# Ichthyological importance of shallow coastal areas for pelagic communities : contributions of echosounding
## [résumé de poster]

Anne Mouget, Viviane David, Anthony Acou, Éric Feunteun, Pierre Thiriet, Yannick Perrot, Loïc Le Goff, Patrice Brehmer

<figure><img src="image_1.png" /></figure>

## ► To cite this version:

Anne Mouget, Viviane David, Anthony Acou, Éric Feunteun, Pierre Thiriet, et al.. Ichthyological importance of shallow coastal areas for pelagic communities : contributions of echosounding [résumé de poster]. Working Group on Fisheries Acoustics, Science and Technology (WGFAST), Apr 2022, Dakar (Sénégal), Senegal. pp.50. mnhn-04153997

HAL Id: mnhn-04153997

https://mnhn.hal.science/mnhn-04153997

Submitted on 6 Jul 2023

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Distributed under a Creative Commons Attribution 4.0 International License
WORKING GROUP OF FISHERIES ACOUSTICS,
SCIENCE AND TECHNOLOGY (WGFAST)

VOLUME 4 | ISSUE 54

ICES SCIENTIFIC REPORTS

RAPPORTS

SCIENTIFIQUES DU CIEM

<figure><img src="image_3.png" /></figure>

ICES
CIEM

INTERNATIONAL COUNCIL FOR THE EXPLORATION OF THE SEA
CONSEIL INTERNATIONAL POUR L'EXPLORATION DE LA MER
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ISSN number: 2618-1371

This document has been produced under the auspices of an ICES Expert Group or Committee. The
contents therein do not necessarily represent the view of the Council.

© 2022 International Council for the Exploration of the Sea

This work is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). For
citation of datasets or conditions for use of data to be included in other databases, please refer to ICES
data policy.

<figure><img src="image_4.png" /></figure>
ICES Scientific Reports

Volume 4 | Issue 54

WORKING GROUP OF FISHERIES ACOUSTICS, SCIENCE AND TECHNOLOGY
(WGFAST)

Recommended format for purpose of citation:

ICES. 2022. Working Group of Fisheries Acoustics, Science and Technology (WGFAST).
ICES Scientific Reports. 4:54. 93 pp. https://doi.org/10.17895/ices.pub.20178464

Editor

Michael Jech

Author

Michael Jech
# Contents

<table><tr><td>i</td><td>Executive summary</td><td>ii</td></tr><tr><td>ii</td><td>Expert group information</td><td>iii</td></tr><tr><td>1</td><td>Report on Terms of Reference</td><td>4</td></tr><tr><td>1.1</td><td>ToR a)</td><td>4</td></tr><tr><td>1.2</td><td>ToR b)</td><td>4</td></tr><tr><td></td><td>Acoustic Methods to Characterize Populations, Ecosystems, Habitat, and Behaviour</td><td>4</td></tr><tr><td></td><td>Acoustic Characterization of Marine Organisms</td><td>5</td></tr><tr><td></td><td>Emerging Technologies, Methodologies, and Protocols</td><td>5</td></tr><tr><td>1.3</td><td>ToR c)</td><td>7</td></tr><tr><td>1.4</td><td>ToR d)</td><td>7</td></tr><tr><td>1.5</td><td>ToR e)</td><td>7</td></tr><tr><td>2</td><td>Discussion Topics</td><td>8</td></tr><tr><td>2.1</td><td>Proposed Symposium in 2023</td><td>8</td></tr><tr><td>2.2</td><td>Symposium Proceedings</td><td>8</td></tr><tr><td>2.3</td><td>WGFAST Chair for 2023-2025</td><td>8</td></tr><tr><td>2.4</td><td>Tribute to Ron Mitson</td><td>8</td></tr><tr><td>2.5</td><td>Tributes to Past Members</td><td>10</td></tr><tr><td>2.6</td><td>Consistency of Terminology with International Standards</td><td>10</td></tr><tr><td>2.7</td><td>SPRFMO Symposium Announcement</td><td>11</td></tr><tr><td>2.8</td><td>WGFAST Response to WGIPS</td><td>11</td></tr><tr><td>2.9</td><td>New ToRs</td><td>11</td></tr><tr><td>3</td><td>References</td><td>12</td></tr><tr><td>Annex 1:</td><td>List of participants</td><td>13</td></tr><tr><td>Annex 2:</td><td>Resolutions</td><td>29</td></tr><tr><td>Annex 3:</td><td>Presentation Abstracts</td><td>32</td></tr><tr><td>Annex 4:</td><td>WGFAST Response to WGIPS</td><td>53</td></tr><tr><td>Annex 5:</td><td>Report on WGAST Resource Surveys</td><td>60</td></tr><tr><td>Annex 6:</td><td>Report on WGAST Research Activities</td><td>76</td></tr><tr><td>Annex 7:</td><td>Report on WGFAST Interactions with other ICES Expert Groups</td><td>87</td></tr><tr><td>Annex 8:</td><td>List of WGFAST Publications</td><td>89</td></tr></table>
i Executive summary

The Working Group of Fisheries Acoustics, Science and Technology (WGFAST) focuses on the
development and application of science and technology to observe the marine environment. In
this report, WGFAST summarizes 40 presentations addressing the three themes: "Acoustic meth-
ods to characterize populations, ecosystems, habitat, and behaviour", "Acoustic characterization
of marine organisms", and "Emerging technologies, methodologies, and protocols", and dis-
cussions addressing these three themes. Common themes throughout these sessions were the
increasing use of autonomous vehicles for collecting data and the increasing use of advanced
statistical methodologies to process and quantitatively interpret acoustic data. Acoustical, envi-
ronmental, and biological data collected by a variety of mobile and stationary platforms provide
multiple data streams to characterize ecosystems, and many of the presentations highlighted
statistical methodologies to utilize long term dataseries to improve our understanding of how
ecosystems change in response to human and natural stressors.

This report also summarizes WGFAST survey, research, and publication output for 2020 through
2022, and connections to other ICES expert groups. WGFAST reviewed its response to the Work-
ing Group on International Pelagic Surveys (WGIPS) query about acoustic extinction and its ef-
fect on abundance estimates of schooling fish, and the ICES Cooperative Research Report Col-
lecting Quality Echosounder Data in Inclement Weather. Additionally, WGFAST discused fu-
ture plans for the working group, including details for the 2023 WGFAST/ICES Symposium and
publication of its proceedings.

WGFAST is a leader in transforming “Big Data” to information that is used to conserve and manage ecosystems and contributes to a number of activities in this area of development. WGFAST is co-hosting a theme session with the Working group on Machine Learning in Marine Science (WGMLEARN) at the 2022 ICES Annual Science Conference. WGFAST will assess how its use of acoustic symbols and definitions corresponds to internationally recognized definitions by the International Organization for Standardization (ISO). With a goal of advancing the use of fisheries acoustic data in fisheries and ecosystem science, WGFAST continues to promote development of open-source data formats (SONAR-netCDF4), metadata (AcMETA), and open-source software through active participation in subgroups and postings to the ICES and WGFAST GitHub sites.

This report pays tribute to Ron Mitson who was a pioneer in the field of fisheries acoustics and
provides a policy to celebrate past members.
## ii Expert group information

<table><tr><td>Expert group name</td><td>Working Group on Fisheries Acoustics, Science and Technology (WGFAST)</td></tr><tr><td>Expert group cycle</td><td>Multiannual fixed term</td></tr><tr><td>Year cycle started</td><td>2020</td></tr><tr><td>Reporting year in cycle</td><td>3/3</td></tr><tr><td>Chair</td><td>Michael Jech, USA</td></tr><tr><td>Meeting venue(s) and dates</td><td>22 April 2020, online meeting hosted by the Institute of Marine Research, Bergen, Norway (120 participants)<br/>19-20, 22-23 April 2021, online meeting hosted by the Institute of Marine Research, Bergen, Norway (156 participants)<br/>25-28 April 2022, hybrid format hosted by Institute for Research and Development, Somone, Senegal (16 in person, 114 online)</td></tr></table>
# 1 Report on Terms of Reference

## 1.1 ToR a)

WGFAST members provided information on their resource surveys, research activities, interactions with other ICES expert groups, and publications that derived from exchange and collaboration with other WGFAST members. The report on resource surveys is provided in Annex 5, the report on research activities is provided in Annex 6, the report on interactions with other expert groups is provided in Annex 7, and a list of publications is provided in Annex 8.

## 1.2 ToR b)

Although the meeting was hybrid format, 38 live and 2 recorded presentations addressed the three topics: "Acoustic methods to characterize populations, ecosystems, habitat, and behaviour"; "Acoustic characterization of marine organisms"; and "Emerging technologies, methodologies, and protocols" were held. Presentations (See Annex 3: for abstracts) and discussions comprised these sessions. Summaries of each session are given here.

### Acoustic Methods to Characterize Populations, Ecosystems, Habitat, and Behaviour

Fifteen presentations addressed this theme. Shifts in spatial distributions of pelagic fish observed in time-series survey data. Lekanda et al. suggested that positional variables (e.g. three-dimensional location of a school) were important to the success of a machine learning method using acoustic school classifications and geographical variables. Sarre et al. showed spatial shifts in pelagic fish species off the coast of western Africa in response to climatic variables, e.g. water temperature. Domokos highlighted the variability in spatio-temporal distributions of micronekton that can complicate predictive models. Zytko et al. showed promising results from a method to estimate the orientation of Baltic herring by comparing Kirchhoff model predictions to data. Larson studied the effects of the target strength (TS) to length intercepts on abundance estimates of Baltic herring, and suggested an improvement in estimates when relationships from other species where integrated. Thorvaldsen et al. developed a three-dimensional self-overlap index to quantify the behaviour of *Maurolicus meulleri* during vertical migrations and suggested that self-overlap was useful for discriminating juveniles who migrate from adults who do not migrate. Horne et al. presented ongoing efforts to collect fisheries data using acoustics on an artificial fishery in Cambodia and highlighted the hurdles involved in sampling a fleet with 1e5 to 1e6 small boats. Lee and Staneva presented a method to decompose time-series acoustic data into components that were useful for defining and eliminating outliers and noise. Rong et al. (presented by T. Forland) showed that TS of salmon in pens, when they were not allowed access to the surface, decreased by 10 dB over about 3.5 weeks. Mouget et al. presented comparisons of spatial location of pelagic species in the Canary Current Large Marine Ecosystem. Izard et al. characterized vertical distributions using functional analysis (e.g. PCA). Fonvieille et al. investigated and compared acoustic data collected by vessel-borne and animal-borne echosounders. Silva et al. investigated whether areas predicted to have higher productivity have increased volume backscatter using shipboard echosounders and a tethered broadband system. Lawrence and Fernandes investigated effects of North Sea oil and gas platforms on fish aggregating behaviour. Annasawmy et al. mapped animal tracks (e.g. dives) on stationary, upward looking echosounder data in the Antarctic.
Discussion centered around the utility of the traditional TS-to-length relationships when estimating abundance of pelagic fish. The consensus was that these relationships are still useful, and sometimes the only method to convert acoustic energy to biological metrics, but that they need to be updated when acoustic data include mixed species. There was also discussion about the shifts in temporal and spatial distributions of pelagic nekton and plankton in response to environmental changes, especially sea temperature. These shifts not only affect how surveys need to adapt, but can have serious consequences for local and regional communities and economies that depend on fish as a primary food source and income.

## Acoustic Characterization of Marine Organisms

Five presentations highlighted the utility of theoretical and empirical models in characterizing and understanding how behaviour affects acoustic backscatter. Barbin et al. computed the predicted forward-scatter based on trawl hauls and showed that trawls seem to underestimate the magnitude of acoustic data by 3-20 dB. Gastauer and Chu investigated the Distorted Wave Born Approximation (DWBA) and effects of orientation of non-spherical targets on TS. Palermino et al. performed TS measurements on tethered chub (*Scomber colias*) and horse mackerel (*Trachurus mediterraneus*) and compared these to backscatter models. Khodabandeloo et al. estimated gas-filled swimbladder elongation of mesopelagic fish using empirical data and theoretical models. Diogoul et al. showed correspondence between net tows and the ability to discriminate copepods using 38 and 120 kHz data collected along the Senegalese coast.

Discussion centered around the utility of broadband data and acoustic backscattering models to improve classification of acoustic data. There are a number of acoustic models available and there was discussion about how to move these forward so that they are available to the broader community, i.e. beyond the theoreticians. There was no consensus on developing a training course, but WGFAST will continue to monitor the use of theoretical and empirical backscatter models for classification and abundance estimates.

## Emerging Technologies, Methodologies, and Protocols

Fourteen presentations showcased emerging technologies, methodologies, and protocols. Due to technical difficulties, McGowan-Yallop was unable to provide a presentation. Lowe et al. (presented by L. McGarry) presented Echofilter, a software implementation of machine learning models designed to identify the ambit of air entrained into the water column and thereby specify the contaminated portion of the water column to be excluded from biological analyses. Andersen et al. presented that Simrad will soon be providing open-source Python code and documentation to read and process Simrad wideband acoustic data. Silverman et al. (presented by J. Horne) have developed “pseudograms” that summarize volume backscatter into EchoMetrics that allow efficient transfer of data from autonomous gliders. Lee et al. provided an update on Echotype, and open-source Python convention for processing acoustic data. Calise et al. are developing ways to quantify data collected during tuna fishing operations by fish attracting devices (FADs) outfitted with echosounders. Fernandes and Lawrence highlighted the benefits and limitations of using autonomous platforms for surveying in and around offshore windfarms and gas and oil platforms. McGarry et al. quantified the seasonal and current speed effects influencing the depth-of-penetration of entrained bubbles in data collected at ocean-energy installations. David et al. investigated the use of acoustic instruments in shallow-water (i.e. < 30 m water depth) ecosystems, such as lagoons. M. Peña showed how the colour representation of acoustic data can influence interpretation of these data. Le Bouffant et al. are developing methods to compare data collected with an autonomous platform with those collected by crewed vessels. Coley et al. presented evidence that an real backscatter can be affected by as much as 40% when an average sound
speed is used instead of a sound-speed profile when processing echosounder data. Handegard et al. provided an update of CRIMAC's progress on developing and implementing machine learning methods on acoustic data. Berger and Le Bouffant highlighted multiple conditions and transducer issues that can contribute to excessive noise in an acoustic data, and mitigation measures to deal with these issues. Urmy et al. are developing a probabilistic approach, e.g., Bayesian, to the inverse problem of estimating densities from multifrequency and wideband data.

Discussion centred on how to best utilize wideband data for classification of acoustic backscatter and how to begin incorporating autonomous vehicles into resource surveys. Both topics are in their beginning stages and while there are no definitive answers, there is increasing effort addressing these topics.

## Poster Presentations

The hybrid format of the meeting did not allow for live viewing of posters by all participants, so authors of the posters were given about five minutes to present their posters to the in-person and online participants. Five presentations were given. N. Diogoul presented two posters that investigated large-scale acoustic/biological phenomena and their responses to environmental forcing. The first addressed the resiliency of an eastern-boundary upwelling ecosystem to multiple environmental stressors, and the second looked at sound-scattering layers on the Senegalese continental shelf as a characteristic of pelagic habitat. A. Mouget presented two posters that investigated different ends of the spatial spectrum. The first looked at the importance of small, shallow coastal areas on the biology of pelagic communities, and the second investigated acoustic scattering layers and their importance to the Atlantic African Large Marine Ecosystems on the continental shelf of West Africa. The final presentation was given by Y. Kande who applied spatial functional analysis to survey data and corresponding fine-scale environmental data.

## Presentations by Commercial Entities

Historically, commercial entities, e.g. sonar manufacturers and software developers, participated in the WGFAST meeting as exhibitors, but were allowed to highlight their company and new innovations to those that chose to stay “after hours”. The online format of the WGFAST meetings in 2020 and 2021 curtailed that exchange of information. During this meeting, WGFAST allowed ten-minute presentations at the end of each day by commercial entities so that members could catch up with the newest innovations. ASL Environmental, Simrad, Zunibal, and Echoview provided presentations showcasing their recent products and software.

## Interesting Sidelights we did During COVID

The COVID pandemic drastically changed the way most of the WGFAST members conducted their science. In many cases, these impacts were temporarily detrimental to the overall goals and missions of each institution. However, the change in work environment also provided opportunities for collaborations that may not have occurred otherwise. Two WGFAST members presented efforts above and beyond the traditional products we normally produce. Claudine Arendt partnered with Sven Gastauer to create functional art based on acoustic backscatter by different types of zooplankton (https://claudinearendt.net). Gildas Roudaut, Anne Lebourges Dhaussy, and Jérémie Habasque collaborated with high school art students to produce a leaflet that
describes the mesopelagic environment with emphasis on lanternfish (https://www-iuem.univ-
brest.fr/lemar/wp-content/uploads/2019/11/La-lanterne-web.pdf).

1.3 ToR c)

Members of WGFAST submitted two theme sessions for the 2022 ICES Annual Science Confer-
ence (ASC) in partnership with other expert groups. “New insights from Combining observa-
tions in ecosystem understanding” was submitted by members of WGFAST (Verena Trenkeland
Michael Jech) and the Working Group on Integrative, Physical-biological and Ecosystem Mod-
elling (WGIPEM; Sonja van Leeuwen), but was not accepted by the ASC scientific steering com-
mittee (SCICOM). “Processing and interpreting big data using machine learning: Acoustic, optic,
and other observations in marine research” was submitted by members of WGFAST (Wu-Jung
Lee and Nils Olav Handegard) and WGMLEARN (Ketil Malde) and was accepted as Theme
Session D.

1.4 ToR d)

The acoustic metadata and open data format conventions have been developed and published
and are available for use. Erin LaBrecque, the new chair of TGMeta, provided an update of the
acoustic metadata convetion, AcMeta, which resides on the ICES Publications GitHub site,
https://github.com/ices-publications/AcMeta. Laurent Berger provided an upadate on the open
data format, SONAR-netCDF4, which resides on the ICES Publications GitHub site,
https://github.com/ices-publications/SONAR-netCDF4. These are living documents in that any-
one interested can use the conventions, suggest revisions, or add new information. Erin LaBrec-
que is the contact for AcMeta, and Gavin Macaulay and Laurent Berger are the primary contacts
for SONAR-netCDF4. Once the full functionality of the ICES Library is online later this year, both
AcMeta and SONAR-netCDF4 will have a landing page in the ICES library that will be searcha-
ble with all other ICES material, and will link to the GitHub repositories.

1.5 ToRe)

An ICES Cooperative Research Report (CRR) was published in 2021 which addresses how to deal with inclement weather when collecting and processing acoustic data: Jech, J. M., Schaber, M., Cox, M., Escobar-Flores, P., Gastauer, S., Haris, K., Horne, J., et al. 2021. Collecting Quality Echosounder Data in Inclement Weather, ICES CRR 352, https://doi.org/10.17895/ices.pub.7539. The practical aims of the CRR were to (i) review current knowledge and experience on the impact of weather conditions on acoustic data collected with a variety of echosounders operating on research vessels at common acoustic frequencies used in fisheries acoustics; (ii) develop standard procedures and methods for identifying unsuitable survey conditions, i.e. situations that are considered too degraded to continue collecting acoustic data; and (iii) propose methods for dealing with degraded data.
## 2 Discussion Topics

### 2.1 Proposed Symposium in 2023

WGFAST has proposed to convene the 8th international symposium of fisheries acoustics in Portland, Maine, USA the 27-30 March 2023. Anne Lebourges-Dhaussy (IRD, France), Gayle Zyndlewski (UMaine, USA), and Michael Jech (NOAA, USA) are co-conveners. The symposium was approved SCICOM, so it will take place during the 50th anniversary of the first symposium held in Bergen, Norway in 1973.

### 2.2 Symposium Proceedings

WGFAST discussed publication of the symposium proceedings. Most of the historical proceedings have followed the format of a traditional proceedings in that the papers have a page limit (5-7 pages) with about 60-70 papers published in a single volume. These were all published by ICES JMS and volume of *Aquatic Living Resources*. ICES JMS no longer allows the “traditional” proceedings format and all papers follow the submission procedures for the ICES JMS. In 2016, this and other factors led to an all-time low publication of 16 papers. WGFAST considered alternative publishers for the symposium in 2023. *Frontiers in Marine Science, Progress in Oceanography*, and *American Society of Linnology and Oceanography* are a few alternatives. Each come with benefits and limitations. For example, *Frontiers in Marine Science*: will allow guest editors, will allow traditional format, $3000 per article. Whereas *ICES JMS*: will not allow guest editors, will not allow traditional format, but does not have page charges $0 per article.

Discussions at this meeting revealed that there were several factors that resulted in a low number of papers published. Submissions not being required prior to the meeting, and the heavy workload by ICES editorial staff were two primary factors that influenced the number of submissions.

The consensus of WGFAST was to i) retain the historical connection to ICES JMS, ii) require manuscript submissions prior to the meeting, iii) and for the scientific steering committee to bring the selected manuscripts up to publication quality before being submitted to the journal so that the burden on editorial staff is alleviated. The symposium conveners discussed these requirements with ICES JMS editorial staff and the decision was made to publish the symposium proceedings in ICES JMS to produce a proceedings volume that is representative of the breadth of innovation within WGFAST and the broader fisheries acoustics community.

### 2.3 WGFAST Chair for 2023-2025

Anne Lebourges-Dhaussy was confirmed by WGFAST as the next chair. Anne and current-chair Michael Jech will work with SCICOM to confirm Anne as the next chair.

### 2.4 Tribute to Ron Mitson

Ron Mitson (1930 - 2021) was a pioneer in the field of fisheries acoustics. Ron was a kind man who always had time to talk to experts and students alike. He was an electrical engineer by training and worked at Cefas (the UK Centre for Environment, Fisheries and Aquaculture Science) and its predecessors from the early 1960s until his retirement.
He worked alongside such luminaries as David Cushing, Roy Harden Jones, and Geoff Arnold developing and adapting technologies for tracking fish and quantifying their abundance. His early work included acoustic target counting (Mitson and Wood, 1962) and development of acoustic transponding tags and application of World War 2 sonar technology for observing flat-fish behavior relative to tidal currents (Mitson and Cook, 1971; Mitson and Storeton West, 1971).

