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A hidden Markov model for reconstructing animal paths from solar geolocation loggers using templates for light intensity

BACKGROUND: Solar archival tags (henceforth called geolocators) are tracking devices deployed on animals to reconstruct their long-distance movements on the basis of locations inferred post hoc with reference to the geographical and seasonal variations in the timing and speeds of sunrise and sunset....

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Autores principales: Rakhimberdiev, Eldar, Winkler, David W., Bridge, Eli, Seavy, Nathaniel E., Sheldon, Daniel, Piersma, Theunis, Saveliev, Anatoly
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4606513/
https://www.ncbi.nlm.nih.gov/pubmed/26473033
http://dx.doi.org/10.1186/s40462-015-0062-5
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author Rakhimberdiev, Eldar
Winkler, David W.
Bridge, Eli
Seavy, Nathaniel E.
Sheldon, Daniel
Piersma, Theunis
Saveliev, Anatoly
author_facet Rakhimberdiev, Eldar
Winkler, David W.
Bridge, Eli
Seavy, Nathaniel E.
Sheldon, Daniel
Piersma, Theunis
Saveliev, Anatoly
author_sort Rakhimberdiev, Eldar
collection PubMed
description BACKGROUND: Solar archival tags (henceforth called geolocators) are tracking devices deployed on animals to reconstruct their long-distance movements on the basis of locations inferred post 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 swallow Tachycineta bicolor migrating along the east and 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 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.
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spelling pubmed-46065132015-10-16 A hidden Markov model for reconstructing animal paths from solar geolocation loggers using templates for light intensity Rakhimberdiev, Eldar Winkler, David W. Bridge, Eli Seavy, Nathaniel E. Sheldon, Daniel Piersma, Theunis Saveliev, Anatoly Mov Ecol Methodology Article BACKGROUND: Solar archival tags (henceforth called geolocators) are tracking devices deployed on animals to reconstruct their long-distance movements on the basis of locations inferred post 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 swallow Tachycineta bicolor migrating along the east and 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 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. BioMed Central 2015-10-15 /pmc/articles/PMC4606513/ /pubmed/26473033 http://dx.doi.org/10.1186/s40462-015-0062-5 Text en © Rakhimberdiev et al. 2015 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Methodology Article
Rakhimberdiev, Eldar
Winkler, David W.
Bridge, Eli
Seavy, Nathaniel E.
Sheldon, Daniel
Piersma, Theunis
Saveliev, Anatoly
A hidden Markov model for reconstructing animal paths from solar geolocation loggers using templates for light intensity
title A hidden Markov model for reconstructing animal paths from solar geolocation loggers using templates for light intensity
title_full A hidden Markov model for reconstructing animal paths from solar geolocation loggers using templates for light intensity
title_fullStr A hidden Markov model for reconstructing animal paths from solar geolocation loggers using templates for light intensity
title_full_unstemmed A hidden Markov model for reconstructing animal paths from solar geolocation loggers using templates for light intensity
title_short A hidden Markov model for reconstructing animal paths from solar geolocation loggers using templates for light intensity
title_sort hidden markov model for reconstructing animal paths from solar geolocation loggers using templates for light intensity
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4606513/
https://www.ncbi.nlm.nih.gov/pubmed/26473033
http://dx.doi.org/10.1186/s40462-015-0062-5
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