Tsai, C.-W., Lai, C.-F., Chao, H.-C. & Vasilakos, A. V. Big data analytics: a survey. J. Big Data 2, 21 (2015).
Lemoine, F. et al. Renewing Felsenstein’s phylogenetic bootstrap in the era of big data. Nature 556, 452–456 (2018).
Google Scholar
Manzoni, C. et al. Genome, transcriptome and proteome: the rise of omics data and their integration in biomedical sciences. Brief. Bioinform. 19, 286–302 (2018).
Google Scholar
Lichtman, J. W., Pfister, H. & Shavit, N. The big data challenges of connectomics. Nat. Neurosci. 17, 1448–1454 (2014).
Google Scholar
Altaf-Ul-Amin, M., Afendi, F. M., Kiboi, S. K. & Kanaya, S. Systems biology in the context of big data and networks. Biomed. Res. Int. 2014, 428570 (2014).
Xia, J., Wang, J. & Niu, S. Research challenges and opportunities for using big data in global change biology. Glob. Change Biol. 26, 6040–6061 (2020).
Google Scholar
Hindell, M. A. et al. Tracking of marine predators to protect Southern Ocean ecosystems. Nature 580, 87–92 (2020).
Google Scholar
Hussey, N. E. et al. Ecology. Aquatic animal telemetry: A panoramic window into the underwater world. Science 348, 1255642 (2015).
Kays, R., Crofoot, M. C., Jetz, W. & Wikelski, M. Terrestrial animal tracking as an eye on life and planet. Science 348, aaa2478 (2015).
Sherub, S., Fiedler, W., Duriez, O. & Wikelski, M. Bio-logging, new technologies to study conservation physiology on the move: a case study on annual survival of Himalayan vultures. J. Comp. Physiol. A Neuroethol. Sens. Neural Behav. Physiol. 203, 531–542 (2017).
Nathan, R. et al. Big-data approaches lead to an increased understanding of the ecology of animal movement. Science 375, eabg1780 (2022).
Google Scholar
Wilson, R. P. et al. Estimates for energy expenditure in free-living animals using acceleration proxies: A reappraisal. J. Anim. Ecol. 89, 161–172 (2020).
Patterson, A., Gilchrist, H. G., Chivers, L., Hatch, S. & Elliott, K. A comparison of techniques for classifying behavior from accelerometers for two species of seabird. Ecol. Evol. 9, 3030–3045 (2019).
Masello, J. F. et al. How animals distribute themselves in space: energy landscapes of Antarctic avian predators. Mov. Ecol. 9, 24 (2021).
Shepard, E. L. C. et al. Energy landscapes shape animal movement ecology. Am. Nat. 182, 298–312 (2013).
Elliott, K. H., Le Vaillant, M., Kato, A., Speakman, J. R. & Ropert-Coudert, Y. Accelerometry predicts daily energy expenditure in a bird with high activity levels. Biol. Lett. 9, 20120919 (2013).
Nickel, B. A., Suraci, J. P., Nisi, A. C. & Wilmers, C. C. Energetics and fear of humans constrain the spatial ecology of pumas. Proc. Natl. Acad. Sci. USA 118, e2004592118 (2021).
Eisaguirre, J. M., Booms, T. L., Barger, C. P., Lewis, S. B. & Breed, G. A. Novel step selection analyses on energy landscapes reveal how linear features alter migrations of soaring birds. J. Anim. Ecol. 89, 2567–2583 (2020).
Wittemyer, G., Northrup, J. M. & Bastille-Rousseau, G. Behavioural valuation of landscapes using movement data. Philos. Trans. R. Soc. Lond. B Biol. Sci. 374, 20180046 (2019).
Chimienti, M. et al. The use of an unsupervised learning approach for characterizing latent behaviors in accelerometer data. Ecol. Evol. 6, 727–741 (2016).
Hounslow, J. L. et al. Assessing the effects of sampling frequency on behavioural classification of accelerometer data. J. Exp. Mar. Bio. Ecol. 512, 22–30 (2019).
