GPS_map_clustering

Citation
Bastille-Rousseau G, Potts JR, Yackulic CB, Frair JL, Ellington EH, Blake S. 2016. GPS_map_clustering. Movebank Data Repository. https://doi.org/10.5441/001/1.356nb5mf/3
Abstract
NOTE: An updated and larger version of this dataset is available. See https://doi.org/10.5441/001/1.6gr485fk. ABSTRACT: Background: Characterizing the movement patterns of animals is an important step in understanding their ecology. Various methods have been developed for classifying animal movement at both coarse (e.g., migratory vs. sedentary behavior) and fine (e.g., resting vs. foraging) scales. A popular approach for classifying movements at coarse resolutions involves fitting time series of net-squared displacement (NSD) to models representing different conceptualizations of coarse movement strategies (i.e., migration, nomadism, sedentarism, etc.). However, the performance of this method in classifying actual (as opposed to simulated) animal movements has been mixed. Here, we develop a more flexible method that uses the same NSD input, but relies on an underlying discrete latent state model. Using simulated data, we first assess how well patterns in the number of transitions between modes of movement and the duration of time spent in a mode classify movement strategies. We then apply our approach to elucidate variability in the movement strategies of eight giant tortoises (Chelonoidis sp.) using a multi-year (2009–2014) GPS dataset from three different Galapagos Islands. Results: With respect to patterns of time spent and the number of transitions between modes, our approach out- performed previous efforts to distinguish among migration, dispersal, and sedentary behavior. We documented marked inter-individual variation in giant tortoise movement strategies, with behaviors indicating migration, dispersal, nomadism and sedentarism, as well as hybrid behaviors such as “exploratory residence”. Conclusions: Distilling complex animal movement into discrete modes remains a fundamental challenge in movement ecology, a problem made more complex by the ever-longer duration, ever-finer resolution, and gap-ridden trajectories recorded by GPS devices. By clustering into modes, we derived information on the time spent within one mode and the number of transitions between modes which enabled finer differentiation of movement strategies over previous methods. Ultimately, the techniques developed here address limitations of previous approaches and provide greater insights with respect to characterization of movement strategies across scales by more fully utilizing long-term GPS telemetry datasets.
Keywords
animal migration,animal movement,animal tracking,Bayesian clustering,Chelonoidis donfaustoi,Chelonoidis hoodensis,Chelonoidis porteri,Chelonoidis vandenburghi,discrete latent state,ectotherm,Galapagos,giant tortoise,mixture model
Taxa
Sensors
Related Workflows
BibTex
@misc{001/1_356nb5mf/3,
  title = {GPS_map_clustering},
  author = {Bastille-Rousseau, G and Potts, JR and Yackulic, CB and Frair, JL and Ellington, EH and Blake, S},
  year = {2016},
  URL = {http://dx.doi.org/10.5441/001/1.356nb5mf/3},
  doi = {doi:10.5441/001/1.356nb5mf/3},
  publisher = {Movebank data repository}
}
RIS
TY  - DATA
ID  - doi:10.5441/001/1.356nb5mf/3
T1  - GPS_map_clustering
AU  - Bastille-Rousseau, Guillaume
AU  - Potts, Jonathan R.
AU  - Yackulic, Charles B.
AU  - Frair, Jacqueline L.
AU  - Ellington, E. Hance
AU  - Blake, Stephen
Y1  - 2016/11/29
KW  - animal migration
KW  - animal movement
KW  - animal tracking
KW  - Bayesian clustering
KW  - Chelonoidis donfaustoi
KW  - Chelonoidis hoodensis
KW  - Chelonoidis porteri
KW  - Chelonoidis vandenburghi
KW  - discrete latent state
KW  - ectotherm
KW  - Galapagos
KW  - giant tortoise
KW  - mixture model
KW  - Chelonoidis donfaustoi
KW  - Chelonoidis hoodensis
KW  - Chelonoidis porteri
KW  - Chelonoidis vandenburghi
PB  - Movebank data repository
UR  - http://dx.doi.org/10.5441/001/1.356nb5mf/3
DO  - doi:10.5441/001/1.356nb5mf/3
ER  - 
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