Behavioral Ecology and Sociobiology

, Volume 67, Issue 6, pp 1013–1026 | Cite as

Splitting animal trajectories into fine-scale behaviorally consistent movement units: breaking points relate to external stimuli in a foraging seabird

Methods

Abstract

Animal movements are widely studied in ecology, and the analysis of tracking data usually gains from splitting the time-series into different parts before interpreting movement strategies. The recent increase in data accuracy and resolution allows for the study of fine-scale movements where each behavioral change is recorded. We propose a simple method to identify the elementary units of movement in a trajectory, resulting in breaks in the track corresponding to the animals' decisions to change its movement. We quantify the movement between successive steps with a vector of speed and direction and represent a movement path in a trigonometric circle space in order to visualize behavioral changes instead of spatial changes. In this space, the distance between successive points informs about their similarity in both speed and direction. We quantify the temporal changes in these distances with a cumulative sum and use a line simplification algorithm to identify breaks in the slope that correspond to breaks in the consistency of successive distance values. We test the algorithm on simulated trajectories and show that the expected number of segments is accurately identified. Moreover, we relate the resulting segmentation from recorded trajectories to events observed using animal-borne video footage and show that the presence of stimuli in the surroundings of the animal is associated with a higher frequency of changes in movement. As an applied example, we propose a descriptive analysis of the segments and show that segments of particular characteristics are not distributed equally along the trajectory, highlighting larger-scale behavioral strategies.