His extensive list of publications in fisheries acoustics includes Ona and Mitson (1996), a classic contribution on the acoustic dead zone, at least two seminal publications, and many others that have influenced much of what we do.

* Fisheries Acoustics, A practical manual for aquatic biomass estimation, K. A. Johanneson and R. B. Mitson (1983) FAO Fisheries Technical Paper 240, https://www.fao.org/3/x5818e/x5818e00.htm. I highly recommend revisiting this publication. It reminds us of our analogue "roots", which is especially important in this age of digital data.

* ICES CRR #209 (1995) Underwater Noise of Research Vessels: Review and Recommendation. This publication is notable because of its influence beyond the fisheries acoustics community to that of vessel design and quality of life aboard research vessels. Most fisheries institutions have built "quiet" vessels based on requirements provided in this document. We have all been the beneficiaries of improved data quality through less ambient and radiated noise. Additionally, passive acoustic data collection and overall "quality of life" while sailing have benefited (I know I don't walk off one of our quiet vessels screaming at everyone I meet).

I (Dick Wood, FIOA, retired) met Ron in the early 1980's as he was then working at the Ministry of Agriculture, Fisheries, and Food in Lowestoft and they were putting out "feelers" for noise consultants to assist them in the development of a low noise research vessel. I was working as a noise consultant for Acoustic Technology Limited (which was later bought out by Bureau Veritas). We were fortunate enough to get the contract despite having little experience in underwater noise. Ron was very patient and understanding with my total lack of knowledge in the field of fisheries acoustics and helped me understand the importance of vessel radiated noise for the onboard scientists - even though my role was very much in the development of engineering orientated noise reduction measures. This first vessel we worked on was RV Corystes - the vessel which later became the template for the seminal publication ICES CRR #209 listed above and Kay et al. (1991). The importance of the ICES publication cannot be overstated as, in the mid-1990's, this was the only reference document which identified specific vessel source strength goals resulting in these criteria being used as a yardstick for numerous other types of vessel. I worked on several vessels with Ron right through from project inception through to the ranging trials when the underwater noise radiation from the vessel was finally fully understood. Ron also introduced me to many other projects where he was sometimes peripherally involved including Scotia, Cefas Endeavour, Oscar Dyson, Celtic Explorer etc. I have provided four photos of Ron taken at the Heggernes range in Norway during the underwater noise trials of Celtic Explorer in November and December 2002. Ron also introduced me to ICES and helped me get involved with study groups such as the Study Group on Fish Avoidance to research Vessels (SGFARV) etc. We always kept in touch until quite recently when he passed away and I will always appreciate his friendship.
<figure><img src="image_5.png" /><figcaption>Figure 2. A picture of Ron Mitson</figcaption></figure>

## 2.5 Tributes to Past Members

WGFAST discussed how to pay tribute to those past members who have had significant influence on the field of fisheries acoustics. Historically, retired members have been recognized during the banquet at the annual meeting where an active member of WGFAST pays recognition to a retired member. In cases where the former member has had significant and profound influence on the field, a more substantial tribute should be provided. The consensus of WGFAST was to provide tributes in the annual report (see section 2.4 for a tribute to Ron Mitson). More substantial efforts (e.g. WGFAST nominated David MacLennon for the ICES Prix d'Excellence award) will be evaluated and pursued if an active member takes the lead.

## 2.6 Consistency of Terminology with International Standards

Toby Jarvis lead a discussion on the consistency of underwater-acoustics terminology used by the WGFAST community with international standards. The preliminary focus was on active solar and the relatively new standard, ISO 18405:2017(E). WGFAST terminology has evolved somewhat separately to ISO 18405:2017(E), and a recent publication by some of the standard's
authors (Ainslie et al. 2022, doi: 10.1109/JOE.2021.3085947) put forward the following case for the benefits of standardization: "By adopting a common language, we facilitate the effective communication of concepts and information in underwater acoustics, whether for research, technology, or regulation". An initial document has been drafted (See WGFAST_Supplementary-file1.pdf) to facilitate discussion and assessment.

The consensus of WGFAST was to continue evaluation by making this a formal ToR, and forming a subgroup of volunteers, led by T. Jarvis, who will continue the effort over the next three years.

## 2.7 SPRFMO Symposium Announcement

Aquiles Sepulveda announced a symposium sponsored by the SPRFMO (South Pacific Regional Fisheries Management Organisation), "State of the Art of Habitat Monitoring". The symposium is to be held in Concepción, Chile, late January 2023. The WGFAST chair will disseminate more details as they become available.

## 2.8 WGFAST Response to WGIPS

WGFAST responded to a request for information by WGIPS as to whether acoustic extinction by dense schools should be considered as their surveys post-process acoustic survey data. Michael Jech presented the response and the response is provided in Annex 4. The two recommendations were

* Inspect historical data for the prevalence of aggregations that may be affected by acoustic shadowing (e.g. $\hat{s}_A$ values greater than 100,000 m² nmi⁻²), and develop metrics to estimate the magnitude of the effects. Metrics include percentage of aggregations with acoustic shadowing, magnitude of acoustic shadowing, and effects on abundance estimates.

* If acoustic shadowing is determined to be significant, devote resources to develop survey protocols and collecting additional data.

## 2.9 New ToRs

WGFAST decided to continue its existence and discussed new ToRs. These ToRs have been developed as a new resolution and submitted to SCICOM for evaluation and approval.
3 References

Kay B.J., Jones D.K. & Mitson R. B. "FRV Corystes: A purpose-built fisheries research vessel". The Royal Institution of Naval Architects, London. Spring Meetings 1991 12p

Ona, E., and Mitson, R. B. 1996. Acoustic sampling and signal processing near the seabed: the deadzone revisited *ICES Journal of Marine Science*, Volume 53, Issue 4, August 1996, Pages 677–690, https://doi.org/10.1006/jmsc.1996.0087.

Mitson, R. B., and Wood, R. J. 1962. An automatic method of counting fish echoes. *Journal du Conseil International pour l'Exploration de la Mer*, 26: 281-291.

Mitson, R. B., and Cook, J. C. 1971. Shipboard installation and trials of an electronic sector scanning sonar. *Radio and Electronic Engineering*, 41: 339-350.

Mitson, R. B., and Storeton West, T. J. 1971. A transponding acoustic fish tag. *Radio Electron. Eng.*, 41: 483-489.
Annex 1: List of participants

Meeting 2022

<table><thead><tr><td>Name</td><td>Institute</td><td>Country (of institute)</td><td>E-mail</td></tr></thead><tbody><tr><td>Alegría, Nicolás</td><td>Instituto de Investigación Pesquera (INPESCA)</td><td>Chile</td><td>nicoalegria1@gmail.com</td></tr><tr><td>Algroy, Tonny</td><td>Kongsberg Maritime AS</td><td>Norway</td><td>tonny.algroy@km.kongsberg.com</td></tr><tr><td>Andersen, Lars Nonboe</td><td>Kongsberg Maritime</td><td>Norway</td><td>lars.nonboe.andersen@km.kongsberg.com</td></tr><tr><td>Anderson, Chuck</td><td>CIRES CU-Boulder / NOAA NCEI</td><td>US</td><td>charles.anderson@noaa.gov</td></tr><tr><td>Annasawmy, Angelee Pavanee</td><td>Lynker Corporation</td><td>US</td><td>angelee-pavanee.annasawmy@ird.fr</td></tr><tr><td>Arendt, Claudine</td><td>Claudine Arendt</td><td>The Netherlands</td><td>claudine.a.c.arendt@gmail.com</td></tr><tr><td>Ariza, Alejandro</td><td>Institut de Recherche pour le Développement IRD</td><td>France</td><td>alejandro.ariza@ird.fr</td></tr><tr><td>Barbin, Laure</td><td>IRD</td><td>New Caledonia, France</td><td>laure.barbin@ird.fr</td></tr><tr><td>Berger, Laurent</td><td>Ifremer</td><td>France</td><td>Laurent.berger@ifremer.fr</td></tr><tr><td>Boyra, Guillermo</td><td>AZTI</td><td>Spain</td><td>gboyra@azti.es</td></tr><tr><td>Brehmer, Patrice</td><td>IRD - CSRP / SRFC</td><td>Senegal</td><td>Patrice.Brehmer@ird.fr</td></tr><tr><td>Bristow, Martina</td><td>University of East Anglia</td><td>UK</td><td>martina.bristow@uea.ac.uk</td></tr><tr><td>Calise, Lucio</td><td>Zunibal S.L</td><td>Spain</td><td>lucio.calise@zunibal.com</td></tr><tr><td>Carlsen, Astrid</td><td>SLU</td><td>Sweden</td><td>astrid.carlsen@slu.se</td></tr><tr><td>Cermak, Joseph</td><td>Coop. Instit. Clim. Ocean, & Ecosys. Studies (CICOES)</td><td>US</td><td>jcerma@uw.edu</td></tr><tr><td>Chawarski, Julek</td><td>Memorial University of Newfoundland / ASL Environmental Science</td><td>Canada</td><td>julek.chawarski@mi.mun.ca</td></tr><tr><td>Chu, Dezhang</td><td>NOAA Fisheries, NWFSC</td><td>US</td><td>Dezhang.chu@noaa.gov</td></tr><tr><td>Coly, Youssouph</td><td>UASZ</td><td>Senegal</td><td>youssouphcoly98@gmail.com</td></tr><tr><td>Condiotty, Jeff</td><td>Kongsberg Underwater Technology LLC</td><td>US</td><td>Jeff.Condiotty@km.kongsberg.com</td></tr><tr><td>Cornejo Urbina, Rodolfo Martín</td><td>IMARPE (Peruvian Marine Institute)</td><td>Peru</td><td>rcornejo@imarpe.gob.pe</td></tr><tr><td>Cox, Martin</td><td>Australian Antarctic Division</td><td>Australia</td><td>martin.cox@aad.gov.au</td></tr><tr><td>De Robertis, Alex</td><td>NOAA Fisheries</td><td>US</td><td>Alex.DeRobertis@noaa.gov</td></tr></tbody></table>
<table><tr><td>Demer, David</td><td>NOAA Fisheries, Southwest Fisheries Science Center</td><td>US</td><td>david.demer@noaa.gov</td></tr><tr><td>Diogoul, Ndague</td><td>IRD-ISRA/CRODT</td><td>Senegal</td><td>Ndague.Diogoul@ird.fr</td></tr><tr><td>Domokos, Réka</td><td>NOAA/NMFS/PIFSC</td><td>US</td><td>reka.domokos@noaa.gov</td></tr><tr><td>Dornan, Tracey</td><td>British Antarctic Survey</td><td>UK</td><td>tarrna70@bas.ac.uk</td></tr><tr><td>Dunn, Muriel</td><td>Memorial University of Newfoundland/Akvaplan-niva</td><td>Norway</td><td>mbd@akvaplan.niva.no</td></tr><tr><td>Fernandes, Paul</td><td>University of Aberdeen</td><td>UK</td><td>fernandespg@abdn.ac.uk</td></tr><tr><td>Fonvieille, Nadège</td><td>Mediterranean In</td><td>France</td><td>nadege.fonvieille@univ-amu.fr</td></tr><tr><td>Forland, Tonje Nesse</td><td>Institute of Marine Research</td><td>Norway</td><td>tonje.nesse.forland@hi.no</td></tr><tr><td>Gastauer, Sven</td><td>Thünen Institute for Sea Fisheries/Scripps Inst. Ocean.</td><td>Germany</td><td>sven.gastauer@thuenen.de</td></tr><tr><td>Gauthier, Stéphane</td><td>DFO</td><td>Canada</td><td>stephane.gauthier@dfo-mpo.gc.ca</td></tr><tr><td>Geoffroy, Maxime</td><td>Marine Institute of Memorial University</td><td>Canada</td><td>maxime.geoffroy@mi.mun.ca</td></tr><tr><td>Gjeitsund Thorvaldsen, Kjetil</td><td>DTU Aqua</td><td>Denmark</td><td>kjgth@aqua.dtu.dk</td></tr><tr><td>Gonzalez, Silvana</td><td>NOAA</td><td>US</td><td>Silvana.gonzalez@noaa.gov</td></tr><tr><td>Gorska, Natalia</td><td>Institute of Oceanology Polish Academy of Sciences</td><td>Poland</td><td>gorska@iopan.pl</td></tr><tr><td>Grados, Daniel</td><td>IMARPE</td><td>Peru</td><td>danny.grados@gmail.com</td></tr><tr><td>Gutierrez, Mariano</td><td>IHMA</td><td>Peru</td><td>mgutierrez@ihma.org.pe</td></tr><tr><td>Habasque, Jérémie</td><td>IRD</td><td>France</td><td>jeremie.habasque@ird.fr</td></tr><tr><td>Handegard, Nils Olav</td><td>IMR</td><td>Norway</td><td>nils.olav handegard@hi.no</td></tr><tr><td>Hassan, Moustahfid</td><td>NOAA</td><td>US</td><td>Hassan.Moustahfid@noaa.gov</td></tr><tr><td>Higashiyama, Hiroyuki</td><td>FURUNO Electric Co. Ltd.</td><td>Japan</td><td>hiroyuki.higashiyama@furuno.co.jp</td></tr><tr><td>Honkalehto, Taina</td><td>NOAA, National Marine Fisheries Service</td><td>US</td><td>taina.honkalehto@noaa.gov</td></tr><tr><td>Horne, John</td><td>CICOES, University of Washington</td><td>US</td><td>jhorne@uw.edu</td></tr><tr><td>Hutton, Briony</td><td>Echoview Software</td><td>Australia</td><td>briony.hutton@echoview.com</td></tr><tr><td>Iglesias, Ilysa</td><td>UCSC</td><td>US</td><td>iiglesia@ucsc.edu</td></tr><tr><td>Iriarte, Federico</td><td>Albor Tecnologico</td><td>Peru</td><td>Federico.iriarte@albortecnologico.com</td></tr></table>
<table><tr><td>Izard, Lloyd</td><td>Sorbonne University; LOCEAN</td><td>France</td><td>lloyd.izard@locean.ipsl.fr</td></tr><tr><td>Jacobsen, Jan Arge</td><td>Faroe Marine Research Institute</td><td>Faroe Islands</td><td>janarge@hav.fo</td></tr><tr><td>Jarvis, Toby</td><td>Echoview Software</td><td>Australia</td><td>toby.jarvis@echoview.com</td></tr><tr><td>Jónsson, Sigurður Pór</td><td>Marine and Freshwater Research Institute</td><td>Iceland</td><td>sigurdur@hafro.is</td></tr><tr><td>Kaljuste, Olavi</td><td>Department of Aquatic Resources, Swedish University of Agricultural Sciences</td><td>Sweden</td><td>olavi.kaljuste@slu.se</td></tr><tr><td>Kamal, Mamza</td><td>INRH</td><td>Morocco</td><td>kamalmamza@gmail.com</td></tr><tr><td>Khodabandeloo, Babak</td><td>Institute of Marine Research (IMR)</td><td>Norway</td><td>babak.khodabandeloo@hi.no</td></tr><tr><td>Klevjer, Thor</td><td>IMR</td><td>Norway</td><td>thor.klevjer@hi.no</td></tr><tr><td>Kubilius, Rokas</td><td>Institute of Marine Research</td><td>Norway</td><td>rokas@hi.no</td></tr><tr><td>LaBrecque, Erin</td><td>Marine Mammal Commission</td><td>US</td><td>erinlab@gmail.com</td></tr><tr><td>Larson, Niklas</td><td>SLU</td><td>Sweden</td><td>Niklas.larson@slu.se</td></tr><tr><td>Lavery, Andone</td><td>Woods Hole Oceanographic Institution</td><td>US</td><td>alavery@whoi.edu</td></tr><tr><td>Lawrence, Joshua</td><td>University of Aberdeen</td><td>Scotland</td><td>joshua.lawrence1@abdn.ac.uk</td></tr><tr><td>Le Bouffant, Naig</td><td>Ifremer</td><td>France</td><td>naig.le.bouffant@ifremer.fr</td></tr><tr><td>Lee, Wu-Jung</td><td>Applied Physics Laboratory, University of Washington</td><td>US</td><td>leewj@uw.edu</td></tr><tr><td>Levine Robert</td><td>University of Washington</td><td>US</td><td>Leviner@uw.edu</td></tr><tr><td>Macaulay, Gavin</td><td>Institute of Marine Research (IMR)</td><td>Norway</td><td>gavin.macaulay@hi.no</td></tr><tr><td>Martin, Antonio</td><td>Furuno Spain</td><td>Spain</td><td>Antonio.martin@furuno.es</td></tr><tr><td>Matsuura, Tomohiko</td><td>Japan Fisheries Research and Education Agency</td><td>Japan</td><td>imat@affrc.go.jp</td></tr><tr><td>Maurice, Alicia</td><td>NIWA</td><td>New Zealand</td><td>alicia.maurice@niwa.co.nz</td></tr><tr><td>Mayorga, Emilio</td><td>Applied Physics Laboratory, University of Washington</td><td>US</td><td>emiliom@uw.edu</td></tr><tr><td>McGarry, Louise</td><td>Fundy Ocean Research Centre for Energy (FORCE)</td><td>Canada</td><td>louise.p.mcgarry@gmail.com</td></tr><tr><td>McGowan-Yallop, Chelsey</td><td>Scottish Association for Marine Science</td><td>Scotland</td><td>chelsey.mcgowan-yallop@sams.ac.uk</td></tr><tr><td>McQuinn, Ian H.</td><td>DFO</td><td>Canada</td><td>ian.mcquinn@dfo-mpo.gc.ca</td></tr><tr><td>Michael, Jech</td><td>NOAA/Northeast Fisheries Science Center</td><td>US</td><td>michael.jech@noaa.gov</td></tr><tr><td>Misonoo, Satoshi</td><td>Furuno electric co., ltd.</td><td>Japan</td><td>Satoshi.misonoo@furuno.co.jp</td></tr></table>
<table><tr><td>Mouget, Anne</td><td>MNHN</td><td>France</td><td>Anne.Mouget@mnhn.fr</td></tr><tr><td>Mouget, Anne</td><td>MNHN C/o IRD</td><td>France</td><td>anne.mouget@mnhn.fr</td></tr><tr><td>Mowbray, Fran</td><td>Department of Fisheries and Oceana</td><td>Canada</td><td>Fran.mowbray@dfo-mpo.gc.ca</td></tr><tr><td>Nogueira, Miguel</td><td>Furuno España</td><td>Spain</td><td>Miguel.nogueira@furuno.es</td></tr><tr><td>O'Connell, Steven</td><td>Marine Scotland</td><td>Scotland</td><td>steven.oconnell@gov.scot</td></tr><tr><td>O'Donnell, Ciaran</td><td>Marine Institute</td><td>Ireland</td><td>Ciaran.odonnell@marine.ie</td></tr><tr><td>O'Driscoll, Richard</td><td>National Institute of Water & Atmospheric Research Ltd</td><td>New Zealand</td><td>richard.odriscoll@niwa.co.nz</td></tr><tr><td>O'Malley, Michael</td><td>Marine Institute</td><td>Ireland</td><td>michael.omalley@marine.ie</td></tr><tr><td>Okunishi, Akira</td><td>FURUNO electric Co., Ltd.</td><td>Japan</td><td>akira.okunishi@furuno.co.jp</td></tr><tr><td>Ona, Egil</td><td>IMR</td><td>Norway</td><td>egil.ona@hi.no</td></tr><tr><td>Ordóñez Cebrián, Patricia</td><td>Zunibal S.L</td><td>Spain</td><td>patricia.ordonez@zunibal.com</td></tr><tr><td>Palermino, Antonio</td><td>Organisation CNR-Irbim; UNIBO</td><td>Italy</td><td>antonio.palermino@irbim.cnr.it</td></tr><tr><td>Parker-Stetter, Sandra</td><td>NOAA-NMFS-AFSC</td><td>US</td><td>sandy.parker-stetter@noaa.gov</td></tr><tr><td>Parner, Hjalte</td><td>ICES</td><td>Denmark</td><td>hjalte@ices.dk</td></tr><tr><td>Pearce, Steve</td><td>ASL Environmental Sciences</td><td>Canada</td><td>spearce@aslenv.com</td></tr><tr><td>Pedersen, Audun Oppedal</td><td>University of Bergen</td><td>Norway</td><td>audun.pedersen@uib.no</td></tr><tr><td>Peltonen, Heikki</td><td>Finnish Environment Institute</td><td>Finland</td><td>Heikki.peltonen@syke.fi</td></tr><tr><td>Pena, Hector</td><td>Institute of Marine Research</td><td>Norway</td><td>hector.pena@hi.no</td></tr><tr><td>Peña, Marián</td><td>Spanish Institute of Oceanography (IEO)</td><td>Spain</td><td>marian.pena@ieo.es</td></tr><tr><td>Petrusevich, Vladislav</td><td>Northwest Atlantic Fisheries Centre, Fish. & Oceans</td><td>Canada</td><td>vladislav.petrusevich@dfo-mpo.gc.ca</td></tr><tr><td>Phillips, Elizabeth</td><td>NOAA Fisheries</td><td>US</td><td>Elizabeth.phillips@noaa.gov</td></tr><tr><td>Proud, Roland</td><td>University of St Andrews</td><td>UK</td><td>rp43@st-andrews.ac.uk</td></tr><tr><td>Renfree, Josiah</td><td>NOAA / Southwest Fisheries Science Center</td><td>US</td><td>Josiah.renfree@noaa.gov</td></tr><tr><td>Ressler, Patrick</td><td>NOAA Alaska Fisheries Science Center</td><td>US</td><td>patrick.ressler@noaa.gov</td></tr><tr><td>Roa, Camilo</td><td>Florida International University</td><td>US</td><td>croa@fiu.edu</td></tr><tr><td>Rodrigues, Eli-zandro</td><td>Instituto do Mar - Imar</td><td>Cabo Verde</td><td>elizandro.rodrigues@oscm.cv</td></tr></table>
<table><tr><td>Roudaut, Gildas</td><td>IRD</td><td>France</td><td>gildas.roudaut@ird.fr</td></tr><tr><td>Rousseau, Shani</td><td>Fisheries and Oceans Canada</td><td>Canada</td><td>shani.rousseau@dfo-mpo.gc.ca</td></tr><tr><td>Ryan, Tim</td><td>CSIRO</td><td>Australia</td><td>tim.ryan@csiro.au</td></tr><tr><td>Sakinan, Serdar</td><td>Wageningen Marine Research</td><td>Netherlands</td><td>serdar.sakinan@wur.nl</td></tr><tr><td>Salvetat, Julie</td><td>University of Montpellier</td><td>France</td><td>ju.salvetat@gmail.com</td></tr><tr><td>Sarré, Abdoulaye</td><td>ISRA, CRODT</td><td>Senegal</td><td>ablaysarrey@yahoo.fr</td></tr><tr><td>Sawada, Kouichi</td><td>Fish. Tech. Institute, Japan Fish. Res. &amp; Educ. Agency</td><td>Japan</td><td>ksawada@fra.affrc.go.jp</td></tr><tr><td>Schaber, Matthias</td><td>Thünen Institute of Sea Fisheries</td><td>Germany</td><td>matthias.schaber@thuenen.de</td></tr><tr><td>Schmidt, Beata</td><td>National Marine Fisheries Research Institute</td><td>Poland</td><td>bschmidt@mir.gdynia.pl</td></tr><tr><td>Sepp, Elor</td><td>Estonian Marine Institute</td><td>Estonia</td><td>elor.sepp@ut.ee</td></tr><tr><td>Sepulveda, Aquiles</td><td>Instituto de Investigación Pesquera</td><td>Chile</td><td>asepulveda@inpesca.cl</td></tr><tr><td>Sidibeh, Momodou</td><td>FD</td><td>The Gambia</td><td>mbailo85@hotmail.com</td></tr><tr><td>Silva, Teresa</td><td>Marine and Freshwater Research Institute</td><td>Iceland</td><td>teresa.silva@hafogvat.is</td></tr><tr><td>Slonimer, Alex</td><td>ASL Environmental Sciences</td><td>Canada</td><td>aslonimer@aslenv.com</td></tr><tr><td>Smith, Abigail</td><td>Australian Antarctic Program Partnership</td><td>Australia</td><td>Abigail.smith@utas.edu.au</td></tr><tr><td>Sørhagen, Robert</td><td>Kongsberg Maritime AS</td><td>Norway</td><td>robert.sorhagen@km.kongsberg.com</td></tr><tr><td>Staneva, Valentina</td><td>University of Washington</td><td>US</td><td>vms16@uw.edu</td></tr><tr><td>Strods, Guntars</td><td>BIOR</td><td>Latvia</td><td>guntars.strods@bior.lv</td></tr><tr><td>Swan, Jackson</td><td>University of Washington</td><td>US</td><td>jswan13@uw.edu</td></tr><tr><td>Taleb, Mohamed Ahmed Jeyid</td><td>IMROP</td><td>Mauritanie</td><td>moahtaje@yahoo.fr</td></tr><tr><td>Taylor, Chris</td><td>NOAA Ocean Service</td><td>US</td><td>Chris.Taylor@noaa.gov</td></tr><tr><td>Thomas, Rebecca</td><td>NOAA/Northwest Fisheries Science Center</td><td>US</td><td>Rebecca.Thomas@noaa.gov</td></tr><tr><td>Urmy, Sam</td><td>NOAA Alaska Fisheries Science Center</td><td>US</td><td>sam.urmy@noaa.gov</td></tr><tr><td>Wall, Carrie</td><td>University of Colorado / NOAA National Centers for Environmental Information</td><td>US</td><td>carrie.wall@noaa.gov</td></tr><tr><td>Wangen, Ivar</td><td>Simrad/Kongsberg Maritime</td><td>Norway</td><td>ivar.wangen@simrad.com</td></tr><tr><td>Warren, Joseph</td><td>Stony Brook University</td><td>US</td><td>joe.warren@stonybrook.edu</td></tr><tr><td>Weber, Tom</td><td>ONR</td><td>US</td><td>Thomas.c.weber16.civ@us.navy.mil</td></tr></table>
<table><tr><td>Whitton, Tim</td><td>Bangor University</td><td>UK</td><td>t.whitton@bangor.ac.uk</td></tr><tr><td>Xu, Jinshan</td><td>Bedford Institute of Oceanography</td><td>Canada</td><td>Jinshan.xu@dfo-mpo.gc.ca</td></tr><tr><td>Zhuang, Zhaozhong</td><td>MIT-WHOI</td><td>US</td><td>zzhuang@whoi.edu</td></tr><tr><td>Żytko, Aleksander</td><td>Institute of Oceanology Polish Academy of Sciences</td><td>Poland</td><td>azytko@iopan.pl</td></tr></table>

