New Ways to Monitor and Analyse Biodiversity in the Marine Environment

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Environmental Sciences".

Deadline for manuscript submissions: closed (10 July 2021) | Viewed by 2955

Special Issue Editors

Division of Environment and Ecosystems, Centre for Environment, Fisheries and Aquaculture Science, Lowestoft, UK
Interests: statistical aspects of monitoring design; litter monitoring; video surveys; measuring biodiversity

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Guest Editor
Sustainable Fisheries and Aquaculture Group, Centre for Applied Marine Science, Bangor University, Wales, UK
Interests: marine fisheries ecology; practical and numerical methods to inform sustainable fisheries’ management; collaborative science–industry projects; fishing gear impacts; developing fishery survey methods

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Guest Editor
Ecology Group, Centre for Environment, Fisheries and Aquaculture Science, Lowestoft, UK
Interests: benthic ecology; biodiversity; monitoring; aggregate dredging; data science; seabed recovery; restoration ecology

Special Issue Information

Dear Colleagues,

Programs to monitor biodiversity directly are widespread in the marine environment, as are factors which may influence results. With the advent of new technological, statistical and computing techniques, the ways in which monitoring is achieved and the range of parameters that are being monitored have expanded. In this Special Edition, we aim to capture some of these new ways of monitoring.

We welcome inputs on the following but will consider any new innovative techniques or analyses:

  • Design of monitoring surveys from a statistical perspective;
  • Integration of different sources of data to achieve successful monitoring programs;
  • Incorporating statistical dependency into the analysis of monitoring data;
  • Monitoring studies that measure aspects of sediment, biogeochemical and faunal parameters;
  • Monitoring based on visual imagery such as video surveys or camera traps;
  • Monitoring based around anthropogenic activities such as pollution, aggregate extraction and litter which may reduce biodiversity;
  • Using machine learning to infer aspects of biodiversity;
  • Automation in terms of data collection and/or analysis;
  • Use of ‘big data’ for addressing environmental issues, with a focus on biodiversity monitoring;
  • Monitoring using eDNA

Dr. Jon Barry
Dr. Claire L. Szostek
Dr. Keith Cooper
Guest Editors

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Published Papers (1 paper)

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Research

16 pages, 2252 KiB  
Article
High-Resolution Acoustic Cameras Provide Direct and Efficient Assessments of Large Demersal Fish Populations in Extremely Turbid Waters
by Céline Artero, Simon Marchetti, Eric Bauer, Christophe Viala, Claire Noël, Christopher C. Koenig, Rachel Berzins and Luis Lampert
Appl. Sci. 2021, 11(4), 1899; https://0-doi-org.brum.beds.ac.uk/10.3390/app11041899 - 22 Feb 2021
Cited by 12 | Viewed by 2489
Abstract
Monitoring fish species populations in very turbid environments is challenging. Acoustic cameras allow work in very poor visibility but are often deployed as a fixed observation point, limiting the scope of the survey. A BlueView P900-130 acoustic camera was deployed in rocky marine [...] Read more.
Monitoring fish species populations in very turbid environments is challenging. Acoustic cameras allow work in very poor visibility but are often deployed as a fixed observation point, limiting the scope of the survey. A BlueView P900-130 acoustic camera was deployed in rocky marine habitats off the coast of French Guiana in order to assess the total abundance, size structure and spatial distribution of a demersal fish population. The relevancy of using an acoustic camera to achieve these three objectives was evaluated by comparing acoustic data to those obtained from fishing surveys. The detection and identification of large demersal fish species were possible with the shape and size of the acoustic signal and acoustic shadow silhouette as well as swimming behavior. Mobile surveys combined with stationary surveys increased the probability of distinguishing individuals from inanimate objects. Estimated total length based on the acoustic signal underestimated the actual length of fish measured on deck, but the data showed the same trends in spatial and temporal variation. Acoustic cameras overcame the extreme lack of visibility by increasing knowledge of fish use of habitat, therefore providing much more efficiency in the effort, more accurate data on the abundance, size structure and spatial distribution than the fishing method. Thus, despite few limitations, acoustic camera surveys are far superior to fishing surveys in evaluating large demersal fish stock status. Full article
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