Deep Learning Resolves Representative Movement Patterns in a Marine Predator Species

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 differen...

Full description

Bibliographic Details
Published in:Applied Sciences
Main Authors: Peng, Chengbin, Duarte, Carlos M., Costa, Daniel P., Guinet, Christophe, Harcourt, R., Hindell, Mark A., Sequeira, Ana M. M., Muelbert, Monica, Thums, Michele, Wong, Ka-Chun, Zhang, Xiangliang
Other Authors: Biological and Environmental Sciences and Engineering (BESE) Division, Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Machine Intelligence & kNowledge Engineering Lab, Marine Science Program, Red Sea Research Center (RSRC), College of Information Science and Engineering, Ningbo University, Ningbo 315211, China, Ningbo Institute of Industrial Technology, Chinese Academy of Sciences, Ningbo 315201, China, Department of Ecology & Evolutionary Biology, University of California, Santa Cruz, CA 95060, USA, Centre d’Études Biologiques de Chizé, UMR 7372 CNRS-Université de La Rochelle, 79360 Villiers-en-Bois, France, Department of Biological Sciences, Macquarie University, Sydney, New South Wales 2109, Australia, Institute for Marine and Antarctic Studies, University of Tasmania, Private Bag 05, Tasmania 7001, Australia, Sydney Institute of Marine Science, 19 Chowder Bay Road, Mosman, New South Wales 2088, Australia, Instituto de Oceanografia, Caixa Postal 474, Rio Grande 96201-900, Brazil, Australian Institute of Marine Science, Indian Ocean Marine Research Centre, University of Western Australia (M096), 35 Stirling Highway, Crawley, Western Australia 6009, Australia, Department of Computer Science, City University of Hong Kong, Hong Kong, China
Format: Article in Journal/Newspaper
Language:unknown
Published: MDPI AG 2020
Subjects:
Online Access:http://hdl.handle.net/10754/661631
https://doi.org/10.3390/app9142935