Meeting 2021

<table><thead><tr><td>Name</td><td>Institute</td><td>Country (of institute)</td><td>E-mail</td></tr></thead><tbody><tr><td>Koki Abe</td><td>Japan Fisheries Research and Education Agency</td><td>Japan</td><td>abec@fra.affrc.go.jp</td></tr><tr><td>Mette Agersted</td><td>Institute of Marine Research, Norway</td><td>Norway</td><td>metteagersted@gmail.com</td></tr><tr><td>Nicolás Alegria Landeros</td><td>INPESCA</td><td>Chile</td><td>nalegria@inpesca.cl</td></tr><tr><td>Tonny Algroey</td><td>Kongsberg Maritime AS</td><td>Norway</td><td>tonny.algroey@simrad.com</td></tr><tr><td>Anibal Aliaga</td><td>TASA</td><td>Peru</td><td>anibaljar@gmail.com</td></tr><tr><td>Lars Nonboe Andersen</td><td>Kongsberg Maritime</td><td>Norway</td><td>lars.nonboe.andersen@kongsberg.com</td></tr><tr><td>Charles Anderson</td><td>NCEI/CIRES</td><td>US</td><td>charles.anderson@noaa.gov</td></tr><tr><td>Angelee Pavanee Annasawmy</td><td>University of Washington</td><td>France</td><td>pannasawmy@yahoo.com</td></tr><tr><td>Alejandro Ariza</td><td>IRD</td><td>Spain</td><td>alejandro.ariza@ird.fr</td></tr><tr><td>Sigvaldi Árnason</td><td>Marine and Freshwater Research Institute</td><td>Iceland</td><td>sigvaldi.arnason@hafogvatn.is</td></tr><tr><td>Fiona Bairstow</td><td>St Andrews</td><td>UK</td><td>fjb5@st-andrews.ac.uk</td></tr><tr><td>Laure Barbin</td><td>IRD</td><td>France</td><td>laure.barbin@ird.fr</td></tr><tr><td>Birkir Bardarson</td><td>MFRI</td><td>Iceland</td><td>birkir.bardarson@hafogvatn.is</td></tr><tr><td>Laurent Berger</td><td>Ifremer</td><td>France</td><td>laurent.berger@ifremer.fr</td></tr><tr><td>Arnaud Bertrand</td><td>IRD</td><td>France</td><td>Arnaud.Bertrand@ird.fr</td></tr><tr><td>Arthur Blanluet</td><td>Queensland University</td><td>France/Australia</td><td>arthur.blanluet@noos.fr</td></tr><tr><td>Kevin Boswell</td><td>Florida International University</td><td>US</td><td>kevin.boswell@fiu.edu</td></tr><tr><td>Guillermo Boyra</td><td>AZTI</td><td>Spain</td><td>gboyra@azti.es</td></tr><tr><td>Patrice Brehmer</td><td>IRD</td><td>Senegal</td><td>Patrice.brehmer@ird.fr</td></tr><tr><td>Andrew Brierley</td><td>University of St Andrews</td><td>UK</td><td>asb4@st-andrews.ac.uk</td></tr><tr><td>Martina Bristow</td><td>University of East Anglia</td><td>UK</td><td>martina.bristow@uea.ac.uk</td></tr></tbody></table>
<table>
  <tr>
    <td>Steven Brodet</td>
    <td>Kongsberg Underwater Technology</td>
    <td>Sweden</td>
    <td>steve.brodet@km.kongsberg.com</td>
  </tr>
  <tr>
    <td>Jan Buermans</td>
    <td>ASL Environmental Sciences Inc.</td>
    <td>Canada</td>
    <td>jbuermans@aslenv.com</td>
  </tr>
  <tr>
    <td>Lucio Calise</td>
    <td>Zunibal SL</td>
    <td>Spain</td>
    <td>lucio.calise@zunibal.com</td>
  </tr>
  <tr>
    <td>Fabio Campanella</td>
    <td>Cefas</td>
    <td>UK</td>
    <td>fabio.campanella@cefas.co.uk</td>
  </tr>
  <tr>
    <td>Julek Chawarski</td>
    <td>Centre for Fisheries Ecosystem Research</td>
    <td>Canada</td>
    <td>julek.chawarski@mi.mun.ca</td>
  </tr>
  <tr>
    <td>Dezhang Chu</td>
    <td>NOAA Fisheries, NWFSC</td>
    <td>US</td>
    <td>dezhang.chu@noaa.gov</td>
  </tr>
  <tr>
    <td>Jeff Condiotty</td>
    <td>Kongsberg Underwater Technology</td>
    <td>US</td>
    <td>jeff.condiotty@km.kongsberg.com</td>
  </tr>
  <tr>
    <td>Rodolfo Cornejo Urbina</td>
    <td>IMARPE</td>
    <td>Peru</td>
    <td>rcornejo@imarpe.gob.pe</td>
  </tr>
  <tr>
    <td>Martin Cox</td>
    <td>Australian Antarctic Division</td>
    <td>Australia</td>
    <td>martin.cox@aad.gov.au</td>
  </tr>
  <tr>
    <td>Alex De Robertis</td>
    <td>NOAA Fisheries</td>
    <td>US</td>
    <td>Alex.DeRobertis@noaa.gov</td>
  </tr>
  <tr>
    <td>David Demer</td>
    <td>NOAA / NMFS / SWFSC</td>
    <td>US</td>
    <td>david.demer@noaa.gov</td>
  </tr>
  <tr>
    <td>Réka Domokos</td>
    <td>Pacific Islands Fisheries Science Center, NMFS, NOAA</td>
    <td>US</td>
    <td>reka.domokos@noaa.gov</td>
  </tr>
  <tr>
    <td>Mathieu Doray</td>
    <td>Ifremer</td>
    <td>France</td>
    <td>mdoray@ifremer.fr</td>
  </tr>
  <tr>
    <td>Tracey Dornan</td>
    <td>British Antarctic Survey</td>
    <td>UK</td>
    <td>tarna70@bas.ac.uk</td>
  </tr>
  <tr>
    <td>Ryan Downie</td>
    <td>CSIRO</td>
    <td>Australia</td>
    <td>ryan.downie@csiro.au</td>
  </tr>
  <tr>
    <td>Muriel Dunn</td>
    <td>Memorial University of Newfoundland / Akvaplan-niva</td>
    <td>Norway</td>
    <td>muriel.dunn@mi.mun.ca</td>
  </tr>
  <tr>
    <td>James Dunning</td>
    <td>University of Aberdeen</td>
    <td>UK</td>
    <td>j.dunning.18@abdn.ac.uk</td>
  </tr>
  <tr>
    <td>Pablo Escobar-Flores</td>
    <td>NIWA</td>
    <td>New Zealand</td>
    <td>pablo.escobar@niwa.co.nz</td>
  </tr>
  <tr>
    <td>Paul Fernandes</td>
    <td>University of Aberdeen</td>
    <td>UK</td>
    <td>fernandespg@abdn.ac.uk</td>
  </tr>
  <tr>
    <td>Sophie Fielding</td>
    <td>British Antarctic Survey</td>
    <td>UK</td>
    <td>sof@bas.ac.uk</td>
  </tr>
  <tr>
    <td>Peer Fietzek</td>
    <td>Kongsberg Maritime</td>
    <td>Germany</td>
    <td>peer.fietzek@km.kongsberg.com</td>
  </tr>
  <tr>
    <td>Tonje Forland</td>
    <td>Institute of Marine Research</td>
    <td>Norway</td>
    <td>tonje.nesse.forland@hi.no</td>
  </tr>
  <tr>
    <td>Sven Gastauer</td>
    <td>Thünen Institute for Sea Fisheries</td>
    <td>Germany</td>
    <td>sven.gastauer@thuenen.de</td>
  </tr>
  <tr>
    <td>Stephane Gauthier</td>
    <td>Fisheries and Oceans Canada</td>
    <td>Canada</td>
    <td>stephane.gauthier@dfo-mpo.gc.ca</td>
  </tr>
  <tr>
    <td>Maxime Geoffroy</td>
    <td>Fisheries and Marine Institute of Memorial University of Newfoundland</td>
    <td>Canada</td>
    <td>maxime.geoffroy@mi.mun.ca</td>
  </tr>
  <tr>
    <td>Teresa Sofia Giesa da Silva</td>
    <td>Marine and Freshwater Research Institute</td>
    <td>Iceland</td>
    <td>teresa.silva@hafogvatn.is</td>
  </tr>
  <tr>
    <td>Silvana Gonzalez</td>
    <td>University of Washington</td>
    <td>US</td>
    <td>silgonz@uw.edu</td>
  </tr>
  <tr>
    <td>Natalia Gorska</td>
    <td>Institute of Oceanology Polish Academy of Science</td>
    <td>Poland</td>
    <td>gorska@iopan.pl</td>
  </tr>
</table>
<table>
  <tr>
    <td>Daniel Grados</td>
    <td>IMARPE</td>
    <td>Peru</td>
    <td>danny.grados@gmail.com</td>
  </tr>
  <tr>
    <td>Jerome Guiet</td>
    <td>UCLA</td>
    <td>US</td>
    <td>Jerome.c.guiet@gmail.com</td>
  </tr>
  <tr>
    <td>Mariano Gutierrez</td>
    <td>Humboldt Institute</td>
    <td>Peru</td>
    <td>msgutierrez@gmail.com</td>
  </tr>
  <tr>
    <td>Jeremie Habasque</td>
    <td>IRD</td>
    <td>France</td>
    <td>jeremie.habasque@ird.fr</td>
  </tr>
  <tr>
    <td>Nils Olav Handegard</td>
    <td>IMR</td>
    <td>Norway</td>
    <td>nilsolav@hi.no</td>
  </tr>
  <tr>
    <td>Daniel Hasselman</td>
    <td>Fundy Ocean Research Center for Energy</td>
    <td>Canada</td>
    <td>dan.hasselman@fundyforce.ca</td>
  </tr>
  <tr>
    <td>Hiroyuki Higashiyama</td>
    <td>FURUNO ELECTRIC CO., LTD.</td>
    <td>Japan</td>
    <td>hiroyuki.higashiyama@furuno.co.jp</td>
  </tr>
  <tr>
    <td>Taina Honkalehto</td>
    <td>NOAA Fisheries</td>
    <td>US</td>
    <td>Taina.Honkalehto@noaa.gov</td>
  </tr>
  <tr>
    <td>John Horne</td>
    <td>University of Washington</td>
    <td>US</td>
    <td>jhorne@uw.edu</td>
  </tr>
  <tr>
    <td>Lawrence Hufnagle</td>
    <td>NOAA Fisheries</td>
    <td>US</td>
    <td>lawrence.c.hufnagle@noaa.gov</td>
  </tr>
  <tr>
    <td>Briony Hutton</td>
    <td>Echoview Software</td>
    <td>Australia</td>
    <td>briony.hutton@echoview.com</td>
  </tr>
  <tr>
    <td>Ilysa Iglesias</td>
    <td>University of California, Santa Cruz and NOAA Southwest Fisheries Science Center</td>
    <td>US</td>
    <td>iiglesia@ucsc.edu</td>
  </tr>
  <tr>
    <td>Tomohito Imaizumi</td>
    <td>National Research Institute of Fisheries Engineering, Japan Fisheries Research and Education Agency</td>
    <td>Japan</td>
    <td>imat@affrc.go.jp</td>
  </tr>
  <tr>
    <td>Jan Arge Jacobsen</td>
    <td>Faroe Marine Research Institute</td>
    <td>Faroe Islands</td>
    <td>janarge@hav.fo</td>
  </tr>
  <tr>
    <td>Michael Jech</td>
    <td>NOAA/NEFSC</td>
    <td>US</td>
    <td>michael.jech@noaa.gov</td>
  </tr>
  <tr>
    <td>Teunis Jensen</td>
    <td>DTU Aqua</td>
    <td>Denmark</td>
    <td>tej@aqua.dtu.dk</td>
  </tr>
  <tr>
    <td>Espen Johnsen</td>
    <td>Institute of Marine Research</td>
    <td>Norway</td>
    <td>espen.johnsen@hi.no</td>
  </tr>
  <tr>
    <td>Jennifer Johnson</td>
    <td>WHOI</td>
    <td>US</td>
    <td>jjjohnson@whoi.edu</td>
  </tr>
  <tr>
    <td>Sigurður Jónsson</td>
    <td>Marine and Freshwater Research Institute, Reykjavík, Iceland</td>
    <td>Iceland</td>
    <td>sigurdur@hafro.is</td>
  </tr>
  <tr>
    <td>Rachel Kahn</td>
    <td>MIT-WHOI Joint Program</td>
    <td>US</td>
    <td>rkahn@whoi.edu</td>
  </tr>
  <tr>
    <td>Olavi Kaljuste</td>
    <td>Swedish University of Agricultural Sciences</td>
    <td>Sweden</td>
    <td>olavi.kaljuste@slu.se</td>
  </tr>
  <tr>
    <td>Zacharias Kapelonis</td>
    <td>Hellenic Centre for Marine research</td>
    <td>Greece</td>
    <td>zkapelonis@hcmr.gr</td>
  </tr>
  <tr>
    <td>Ailbhe Kavanagh</td>
    <td>Marine Institute</td>
    <td>Ireland</td>
    <td>ailbhe.kavanagh@marine.ie</td>
  </tr>
  <tr>
    <td>Babak Khodabandeloo</td>
    <td>Norwegian Institute of Marine Research</td>
    <td>Norway</td>
    <td>babak.khodabandeloo@hi.no</td>
  </tr>
  <tr>
    <td>Thor Klevjer</td>
    <td>IMR</td>
    <td>Norway</td>
    <td>thor.klevjer@hi.no</td>
  </tr>
  <tr>
    <td>Rolf Korneliussen</td>
    <td>Institute of Marine Research</td>
    <td>Norway</td>
    <td>rolf.korneliussen@hi.no</td>
  </tr>
</table>
<table>
  <tr>
    <td>Rokas Kubilius</td>
    <td>Institute of Marine Research</td>
    <td>Norway</td>
    <td>rokas@hi.no</td>
  </tr>
  <tr>
    <td>Erin LaBrecque</td>
    <td>US Marine Mammal Commission</td>
    <td>US</td>
    <td>erinlab@gmail.com</td>
  </tr>
  <tr>
    <td>Niklas Larson</td>
    <td>IMR, Aqua, SLU</td>
    <td>Sweden</td>
    <td>niklas.larson@slu.se</td>
  </tr>
  <tr>
    <td>Andone Lavery</td>
    <td>Woods Hole Oceanographic Institution</td>
    <td>US</td>
    <td>alavery@whoi.edu</td>
  </tr>
  <tr>
    <td>Naig Le Bouffant</td>
    <td>Ifremer</td>
    <td>France</td>
    <td>naig.le.bouffant@ifremer.fr</td>
  </tr>
  <tr>
    <td>Anne Lebourges-Dhaussy</td>
    <td>IRD</td>
    <td>France</td>
    <td>anne.lebourges.dhaussy@ird.fr</td>
  </tr>
  <tr>
    <td>Wu-Jung Lee</td>
    <td>University of Washington Applied Physics Laboratory</td>
    <td>US</td>
    <td>wjlee@apl.washington.edu</td>
  </tr>
  <tr>
    <td>Robert Levine</td>
    <td>University of Washington</td>
    <td>US</td>
    <td>leviner@uw.edu</td>
  </tr>
  <tr>
    <td>Lilian Leiber</td>
    <td>University Belfast Marine Laboratory</td>
    <td>UK</td>
    <td>L.Lieber@qub.ac.uk</td>
  </tr>
  <tr>
    <td>Scott Loranger</td>
    <td>Woods Hole Oceanographic Institution</td>
    <td>US</td>
    <td>sloranger@whoi.edu</td>
  </tr>
  <tr>
    <td>Zhen Lu</td>
    <td>postdoctoral researcher</td>
    <td>Japan</td>
    <td>roshin@affrc.go.jp</td>
  </tr>
  <tr>
    <td>Susan Lusseau</td>
    <td>DTU Aqua</td>
    <td>Denmark</td>
    <td>smalu@aqua.dtu.dk</td>
  </tr>
  <tr>
    <td>Gavin Macaulay</td>
    <td>Institute of Marine Research</td>
    <td>Norway</td>
    <td>gavin.macaulay@hi.no</td>
  </tr>
  <tr>
    <td>Nicholas Makris</td>
    <td>MIT</td>
    <td>US</td>
    <td>makris@mit.edu</td>
  </tr>
  <tr>
    <td>Alicia Maurice</td>
    <td>Ifremer</td>
    <td>France</td>
    <td>alicia.maurice@ifremer.fr</td>
  </tr>
  <tr>
    <td>Emilio Mayorga</td>
    <td>University of Washington</td>
    <td>US</td>
    <td>mayorga@apl.washington.edu</td>
  </tr>
  <tr>
    <td>Louise McGarry</td>
    <td>FORCE: Fundy Ocean Research Center for Energy</td>
    <td>Canada</td>
    <td>louise.mcgarry@fundyforce.ca</td>
  </tr>
  <tr>
    <td>David McGowan</td>
    <td>NOAA AFSC</td>
    <td>US</td>
    <td>david.mcgowan@noaa.gov</td>
  </tr>
  <tr>
    <td>Chelsey McGowan-Yallop</td>
    <td>Scottish Association for Marine Science</td>
    <td>UK</td>
    <td>sa06cm@sams.ac.uk</td>
  </tr>
  <tr>
    <td>Denise McKelvey</td>
    <td>Alaska Fisheries Science Center, NOAA Fisheries</td>
    <td>US</td>
    <td>denise.mckelvey@noaa.gov</td>
  </tr>
  <tr>
    <td>Satoshi Misonoo</td>
    <td>FURUNO ELECTRIC CO., LTD.</td>
    <td>Japan</td>
    <td>satoshi.misonoo@furuno.co.jp</td>
  </tr>
  <tr>
    <td>Hassan Moustahfid</td>
    <td>NOAA</td>
    <td>US</td>
    <td>hassan.moustahfid@noaa.gov</td>
  </tr>
  <tr>
    <td>Fran Mowbray</td>
    <td>Fisheries and Oceans Canada</td>
    <td>Canada</td>
    <td>Fran.Mowbray@dfo-mpo.gc.ca</td>
  </tr>
  <tr>
    <td>Miad Al Mursaline</td>
    <td>MIT/WHOI</td>
    <td>US</td>
    <td>mmursaline@whoi.edu</td>
  </tr>
  <tr>
    <td>Lauren Nelson</td>
    <td>IOTC</td>
    <td>Seychelles</td>
    <td>lauren.nelson@fao.org</td>
  </tr>
  <tr>
    <td>Miguel Nogueira</td>
    <td>FURUNO</td>
    <td>Spain</td>
    <td>miguel.nogueira@furuno.es</td>
  </tr>
  <tr>
    <td>Michael O'Malley</td>
    <td>Marine Institute</td>
    <td>Ireland</td>
    <td>Michael.O'Malley@Marine.ie</td>
  </tr>
  <tr>
    <td>Steven O'Connell</td>
    <td>Marine Scotland Science</td>
    <td>UK</td>
    <td>Steven.O'connell@gov.scot</td>
  </tr>
</table>
<table><tr><td>Ciaran O'Donnell</td><td>Marine Institute</td><td>Ireland</td><td>Ciaran.odonnell@marine.ie</td></tr><tr><td>Akira Okunishi</td><td>FURUNO ELECTRIC CO., LTD.</td><td>Japan</td><td>akira.okunishi@furuno.co.jp</td></tr><tr><td>Egil Ona</td><td>Institute of Marine Research</td><td>Norway</td><td>egil.ona@imr.no</td></tr><tr><td>Patricia Ordonez</td><td>ZUNIBAL SL</td><td>Spain</td><td>patricia.ordonez@zunibal.com</td></tr><tr><td>Egoitz Ormaechea</td><td>Zunibal</td><td>Spain</td><td>egoitz.ormaetxea@zunibal.com</td></tr><tr><td>Antonio Palermino</td><td>CNR-National Research Council, IRBIM-Institute for Marine Biological Resources and Biotechnologies</td><td>Italia</td><td>antonio.palermino@irbim.cnr.it</td></tr><tr><td>Sandra Parker-Stetter</td><td>NOAA-AFSC-MACE</td><td>US</td><td>sandy.parker-stetter@noaa.gov</td></tr><tr><td>Hjalte Parner</td><td>ICES</td><td>Denmark</td><td>hjalte.parner@gmail.com</td></tr><tr><td>Audun Oppedal Pedersen</td><td>University of Bergen</td><td>Norway</td><td>audun.pedersen@outlook.com</td></tr><tr><td>Geir Pedersen</td><td>Institute of Marine Research</td><td>Norway</td><td>geir.pedersen@hi.no</td></tr><tr><td>Heikki Peltonen</td><td>Finnish Environment Institute</td><td>Finland</td><td>heikki.peltonen@syke.fi</td></tr><tr><td>Hector Pena</td><td>Institute of Marine Research</td><td>Norway</td><td>hector.pena@hi.no</td></tr><tr><td>Marian Peña</td><td>IEO</td><td>Spain</td><td>marian.pena@ieo.es</td></tr><tr><td>Salvador Peraltilla</td><td>Peruvian National Fisheries Society</td><td>Peru</td><td>speraltilla@snp.org.pe</td></tr><tr><td>Vladislav Petrusevich</td><td>DFO, NAFC</td><td>Canada</td><td>vladislav.petrusevich@dfo-mpo.gc.ca</td></tr><tr><td>Elizabeth Phillips</td><td>NOAA</td><td>US</td><td>elizabeth.phillips@noaa.gov</td></tr><tr><td>Marissela Pozada</td><td>IMARPE</td><td>Peru</td><td>marissela.pozada.herrera@gmail.com</td></tr><tr><td>Roland Proud</td><td>University of St Andrews</td><td>UK</td><td>rp43@st-andrews.ac.uk</td></tr><tr><td>Madhu Ramakrishnan</td><td>ICAR-Central Institute of Fisheries Technology</td><td>India</td><td>madhucift@gmail.com</td></tr><tr><td>Aurore Receveur</td><td>Thuenen Institute</td><td>Germany</td><td>aurore.receveur@9online.fr</td></tr><tr><td>Frank Reier Knudsen</td><td>Kongsberg Maritime</td><td>Norway</td><td>frank.reier.knudsen@km.kongsberg.com</td></tr><tr><td>Josiah Renfree</td><td>NOAA</td><td>US</td><td>josiah.renfree@noaa.gov</td></tr><tr><td>Patrick Ressler</td><td>NOAA Alaska Fisheries Science Center</td><td>US</td><td>patrick.ressler@noaa.gov</td></tr><tr><td>Shani Rousseau</td><td>Fisheries and Oceans Canada</td><td>Canada</td><td>shani.rousseau@dfo-mpo.gc.ca</td></tr><tr><td>Tim Ryan</td><td>CSIRO</td><td>Australia</td><td>Tim.ryan@csiro.au</td></tr><tr><td>Serdar Sakinan</td><td>Wageningen Marine Research</td><td>Netherlands</td><td>serdar.sakinan@wur.nl</td></tr><tr><td>Julie Salvetat</td><td>UFRPE, IRD</td><td>France</td><td>ju.salvetat@gmail.com</td></tr></table>
<table><tr><td>Kouichi Sawada</td><td>Japan Fisheries Research and Education Agency, Fisheries Technology Institute</td><td>Japan</td><td>ksawada@fra.affrc.go.jp</td></tr><tr><td>Matthias Schaber</td><td>Thünen Institute of Sea Fisheries</td><td>Germany</td><td>matthias.schaber@thuenen.de</td></tr><tr><td>Ben Scoulding</td><td>CSIRO</td><td>Australia</td><td>ben.scoulding@csiro.au</td></tr><tr><td>Elor Sepp</td><td>Estonian Marine Institute</td><td>Estonia</td><td>elor.sepp@ut.ee</td></tr><tr><td>Jacob Sobin</td><td>Kongsberg Maritime</td><td>US</td><td>jacob.sobin@km.kongsberg.com</td></tr><tr><td>Valentina Staneva</td><td>University of Washington</td><td>US</td><td>vms16@uw.edu</td></tr><tr><td>Chelsea Stanley</td><td>Institute of Ocean Sciences</td><td>Canada</td><td>chelsea.stanley@dfo-mpo.gc.ca</td></tr><tr><td>Jonas Sundberg</td><td>Swedish University of Agricultural Sciences</td><td>Sweden</td><td>jonas.sundberg@slu.se</td></tr><tr><td>Chris Taylor</td><td>NOAA Ocean Service</td><td>US</td><td>Chris.Taylor@noaa.gov</td></tr><tr><td>Michala Techau</td><td>Strategic Innovation Ltd</td><td>UK</td><td>m.techau@strategic-innovation.co.uk</td></tr><tr><td>Rebecca Thomas</td><td>NOAA/NWFSC</td><td>US</td><td>rebecca.thomas@noaa.gov</td></tr><tr><td>Verena Trenkel</td><td>Ifremer</td><td>France</td><td>verena.trenkel@ifremer.fr</td></tr><tr><td>Sam Urmy</td><td>NOAA Alaska Fisheries Science Center</td><td>US</td><td>sam.urmy@noaa.gov</td></tr><tr><td>Jeroen Van Der Kooij</td><td>Cefas</td><td>UK</td><td>jeroen.vanderkooij@cefas.co.uk</td></tr><tr><td>Gary Vargas</td><td>Universidade Federal Rural de Pernambuco</td><td>Peru</td><td>garyrvc@gmail.com</td></tr><tr><td>Carrie Wall</td><td>University of Colorado Boulder / NOAA NCEI</td><td>US</td><td>carrie.wall@noaa.gov</td></tr><tr><td>Ivar Wangen</td><td>Kongsberg Maritime</td><td>Norway</td><td>ivar.wangen@simrad.com</td></tr><tr><td>Joseph Warren</td><td>Stony Brook University</td><td>US</td><td>joe.warren@stonybrook.edu</td></tr><tr><td>David Warwick</td><td>Seafish</td><td>UK</td><td>david.warwick@seafish.co.uk</td></tr><tr><td>Tim Whitton</td><td>Bangor University</td><td>UK</td><td>t.whitton@bangor.ac.uk</td></tr><tr><td>Alina Wieczorek</td><td>Marine Institute Ireland</td><td>Ireland</td><td>Alina.Wieczorek@marine.ie</td></tr><tr><td>Jinshan Xu</td><td>Fisheries and Oceans of Canada</td><td>Canada</td><td>jinshan.xu@dfo-mpo.gc.ca</td></tr><tr><td>Yawen Zhang</td><td>University of Colorado Boulder</td><td>China</td><td>yawen.zhang@colorado.edu</td></tr><tr><td>Zhaozhong Zhuang</td><td>MIT-WHOI</td><td>US</td><td>zzhuang@whoi.edu</td></tr><tr><td>Aleksander Żytko</td><td>Institute of Oceanology, Polish Academy of Science</td><td>Poland</td><td>azytko@iopan.pl</td></tr></table>
Meeting 2020

