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Deep learning resolves representative movement patterns in a marine predator species


Peng, C and Duarte, CM and Costa, DP and Guinet, C and Harcourt, RG and Hindell, MA and McMahon, CR and Muelbert, M and Thums, M and Wong, K-C and Zhang, X, Deep learning resolves representative movement patterns in a marine predator species, Applied Sciences, 9, (14) Article 2935. ISSN 2076-3417 (2019) [Refereed Article]


Copyright Statement

Copyright 2019 The Authors. Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0)

DOI: doi:10.3390/app9142935


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.

Item Details

Item Type:Refereed Article
Keywords:animal tracking, marine animal movement analysis, recurrent neural networks, representative patterns
Research Division:Earth Sciences
Research Group:Oceanography
Research Field:Biological oceanography
Objective Division:Environmental Management
Objective Group:Terrestrial systems and management
Objective Field:Assessment and management of terrestrial ecosystems
UTAS Author:Hindell, MA (Professor Mark Hindell)
UTAS Author:Muelbert, M (Dr Monica Muelbert)
ID Code:136821
Year Published:2019
Web of Science® Times Cited:3
Deposited By:Ecology and Biodiversity
Deposited On:2020-01-20
Last Modified:2022-08-29
Downloads:10 View Download Statistics

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