Animal-borne acoustic data alone can provide high accuracy classification of activity budgets
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Background: Studies on animal behaviour often involve the quantification of the occurrence and duration of various activities. When direct observations are challenging (e.g., at night, in a burrow, at sea), animal-borne devices can be used to remotely record the movement and behaviour of an animal (e.g., changing body posture and movement, geographical position) and/or its immediate surrounding environment (e.g., wet or dry, pressure, temperature, light). Changes in these recorded variables are related to different activities undertaken by the animal. Here we explored the use of animal-borne acoustic recorders to automatically infer activities in seabirds. Results: We deployed acoustic recorders on Cape gannets and analysed sound data from 10 foraging trips. The different activities (flying, floating on water and diving) were associated with clearly distinguishable acoustic features. We developed a method to automatically identify the activities of equipped individuals, exclusively from animal-borne acoustic data. A random subset of four foraging trips was manually labelled and used to train a classification algorithm (k-nearest neighbour model). The algorithm correctly classified activities with a global accuracy of 98.46%. The model was then used to automatically assess the activity budgets on the remaining non-labelled data, as an illustrative example. In addition, we conducted a systematic review of studies that have previously used data from animal-borne devices to automatically classify animal behaviour (n = 61 classifications from 54 articles). The majority of studies (82%) used accelerometers (alone or in combination with other sensors, such as gyroscopes or magnetometers) for classifying activities, and to a lesser extent GPS, acoustic recorders or pressure sensors, all potentially providing a good accuracy of classification (> 90%). Conclusion: This article demonstrates that acoustic data alone can be used to reconstruct activity budgets with very good accuracy. In addition to the animal’s activity, acoustic devices record the environment of equipped animals (biophony, geophony, anthropophony) that can be essential to contextualise the behaviour of animals. They hence provide a valuable alternative to the set of tools available to assess animals’ behaviours and activities in the wild.
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Wearable acoustic tags can capture vocalizations, movement-generated sounds, and environmental noise, enabling automated inference of behavioural states such as flying, diving, or resting from acoustic patterns alone.
“Wearable acoustic tags can capture not only vocalizations but also movement-generated sounds and environmental noise, enabling automated inference of behavioural states such as flying, diving, or resting from acoustic patterns alone”
From Listening forward: emerging roles of bioacoustics in ecology, evolution, and conservation (2026) · cites this paper as Thiebault et al. 2021
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Observational study of behavior: sampling methods
Altmann, J. (1974) · Behaviour · doi:10.1163/156853974x00534 · Cited by 13,891 · see citing works
On aims and methods of ethology
Tinbergen, N. (1963) · Zeitschrift für Tierpsychologie · doi:10.1111/j.1439-0310.1963.tb01161.x · Cited by 4,551 · see citing works
Handbook of ethological methods
(1997) · Choice Reviews Online · doi:10.5860/choice.34-4483 · Cited by 1,706 · see citing works
Vervet monkey alarm calls: Semantic communication in a free-ranging primate
Seyfarth, R., Cheney, D., & Marler, P. (1980) · Animal Behaviour · doi:10.1016/s0003-3472(80)80097-2 · Cited by 957 · see citing works
Tinbergen, N. (1952) · The Quarterly Review of Biology · doi:10.1086/398642 · Cited by 864 · see citing works
Biotelemetry: a mechanistic approach to ecology
Cooke, S. J., Hinch, S. G., Wikelski, M. C., Andrews, R. D., Kuchel, L. J., Wolcott, T. G., & Butler, P. J. (2004) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2004.04.003 · Cited by 852 · see citing works
Plummeting gannets: a paradigm of ecological optics
Lee, D. N., & Reddish, P. E. (1981) · Nature · doi:10.1038/293293a0 · Cited by 743 · see citing works
Biotelemetry: a mechanistic approach to ecology
