Data from: Flexible characterization of animal movement pattern using net squared displacement and a latent state model

When using this dataset, please cite the original article.

Bastille-Rousseau G, Potts JR, Yackulic CB, Frair JL, Ellington EH, Blake S (2016) Flexible characterization of animal movement pattern using net squared displacement and a latent state model. Movement Ecology 4:15. doi:10.1186/s40462-016-0080-y

Additionally, please cite the Movebank data package:

Bastille-Rousseau G, Potts JR, Yackulic CB, Frair JL, Ellington EH, Blake S (2016) Data from: Flexible characterization of animal movement pattern using net squared displacement and a latent state model. Movebank Data Repository. doi:10.5441/001/1.356nb5mf
Cite | Share
Download the data package citation in the following formats:
   RIS (compatible with EndNote, Reference Manager, ProCite, RefWorks)
   BibTex (compatible with BibDesk, LaTeX)

Package Identifier doi:10.5441/001/1.356nb5mf  
 
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,

Movement strategies of Galapagos tortoises (data from Bastille-Rousseau et al. 2016) View File Details
Download: README.txt ( 16.39Kb )
Download: Movement strategies of Galapagos tortoises (data from Bastille-Rousseau et al. 2016).csv ( 316.7Mb )
To the extent possible under law, the authors have waived all copyright and related or neighboring rights to this data.  



Movement strategies of Galapagos tortoises (data from Bastille-Rousseau et al. 2016)-reference-data View File Details
Download: Movement strategies of Galapagos tortoises (data from Bastille-Rousseau et al. 2016)-reference-data.csv ( 22.18Kb )
To the extent possible under law, the authors have waived all copyright and related or neighboring rights to this data.  



GPS_map_clustering View File Details
Download: README.txt ( 16.39Kb )
Download: GPS_map_clustering.csv ( 1.764Mb )
To the extent possible under law, the authors have waived all copyright and related or neighboring rights to this data.  


Submission