GPSeqClus: An R package for sequential clustering of animal location data for model building, model application and field site investigations

Abstract

Evaluating the concentration of animal locations, in space and time, provides insight regarding animal activity and behaviour. Most commonly collected via global positioning system technology, these concentrations are identified using rule sets resulting in location ‘clusters’. Cluster algorithms provide a framework for modelling and predicting behavioural states when paired with field data to evaluate habitat characteristics and validate results. We provide a sequential clustering algorithm package (GPSeqClus) to process location datasets based on user-defined parameters. GPSeqClus also calculates an array of movement attributes commonly applied as covariates to develop cluster-based models. Our package maps locations and clusters, with additional functions to assess specific clusters for site investigations and to export GPS Exchange format (.gpx) files for navigation. We highlight the ability of GPSeqClus to modify existing cluster attributes or to append additional attributes, and the flexibility to accommodate archived or near real-time (satellite-driven) datasets. Although spatio-temporal clustering is widely used in animal ecology, clustering routines are commonly applied to assess carnivore movement patterns to predict denning or feeding locations, evaluate prey composition and estimate kill rates and handling times. We demonstrate the applicability of GPSeqClus by constructing clusters using mountain lion Puma concolor location data. Our package provides an efficient data processing routine to build, characterize, visualize and navigate to clusters. Attributes provided within GPSeqClus can be used when developing predictive cluster models, or can be applied to other modelling procedures to advance understandings of animal behaviour.