A hierarchical machine learning framework for the analysis of large scale animal movement data

Torney, C. J. , Morales, J. M. and Husmeier, D. (2021) A hierarchical machine learning framework for the analysis of large scale animal movement data. Movement Ecology, 9, 6. (doi: 10.1186/s40462-021-00242-0) (PMID:33602302)

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Background: In recent years the field of movement ecology has been revolutionized by our ability to collect high-accuracy, fine scale telemetry data from individual animals and groups. This growth in our data collection capacity has led to the development of statistical techniques that integrate telemetry data with random walk models to infer key parameters of the movement dynamics. While much progress has been made in the use of these models, several challenges remain. Notably robust and scalable methods are required for quantifying parameter uncertainty, coping with intermittent location fixes, and analysing the very large volumes of data being generated. Methods: In this work we implement a novel approach to movement modelling through the use of multilevel Gaussian processes. The hierarchical structure of the method enables the inference of continuous latent behavioural states underlying movement processes. For efficient inference on large data sets, we approximate the full likelihood using trajectory segmentation and sample from posterior distributions using gradient-based Markov chain Monte Carlo methods. Results: While formally equivalent to many continuous-time movement models, our Gaussian process approach provides flexible, powerful models that can detect multiscale patterns and trends in movement trajectory data. We illustrate a further advantage to our approach in that inference can be performed using highly efficient, GPU-accelerated machine learning libraries. Conclusions: Multilevel Gaussian process models offer efficient inference for large-volume movement data sets, along with the fitting of complex flexible models. Applications of this approach include inferring the mean location of a migration route and quantifying significant changes, detecting diurnal activity patterns, or identifying the onset of directed persistent movements.

Item Type:Articles
Additional Information:Funding: CT is supported by a James S. McDonnell Foundation Complex Systems Scholar Award.
Glasgow Author(s) Enlighten ID:Morales, Dr Juan and Husmeier, Professor Dirk and Torney, Dr Colin
Authors: Torney, C. J., Morales, J. M., and Husmeier, D.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Mathematics
College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Movement Ecology
Publisher:BioMed Central
ISSN (Online):2051-3933
Copyright Holders:Copyright © 2021 The Authors
First Published:First published in Movement Ecology 9: 6
Publisher Policy:Reproduced under a Creative Commons License
Data DOI:10.5525/gla.researchdata.1115

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