[ad_1]
Anderson, Okay. & Gaston, Okay. J. Light-weight unmanned aerial automobiles will revolutionize spatial ecology. Entrance. Ecol. Environ. 11, 138–146 (2013).
Google Scholar
Weinstein, B. G. et al. A common deep studying mannequin for fowl detection in high-resolution airborne imagery. Ecol. Appl. 4, e2694 (2022).
Google Scholar
Lyons, M. B. et al. Monitoring massive and complicated wildlife aggregations with drones. Strategies Ecol. Evol. 10, 1024–1035 (2019).
Google Scholar
Schad, L. & Fischer, J. Alternatives and dangers in the usage of drones for finding out animal behaviour. Strategies Ecol. Evol. https://doi.org/10.1111/2041-210X.13922 (2022).
Google Scholar
Evans, L., Jones, T., Pang, Okay., Saimin, S. & Goossens, B. Spatial ecology of estuarine crocodile (Crocodylus porosus) nesting in a fragmented panorama. Sensors 16, 1527 (2016).
Google Scholar
Bonnin, N. et al. Evaluation of chimpanzee nest detectability in drone-acquired photographs. Drones 2, 17 (2018).
Google Scholar
Seymour, A. C., Dale, J., Hammill, M., Halpin, P. N. & Johnston, D. W. Automated detection and enumeration of marine wildlife utilizing unmanned plane methods (UAS) and thermal imagery. Sci. Rep. 7, 45127 (2017).
Google Scholar
Chabot, D. & Francis, C. M. Pc-automated fowl detection and counts in high-resolution aerial photographs: A evaluate. J. Area Ornithol. 87, 343–359 (2016).
Google Scholar
Sardà-Palomera, F. et al. Fantastic-scale fowl monitoring from gentle unmanned plane methods: Chicken monitoring from UAS. Ibis 154, 177–183 (2012).
Google Scholar
Afán, I., Máñez, M. & Díaz-Delgado, R. Drone monitoring of breeding waterbird populations: The case of the shiny ibis. Drones 2, 42 (2018).
Google Scholar
Weissensteiner, M. H., Poelstra, J. W. & Wolf, J. B. W. Low-budget ready-to-fly unmanned aerial automobiles: An efficient instrument for evaluating the nesting standing of canopy-breeding fowl species. J. Avian Biol. 46, 425–430 (2015).
Google Scholar
Valle, R. G. & Scarton, F. Speedy evaluation of productiveness of purple herons Ardea purpurea by drone performed monitoring. Ardeola 69, 231–248 (2022).
Google Scholar
Sardà-Palomera, F., Bota, G., Padilla, N., Brotons, L. & Sardà, F. Unmanned plane methods to unravel spatial and temporal components affecting dynamics of colony formation and nesting success in birds. J. Avian Biol. 48, 1273–1280 (2017).
Google Scholar
Hodgson, J. C. et al. Drones rely wildlife extra precisely and exactly than people. Strategies Ecol. Evolut. 9, 1160–1167 (2018).
Google Scholar
Barr, J. R., Inexperienced, M. C., DeMaso, S. J. & Hardy, T. B. Detectability and visibility biases related to utilizing a consumer-grade unmanned plane to survey nesting colonial waterbirds. J. Area Ornithol. 89, 242–257 (2018).
Google Scholar
Nichols, J. D., Thomas, L. & Conn, P. B. Inferences about landbird abundance from rely knowledge: Latest advances and future instructions. In Modeling Demographic Processes in Marked Populations (eds. Thomson, D. L., Cooch, E. G. & Conroy, M. J.). 201–235 (Springer, 2009).
Riddle, J. D., Stanislav, S. J., Pollock, Okay. H., Moorman, C. E. & Perkins, F. S. Separating parts of the detection course of with mixed strategies: An instance with northern bobwhite. J. Wildl. Manag. 74, 1319–1325 (2010).
