Animal movement constraints improve resource selection inference in the presence of telemetry error

摘要: Multiple factors complicate the analysis of animal telemetry location data. Recent advancements address issues such as temporal autocorrelation and measurement error, but additional challenges remain. Difficulties introduced by complicated error structures or barriers to movement can weaken inference. We propose an approach for obtaining resource selection inference from data that accounts structures, constraints, temporally autocorrelated observations. specify a model observed with conditional on unobserved true locations reflects prior knowledge about constraints in process. The are modeled using flexible distribution accommodates extreme errors structures. Although often viewed nuisance, we use simultaneously estimate account error. apply simulated data, showing it outperforms common ad hoc approaches used when confronted constraints. then our framework Argos satellite set harbor seals (Phoca vitulina) Gulf Alaska, species is constrained move within marine environment adjacent coastlines.