<table><thead><tr><td>Name</td><td>Institute</td><td>Country (of institute)</td><td>E-mail</td></tr></thead><tbody><tr><td>Alejandro Ariza</td><td>British Antarctic Survey</td><td>UK</td><td>aletea@bas.ac.uk</td></tr><tr><td>Alex de Robertis</td><td>Alaska Fisheries Science Center</td><td>US</td><td>Alex.DeRobertis@noaa.gov</td></tr><tr><td>Alireza Rezvanifar</td><td>University of Victoria</td><td>Canada</td><td>arezvani@uvic.ca</td></tr><tr><td>Andone C. Lavery</td><td>Woods Hole Oceanographic Institution</td><td>US</td><td>alavery@whoi.edu</td></tr><tr><td>Anne Lebourges-Dhaussy</td><td>L'IRD en Bretagne</td><td>France</td><td>Anne.Lebourges.Dhaussy@ird.fr</td></tr><tr><td>Aquiles Sepulveda</td><td>Instituto de Investigación Pesquera</td><td>Chile</td><td>asepulveda@inpesca.cl</td></tr><tr><td>Arnaud Bertrand</td><td>Centre de Recherche Halieutique Méditerranée et Tropicale</td><td>France</td><td>arnaud.bertrand@ird.fr</td></tr><tr><td>Arthur Blanluet</td><td>Ifremer Nantes Centre</td><td>France</td><td>arthur.blanluet@ifremer.fr</td></tr><tr><td>Ben Scoulding</td><td>CSIRO Marine and Atmospheric Research</td><td>Australia</td><td>ben.scoulding@csiro.au</td></tr><tr><td>Benoit Berges</td><td>Wageningen University &amp; Research</td><td>Netherlands</td><td>benoit.berges@wur.nl</td></tr><tr><td>Birkir Bardarson</td><td>Marine and Freshwater Research Institute</td><td>Iceland</td><td>birkir.bardarson@hafogvatn.is</td></tr><tr><td>Carlos Robinson</td><td>Instituto de Ciencias del Mar y Limnologia UNAM</td><td>Mexico</td><td>robmen@servidor.unam.mx</td></tr><tr><td>Carrie Wall</td><td>Cooperative Institute for Research in Environmental Sciences</td><td>US</td><td>carrie.wall@noaa.gov</td></tr><tr><td>Charles Anderson</td><td>National Oceanic and Atmospheric Administration (NOAA)</td><td>US</td><td>charles.anderson@noaa.gov</td></tr><tr><td>Christopher Taylor</td><td>Southeast Fisheries Science Center</td><td>US</td><td>Chris.Taylor@noaa.gov</td></tr><tr><td>Ciaran O'Donnell</td><td>Marine Institute</td><td>Ireland</td><td>Ciaran.ODonnell@Marine.ie</td></tr><tr><td>Daniel Grados</td><td>Instituto del Mar del Peru</td><td>Peru</td><td>dgrados@imarpe.gob.pe</td></tr><tr><td>Danny Copland</td><td>Marine Science Scotland</td><td>UK</td><td>danny.copland@gov.scot</td></tr><tr><td>David Demer</td><td>Southwest Fisheries Science Center</td><td>US</td><td>david.demer@noaa.gov</td></tr><tr><td>David McGowan</td><td>University of Washington</td><td>US</td><td>mcgowand@uw.edu</td></tr><tr><td>Denise McKelvey</td><td>Alaska Fisheries Science Center</td><td>US</td><td>Denise.mckelvey@noaa.gov</td></tr><tr><td>Dezhang Chu</td><td>Northwest Fisheries Science Center</td><td>US</td><td>dezhang.chu@noaa.gov</td></tr><tr><td>Egoitz Burggraaf</td><td>Zunibal, S.L.</td><td>Spain</td><td>egoitz.ormaetxea@zunibal.com</td></tr><tr><td>Elizabeth Phillips</td><td>University of Washington</td><td>US</td><td>emp11@uw.edu</td></tr><tr><td>Erin LaBrecque</td><td>Independent Consultant</td><td>US</td><td>erinlab@gmail.com</td></tr></tbody></table>
<table><tr><td>Fabio Campanella</td><td>Centre for Environment, Fisheries and Aquaculture Science</td><td>US</td><td>fabio.campanella@cefas.co.uk</td></tr><tr><td>Fletcher Thompson</td><td>DTU Aqua, National Institute of Aquatic Resources</td><td>Denmark</td><td>fletho@aqua.dtu.dk</td></tr><tr><td>Geir Pedersen</td><td>Institute of Marine Research</td><td>Norway</td><td>geir.pedersen@hi.no</td></tr><tr><td>Guillermo Boyra</td><td>AZTI-Tecnalia</td><td>Spain</td><td>gboyra@azti.es</td></tr><tr><td>Haris Kunnath</td><td>CSIRO Marine and Atmospheric Research</td><td>Australia</td><td>haris.kunnath@csiro.au</td></tr><tr><td>Hassan Moustahfid</td><td>NOAA Integrated Ocean Observing System (IOOS)</td><td>US</td><td>Hassan.Moustahfid@noaa.gov</td></tr><tr><td>Hector Peña</td><td>Institute of Marine Research</td><td>Norway</td><td>hector.pena@hi.no</td></tr><tr><td>Hjalte Parner</td><td>ICES Secretariat</td><td>Denmark</td><td>hjalte@ices.dk</td></tr><tr><td>Ilysa Iglesias</td><td>University of California Santa Cruz</td><td>US</td><td>ilysa.iglesias@noaa.gov</td></tr><tr><td>Ivar Wangen</td><td>Simrad</td><td>Norway</td><td>ivar.wangen@simrad.com</td></tr><tr><td>J. Michael Jech</td><td>NOAA Fisheries</td><td>US</td><td>michael.jech@noaa.gov</td></tr><tr><td>Jan Buermans</td><td>ASL Environmental Sciences Inc.</td><td>Canada</td><td>jbuermans@aslenv.com</td></tr><tr><td>Jeff Condiotty</td><td>Simrad Fisheries</td><td>US</td><td>jeff.condiotty@simrad.com</td></tr><tr><td>Jennifer Johnson</td><td>University of New Hampshire</td><td>US</td><td>jenniferj@ccom.unh.edu</td></tr><tr><td>Jenni McDermid</td><td>Fisheries and Oceans Canada Charlotte-town</td><td>Canada</td><td>Jenni.McDermid@dfo-mpo.gc.ca</td></tr><tr><td>Jeremie Habasque</td><td>French National Research Institute for Sustainable Development</td><td>France</td><td>jeremie.habasque@ird.fr</td></tr><tr><td>John Horne</td><td>University of Washington</td><td>US</td><td>jhorne@u.washington.edu</td></tr><tr><td>Jose Rojas</td><td>Austral Group S.A.A. Peru</td><td>Peru</td><td>jrojas@austral.com.pe</td></tr><tr><td>Joseph Warren</td><td>Stony Brook University</td><td>US</td><td>joe.warren@stonybrook.edu</td></tr><tr><td>Julek Chawarski</td><td>Memorial University of Newfoundland</td><td>US</td><td>Julek.Chawarski@mi.mun.ca</td></tr><tr><td>Kathryn Gavira O'Neill</td><td>Satlink</td><td>Spain</td><td>kgo@satlink.es</td></tr><tr><td>Kevin M. Boswell</td><td>Florida International University</td><td>US</td><td>kevin.boswell@fiu.edu</td></tr><tr><td>Kjetil Gjeitsund Thorvaldsen</td><td>DTU Aqua, National Institute of Aquatic Resources</td><td>Denmark</td><td>kjgth@aqua.dtu.dk</td></tr><tr><td>Lars Nonboe Andersen</td><td>Simrad</td><td>Norway</td><td>lars.nonboe.andersen@simrad.com</td></tr><tr><td>Laurent Berger</td><td>Ifremer Centre de Brest</td><td>France</td><td>Laurent.Berger@ifremer.fr</td></tr><tr><td>Lawrence Hufnagle</td><td>Northwest Fisheries Science Center</td><td>US</td><td>Lawrence.C.Hufnagle@noaa.gov</td></tr><tr><td>Lilian Lieber</td><td>Queen's University Belfast</td><td>UK</td><td>l.lieber@qub.ac.uk</td></tr></table>
<table><tr><td>Lucio Calise</td><td>Zunibal, S.L.</td><td>Spain</td><td>lucio.calise@zunibal.com</td></tr><tr><td>Lu Guan</td><td>University of Victoria</td><td>Canada</td><td>Iguan@uvic.ca</td></tr><tr><td>Marian Peña</td><td>Centro Oceanografico de Baleares</td><td>Spain</td><td>marian.pena@ieo.es</td></tr><tr><td>Mariano Gutierrez Torero</td><td>Humboldt Institute of Marine and Aquaculture Research</td><td>Peru</td><td>msgutierrez@gmail.com</td></tr><tr><td>Marissela Pozada</td><td>Instituto del Mar del Peru</td><td>Peru</td><td>mpozada@imarpe.gob.pe</td></tr><tr><td>Mathieu Doray</td><td>Ifremer Nantes Centre</td><td>France</td><td>Mathieu.Doray@ifremer.fr</td></tr><tr><td>Matthias Schaber</td><td>Institute of Sea Fisheries</td><td>Germany</td><td>matthias.schaber@thuenen.de</td></tr><tr><td>Maxime Geoffroy</td><td>Memorial University of Newfoundland</td><td>Canada</td><td>Maxime.Geoffroy@mi.mun.ca</td></tr><tr><td>Mei Sato</td><td>College of Earth, Ocean and Atmospheric Sciences</td><td>US</td><td>msato@coas.oregonstate.edu</td></tr><tr><td>Miles Parsons</td><td>Center for Marine Science and Technology</td><td>Australia</td><td>miles.parsons@student.curtin.edu.au</td></tr><tr><td>Muriel Barbara Dunn</td><td>Akvaplan-niva AS/ Polar Environmental Center</td><td>Norway</td><td>mbd@akvaplan.niva.no</td></tr><tr><td>Naig Le Bouffant</td><td>Ifremer Centre de Brest</td><td>France</td><td>Naig.Le.Bouffant@ifremer.fr</td></tr><tr><td>Natalia Gorska</td><td>University of Gdańsk</td><td>Poland</td><td>natalia.gorska@univ.gda.pl</td></tr><tr><td>Nicolás Algeria Landeros</td><td>Institutio de Investigacion Pesquera</td><td>Chile</td><td>nicoalegria1@gmail.com</td></tr><tr><td>Nils Olav Handegard</td><td>Institute of Marine Research</td><td>Norway</td><td>nils.olav handegard@hi.no</td></tr><tr><td>Olavi Kaljuste</td><td>Institute of Coastal Research</td><td>Sweden</td><td>olavi.kaljuste@slu.se</td></tr><tr><td>Pablo Carrera</td><td>Centro Oceanografico de Vigo</td><td>Spain</td><td>pablo.carrera@ieo.es</td></tr><tr><td>Pablo Escobar-Flores</td><td>NIWA Wellington</td><td>New Zealand</td><td>pablo.escobar-flores@niwa.co.nz</td></tr><tr><td>Patrice Brehmer</td><td>L'IRD en Bretagne</td><td>France</td><td>patrice.brehmer@ird.fr</td></tr><tr><td>Patricia Ordoñez</td><td>Polytechnic University of Valencia</td><td>Spain</td><td>patricia.ordonez@zunibal.com</td></tr><tr><td>Patrick Schneider</td><td>AQUASON</td><td>Spain</td><td>patrick@aquason.com</td></tr><tr><td>Paul Fernandes</td><td>University of Aberdeen</td><td>UK</td><td>fernandes pg@abdn.ac.uk</td></tr><tr><td>Pilar Cordoba</td><td>Instituto Espanol de Oceanografia</td><td>Spain</td><td>pilar.cordoba@ieo.es</td></tr><tr><td>Rebecca Thomas</td><td>Northwest Fisheries Science Center</td><td>US</td><td>rebecca.thomas@noaa.gov</td></tr><tr><td>Richard O'Driscoll</td><td>NIWA Wellington</td><td>New Zealand</td><td>richard.odriscoll@niwa.co.nz</td></tr><tr><td>Rika Shiraki</td><td>Furuno Espana</td><td>Japan</td><td>rika.shiraki.hc@furuno.co.jp</td></tr><tr><td>Rokas Kubilius</td><td>Institute of Marine Research</td><td>Norway</td><td>rokas.kubilius@hi.no</td></tr></table>
<table><tr><td>Rolf Korneliussen</td><td>Institute of Marine Research</td><td>Norway</td><td>rolf.korneliussen@hi.no</td></tr><tr><td>Rudy Kloser</td><td>CSIRO Marine and Atmospheric Research</td><td>Australia</td><td>rudy.kloser@csiro.au</td></tr><tr><td>Ryan Downie</td><td>CSIRO Marine and Atmospheric Research</td><td>Australia</td><td>ryan.downie@csiro.au</td></tr><tr><td>Salvador Peraltilla Neyra</td><td>Instituto del Mar del Peru</td><td>Peru</td><td>speraltilla@snp.org.pe</td></tr><tr><td>Sandra Parker-Stetter</td><td>Northwest Fisheries Science Center</td><td>US</td><td>sandy.parker-stetter@noaa.gov</td></tr><tr><td>Sebastien de Hal-leux</td><td>Saildrone Inc.</td><td>US</td><td>sebastien@saildrone.com</td></tr><tr><td>Sigurdur Thor Jónsson</td><td>Marine and Freshwater Research Institute</td><td>Iceland</td><td>sigurdur.thor.jonsson@hafogvatn.is</td></tr><tr><td>Stephane Gauthier</td><td>Institute of Ocean Sciences</td><td>Canada</td><td>stephane.gauthier@dfo-mpo.gc.ca</td></tr><tr><td>Stephen Brandt</td><td>Department of Fisheries and Wildlife</td><td>US</td><td>Stephen.brandt@oregonstate.edu</td></tr><tr><td>Steve Pearce</td><td>ASL Environmental Sciences Inc.</td><td>Canada</td><td>spearce@aslenv.com</td></tr><tr><td>Sophie Fielding</td><td>British Antarctic Survey</td><td>UK</td><td>s.fielding@bas.ac.uk</td></tr><tr><td>Susan Mærsk Lus-seau</td><td>Marine Laboratory</td><td>UK</td><td>s.lusseau@marlab.ac.uk</td></tr><tr><td>Sven Gastauer</td><td>Center for Marine Science and Technology</td><td>Australia</td><td>sven.gastauer@postgrad.curtin.edu.au</td></tr><tr><td>Tim Acker</td><td>BioSonics Inc.</td><td>US</td><td>tacker@biosonicsinc.com</td></tr><tr><td>Tim Ryan</td><td>CSIRO Marine and Atmospheric Research</td><td>Australia</td><td>Tim.Ryan@csiro.au</td></tr><tr><td>Timothy Whitton</td><td>School of Ocean Sciences</td><td>UK</td><td>t.whitton@bangor.ac.uk</td></tr><tr><td>Toby Jarvis</td><td>Southern Ocean Ecosystems Program</td><td>Australia</td><td>toby.jarvis@echoview.com</td></tr><tr><td>Tonny Algroy</td><td>Simrad</td><td>Norway</td><td>tonny.algroy@simrad.com</td></tr><tr><td>Tunia Porto Marques</td><td>University of Victoria</td><td>Canada</td><td>tunaip@uvic.ca</td></tr><tr><td>Wu-Jung Lee</td><td>University of Washington</td><td>US</td><td>wjlee@apl.washington.edu</td></tr><tr><td>Zacharias Kapelo-nis</td><td>Institute of Marine Biological Resources and Inland Waters</td><td>Greece</td><td>zkapelonis@hcmr.gr</td></tr></table>

