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

Ecology
By: , and 

Links

Abstract

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.

Additional publication details

Publication type Article
Publication Subtype Journal Article
Title Animal movement constraints improve resource selection inference in the presence of telemetry error
Series title Ecology
DOI 10.1890/15-0472.1
Volume 96
Issue 10
Year Published 2016
Language English
Publisher Ecological Society of America, Wiley
Contributing office(s) Coop Res Unit Seattle
Description 8 p.
First page 2590
Last page 2597
Online Only (Y/N) N
Additional Online Files (Y/N) N