Monitoring abundance of aggregated animals (Florida manatees) using an unmanned aerial system (UAS)
1.Williams, B. K., Nichols, J. D. & Conroy, M. J. Analysis and Management of Animal Populations, Modeling, Estimation, and Decision Making (eds. Wood, J. M. & Tanner, G. W.) (Academic Press, 2002).2.Krause, J. & Ruxton, G. D. Living in Groups. Oxford Series in Ecology and Evolution (Oxford University Press, 2002).
Google Scholar
3.Riipi, M. et al. Multiple benefits of gregariousness cover detectability costs in aposematic aggregations. Nature 413, 512–514. https://doi.org/10.1038/35097061 (2001).ADS
CAS
Article
PubMed
Google Scholar
4.Griffin, A. S., Savani, R. S., Hausmanis, K. & Lefebvre, L. Mixed-species aggregations in birds: Zenaida doves, Zenaida aurita, respond to alarm call of carib grackles, Quiscalus lugubris. Anim. Behav. 70, 507–515. https://doi.org/10.1016/j.anbehav.2004.11.023 (2005).Article
Google Scholar
5.Kunz, T. H. Roosting ecology of bats. In Ecology of Bats (ed. Kunz, T. H.) 1–55 (Springer, 1982).6.Dobson, A. & Poole, J. Conspecific aggregation and conservation biology. In Behavioral Ecology and Conservation Biology (ed. Caro, T. M.) 193–208 (Oxford University Press, 1998).7.Laist, D. W. & Reynolds, J. E. Influence of power plants and other warm-water refuges on Florida manatees. Mar. Mamm. Sci. 21, 739–764 (2005).Article
Google Scholar
8.Bossart, G. D. et al. Pathological features of the Florida manatee cold stress syndrome. Aquat. Mamm. 29, 9–17 (2002).Article
Google Scholar
9.Laist, D. W., Taylor, C. & Reynolds, J. E. III. Winter habitat preferences for Florida manatees and vulnerability to cold. PLoS One 8(3), e58978 (2013).ADS
CAS
Article
Google Scholar
10.Chabot, D. & Bird, D. M. Wildlife research and management methods in the 21st century: Where do unmanned aircraft fit in?. J. Unmanned Veh. Sys. 3, 137–155 (2015).Article
Google Scholar
11.Hodgson, A., Kelly, N. & Peel, D. Unmanned aerial vehicles (UAVs) for surveying marine fauna: A dugong case study. PLoS One 8, 1–15. https://doi.org/10.1371/journal.pone.0079556 (2013).CAS
Article
Google Scholar
12.Hodgson, A., Peel, D. & Kelly, N. Unmanned aerial vehicles for surveying marine fauna: Assessing detection probability. Ecol. Appl. 27, 1253–1267 (2017).Article
Google Scholar
13.Landeo-Yauri, S. S., Ramos, E. A., Castelblanco-Martínez, D. N., Niño-Torres, C. A. & Searle, L. Using small drones to photo-identify Antillean manatees: A novel method for monitoring an endangered marine mammal in the Caribbean Sea. Endanger. Species Res. 41, 79–90. https://doi.org/10.3354/esr01007 (2020).Article
Google Scholar
14.Linchant, J., Lisein, J., Smeki, J., Lejeune, P. & Vermeulen, C. Are unmanned aircraft systems (UASs) the future of wildlife monitoring? A review of accomplishments and challenges. Mamm. Rev. 45, 239–252. https://doi.org/10.1111/mam.12046 (2015).Article
Google Scholar
15.Martin, J. et al. Estimating distribution of hidden objects with drones: From tennis balls to manatees. PLoS One 7(6), 1–8. https://doi.org/10.1371/journal.pone.0038882 (2012).CAS
Article
Google Scholar
16.Fiori, L., Martinez, E., Bader, M. K. F., Orams, M. B. & Bollard, B. Insights into the use of an unmanned aerial vehicle (UAV) to investigate the behavior of humpback whales (Megaptera novaeangliae) in Vava’u, Kingdom of Tonga. Mar. Mamm. Sci. 36, 209–223 (2020).Article
Google Scholar
17.Hodgson, J. C. et al. Drones count wildlife more accurately and precisely than humans. Methods Ecol. Evol. 9, 1160–1167 (2018).Article
Google Scholar
18.Edwards, H. H., Pollock, K. H., Ackerman, B. B., Reynolds, J. E. III. & Powell, J. A. Estimation of detection probability in manatee aerial surveys at a winter aggregation site. J. Wildl. Manag. 71, 2052–2060 (2007).Article
Google Scholar
19.Stith, B. M. et al. Passive thermal refugia provided warm water for Florida manatees during the severe winter of 2009–2010. Mar. Ecol. Prog. Ser. 462, 287–301. https://doi.org/10.3354/meps09732 (2012).ADS
Article
Google Scholar
20.Edwards, H. H. & Ackerman, B. B. (eds.) Aerial surveys of manatee distribution in Florida, 1984–2004. In Florida Fish and Wildlife Conservation Commission, Fish and Wildlife Research Institute, Fish and Wildlife Research Institute Technical Report, TR-19, 273 (2016).21.Hartman, D. S. Ecology and behavior of the manatee (Trichechus manatus) in Florida. Am. Soc. Mamm. Spec. Publ. 5, 1–153 (1979).