Because of the remote meeting, we only have the participants Webex login name and their e-mail address for some participants.

<table><caption>List of participants: guests to the 2020 WGFAST meeting</caption><thead><tr><td>Name</td><td>Institute</td><td>Country</td><td>E-mail</td></tr></thead><tbody><tr><td>Adrian Ibieta</td><td>Fisheries Development Institute (IFOP)</td><td>Chile</td><td>adrian.ibieta@ifop.cl</td></tr><tr><td>Aron Paz</td><td></td><td></td><td>Aron.pazv@gmail.com</td></tr><tr><td>Camilo</td><td></td><td></td><td>croa@fiu.edu</td></tr></tbody></table>
<table><tr><td>Carlos Valdez Mego</td><td></td><td></td><td>car-losvaldezmego@gmail.com</td></tr><tr><td>Collins Onyongo On-gore</td><td>Kenya Marine Fisheries Institute</td><td>Kenya</td><td>collongore@gmail.com</td></tr><tr><td>Cyrille Poncelet</td><td>Ifremer</td><td>France</td><td>cyrille.poncelet@ifremer.fr</td></tr><tr><td>Isobel Cave</td><td>Saildrone Inc.</td><td>US</td><td>isobel.cave@saildrone.com</td></tr><tr><td>Jennifer Herbig</td><td>Marine Institute of Memorial University of Newfoundland</td><td>Canada</td><td>jennifer.herbig@mi.mun.ca</td></tr><tr><td>Jinshan Xu</td><td>Fisheries and Oceans Canada</td><td>Canada</td><td>jinshan.xu@dfo-mpo.gc.ca</td></tr><tr><td>Jsalveta</td><td></td><td></td><td>Ju.salvetat@gmail.com</td></tr><tr><td>Kimberly Sparling</td><td>Saildrone Inc</td><td>US</td><td>kim@saildrone.com</td></tr><tr><td>Luis</td><td></td><td></td><td>Lacruz.luis2886@gmail.com</td></tr><tr><td>Marta D'Elia</td><td>Florida International University</td><td>US</td><td>madelia@fiu.edu</td></tr><tr><td>Pierre Priou</td><td>Marine Institute of Memorial University of Newfoundland</td><td>Canada</td><td>pierre.priou@mi.mun.ca</td></tr><tr><td>Ramilla Assuncao</td><td></td><td></td><td>ramillavieira@hotmail.com</td></tr><tr><td>Richard Mangeni Sand</td><td></td><td></td><td>Rms28@st-andrews.ac.uk</td></tr><tr><td>Savannah LaBua</td><td>Florida International University</td><td>US</td><td>slabua92@gmail.com</td></tr><tr><td>SG Conti</td><td></td><td></td><td>Stephane.conti@espci.org</td></tr><tr><td>Youssouph Coly</td><td>University of Ziguinchor (UASZ)</td><td>Senegal</td><td>Youssou-phcoly98@gmail.com</td></tr><tr><td>Yang</td><td>University of St. Andrews</td><td>UK</td><td>yyy1@st-andrews.ac.uk</td></tr></table>
# Annex 2: Resolutions

## Working Group on Fisheries Acoustics, Science and Technology (WGFAST)

2019/FT/EOSG09 A Working Group on Fisheries Acoustics, Science and Technology (WGFAST), chaired by J. Michael Jech, USA, will work on ToRs and generate deliverables as listed in the Table below.

<table><thead><tr><td></td><td>Meeting dates</td><td>Venue</td><td>Reporting details</td><td>Comments (change in Chair, etc.)</td></tr></thead><tbody><tr><td>Year 2020</td><td>22 April</td><td>Online meeting</td><td>Interim report by 22 May 2020 to ACOM-SCICOM</td><td>Michael Jech takes over as chair</td></tr><tr><td>Year 2021</td><td>19-23 April</td><td>Online meeting</td><td>Interim report by 30 June 2021 to ACOM-SCICOM</td><td></td></tr><tr><td>Year 2022</td><td>25-28 April</td><td>Dakar, Senegal</td><td>Final report by 30 June 2022 to ACOM-SCICOM</td><td></td></tr></tbody></table>

### ToR descriptors

<table><thead><tr><td>ToR</td><td>Description</td><td>Background</td><td>Science Plan codes</td><td>Duration</td><td>Expected Deliverables</td></tr></thead><tbody><tr><td>a</td><td>Collate information on acoustic related research and surveys, and interactions with ecosystem and assessment expert groups.</td><td>a) Science Requirements<br/>b) Advisory Requirements<br/>A summary of the information will be presented in the final report</td><td>3.1, 3.2, 3.4</td><td>3</td><td></td></tr><tr><td>b</td><td>Review presented recent work within the topics: "Acoustic methods to characterize populations, ecosystems, habitat, and behaviour"; "Acoustic characterization of marine organisms"; and "Emerging technologies, methodologies, and protocols". Provide guidance by identifying: (1) where training opportunities could be developed; and (2) gaps in knowledge and challenges that should be prioritized by the community.</td><td>Create a venue for informing the group members on recent activities and seeking input to further development. An overview of the different contributions and guidance will be presented in the annual report</td><td>3.3, 4.1, 4.4</td><td>1, 2, 3</td><td></td></tr><tr><td>c</td><td>Organize a conference session on integrating fisheries acoustics with ecosystem assessment and monitoring at an international scientific meeting such as ASC.</td><td></td><td>3.1, 3.2, 4.1</td><td>2 or 3</td><td></td></tr></tbody></table>
<table><tr><td>d</td><td>Develop, and maintain acoustic metadata and data format conventions and coordinate with acoustic survey groups.</td><td>Data format conventions for acoustic metadata and data are required for efficient data interchange and processing of acoustic data, but are lacking in the fisheries acoustics field. CRR 341 (2018) and SISP 4 (2016) have partially addressed this need, but further types of data and acoustic equipment need to be supported.</td><td>3.2, 3.5, 4.2</td><td>1, 2, 3</td><td>Updated metadata convention publication (new guide/handbook series)<br>Revised sonar-netcdf4 convention publication that includes echosounder data (new guide/handbook series)</td></tr><tr><td>e</td><td>Develop and recommend procedures for collecting and processing quality acoustic data in inclement weather.</td><td>Acoustic data are collected from a variety of vessels that respond to inclement weather in diverse ways. Procedures are needed to provide quality control for data collected in inclement weather to stock assessment.</td><td>3.3, 3.6</td><td>1</td><td>CRR; recommendations on methodology improvements to acoustic survey coordination groups to implement on surveys and update SISPs</td></tr></table>

## Summary of the Work Plan

<table><tr><td>Year 1</td><td>Produce the annual overview of recent developments within the field. Produce an ICES CRR recommending procedures for collecting and processing quality acoustic data in inclement weather. Develop and maintain metadata and acoustic data formats.</td></tr><tr><td>Year 2</td><td>Produce the annual overview of recent developments within the field. Propose a conference session at an international scientific meeting. Develop and maintain metadata and acoustic data formats.</td></tr><tr><td>Year 3</td><td>Produce the annual overview of recent developments within the field. Collate information on acoustic related research and surveys. Develop and maintain metadata and acoustic data formats. Publish new guides with updated metadata convention and revised sonar-netcdf4 convention publication that includes echosounder data.</td></tr></table>

## Supporting information

<table><tr><td>Priority</td><td>Fisheries acoustics and complementary technologies provide the necessary tools and methods to implement the ecosystem approach to fisheries management within ICES and research into their application and further development is vital.</td></tr><tr><td>Justification for venue 2022 (in non-ICES member country)</td><td>WGFAST has a long and rich history of collaborating with our West African partners, and hosting a meeting in Senegal will facilitate the participation of scientists from Africa (particularly West Africa and the south Mediterranean area), improve the exchange of science and communication on Fisheries Acoustics, Science and Technology between European and African colleagues, and promote the UN Ocean decade initiative. We expect to recruit several new members to WGFAST and even at higher levels, gain new "observatory" countries for ICES in Africa.</td></tr><tr><td>Resource requirements</td><td>No new resources will be required. Having overlaps with the other meetings of the Working, Planning, Study and Topic Groups increases efficiency and reduces travel costs.</td></tr><tr><td>Participants</td><td>The Group is normally attended by some 60-100 members and guests.</td></tr><tr><td>Secretariat facilities</td><td>None.</td></tr><tr><td>Financial</td><td>No financial implications.</td></tr></table>
<table><tr><td>Linkages to ACOM and groups under ACOM</td><td>Stock assessment groups using acoustic abundance indices.</td></tr><tr><td>Linkages to other committees or groups</td><td>The work in this group is closely aligned with complementary work in the FTFB Working Group. The work is of direct relevance to a number of data collection and coordination groups within EOSG (e.g. WGIPS, WGBITS, WGISUR)</td></tr><tr><td>Linkages to other organizations</td><td>The work of this group is closely aligned with similar work in FAO, the Acoustical Society of America, the South Pacific Regional Fisheries Management Organization, the Commission for the Conservation of Antarctic Marine Living Resources, and the American Fisheries Society.</td></tr></table>
# Annex 3: Presentation Abstracts

## Acoustic Methods to Characterize Populations, Ecosystems, Habitat, and Behaviour

### Probabilistic school classification of multiple species in acoustic echograms based on machine learning

A. Lekanda<fnref n="1" />, G. Boyra<fnref n="1" /> and M. Louzao<fnref n="1" />

¹ AZTI, Txatxarramendi Ugartea z/g, 48395 Sukarrieta (Bizkaia, Spain), e-mail: alekanda@azti.es

Multifrequency trawl-acoustic surveys are used worldwide for continuous monitoring of pelagic ecosystems. Acoustic backscattering energy partitioning in different species is typically done by visual scrutiny of the echograms with the aid of trawl species composition, which may be subjective and time-consuming. Alternatively, machine learning techniques may provide well-stabilized, objective, and reproducible methods for automatic school classification in acoustic echograms. The pelagic ecosystem is a diverse one, where many species co-occur in space and time, being mixed catches very common during scientific surveys. However, most of the school classification models are built using single species composition trawls due to difficulties to assign a class to each school in multispecific trawls. The present study has the aim of developing and comparing different probabilistic multivariate models to identify pelagic species in mixed scenarios based on trawl catch proportions. In addition to the standard predictors, a novel variable, collective mean TS per nautical mile measured on the periphery of the schools, has shown to play an important role in species discrimination. The methods were applied on data from 7 consecutive years of an acoustic survey in the Bay of Biscay. Preliminary results yielded classification performances near 95 % in classifying 10 different pelagic species.

### Acoustics surveys in North-West Africa reveal a spatial shift of small pelagic fish related to intense warming

Abdoulaye SARRE<fnref n="1" />, Hervé DEMARCQ<fnref n="2" />, Noel KEENLYSIDE<fnref n="3" />, Jens-Otto KRAKSTAD<fnref n="4" />, Salaheddine EL AYOUBI<fnref n="5" />, Ahmed Mohamed JEYID<fnref n="6" />, Saliou FAYE<fnref n="1" />, Adama MBAYE<fnref n="1" />, Momodou SIDIBEH<fnref n="7" />, Patrice BREHMER<fnref n="8" />,<fnref n="1" />

In the southern part of the CCLME, northward shifts in the distribution of sardinella and other species have been attributed to an intense warming trend in sea surface temperature. Such

<footnote n="1">¹ISRA, Centre de Recherches Océanographiques de Dakar-Thiaroye, CRODT, BP 2241, Dakar, Sénégal</footnote>

<footnote n="2">²IRD, Institut de Recherche pour le Développement, UMR Marbec, Centre de Sète, Avenue Jean Monnet, CS 30171, 34203 Sète cedex, France;</footnote>

<footnote n="3">³Geophysical Institute, Univ of Bergen and Bjerknes Centre for Climate Research, Norway;</footnote>

<footnote n="4">⁴Institute of Marine Research, P.O. Box 1870 Nordnes, N-5817 Bergen, Norway;</footnote>

<footnote n="5">⁵INRH, Institut National de Recherche Halieutique, Casablanca, Maroc;</footnote>

<footnote n="6">⁶Institut Mauritanien de Recherche Océanographique et des Pêches (IMROP), BP 22, Nouadhibou, Mauritanie,</footnote>

<footnote n="7">⁷Fisheries Department (FD), Banjul, The Gambia;</footnote>

<footnote n="8">⁸IRD, Univ Brest, CNRS, Ifremer, LEMAR, Dakar, Sénégal</footnote>
warming is higher than 0.5 °C per decade in the southern part of the CCLME, the greatest increase in SST observed in the tropical Atlantic. The acoustics abundance of *Sardinella aurita*, the most abundant species along the coast, has increased in the subtropics and fallen in the inter-tropical region. Small pelagic acoustics assessment surveys confirm a robust northward shift of around 180 km per decade in *S. aurita* habitat, while *S. maderensis* did not move significantly. Spatial shifts in biomass from 70 to 230 kilometres were observed for six others exploited small pelagic species during the last 20 years, at similar ranges to those recorded for surface isotherms in their habitat. The change occurs more quickly in the central part of the CCLME. This shift widely overlaps national boundaries and combined to overexploitation adds a new threat on the pelagic fish resources. Such results are an advocacy to continue to lead acoustics survey on small pelagic in the West Africa.

## Spatiotemporal variability of micronekton at two fronts in the central North Pacific

R. Domokos

Pacific Islands Fisheries Science Center, NMFS, NOAA, 1845 Wasp Blvd, Bldg. 176, Honolulu, Hawaii,
96819, USA.

The North Pacific Subtropical Frontal Zone (STFZ) seasonally aggregates economically important fish and protected species, hypothesized in results of enhanced prey biomass due to convergence at the Subtropical Front (STF) and a sharp northwards increase in primary productivity, the Transition Zone Chlorophyll Front (TZCF), both prominent in the STFZ. Given existing data gaps, characteristics of micronekton, forage for top predators, were investigated using multi-frequency active acoustics and the effects of STF and TZCF accessed from a combination of *in situ* and satellite environmental data. Results of this study show a significant increase in micronekton biomass across the STF with differing taxonomic composition from south to its north. The Pacific Decadal Oscillation as well as mesoscale events and subsurface processes were indicated to play important roles in affecting micronekton distribution and/or biomass. The largescale 2014-2017 extreme warming event positively corresponded with micronekton biomass and changes in its composition in the region, findings that are in agreement with expectations. Results of this work highlight the importance of our need to further our understanding of the role of largescale variability, extreme events, and subsurface processes on micronekton in the region's ecosystem to improve management of our living marine resources.

## Development of a hydroacoustic technique for determination of the orientation of aggregated Baltic herring

A. Żytko<fnref n="1" />, N. Gorska<fnref n="2" />, D. Chu<fnref n="3" />, and B. Schmidt<fnref n="4" />

The spatial distribution of fish orientation is a very important factor influencing their target strength (TS), and thus the hydroacoustic assessment of fish biomass. A method is being developed to estimate the orientation distribution of the Baltic herring in schools by comparing the measured herring TS histograms with the TS histograms obtained from the theoretical backscattering model. The target strength data were collected by the National Marine Fisheries Research

<footnote n="1">¹Institute of Oceanology PAS, Powstańców Warszawy 55, 81-712 Sopot, Poland, azytko@iopan.pl;</footnote>

<footnote n="2">²Institute of Oceanology PAS, Powstańców Warszawy 55, 81-712 Sopot, Poland, gorska@iopan.pl;</footnote>

<footnote n="3">³NOAA Fisheries, Northwest Fisheries Science Center, 2725 Montlake Blvd. E., Seattle, WA 98112, USA;</footnote>

<footnote n="4">⁴National Marine Fisheries Research Institute, ul. Kołtaja 1, 81-332 Gdynia, Poland, bschmidt@mir.gdynia.pl;</footnote>
Institute in Gdynia (R/V *Baltica*) in the Polish component of the Baltic International Acoustic Survey (*BIAS*) surveys under the EU Data Collection Framework (CDF). We employed a modified resonance scattering model to describe backscattering by herring swimbladders, using available morphometry data of Baltic Herring. Target strength histograms are generated based on the scattering models for different probability density functions of fish orientation, and are then compared with the measured TS histograms. Based on the similarity of the histograms, the most likely distribution of herring orientation can be inferred.

## Can target strength research improve acoustic survey indices in the Baltic?

N. Larson

SLU, Sweden

This study analyses the effects of target strength on the acoustic indices of abundance produced for stock assessment. The BIAS and BASS surveys produce indices of abundance for herring and sprat in the Baltic Sea, based on a $b_{20}$ value of -71,2 for both these two species and stickleback. The three species together constitute more than 99% of the abundance. Using published $b_{20}$ values from the Baltic Sea, different indices of abundance were calculated. Then, systematic changes were made to these $b_{20}$ values and new indices were created, which were compared with the original indices, in order to analyse the tolerance of a wrong target strength. Results show that changing one or two $b_{20}$ to a more accurate value can produce a bigger error than keeping the $b_{20}$ values used in BIAS. Furthermore, the difference in $b_{20}$ between the species in the community has greater influence on index error than the difference between the TS found in the literature and the TS used in BIAS. We propose a more ecological approach to the study of target strength, with a focus on investigating the difference in TS between the species, instead of solely studying TS for individual species. We discuss as well how to best introduce a new TS value in a running time series when recalculation of the whole time series is impossible.

## Hiding in plain sight: Predator avoidance behaviour of mesopelagic fish during foraging

K.G Thorvaldsen<fnref n="1" />, S. Neuenfeldt<fnref n="1" />, P. Mariani<fnref n="1" />, and J.R. Nielsen<fnref n="1" />

Mesopelagic fishes are ubiquitous, ecologically important as well as a potential protein resource. However, how mesopelagic fish maneuver in their 3D environment, facilitating encounters with prey and avoiding predators is relatively unknown. Individual behavioral studies have been historically challenging due to previous limitations to technology. During a short period, we observed high-resolution 3d-trajectories of mesopelagic fishes within a Norwegian fjord. We acoustically tracked the swimming trajectories of juvenile and adult *M. muelleri* and *B. glaciale* separated within two distinct vertical layers, measured swimming speed, and used a self-overlap model (Ψ) to analyse the geometry of the trajectories. Our aim was to investigate, if and how the fishes were optimizing their swimming behaviour. We found that mesopelagic were moving actively within a large range between ballistic to convoluted movements. Some of the fishes were moving in a manner that minimized self-overlap in relation to prey search (Ψ<0.1), while increasing self-overlap with regards to a piscivorous predator (Ψ>0.6) with a hypothetical visual range of 1 m, while the large variation can possibly be explained by several factors driving the different behaviors.

