Jones, G. P., Pearlstine, L. G. & Percival, H. F. An assessment of small unmanned aerial vehicles for wildlife research. Wildl. Soc. Bull. 34, 750–758 (2006).
Google Scholar
Jones, G. P. The feasibility of using small unmanned aerial vehicles for wildlife research. Masters Thesis. (University of Florida, 2003).
Watts, A. C. et al. Unmanned aircraft systems (UASs) for ecological research and natural-resource monitoring (Florida). Ecol. Restor. 26, 13–14 (2008).
Google Scholar
Chabot, D. Systematic Evaluation of a Stock Unmanned Aerial Vehicle (UAV) System for Small-Scale Wildlife Survey Applications. Masters Thesis. (McGill University, 2009).
Koski, W. R. et al. Evaluation of an unmanned airborne system for monitoring marine mammals. Aquat. Mamm. 35, 347–357 (2009).
Google Scholar
Soriano, P., Caballero, F. & Ollero, A. RF-based particle filter localization for wildlife tracking by using an UAV. Int. Symp. Robot. 40, 239–244 (2009).
Sukkarieh, S. UAV based search for a radio tagged animal using particle filters at Stuttgart. In Australasian Conference on Robotics and Automation (ACRA) (2009).
Abd-Elrahman, A., Pearlstine, L. & Percival, F. Development of pattern recognition algorithm for automatic bird detection from unmanned aerial vehicle imagery. Surv. L. Inf. Sci. 65, 37–45 (2005).
Singh, K. K., Frazier, A. E. & Frazier, A. E. A meta-analysis and review of unmanned aircraft system (UAS) imagery for terrestrial applications. Int. J. Remote Sens. 00, 1–21 (2018).
Rebolo-Ifran, N., Grilli, M. G. & Lambertucci, S. Drones as a threat to wildlife: YouTube complements science in providing evidence about their effect. Environ. Conserv. https://doi.org/10.1017/S0376892919000080 (2019).
Google Scholar
Weston, M. A., O’Brien, C., Kostoglou, K. N. & Symonds, M. R. E. E. Escape responses of terrestrial and aquatic birds to drones: Towards a code of practice to minimize disturbance. J. Appl. Ecol. 57, 777–785 (2020).
Google Scholar
Vas, E., Lescroël, A., Duriez, O., Boguszewski, G. & Grémillet, D. Approaching birds with drones: First experiments and ethical guidelines. Biol. Lett. 11, 20140754 (2015).
Google Scholar
Pomeroy, P., O’Connor, L. & Davies, P. Assessing use of and reaction to unmanned aerial systems in gray and harbor seals during breeding and molt in the UK. J. Unmanned Veh. Syst. 3, 102–113 (2015).
Google Scholar
Giles, A. B. et al. Responses of bottlenose dolphins (Tursiops spp.) to small drones. Aquat. Conserv. Mar. Freshw. Ecosyst. https://doi.org/10.1002/aqc.3440 (2020).
Google Scholar
Mulero-Pázmány, M. et al. Unmanned aircraft systems as a new source of disturbance for wildlife: A systematic review. PLoS ONE 12, 1–14 (2017).
Google Scholar
Bennitt, E., Bartlam-Brooks, H. L. A., Hubel, T. Y. & Wilson, A. M. Terrestrial mammalian wildlife responses to unmanned aerial systems approaches. Sci. Rep. 9, 2142 (2019).
Google Scholar
Irigoin-Lovera, C., Luna, D. M., Acosta, D. A. & Zavalaga, C. B. Response of colonial Peruvian guano birds to flying UAVs: Effects and feasibility for implementing new population monitoring methods. PeerJ 7, e8129 (2019).
Google Scholar
McEvoy, J. F., Hall, G. P. & McDonald, P. G. Evaluation of unmanned aerial vehicle shape, flight path and camera type for waterfowl surveys: Disturbance effects and species recognition. PeerJ 2016, e1831 (2016).
Google Scholar
Barnas, A. F., Felege, C. J., Rockwell, R. F. & Ellis-Felege, S. N. A pilot(less) study on the use of an unmanned aircraft system for studying polar bears (Ursus maritimus). Polar Biol. 41, 1055–1062 (2018).