Cooke, S. J. (2004) · Trends in Ecology & Evolution · doi:10.1016/s0169-5347(04)00112-0 · Cited by 670 · see citing works
The golden age of bio‐logging: how animal‐borne sensors are advancing the frontiers of ecology
Wilmers, C. C., Nickel, B. A., Bryce, C. M., Smith, J. A., Wheat, R. E., & Yovovich, V. (2015) · Ecology · doi:10.1890/14-1401.1 · Cited by 598 · see citing works
How to reliably estimate the tortuosity of an animal's path:
Benhamou, S. (2004) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2004.03.016 · Cited by 586 · see citing works
Trends and perspectives in animal‐attached remote sensing
Ropert-Coudert, Y., & Wilson, R. (2005) · Frontiers in Ecology and the Environment · doi:10.1890/1540-9295(2005)003[0437:tapiar]2.0.co;2 · Cited by 560 · see citing works
BEHAVIORAL SAMPLING METHODS FOR CETACEANS: A REVIEW AND CRITIQUE
Mann, J. (1999) · Marine Mammal Science · doi:10.1111/j.1748-7692.1999.tb00784.x · Cited by 558 · see citing works
Identification of animal movement patterns using tri-axial accelerometry
Shepard, E. L. C., Wilson, R., Quintana, F., Laich, A. G., Liebsch, N., Albareda, D., Halsey, L. G., Gleiss, A. C., Morgan, D., Myers, A., et al. (2008) · Endangered Species Research · doi:10.3354/esr00084 · Cited by 535 · see citing works
Martiskainen, P., Järvinen, M., Skön, J.-P., Tiirikainen, J., Kolehmainen, M., & Mononen, J. (2009) · Applied Animal Behaviour Science · doi:10.1016/j.applanim.2009.03.005 · Cited by 405 · see citing works
Time Budgeting and Group Size: A Test of Theory
Caraco, T. B. (1979) · Ecology · doi:10.2307/1936082 · Cited by 391 · see citing works
Baboons, Space, Time, and Energy
Altmann, S. A. (1974) · American Zoologist · doi:10.1093/icb/14.1.221 · Cited by 345 · see citing works
An Ethological Analysis of Social Agonism and Dominance Relations among Preschool Children
Strayer, F. F., & Strayer, J. (1976) · Child Development · doi:10.2307/1128434 · Cited by 296 · see citing works
A New Technique for Monitoring the Behaviour of Free-Ranging Adélie Penguins
Yoda, K., Naito, Y., Sato, K., Takahashi, A., Nishikawa, J., Ropert‐Coudert, Y., Kurita, M., & Maho, Y. L. (2001) · Journal of Experimental Biology · doi:10.1242/jeb.204.4.685 · Cited by 237 · see citing works
Sperm whale sound production studied with ultrasound time/depth-recording tags
Madsen, P. T., Payne, R. S., Kristiansen, N. U., Wahlberg, M., Kerr, I., & Møhl, B. (2002) · Journal of Experimental Biology · doi:10.1242/jeb.205.13.1899 · Cited by 231 · see citing works
Anthony, E. L. P., Stack, M. H., & Kunz, T. (1981) · Oecologia · doi:10.1007/bf00540593 · Cited by 218 · see citing works
Vandenabeele, S. P., Shepard, E. L. C., Grogan, A., & Wilson, R. P. (2011) · Marine Biology · doi:10.1007/s00227-011-1784-6 · Cited by 205 · see citing works
Hanya, G. (2004) · American Journal of Primatology · doi:10.1002/ajp.20049 · Cited by 203 · see citing works
Dynamic cattle behavioural classification using supervised ensemble classifiers
Dutta, R., Smith, D., Rawnsley, R., Bishop-Hurley, G. J., Hills, J. L., Timms, G. P., & Henry, D. A. (2014) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2014.12.002 · Cited by 200 · see citing works
Behavioral classification of data from collars containing motion sensors in grazing cattle
González, L. A., Bishop-Hurley, G. J., Handcock, R. N., & Crossman, C. (2014) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2014.10.018 · Cited by 178 · see citing works
Yoda, K., Sato, K., Niizuma, Y., Kurita, M., Bost, C., Maho, Y. L., & Naito, Y. (1999) · Journal of Experimental Biology · doi:10.1242/jeb.202.22.3121 · Cited by 178 · see citing works
Has the fishery contributed to a major shift in the distribution of South African sardine?
Coetzee, J., van der Lingen, C. D., Hutchings, L., & Fairweather, T. P. (2008) · ICES Journal of Marine Science · doi:10.1093/icesjms/fsn184 · Cited by 170 · see citing works
From Sensor Data to Animal Behaviour: An Oystercatcher Example
Shamoun-Baranes, J. Z., Bom, R. A., van Loon, E. E., Ens, B. J., Oosterbeek, K., & Bouten, W. (2012) · PLoS ONE · doi:10.1371/journal.pone.0037997 · Cited by 162 · see citing works
Perspectives – Minimizing Observer Bias in Behavioral Studies: A Review and Recommendations
Burghardt, G. M., Bartmess-LeVasseur, J., Browning, S. A., Morrison, K. E., Stec, C. L., Zachau, C. E., & Freeberg, T. M. (2012) · Ethology · doi:10.1111/j.1439-0310.2012.02040.x · Cited by 158 · see citing works