Glass, T. W., Breed, G. A., Robards, M. D., Williams, C. T. & Kielland, K. Accounting for unknown behaviors of free-living animals in accelerometer-based classification models: Demonstration on a wide-ranging mesopredator. Ecol. Inf. 60, 101152 (2020).
Wang, Y. et al. Movement, resting, and attack behaviors of wild pumas are revealed by tri-axial accelerometer measurements. Mov. Ecol. 3, 2 (2015).
Chakravarty, P., Cozzi, G., Ozgul, A. & Aminian, K. A novel biomechanical approach for animal behaviour recognition using accelerometers. Methods Ecol. Evol. https://doi.org/10.1111/2041-210X.13172 (2019).
Google Scholar
Clarke, T. M. et al. Using tri-axial accelerometer loggers to identify spawning behaviours of large pelagic fish. Mov. Ecol. 9, 26 (2021).
Zhang, J., O’Reilly, K. M., Perry, G. L. W., Taylor, G. A. & Dennis, T. E. Extending the functionality of behavioural change-point analysis with k-Means clustering: a case study with the little penguin (Eudyptula minor). PLoS ONE 10, e0122811 (2015).
Korpela, J. et al. Machine learning enables improved runtime and precision for bio-loggers on seabirds. Commun. Biol. 3, 633 (2020).
Jeantet, L. et al. Behavioural inference from signal processing using animal-borne multi-sensor loggers: a novel solution to extend the knowledge of sea turtle ecology. R. Soc. Open Sci. 7, 200139 (2020).
Google Scholar
Wang, G. Machine learning for inferring animal behavior from location and movement data. Ecol. Inf. 49, 69–76 (2019).
Dunford, C. E. et al. Surviving in steep terrain: a lab-to-field assessment of locomotor costs for wild mountain lions (Puma concolor). Mov. Ecol. 8, 34 (2020).
Jeanniard-du-Dot, T., Guinet, C., Arnould, J. P. Y., Speakman, J. R. & Trites, A. W. Accelerometers can measure total and activity-specific energy expenditures in free-ranging marine mammals only if linked to time-activity budgets. Funct. Ecol. 31, 377–386 (2017).
Hicks, O. et al. Acceleration predicts energy expenditure in a fat, flightless, diving bird. Sci. Rep. 10, 21493 (2020).
Google Scholar
Dentinger, J. E. et al. A probabilistic framework for behavioral identification from animal-borne accelerometers. Ecol. Model. 464, 109818 (2022).
Chakravarty, P., Maalberg, M., Cozzi, G., Ozgul, A. & Aminian, K. Behavioural compass: animal behaviour recognition using magnetometers. Mov. Ecol. 7, 28 (2019).
Hammond, T. T., Palme, R. & Lacey, E. A. Ecological specialization, variability in activity patterns and response to environmental change. Biol. Lett. 14, 20180115 (2018).
Lynch, H. J. & LaRue, M. A. First global census of the Adélie Penguin. Auk 131, 457–466 (2014).
Riaz, J., Bestley, S., Wotherspoon, S., Freyer, J. & Emmerson, L. From trips to bouts to dives: temporal patterns in the diving behaviour of chick-rearing Adélie penguins East Antarctica. Mar. Ecol. Prog. Ser. 654, 177–194 (2020).
Google Scholar
Cherel, Y. Isotopic niches of emperor and Adélie penguins in Adélie Land, Antarctica. Mar. Biol. 154, 813–821 (2008).
Little Penguin (Eudyptula minor) – BirdLife species factsheet. at <http://datazone.birdlife.org/species/factsheet/little-penguin-eudyptula-minor/details>
Carroll, G., Harcourt, R., Pitcher, B. J., Slip, D. & Jonsen, I. Recent prey capture experience and dynamic habitat quality mediate short-term foraging site fidelity in a seabird. Proc. Biol. Sci. 285, 20180788 (2018).