<footnote n="1">¹Kemitorvet 201, 2800 Kgs Lyngby, kjgth@aqua.dtu.dk, stn@aqua.dtu.dk;</footnote>

<footnote n="2">²Kemitorvet 201, 2800 Kgs Lyngby, pat@aqua.dtu.dk:Kemitorvet 201, 2800 Kgs Lyngby,</footnote>

<footnote n="2">²Kemitorvet 201, 2800 Kgs Lyngby, m@aqua.dtu.dk</footnote>
# Monitoring Seasonal Fish Migration and Fishing Mortality in the Tonle Sap River, Cambodia

J. K. Horne¹, J. A. Swan¹, G. W. Holtgrieve¹

¹School of Aquatic and Fishery Sciences, Box 355020, Seattle, WA 98195, USA, jhorne@uw.edu, jswan13@uw.edu, gholt@uw.edu

Over 60 million Cambodians rely on fisheries and aquaculture to supply 80% of their animal protein and micronutrients, with the majority sourced from the Tonle Sap River. From November through February, commercial and artisanal fisheries catch over 130 species that migrate from the Tonle Sap Lake to the ocean after the rainy season. Two monitoring stations (solar powered, WBTminis, 200 kHz) were installed on upstream and downstream commercial fishing platforms and programmed to sample at 1 Hz for 15 minutes every hour of the day. Data are sent through a wireless network to an AWS server in Singapore and then downloaded to a local server. Scripts are used to automate data file creation, most processing, exports, and standard graphic production for each week. Fish densities peaked at night and in association with a four-day window around the full moon. River depth decreased 2.5 – 3 m through the two-month fishing season. Differences in fish flux at the two stations is being developed into a fishing mortality index. This system can be used as a near-real-time monitoring system for in season fisheries management.

# Summarizing low-dimensional patterns in long-term echosounder time series from the U.S. Ocean Observatories Initiative network

W.-J. Lee¹ and V. Staneva²

¹Applied Physics Laboratory, University of Washington, 1013 NE 106th St, Seattle, WA 98105, USA, leewj@uw.edu;

²eScience Institute, University of Washington, 3910 15th Ave NE, Seattle, WA 98195, USA, vms16@uw.edu.

As a remote sensing tool, moored echosounders have played an important role in observing temporal changes of animal distributions in the water column over large temporal scales, ranging from days, months to seasons and even years. In this work we take advantage of the power of matrix decomposition techniques in exploiting regularity in the data to automatically discover low-dimensional structures in large data sets, and develop a methodology that can effectively remove noise and extract dominant daily echogram patterns from long-term echosounder series collected by moored echosounders deployed by the U.S. Ocean Observatories Initiative (OOI). These echosounders are located on the continental shelf and the shelf break in the rich Northern California Current System that is strongly influenced by seasonal upwelling. The echosounders are collocated with a suite of oceanographic sensors, allowing systematic analysis of multi-modal data streams. Our analysis results in an array of daily echogram patterns (components) whose time-varying linear combination (activations) captures major structures in these time series. Together, these components and variations provide a compact representation that allows intuitive interpretation of such a long-term observational dataset.
**Monitoring salmon with broad band echo sounders – investigate acoustic parameters as indicators for welfare**

M. F. Rong<fnref n="1" />, F. Oppedal<fnref n="2" />, G. Pedersen<fnref n="2" />, N. O. Handegard<fnref n="2" /> and T. N. Forland<fnref n="2" />

Salmon lice is challenging for salmon aquaculture. A new innovative preventative solution is to submerge the salmon net-pen to avoid the surface-dwelling infestative lice larvae. However, the physostomous salmon require daily air surface access or the swim bladder will deflate. The negative buoyancy lead to increased swimming and positive tilt angles. Over extended time (2-3 weeks), the appetite, growth rate is reduced, spine compressed and more injuries appear resulting in poor welfare. To study the swim bladder inflation level as an indicator for fish well-being, we deployed three split beam 70, 120 and 200 kHz broad-band transducers under a net-pen for 30 days. Using an underwater net roof, salmon was denied air access for 23 days and the change in swim bladder inflation and fish behavior was observed. A method for early detection of swim bladder deflation from acoustic FM pulses and CW pulses are being developed, and we already see clear patterns in the TS attenuating by 10 dB. Results can be used to develop automatic alarms on lack of swim bladder inflation/poor welfare and have already given us new key knowledge of backscattering from the salmon swim bladder with degree of fulness.

**Comparative analysis of pelagic compartment organization by bathymetric strata in the Canary Current Large Marine Ecosystem**

Anne Mouget<fnref n="1" />,<fnref n="2" />, Patrice Brehmer<fnref n="2" />,<fnref n="3" />, Mohamed Ahmed Jeyid<fnref n="4" /> Abdoulaye Sarré<fnref n="5" />, Salahedine El-Ayoubi<fnref n="6" />, Ndague Diogoul<fnref n="7" />,<fnref n="5" />,<fnref n="2" />, Momodou Sidibeh<fnref n="8" />, Yannick Perrot<fnref n="2" />

The Canary Current Large Marine Ecosystem (CCLME) is located along the North West African coast from Morocco to Guinea Bissau. In this work, 14 Nansen fisheries acoustics surveys have been led from the southern border of Senegal (12.15°N) to the Cape Blanc (20.77°N) during the hot wet season, i.e., outside the seasonal upwelling period, from 1995 to 2015. The aim of this study was to scrutinize the water column organization in a bathymetric gradient starting from the coast to the offshore area, discretised in three areas: inshore (< 150 m), transition (150-500 m) and off shore (> 500 m). Here, we worked on acoustic sound scattering layers (SSL) and on the whole water column through echointegrated data (EI). SSL and EI descriptors highlighted significant difference between the three depth strata. Study of the Diel Vertical Migration (DVM) highlighted that volume backscattering coefficient (proxy of pelagic abundance) vertical profile

<footnote n="1">¹University of Bergen, 5007 Bergen, Maren.Rong@student.uib.no;</footnote>

<footnote n="2">²Institute of Marine Research, 5004 Bergen, Tonje.Nesse.Forland@hi.no</footnote>

<footnote n="1">¹Muséum National d'Histoire Naturelle (MNHN), Station de biologie marine, CRESCO, Dinard, France</footnote>

<footnote n="2">²Institut de Recherche pour le Développement (IRD), UMR Lemar, Campus Ifremer, Délégation régionale IRD ouest France, 29280, Plouzané, France</footnote>

<footnote n="3">³Commission Sous Régional des Pêches, CSRP, SRFC, Dakar, Sénégal</footnote>

<footnote n="4">⁴Institut Mauritanien de Recherche Océanographique et des Pêches (IMROP), BP 22, Nouadhibou, Mauritania</footnote>

<footnote n="5">⁵Institut Sénégalais de Recherche Agricole (ISRA), Centre de Recherche Océanographique de Dakar Thiroye (CRODT), Pôle de recherche de Hann, Dakar, Sénégal</footnote>

<footnote n="6">⁶Institut National de Recherche Halieutique (INRH), Casablanca, Maroc</footnote>

<footnote n="7">⁷Centre de Recherches Océanologiques (CRO), BP V 18 Abidjan, Côte d'Ivoire</footnote>

<footnote n="8">⁸The Fisheries Department (FD), 6 marina parade, Banjul, The Gambia</footnote>
from transition area has a pattern globally similar to offshore one while inshore presented an inversed pattern. Analyse of annual change using EI descriptors reveal one common trait: the number of SSLs significantly increase whatever the depth strata considered. This study highlights differences and similarity in water column organization between depth strata.

## Switching off the Sun to observe the twilight zone spatial dynamics across Saint-Paul and New-Amsterdam Islands, Southern Indian Ocean

L. Izard1, N. Fonvieille2, D. Nerini2, A. Lebourges-dhaussy3, G. Roudaut3, J. Habasque3, C. Cotté1

¹Sorbonne University, CNRS, IRD, MNHN, Oceanography and Climate Laboratory: Experiments and Numerical Approaches (LOCEAN-IPSL), Paris, France

²Aix-Marseille University, Toulon University, CNRS, IRD, Mediterranean Institute of Oceanography (MIO), Marseille, France

³Marine Environmental Sciences Laboratory (LEMAR), UBO-CNRS-IRD-Ifremer IUEM, Plouzané, France

Information on micronekton (> 1 cm organisms) is globally scarce in the open ocean, and its vertical and horizontal distribution in relation to oceanographic structures is poorly known. The complex biodiversity composing micronektonic functional groups lead to even more challenging interpretations of their spatial dynamics. Advanced generations of echosounders emit simultaneously several acoustic signals (multi-frequency device), allowing a finer view of the micronektonic community. While data becomes more abundant and complex, it is crucial to develop statistical tools aiming to objectively extract key components of its variability. In this study, we analyse data recorded onboard the R/V Marion Dufresne from an EK80 echosounder emitting at 18, 38, 70, 120 and 200 kHz. We developed a Multivariate Functional Data Analysis method to identify patterns in micronekton structures across Saint-Paul and New Amsterdam economic exclusive zone, at the boundary between vast oceanic domains. This approach proposes an objective method to analyse the vertical backscatter distribution and quantify temporal and spatial modes of variability in multivariate acoustic data. By filtering the temporal mode, we uncovered a latitudinal acoustic pattern in concordance with hydrological features and biological samples distribution. Such methods could be implemented at a global or local scale and allow 3-D modelling of micronekton structuring.

## A finer look into the twilight zone: comparing acoustic records from an animal-borne miniature sonar and a multifrequency echosounder

N. Fonvieille1*, L. Izard3, P. Goulet2, C. Guinet2, D. Nerini1, A. Lebourges, G. Roudaut, J. Habasque, J. Chevassu, C. Castrec, M. Tournier2, B. Picard2, M. Johnson and C. Cotté3

¹Mediterranean Institute of Oceanography (MIO), Marseille, France,

²Centre of Biological Studies Chizé, Villiers-en-Bois, France

³Laboratory of Oceanography and Climate, Experiments and numerical Approaches (LOCEAN), Paris, France

An animal-borne miniature active echo-sounder has been recently deployed on southern elephant seals (*Mirounga leonina*) from the Kerguelen and Argentinian colonies. This high frequency sonar (1.5 MHz) has shown a strong potential in detecting small mid-trophic level targets (zooplankton and micronekton). Relative abundance and distribution can be assessed, allowing to observe temporal (diel migration) and spatial patterns of plankton. However, the interpretation
of the collected data remains uncertain. To address this lack of information, we conducted *in situ*
experiments onboard the Marion Dufresne vessel in the Southern Ocean (10th February to 6th
March 2022). The microsonar was fixed on the rosette sampler at 13 locations and attached 16
times to a trawling net (4 mm mesh). Records will be analyzed applying a recent method devel-
oped on elephant seals data to detect targets in the beam and estimate organisms abundance.
The result will be compared with biological samples and backscattering layers detected by a
multifrequency EK80 echosounder (18, 38, 70, 120 and 200 kHz), offering an acoustic landscape
of the seals foraging area. This study will benefit to ongoing research regarding biological fields
visited by the elephant seals, bringing precision on microsonar target detection capacities.

Fish aggregation around North Sea oil and gas platforms

J. M. Lawrence¹ and P.G. Fernandes¹

¹School of Biological Sciences, Tillydrone Avenue, University of Aberdeen, Scotland. AB24 2TZ

Many offshore oil and gas rigs will soon require decommissioning. In northern Europe, current legislation requires their complete removal, but it is unclear what the environmental impacts of this will be. Fish aggregate at offshore structures, but the horizontal extent and strength of this aggregation remains unknown. Here, we use fisheries acoustic data collected during a bottom-trawl survey of the North Sea to investigate the relationship between fish distributions and rig densities. Acoustic backscatter from schooling fish was isolated and quantified, and echoes from individual, non-schooling, fish were processed to give relative areal fish densities. The distribution of offshore oil and gas rigs was estimated using a surface of rig kernel density. Parametric (generalised linear modelling) and non-parametric (random forests) modelling revealed relationships between rig density and the fish density of both schooling and individual fish, with higher densities of fish found in areas of higher rig density. However, very few data were collected within 500m of rigs due to the exclusion zones in force. Future work will address this by negotiating access to the exclusion zones with operators and using a novel platform (an uncrewed surface vehicle) which can safely survey much closer to rigs (within 10s of metres).

Characterizing predator dive patterns on a common prey base from stationary echo-
sounders in Antarctica

A. P. Annasawmy¹, J. K. Horne¹, Christian S Reiss², and Gavin J Macaulay³

¹School of Aquatic and Fisheries Sciences (SAFS) and Cooperative Institute for Climate, Ocean and Ecosystem Studies (CICOES), University of Washington, Seattle, USA

²Antarctic Ecosystem Research Division, NOAA Southwest Fisheries Science Center, La Jolla, California, USA

³Institute of Marine Research, Norway

The Antarctic Peninsula, which stretches from Antarctica towards South America, is a critical habitat to penguins and other seabirds, seals, and whales, with Antarctic krill (Euphausia superba) serving as a significant diet component. Kongsberg WBAT echosounders fixed on moorings were deployed close to Deception and Nelson Islands in Bransfield Strait. Aggregations of krill were detected using Echoview's school detection module and classified in three categories using hierarchical clustering on a metric suite including NASC, mean depth, center of mass, inertia, equivalent area, aggregation index, and proportion occupied. 'V', 'U' and 'W' - shaped predator dive profiles were visible in the echograms from the moorings at both sites. A dive consisted of a descent from the surface, time at depth and an ascent. Additional dive metrics related to the descent, bottom, and ascent durations, maximum dive depth and wiggle counts, were measured
using Echoview. Dive angles were measured and used to calculate descent and ascent velocities using ImageJ software. Potential predators were determined by hierarchical clustering of dive metrics and classified in four groups. This study advances knowledge on detection and classification of predator dive profiles for predator-prey interaction studies using stationary platforms with echosounders.

# Acoustic Characterization of Marine Organisms

## Abundance estimates of micronekton organisms in tropical Pacific Ocean from trawl sampling, acoustic survey & backscatter models

L. Barbin<fnref n="1" />, A. Lebourges-Dhaussy<fnref n="2" />, V. Allain<fnref n="3" />, G. Roudaut<fnref n="4" />, J. Habasque<fnref n="5" />, A. Receveur⁶, P. Lehodey⁷ and C. Menkes<fnref n="8" />

Micronekton, ubiquitous to all oceans plays a pivotal role in the trophic organisation and constitutes the food of most top predators. Due to the paucity of sampling, estimates of these organisms abundance and specie distribution is largely unknown. Such sampling comes either from trawls or active acoustic. One key question remains on how these two means of observations compare and complement each other. Our study focuses on the analysis of active acoustic data from 8 oceanographic surveys in South Pacific. Two active acoustic methods were used simultaneously: hull-mounted echo sounders and a wideband profiler. These data are examined together with biological samples obtained by trawling which brings a ground truth to the acoustic measurements. By comparing the *in situ* acoustic response, the modelled response from biological sampling and the density of organisms calculated from wideband profiles, we obtain an order of abundance estimates of micronekton in depth layers. This comparison enables us to estimate the observed differences of organisms abundance between the three methods and helps understanding. Over the entire cruises, the average ratio between abundances derived from acoustic sounders and those obtained from trawled samples is on the order of 10 but varies strongly with depth.

## A DWBA based fluid shell model towards improved modelling of weakly scattering organisms

S. Gastauer<fnref n="1" />,<fnref n="2" />, D. Chu<fnref n="3" />

<footnote n="1">¹IRD Noumea, New-Caledonia, laure.barbin@ird.fr;</footnote>

<footnote n="2">²anne.lebourges.dhaussy@ird.fr, IRD Brest, France;</footnote>

<footnote n="3">³ValerieA@spc.int, SPC (Pacific Community), Noumea, New-Caledonia;</footnote>

<footnote n="4">⁴gildas.roudaut@ird.fr, IRD Brest, France;</footnote>

<footnote n="5">⁵jeremie.habasque@ird.fr, IRD Brest, France; ⁷aurore.receveur@fondationbiodiversite.fr, CESAB – FRB, Montpellier, France;</footnote>

<footnote n="8">⁸patrickl@spc.int, (Pacific Community), Noumea, New-Caledonia & CLS, Toulouse, France;</footnote>

<footnote n="8">⁸IRD Noumea, New-Caledonia, christophe.menkes@ird.fr;</footnote>

<footnote n="1">¹Thünen-Institute of Sea Fisheries, 27572 Bremerhaven, Germany sven.gastauer@thuenen.de;</footnote>

<footnote n="2">²Scripps Institution of Oceanography, University of California, San Diego, La Jolla CA 92037, United States;</footnote>

<footnote n="3">³NOAA Fisheries, NWFSC, 2725 Montlake Blvd. E., Seattle, WA 98112</footnote>
Acoustic scattering is largely dependent on the acoustic impedance, shape and orientation of marine organisms. Including information on the internal structure, such as lipid sacks or other body parts with a density or sound speed different from the surrounding body can improve the accuracy of scattering models. Acoustic scattering models are especially useful to help us better understand the complex signals we receive from broadband frequency spectra and can help us to better interpret collected acoustic data. Here we present an analytical 3D Distorted Wave Born Approximation (DWBA) model with a fluid shell. We compare the results of our model outputs to those from a computationally more intense Boundary Element Mode model outputs, provide examples on how to size weakly scattering targets theoretically based on the broadband spectrum, and discuss limitations of the presented models.

**Study of the acoustic reflectivity of pelagic fishes in the Mediterranean Sea: from ex-situ experiments to backscattering model.**

A. Palermino¹,², A. De Felice², G. Pedersen³, G. Canduci⁴, I. Biagiotti⁵,¹, I. Costantini⁶,¹, M. Centurelli⁷,¹, R. Korneliussen⁸,³, and I. Leonori⁹¹

¹CNR-National Research Council, IRBIM-Institute of Marine Biological Resources and Biotechnologies, Largo Fiera della Pesca, 1-60125 Ancona, Italy; antonio.palermino@irbim.cnr.it 1; andrea.defelice@cnr.it 2; giovanni.canduci@cnr.it 4; ilaria.biagiotti@cnr.it 5; ilaria costantini@cnr.it 6; michele.centurelli@irbim.cnr.it 7; iole.leonori@cnr.it 9

²ALMA MATER STUDIORUM, Università di Bologna, Via Zamboni, 33 - 40126 Bologna, Italy, antonio.palermino2@unibo.it 1

³Institute of Marine Research, Nykirkekaien 1, 5004, Bergen, geir.pedersen@hi.no, 3; rolf.korneliusen@hi.no 8

Accuracy of the target strength is one of the most important sources of uncertainty in fish bio-
mass estimates by acoustic methods. In order to convert the volume backscattering strength to
an absolute number-density of species, the knowledge of species-specific acoustic reflectivity is
essential. Likewise, observation of broadband backscatter proprieties might be powerful in dis-
tinguishing between species, especially for regions characterized by mixed-species fisheries such
as the Mediterranean Sea. However, at date, there are no works on the use of broadband in this
basin and only sardine and anchovy are well studied using discrete frequencies. We performed
four ex-situ experiments in the Adriatic Sea using a novel approach on tethered specimens
of *Scomber colias* and *Trachurus mediterraneus*. Successively, 45 individuals of the two species
were collected during the MEDiterranean International Acoustic Survey (MEDIAS) 2020 and
2021 for the application of backscattering models. Here the results on new conversion parameter
values (b20) obtained through these two methods are compared. Moreover, the first insights on
the broadband backscatter of fishes in the Mediterranean Sea are given. Our results on backscat-
tering models show the potential of broadband and multi-frequencies approaches to distinguish
between species and sizes for *S. colias* and *T. mediterraneus*.

Gas-baldder elongation estimation of mesopelagic organisms from wideband target strength frequency response

B. Khodabandeloo¹, E. Ona², G. Pedersen³, R. Korneliussen⁴, W. Melle⁵ and T. Klevjer⁶

1,2,3,4Ecosystem Acoustics Research Group, Institute of Marine Research, P.O. Box 1870 Nordnes, 5817 Bergen, Norway, 1babak.khodabandeloo@hi.no; 2egil.ona@hi.no; 3geir.pedersen@hi.no, 4rolf.korneliusen@hi.no
5,6 Plankton Research Group, Institute of Marine Research, P.O. Box 1870 Nordnes, 5817 Bergen, Norway,

⁵webjoern.melle@hi.no, ⁶thor.klevjer@hi.no

Backscattered acoustic energy from a target contains information about its shape, size, orientation, and material properties. The high uncertainties in the worldwide biomass estimates of mesopelagic fish limits our understanding of their actual importance and role in the ocean ecosystems. The major proportion (~99%) of the volume backscattering of deep scattering layers measured by a 38 kHz vessel-mounted echosounder can be due to the gas-bearing organisms, even if these organisms might make up a small fraction of the total biomass. Morphological features of the gas-filled organs have noticeable effects on the backscattering. Improved knowledge about the volume and actual shape (elongation) of swimbladders of mesopelagic fishes has been identified as important factors to reduce the overall uncertainties in an acoustic survey estimates of mesopelagic biomass. Here, using the first and second resonance frequencies of a gas bubble's TS frequency response, a method is suggested to estimate its elongation. The method was applied to the *in situ* measured wideband (33-380 kHz) TS of single mesopelagic gas-bearing organisms from two stations in the North Atlantic (NA) and Norwegian Sea (NS). For the selected targets, the elongation of gas-bladder from the NS and NA stations were 2.86±0.50 and 1.49±0.52, respectively.

