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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.

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Mendeley readers

The data shown below were compiled from readership statistics for 238 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 234 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 60 25%
Student > Master 48 20%
Researcher 42 18%
Student > Bachelor 13 5%
Student > Doctoral Student 11 5%
Other 17 7%
Unknown 47 20%
Readers by discipline Count As %
Agricultural and Biological Sciences 108 45%
Environmental Science 52 22%
Earth and Planetary Sciences 5 2%
Computer Science 2 <1%
Veterinary Science and Veterinary Medicine 2 <1%
Other 11 5%
Unknown 58 24%
Attention Score in Context

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
#18,461,618
of 22,875,477 outputs
Outputs from Movement Ecology
#285
of 315 outputs
Outputs of similar age
#254,747
of 339,120 outputs
Outputs of similar age from Movement Ecology
#7
of 7 outputs
Altmetric has tracked 22,875,477 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 315 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 22.3. This one is in the 3rd percentile – i.e., 3% of its peers scored the same or lower than it.
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