A hidden Markov model for reconstructing animal paths from solar geolocation loggers using templates for light intensity. Issue 1 (December 2015)

• Record Type: Journal Article
• Authors: Rakhimberdiev, Eldar; Winkler, David; Bridge, Eli; Seavy, Nathaniel; Sheldon, Daniel; Piersma, Theunis; Saveliev, Anatoly
• Is Part Of: Movement ecology. Volume 3:Issue 1(2015)
• Page Start: 1
• Page End: 15
• Publication Date: 2015-12
• Subjects: Bird migration -- FLightR -- Hidden Markov models -- Particle filter -- Solar geolocation -- Template fitting; Animal ecology -- Periodicals; Plant ecology -- Periodicals; Microbial ecology -- Periodicals; 577.05
• DOI: 10.1186/s40462-015-0062-5
• Languages: English
• ISSNs: 2051-3933

Abstract

Abstract Background Solar archival tags (henceforth called geolocators) are tracking devices deployed on animals to reconstruct their long-distance movements on the basis of locations inferredpost hoc with reference to the geographical and seasonal variations in the timing and speeds of sunrise and sunset. The increased use of geolocators has created a need for analytical tools to produce accurate and objective estimates of migration routes that are explicit in their uncertainty about the position estimates. Results We developed a hidden Markov chain model for the analysis of geolocator data. This model estimates tracks for animals with complex migratory behaviour by combining: (1) a shading-insensitive, template-fit physical model, (2) an uncorrelated random walk movement model that includes migratory and sedentary behavioural states, and (3) spatially explicit behavioural masks. The model is implemented in a specially developed open source R package FLightR. We used the particle filter (PF) algorithm to provide relatively fast model posterior computation. We illustrate our modelling approach with analysis of simulated data for stationary tags and of real tracks of both a tree swallowTachycineta bicolor migrating along the east and a golden-crowned sparrowZonotrichia atricapilla migrating along the west coast of North America. Conclusions We provide a model that increases accuracy in analyses of noisy data and movements of animals with complicated migration behaviour. It provides posterior distributions for the positions of animals, their behavioural states (e.g., migrating or sedentary), and distance and direction of movement. Our approach allows biologists to estimate locations of animals with complex migratory behaviour based on raw light data. This model advances the current methods for estimating migration tracks from solar geolocation, and will benefit a fast-growing number of tracking studies with this technology.