Van Vessem, J. & Draulans, D. Elements affecting the size of the breeding cycle and the frequency of nest attendance by Gray Herons Ardea cinerea. Chicken Research 33, 98–104 (1986).
Google Scholar
Jakubas, D. The affect of local weather circumstances on breeding phenology of the Gray Heron Ardea cinerea L. in northern Poland. Pol. J. Ecol. 59, 179–192 (2011).
Maccarone, A. D., Brzorad, J. N. & Stone, H. M. Nest-activity patterns and food-provisioning charges by Nice Egrets (Ardea alba). Waterbirds Int. J. Waterbird Biol. 33, 504–510 (2010).
Google Scholar
Chabot, D., Craik, S. R. & Chicken, D. M. Inhabitants census of a giant widespread tern colony with a small unmanned plane. PLOS ONE 10, e0122588 (2015).
Google Scholar
Brisson-Curadeau, É. et al. Seabird species differ in behavioural response to drone census. Sci. Rep. 7, 113 (2017).
Google Scholar
Williams, Okay. A., Frederick, P. C., Kubilis, P. S. & Simon, J. C. Bias in aerial estimates of the variety of nests in White Ibis and Nice Egret colonies. J. Area Ornithol. 79, 438–447 (2008).
Google Scholar
Pollock, Okay. H. & Kendall, W. L. Visibility bias in aerial surveys: A evaluate of estimation procedures. J. Wildl. Manag. 51, 502–510 (1987).
Google Scholar
Conroy, M. J. et al. Sources of variation in detection of wading birds from aerial surveys within the Florida Everglades. Auk 125, 731–743 (2008).
Google Scholar
Rodgers, J. A., Kubilis, P. & Nesbitt, S. A. Accuracy of aerial surveys of waterbird colonies. Waterbirds 28, 230–237 (2005).
Google Scholar
Corcoran, E., Denman, S. & Hamilton, G. Evaluating new expertise for biodiversity monitoring: Are drone surveys biased?. Ecol. Evol. 11, 6649–6656 (2021).
Google Scholar
Sauer, J. R., Peterjohn, B. G. & Hyperlink, W. A. Observer variations within the North American breeding fowl survey. Auk 111, 50–62 (1994).
Google Scholar
Kendall, W. L., Peterjohn, B. G. & Sauer, J. R. First-time observer results within the North American breeding fowl survey. Auk 113, 823–829 (1996).
Google Scholar
Williams, B. Okay., Nichols, J. D. & Conroy, M. J. Evaluation and Administration of Animal Populations (Educational Press, 2002).
Otis, D., Burnham, Okay., White, G. & Anderson, D. Statistical inference from seize knowledge on closed animal populations. Wildl. Monogr. 62, 1–135 (1978).
Buckland, S. T. et al. Introduction to Distance Sampling: Estimating Abundance of Organic Populations (2001).
Landeo-Yauri, S., Ramos, E., Castelblanco-Martínez, D., Torres, C. & Searle, L. Utilizing small drones to photo-identify Antillean manatees: A novel technique for monitoring an endangered marine mammal within the Caribbean Sea. Endanger. Spec. Res. 41, 79–90 (2020).
Google Scholar
Vas, E., Lescroël, A., Duriez, O., Boguszewski, G. & Grémillet, D. Approaching birds with drones: first experiments and moral pointers. Biol. Lett. 11, 20140754 (2015).
Google Scholar
Weston, M. A., O’Brien, C., Kostoglou, Okay. N. & Symonds, M. R. E. Escape responses of terrestrial and aquatic birds to drones: In the direction of a code of follow to attenuate disturbance. J. Appl. Ecol. 57, 777–785 (2020).
Google Scholar
Prepare dinner, R. D. & Jacobson, J. O. A design for estimating visibility bias in aerial surveys. Biometrics 35, 735–742 (1979).
