moveHMM: an R package for the statistical modelling of animal movement data using hidden Markov models

Abstract

Summary1. Due to the substantial progress in tracking technology, recent years have seen an explosion in the amount of movement data being collected. This has led to a huge demand for statistical tools that allow ecologists to draw meaningful inference from large tracking data sets. 2. The class of hidden Markov models (HMMs) matches the intuitive understanding that animal movement is driven by underlying behavioural modes and has proven to be very useful for analysing movement data. For data that involve a regular sampling unit and negligible measurement error, these models usually are sufficiently flexible to capture the complex correlation structure found in movement data, yet are computationally inexpensive compared to alternative methods. 3. The R package moveHMM allows ecologists to process GPS tracking data into series of step lengths and turning angles, and to fit an HMM to these data, allowing, in particular, for the incorporation of environmental covariates. The package includes assessment and visualization tools for the fitted model. 4. We illustrate the use of moveHMM using (simulated) movement of the legendary wild haggis Haggis scoticus. Our findings illustrate the role our software, and movement modelling in general, can play in conservation and management by illuminating environmental constraints.

Bayesian Inference for Multistate ‘Step and Turn’ Animal Movement in Continuous Time

“…Although continuous-time models based (λ1) ln(λ2) CT behav 2 prob Upper right plot probability of residing in behaviour 2 ('travelling') over time. Lower right plot probability of residing in behaviour 2 using the R package moveHMM (Michelot et al 2016). In both plots on the right, points are included to highlight the times/frequency of observations.…”

“…These observations were introduced and modelled as part of a larger set consisting of four elk in the discrete-time 'step and turn' model of Morales et al (2004), and more recently modelled in the vignette of the R package moveHMM (Michelot et al 2016) applying the hidden Markov model of Langrock et al (2012). Observations are shown in Fig.…”

Incorporating Telemetry Error into Hidden Markov Models of Animal Movement Using Multiple Imputation

“…To provide some insight into the number of imputations that may be required in practice, pooled estimates from the n = 400 model fits were compared to those of randomly selected subsets of n = 30 and n = 5 imputations. All analyses were performed in R (R Core Team 2016) using an extension of the "moveHMM" package (Michelot et al 2016a) that is currently under development (https://github.com/bmcclintock/momentuHMM). The "momentuHMM" package allows for additional data streams, as well as user-specified design matrices and constraints for the state-dependent probability distribution parameters.…”

“…When data streams are observed without error and at regular time intervals, a major advantage of HMMs is the relatively fast and efficient maximization of the likelihood using the forward algorithm (Zucchini et al 2016), and user-friendly software is available for fitting movement HMMs under these circumstances (e.g., Michelot et al 2016a). However, location measurement error is rarely nonexistent and depends on both the telemetry device and the system under study.…”

Guest Editor’s Introduction to the Special Issue on “Animal Movement Modeling”

“…Discrete-time movement models from the class of HMM are proliferating in the literature, due at least in part to the availability of statistical software to fit the models to data and their intuitive nature (Michelot et al 2016). However, discrete-time formulations have an associated set of challenges.…”