Meyer, X. et al. Oceanic thermal structure mediates dive sequences in a foraging seabird. Ecol. Evol. 10, 6610–6622 (2020).
Cavallo, C. et al. Quantifying prey availability using the foraging plasticity of a marine predator, the little penguin. Funct. Ecol. https://doi.org/10.1111/1365-2435.13605 (2020).
Google Scholar
Ropert-Coudert, Y., Chiaradia, A. & Kato, A. An exceptionally deep dive by a Little Penguin Eudyptula minor. Mar. Ornithol 34, 71–74 (2006).
Ropert-Coudert, Y., Kato, A., Wilson, R. P. & Cannell, B. Foraging strategies and prey encounter rate of free-ranging Little Penguins. Mar. Biol. 149, 139–148 (2006).
Rodríguez, A., Chiaradia, A., Wasiak, P., Renwick, L. & Dann, P. Waddling on the dark side: ambient light affects attendance behavior of little penguins. J. Biol. Rhythms 31, 194–204 (2016).
Ropert-Coudert, Y. et al. Happy feet in a hostile world? the future of penguins depends on proactive management of current and expected threats. Front. Mar. Sci. 6, 248 (2019).
Shuert, C. R., Pomeroy, P. P. & Twiss, S. D. Assessing the utility and limitations of accelerometers and machine learning approaches in classifying behaviour during lactation in a phocid seal. Anim. Biotelemetry 6, 14 (2018).
Dickinson, E. R. et al. Limitations of using surrogates for behaviour classification of accelerometer data: refining methods using random forest models in Caprids. Mov. Ecol. 9, 28 (2021).
Conway, A. M., Durbach, I. N., McInnes, A. & Harris, R. N. Frame-by-frame annotation of video recordings using deep neural networks. Ecosphere 12, e03384 (2021).
Ravindran, S. Five ways deep learning has transformed image analysis. Nature 609, 864–866 (2022).
Google Scholar
Del Caño, M. et al. Fine-scale body and head movements allow to determine prey capture events in the Magellanic Penguin (Spheniscus magellanicus). Mar. Biol. 168, 84 (2021).
Johnson, J. M. & Khoshgoftaar, T. M. Survey on deep learning with class imbalance. J. Big Data 6, 27 (2019).
Hazen, E. L. et al. Marine top predators as climate and ecosystem sentinels. Front. Ecol. Environ. 17, 565–574 (2019).
Sánchez, S. et al. Within-colony spatial segregation leads to foraging behaviour variation in a seabird. Mar. Ecol. Prog. Ser. 606, 215–230 (2018).
Google Scholar
Patrick, S. C., Martin, J. G. A., Ummenhofer, C. C., Corbeau, A. & Weimerskirch, H. Albatrosses respond adaptively to climate variability by changing variance in a foraging trait. Glob. Change Biol. https://doi.org/10.1111/gcb.15735 (2021).
Google Scholar
Bonar, M. et al. Geometry of the ideal free distribution: individual behavioural variation and annual reproductive success in aggregations of a social ungulate. Ecol. Lett. 23, 1360–1369 (2020).
Michelot, C., Kato, A., Raclot, T. & Ropert-Coudert, Y. Adélie penguins foraging consistency and site fidelity are conditioned by breeding status and environmental conditions. PLoS ONE 16, e0244298 (2021).
Google Scholar
Mahoney, P. J. et al. Navigating snowscapes: scale-dependent responses of mountain sheep to snowpack properties. Ecol. Appl. 28, 1715–1729 (2018).
Watanabe, Y. Y., Ito, K., Kokubun, N. & Takahashi, A. Foraging behavior links sea ice to breeding success in Antarctic penguins. Sci. Adv. 6, eaba4828 (2020).
Google Scholar
Lescroël, A. et al. Working less to gain more: when breeding quality relates to foraging efficiency. Ecology 91, 2044–2055 (2010).
