Alaska Science Center
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. Specifically, tags were targeted to attach midway between the shoulders and each tag had a conductivity sensor and pressure transducer sensor integrated with an Argos satellite telemetry tag. At 1 s intervals, the pressure transducer recorded the depth of the tag and the conductivity sensor indicated whether the tag was in salt water. Two simple algorithms that ran onboard the tag summarized behavior information within 1-hr intervals to facilitate behavior data transmission through the Argos system using two indicator variables. One algorithm set the forage indicator variable to 1 if >50% of depth measurements exceeded 10 m during a 1-hr interval and to 0 if otherwise. A second algorithm set the wet indicator variable to 1 if >10% of conductivity measurements indicated the tag was in salt water during that 1-hr interval and to 0 if otherwise. Based on the values of these two indicator variables, we categorized each 1-hr interval into one of three behavior states. A combination of wet = 0 and forage = 0 for a 1-hr interval indicated the animal was primarily hauled-out during that period. Variable indicators of wet = 1 and forage = 0 indicated a walrus was primarily in water and not foraging (swimming) during the associated 1-hr interval. Finally, combinations of wet = 1 and foraging = 1 represented an individual foraging at depth for the corresponding 1-hr interval. To compare these real behaviors to modeled behaviors, 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.
We summarize available information on Pacific walrus haulouts from available reports, interviews with coastal residents and aviators, and personal observations of the authors. We provide this in the form of a georeferenced database that may be queried and displayed with standard geographic information system and database management software. The database contains 150 records of Pacific walrus haulouts, with a summary of basic characteristics on maximum haulout size, age-sex composition, season of use, and decade of most recent use. Citations to reports are provided as a bibliographic database.
This data set contains banding, morphology, and satellite telemetry information for Bristle-thighed curlews (Numenius tahitiensis) that were captured between 2012 and 2014 on the James Campbell National Wildlife Refuge on the island of O'ahu, Hawaii (21.68 N, -157.95 W).
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