Google Scholar
Graham, A. & Bell, R. Investigating observer bias in aerial survey by simultaneous double-counts. J. Wildl. Manag. 53, 1009–1016 (1989).
Google Scholar
Seber, G. A. F. Closed inhabitants: Single mark launch. In The Estimation of Animal Abundance and Associated Parameters. 59–125 (Griffin, 1982).
Collins, S. A., Giffin, G. J. & Robust, W. T. Utilizing flight initiation distance to judge responses of colonial-nesting Nice Egrets to the method of an unmanned aerial automobile. J. Area Ornithol. 90, 382–390 (2019).
Google Scholar
Barr, J. R., Inexperienced, M. C., DeMaso, S. J. & Hardy, T. B. Drone surveys don’t improve colony-wide flight behaviour at waterbird nesting websites, however sensitivity varies amongst species. Sci. Rep. 10, 34 (2020).
Google Scholar
Jeanne, F. et al. Plan Nationwide d’Actions en Faveur du Crabier Blanc (Ardeola idae) sur l’île de Mayotte 2019–2023. Vol. 71 (2018).
Rabarisoa, R. et al. Standing evaluation and inhabitants tendencies of the Madagascar Pond-Heron (Ardeola idae) from 1993–2016. Waterbirds 43, 45 (2020).
Google Scholar
BirdLife Worldwide. The IUCN Crimson Checklist of Threatened Species 2021. (2021).
Sandilyan, S. & Kathiresan, Okay. Mangrove conservation: A world perspective. Biodivers. Conserv. 21, 3523–3542 (2012).
Google Scholar
Bunbury, N. Distribution, seasonality and habitat preferences of the endangered Madagascar Pond-heron Ardeola idae on Aldabra Atoll: 2009–2012. Ibis 156, 233–235 (2014).
Google Scholar
Rocamora, G. Les Oiseaux des Espaces Naturels Remarquables de Mayotte. Vol. 247 (2004).
UICN Comité Français. La Liste Rouge des Ecosystèmes en France—Chapitre Mangroves de Mayotte. Vol. 72 (2017).
Ndang’ang’a, P. Okay. & Sande, E. Worldwide Single Species Motion Plan for the Madagascar Pond-heron (Ardeola idae) (2008).
Wooden, S. N. Quick secure REML and ML estimation of semiparametric GLMs. J. R. Stat. Soc. Ser. B Stat. Methodol. 73, 3–36 (2011).
R Core Workforce. R: A Language and Surroundings for Statistical Computing (2017).
Burnham, Okay. P. & Anderson, D. R. Mannequin Choice and Multimodel Inference: A Sensible Info Theoretic Strategy (2002).
Laake, J. L. RMark: An R Interface for Evaluation of Seize-Recapture Knowledge with MARK. Vol. 33 (2013).
Rush, G. P., Clarke, L. E., Stone, M. & Wooden, M. J. Can drones rely gulls? Minimal disturbance and semiautomated picture processing with an unmanned aerial automobile for colony-nesting seabirds. Ecol. Evolut. 8, 12322–12334 (2018).
Google Scholar
Santangeli, A. et al. Integrating drone-borne thermal imaging with synthetic intelligence to find fowl nests on agricultural land. Sci. Rep. 10, 10993 (2020).
Google Scholar
Burke, C. et al. Optimizing observing methods for monitoring animals utilizing drone-mounted thermal infrared cameras. Int. J. Distant Sens. 40, 439–467 (2019).
Google Scholar
Kays, R. et al. Sizzling monkey, chilly actuality: surveying rainforest cover mammals utilizing drone-mounted thermal infrared sensors. Int. J. Distant Sens. 40, 407–419 (2019).
Google Scholar
McKellar, A. E., Shephard, N. G. & Chabot, D. Twin visible-thermal digicam method facilitates drone surveys of colonial marshbirds. Distant Sens. Ecol. Conserv. 7, 214–226 (2021).
Google Scholar
[ad_2]
Source link
