Abstract
The identification of when, how and where animals feed is essential to estimate the amount of energy they obtain and to study the processes associated with prey search and consumption. We combined the use of animal-borne video cameras and accelerometers to characterise the body and head movements associated to four types of prey capture behaviours in the Magellanic Penguin (Spheniscus magellanicus). In addition, we evaluated how the K-Nearest Neighbour (K-NN) algorithm recognized these behaviours from acceleration data. Finally, we compared the total capture and the capture per unit time (CPUT) derived by identifying prey capture events using the K-NN algorithm to that derived by counting undulations in the dive profile (“wiggles”). During captures, body and head movements were highly variable in the tridimensional space. Energy expenditure (i.e., VeDBA values) during diving periods with prey captures was from three to four times higher than during controls diving periods (i.e., with no capture events). The K-NN classification resulted effective and showed accuracy scores above 90% when considering both head and body related features. In addition, when captures were estimated using the K-NN method, the CPUT was similar or higher to that estimated by counting wiggles. Our study contributes to the knowledge of the trophic ecology of this species and provides an alternative method for estimating prey consumption in the Magellanic Penguin and other diving seabirds.
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The datasets generated during and/or analysed during the current study are available from the corresponding author on request.
References
Arai N, Kuroki M, Sakamoto W, Naito Y (2000) Analysis of diving behavior of Adélie penguins using acceleration data logger. Polar Biol 13:95–100
Austin D, Bowen WD, McMillan JI, Boness DJ (2006) Stomach temperature telemetry reveals temporal patterns of foraging success in a free-ranging marine mammal. J Anim Ecol 75(2):408–420
Bidder OR, Campbell HA, Gómez-Laich A, Urgé P, Walker J, Cai Y et al (2014) Love thy neighbour: automatic animal behavioural classification of acceleration data using the k-nearest neighbour algorithm. PLoS ONE 9(2):e88609
Bidder OR, di Virgilio A, Hunter JS, McInturff A, Gaynor KM, Smith AM et al (2020) Monitoring canid scent marking in space and time using a biologging and machine learning approach. Sci Rep 10(1):1–13
Bost CA, Handrich Y, Butler PJ, Fahlman A, Halsey LG, Woakes AJ, Ropert-Coudert Y (2007) Changes in dive profiles as an indicator of feeding success in king and Adélie penguins . Deep Sea Res Part II Top Stud Oceanogr 54(3–4):248–255
Breiman L (2001) Random forests. Mach Learn 45(1):5–32
Brooks ME, Kristensen K, van Benthem KJ, Magnusson A, Berg CW, Nielsen A et al (2017) glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J 9(2):378–400
Carroll G, Slip D, Jonsen I, Harcourt R (2014) Supervised accelerometry analysis can identify prey capture by penguins at sea. J Exp Biol 217(24):4295–4302
Carroll G, Cox M, Harcourt R, Pitcher BJ, Slip D, Jonsen I (2017) Hierarchical influences of prey distribution on patterns of prey capture by a marine predator. Funct Ecol 31(9):1750–1760
Carroll G, Harcourt R, Pitcher BJ, Slip D, Jonsen I (2018) Recent prey capture experience and dynamic habitat quality mediate short-term foraging site fidelity in a seabird. Proc Royal Soc B 285:20180788
Castillo J, Yorio P, Gatto A (2019) Shared dietary niche between sexes in Magellanic Penguins. Austral Ecol 44(4):635–647
