Identifying resting locations of a small elusive forest carnivore using a two-stage model accounting for GPS measurement error and hidden behavioral states

Dalton J Hance,Russell W Perry,Bruce A Hollen,Katie M Moriarty

doi:10.1186/s40462-021-00256-8

Abstract

BackgroundStudies of animal movement using location data are often faced with two challenges. First, time series of animal locations are likely to arise from multiple behavioral states (e.g., directed movement, resting) that cannot be observed directly. Second, location data can be affected by measurement error, including failed location fixes. Simultaneously addressing both problems in a single statistical model is analytically and computationally challenging. To both separate behavioral states and account for measurement error, we used a two-stage modeling approach to identify resting locations of fishers (Pekania pennanti) based on GPS and accelerometer data.MethodsWe developed a two-stage modelling approach to estimate when and where GPS-collared fishers were resting for 21 separate collar deployments on 9 individuals in southern Oregon. For each deployment, we first fit independent hidden Markov models (HMMs) to the time series of accelerometer-derived activity measurements and apparent step lengths to identify periods of movement and resting. Treating the state assignments as given, we next fit a set of linear Gaussian state space models (SSMs) to estimate the location of each resting event.ResultsParameter estimates were similar across collar deployments. The HMMs successfully identified periods of resting and movement with posterior state assignment probabilities greater than 0.95 for 97% of all observations. On average, fishers were in the resting state 63% of the time. Rest events averaged 5 h (4.3 SD) and occurred most often at night. The SSMs allowed us to estimate the 95% credible ellipses with a median area of 0.12 ha for 3772 unique rest events. We identified 1176 geographically distinct rest locations; 13% of locations were used on > 1 occasion and 5% were used by > 1 fisher. Females and males traveled an average of 6.7 (3.5 SD) and 7.7 (6.8 SD) km/day, respectively.ConclusionsWe demonstrated that if auxiliary data are available (e.g., accelerometer data), a two-stage approach can successfully resolve both problems of latent behavioral states and GPS measurement error. Our relatively simple two-stage method is repeatable, computationally efficient, and yields directly interpretable estimates of resting site locations that can be used to guide conservation decisions.

Highlights

Studies of animal movement using location data are often faced with two challenges
The models were fit independently to each collar deployment time series, parameter estimates were qualitatively similar across all 21 models (Fig. 3), which suggests that the models describe a consistent suite of behaviors across the population of 9 fishers
We propose that direct observation from crew-based telemetry, cameras, and Global Positioning System (GPS) data would best provide an understanding of which structures fishers perceive to be most important for survival and recruitment as the State space model (SSM) produced a catalogue of resting event location estimates that can be used to prioritize confirmatory field surveys

Summary

Introduction

Studies of animal movement using location data are often faced with two challenges. First, time series of animal locations are likely to arise from multiple behavioral states (e.g., directed movement, resting) that cannot be observed directly. Lightweight biotelemetry devices containing Global Positioning System (GPS) or similar technologies have increasingly been adopted by researchers targeting a variety of terrestrial and aquatic species to collect time series of individual animal locations These telemetry devices often contain multiple sensors (e.g., thermometer, accelerometer), allowing time series of location data to be paired to matching time series of physiological measurements or other auxiliary information [1]. There is no generally applicable movement model for animal location data because each analysis must be tailored both to the set of research questions under study and the particular structure of a given dataset (e.g. regular or irregular recording intervals or availability of auxiliary data) [4] Given these issues, examples of using statistical movement models to directly inform management of animal populations are limited

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