|A hidden Markov model for reconstructing animal paths from solar geolocation loggers using templates for light intensity|Rakhimberdiev, E.; Winkler, D.W.; Bridge, E.; Seavy, N.E.; Sheldon, D.; Piersma, T.; Saveliev, A. (2015). A hidden Markov model for reconstructing animal paths from solar geolocation loggers using templates for light intensity. Movement Ecology 3: 1-15. dx.doi.org/10.1186/s40462-015-0062-5
In: Movement Ecology. BioMed Central: London. ISSN 2051-3933, meer
Bird migration; FLightR; Hidden Markov models; Particle filter; Solar geolocation; Template fitting
|Auteurs|| || Top |
- Rakhimberdiev, E., meer
- Winkler, D.W.
- Bridge, E.
- Seavy, N.E.
- Sheldon, D.
- Piersma, T., meer
- Saveliev, A.
Background: Solar archival tags (henceforth called geolocators) are tracking devices deployed on animals to reconstructtheir long-distance movements on the basis of locations inferred post hoc with reference to the geographical andseasonal variations in the timing and speeds of sunrise and sunset. The increased use of geolocators has created a needfor analytical tools to produce accurate and objective estimates of migration routes that are explicit in their uncertaintyabout the position estimates.Results: We developed a hidden Markov chain model for the analysis of geolocator data. This model estimates tracks foranimals with complex migratory behaviour by combining: (1) a shading-insensitive, template-fit physical model, (2) anuncorrelated random walk movement model that includes migratory and sedentary behavioural states, and (3) spatiallyexplicit 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 ofsimulated data for stationary tags and of real tracks of both a tree swallow Tachycineta bicolor migrating along the eastand a golden-crowned sparrow Zonotrichia 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 withcomplicated 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 lightdata. This model advances the current methods for estimating migration tracks from solar geolocation, and will benefit afast-growing number of tracking studies with this technology.