Deep learning resolves representative movement patterns in a marine predator species

Chengbin Peng, Carlos M. Duarte, Daniel P. Costa, Christophe Guinet, Robert G. Harcourt, Mark A. Hindell, Clive R. McMahon, Monica Muelbert, Michele Thums, Ka-Chun Wong, Xiangliang Zhang*

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

The analysis of animal movement from telemetry data provides insights into how and why animals move. While traditional approaches to such analysis mostly focus on predicting animal states during movement, we describe an approach that allows us to identify representative movement patterns of different animal groups. To do this, we propose a carefully designed recurrent neural network and combine it with telemetry data for automatic feature extraction and identification of non-predefined representative patterns. In the experiment, we consider a particular marine predator species, the southern elephant seal, as an example. With our approach, we identify that the male seals in our data set share similar movement patterns when they are close to land. We identify this pattern recurring in a number of distant locations, consistent with alternative approaches from previous research.

Original languageEnglish
Article number2935
Pages (from-to)1-13
Number of pages13
JournalApplied Sciences (Switzerland)
Volume9
Issue number14
DOIs
Publication statusPublished - 2 Jul 2019

Bibliographical note

Copyright the Author(s) 2019. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

  • marine animal movement analysis
  • recurrent neural networks
  • representative patterns

Cite this

Peng, C., Duarte, C. M., Costa, D. P., Guinet, C., Harcourt, R. G., Hindell, M. A., ... Zhang, X. (2019). Deep learning resolves representative movement patterns in a marine predator species. Applied Sciences (Switzerland), 9(14), 1-13. [2935]. https://doi.org/10.3390/app9142935