Ropert‐Coudert, Y., Grémillet, D., Kato, A., Ryan, P. G., Naito, Y., & Maho, Y. L. (2004) · Animal Behaviour · doi:10.1016/j.anbehav.2003.09.010 · Cited by 151 · see citing works
Schlecht, E., Hülsebusch, C., Mahler, F., & Becker, K. (2004) · Applied Animal Behaviour Science · doi:10.1016/j.applanim.2003.11.003 · Cited by 145 · see citing works
Mansbridge, N., Mitsch, J., Bollard, N. J., Ellis, K. A., Miguel-Pacheco, G. G., Dottorini, T., & Kaler, J. (2018) · Sensors · doi:10.3390/s18103532 · Cited by 143 · see citing works
Nadimi, E. S., Jørgensen, R. N., Blanes‐Vidal, V., & Christensen, S. (2012) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2011.12.008 · Cited by 142 · see citing works
Foraging and provisioning in Antarctic fur seals: interannual variability in time-energy budgets
Boyd, I. L. (1999) · Behavioral Ecology · doi:10.1093/beheco/10.2.198 · Cited by 141 · see citing works
Changes in dive profiles as an indicator of feeding success in king and Adélie penguins
Bost, C., Handrich, Y., Butler, P. J., Fahlman, A., Halsey, L. G., WOAKES, A. J., & Ropert‐Coudert, Y. (2007) · Deep Sea Research Part II Topical Studies in Oceanography · doi:10.1016/j.dsr2.2006.11.007 · Cited by 139 · see citing works
A Bird’s Eye View of Discard Reforms: Bird-Borne Cameras Reveal Seabird/Fishery Interactions
Votier, S. C., Bicknell, A. W. J., Cox, S. L., Scales, K. L., & Patrick, S. C. (2013) · PLoS ONE · doi:10.1371/journal.pone.0057376 · Cited by 126 · see citing works
Smith, D., Rahman, A., Bishop-Hurley, G. J., Hills, J. L., Shahriar, M. S., Henry, D. A., & Rawnsley, R. (2016) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2016.10.006 · Cited by 125 · see citing works
Behaviour classification of extensively grazed sheep using machine learning
Fogarty, E. S., Swain, D. L., Cronin, G. M., Moraes, L. E., & Trotter, M. G. (2019) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2019.105175 · Cited by 124 · see citing works
Walton, E., Casey, C., Mitsch, J., Vázquez-Diosdado, J. A., Yan, J., Dottorini, T., Ellis, K. A., Winterlich, A., & Kaler, J. (2018) · Royal Society Open Science · doi:10.1098/rsos.171442 · Cited by 119 · see citing works
Peng, Y., Kondo, N., Fujiura, T., Suzuki, T., Wulandari, W., Yoshioka, H., & Itoyama, E. (2019) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2018.12.023 · Cited by 118 · see citing works
Wauters, L. A., Swinnen, C., & Dhondt, A. A. (1992) · Journal of Zoology · doi:10.1111/j.1469-7998.1992.tb04345.x · Cited by 117 · see citing works
Identification of Behaviour in Freely Moving Dogs (Canis familiaris) Using Inertial Sensors
Gerencsér, L., Vásárhelyi, G., Nagy, M., Vicsek, T., & Miklósi, Á. (2013) · PLoS ONE · doi:10.1371/journal.pone.0077814 · Cited by 106 · see citing works
Brewster, L. R., Dale, J. J., Guttridge, T. L., Gruber, S. H., Hansell, A. C., Elliott, M., Cowx, I. G., Whitney, N. M., & Gleiss, A. C. (2018) · Marine Biology · doi:10.1007/s00227-018-3318-y · Cited by 104 · see citing works
Tremblay, Y., Thiebault, A., Mullers, R. H. E., & Pistorius, P. A. (2014) · PLoS ONE · doi:10.1371/journal.pone.0088424 · Cited by 103 · see citing works
Self-citationTremblay, Y., Cherel, Y., Oremus, M., Tveraa, T., & Chastel, O. (2003) · Journal of Experimental Biology · doi:10.1242/jeb.00363 · Cited by 103 · see citing works
Activity Budgets and Foraging Ranges of Breeding Common Murres
Cairns, D. K., Bredin, K. A., & Montevecchi, W. A. (1987) · The Auk · doi:10.1093/auk/104.2.218 · Cited by 101 · see citing works
Benaissa, S., Tuyttens, F., Plets, D., Cattrysse, H., Martens, L., Vandaele, L., Joseph, W., & Sonck, B. (2018) · Applied Animal Behaviour Science · doi:10.1016/j.applanim.2018.12.003 · Cited by 100 · see citing works
Pichegru, L., Ryan, P. G., van der Lingen, C. D., Coetzee, J., Ropert‐Coudert, Y., & Grémillet, D. (2007) · Marine Ecology Progress Series · doi:10.3354/meps07128 · Cited by 99 · see citing works
Gaussian Mixture Modeling of Short-Time Fourier Transform Features for Audio Fingerprinting
Ramalingam, A., & Krishnan, S. (2006) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2006.885036 · Cited by 87 · see citing works
An automated sensor-based method of simple behavioural classification of sheep in extensive systems
