Deep Learning Resolves Representative Movement Patterns in a Marine Predator Species
Duarte, Carlos M.
Costa, Daniel P.
Hindell, Mark A.
Sequeira, Ana M. M.
KAUST DepartmentBiological 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)
Permanent link to this recordhttp://hdl.handle.net/10754/661631
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AbstractThe 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.
CitationPeng, 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, 9(14), 2935. doi:10.3390/app9142935
SponsorsSeal data from Macquarie Island, Davis and Casey Stations were sourced from the Integrated Marine Observing System (IMOS). IMOS is a national collaborative research infrastructure, supported by the Australian Government. It is operated by a consortium of institutions as an unincorporated joint venture, with the University of Tasmania as Lead Agent. M.M. acknowledges support from CNPq. The Kerguelen Island work was supported by the CNES-TOSCA and the French Polar Institute as part of the SNO-MEMO.
This research was funded by King Abdullah University of Science and Technology’s (KAUST) Sensor Innovation Initiative, the National Natural Science Foundation of China (NO. 61802372), the Qianjiang Talent Plan (NO. QJD1702031), and Natural Science Foundation of Ningbo, China (NO. 2018A610050).
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