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

October 1, 2015

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

Publication Year 2016
Title Animal movement constraints improve resource selection inference in the presence of telemetry error
DOI 10.1890/15-0472.1
Authors Brian M. Brost, Mevin Hooten, Ephraim M. Hanks, Robert J. Small
Publication Type Article
Publication Subtype Journal Article
Series Title Ecology
Index ID 70173663
Record Source USGS Publications Warehouse
USGS Organization Coop Res Unit Seattle
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