10.1038/s41598-019-38610-x · Cited by 85 · see citing works
Marino, A., & Baldi, R. (2008) · Ethology · doi:10.1111/j.1439-0310.2008.01485_1.x · Cited by 79 · see citing works
Longmore, S. N., Collins, R. P., Pfeifer, S., et al. (2017) · International Journal of Remote Sensing · doi:10.1080/01431161.2017.1280639 · Cited by 78 · see citing works
Lhoest, S., Linchant, J., Quevauvillers, S., Vermeulen, C., & Lejeune, P. (2015) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprsarchives-xl-3-w3-355-2015 · Cited by 67 · see citing works
Bears habituate to the repeated exposure of a novel stimulus, unmanned aircraft systems
Ditmer, M. A., Werden, L. K., Tanner, J. C., et al. (2018) · Conservation Physiology · doi:10.1093/conphys/coy067 · Cited by 64 · see citing works
Schroeder, N., Matteucci, S. D., Moreno, P. G., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0085960 · Cited by 59 · see citing works
Self-citationMeasuring ungulate tolerance to human with flight distance: a reliable visitor management tool?
Malo, J. E., Acebes, P., & Traba, J. (2011) · Biodiversity and Conservation · doi:10.1007/s10531-011-0136-7 · Cited by 49 · see citing works
Arona, L., Dale, J., Heaslip, S. G., et al. (2018) · PeerJ · doi:10.7717/peerj.4467 · Cited by 43 · see citing works
Cooperative vigilance: The guanaco's (Lama guanicoe) key antipredator mechanism
Taraborelli, P. A., Gregorio, P. F., Moreno, P., et al. (2012) · Behavioural Processes · doi:10.1016/j.beproc.2012.06.002 · Cited by 41 · see citing works
A comparison of image and observer based aerial surveys of narwhal
Bröker, K., Hansen, R. G., Leonard, K. E., et al. (2019) · Marine Mammal Science · doi:10.1111/mms.12586 · Cited by 32 · see citing works
Marino, A., & Johnson, A. (2012) · Wildlife Research · doi:10.1071/wr11174 · Cited by 25 · see citing works
Will Drones Reduce Investigator Disturbance to Surface-nesting Birds?
Borrelle, S. B., & Fletcher, A. (2017) · Marine ornithology · doi:10.5038/2074-1235.45.1.1205 · Cited by 19 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [27]Beauchamp G 2015 Animal vigilance. Monitoring predators and competitors. New York, NY: Academic Press.
Cited by
Sorted by citations
Perspectives in machine learning for wildlife conservation
Tuia, D., Kellenberger, B., Beery, S., Costelloe, B. R., Zuffi, S., Risse, B., Mathis, A., Mathis, M. W., van Langevelde, F., Burghardt, T., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-27980-y · Cited by 740 · see citing works
Networked Unmanned Aerial Vehicles for Surveillance and Monitoring: A Survey
Li, X., & Savkin, A. V. (2021) · Future Internet · doi:10.3390/fi13070174 · Cited by 107 · see citing works
- Authors
- Schroeder, N. M., Panebianco, A., Gonzalez Musso, R., & Carmanchahi, P.
- Affiliations
- Consejo Nacional de Investigaciones Científicas y Técnicas (AR)
- Asentamiento Universitario San Martín de los Andes, Universidad Nacional del Comahue, Pasaje de la paz 235, CP 8370, San Martín de los Andes, Neuquén, Argentina
- Corresponding author
- Schroeder, Natalia M.
- Venue
- Royal Society Open Science
- Publisher
- The Royal Society
- Published
- 2020-01
- Volume
- 7
- Issue
- 1
- Pages
- 191482
- ISSN
- 2054-5703 (ISSN-L)
- PMC
- PMC7029930
- OpenAlex
- W2999135164
- Cited by
- 66
- References
- 36
- Citations by year
- 2026: 5 · 2025: 7 · 2024: 12 · 2023: 9 · 2022: 16 · 2021: 13 · 2020: 4
- FWCI
- 5.58
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Royal Society Open Science (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Americanae (AECID Library) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, cc-by)
- Funders
- Consejo Nacional de Investigaciones Científicas y Técnicas (PIP 2015 0425)
- Centro de Investigación y Extensión Forestal Andino Patagónico (CIEFAP)
- Fondo para la Investigación Científica y Tecnológica (PICT 2015 1780)
- Topics
- Physical Sciences › Environmental Science › Ecology › Wildlife Ecology and Conservation
- Physical Sciences › Engineering › Aerospace Engineering › UAV Applications and Optimization
- Physical Sciences › Environmental Science › Ecology › Rangeland and Wildlife Management
- Keywords
- unmanned aircraft systems, unmanned aerial vehicles, guanaco, ungulates, animal disturbance, aerial survey, animal detection, flight altitude, flight speed, social aggregation, body size, wildlife monitoring
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