Charrassin JB, Kato A, Handrich Y, Sato K, Naito Y, Ancel A, Bost CA, Gauthier-Clerc M, Ropert-Coudert Y, Le Maho Y (2001) Feeding behaviour of free–ranging penguins determined by oesophageal temperature. Proc R Soc 268(1463):151–157
Chakravarty P, Cozzi G, Dejnabadi H, Léziart PA, Manser M, Ozgul A, Aminian K (2020) Seek and learn: Automated identification of microeventsin animal behaviour using envelopes of acceleration data and machine learning. Methods Ecol Evol 11(12):1639–1651
Chimienti M, Cornulier T, Owen E, Bolton M, Davies IM, Travis JM, Scott BE (2017) Taking movement data to new depths: Inferring prey availability and patch profitability from seabird foraging behavior. Ecol Evol 7(23):10252–10265
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297
Cutler DR, Edwards TC Jr, Beard KH, Cutler A, Hess KT, Gibson J, Lawler JJ (2007) Random forests for classification in ecology. Ecology 88(11):2783–2792
Fernandez SJ, Yorio P, Ciancio JE (2019) Diet composition of expanding breeding populations of the Magellanic Penguin. Mar Biol Res 15(1):84–96
Foo D, Semmens JM, Arnould JP, Dorville N, Hoskins AJ, Abernathy K et al (2016) Testing optimal foraging theory models on benthic divers. Anim Behav 112:127–138
Fossette S, Gaspar P, Handrich Y, Le Maho Y, Georges JY (2008) Dive and beak movement patterns in leatherback turtles Dermochelys coriacea during internesting intervals in French Guiana. J Anim Ecol 77(2):236–246
Frere E, Gandini P, Lichtschein V (1996) Variación latitudinal en la dieta del Pingüino de Magallanes (Spheniscus magellanicus) en la costa Patagónica, Argentina. Ornitol Neotrop 7:35–41
Gallon S, Bailleul F, Charrassin JB, Guinet C, Bost CA, Handrich Y, Hindell M (2013) Identifying foraging events in deep diving southern elephant seals, Mirounga leonina, using acceleration data loggers. Deep Sea Res Part II Top Stud Oceanogr 88:14–22
Gómez-Laich A, Yoda K, Quintana F (2018) Insights into the foraging behavior of Magellanic penguins (Spheniscus magellanicus). Waterbirds 41(3):332–336
Grünewälder S, Broekhuis F, Macdonald DW, Wilson AM, McNutt JW, Shawe-Taylor J, Hailes S (2012) Movement activity based classification of animal behaviour with an application to data from cheetah (Acinonyx jubatus). PLoS ONE 7(11):e49120
Guinard G, Marchand D, Courant F, Gauthier-Clerc M, Le Bohec C (2010) Morphology, ontogenesis and mechanics of cervical vertebrae in four species of penguins (Aves: Spheniscidae). Pol Biol 33(6):807–822
Guinet C, Vacquié-Garcia J, Picard B, Bessigneul G, Lebras Y, Dragon AC et al (2014) Southern elephant seal foraging success in relation to temperature and light conditions: insight into prey distribution. Mar Ecol Prog Ser 499:285–301
Gunner RM, Wilson RP, Holton MD, Scott R, Hopkins P, Duarte CM (2020) A new direction for differentiating animal activity based on measuring angular velocity about the yaw axis. Ecol Evol 10(14):7872–7886
Gutierrez-Galan D, Dominguez-Morales JP, Cerezuela-Escudero E, Rios-Navarro A, Tapiador-Morales R, Rivas-Perez M et al (2018) Embedded neural network for real-time animal behavior classification. Neurocomputing 272:17–26
Halsey LG, Shepard EL, Wilson RP (2011) Assessing the development and application of the accelerometry technique for estimating energy expenditure. Comp Biochem Physiol Part A Mol Integr Physiol 158(3):305–314
Hansen JE, Martos P, Madirolas A (2001) Relationship between spatial distribution of the Patagonian stock of Argentine anchovy, Engraulis anchoita, and sea temperature during late spring to early summer. Fish Oceanogr 10(2):193–206