Keywords

Movement ecology Animal behavior Segmentation Biologging GPS 

References

  1. Adams N, Navarro R (2005) Foraging of a coastal seabird: flight patterns and movements of breeding Cape gannets Morus capensis. Afr J Mar Sci 27:239CrossRefGoogle Scholar
  2. Barraquand F, Benhamou S (2008) Animal movements in heterogeneous landscapes: identifying profitable places and homogeneous movement bouts. Ecology 89:3336–3348PubMedCrossRefGoogle Scholar
  3. Bartumeus F, da Luz MGE, Viswanathan GM, Catalan J (2005) Animal search strategies: a quantitative random-walk analysis. Ecology 86:3078–3087CrossRefGoogle Scholar
  4. Bascompte J, Vilà C (1997) Fractals and search paths in mammals. Landscape Ecol 12:213–221CrossRefGoogle Scholar
  5. Batschelet E (1981) Circular statistics in biology. Academic, LondonGoogle Scholar
  6. Benhamou S (1992) Efficiency of area-concentrated searching behaviour in a continuous patchy environment. J Theor Biol 159:67–81CrossRefGoogle Scholar
  7. Benhamou S (2004) How to reliably estimate the tortuosity of an animal's path: straightness, sinuosity, or fractal dimension? J Theor Biol 229:209–220PubMedCrossRefGoogle Scholar
  8. Buchin M, Driemel A, van Kreveld M, Sacristan V (2011) Segmenting trajectories: a framework and algorithms using spatiotemporal criteria. J Spat Inf Sci 0:33–63Google Scholar
  9. Calenge C, Dray S, Royer-Carenzi M (2009) The concept of animals' trajectories from a data analysis perspective. Ecol Inform 4:34–41CrossRefGoogle Scholar
  10. Camphuysen CJ, Webb A (1999) Multi-species feeding associations in North Sea seabirds: jointly exploiting a patchy environment. Ardea 87:177–198Google Scholar
  11. Clauset A, Shalizi CR, Newman MEJ (2009) Power-law distributions in empirical data. SIAM Rev 51:661–703CrossRefGoogle Scholar
  12. Codling EA, Plank MJ, Benhamou S (2008) Random walk models in biology. J R Soc Interface 5:813–834PubMedCrossRefGoogle Scholar
  13. Dean B, Freeman R, Kirk H, Leonard K, Phillips RA, Perrins CM, Guilford T (2013) Behavioural mapping of a pelagic seabird: combining multiple sensors and a hidden Markov model reveals the distribution of at-sea behaviour. J R Soc Interface. doi:10.1098/rsif.2012.0570
  14. Dodge S, Laube P, Weibel R (2012) Movement similarity assessment using symbolic representation of trajectories. Int J Geogr Inf Sci 26:1563–1588CrossRefGoogle Scholar
  15. Douglas DH, Peucker TK (1973) Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. Cartogr Int J Geogr Inf GeoVis 10:112–122Google Scholar
  16. Dowd M, Joy R (2010) Estimating behavioral parameters in animal movement models using a state-augmented particle filter. Ecology 92:568–575CrossRefGoogle Scholar
  17. Fauchald P, Tveraa T (2003) Using first-passage time in the analysis of area-restricted search and habitat selection. Ecology 84:282–288CrossRefGoogle Scholar
  18. Fritz H, Said S, Weimerskirch H (2003) Scale-dependent hierarchical adjustments of movement patterns in a long-range foraging seabird. Proc R Soc Lond B 270:1143–1148CrossRefGoogle Scholar
  19. Fronhofer EA, Hovestadt T, Poethke H-J (2012) From random walks to informed movement. Oikos :001–010Google Scholar
  20. Garthe S, Montevecchi WA, Davoren GK (2007) Flight destinations and foraging behaviour of northern gannets (Sula bassana) preying on a small forage fish in a low-Arctic ecosystem. Deep-Sea Res Pt II 54:311–320CrossRefGoogle Scholar
  21. Gaucherel C (2011) Wavelet analysis to detect regime shifts in animal movement. Comput Ecol Softw 1:69–85Google Scholar
  22. Getz WM, Saltz D (2008) A framework for generating and analyzing movement paths on ecological landscapes. P Natl Acad Sci USA 105:19066–19071Google Scholar
  23. Grémillet D, DellOmo G, Ryan PG, Peters G, RopertCoudert Y, Weeks SJ (2004) Offshore diplomacy, or how seabirds mitigate intra-specific competition: a case study based on GPS tracking of Cape gannets from neighbouring colonies. Mar Ecol Prog Ser 268:265–279CrossRefGoogle Scholar
  24. Grimm V, Railsback SF (2005) Individual-based modeling and ecology. Princeton University Press, PrincetonGoogle Scholar
  25. Guilford T, Roberts S, Biro D, Rezek I (2004) Positional entropy during pigeon homing II: navigational interpretation of Bayesian latent state models. J Theor Biol 227:25–38PubMedCrossRefGoogle Scholar
  26. Gurarie E, Andrews RD, Laidre KL (2009) A novel method for identifying behavioural changes in animal movement data. Ecol Lett 12:395–408PubMedCrossRefGoogle Scholar
  27. Hamer KC, Humphreys EM, Magalhães MC, Garthe S, Hennicke J, Peters G, Grémillet D, Skov H, Wanless S (2009) Fine-scale foraging behaviour of a medium-ranging marine predator. J Anim Ecol 78:880–889PubMedCrossRefGoogle Scholar
  28. Holyoak M, Casagrandi R, Nathan R, Revilla E, Spiegel O (2008) Trends and missing parts in the study of movement ecology. P Natl Acad Sci USA 105:19060–19065Google Scholar
  29. Horne JS, Garton EO, Krone SM, Lewis JS (2007) Analyzing animal movements using Brownian bridges. Ecology 88:2354–2363PubMedCrossRefGoogle Scholar
