Velocity-Based Movement Modeling for Individual and Population Level Inference

Ephraim M Hanks,Jeremy T Sterling,Mevin B Hooten,Devin S Johnson,Wayne M Getz

doi:10.1371/journal.pone.0022795

Abstract

Understanding animal movement and resource selection provides important information about the ecology of the animal, but an animal's movement and behavior are not typically constant in time. We present a velocity-based approach for modeling animal movement in space and time that allows for temporal heterogeneity in an animal's response to the environment, allows for temporal irregularity in telemetry data, and accounts for the uncertainty in the location information. Population-level inference on movement patterns and resource selection can then be made through cluster analysis of the parameters related to movement and behavior. We illustrate this approach through a study of northern fur seal (Callorhinus ursinus) movement in the Bering Sea, Alaska, USA. Results show sex differentiation, with female northern fur seals exhibiting stronger response to environmental variables.

Highlights

The analysis of animal movement data can provide insight into the relationship between animal behavior and the heterogeneous environment they inhabit [1,2,3]
Some challenges arise in the use of telemetry data, which are typically irregular in time, have measurement error that is varying in severity, and may exhibit temporal autocorrelation [4,5]
Includes only the distance to rookery covariate, indicating that these animals were not strongly influenced by the environmental factors represented by the other covariates (Table 1)

Summary

Introduction

The analysis of animal movement data can provide insight into the relationship between animal behavior and the heterogeneous environment they inhabit [1,2,3]. Tremblay et al [16] utilize a forward particle filtering method to create an ensemble of possible movement paths with regular temporal intervals, and Sumner et al [17] utilize Bayesian methods to incorporate multiple data sources and prior information to obtain a posterior distribution of the animal’s path [18]. Methods such as these are especially appealing because they provide information about the individual’s location at any given time as well as the innate uncertainty associated with our knowledge of it

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Results

Conclusion