Hanuise N, Bost CA, Huin W, Auber A, Halsey LG, Handrich Y (2010) Measuring foraging activity in a deep-diving bird: comparing wiggles, oesophageal temperatures and beak-opening angles as proxies of feeding. J Exp Biol 213(22):3874–3880
Handley JM, Thiebault A, Stanworth A, Schutt D, Pistorius P (2018) Behaviourally mediated predation avoidance in penguin prey: in situ evidence fromanimal-borne camera loggers. R Soc Open Sci 5(8):171449
Heithaus MR, McLash JJ, Frid A, Dill LM, Marshall GJ (2002) Novel insights into green sea turtle behaviour using animal-borne video cameras. J Mar Biolog Assoc 82(6):1049–1050
Hutchinson JM, Gigerenzer G (2005) Simple heuristics and rules of thumb: Where psychologists and behavioural biologists might meet. Behav Processes 69(2):97–124
Jeanniard-du-Dot T, Trites AW, Arnould JP, Speakman JR, Guinet C (2016) Flipper strokes can predict energy expenditure and locomotion costs in free-ranging northern and Antarctic fur seals. Scie Rep 6(1):1–12
Jeantet L, Dell’Amico F, Forin-Wiart MA, Coutant M, Bonola M, Etienne D et al (2018) Combined use of two supervised learning algorithms to model sea turtle behaviours from tri-axial acceleration data. J Exp Biol 221(10):jeb177378
Kato A, Ropert-Coudert Y, Grémillet D, Cannell B (2006) Locomotion and foraging strategy in foot-propelled and wing-propelled shallow-diving seabirds. Mar Ecol Prog Ser 308:293–301
Kinovea (2006) Kinovea v. 0.8.15 for Windows. Kinovea Paris France. http://www.kinovea.org. Accessed 10 March 2014
Kokubun N, Kim JH, Shin HC, Naito Y, Takahashi A (2011) Penguin head movement detected using small accelerometers: a proxy of prey encounter rate. J Exp Biol 214(22):3760–3767
Kuhn M, Contributions from Wing J, Weston S, Williams A, Keefer C, Engelhardt A, Cooper T, Mayer Z, Kenkel B, the R Core Team, Benesty M, Lescarbeau R, Ziem A, Scrucca L, Tang Y, Candan C (2016) caret: Classification and Rgeression Training. R package version 6.0–71. https://CRAN.R-project.org/package=caret
Ladds MA, Thompson AP, Kadar JP, Slip DJ, Hocking DP, Harcourt RG (2017) Super machine learning: improving accuracy and reducing variance of behaviour classification from accelerometry. Anim Biotelemetry 5(1):8
Lantz B (2015) Machine learning with R. Ltd, Birmingham
Liebsch N, Wilson RP, Bornemann H, Adelung D, Plötz J (2007) Mouthing off about fish capture: jaw movement in pinnipeds reveals the real secrets of ingestion. Deep Sea Res Part II Top Stud Oceanogr 54(3–4):256–269
Marshall GJ (1998) Crittercam: an animal-borne imaging and data logging system. Mar Technol Mar Technol Soc J 32(1):11
Martiskainen P, Järvinen M, Skön JP, Tiirikainen J, Kolehmainen M, Mononen J (2009) Cow behaviour pattern recognition using a three-dimensional accelerometer and support vector machines. Appl Anim Behav Sci 119(1–2):32–38
McClune DW, Marks NJ, Wilson RP, Houghton JD, Montgomery IW, McGowan NE et al (2014) Tri-axial accelerometers quantify behaviour in the Eurasian badger (Meles meles): towards an automated interpretation of field data. Anim Biotelemetry 2(1):5
Nadimi ES, Søgaard HT, Bak T (2008) ZigBee-based wireless sensor networks for classifying the behaviour of a herd of animals using classification trees. Biosyst Eng 100(2):167–176
Nadimi ES, Jørgensen RN, Blanes-Vidal V, Christensen S (2012) Monitoring and classifying animal behavior using ZigBee-based mobile ad hoc wireless sensor networks and artificial neural networks. Comput Electron Agric 82:44–54
Naito Y (2007) How can we observe the underwater feeding behavior of endotherms? Polar Scie 1:101–111