Google Scholar
Jarrett, D., Calladine, J., Cotton, A., Wilson, M. W. & Humphreys, E. Behavioural responses of non-breeding waterbirds to drone approach are associated with flock size and habitat. Bird Study 67, 190–196 (2020).
Google Scholar
Bevan, E. et al. Measuring behavioral responses of sea turtles, saltwater crocodiles, and crested terns to drone disturbance to define ethical operating thresholds. PLoS One 13, e0194460 (2018).
Google Scholar
Stankowich, T. Ungulate flight responses to human disturbance: A review and meta-analysis. Biol. Conserv. 141, 2159–2173 (2008).
Google Scholar
Weston, M. A., Mcleod, E. M., Blumstein, D. T. & Guay, P. J. A review of flight-initiation distances and their application to managing disturbance to Australian birds. Emu 112, 269–286 (2012).
Google Scholar
Wisdom, M. J., Ager, A. A., Preisler, H. K., Cimon, N. J. & Johnson, B. K. Effects of off-road recreation on mule deer and elk. In Transactions of the 69th North American Wildlife and Natural Resources Conference 531–550 (2004).
Penny, S. G., White, R. L., Scott, D. M., MacTavish, L. & Pernetta, A. P. Using drones and sirens to elicit avoidance behaviour in white rhinoceros as an anti-poaching tactic. Proc. R. Soc. B Biol. Sci. 286, 20191135 (2019).
Google Scholar
Frid, A. & Dill, L. M. Human-caused disturbance stimuli as a form of predation risk. Conserv. Ecol. 6, 11 (2002).
Dill, L. M. & Frid, A. Behaviourally mediated biases in transect surveys: A predation risk sensitivity approach. Can. J. Zool. 98, 697–704 (2020).
Google Scholar
Pulliam, R. H. On the advantage of flocking. J. Theor. Biol. 38, 419–422 (1973).
Google Scholar
Taraborelli, P., Gregorio, P., Moreno, P., Novaro, A. & Carmanchahi, P. Cooperative vigilance: The guanaco’ s (Lama guanicoe) key antipredator mechanism. Behav. Process. 91, 82–89 (2012).
Google Scholar
Delm, M. M. Vigilance for predators: Detection and dilution effects. Behav. Ecol. Sociobiol. 26, 337–342 (1990).
Google Scholar
Roberts, G. Why individual vigilance declines as group size increases. Anim. Behav. 51, 1077–1086 (1996).
Google Scholar
Brunton, E., Bolin, J., Leon, J. & Burnett, S. Fright or flight? Behavioural responses of kangaroos to drone-based monitoring. Drones 3, 1–11 (2019).
Google Scholar
Lent, P. C. Mother-infant relationships in ungulates. Behav. Ungulates Relat. Manag. I, 14–55 (1974).
Franklin, W. Contrasting socioecologies of South America´s wild camelids: The vicuña and the guanaco. Adv. Study Mamm. Behav. 7, 573–629 (1983).
Ortega, I. M. & Franklin, W. L. Social organization, distribution and movements of a migratory guanaco population in the Chilean Patagonia. Rev. Chil. Hist. Nat. 68, 489–500 (1995).
Schroeder, N. M., Panebianco, A., Musso, R. G. & Carmanchahi, P. An experimental approach to evaluate the potential of drones in terrestrial mammal research: A gregarious ungulate as a study model. R. Soc. Open Sci. 7, 191482 (2020).
Google Scholar
Lima, S. L. Back to the basics of anti-predatory vigilance: the group size effect. Anim. Behav. 49, 11–20 (1995).
Google Scholar
Marino, A. & Baldi, R. Vigilance patterns of territorial guanacos (Lama guanicoe): The role of reproductive interests and predation risk. Ethology 114, 413–423 (2008).
Google Scholar
Taraborelli, P. et al. Different factors that modify anti-predator behaviour in guanacos (Lama guanicoe). Acta Theriol. (Warsz) 59, 529–539 (2014).
Google Scholar
Donadio, E. & Buskirk, S. W. Flight behavior in guanacos and vicuñas in areas with and without poaching in western Argentina. Biol. Conserv. 127, 139–145 (2006).