Umstätter, C., Waterhouse, A., & Holland, J. P. (2008) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2008.05.004 · Cited by 87 · see citing works
Using accelerometers to remotely and automatically characterize behavior in small animals
Hammond, T. T., Springthorpe, D., Walsh, R. E., & Berg-Kirkpatrick, T. (2016) · Journal of Experimental Biology · doi:10.1242/jeb.136135 · Cited by 87 · see citing works
Bonadonna, F., Bajzak, C., Benhamou, S., Iglói, K., Jouventin, P., Lipp, H., & Dell’Omo, G. (2005) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2004.2984 · Cited by 83 · see citing works
Classifying season long livestock grazing behavior with the use of a low-cost GPS and accelerometer
Brennan, J. R., Johnson, P. S., & Olson, K. C. (2021) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2020.105957 · Cited by 79 · see citing works
Synchronous Underwater Foraging Behavior in Penguins
Tremblay, Y., & Cherel, Y. (1999) · Ornithological Applications · doi:10.2307/1370462 · Cited by 78 · see citing works
From colony to first patch: Processes of prey searching and social information in Cape Gannets
Thiebault, A., Mullers, R. H. E., Pistorius, P. A., Meza, M. A., Dubroca, L., Green, D. B., & Tremblay, Y. (2014) · The Auk · doi:10.1642/auk-13-209.1 · Cited by 77 · see citing works
Self-citationCrawford, R. J. M., Dundee, B. L., Dyer, B. M., Klages, N. T. W., Meÿer, M. A., & Upfold, L. (2006) · ICES Journal of Marine Science · doi:10.1093/icesjms/fsl011 · Cited by 74 · see citing works
Elk Diets and Activities on Different Lodgepole Pine Habitat Segments
Collins, W., Urness, P. J., & Austin, D. D. (1978) · Journal of Wildlife Management · doi:10.2307/3800770 · Cited by 74 · see citing works
Wijers, M., Trethowan, P., Markham, A., du Preez, B., Chamaillé-Jammes, S., Loveridge, A., & Macdonald, D. (2018) · Frontiers in Ecology and Evolution · doi:10.3389/fevo.2018.00171 · Cited by 73 · see citing works
Evaluation of pre-processing methods for the prediction of cattle behaviour from accelerometer data
Riaboff, L., Aubin, S., Bédère, N., Couvreur, S., Madouasse, A., Goumand, E., Chauvin, A., & Plantier, G. (2019) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2019.104961 · Cited by 73 · see citing works
Sakai, K., Oishi, K., Masafumi, M. I. W. A., Kumagai, H., & Hirooka, H. (2019) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2019.105027 · Cited by 72 · see citing works
Activity Budgets of Foraging Gray-Headed Albatrosses
Prince, P. A., & Francis, M. D. (1984) · Ornithological Applications · doi:10.2307/1366998 · Cited by 66 · see citing works
The adaptive nature of the social system and behavior in the eastern chipmunk, Tamias striatus
Yahner, R. H. (1978) · Behavioral Ecology and Sociobiology · doi:10.1007/bf00303202 · Cited by 65 · see citing works
Tamura, T., Okubo, Y., Deguchi, Y., Koshikawa, S., Takahashi, M., Chida, Y., & Okada, K. (2019) · Animal Science Journal · doi:10.1111/asj.13184 · Cited by 64 · see citing works
Barwick, J., Lamb, D. W., Dobos, R. C., Welch, M., Schneider, D. A., & Trotter, M. G. (2020) · Remote Sensing · doi:10.3390/rs12040646 · Cited by 62 · see citing works
Garthe, S., Grémillet, D., & Furness, R. (1999) · Marine Ecology Progress Series · doi:10.3354/meps185093 · Cited by 61 · see citing works
Assessing the effects of sampling frequency on behavioural classification of accelerometer data
Hounslow, J. L., Brewster, L. R., Lear, K. O., Guttridge, T. L., Daly, R., Whitney, N. M., & Gleiss, A. C. (2019) · Journal of Experimental Marine Biology and Ecology · doi:10.1016/j.jembe.2018.12.003 · Cited by 61 · see citing works
Jeantet, L., Planas-Bielsa, V., Benhamou, S., Geiger, S., Martin, J., Siegwalt, F., Lelong, P., Gresser, J., Etienne, D., Hiélard, G., et al. (2020) · Royal Society Open Science · doi:10.1098/rsos.200139 · Cited by 61 · see citing works
Kawabe, R., KAWANO, T., NAKANO, N., Yamashita, N., Hiraishi, T., & Naito, Y. (2003) · Fisheries Science · doi:10.1046/j.1444-2906.2003.00713.x · Cited by 60 · see citing works
On-Bird Sound Recordings: Automatic Acoustic Recognition of Activities and Contexts
Stowell, D., Benetos, E., & Gill, L. F. (2017) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2017.2690565 · Cited by 60 · see citing works
Grémillet, D., Péron, C., Kato, A., Amélineau, F., Ropert‐Coudert, Y., Ryan, P. G., & Pichegru, L. (2016) · Marine Biology · doi:10.1007/s00227-015-2798-2 · Cited by 59 · see citing works
