Abstract
BackgroundCharacterizing 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.ResultsWith 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”.ConclusionsDistilling 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s40462-016-0080-y) contains supplementary material, which is available to authorized users.
Highlights
Characterizing the movement patterns of animals is an important step in understanding their ecology
net-squared displacement (NSD) approaches suffer from inherent statistical problems related to temporal autocorrelation, that limit their applicability
Advances in our ability to track animal movement coupled with greater availability and resolution of environmental data offers new opportunities for movement ecologists [31, 32]
Summary
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. Movement patterns at coarse temporal scales (e.g., annual) can be classified in terms of broad strategies (e.g., migration, dispersal, residency, or nomadism; hereafter referred to as “movement strategies”), while variation at finer temporal scales is frequently thought of in terms of latent or behavioral modes (e.g., “encamped” vs “exploratory” [3, 4]; hereafter referred to as “modes”). Movement strategies at even finer temporal scales can be envisioned when animals transition among different behavioral states (e.g., resting, moving, foraging, hereafter referred to as “states”). Recent studies have shown that broadscale movement strategies vary substantially among and within species, and even among individuals within a population [5,6,7,8]
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