Using tri-axial acceleration data to identify behavioral modes of free-ranging animals: General concepts and tools illustrated for griffon vultures

Journal of Experimental Biology

Volumn 215, Issue 6, 2012, Pages 986-996

  Nathan, Ran ^a   Spiegel, Orr ^a   Fortmann Roe, Scott ^b   Harel, Roi ^a   Wikelski, Martin ^c,d   Getz, Wayne M ^b,e  

^a HEBREW UNIVERSITY OF JERUSALEM   (Israel)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c MAX PLANCK INSTITUTE FOR ORNITHOLOGY   (Germany)

^d UNIVERSITY OF KONSTANZ   (Germany)

^e UNIVERSITY OF KWAZULU NATAL   (South Africa)

Author keywords

Animal behavior; Biomechanics; Flight characteristic; Foraging; Free ranging wild animal; Gyps fulvus; Long range foray; Machine learning algorithm; Movement ecology; Tri axial acceleration; Vulture

Indexed keywords

ACCELERATION; ANIMAL; ANIMAL BEHAVIOR; BIOMECHANICS; CONFERENCE PAPER; FALCONIFORMES; GEOGRAPHIC INFORMATION SYSTEM; MOVEMENT (PHYSIOLOGY); PHYSIOLOGY;

ACCELERATION; ANIMALS; BEHAVIOR, ANIMAL; BIOMECHANICS; FALCONIFORMES; GEOGRAPHIC INFORMATION SYSTEMS; MOVEMENT;

ANIMALIA; GYPS FULVUS;

EID: 84858640770     PISSN: 00220949     EISSN: None     Source Type: Journal    
DOI: 10.1242/jeb.058602     Document Type: Conference Paper

References (60)