Variability in foraging in response to changing prey distributions in rhinoceros auklets
Davoren, G. (2000) · Marine Ecology Progress Series · doi:10.3354/meps198283 · Cited by 58 · see citing works
Studd, E. K., Landry‐Cuerrier, M., Menzies, A. K., Boutin, S. A., McAdam, A. G., Lane, J. E., & Humphries, M. M. (2018) · Ecology and Evolution · doi:10.1002/ece3.4786 · Cited by 58 · see citing works
Chimienti, M., Desforges, J.-P. W., Beumer, L. T., Nabe‐Nielsen, J., van Beest, F. M., & Schmidt, N. M. (2020) · Ecological Modelling · doi:10.1016/j.ecolmodel.2020.109250 · Cited by 57 · see citing works
Optimal hunting conditions drive circalunar behavior of a diurnal carnivore
Broekhuis, F., Grünewälder, S., McNutt, J., & Macdonald, D. (2014) · Behavioral Ecology · doi:10.1093/beheco/aru122 · Cited by 56 · see citing works
A Device for Measuring Seabird Activity at Sea
Wilson, R. P., Weimerskirch, H., & Lys, P. (1995) · Journal of Avian Biology · doi:10.2307/3677067 · Cited by 55 · see citing works
Diving Behavior of Adelie Penguins Determined by Time-Depth Recorder
Naito, Y., Asaga, T., & Ohyama, Y. (1990) · Ornithological Applications · doi:10.2307/1368676 · Cited by 54 · see citing works
Achour, B., Belkadi, M., Aoudjit, R., & Laghrouche, M. (2019) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2019.105068 · Cited by 54 · see citing works
An Inexpensive Depth Gauge for Penguins
Wilson, R. P., & Bain, C. A. R. (1984) · Journal of Wildlife Management · doi:10.2307/3801769 · Cited by 53 · see citing works
Studd, E. K., Boudreau, M. R., Majchrzak, Y. N., Menzies, A. K., Peers, M. J. L., Seguin, J. L., Lavergne, S. G., Boonstra, R., Murray, D. L., Boutin, S. A., et al. (2019) · Frontiers in Ecology and Evolution · doi:10.3389/fevo.2019.00154 · Cited by 53 · see citing works
Ladds, M., Thompson, A. P., Slip, D. J., Hocking, D. P., & Harcourt, R. G. (2016) · PLoS ONE · doi:10.1371/journal.pone.0166898 · Cited by 52 · see citing works
den Uijl, I., Álvarez, C. B. G., Bartram, D. J., Dror, Y., Holland, R. E., & Cook, A. J. C. (2017) · PLoS ONE · doi:10.1371/journal.pone.0188481 · Cited by 52 · see citing works
The use of on‐animal acoustical recording devices for studying animal behavior
Lynch, E., Angeloni, L. M., Fristrup, K. M., Joyce, D., & Wittemyer, G. (2013) · Ecology and Evolution · doi:10.1002/ece3.608 · Cited by 51 · see citing works
Changes in prey availability impact the foraging behaviour and fitness of Cape gannets over a decade
Cohen, L., Pichegru, L., Grémillet, D., Coetzee, J., Upfold, L., & Ryan, P. G. (2014) · Marine Ecology Progress Series · doi:10.3354/meps10762 · Cited by 51 · see citing works
Diurnal patterning of eight activities in 14 species of muroid rodents
Baumgardner, D. J., Ward, S. E., & Dewsbury, D. A. (1980) · Animal Learning & Behavior · doi:10.3758/bf03199612 · Cited by 50 · see citing works
Inferring Behavioral States of Grazing Livestock from High-Frequency Position Data Alone
Homburger, H., Schneider, M. K., Hilfiker, S., & LÜSCHER, A. (2014) · PLoS ONE · doi:10.1371/journal.pone.0114522 · Cited by 49 · see citing works
An Analysis of Time-Budgeting By Roe Deer (Capreol Us Capreol Us) in an Agricultural Area
Turner, D. C. (1979) · Behaviour · doi:10.1163/156853979x00188 · Cited by 49 · see citing works
Automatic equine activity detection by convolutional neural networks using accelerometer data
Eerdekens, A., Deruyck, M., Fontaine, J., Martens, L., Poorter, E. D., & Joseph, W. (2019) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2019.105139 · Cited by 48 · see citing works
Jeantet, L., Dell’Amico, F., Forin-Wiart, M.-A., Coutant, M., Bonola, M., Etienne, D., Gresser, J., Régis, S., Lecerf, N., Lefebvre, F., et al. (2018) · Journal of Experimental Biology · doi:10.1242/jeb.177378 · Cited by 44 · see citing works
Green, D., Klages, N., Crawford, R., Coetzee, J., Dyer, B., Rishworth, G., & Pistorius, P. (2015) · ICES Journal of Marine Science · doi:10.1093/icesjms/fsu203 · Cited by 42 · see citing works
Self-citationHurme, E., Gurarie, E. D., Greif, S., M., L. G. H., Flores‐Martínez, J. J., Wilkinson, G. S., & Yovel, Y. (2019) · Movement Ecology · doi:10.1186/s40462-019-0163-7 · Cited by 42 · see citing works
Daily activity budgets reveal a quasi-flightless stage during non-breeding in Hawaiian albatrosses