Zimmer, I., Ropert-Coudert, Y., Kato, A., Ancel, A. & Chiaradia, A. Does foraging performance change with age in female little penguins (Eudyptula minor)?. PLoS ONE 6, e16098 (2011).
Google Scholar
Hertel, A. G., Royauté, R., Zedrosser, A. & Mueller, T. Biologging reveals individual variation in behavioural predictability in the wild. J. Anim. Ecol. 90, 723–737 (2021).
Dickinson, E. R., Stephens, P. A., Marks, N. J., Wilson, R. P. & Scantlebury, D. M. Best practice for collar deployment of tri-axial accelerometers on a terrestrial quadruped to provide accurate measurement of body acceleration. Anim. Biotelemetry 8, 9 (2020).
Garde, B. et al. Ecological inference using data from accelerometers needs careful protocols. Methods Ecol. Evol. https://doi.org/10.1111/2041-210X.13804 (2022).
Google Scholar
Watanabe, Y. Y., Ito, M. & Takahashi, A. Testing optimal foraging theory in a penguin-krill system. Proc. Biol. Sci. 281, 20132376 (2014).
Grémillet, D. et al. Energetic fitness: Field metabolic rates assessed via 3D accelerometry complement conventional fitness metrics. Funct. Ecol. 32, 1203–1213 (2018).
Chimienti, M. et al. Quantifying behavior and life-history events of an Arctic ungulate from year-long continuous accelerometer data. Ecosphere 12, e03565 (2021).
Sutton, G. J., Botha, J. A., Speakman, J. R. & Arnould, J. P. Y. Validating accelerometry-derived proxies of energy expenditure using the doubly-labelled water method in the smallest penguin species. Biol. Open 10, bio055475 (2021).
Pagano, A. M. & Williams, T. M. Estimating the energy expenditure of free-ranging polar bears using tri-axial accelerometers: A validation with doubly labeled water. Ecol. Evol. 9, 4210–4219 (2019).
Ballance, L. T., Ainley, D. G., Ballard, G. & Barton, K. An energetic correlate between colony size and foraging effort in seabirds, an example of the Adélie penguin Pygoscelis adeliae. J. Avian Biol. 40, 279–288 (2009).
Wilson, R. P. et al. Long-term attachment of transmitting and recording devices to penguins and other seabirds. Wildl. Soc. Bull. 25, 101–106 (1997).
Shepard, E. L. C. et al. Identification of animal movement patterns using tri-axial accelerometry. Endanger. Species Res. 10, 47–60 (2008).
Google Scholar
Kato, A., Ropert-Coudert, Y., Grémillet, D. & Cannell, B. Locomotion and foraging strategy in foot-propelled and wing-propelled shallow-diving seabirds. Mar. Ecol. Prog. Ser. 308, 293–301 (2006).
Google Scholar
Team, R. C. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://url.org/www.R-project.org/ (2021).
Ainley, D. The Adélie Penguin: Bellwether of Climate Change (New York: Columbia University Press) (2006).
Langrognet, F. et al. Rmixmod: Classification with Mixture Modelling. (2020).
Bishop, C. M. Pattern Recognition and Machine Learning. Springer Science+Business Media, LLC, New
York, NY. (2006).
Amélineau, F. et al. Intra- and inter-individual changes in little penguin diving and isotopic composition over the breeding season. Mar. Biol. 168, 62 (2021).
Stoffel, M. A., Nakagawa, S. & Schielzeth, H. rptR: repeatability estimation and variance decomposition by generalized linear mixed-effects models. Methods Ecol. Evol. 8, 1639–1644 (2017).
Kuhn, M. & Wickham, H. Tidymodels: a collection of packages for modeling and machine learning using tidyverse principles. https://www.tidymodels.org (2020).
Wright, M. N. & Ziegler, A. Ranger : A fast implementation of random forests for high dimensional data in C++ andR. J. Stat. Softw. 77, 1–17 (2017).
Source: Ecology - nature.com