1. Altun, K., Barshan, B. and Tuncel, O. (2010). Comparative study on classifying human activities with miniature inertial and magnetic sensors. Pattern Recognit. 43, 3605-3620.
2. Boettiger, A., Wittemyer, G., Starfield, R., Volrath, F., Douglas-Hamilton, I. and Getz, W. M. (2011). Inferring ecological and behavioral drivers of elephant movement using a linear filtering approach. Ecology 92, 1648-1657.
3. Bouten, C. V., Westerterp, K. R., Verduin, M. and Janssen, J. D. (1994). Assessment of energy expenditure for physical activity using a triaxial accelerometer. Med. Sci. Sports Exerc. 26, 1516-1523.
4. Breiman, L. (2001). Random forests. Mach. Learn. 45, 5-32. (Pubitemid 32933532)
5. Byrnes, G., Libby, T., Lim, N. T. L. and Spence, A. J. (2011). Gliding saves time but not energy in Malayan colugos. J. Exp. Biol. 214, 2690-2696.
6. Chen, K. Y. and Bassett, D. R. (2005). The technology of accelerometry-based activity monitors: Current and future. Med. Sci. Sports Exerc. 37, S490-S500. (Pubitemid 41691812)
7. Cooke, S. J., Hinch, S. G., Wikelski, M., Andrews, R. D., Kuchel, L. J., Wolcott, T. G. and Butler, P. J. (2004). Biotelemetry: A mechanistic approach to ecology. Trends Ecol. Evol. 19, 334-343. (Pubitemid 38729836)
8. Cortes, C. and Vapnik, V. (1995). Support-vector networks. Mach. Learn. 20, 273-297.
9. Crouter, S. E., Churilla, J. R. and Bassett, D. R. (2006). Estimating energy expenditure using accelerometers. Eur. J. Appl. Physiol. 98, 601-612. (Pubitemid 44762582)
10. Dalziel, B. D., Morales, J. M. and Fryxell, J. M. (2008). Fitting probability distributions to animal movement trajectories: Using artificial neural networks to link distance, resources, and memory. Am. Nat. 172, 248-258.
11. DeVaul, R. and Dunn, S. (2001). Real-time motion classification for wearable computing applications. Technical Report, MIT Media Laboratory.
12. Fahlman, A., Wilson, R., Svard, C., Rosen, D. A. S. and Trites, A. W. (2008). Activity and diving metabolism correlate in Steller sea lion Eumetopias jubatus. Aquat. Biol. 2, 75-84.
13. Fryxell, J. M., Hazell, M., Borger, L., Dalziel, B. D., Haydon, D. T., Morales, J. M., McIntosh, T. and Rosatte, R. C. (2008). Multiple movement modes by large herbivores at multiple spatiotemporal scales. Proc. Natl. Acad. Sci. USA 105, 19114-19119.
14. Getz, W. M. and Saltz, D. (2008). A framework for generating and analyzing movement paths on ecological landscapes. Proc. Natl. Acad. Sci. USA 105, 19066-19071.
15. Gleiss, A. C., Dale, J. J., Holland, K. N. and Wilson, R. P. (2010). Accelerating estimates of activity-specific metabolic rate in fishes: Testing the applicability of acceleration data-loggers. J. Exp. Mar. Biol. Ecol. 385, 85-91.
16. Godfrey, A., Conway, R., Meagher, D. and Olaighin, G. (2008). Direct measurement of human movement by accelerometry. Med. Eng. Phys. 30, 1364-1386.
17. Goldreich, Y. (2003). The Climate of Israel: Observation, Research and Application. New York: Kluwer Academic/Plenum Publishers.
18. Green, J. A., Boyd, I. L., Woakes, A. J., Warren, N. L. and Butler, P. J. (2009a). Evaluating the prudence of parents: Daily energy expenditure throughout the annual cycle of a free ranging bird, the macaroni penguin Eudyptes chrysolophus. J. Avian Biol. 40, 529-538.
19. Green, J. A., Halsey, L. G., Wilson, R. P. and Frappell, P. B. (2009b). Estimating energy expenditure of animals using the accelerometry technique: Activity, inactivity and comparison with the heart-rate technique. J. Exp. Biol. 212, 471-482.
20. Halsey, L. G., Shepard, E. L. C., Hulston, C. J., Venables, M. C., White, C. R., Jeukendrup, A. E. and Wilson, R. P. (2008). Acceleration versus heart rate for estimating energy expenditure and speed during locomotion in animals: Tests with an easy model species, Homo sapiens. Zoology 111, 231-241. (Pubitemid 351787341)
21. Halsey, L. G., Portugal, S. J., Smith, J. A., Murn, C. P. and Wilson, R. P. (2009a). Recording raptor behavior on the wing via accelerometry. J. Field Ornithol. 80, 171-177.