## A bi-frequency discrimination method of copepods in the Senegalese coast

Ndague DIOGOUL¹,⁶*, Patrice BREHMER¹,²,⁶, Yannick PERROT², Elizandro Rodrigues RODRIGUES³, Abou THIAM⁴, Salaheddine EL AYOUBI⁵, Anne MOUGET², Abdoulaye SARRÉ⁶, and Anne Lebourges-Dhaussy²

¹IRD, Univ Brest, CNRS, Ifremer, LEMAR, Campus UCAD-IRD de Hann, Dakar, Senegal

²IRD, Univ Brest, CNRS, Ifremer, LEMAR, DR Ouest, Plouzané, France

³Instituto Nacional de Desenvolvimento das pescas, Cabo Verde

⁴University Cheikh Anta Diop UCAD, Institute of Environmental Science (ISE), BP 5005, Senegal

⁵Institut National de Recherche Halieutique INRH, Agadir, Morocco

⁶Institut Sénégalais de Recherches agricoles ISRA, Centre de Recherches Océanographiques de Dakar-Thiaryoe (CRODT), BP 2221 Dakar, Senegal

The Canary Current Large Marine Ecosystem (CCLME) is one of the most productive marine ecosystem worldwide and is key for food security for numerous African countries. Nevertheless, its function remains poorly described and ecosystemic data collection are rare. Copepods are the key macrozooplankton group in the CCLME but their dynamic, their distribution and even their abundance remain poorly documented. Multinet net data allowed identifying large Copepod in CCLME. As small pelagic fish assessment acoustics survey were routinely done using 38 and 120 kHz frequencies, we used the same frequencies to propose a bi-frequencies inversion method to discriminate Copepod. We identified copepod backscatter using differences in volume backscattering strength ($S_v$). A close significant relationship were found between the size values of Copepod from multinet samples with those calculated by the acoustic highpass model. The correlation between copepod abundance and corresponding $S_v$ were positive. This work showed that 38-120 kHz frequency can be used on Copepod and thus open the way to retrospective analysis in the CCLME. These results were important to better understand marine ecosystem, and constitute a first step for Copepod biomass estimation in the context of ecosystemic approach of small pelagic fish management and climate change.
Emerging Technologies, Methodologies, and Protocols

Echofilter: A Machine Learning Model Improves the Automated Placement and Standardization of the Line Defining the Ambit of Entrained Air

Scott C. Lowe<sup>2,3</sup>, Louise P. McGarry<sup>1</sup>, Jessica Douglas<sup>1</sup>, Jason Newport<sup>4</sup>, Sageev Oore<sup>2,3</sup>, Christopher Whidden<sup>2,4</sup>, and Daniel J. Hasselman<sup>1</sup>

Understanding fish abundance and distribution in tidal energy streams is important for assessing risk presented by the introduction of tidal energy devices. Tidal currents favourable for development are characterized by complex hydrodynamics entraining air into the water. Algorithms available in Echoview are sufficient for automated detection of distinct and strongly reflective boundaries at the sea floor and sea surface. However, applying a single algorithm to identify the depth-of-penetration of entrained air is insufficient for a boundary that is discontinuous, depth-dynamic, porous, and widely variable across current flow speeds from slack tide to full flow at 5 m/s. We describe the development of a deep machine learning model that produces a pronounced and consistent improvement for the automated detection of the ambit of entrained air. Our model, Echofilter, is highly responsive to the dynamic range of turbulence conditions and sensitive to the fine-scale nuances in the boundary position, yielding a 95% agreement with human annotations. The time required to manually edit the line placement was reduced by half - doubling user productivity. The machine learning contribution to assessing the ecological impacts of tidal-stream energy devices is the improved analytical consistency and substantial improvement in the timeliness of analyses and subsequent reporting.

Quantitative processing of broadband data as implemented in a scientific split-beam
echosounder

L. N. Andersen<fnref n="1" />, D. Chu<fnref n="2" />, N. O. Handegard<fnref n="3" />, H. Heimvoll<fnref n="1" />, R. Korneliussen<fnref n="3" />, G. J. Macaulay<fnref n="3" />, E. Ona<fnref n="3" />, R. Patel<fnref n="4" /> and G. Pedersen<fnref n="3" />

<footnote n="1"><sup>1</sup>Fundy Ocean Research Centre for Energy (FORCE), 75 Alderney Drive, Dartmouth, Nova Scotia B2Y 2N7 Canada, louise.mcgarry@fundyforce.ca, jessica.douglas@fundyforce.ca, dan.hasselman@fundyforce.ca</footnote>

<footnote n="2"><sup>2</sup>Faculty of Computer Science, 6050 University Avenue, Halifax, Nova Scotia B3H 4R2 Canada, scott.lowe@dal.ca, sageev@gmail.com, cwhidden@dal.ca</footnote>

<footnote n="3"><sup>3</sup>Vector Institute, MaRS Centre, West Tower, 661 University Avenue, Toronto, Ontario M5G 1M1 Canada</footnote>

<footnote n="4"><sup>4</sup>DeepSense, Dalhousie University, 6050 University Avenue, Halifax, Nova Scotia B3H 4R2 Canada</footnote>

<footnote n="1">¹ Kongsberg Maritime AS, Strandpromenaden 50, 3191, Horten, Norway, lars.nonboe.ander-
sen@km.kongsberg.com, harald.heimvoll@km.kongsberg.com;</footnote>

<footnote n="2">² Fishery Resource Analysis and Monitoring Division, Northwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, 2725 Montlake Blvd. E. Seattle, WA, 98112, USA, dezhang.chu@noaa.gov;</footnote>

<footnote n="3">³ Marine Ecosystem Acoustics, Institute of Marine Research, Nykirkekaien 1, 5004 Bergen, nils.olav handegard@hi.no, rolf.korneliussen@hi.no, gavin.macaulay@hi.no, egil.ona@hi.no, geir.peder-sen@hi.no;</footnote>

<footnote n="4">⁴ Codelab Bergen AS, Thormøhlensgate 51, 5006 Bergen, Norway, ruben.patel@codelab.no</footnote>
The use of quantitative broadband echosounders for biological studies and surveys offers considerable advantages over narrowband echosounders. These include improved spectral-based target identification and significantly increased ability to resolve individual targets. An understanding of current processing steps is required to fully utilize and further develop broadband acoustic methods in fisheries acoustics. We describe the steps involved in processing broadband acoustic data from raw data to frequency dependent target strength (TS(f)) and volume backscattering strength (Sv(f)) using data from the EK80 broadband scientific echosounder as examples. Although the overall processing steps are described and built on established methods from literature, multiple choices need to be made during implementation. To highlight and discuss some of these choices and facilitating a common understanding within the community, we have also developed a code which will be made publicly available and open source. The code follows the steps using raw data from two single pings, showing the step-by-step processing from raw data to TS(f) and Sv(f). This code can serve as a reference for developing own code or implementation in existing processing pipelines, as an educational tool and as a starting point for further development of broadband acoustic methods in fisheries acoustics.

## Pseudograms: adding spatial context to EchoMetrics by embedding Sv values in output from underwater gliders

A. Silverman¹, J. R. Cermak², S.L. Danielson², and J. K. Horne³

¹University of South Florida, asilverman@usf.edu; ²University of Alaska Fairbanks, jrcermakiii@alaska.edu, sldanielson@alaska.edu; ³University of Washington, jhorne@uw.edu

The EchoMetrics suite was developed to parsimoniously characterize acoustically detected, water column biomass. Data acquisition from alternate platforms, including underwater gliders, enables near-real-time metric value transmission through bandwidth limited satellites. While representing overall biomass distribution in the water column, metric only information limits locating depth specific biological features such as layers. To provide a parallel visual representation, Sv values from the WBT mini VBS 20-layer output are encoded within the seven Echometric output variable data stream without increasing data volume. Metric and Sv values are then sent through the glider science computer to an Iridium satellite connection and received by a shoreside server. Deconvolution of Echometric and Sv values provides separate data streams for plotting, interpretation, and can be used to target direct sampling. Data streams are formatted to update displays in platform or project web pages. Recent deployments have collected data in the Gulf of Mexico and in the Gulf of Alaska. Coincident graphic and statistical characterization increases our understanding of water column biomass distribution during autonomous platform deployment.

## Updates from Echotype developers: changes and roadmap

W.-J. Lee¹, E. Mayorga¹, B.C. Reyes¹, L. Setiawan², I. Majeed³, V. Staneva⁴, and K. Nguyen³

¹Applied Physics Laboratory, University of Washington, 1013 NE 106th St, Seattle, WA 98105, USA, leewj@uw.edu, emiliom@uw.edu, bcreyes@uw.edu;

²School of Oceanography, University of Washington, 1501 NE Boat St, Seattle, WA 98195, USA, landungs@uw.edu;

³Department of Electrical and Computer Engineering, Paul Allen Center, 185 E Stevens Way NE AE100R, Seattle, WA 98195, USA, imranmaj@uw.edu, ngkavin@uw.edu,

⁴eScience Institute, University of Washington, 3910 15th Ave NE, Seattle, WA 98195, USA, vms16@uw.edu.
Echotype is an open-source Python software package built to enhance the interoperability and scalability of fisheries acoustics data. By standardizing data from diverse instrument sources following a community convention and utilizing the widely embraced netCDF data model to encode data as labeled, multi-dimensional arrays, Echotype facilitates intuitive, user-friendly exploration and use of echosounder data in an instrument-agnostic manner. In addition, it directly enables computational interoperability and scalability in both local and cloud computing environments by leveraging existing open-source Python libraries optimized for distributed computing. Echotype is currently used by the US Ocean Observatories Initiative (OOI) Data Center to parse and serve echosounder data, and by the NOAA National Centers for Environmental Information as the data ingestion backend for interactive visualization on the cloud. In this presentation, we will summarize content of recent Echotype releases, including support for additional echosounder models, direct reading and writing interface with cloud object storage, enhanced data access and integration functionalities, documentation upgrades, and improved data structure adherence to the SONAR-netCDF4 convention. We will conclude by discussing our development roadmap and hope that you will join us to make this a community-driven effort.

## The Zunibal Precatch System: a new tool to minimize bycatch in Tropical Tuna Fisheries

Lucio Calise, Patricia Ordoñez and Egoitz Ormaechea

Zunibal S.L., Idorsolo 1, 48160 Derio, Spain, lucio.calise@zunibal.com

The most common fishing method in tropical tuna fisheries is the deployment of seine nets around drifting Fish Aggregating Devices (dFADs). Although efficient, this method has significant bycatch, especially the “size-bycatch”, i.e. individuals of target species of unmarketable size. Since there are no suitable tools capable of discriminating schools underneath the dFADs, fishing is always carried out if a sufficient amount of biomass is predicted. Therefore, the catch is composed by individuals of different species and sizes, and those belonging to non-target species and unmarketable size of target species are discarded into the sea lifeless. Here, a new tool to minimize bycatch is presented: the Zunibal Precatch System, which is being used during the precatch phase of checking the satisfactory fishable quantity of biomass underneath the dFAD. The purpose is to acoustically detect, identify and estimate the fish schools underneath a dFAD in near real-time to help the fishing decision. Three elements compose the system: 1) a multi-frequency data acquisition buoy; 2) a buoy-vessel communication system and 3) an onboard processing unit. This presentation will illustrate the practical and marketing requests and consequently how the elements were designed and realized, with particular focus on the acoustic performances evaluation and data processing strategy.

## Acoustic surveys at your desk: deploying a USV into an offshore windfarm and up to oil and gas platforms.

P.G.Fernandes<fnref n="1" />, J.M.Lawrence<fnref n="1" />

Acoustic surveys can provide precise estimates of the abundance and distribution of a variety of marine organisms at high spatial resolution. Uptake of broadband sonar has increased this capability, but circumstances may limit the technology, notably the platform that acoustic devices are typically deployed on and/or the type of habitat being surveyed. Traditional platforms (ships) suffer from high costs, a large carbon footprint, and, in the case of the coronavirus

<footnote n="1">¹School of Biological Sciences, Zoology Building, University of Aberdeen, Tillydrone Avenue, Aberdeen, AB24 2TZ. Corresponding author: Fernandespg@abdn.ac.uk</footnote>
pandemic, limits on personnel; furthermore, they struggle to get close to certain habitats, partic-
ularly man-made marine structures such as oil and gas platforms and offshore windfarms. Here
we present experience of an uncrewed surface vehicle (USV) survey of these habitats. We focus
on some of the challenges and benefits of the USV and describe how it was operated remotely
from the desk of navigators and sensor operators. Notable benefits include, in-situ calibration,
getting very close to certain platforms, surveying straight through an offshore windfarm, and
obtaining ancillary data such as regular 360° imagery for seabird detection. Challenges include
the lack of alternative evidence for the acoustic data, acoustic noise at longer ranges and some-
what limited survey range. However, these challenges are not unsurmountable, so prospects
for USV applications look good.

Recognizing the Influence of Turbulence-Induced Entrained Air When Monitoring for Risk to Fish at Ocean Energy Installations

Louise P. McGarry<fnref n="1" />, Haley A. Viehman<fnref n="2" />, Jessica Douglas<fnref n="1" />, and Daniel J. Hasselman<fnref n="1" />

For those tidal energy installations where it is required to quantify the potential risk to fish posed
by the introduction of energy conversion devices into the habitat, scientific hydroacoustic echo-
sounders provide quantifiable, stratified sampling of the whole water column with sufficiently
high resolution in time and space. However, the impressive currents that are favourable to en-
ergy development are often turbulent and result in air entrainment into the water column. En-
trained air limits the use of acoustic-based sampling systems to only those portions of the water
column not contaminated with entrained air. To help regulators and developers recognize the
potential implications to the understanding of fish presence, distribution, and abundance in
ocean energy sites, we undertook a study of the proportion of water column obfuscated by en-
trained air as a function of tide direction, current speed, and season. Our findings demonstrate
that site-specific localized hydrodynamics and seasonal winds can have a major impact on the
observable portion of the water column. This information is critical for determining an optimal
data collection site, establishing reasonable monitoring goals in dialogue with regulators, devel-
opers, and stakeholders, and for identifying periods of time when active acoustic technologies
may not be an effective tool for monitoring.

Species identification of fish shoals combining multibeam and split-beam echo-
sounders with visual observations from diving

Viviane David<fnref n="1" />,<fnref n="2" />*, Corentin Minart<fnref n="1" />,<fnref n="2" />, Anne Mouget<fnref n="3" />,<fnref n="4" />, Yannick Perrot<fnref n="2" />, Pierre Thiriet<fnref n="1" />, Eric Feun-
teun<fnref n="3" />,<fnref n="4" />, Adrien Cheminée⁵, Loïc Le Goff<fnref n="1" />, Anthony Acou<fnref n="1" />, Patrice Brehmer<fnref n="2" />

<footnote n="1">¹Fundy Ocean Research Centre for Energy (FORCE), 75 Alderney Drive, Dartmouth, Nova Scotia B2Y 2N7 Canada, louise.mcgarry@fundyforce.ca, jessica.douglas@fundyforce.ca, dan.hasselman@fundyforce.ca</footnote>

<footnote n="2">²Echoview Software Pty Ltd., GPO Box 1387, Hobart, Tasmania 7001, Australia, haley.viehman@echoview.com</footnote>

<footnote n="1">¹ *PatriNat* (OFB, CNRS, MNHN) – *Centre de d'expertise et de données sur la nature* – *Station Marine de Dinard, CRESCO, 38 rue de Port Blanc, Dinard, France*</footnote>

<footnote n="2">²IRD, Univ Brest, CNRS, Ifremer, UMR Lemar, Plouzané, France</footnote>

<footnote n="3">³ Laboratoire BOREA (Museum National d'Histoire Naturelle, CNRS, Sorbonne Université, IRD, Uni-
Caen, Univ Antilles Guadeloupe), 57 rue de cuvier, 75005 Paris, France</footnote>

<footnote n="4">⁴ Station Marine de Dinard, CRESCO, 38, rue du port Blanc, 35800 Dinard, France</footnote>
⁵ *Septentrion Environnement, Campus Nature Provence, 89 Tra. Paragon, 13008, Marseille, France*

One challenge of acoustic observations of marine organisms is the identification of species, particularly in shallow waters where high diversity occurs. We deployed combining split-beam EK80 (70, 120 and 200 kHz) and M3 multibeam (500 kHz) echosounders to detect monospecific fish shoals in coastal shallow waters (5-60 m). Innovative protocols for the specific allocation were tested, using (i) a scuba divers census on fish shoals and (ii) a towed scuba diver. Stereoscopic video system was also used to assess fish length and abundance and compare with the visual estimations of divers. Several independent replicates of monospecific shoals from 5 fish species were obtained. The combined use of the echosounders allows to have complementary morphologic, acoustic and spatial descriptors to correctly discriminate the shoals. In addition, as the stereoscopic system has shown to provide precise measurements of individuals and could overcome the visual diver observations, our results suggest that a system equipped with cameras like a remotely controlled towed instrumentation platform could be used in a near future for ground trust in shallow and clear waters.

## Using histogram equalization to visualize acoustic and ancillary data

M. Peña<fnref n="1" />

¹Instituto Español de Oceanografía, Muelle de Poiente s/n Palma, Spain, marian.pena@ieo.es

Echogram visualization and processing is one of the most time-consuming tasks for fisheries acousticians. Analyzing target species or features is often based on experience while visualization settings are inherited from colleagues or established for standardization purposes within international efforts. Acoustic data is often visualized with a standard minimum threshold for Sv that varies with target species. For instance, small pelagic fishes are often visualized in European waters from - 60 dB re 1 m2 m-3 while echograms of the mesopelagic zone (200-1000 m depth) usually employ a -90 or -80 dB re 1 m2 m-3 minimum threshold. However, numerical volume density changes greatly with depth (particularly beyond the shelf), time of the day and season, and thus setting an incorrect threshold may mask part of the population at some times or areas. Following on Blackwell et al. (2019) that showed the best colormaps to be employed in fisheries acoustics, this presentation focus on further parameters (general thresholds and location of the color limits) of the colorbar. Most colormaps employ linear relationship between data and color, where every color represents a similar range of values. This can hide interesting features in only one or two colors. Histogram equalization is a non-linear interpolation technique that locates thresholds and color limits at the quantiles of the image data. Examples using acoustic and ancillary data will be shown to highlight the benefits of this technique. While no definite conclusion is provided, some food for thought on the influence of the number of colors employed will also be provided.

## Target classification of individual zooplankton in ex-situ broadband acoustic data using supervised machine learning

C. McGowan-Yallop<fnref n="1" />, K. Last<fnref n="2" />, F. Cottier<fnref n="3" />, S. Fielding<fnref n="4" /> and A.S. Brierley⁵

<footnote n="1">¹Scottish Association for Marine Science, Oban, Argyll, PA37 1QA, UK, chelsey.mcgowan-yallop@sams.ac.uk,</footnote>

<footnote n="2">²Scottish Association for Marine Science, Oban, Argyll, PA37 1QA, UK, kim.last@sams.ac.uk,</footnote>

<footnote n="3">³Scottish Association for Marine Science, Oban, Argyll, PA37 1QA, UK, finlo.cottier@sams.ac.uk,</footnote>

<footnote n="4">⁴British Antarctic Survey, High Cross, Madingley Road, Cambridge, CB3 0ET, UK, sof@bas.ac.uk,</footnote>
⁵Scottish Oceans Institute, Gatty Marine Laboratory, University of St Andrews, St Andrews, KY16 8LB, UK, asb4@st-andrews.ac.uk

Broadband echosounders offer the potential for improved target classification of zooplankton using measurements of backscattering strength across a wide frequency range (frequency response). Previous work has shown that supervised machine learning is a powerful tool for classifying unknown, single-species aggregations in acoustic data using frequency response. However, this volume backscatter-based approach is less effective for mixed-species aggregations. Here, a method for classification of individual zooplankton using target strength frequency response, TS(f), is proposed. Supervised classification algorithms were trained using *ex-situ* TS(f) measurements for future application to *in-situ* survey data. TS(f) measurements (283-383 kHz) were made of the copepods *Paraeuchaeta norvegica* and krill (a mixture of Northern krill, *Meganycriphanes norvegica*, and *Thysanoessa* spp.) in a tank. Using these data, 12 supervised classification algorithms were compared. This method was then applied to a more realistic scenario using tank TS(f) measurements of *P. norvegica* and a community sample with *P. norvegica* removed ('non-target zooplankton'). The best-performing classification algorithm, XGBoost, classified *P. norvegica* or krill with 95.95% (± 0.47) accuracy, and *P. norvegica* or non-target zooplankton with 97.30% (± 0.41) accuracy. Results suggest that, where single target detection is possible, this is a highly accurate and robust method for target classification of zooplankton.

## Comparing acoustic data acquired with research vessel l'Europe and USV DriX:

Naig Le Bouffant, Emeline Veit, Laurent Berger

Unité Navires et Systèmes Embarqués, Ifremer Brest, CS 10070-29280 Plouzané, France.

Developments of Unmanned Surface Vehicules substantially progressed over the past few years, enabling to provide extensive monitoring capacities for physical and biological processes and achieve sustainable management of the maritime domain. In order to investigate their performance and the way they could be used to complete traditional vessel survey acquisitions, a five-days technical survey with a 7 meters-long DriX USV has been carried out along Mediterranean coast. USV was equipped with ES70 and ES200 EK80, along with IcListen hydrophone and EM2040 multibeam. Simultaneous acquisitions were performed with l'Europe research vessel fitted with similar echosounders. First analysis of comparative data will be presented, in terms of data quality (detection range and surface bubbling) as well as biomass observation and single target detection.

## Estimation and correction of depth range and echo level errors due to water-column temperature and salinity on sound scattering layer.

