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

Abstract

Background 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. 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. Methods We 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. Results Parameter 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. Conclusions We 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.

Animal behaviour on the move: the use of auxiliary information and semi-supervision to improve behavioural inferences from Hidden Markov Models applied to GPS tracking datasets

“…In tandem, several statistical methods and modelling approaches have been developed which mathematically analyse step length (the distance between consecutive positions), angle, tortuosity, and other traits of a trajectory to infer what segments of an animal’s track are spent in specific behaviours based on knowledge of their locomotion and ecology [ 7 ]. This can be particularly useful for conservation and management [ 8 ], enabling the identification and protection of areas important for animal ecology, such as those associated with foraging [ 9 , 10 ], and/or resting [ 11 , 12 ]. However, whilst the study of animal movement is progressing rapidly, transforming tracking data into meaningful behavioural states still remains a challenge for many species.…”

“…Outside of these foraging patches, OFT predicts that animals will minimise time in transit to, from, and between foraging areas by taking the most direct route over unsuitable environments, resulting in fast,directed movements [ 15 ]. The identification of rest is often associated with a long period without movement in terrestrial environments or with movement associated with drift in aquatic environments [ 11 , 12 ]. However, while several methods are commonly used to infer behaviour from GPS tracks, their results are rarely cross-validated, and when they are, show a disparate ability to correctly predict behavioural states (S1).…”