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

Summary 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. The class of hidde...

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Bibliographic Details
Published in:Methods in ecology and evolution 2016-11, Vol.7 (11), p.1308-1315
Main Authors: Michelot, Théo, Langrock, Roland, Patterson, Toby A., McInerny, Greg
Format: Article
Language:English
Subjects:
behavioural state
Computer simulation
Ecologists
Environmental assessment
Environmental management
Error analysis
Markov chains
Mathematical models
maximum likelihood
random walk
state‐switching model
Statistical analysis
Statistical inference
Statistical models
telemetry data
Tracking
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Summary:Summary 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. 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. 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. 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.
ISSN:2041-210X
2041-210X
DOI:10.1111/2041-210X.12578