Gutowsky, S. E., Gutowsky, L. F. G., Jonsen, I. D., Leonard, M. L., Naughton, M. B., Romano, M. D., & Shaffer, S. A. (2014) · Movement Ecology · doi:10.1186/s40462-014-0023-4 · Cited by 41 · see citing works
Use of tri‐axial accelerometers to assess terrestrial mammal behaviour in the wild
Lush, L., Ellwood, S. A., Markham, A., Ward, A. I., & Wheeler, P. M. (2015) · Journal of Zoology · doi:10.1111/jzo.12308 · Cited by 41 · see citing works
Energetic costs of foraging in breeding Cape gannets Morus capensis
Mullers, R. H. E., Navarro, R., Daan, S., Tinbergen, J. M., & Meijer, H. A. J. (2009) · Marine Ecology Progress Series · doi:10.3354/meps08250 · Cited by 39 · see citing works
Activity Budget of Male Gray Seals, Halichoerus grypus
Boness, D. J. (1984) · Journal of Mammalogy · doi:10.2307/1381168 · Cited by 35 · see citing works
Hu, S., Ingham, A. B., Schmoelzl, S., McNally, J., Little, B., Smith, D., Bishop-Hurley, G. J., Wang, Y., & Li, Y. (2020) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2020.105857 · Cited by 34 · see citing works
The activity budget and use of territory by a tropical blenniid fish
Nursall, J. R. (1981) · Zoological Journal of the Linnean Society · doi:10.1111/j.1096-3642.1981.tb01652.x · Cited by 33 · see citing works
Coupling spectral analysis and hidden Markov models for the segmentation of behavioural patterns
Heerah, K., Woillez, M., Fablet, R., Garren, F., Martin, S., & de Pontual, H. (2017) · Movement Ecology · doi:10.1186/s40462-017-0111-3 · Cited by 31 · see citing works
Acoustic determination of activity and flipper stroke rate in foraging northern fur seal females
Insley, S. J., Robson, B. W., Yack, T. M., Ream, R. R., & Burgess, W. C. (2007) · Endangered Species Research · doi:10.3354/esr00050 · Cited by 29 · see citing works
Couchoux, C., Aubert, M., Garant, D., & Réale, D. (2015) · Scientific Reports · doi:10.1038/srep10118 · Cited by 29 · see citing works
Using on-board sound recordings to infer behaviour of free-moving wild animals
Greif, S., & Yovel, Y. (2019) · Journal of Experimental Biology · doi:10.1242/jeb.184689 · Cited by 29 · see citing works
Sutton, G. J., Bost, C., Kouzani, A. Z., Adams, S. D., Mitchell, K., & Arnould, J. P. Y. (2021) · Marine Biology · doi:10.1007/s00227-020-03811-w · Cited by 28 · see citing works
Mullers, R. H. E., & Tinbergen, J. M. (2009) · Ardea · doi:10.5253/078.097.0111 · Cited by 27 · see citing works
At sea vocal repertoire of a foraging seabird
Thiebault, A., Charrier, I., Pistorius, P. A., & Aubin, T. (2019) · Journal of Avian Biology · doi:10.1111/jav.02032 · Cited by 26 · see citing works
Self-citationYang, Y., Yeh, H., Zhang, W., Lee, C., Meese, E. N., & Lowe, C. G. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.3038660 · Cited by 25 · see citing works
le Roux, S. P., Wolhuter, R., & Niesler, T. (2018) · IEEE Sensors Journal · doi:10.1109/jsen.2018.2886890 · Cited by 24 · see citing works
The status and conservation of the Cape Gannet Morus capensis
Sherley, R. B., Crawford, R. J. M., Dyer, B. M., Kemper, J., Makhado, A. B., Masotla, M. J., Pichegru, L., Pistorius, P. A., Roux, J.-P., Ryan, P. G., et al. (2019) · Ostrich · doi:10.2989/00306525.2019.1684396 · Cited by 24 · see citing works
Self-citationDeSantis, D. L., Mata‐Silva, V., Johnson, J. D., & Wagler, A. E. (2020) · Frontiers in Ecology and Evolution · doi:10.3389/fevo.2020.00169 · Cited by 23 · see citing works
Foraging behaviour of Cape gannets as an indicator of colony health status
Mullers, R., & Navarro, R. (2010) · Endangered Species Research · doi:10.3354/esr00306 · Cited by 22 · see citing works
The new design of cows' behavior classifier based on acceleration data and proposed feature set
Khanh, P. C. P., Tran, D., Duong, V. T., Thinh, N. H., & Tran, D.-N. (2020) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2020151 · Cited by 22 · see citing works
Chakravarty, P., Cozzi, G., Dejnabadi, H., Léziart, P., Manser, M. B., Özgül, A., & Aminian, K. (2020) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13491 · Cited by 22 · see citing works
Social context and prey composition are associated with calling behaviour in a diving seabird
McInnes, A., Thiebault, A., Cloete, T., Pichegru, L., Aubin, T., McGeorge, C., & Pistorius, P. A. (2019) · Ibis · doi:10.1111/ibi.12806 · Cited by 19 · see citing works
Self-citationRishworth, G., & Pistorius, P. A. (2015) · Marine Ecology Progress Series · doi:10.3354/meps11317 · Cited by 18 · see citing works
