Splitting animal trajectories into fine-scale behaviorally consistent movement units: breaking points relate to external stimuli in a foraging seabird
- 1.3k Downloads
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.
KeywordsMovement ecology Animal behavior Segmentation Biologging GPS
We thank SANParks for access to Bird Island, as well as Pierre Pistorius and Ralf Mullers for support and help during the fieldwork. The wind data were collected and provided by the South African Weather Service. We thank Nicolas Bez, Gabriel Reygondeau, and Laurent Dubroca for constructive contributions during the initial phase of methodological implementation and the anonymous referees for helpful comments on the manuscript.
We declare that all experiments comply with the current laws of the country in which they were performed. Fieldwork, handling, and deployments were conducted following standard processes in seabird ecology (see “Materials and methods”, “Test of the method”, and “Collected data”).
- Batschelet E (1981) Circular statistics in biology. Academic, LondonGoogle Scholar
- 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
- Camphuysen CJ, Webb A (1999) Multi-species feeding associations in North Sea seabirds: jointly exploiting a patchy environment. Ardea 87:177–198Google Scholar
- 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
- 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
- Fronhofer EA, Hovestadt T, Poethke H-J (2012) From random walks to informed movement. Oikos :001–010Google Scholar
- Gaucherel C (2011) Wavelet analysis to detect regime shifts in animal movement. Comput Ecol Softw 1:69–85Google Scholar
- 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
- Grimm V, Railsback SF (2005) Individual-based modeling and ecology. Princeton University Press, PrincetonGoogle Scholar
- 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
- Naito Y (2004) New steps in bio-logging science. Mem Nat Inst Polar Res 58:50–57Google Scholar
- Turchin P (1998) Quantitative analysis of movement: measuring and modeling population redistribution in animals and plants. Sinauer, SunderlandGoogle Scholar
- 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