22. Halsey, L. G., Shepard, E. L. C., Quintana, F., Laich, A. G., Green, J. A. and Wilson, R. P. (2009b). The relationship between oxygen consumption and body acceleration in a range of species. Comp. Biochem. Physiol. A Mol. Integr. Physiol. 152, 197-202.
23. Halsey, L. G., Shepard, E. L. C. and Wilson, R. P. (2011). Assessing the development and application of the accelerometry technique for estimating energy expenditure. Comp. Biochem. Physiol. A Mol. Integr. Physiol. 158, 305-314.
24. Hebblewhite, M. and Haydon, D. T. (2010). Distinguishing technology from biology: A critical review of the use of GPS telemetry data in ecology. Philos. Trans. R. Soc. Lond. B 365, 2303-2312.
25. Holyoak, M., Casagrandi, R., Nathan, R., Revilla, E. and Spiegel, O. (2008). Trends and missing parts in the study of movement ecology. Proc. Natl. Acad. Sci. USA 105, 19060-19065.
26. Laich, A. G., Wilson, R. P., Quintana, F. and Shepard, E. L. C. (2008). Identification of imperial cormorant Phalacrocorax atriceps behavior using accelerometer. Endang. Species Res. 10, 29-37.
27. Martiskainen, P., Jarvinen, M., Skon, J. P., Tiirikainen, J., Kolehmainen, M. and Mononen, J. (2009). Cow behaviour pattern recognition using a three-dimensional accelerometer and support vector machines. Appl. Anim. Behav. Sci. 119, 32-38.
28. Mendelssohn, H. and Leshem, Y. (1983). Observations on reproduction and growth of Old World vultures. In Vulture Biology and Management (ed. S. R. Wilbur and J. A. Jackson), pp. 214-241. Los Angeles, CA: University of California Press.
29. Montoye, H. J., Washburn, R., Servais, S., Ertl, A., Webster, J. G. and Nagle, F. J. (1983). Estimation of energy expenditure by a portable accelerometer. Med. Sci. Sports Exerc. 15, 403-407. (Pubitemid 14239236)
30. Moreau, M., Siebert, S., Buerkert, A. and Schlecht, E. (2009). Use of a tri-axial accelerometer for automated recording and classification of goats' grazing behaviour. Appl. Anim. Behav. Sci. 119, 158-170.
31. Mundy, P. J., Bunchart, D., Ledger, J. and Piper, S. E. (1992). The Vultures of Africa. Halfway House, RSA: Acorn and Russel Friedman Books.
32. Nathan, R., Getz, W. M., Revilla, E., Holyoak, M., Kadmon, R., Saltz, D. and Smouse, P. E. (2008). A movement ecology paradigm for unifying organismal movement research. Proc. Natl. Acad. Sci. USA 105, 19052-19059.
33. Nielsen, L. R., Pedersen, A. R., Herskin, M. S. and Munksgaard, L. (2010). Quantifying walking and standing behaviour of dairy cows using a moving average based on output from an accelerometer. Appl. Anim. Behav. Sci. 127, 12-19.
34. Payne, N. L., Gillanders, B. M., Seymour, R. S., Webber, D. M., Snelling, E. P. and Semmens, J. M. (2011). Accelerometry estimates field metabolic rate in giant Australian cuttlefish Sepia apama during breeding. J. Anim. Ecol. 80, 422-430.
35. Plasqui, G. and Westerterp, K. R. (2007). Physical activity assessment with accelerometers: An evaluation against doubly labeled water. Obesity 15, 2371-2379. (Pubitemid 350034029)
36. Preece, S. J., Goulermas, J. Y., Kenney, L. P. J. and Howard, D. (2009). A comparison of feature extraction methods for the classification of dynamic activities from accelerometer data. IEEE Trans. Biomed. Eng. 56, 871-879.
37. Prinzinger, R., Nagel, B., Bahat, O., Bögel, R., Karl, E., Weihs, D. and Walzer, C. (2002). Energy metabolism and body temperature in the Griffon Vulture (Gyps fulvus) with comparative data on the Hooded Vulture (Necrosyrtes monachus) and the White-backed Vulture (Gyps africanus). J. Ornithol. 143, 456-467. (Pubitemid 35274011)
38. Ravi, N., Dandekar, N., Mysore, P. and Littman, M. (2005). Activity recognition from accelerometer data. Proc. 20th Nat. Conf. Artif. Intell. 20, 1541-1546. (Pubitemid 43006650)
39. Ropert-Coudert, Y. and Wilson, R. P. (2005). Trends and perspectives in animalattached remote sensing. Front. Ecol. Environ. 3, 437-444. (Pubitemid 41407518)
40. Rutz, C. and Hays, G. C. (2009). New frontiers in biologging science. Biol. Lett. 5, 289-292.
41. Ruxton, G. D. and Houston, D. C. (2004). Obligate vertebrate scavengers must be large soaring fliers. J. Theor. Biol. 228, 431-436. (Pubitemid 38591468)