  30. Jonsen ID, Myers RA, Flemming JM (2003) Meta-analysis of animal movement using state-space models. Ecology 84:3055–3063CrossRefGoogle Scholar
  31. Kareiva P, Odell G (1987) Swarms of predators exhibit “preytaxis” if individual predators use area-restricted search. Am Nat 130:233–270CrossRefGoogle Scholar
  32. Kranstauber B, Kays R, LaPoint SD, Wikelski M, Safi K (2012) A dynamic Brownian bridge movement model to estimate utilization distributions for heterogeneous animal movement. J Anim Ecol 81:738–746PubMedCrossRefGoogle Scholar
  33. Morales JM, Haydon DT, Frair J, Holsinger KE, Fryxell JM (2004) Extracting more out of relocation data: building movement models as mixtures of random walks. Ecology 85:2436–2445CrossRefGoogle Scholar
  34. Naito Y (2004) New steps in bio-logging science. Mem Nat Inst Polar Res 58:50–57Google Scholar
  35. Nams VO (1996) The VFractal: a new estimator for fractal dimension of animal movement paths. Landscape Ecol 11:289–297CrossRefGoogle Scholar
  36. Nams VO (2005) Using animal movement paths to measure response to spatial scale. Oecologia 143:179–188PubMedCrossRefGoogle Scholar
  37. Nathan R, Getz WM, Revilla E, Holyoak M, Kadmon R, Saltz D, Smouse PE (2008) A movement ecology paradigm for unifying organismal movement research. PNAS 105:19052–19059PubMedCrossRefGoogle Scholar
  38. Newlands NK, Lutcavage ME, Pitcher TJ (2004) Analysis of foraging movements of Atlantic bluefin tuna (Thunnus thynnus): individuals switch between two modes of search behaviour. Popul Ecol 46:39–53CrossRefGoogle Scholar
  39. O'Donoghue SH, Drapeau L, Peddemors VM (2010) Broad-scale distribution patterns of sardine and their predators in relation to remotely sensed environmental conditions during the KwaZulu-Natal sardine run. Afr J Mar Sci 32:279–291CrossRefGoogle Scholar
  40. Patterson TA, Thomas L, Wilcox C, Ovaskainen O, Matthiopoulos J (2008) State-space models of individual animal movement. Trends Ecol Evol 23:87–94PubMedCrossRefGoogle Scholar
  41. Pichegru L, Ryan PG, van der Lingen CD, Coetzee J, RopertCoudert Y, Grmillet D (2007) Foraging behaviour and energetics of Cape gannets Morus capensis feeding on live prey and fishery discards in the Benguela upwelling system. Mar Ecol Prog Ser 350:127–136CrossRefGoogle Scholar
  42. Polansky L, Wittemyer G, Cross PC, Tambling CJ, Getz WM (2010) From moonlight to movement and synchronized randomness: Fourier and wavelet analyses of animal location time series data. Ecology 91:1506–1518PubMedCrossRefGoogle Scholar
  43. Postlethwaite CM, Brown P, Dennis TE (2013) A new multi-scale measure for analysing animal movement data. J Theor Biol 317:175–185PubMedCrossRefGoogle Scholar
  44. Roberts S, Guilford T, Rezek I, Biro D (2004) Positional entropy during pigeon homing I: application of Bayesian latent state modelling. J Theor Biol 227:39–50PubMedCrossRefGoogle Scholar
  45. Ropert-Coudert Y, Wilson RP (2005) Trends and perspectives in animal-attached remote sensing. Front Ecol Environ 3:437–444CrossRefGoogle Scholar
  46. Shamoun-Baranes J, van Loon EE, Purves RS, Speckmann B, Weiskopf D, Camphuysen CJ (2012) Analysis and visualization of animal movement. Biol Lett 8:6–9PubMedCrossRefGoogle Scholar
  47. Silverman ED, Veit RR, Nevitt G (2004) Nearest neighbors as foraging cues: information transfer in a patchy environment. Mar Ecol Prog Ser 277:25–36CrossRefGoogle Scholar
  48. Tremblay Y, Bertrand S, Henry RW, Kappes MA, Costa DP, Shaffer SA (2009) Analytical approaches to investigating seabird–environment interactions: a review. Mar Ecol Prog Ser 391:153–163CrossRefGoogle Scholar
  49. Tremblay Y, Roberts AJ, Costa DP (2007) Fractal landscape method: an alternative approach to measuring area-restricted searching behavior. J Exp Biol 210:935–945PubMedCrossRefGoogle Scholar
  50. Tremblay Y, Shaffer SA, Fowler SL, Kuhn CE, McDonald BI, Weise MJ, Bost C-A, Weimerskirch H, Crocker DE, Goebel ME, Costa DP (2006) Interpolation of animal tracking data in a fluid environment. J Exp Biol 209:128–140PubMedCrossRefGoogle Scholar
  51. Turchin P (1998) Quantitative analysis of movement: measuring and modeling population redistribution in animals and plants. Sinauer, SunderlandGoogle Scholar
  52. Walker E, Bez N (2010) A pioneer validation of a state-space model of vessel trajectories (VMS) with observers' data. Ecol Model 221:2008–2017CrossRefGoogle Scholar
  53. Weimerskirch H, Pinaud D, Pawlowski F, Bost C (2007) Does prey capture induce area-restricted search? A fine-scale study using GPS in a marine predator, the wandering albatross. Am Nat 170:734–743PubMedCrossRefGoogle Scholar
  54. Wittemyer G, Polansky L, Douglas-Hamilton I, Getz WM (2008) Disentangling the effects of forage, social rank, and risk on movement autocorrelation of elephants using Fourier and wavelet analyses. P Natl Acad Sci USA 105:19108–19113Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Institut de Recherche pour le Développement, UMR EME-212 Exploited Marine EcosystemsCentre de Recherche Halieutique Méditerranéenne et TropicaleSète cedexFrance
  2. 2.Instituto del mar del Peru (IMARPE)CallaoPeru

Personalised recommendations