Self-citationContrasting Daily and Seasonal Activity and Movement of Sympatric Elk and Cattle
Clark, P. E., Johnson, D. E., Ganskopp, D. C., Varva, M., Cook, J. G., Cook, R. C., Pierson, F. B., & Hardegree, S. P. (2016) · Rangeland Ecology & Management · doi:10.1016/j.rama.2016.09.003 · Cited by 18 · see citing works
Activity patterns of free-ranging beef cattle in Norway
Tofastrud, M., Hegnes, H., Devineau, O., & Zimmermann, B. (2018) · Acta Agriculturae Scandinavica Section A – Animal Science · doi:10.1080/09064702.2018.1524928 · Cited by 18 · see citing works
Beltramino, L. E., Venerus, L. A., Trobbiani, G., Wilson, R. P., & Ciancio, J. E. (2018) · Austral Ecology · doi:10.1111/aec.12696 · Cited by 17 · see citing works
Fannjiang, C., Mooney, T. A., Cones, S., Mann, D. A., Shorter, K. A., & Katija, K. (2019) · Journal of Experimental Biology · doi:10.1242/jeb.207654 · Cited by 17 · see citing works
Sex-specific foraging over space and time in Cape gannets during chick rearing
Botha, J. A., Rishworth, G., Thiebault, A., Green, D. B., & Pistorius, P. A. (2017) · Marine Ecology Progress Series · doi:10.3354/meps12299 · Cited by 16 · see citing works
Self-citationAn automated approach towards measuring time‐activity budgets in colonial seabirds
Rishworth, G. M., Tremblay, Y., Green, D. B., & Pistorius, P. A. (2014) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.12213 · Cited by 13 · see citing works
Self-citationle Roux, S. P., Wolhuter, R., Stevens, N., & Niesler, T. (2018) · IEEE Sensors Journal · doi:10.1109/jsen.2018.2816965 · Cited by 13 · see citing works
Green, D. B., Coetzee, J., Rishworth, G., & Pistorius, P. A. (2015) · Marine Ecology Progress Series · doi:10.3354/meps11428 · Cited by 12 · see citing works
Self-citationQuantifying the swimming gaits of veined squid (Loligo forbesi) using bio-logging tags
Flaspohler, G., Caruso, F., Mooney, T. A., Katija, K., Fontes, J., Afonso, P., & Shorter, K. A. (2019) · Journal of Experimental Biology · doi:10.1242/jeb.198226 · Cited by 12 · see citing works
Abrol, D. P. (1986) · Proceedings Animal Sciences · doi:10.1007/bf03179421 · Cited by 11 · see citing works
Determining Activity Types and Budgets from Movement Speed of Radio-Marked Mongooses
Palomares, F., & Delibes, M. (1993) · Journal of Wildlife Management · doi:10.2307/3809013 · Cited by 9 · see citing works
Calibrating tri-axial accelerometers for remote behavioural observations in Bewick's swans
Nuijten, R. J. M., Prins, E. F., Lammers, J., Mager, C., & Nolet, B. A. (2020) · The Journal of zoo and aquarium research · doi:10.19227/jzar.v8i4.522 · Cited by 9 · see citing works
Thiebault, A., Dubroca, L., Mullers, R. H. E., Tremblay, Y., & Pistorius, P. A. (2018) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.12989 · Cited by 8 · see citing works
Self-citationA Flight-Time Integrator for Birds
LeFebvre, E. A., & BIRKEBAK, R. C. (1967) · The Auk · doi:10.2307/4083268 · Cited by 6 · see citing works
Choosing Sampling Interval Durations for Remotely Classifying Rocky Mountain Elk Behavior
Gaylord, A. J., Sanchez, D. M., & Sickle, J. V. V. (2016) · Journal of Fish and Wildlife Management · doi:10.3996/042015-jfwm-034 · Cited by 4 · see citing works
A Storing Telemetry-Transmitter for Recording Bird Activity
Mohus, I. (1987) · Ornis Scandinavica · doi:10.2307/3676770 · Cited by 4 · see citing works
Busch, P., LAHTI, P. J., Pellonpää, J.-P., & Ylinen, K. (2016) · Theoretical and mathematical physics · doi:10.1007/978-3-319-43389-9_17 · Cited by 4 · see citing works
Morus capensis: BirdLife International
IUCN (2018) · IUCN Red List of Threatened Species · doi:10.2305/iucn.uk.2018-2.rlts.t22696668a132587992.en · Cited by 4 · see citing works
Weather dependent behaviour of captive striped hyaenas (Hyaena hyaena)
Rieger, I., & Weihe, W. H. (1975) · International Journal of Biometeorology · doi:10.1007/bf01463868 · Cited by 3 · see citing works
Mattern, T., McPherson, M. D., Ellenberg, U., van Heezik, Y., & Seddon, P. J. (2017) · doi:10.7287/peerj.preprints.2765v1 · Cited by 2 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- Puttick GM. Foraging behaviour and activity budgets of Curlew Sandpi- pers. Ardea. 1979;67:1-122.
- Mullers RHE, Tinbergen JM. Parental provisioning behaviour pre- dicts survival of cape gannet chicks under poor conditions. Ardea. 2009;97:89-98.