Google Scholar
Marino, A. & Johnson, A. Behavioural response of free-ranging guanacos (Lama guanicoe) to land-use change: Habituation to motorised vehicles in a recently created reserve. Wildl. Res. 39, 503–511 (2012).
Google Scholar
Malo, J. E., Acebes, P. & Traba, J. Measuring ungulate tolerance to human with flight distance: A reliable visitor management tool?. Biodivers. Conserv. 20, 3477e3488 (2011).
Google Scholar
Marino, A. Indirect measures of reproductive effort in a resource-defense polygynous ungulate: Territorial defense by male guanacos. J. Ethol. 30, 83–91 (2012).
Google Scholar
Marino, A. & Ricardo, B. Vigilance patterns of territorial guanacos (Lama guanicoe): the role of reproductive interests and predation risk. Ethology 114, 413–423 (2008).
Google Scholar
Merino, M. L. & Cajal, C. J. Estructura social de la población de guanacos (Lama guanicoe Muller, 1776) en la costa norte de Península Mitre, Tierra del Fuego, Argentina. Stud. Neotrop. Fauna Environ. 28, 129–138 (1993).
Google Scholar
Marino, A. & Baldi, R. Ecological correlates of group-size variation in a resource-defense ungulate, the sedentary Guanaco. PLoS ONE 9, e89060 (2014).
Google Scholar
Fattorini, N. et al. Temporal variation in foraging activity and grouping patterns in a mountain-dwelling herbivore: Environmental and endogenous drivers. Behav. Process. 167, 103909 (2019).
Google Scholar
Blank, D., Ruckstuhl, K. & Yang, W. Influence of population density on group sizes in goitered gazelle (Gazella subgutturosa Guld., 1780). Eur. J. Wildl. Res. 58, 981–989 (2012).
Google Scholar
Isvaran, K. Intraspecific variation in group size in the blackbuck antelope: The roles of habitat structure and forage at different spatial scales. Oecologia 154, 435–444 (2007).
Google Scholar
Mahoney, S. P., Mawhinney, K., McCarthy, C., Anions, D. & Taylor, S. Caribou reactions to provocation by snowmachines in Newfoundland. Rangifer 21, 35 (2001).
Google Scholar
Ruiz Blanco, M. et al. Supervivencia y causas de mortalidad durante el primer año de vida de guanacos en el norte de patagonia. In XXVII Jornadas Argentinas de Mastozoología 151 (2014).
Weimerskirch, H., Prudor, A. & Schull, Q. Flights of drones over sub-Antarctic seabirds show species- and status-specific behavioural and physiological responses. Polar Biol. 41, 259–266 (2018).
Google Scholar
McIntosh, R. R., Holmberg, R. & Dann, P. Looking without landing-using Remote Piloted Aircraft to monitor fur seal populations without disturbance. Front. Mar. Sci. 5, (2018).
Mesquita, G. P., Rodríguez-Teijeiro, J. D., Wich, S. A. & Mulero-Pázmány, M. Measuring disturbance at swift breeding colonies due to the visual aspects of a drone: a quasi-experiment study. Curr. Zool. https://doi.org/10.1093/cz/zoaa038 (2020).
Google Scholar
Scobie, C. A. & Hugenholtz, C. H. Wildlife monitoring with unmanned aerial vehicles: Quantifying distance to auditory detection. Wildl. Soc. Bull. 40, 781–785 (2016).
Google Scholar
Rümmler, M. C., Esefeld, J., Hallabrin, M. T., Pfeifer, C. & Mustafa, O. Emperor penguin reactions to UAVs: First observations and comparisons with effects of human approach. Remote Sens. Appl. Soc. Environ. 23, 100545 (2021).
Zbyryt, A., Dylewski, Ł, Morelli, F., Sparks, T. H. & Tryjanowski, P. Behavioural responses of adult and young White Storks Ciconia ciconia in nests to an unmanned aerial vehicle. Acta Ornithol. 55, 243–251 (2020).
Christiansen, F., Rojano-Doñate, L., Madsen, P. T. & Bejder, L. Noise levels of multi-rotor unmanned aerial vehicles with implications for potential underwater impacts on marine mammals. Front. Mar. Sci. 3, 277 (2016).
