Walrus Bayesian State-Space Bering Sea Chukchi Sea 2008-2012
State-space models offer researchers an objective approach to modeling complex animal location datasets, and state-space model behavior classifications are often assumed to have a link to animal behavior. We evaluated the behavioral classification accuracy of a Bayesian state-space model in Pacific...
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| Format: | Dataset |
| Language: | unknown |
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U.S. Geological Survey
2016
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| Online Access: | https://dx.doi.org/10.5066/f77m060g https://alaska.usgs.gov/products/data.php?dataid=75 |
| Summary: | State-space models offer researchers an objective approach to modeling complex animal location datasets, and state-space model behavior classifications are often assumed to have a link to animal behavior. We evaluated the behavioral classification accuracy of a Bayesian state-space model in Pacific walruses using Argos satellite tags outfitted with sensors to detect animal behavior in real time. We fit a two-state discrete-time continuous-space Bayesian state-space model to data from 306 Pacific walruses tagged in the Chukchi Sea. We matched predicted locations and behaviors from the state-space model (resident, transient behavior) to true animal behavior (foraging, swimming, hauled-out) and evaluated classification accuracy with kappa statistics and root mean square error. These data represent Bayesian state-space model output for 8 hr and 12 hr time steps. |
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