Nathan R, Spiegel O, Fortmann-Roe S, Harel R, Wikelski M, Getz WM (2012) Using tri-axial acceleration data to identify behavioral modes of free-ranging animals: general concepts and tools illustrated for griffon vultures. J Exp Biol 215(6):986–996
Okuyama J, Nakajima K, Noda T, Kimura S, Kamihata H, Kobayashi M et al (2013) Ethogram of immature green turtles: behavioral strategies for somatic growth in large marine herbivores. PLoS ONE 8(6):e65783
Pavey TG, Gilson ND, Gomersall SR, Clark B, Trost SG (2017) Field evaluation of a random forest activity classifier for wrist-worn accelerometer data. J Sci Med Sport 20(1):75–80
Ponganis PJ, Van Dam RP, Marshall G, Knower T, Levenson DH (2000) Sub-ice foraging behavior of emperor penguins. J Exp Biol 203(21):3275–3278
Pozzi LM, Borboroglu PG, Boersma PD, Pascual MA (2015) Population regulation in Magellanic penguins: what determines changes in colony size? PLoS ONE 10(3):e0119002
Qasem L, Cardew A, Wilson A, Griffiths I, Halsey LG, Shepard EL et al (2012) Tri-axial dynamic acceleration as a proxy for animal energy expenditure; should we be summing values or calculating the vector? PLoS ONE 7(2):e31187
R Core Team (2019) R: a language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria http://www.R-project.org
Redfern JV, Ferguson MC, Becker EA, Hyrenbach KD, Good C, Barlow J et al (2006) Techniques for cetacean-habitat modeling. Mar Ecol Prog Ser 310:271–295
Resheff YS, Rotics S, Harel R, Spiegel O, Nathan R (2014) AcceleRater: a web application for supervised learning of behavioral modes from acceleration measurements. Mov Ecol 2(1):27
Rodary D, Wienecke BC, Bost CA (2000) Diving behaviour of Adelie penguins (Pygoscelis adeliae) at Dumont D’Urville, Antarctica: nocturnal patterns of diving and rapid adaptations to changes in sea-ice condition. Pol Biol 23(2):113–120
Ropert-Coudert Y, Kato A, Baudat J, Bost CA, Le Maho Y, Naito Y (2001) Feeding strategies of free-ranging Adélie penguins Pygoscelis adeliae analysed by multiple data recording. Pol Biol 24(6):460–466
Ropert-Coudert Y, Kato A, Liebsch N, Wilson RP, Muller G, Baubet E (2004) Monitoring jaw movements: a cue to feeding activity. Game Wildl Sci 21(1):1–20
Rutz C, Troscianko J (2013) Programmable, miniature video loggers for deployment on wild birds and other wildlife. Methods Ecol Evol 4(2):114–122
Sala JE (2013) Ecología pelágica del Pingüino de Magallanes (Spheniscus magellanicus): determinación de áreas de uso, comportamiento y gasto energético, asociados a la obtención de alimento. Ph.D dissertation, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires
Sala JE, Wilson RP, Frere E, Quintana F (2012a) Foraging effort in Magellanic penguins in coastal Patagonia, Argentina. Mar Ecol Prog Ser 464:273–287
Sala JE, Wilson RP, Quintana F (2012a) How much is too much? Assessment of prey consumption by Magellanic penguins in Patagonian colonies. PLoS ONE 7(12):e51487.012
Sala JE, Wilson RP, Frere E, Quintana F (2014) Flexible foraging for finding fish: variable diving patterns in Magellanic penguins Spheniscus magellanicus from different colonies. J Ornithol 155(3):801–817
Schiavini A, Yorio P, Gandini P, Raya Rey A, Boersma PD (2005) Los pingüinos de las costas argentinas: estado poblacional y conservación. Hornero 20(1):5–23
Shepard EL, Wilson RP, Quintana F, Gómez Laich AG, Liebsch N, Albareda DA et al (2008) Identification of animal movement patterns using tri-axial accelerometry. Endanger Species Res 10:47–60