Google Scholar
Arona, L., Dale, J., Heaslip, S. G., Hammill, M. O. & Johnston, D. W. Assessing the disturbance potential of small unoccupied aircraft systems (UAS) on gray seals (Halichoerus grypus) at breeding colonies in Nova Scotia, Canada. PeerJ https://doi.org/10.7717/peerj.4467 (2018).
Google Scholar
Goebel, M. E. et al. A small unmanned aerial system for estimating abundance and size of Antarctic predators. Polar Biol. 38, 619–630 (2015).
Google Scholar
Cracknell, A. P. UAVs: Regulations and law enforcement. Int. J. Remote Sens. 38, 3054–3067 (2017).
Google Scholar
ANAC, A. N. de A. C. Reglamento de Vehículos Aéreos no Tripulados (VANT) y de Sistemas de Vehículos Aéreos no Tripulados (SVANT). (2019).
Brisson-Curadeau, É. et al. Seabird species vary in behavioural response to drone census. Sci. Rep. 7, 17884 (2017).
Google Scholar
Rümmler, M.-C., Mustafa, O., Maercker, J., Peter, H.-U. & Esefeld, J. Sensitivity of Adélie and Gentoo penguins to various flight activities of a micro UAV. Polar Biol. 41, 2481–2493 (2018).
Google Scholar
Ditmer, M. A. et al. Bears habituate to the repeated exposure of a novel stimulus, unmanned aircraft systems. Conserv. Physiol. 7, 1–7 (2019).
Google Scholar
Young, J. K. & Franklin, W. L. Territorial Fidelity of male guanacos in the Patagonia of Southern Chile. J. Mammal. 85, 72–78 (2004).
Google Scholar
Martínez Carretero, E. La Provincia Fitogeográfica de la Payunia. Boletín la Soc. Argentina Botánica 39, 195–226 (2004).
Schroeder, N. M. et al. Spatial and seasonal dynamic of abundance and distribution of guanaco and livestock: Insights from using density surface and null models. PLoS One 9, e85960 (2014).
Google Scholar
Bolgeri, M. J. Caracterización de movimientos migratorios en guanacos (Lama guanicoe) y patrones de depredación por pumas (Puma concolor) en la Payunia, Mendoza. Phd Thesis. (Universidad Nacional del Comahue, 2016).
Bolgeri, M. J. & Novaro, A. J. Variación espacial en la depredación por puma (Puma concolor) sobre guanacos (Lama guanicoe) en la Payunia, Mendoza,Argentina. Mastozoología Neotrop. 22, 255–264 (2015).
Candia, R., Puig, S., Dalmasso, A., Videla, F. & Martínez Carretero, E. Diseño del Plan de Manejo para la reserva provincial La Payunia (Malargüe, Mendoza). Multequina 2, 5–87 (1993).
Carmanchahi, P. D. et al. Physiological response of wild guanacos to capture for live shearing. Wildl. Res. 38, 61–68 (2011).
Google Scholar
Martin, P. & Bateson, P. Measuring Behaviour. An Introductory Guide. (Cambridge University Press, 2007).
Ydenberg, R. C. & Dill, L. M. The economics of fleeing from predators. Adv. Study Behav. 16, 229–249 (1986).
Google Scholar
McCullagh, P. & Nelder, J. Generalized Linear Models. Second Edition. (Chapman & Hall, 1989).
Fox, J. & Monette, G. Generalized collinearity diagnostics. J. Am. Stat. Assoc. 87, 178–183 (1992).
Google Scholar
Zuur, A. F., Ieno, E. N. & Smith, G. M. Analysing Ecological Data. (Springer, 2007).
Gelman, A. & Hill, J. Data analysis using regression and multilevel/hierarchical models. Cambridge 651 (2007). https://doi.org/10.2277/0521867061
Korner-Nievergelt, F. et al. Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and Stan. (2015). https://doi.org/10.1007/s13398-014-0173-7.2
R Core Team. R Development Core Team. R A Lang. Environ. Stat. Comput. 55, 275–286 (2016).
Fox, J. & Weisberg, S. An R Companion to Applied Regression, 3rd edn. (2019).
Gelman, A. et al. Data analysis using regression and multilevel/hierarchical models. R package version 1, 10–1 (2018).
Source: Ecology - nature.com