Google Scholar
22.Otis, D. L., Burnham, K. P., White, G. C. & Anderson, D. R. Statistical inference from capture data on closed animal populations. Wildl. Monogr. 62, 1–133 (1978).MATH
Google Scholar
23.Kéry, M. & Schaub, M. Bayesian Population Analysis Using WinBUGS: A Hierarchical Perspective (Elsevier, Amsterdam, 2012).
Google Scholar
24.Dorazio, R. M., Martin, J. & Edwards, H. H. Estimating abundance while accounting for rarity, correlated behavior, and other sources of variation in counts. Ecology 94, 1472–1478 (2013).Article
Google Scholar
25.Martin, J. et al. Accounting for non-independent detection when estimating abundance of organisms with a Bayesian approach. Methods Ecol. Evol. 2, 595–601 (2011).ADS
Article
Google Scholar
26.Hostetler, J. A., Edwards, H. H., Martin, J. & Schueller, P. Updated statewide abundance estimates for the Florida manatee. https://f50006a.eos-intl.net/F50006A/OPAC/Details/Record.aspx?BibCode=1864664. Accessed 12 June 2021 (2018).27.Craig, B. A. & Reynolds, J. E. III. Determination of manatee population trends along the Atlantic coast of Florida using a Bayesian approach with temperature adjusted aerial survey data. Mar. Mamm. Sci. 20, 386–400 (2004).Article
Google Scholar
28.Hisakado, M., Kitsukawa, K. & Mori, S. Correlated binomial models and correlation structures. J. Phys. A Math. Gen. 39, 15365–15378 (2006).MathSciNet
Article
Google Scholar
29.Royle, A. J., Dorazio, R. M. & Link, W. A. Analysis of multinomial models with unknown index using data augmentation. J. Comput. Graph. Stat. 16(1), 67–85. https://doi.org/10.1198/106186007X181425 (2007).MathSciNet
Article
Google Scholar
30.Royle, A. J. & Dorazio, R. M. Parameter-expanded data augmentation for Bayesian analysis of capture–recapture models. J. Ornithol. 152, 521–537 (2012).Article
Google Scholar
31.Kellner, K. jagsUI: a wrapper around “rjags” to streamline “JAGS” analyses. R package. version 1.4.4. (2016).32.R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2020) www.R-project.org/. Accessed 12 June 2021.33.Runge, M.C. et al. Status and threats analysis for the Florida manatee (Trichechus manatus latirostris), 2016. U.S. Geological Survey Scientific Investigations Report 2017–5030, Reston, VA, 2017. https://doi.org/10.3133/sir2017503034.U.S. Fish and Wildlife Service. Florida Manatee Recovery Plan, Trichechus manatus latirostris, Third Revision. (U.S. Fish and Wildlife Service, 2001).35.Flamm, R. O., Reynolds, J. E. III. & Harmak, C. Improving conservation of Florida manatees (Trichechus manatus latirostris): Conceptualization and contributions toward a regional warm-water network management strategy for sustainable winter habitat. Environ. Manag. 51, 154–166 (2013).ADS
Article
Google Scholar
36.Martin, J. et al. Combining information for monitoring at large spatial scales: First statewide abundance estimate of the Florida manatee. Biol. Conserv. 186, 44–51 (2015).Article
Google Scholar
37.Valade, J., Mezich, R., Smith, K., Merrill, M. & Calleson, T. Florida Manatee Warm-Water Habitat Action Plan. Florida Fish & Wildlife Service and Florida Fish and Wildlife Conservation Commission. 1–43 (2020).38.Wang, D., Shao, Q. & Yue, H. Surveying wild animals from satellites, manned aircraft and unmanned aerial systems (UASs): A review. Remote Sens. 11(1308), 1–28 (2019).ADS
Google Scholar
39.Colefax, A. P., Butcher, P. A. & Kelaher, B. P. The potential for unmanned aerial vehicles (UAVs) to conduct marine fauna surveys in place of manned aircraft. ICES J. Mar. Sci. 75, 1–8 (2018).Article
Google Scholar
40.Linchant, et al. UAS imagery reveals new survey opportunities for counting hippos. PLoS One 13, 1–17. https://doi.org/10.1371/journal.pone.0206413 (2018).CAS
Article
Google Scholar
41.Ezat, M. A., Fritsch, C. J. & Downs, C. T. Use of an unmanned aerial vehicle (drone) to survey Nile crocodile populations: A case study at Lake Nyamithi, Ndumo game reserve, South Africa. Biol. Conserv. 223, 76–81 (2018).Article
Google Scholar
42.Pӧysӓ, H., Kotilainen, J., Väänänen, V. & Kunnasranta, M. Estimating production in ducks: A comparison between ground surveys and unmanned aircraft surveys. Eur. J. Wildl. Res. 64(74), 1–4. https://doi.org/10.1007/s10344-018-1238-2 (2018).Article
Google Scholar
43.Ratcliffe, N. et al. A protocol for the aerial survey of penguin colonies using UAVs. J. Unmanned Veh. Syst. 3, 95–101. https://doi.org/10.1139/juvs-2015-0006 (2015).Article
Google Scholar
44.Brack, I. V., Kindel, A. & Oliveira, L. F. B. Detection error in wildlife abundance estimates from unmanned aerial systems (UAS) surveys: Synthesis, solutions, and challenges. Methods Ecol. Evol. 9, 1864–1873 (2018).Article
Google Scholar
45.Goebel, M. et al. A small unmanned aerial system for estimating abundance and size of Antarctic predators. Polar Biol. 38, 619–630 (2015).Article
Google Scholar More