Simeone A, Wilson RP (2003) In-depth studies of Magellanic penguin (Spheniscus magellanicus) foraging: can we estimate prey consumption by perturbations in the dive profile? Mar Biol 143(4):825–831
Skinner JP, Mitani Y, Burkanov VN, Andrews RD (2014) Proxies of food intake and energy expenditure for estimating the time–energy budgets of lactating northern fur seals Callorhinus ursinus. J Exp Mar Biol Ecol 461:107–115
Sur M, Suffredini T, Wessells SM, Bloom PH, Lanzone M, Blackshire S et al (2017) Improved supervised classification of accelerometry data to distinguish behaviors of soaring birds. PLoS ONE 12(4):e0174785
Takahashi A, Dunn MJ, Trathan PN, Croxall JP, Wilson RP, Sato K, Naito Y (2004) Krill-feeding behaviour in a chinstrap penguin compared to fish-eating in Magellanic penguins: a pilot study. Mar Ornithol 32:47–54
Takahashi A, Sato K, Naito Y, Dunn MJ, Trathan PN, Croxall JP (2004) Penguin–mounted cameras glimpse underwater group behaviour. Proc Royal Soc B 271(suppl_5):S281–S282
Tennessen JB, Holt MM, Hanson MB, Emmons CK, Giles DA, Hogan JT (2019) Kinematic signatures of prey capture from archival tags reveal sex differences in killer whale foraging activity. J Exp Biol 222(3):jeb191874
Valletta JJ, Torney C, Kings M, Thornton A, Madden J (2017) Applications of machine learning in animal behaviour studies. Anim Behav 124:203–220
Viviant M, Trites AW, Rosen DA, Monestiez P, Guinet C (2010) Prey capture attempts can be detected in Steller sea lions and other marine predators using accelerometers. Pol Biol 33(5):713–719
Viviant M, Monestiez P, Guinet C (2014) Can we predict foraging success in a marine predator from dive patterns only? Validation with prey capture attempt data. PLoS ONE 9(3):e88503
Volpov BL, Hoskins AJ, Battaile BC, Viviant M, Wheatley KE, Marshall G et al (2015) Identification of prey captures in Australian fur seals (Arctocephalus pusillus doriferus) using head-mounted accelerometers: field validation with animal-borne video cameras. PLoS ONE 10(6):e0128789
Watanabe YY, Takahashi A (2013) Linking animal-borne video to accelerometers reveals prey capture variability. Proc Natl Acad Sci 110:2199–2204
Watanabe YY, Ito M, Takahashi A (2014) Testing optimal foraging theory in a penguin–krill system. Proc Biol Sci 281(1779):20132376
Watanabe YY, Payne NL, Semmens JM, Fox A, Huveneers C (2019) Swimming strategies and energetics of endothermic white sharks during foraging. J Exp Biol 222(4):jeb185603
Watanuki Y, Niizuma Y, Geir WG, Sato K, Naito Y (2003) Stroke and glide of wing–propelled divers: deep diving seabirds adjust surge frequency to buoyancy change with depth. Proc Royal Soc B 270(1514):483–488
Wilkinson DM, Ruxton GD (2012) Understanding selection for long necks in different taxa. Biol Rev 87(3):616–630
Williams HJ, EL Shepard C, Duriez O, Lambertucci SA (2015) Can accelerometry be used to distinguish between flight types in soaring birds? Anim Biotelemetry 3(1):45
Wilson RP, Duffy DC (1986) Prey seizing in African penguins Spheniscus-demersus. Ardea 74(2):211–214
Wilson R, Liebsch N (2003) Up-beat motion in swinging limbs: new insights into assessing movement in free-living aquatic vertebrates. Mar Biol 142(3):537–547
Wilson RP, Wilson MPT (1990) Foraging ecology of breeding Spheniscus penguins. In: Davis L, Darby J (eds) Penguin biology, 1st edn. Academic Press, San Diego, pp 181–206
Wilson RP, Gómez-Laich A, Sala JE, Dell'Omo G, Holton MD, Quintana F (2017) Long necks enhance and constrain foraging capacity in aquatic vertebrates. Proc Royal Soc B 284(1867):20172072