42. Sakamoto, K. Q., Sato, K., Ishizuka, M., Watanuki, Y., Takahashi, A., Daunt, F. and Wanless, S. (2009). Can ethograms be automatically generated using body acceleration data from free-ranging birds? PLoS ONE 4, e5379.
43. Sapir, N., Wikelski, M., McCue, M. D., Pinshow, B. and Nathan, R. (2010). Flight modes in migrating European Bee-eaters: Heart rate may indicate low metabolic rate during soaring and gliding. PLoS ONE 5, e13956.
44. Sapir, N., Wikelski, M., Avissar, R. and Nathan, R. (2011). Timing and flight mode of departure in migrating European bee-eaters in relation to multi-scale meteorological processes. Behav. Ecol. Sociobiol. 65, 1353-1365.
45. Sarrazin, F., Bagnolini, C., Pinna, J. L., Danchin, E. and Clobert, J. (1994). High survival estimates of griffon vultures (Gyps fulvus fulvus) in a reintroduced population. Auk 111, 853-862.
46. Scheibe, K. M. and Gromann, C. (2006). Application testing of a new threedimensional acceleration measuring system with wireless data transfer (WAS) for behavior analysis. Behav. Res. Methods 38, 427-433.
47. Shepard, E. L. C., Wilson, R. P., Quintana, F., Laich, A. G., Liebsch, N., Albareda, D. A., Halsey, L. G., Gleiss, A., Morgan, D. T., Myers, A. E. et al. (2008). Identification of animal movement patterns using tri-axial accelerometry. Endang. Species Res. 10, 47-60.
48. Shepard, E. L. C., Wilson, R. P., Halsey, L. G., Quintana, F., Laich, A. G., Gleiss, A. C., Liebsch, N., Myers, A. E. and Norman, B. (2009a). Derivation of body motion via appropriate smoothing of acceleration data. Aquat. Biol. 4, 235-241.
49. Shepard, E. L. C., Wilson, R. P., Quintana, F., Laich, A. G. and Forman, D. W. (2009b). Pushed for time or saving on fuel: Fine-scale energy budgets shed light on currencies in a diving bird. Proc. R. Soc. Lond. B 276, 3149-3155.
50. Shirihai, H. (1996). The Birds of Israel. London: Academic Press.
51. Smouse, P. E., Focardi, S., Moorcroft, P. R., Kie, J. G., Forester, J. D. and Morales, J. M. (2010). Stochastic modelling of animal movement. Philos. Trans. R. Soc. Lond. B 365, 2201-2211.
52. Vanak, A. T., Thaker, M. and Slotow, R. (2010). Do fences create an edge-effect on the movement patterns of a highly mobile mega-herbivore? Biol. Conserv. 143, 2631-2637.
53. Watanabe, S., Izawa, M., Kato, A., Ropert-Coudert, Y. and Naito, Y. (2005). A new technique for monitoring the detailed behaviour of terrestrial animals: A case study with the domestic cat. Appl. Anim. Behav. Sci. 94, 117-131. (Pubitemid 41253682)
54. Watanabe, Y. Y., Takahashi, A., Sato, K., Viviant, M. and Bost, C. A. (2011). Poor flight performance in deep-diving cormorants. J. Exp. Biol. 214, 412-421.
55. Wikelski, M., Kays, R. W., Kasdin, N. J., Thorup, K., Smith, J. A. and Swenson, G. W. (2007). Going wild: What a global small-animal tracking system could do for experimental biologists. J. Exp. Biol. 210, 181-186.
56. Wilson, R. P., White, C. R., Quintana, F., Halsey, L. G., Liebsch, N., Martin, G. R. and Butler, P. J. (2006). Moving towards acceleration for estimates of activityspecific metabolic rate in free-living animals: The case of the cormorant. J. Anim. Ecol. 75, 1081-1090. (Pubitemid 44175175)
57. Yang, C. C. and Hsu, Y. L. (2010). A review of accelerometry-based wearable motion detectors for physical activity monitoring. Sensors 10, 7772-7788.
58. Yoda, K., Sato, K., Niizuma, Y., Kurita, M., Bost, C. A., Le Maho, Y. and Naito, Y. (1999). Precise monitoring of porpoising behaviour of Adelie penguins determined using acceleration data loggers. J. Exp. Biol. 202, 3121-3126. (Pubitemid 30030912)
59. Yoda, K., Naito, Y., Sato, K., Takahashi, A., Nishikawa, J., Ropert-Coudert, Y., Kurita, M. and Le Maho, Y. (2001). A new technique for monitoring the behaviour of free-ranging Adelie penguins. J. Exp. Biol. 204, 685-690. (Pubitemid 34861168)
60. Yott, A., Rosatte, R., Schaefer, J. A., Hamr, J. and Fryxell, J. (2011). Movement and spread of a founding population of reintroduced elk (Cervus elaphus) in Ontario, Canada. Restor. Ecol. 19, 70-77.