Cited by
Sorted by citations
Listening to animal behavior to understand changing ecosystems
Oestreich, W. K., Oliver, R. Y., Chapman, M., Go, M. C., & McKenna, M. F. (2024) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2024.06.007 · Cited by 43 · see citing works
A benchmark for computational analysis of animal behavior, using animal-borne tags
Hoffman, B., Cusimano, M., Baglione, V., Canestrari, D., Chevallier, D., DeSantis, D. L., Jeantet, L., Ladds, M., Maekawa, T., Mata‐Silva, V., et al. (2024) · Movement Ecology · doi:10.1186/s40462-024-00511-8 · Cited by 28 · see citing works
Context-Aware Edge-Based AI Models for Wireless Sensor Networks—An Overview
Al-Saedi, A. A., Boeva, V., Casalicchio, E., & Exner, P. (2022) · Sensors · doi:10.3390/s22155544 · Cited by 26 · see citing works
Artificial Intelligence for Climate Change Biology: From Data Collection to Predictions
Levy, O., & Shahar, S. (2024) · Integrative and Comparative Biology · doi:10.1093/icb/icae127 · Cited by 19 · see citing works
Plugging biologging into animal welfare: An opportunity for advancing wild animal welfare science
Beaulieu, M., & Masilkova, M. (2024) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.14441 · Cited by 14 · see citing works
Resheff, Y. S., Bensch, H. M., Zöttl, M., Harel, R., Matsumoto–Oda, A., Crofoot, M. C., Gómez, S., Börger, L., & Rotics, S. (2024) · Movement Ecology · doi:10.1186/s40462-024-00485-7 · Cited by 10 · see citing works
Semi-Supervised Machine Learning for Livestock Threat Classification Using GPS Data
Swardt, U. J. D., & Kamper, H. (2023) · IEEE Access · doi:10.1109/access.2023.3258621 · Cited by 6 · see citing works
Enari, H., Enari, H. S., Uno, T., Sembongi, Y., Akamatsu, M., Kanayama, N., Enomoto, T., & Yamashita, J. (2025) · Mammalian Biology · doi:10.1007/s42991-025-00537-4 · Cited by 5 · see citing works
Baglione, V., Canestrari, D., Cusimano, M., Hoffman, B., Moreno‐González, V., & Trapote, E. (2025) · Animal Cognition · doi:10.1007/s10071-025-02018-0 · Cited by 4 · see citing works
Modelling reindeer rut activity using on‐animal acoustic recorders and machine learning
Boucher, A. J., Weladji, R. B., Holand, Ø., & Kumpula, J. (2024) · Ecology and Evolution · doi:10.1002/ece3.11479 · Cited by 4 · see citing works
Animal-Borne Adaptive Acoustic Monitoring
Jean, D., Turner, J., Hedgecock, W., Kalmár, G., Wittemyer, G., & Lédeczi, Á. (2025) · Journal of Sensor and Actuator Networks · doi:10.3390/jsan14040066 · Cited by 1 · see citing works
Listening forward: emerging roles of bioacoustics in ecology, evolution, and conservation
Zsebők, S. (2026) · Biologia Futura · doi:10.1007/s42977-026-00322-5
“…acoustic tags can capture not only vocalizations but also movement-generated sounds and environmental noise, enabling automated inference of behavioural states such as flying, diving, or resting from acoustic patterns alone (Stowell et al. 2017 ; Greif and Yovel 2019; Thiebault et al. 2021).”
Cerqueira, A. D. S., Freeman, R., Phillips, R. A., & Dawson, T. P. (2025) · Ecological Informatics · doi:10.1016/j.ecoinf.2025.103474
Resource-efficient data transmission for WiFi-capable bio-loggers based on machine learning
Kerle-Malcharek, W., Klein, K., Wikelski, M. C., Schreiber, F., & Wild, T. A. (2026) · PLoS ONE · doi:10.1371/journal.pone.0354146
Reis‐Santos, P., Madgett, A. S., Gillanders, B. M., Marnane, M. J., Harvey, E. S., & Elsdon, T. S. (2026) · Frontiers in Marine Science · doi:10.3389/fmars.2026.1865430
- Authors
- Thiebault, A., Huetz, C., Pistorius, P., Aubin, T., & Charrier, I.
- Affiliations
- Nelson Mandela University (ZA)
- Centre National de la Recherche Scientifique (FR)
- Corresponding author
- Thiebault, Andréa
- Venue
- Animal Biotelemetry
- Published
- 2021-08-14
- Volume
- 9
- Issue
- 1
- ISSN
- 2050-3385 (ISSN-L)
- OpenAlex
- W3193334884
- Cited by
- 15
- References
- 131
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 6 · 2023: 1 · 2022: 1
- FWCI
- 1.08
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Animal Biotelemetry (publishedVersion, cc-by)
- HAL (submittedVersion)
- HAL (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Nelson Mandela University Research Repository (submittedVersion)
- Funders
- National Research Foundation
- Centre National de la Recherche Scientifique
- Nelson Mandela University
- Topics
- Physical Sciences › Environmental Science › Ecology › Marine animal studies overview
- Physical Sciences › Environmental Science › Ecology › Wildlife Ecology and Conservation
- Physical Sciences › Environmental Science › Ecology › Avian ecology and behavior
- Keywords
- seabird foraging behavior, Cape gannet, k-nearest neighbors, biophony, geophony, anthropophony, animal-borne sensors, accelerometer, GPS, pressure sensors, bioacoustics
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