A hidden Markov model for reconstructing animal paths from solar geolocation loggers using templates for light intensity
Identifieur interne : 000676 ( Ncbi/Curation ); précédent : 000675; suivant : 000677A hidden Markov model for reconstructing animal paths from solar geolocation loggers using templates for light intensity
Auteurs : Eldar Rakhimberdiev [États-Unis, Pays-Bas, Russie] ; David W. Winkler [États-Unis] ; Eli Bridge [États-Unis] ; Nathaniel E. Seavy [États-Unis] ; Daniel Sheldon [États-Unis] ; Theunis Piersma [Pays-Bas] ; Anatoly Saveliev [Russie]Source :
- Movement Ecology [ 2051-3933 ] ; 2015.
Abstract
Solar archival tags (henceforth called geolocators) are tracking devices deployed on animals to reconstruct their long-distance movements on the basis of locations inferred
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
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 (
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.
Url:
DOI: 10.1186/s40462-015-0062-5
PubMed: 26473033
PubMed Central: 4606513
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Theunis Piersma<affiliation><nlm:aff id="Aff8">Chair in Global Flyway Ecology, Conservation Ecology Group, Groningen Institute for Evolutionary Life Sciences (GELIFES), University of Groningen, PO Box 11103, Groningen, 9700 CC The Netherlands</nlm:aff>
<wicri:noCountry code="subfield">9700 CC The Netherlands</wicri:noCountry>
</affiliation>
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<author><name sortKey="Saveliev, Anatoly" sort="Saveliev, Anatoly" uniqKey="Saveliev A" first="Anatoly" last="Saveliev">Anatoly Saveliev</name>
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<series><title level="j">Movement Ecology</title>
<idno type="eISSN">2051-3933</idno>
<imprint><date when="2015">2015</date>
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<front><div type="abstract" xml:lang="en"><sec><title>Background</title>
<p>Solar archival tags (henceforth called geolocators) are tracking devices deployed on animals to reconstruct their long-distance movements on the basis of locations inferred <italic>post hoc</italic>
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.</p>
</sec>
<sec><title>Results</title>
<p>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.</p>
<p>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 <italic>Tachycineta bicolor</italic>
migrating along the east and a golden-crowned sparrow <italic>Zonotrichia atricapilla</italic>
migrating along the west coast of North America.</p>
</sec>
<sec><title>Conclusions</title>
<p>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 (<italic>e.g.</italic>
, migrating or sedentary), and distance and direction of movement.</p>
<p>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.</p>
</sec>
</div>
</front>
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