Wilson RP, Ryan PG, James A, Wilson MPT (1987) Conspicuous coloration may enhance prey capture in some piscivores. Anim Behav 35:1558–1560
Wilson RP, Cooper J, Plötz J (1992) Can we determine when marine endotherms feed? A case study with seabirds. J Exp Biol 167:267–275
Wilson RP, Pütz K, Peters G, Culik B, Scolaro JA, Charrassin JB, Ropert-Coudert Y (1997) Long-term attachment of transmitting and recording devices to penguins and other seabirds. Wildl Soc Bull 25(1):101–106
Wilson RP, Ropert-Coudert Y, Kato A (2002) Rush and grab strategies in foraging marine endotherms: the case for haste in penguins. Anim Behav 63(1):85–95
Wilson R, Steinfurth A, Ropert-Coudert Y, Kato A, Kurita M (2002) Lip-reading in remote subjects: an attempt to quantify and separate ingestion, breathing and vocalisation in free-living animals using penguins as a model. Mar Biol 140(1):17–27
Wilson AM, Lowe JC, Roskilly K, Hudson PE, Golabek KA, McNutt JW (2013) Locomotion dynamics of hunting in wild cheetahs. Nature 498(7453):185–189
Wilson RP, Sala JE, Gómez-Laich A, Ciancio J, Quintana F (2015) Pushed to the limit: food abundance determines tag-induced harm in penguins. Anim Welf 24(1):37–44
Wilson RP, Börger L, Holton MD, Scantlebury DM, Gómez-Laich A, Quintana F et al (2019) Estimates for energy expenditure in free-living animals using acceleration proxies; a reappraisal. J Anim Ecol 89(1):161–172
Yorio P, Frere E, Gandini P, Conway W (1999) Status and conservation of seabirds breeding in Argentina Bird Conserv Int 9(4):299–314
Davis RW, Fuiman LA, Williams TM, Collier SO, Hagey WP, Kanatous SB et al (1999) Hunting behavior of a marine mammal beneath the Antarctic fast ice. Science 283(5404):993–996
Acknowledgements
We would like to express our gratitude to Juan Emilio Sala and Flavio Monti for their helpful assistance during fieldwork. We also thank the Conservation Agency from the Chubut Province for the permits to work at Cabo dos Bahías and Península Valdés, people from Ea. San Lorenzo and the Instituto de Biología de Organismos Marinos (IBIOMAR)—CONICET and the CCT CENPAT—CONICET for institutional and logistical support.
Funding
This study was funded by grants from the Agencia Nacional de Promoción Científica y Tecnológica (Grant Number: PICT-2013-1229) and from the Japan Society for the Promotion of Science KAKENHI (Grant Number: JP16H06541).
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A.G-L. and F.Q. conceived the study. A.G-L., F.Q., G.S.B. and G.D-O. collected the data. M.D-C. completed statistical analysis with the help of A.G-L and F.Q. M.D-C., A.G-L. and F.Q. wrote the initial manuscript. All authors contributed to the reviewing and editing. F.Q, K.Y and G.S.B. obtained funding. F.Q., K.Y. and G. D-O. provided resources. A.G-L. and F.Q. supervised the project.
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All penguin handling procedures were reviewed and approved by the Dirección de Fauna y Flora Silvestre and the Ministerio de Turismo y Áreas Protegidas de la Provincia de Chubut (permits to work at Punta Norte during 2015 and 2016: No. 096-SsCyAP/15 and No. 096-SsCyAP/16, permit to work at Cabo dos Bahías during 2015: No. 075-SsCyAP/15). During instrumentation, birds were handled as quickly and efficiently as possible.
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Del Caño, M., Quintana, F., Yoda, K. 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). https://doi.org/10.1007/s00227-021-03892-1
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DOI: https://doi.org/10.1007/s00227-021-03892-1