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Mobile robotic platforms for the acoustic tracking of deep-sea demersal fishery resources.

Authors :
Masmitja I
Navarro J
Gomariz S
Aguzzi J
Kieft B
O'Reilly T
Katija K
Bouvet PJ
Fannjiang C
Vigo M
Puig P
Alcocer A
Vallicrosa G
Palomeras N
Carreras M
Del Rio J
Company JB
Source :
Science robotics [Sci Robot] 2020 Nov 25; Vol. 5 (48).
Publication Year :
2020

Abstract

Knowing the displacement capacity and mobility patterns of industrially exploited (i.e., fished) marine resources is key to establishing effective conservation management strategies in human-impacted marine ecosystems. Acquiring accurate behavioral information of deep-sea fished ecosystems is necessary to establish the sizes of marine protected areas within the framework of large international societal programs (e.g., European Community H2020, as part of the Blue Growth economic strategy). However, such information is currently scarce, and high-frequency and prolonged data collection is rarely available. Here, we report the implementation of autonomous underwater vehicles and remotely operated vehicles as an aid for acoustic long-baseline localization systems for autonomous tracking of Norway lobster ( Nephrops norvegicus ), one of the key living resources exploited in European waters. In combination with seafloor moored acoustic receivers, we detected and tracked the movements of 33 tagged lobsters at 400-m depth for more than 3 months. We also identified the best procedures to localize both the acoustic receivers and the tagged lobsters, based on algorithms designed for off-the-shelf acoustic tags identification. Autonomous mobile platforms that deliver data on animal behavior beyond traditional fixed platform capabilities represent an advance for prolonged, in situ monitoring of deep-sea benthic animal behavior at meter spatial scales.<br /> (Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

Details

Language :
English
ISSN :
2470-9476
Volume :
5
Issue :
48
Database :
MEDLINE
Journal :
Science robotics
Publication Type :
Academic Journal
Accession number :
33239320
Full Text :
https://doi.org/10.1126/scirobotics.abc3701