Abstract
Understanding movement is critical in several disciplines but analysis methods often neglect key information by adopting each location as sampling unit, rather than each individual. We introduce a novel statistical method that, by focusing on individuals, enables better identification of temporal dynamics of connectivity, traits of individuals that explain emergent movement patterns, and sites that play a critical role in connecting subpopulations. We apply this method to two examples that span movement networks that vary considerably in size and questions: movements of an endangered raptor, the snail kite (Rostrhamus sociabilis plumbeus), and human movement in Florida inferred from Twitter. For snail kites, our method reveals substantial differences in movement strategies for different bird cohorts and temporal changes in connectivity driven by the invasion of an exotic food resource, illustrating the challenge of identifying critical connectivity sites for conservation in the presence of global change. For human movement, our method is able to reliably determine the origin of Florida visitors and identify distinct movement patterns within Florida for visitors from different places, providing near real-time information on the spatial and temporal patterns of tourists. These results emphasize the need to integrate individual variation to generate new insights when modeling movement data.
Highlights
Movement lies at the heart of several important problems
We compare and contrast inference from our method (IndividualBased Clustering [Individual-Based Clustering (IBC)], fit using Markov-Chain Monte Carlo [MCMC] methods; see Methods) to that provided by network analysis methods commonly used for movement data
With greater level of overlap (Fig. 1D), we find that all of the traditional methods used to cluster locations failed to identify the four existing groups, inferring instead the presence of only 1 (ME and latent cluster model (LC); Fig. 1F) or 3 groups (MM)
Summary
Movement lies at the heart of several important problems. For instance, there has been considerable interest in analyzing human movement data to improve our understanding of human behavior[1], of how people respond to disasters[2], to determine the potential for disease spread or elimination[3,4,5], and to aid urban planning[6]. Network analysis algorithms have been devised to identify groups of users (often called communities or modules), such that these users have more connections (or stronger connections) within their group than expected by chance[12,17,18,19] This is a useful task because it allows for the simplification and discovery of the underlying structure of these complex systems[20]. The first focuses on mark-recapture data from an endangered raptor, the snail kite (Rostrhamus sociabilis) This bird species has become a key indicator species for the Everglades’ restoration and, as a consequence, there is considerable interest in understanding how the landscape is used by this species and in identifying which sites are important for long-term connectivity[26]. The tourism industry is a major industry in Florida and near real-time insights regarding Florida visitors (e.g., where they come from and where they visit within Florida) is important to improve planning of tourism-related activities and investments (e.g., tourism marketing)
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