The potential of video imagery from worldwide cabled observatory networks to provide information supporting fish-stock and biodiversity assessment

Author:

Aguzzi J12ORCID,Chatzievangelou D3ORCID,Company J B1,Thomsen L3,Marini S24ORCID,Bonofiglio F4ORCID,Juanes F5,Rountree R56,Berry A7,Chumbinho R8ORCID,Lordan C7ORCID,Doyle J7,del Rio J9ORCID,Navarro J1ORCID,De Leo F C510,Bahamon N1,García J A111,Danovaro P R212,Francescangeli M9,Lopez-Vazquez V13,Gaughan P7

Affiliation:

1. Instituto de Ciencias del Mar (ICM-CSIC), Barcelona 08003, Spain

2. Stazione Zoologica Anton Dohrn (SZN), Naples 80122, Italy

3. Jacobs University, Bremen 28759, Germany

4. National Research Council of Italy (CNR), Institute of Marine Sciences, La Spezia 19032, Italy

5. Department of Biology, University of Victoria, Victoria BC V8P 5C2, Canada

6. The Fish Listener, Waquoit, MA 02536, USA

7. Marine Institute, Oranmore, Galway H91 R673, Ireland

8. SmartBay Ireland, Galway H91 DCH9, Ireland

9. SARTI, Universitat Politècnica de Catalunya (UPC), Barcelona 08800, Spain

10. Ocean Networks Canada (ONC), University of Victoria, Victoria BC V8N 1V8, Canada

11. Universitat Oberta de Catalunya (UOC), Barcelona 08018, Spain

12. Department of Life and Environmental Science, Polytechnic University of Marche, Ancona 60131, Italy

13. DS Labs, Vitoria-Gasteiz E-01015, Spain

Abstract

Abstract Seafloor multiparametric fibre-optic-cabled video observatories are emerging tools for standardized monitoring programmes, dedicated to the production of real-time fishery-independent stock assessment data. Here, we propose that a network of cabled cameras can be set up and optimized to ensure representative long-term monitoring of target commercial species and their surrounding habitats. We highlight the importance of adding the spatial dimension to fixed-point-cabled monitoring networks, and the need for close integration with Artificial Intelligence pipelines, that are necessary for fast and reliable biological data processing. We then describe two pilot studies, exemplary of using video imagery and environmental monitoring to derive robust data as a foundation for future ecosystem-based fish-stock and biodiversity management. The first example is from the NE Pacific Ocean where the deep-water sablefish (Anoplopoma fimbria) has been monitored since 2010 by the NEPTUNE cabled observatory operated by Ocean Networks Canada. The second example is from the NE Atlantic Ocean where the Norway lobster (Nephrops norvegicus) is being monitored using the SmartBay observatory developed for the European Multidisciplinary Seafloor and water column Observatories. Drawing from these two examples, we provide insights into the technological challenges and future steps required to develop full-scale fishery-independent stock assessments.

Funder

Autonomous Robotic Networks to Help Modern Societies

German Helmholtz Association

RESBIO

Ministerio de Ciencia, Innovación y Universidades, Spanish Government

RESNEP

Science Foundation Ireland

SFI Research Infrastructure Award

Publisher

Oxford University Press (OUP)

Subject

Ecology,Aquatic Science,Ecology, Evolution, Behavior and Systematics,Oceanography

Reference145 articles.

1. A flexible autonomous robotic observatory infrastructure for bentho-pelagic monitoring;Aguzzi;Sensors-Basel,2020

2. The hierarchic treatment of marine ecological information from spatial networks of benthic platforms;Aguzzi;Sensors,2020

3. New high-tech flexible networks for the monitoring of deep-sea ecosystems;Aguzzi;Environmental Science and Technology,2019

4. Challenges to assessment of benthic populations and biodiversity as a result of rhythmic behaviour: video solutions from cabled observatories;Aguzzi;Oceanography and Marine Biology: An Annual Review,2012

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3