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

Overview of attention for article published in Movement Ecology, June 2016
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Title
Flexible characterization of animal movement pattern using net squared displacement and a latent state model
Published in
Movement Ecology, June 2016
DOI 10.1186/s40462-016-0080-y
Pubmed ID
Authors

Guillaume Bastille-Rousseau, Jonathan R. Potts, Charles B. Yackulic, Jacqueline L. Frair, E. Hance Ellington, Stephen Blake

Abstract

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. 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". 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.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 188 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 <1%
United States 1 <1%
South Africa 1 <1%
Canada 1 <1%
Unknown 184 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 51 27%
Researcher 38 20%
Student > Master 35 19%
Student > Bachelor 13 7%
Student > Doctoral Student 8 4%
Other 14 7%
Unknown 29 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 92 49%
Environmental Science 38 20%
Earth and Planetary Sciences 5 3%
Computer Science 2 1%
Social Sciences 2 1%
Other 10 5%
Unknown 39 21%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 08 June 2016.
All research outputs
#13,264,580
of 16,697,177 outputs
Outputs from Movement Ecology
#201
of 219 outputs
Outputs of similar age
#189,032
of 270,022 outputs
Outputs of similar age from Movement Ecology
#1
of 1 outputs
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