Artículo| Año: | 2016 |
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| Autor(es): | J. Alós, M. Palmer, S. Balle, R. Arlinghaus |
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| Título: | Bayesian State-Space Modelling of Conventional Acoustic Tracking Provides Accurate Descriptors of Home Range Behavior in a Small-Bodied Coastal Fish Species |
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| Revista: | PLoS One |
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| ISSN: | 1932-6203 |
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| JCR Impact Factor: | 2.806 |
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| Volumen: | 11 |
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| Número: | 0154089 |
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| Páginas: | 1-23 |
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| D.O.I.: | 10.1371/journal.pone.0154089 |
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| Web: | https://www.researchgate.net/publication/301688392_Bayesian_State-Space_Modelling_of_Conventional_Acoustic_Tracking_Provides_Accurate_Descriptors_of_Home_Range_Behavior_in_a_Small-Bodied_Coastal_Fish_Species?_sg=VufpsmE2mWHIF4PeFlgFvJ2Q150vs9Va03k3gTqbbu7Eb36Xr1DrEnGOD9o1CDV4TgmjSKsxoWkuUYHu_mpk2g |
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| Resumen: | State-space
models (SSM) are increasingly applied in studies involving
biotelemetry-generated positional data because they are able to estimate
movement parameters from positions that are unobserved or have been
observed with non-negligible observational error. Popular telemetry
systems in marine coastal fish consist of arrays of omnidirectional
acoustic receivers, which generate a multivariate time-series of
detection events across the tracking period. Here we report a novel
Bayesian fitting of a SSM application that couples mechanistic movement
properties within a home range (a specific case of random walk weighted
by an Ornstein-Uhlenbeck process) with a model of observational error
typical for data obtained from acoustic receiver arrays. We explored the
performance and accuracy of the approach through simulation modelling
and extensive sensitivity analyses of the effects of various
configurations of movement properties and time-steps among positions.
Model results show an accurate and unbiased estimation of the movement
parameters, and in most cases the simulated movement parameters were
properly retrieved. Only in extreme situations (when fast swimming
speeds are combined with pooling the number of detections over long
time-steps) the model produced some bias that needs to be accounted for
in field applications. Our method was subsequently applied to real
acoustic tracking data collected from a small marine coastal fish
species, the pearly razorfish, Xyrichtys novacula. The Bayesian SSM we
present here constitutes an alternative for those used to the Bayesian
way of reasoning. Our Bayesian SSM can be easily adapted and generalized
to any species, thereby allowing studies in freely roaming animals on
the ecological and evolutionary consequences of home ranges and
territory establishment, both in fishes and in other taxa. |
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Personal relacionadoMiguel Palmer VidalJosep Alós CrespíProyectos relacionadosPHENOFISH CTA 137.1Grupos de investigación relacionadosTecnologías Marinas, Oceanografía Operacional y Costera
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