Y. Coly<fnref n="1" />*, Y. Perrot<fnref n="2" />, P. Brehmer<fnref n="2" />,<fnref n="3" />, E. Machu<fnref n="4" /> and B. A. Sow<fnref n="1" />

Water temperature and salinity are the key environmental parameters involved in the acoustic signal processing. In absence of clear procedure, temperature and salinity value are usually used at a fixed point in the water column to estimate the sound celerity in the studied area. Considering temperature and salinity as having negligible effects in estimates of acoustic variables may

<footnote n="1">¹LOSEC, Université Assane Seck de Ziguinchor, Ziguinchor, Sénégal; youssouphcoly98@gmail.com</footnote>

<footnote n="2">²IRD, CNRS, Univ Brest, Ifremer, DR Ouest, Lemar, Plouzané, France</footnote>

<footnote n="3">³IRD, CNRS, Univ Brest, Ifremer, Lemar, CSRP, SRFC, Dakar, Sénégal</footnote>

<footnote n="4">⁴Univ. Brest, CNRS, IRD, Ifremer, Laboratoire d'Océanographie Physique et Spatiale (LOPS), IUEM, Plouzané, France</footnote>
lead to errors in dependent acoustic variables as the volume backscattering coefficient $S_v$, the nautical area backscattering coefficient $S_A$ and the target distance, i.e., depth for vertical echosounder. We examine the impact of environmental errors and their effects on the level of $S_v$, $S_A$ and ranger. The results shown that the effect may not be negligible. The nautical area backscattering coefficient $S_A$ was the most affected by environmental errors. The deep ocean areas were the most concerned by these errors vs. surface and shallow coastal areas. Failure to correct for environmental errors in acoustic studies can lead to inaccurate results on the positions of the targets studied and biomass assessment. Abacuses were built to identify areas of interest where environmental corrections should be implemented and we share corrective code integrating temperature and salinity water profile for fisheries acoustics data correction.

## Developing and deploying machine learning methods for acoustic data

N.O. Handegard<fnref n="1" />, O. Brautaset<fnref n="2" />, C. Choi<fnref n="3" />, T. Furmanek<fnref n="1" />, A.J. Hestnes, E. Johnsen<fnref n="1" />, A. Ordonez<fnref n="2" />, I. Utseth<fnref n="2" />, S. Vatnehol<fnref n="1" />, G. Huse<fnref n="1" />

Machine learning methods are well suited for classification tasks and has been extensively used for acoustic data in the recent years. This presentation gives an overview of the effort on acoustic target classification using machine learning methods in Norway. The work includes new machine learning methods adapted to acoustic data, both fully supervised methods as well as semi supervised methods. We also investigate how to combine auxiliary information with classical convolutional methods. To efficiently use these new methods on acoustic data, the data needs to be prepared for efficient access and we have developed a cloud solution for efficient data access. Automated deployment of the methods on platforms like unmanned surface vehicles, research vessels and ships of opportunity is made possible through a combination of docker containers and Kongsberg Maritime's Blue Insight platform. This allows adaptive survey strategies, which is a step towards fully autonomous acoustic surveys. Finally, we present both the short term and the long term plan for how this will be used on IMRs surveys.

## Investigating EK80 data quality on different platforms

Laurent Berger and Naig Le Bouffant

Unité Navires et Systèmes Embarqués, Ifremer Brest, CS 10070-29280 Plouzané, France.

Each time a new acoustic equipment is integrated on a new or existing platform, much care is taken to optimize its location and electronic integration. The performance of a system and the data quality remains however platform dependent and reduced performances sometimes require posterior investigation. Case of degradation of surface data of ES18 transducers on research vessels and example of broadband electric noise investigation on different platforms will be presented in order to exchange about possible issues and best practices.

<footnote n="1">¹Institute of Marine Research, Bergen, Norway, nilsolav@hi.no</footnote>

<footnote n="2">²Norwegian Computing Center, P.O.Box 114, Blindern, 0314 Oslo, Norway</footnote>

<footnote n="3">³Arctic University of Tromsø, Norway ⁴Kongsberg Maritime, Horten, Norway.</footnote>
# Automated probabilistic echo solving: A scalable Bayesian inverse approach applied to echo integration

Samuel S. Urmy, Alex De Robertis, and Christopher Bassett

Identifying scatterers is a perennial challenge in fisheries acoustics. Most practitioners classify backscatter based on direct sampling and frequency-difference thresholds, then integrate at a single frequency. However, this approach struggles with species mixtures, and discards multi-frequency information when integrating. Inverse methods do not have these limitations, but are not widely used because their species identifications are often ambiguous and the algorithms are complicated to implement. We address these shortcomings using a probabilistic, Bayesian inversion method. Like other inversion methods, it handles species mixtures, uses all available frequencies, and extends naturally to broadband signals. Unlike prior approaches, it leverages Bayesian priors to rigorously incorporate information from direct sampling and biological knowledge, constraining the inversion and reducing ambiguity in species identification. Because it is probabilistic, it can be trusted to run automatically: it should not produce solutions that are both wrong and confident. Unlike some machine learning methods, it is based on physical scattering processes, so its output is fully interpretable. Finally, the approach is straightforward to implement using existing Bayesian libraries, and is easily parallelized for large datasets. We present narrowband and broadband examples using simulations and field data from the Gulf of Alaska, and discuss possible extensions and applications of the method.

# Poster Presentations

## On the resiliency of an eastern boundary upwelling ecosystem exposed to multiple stressors: an acoustic approach

Ndague DIOGOUL<fnref n="1" />,<fnref n="5" />, Patrice BREHMER<fnref n="1" />,<fnref n="5" />, Hervé DEMARCQ<fnref n="2" />, Salaheddine El Ayoubi<fnref n="3" />, Abou THIAM<fnref n="4" />, Anne Mouget<fnref n="6" />, Abdoulaye Sarré<fnref n="5" /> and Yannick PERROT<fnref n="6" />

The resistance of an east border upwelling system was investigated using relative index of marine pelagic biomass estimates under a changing environment spanning 20-years in the strongly exploited southern Canary Current Large marine Ecosystem (sCCLME). We divided the sCCLME in two parts (north and south of Cap Blanc), based on oceanographic regimes. We delineated two size-based groups (“plankton” and “pelagic fish”) corresponding to lower and higher trophic levels, respectively. Over the 20-year period, all spatial remote sensing environmental variables increased significantly, except in the area south of Cap Blanc where sea surface Chlorophyll-a concentrations declined and the upwelling favorable wind was stable. Relative index of marine pelagic abundance was higher in the south area compared to the north area of Cap Blanc. No significant latitudinal shift to the mass center was detected, regardless of trophic level. Relative pelagic abundance did not change, suggesting sCCLME pelagic organisms were

<footnote n="1">¹IRD, Univ Brest, CNRS, Ifremer, Lemar, CSRP, Dakar, Sénégal;</footnote>

<footnote n="2">²IRD, IFREMER, CNRS, Univ Montpellier, MARBEC, Sète, France;</footnote>

<footnote n="3">³Institut National de Recherche Halieutique INRH, Agadir, Morocco;</footnote>

<footnote n="4">⁴University Cheikh Anta Diop UCAD, Institute of Environmental Science (ISE), BP 5005, Dakar, Senegal;</footnote>

<footnote n="5">⁵Institut Sénégalais de Recherches agricoles (ISRA), Centre de Recherches Océanographiques de Dakar-Thiaroye (CRODT), BP 2221 Dakar, Senegal;</footnote>

<footnote n="6">⁶LEMAR, IRD, Univ Brest, CNRS, Ifremer, DR Ouest, Plouzané, France</footnote>
able to adapt to changing environmental conditions. Despite strong annual variability and the presence of major stressors (overfishing, climate change), the marine pelagic resources, mainly fish and plankton remained relatively stable over the two decades, advancing our understanding on the resistance of this east border upwelling system.

## Ichthyological importance of shallow coastal areas for pelagic communities: contributions of echosounding

Anne Mouget<fnref n="1" />,<fnref n="2" />, Viviane David<fnref n="3" />,<fnref n="4" />, Anthony Acou<fnref n="3" />, Eric Feunteun<fnref n="1" />,<fnref n="2" />, Pierre Thiriet<fnref n="3" />, Yannick Perrot<fnref n="4" />, Loïc Le Goff<fnref n="2" />, Patrice Brehmer<fnref n="4" />,<fnref n="5" />

Marine communities are strongly structured by bathymetry and distance from the coast. Shallow coastal areas host diverse and abundant fish communities and are subjected to strong anthropogenic pressures. However, assessments of good ecological status of pelagic fish populations do not generally take into account the ultra-coastal fringe of the coastline (<20m depth and <5km from coast). Data presented in this study were acquired in Brittany (France) during eleven acoustic surveys conducted in 2020 and 2021 using a splitbeam EK80 echosounder (70, 120 and 200 kHz). Pelagic fish shoals were extracted from the echogram and characterized by spatial (location in the water column), morphological (size and shape of the shoal) and acoustic descriptors. Shoal descriptors were compared between coastal and ultra-coastal areas, taking into account variability between sites, seasons and years. Results showed different shoal structures with notably smaller shoals of pelagic fish in the ultracoastal zone but with a stronger acoustic response, suggesting a higher density per school than offshore and/or different species. This study highlights the uniqueness of ultra-coastal areas for marine pelagic fish communities and underlines the need to integrate their monitoring into marine management and action strategies to improve management and protection systems for these biocenoses.

## Sound-scattering layers related to pelagic habitat characteristics: the case

Ndague Diogoul<fnref n="1" />,<fnref n="2" />,<fnref n="6" />, Patrice Brehmer<fnref n="2" />,<fnref n="3" />,<fnref n="6" />, Yannick Perrot<fnref n="3" />, Maik Tiedeman<fnref n="4" />, Abou Thiam<fnref n="1" />, Salaheddine El Ayoubi<fnref n="5" />, Anne Mouget<fnref n="3" />, Abdoulaye Sarré<fnref n="6" />

<footnote n="1">¹Laboratoire BOREA (Museum National d'Histoire Naturelle, CNRS, Sorbonne Université, IRD, Uni-Caen, Univ Antilles Guadeloupe), 57 rue de cuvier, 75005 Paris, France</footnote>

<footnote n="2">²Station Marine de Dinard, CRESCO, 38, rue du port Blanc, 35800 Dinard, France</footnote>

<footnote n="3">³PatriNat (OFB, CNRS, MNHN), Centre d'expertise et de données sur la nature – Station Marine de Dinard, CRESCO, 38 rue de Port Blanc, Dinard, FRANCE</footnote>

<footnote n="4">⁴IRD, Univ Brest, CNRS, Ifremer, Lemar, Délégation régionale IRD ouest France, 29280, Plouzané, France</footnote>

<footnote n="5">⁵Commission sous régionale des pêches, CSRP, SRFC, Dakar, Sénégal</footnote>

<footnote n="1">¹University Cheikh Anta Diop UCAD, Institute of Environmental Science (ISE), BP 5005, Dakar, Senegal</footnote>

<footnote n="2">²IRD, Univ. Brest, CNRS, Ifremer, LEMAR, Campus UCAD-IRD de Hann, Dakar, Senegal</footnote>

<footnote n="3">³IRD, Univ. Brest, CNRS, Ifremer, LEMAR, DR Ouest, Plouzané, France</footnote>

<footnote n="4">⁴Institute of Marine Research IMR, Pelagic Fish, P.O. Box 1870 Nordnes, 5817 Bergen, Norway</footnote>

<footnote n="5">⁵Institut National de Recherche Halieutique INRH, Agadir, Morocco</footnote>

<footnote n="6">⁶Institut Sénégalais de Recherches agricoles ISRA, Centre de Recherches Océanographiques de Dakar-Thiaroye (CRODT), BP 2221 Dakar, Senegal</footnote>
Understanding the relationship between sound scattering layers (SSLs) and pelagic habitat characteristics is a substantial step to apprehend ecosystem dynamics. SSLs are detected on echo sounders representing aggregated marine pelagic organisms. In this study, SSL characteristics of zooplankton and micronekton were identified during an upwelling event in two contrasting areas of the Senegalese continental shelf. Here a cold upwelling-influenced inshore area was sharply separated by a strong thermal boundary from a deeper, warmer, stratified offshore area. Mean SSL thickness and SSL vertical depth increased with the shelf depth. The thickest and deepest SSLs were observed in the offshore part of the shelf. Hence, zooplankton and micronekton seem to occur more frequently in stratified water conditions rather than in fresh upwelled water. Diel vertical and horizontal migrations of SSLs were observed in the study area. Diel period and physicochemical water characteristics influenced SSL depth and SSL thickness. Although chlorophyll-a concentration insignificantly affected SSL characteristics, the peak of chlorophyll a was always located above or in the middle of the SSLs, regularly matching with the peak of SSL biomass. Such observations indicate trophic relationships, suggesting SSLs to be mainly composed of phytoplanktivorous zooplankton and micronekton. Despite local hypoxia, below 30m depth, distribution patterns of SSLs indicate no vertical migration boundary. The results increase the understanding of the spatial organization of mid-trophic species and migration patterns of zooplankton and micronekton, and they will also improve dispersal models for organisms in upwelling regions.

## Applying Acoustic Scattering Layer Descriptors to Depict Mid-Trophic Pelagic Organisation: The Case of Atlantic African Large Marine Ecosystems Continental Shelf

Anne Mouget¹,², Patrice Brehmer¹,³, Yannick Perrot¹, Uatjavi Uanivi⁴, Ndague Diogoul¹,⁵, Salahedine El Ayoubi⁶, Mohamed Ahmed Jeyid⁷, Abdoulaye Sarré⁵, Nolwenn Béhagle¹, and Aka Marcel Kouassi⁸

¹IRD, Univ Brest, CNRS, Ifremer, Lemar, Délégation régionale IRD ouest France, 29280, Plouzané, France

²Unité Biologie des organismes et écosystèmes aquatiques (BOREA), CNRS, IRD, Museum National d'Histoire Naturelle, Sorbonne Université, Université de Caen Normandie, Université des Antilles, 38, rue du port Blanc, 35800 Dinard, France

³Commission Sous Régional des Pêches, CSRP, SRFC, Dakar, Sénégal

⁴Ministry of Fisheries and Marine Resources (MFMR), P.O. Box 912 Swakopmund, Namibia

⁵Institut Sénégalais de Recherche Agricole (ISRA), Centre de Recherche Océanographique de Dakar Thiarye (CRODT), Pôle de recherche de Hann, Dakar, Sénégal

⁶Institut National de Recherche Halieutique (INRH), Casablanca, Maroc

⁷Institut Mauritanien de Recherche Océanographique et des Pêches (IMROP), BP 22, Nouadhibou, Mauritania

⁸Centre de Recherches Océanologiques (CRO), BP V 18 Abidjan, Côte d'Ivoire

Hydroacoustic is a reliable and often used tool to monitor and study marine ecosystems. This study focus on acoustics scattered layers, which are the echosounder detection of pelagic marine organism of low trophic level, important in ecosystems functioning. Data have been recorded at 38 kHz in the three Atlantic African Large Marine Ecosystems (AA LME). To describe parsimoniously ecosystems, compare them and understand the difference, 14 descriptors have been used. Some of them are based on already used descriptors and others are new. The aim of this study is to ensure that these descriptors are relevant to monitor and compare systems. So, we first explore spatial (intra- and inter-LME comparisons) and then temporal dimension (inter-
annual variability). For such purpose, we use a large acoustic database collected over 15 years in the three AA LME: Canary Current LME, Guinea Current LME and Benguela Current LME. Our methodology is innovative, introducing original new descriptors to monitor pelagic compartment of each LME and should be efficiently used for environmental monitoring in case of perturbation as overfishing, climate change or marine pollution. Indeed the acoustic scattered layer are mainly composed of macrozooplankton and ichtyoplankton which are sensitive to environmental change.

**Spatial functionnal analysis application on fisheries acoustics data coupled with fine scale environmental data**

Yoba Kande¹,²,³, Sophie Dabo-Niang², Ndague Diogoul¹,³, and Patrice Brehmer¹,³,⁴

¹ISRA, Centre de Recherches Océanographiques de Dakar-Thiaroye, CRODT, BP 2241, Dakar, Sénégal;

²University of Lille, Laboratory PAINLEVE UMR 8524;

³IRD, Univ Brest, CNRS, Ifremer, LEMAR, Dakar, Sénégal; 4IRD, CSRP / SRFC, Dakar, Sénégal

In this work, we were interested in the application of functional, spatial data analysis (FSDA) on coupling acoustic (Sv) and environmental (water temperature, fluorescence, salinity and turbidity) data. To do this we use data from an acoustics fisheries surveys (R/V Thalassa, Ifremer, AWA campaign) carried out in West African waters using multifrequency echosounder (18, 38, 70, 120, 333 kHz) and a scanfish (high performance towed undulator). FSDA were compared to classical statistical methods namely multivariate functional principal component analysis, classical principal component analysis, classification on principal component scores, classical additive model, spatial functional additive model. The interest to improve such statistical analysis is applied here to the study the effect at fine scale of environmental parameters on the distribution of coastal sound scattered layers. We first considered an aggregated analysis of the environmental data then we considered a more complete analysis of the data via their functional characters.
# Annex 4: WGFAST Response to WGIPS

## Recommendation from WGIPS to WGFAST

The Working Group on International Pelagic Surveys (WGIPS) acknowledges that acoustic backscatter values collected during surveys coordinated by the group and used to calculate bio-mass estimates for stock assessments, may be affected by acoustic shadowing when very dense schools are encountered, thereby potentially adversely impacting the quality of the stock assessment. While a handful of papers report on shadowing, to the best of our knowledge, there are currently no standardized guidelines in the peer review literature on how to robustly test for the occurrence of shadowing, to quantify it, or to correct for these biases. The group seeks advice from WGFAST on standardized methods to identify, measure and correct for acoustic shadowing.

## WGFAST Response

Rolf Korneliussen, Sven Gastauer, and Michael Jech provided a response to this request as follows.

### Response from Rolf Korneliussen (This is an excerpt of an e-mail from Rolf)

Regarding the shadowing effect, I would claim that quite much is done there:

* Foote (1983): Theory (after measuring)

* Toresen (1991; some new measurements)

* Foote, Ona, Toresen (1992), Theory and measurements

* Zhao & Ona (2003) improvements in correction methods (with the help from Gorska).

* Utne & Ona (2006) ICES paper: Measurements with bottom fixed transducers: Same result at 2! On mean ext cross section, much meter data.

* Martha Uumati et al. (2010), on single schools from Marocco. Method development using BEI etc.

Both Foote (1983) and Zhao & Ona (2003) methods were implemented in the Bergen Echo Integrator (BEI). These assumed a known, measured mean extinction cross section that was provided by R. Korneliussen. The Foote (1983) theory is approximate and potentially inaccurate depending on how measurements are made available. Implementation in LSSS would be easy. The problems are similar for sonar. I have myself collected data for this for MS70, and so have others at IMR.

The Foote (1983) theory resulted in a Taylor expansion. The expansion was an approximation that relied on high resolution in the data to be accurate. The Zhao & Ona (2003) theory was accurate and did not need high vertical resolution of the data. When herring data were stored at a vertical resolution of 1 m, the Zhao & Ona (2003) and Foote (1983) theories gave the same result. The extinction cross section was necessary for both theories for the estimation of extinction.

## Background

The conversion of echo intensity to abundance estimates is a major goal of acoustic surveys of living marine and freshwater resources. Because these estimates are a (if not "the") fundamental product of acoustic surveys, considerable effort has been dedicated to quantifying the relationship between echo intensity and fish density when using echo integration (Foote, 1999). Ideally,
that relationship is linear over the range of animal densities encountered during a survey, and indeed that relationship is indeed linear for most aggregations of animals. For example, Foote (1983) found a linear relationship for fish densities up to 40 pollack (*Pollachius pollachius*) and Atlanticherring (*Clupea harengus*) per m³. We take linearity for granted now, but in the course of determining that relationship, dense aggregations were found to have measured echo intensities that were not linearly proportional to their number densities. For example, early measurements of caged saithe (*Pollachius virens*) and sprat (*Sprattus sprattus*) by Røttingen (1976) suggested a linear relationship of echo intensity to fish density of up to 100 saithe m⁻³ and 2000 sprat m⁻³, and up to 120 saithe m⁻³ and 2500 sprat m⁻³ at 38 and 120 kHz, respectively, but echo intensity deviated from linear at higher fish densities.

As the transmitted sound interacts with targets, a proportion of the acoustic energy is absorbed and scattered by each target. The combined effect of absorption and scattering by a target is called extinction and the extinction cross section of a target is denoted as $σ_e$ (m²). In addition, the scattered sound can interact with the other targets before it travels back to the receiver. This is called multiple scattering, and is often considered a second-order term because it has been shown to be of lesser magnitude than extinction (a first order term) under common survey conditions (Stanton, 1983), and is often assumed to be negligible when compared to the effects of extinction (Foote, 1983; Zhao and Ona, 2003). For the purposes of this response, we assume multiple scattering to be negligible.

When the relationship between echo intensity and fish density is linear, $σ_e$ is negligible and the resulting estimates of fish density ($ρ, \# m⁻³$) are calculated directly using echo integration. As fish density increases, more of the acoustic energy is scattered and absorbed, and increased levels of extinction will reduce the measured acoustic energy at proportionally greater magnitude. In this case, the measured acoustic energy is less than what it should be at the transducer. In terms of volume backscattering ($s_v, m² m⁻³$), the measured volume backscatter ($\hat{s}_v$) is reduced by a factor of $exp(2ρσ_eΔz)$, i.e. $\hat{s}_v \sim s_vexp(2ρσ_eΔz)$, where $s_v$ is the true density, $Δz$ is the vertical extent of the integration layer, and the factor 2 accounts for two-way travel (Foote, 1990). Excess extinction is often called acoustic shadowing (Zhao and Ona, 2003).

To estimate the level of acoustic shadowing, we need to know or estimate the density of scatterers and the extinction cross section of those scatterers. Unfortunately, these are not easy to obtain directly, so methods have been developed to estimate them. The next two sections review methods to identify and correct for acoustic shadowing when there is a reference target and when there is no reference target available.

## Corrections for Acoustic Shadowing Using a Reference Target

The effect of acoustic shadowing in an aggregation can be estimated by comparing the echo intensities of a reference target with and without an intervening aggregation in the acoustic beam. A decline in the echo intensity of the reference target when an aggregation is present is proportional to the extinction cross section of that aggregation (Foote et al., 1992), and this information can be used to derive a ratio between the measured acoustic energy from an aggregation and what that energy would be without shadowing, such as the extinction coefficient ($\frac{σ_e}{σ_b}$) where $σ_b$ is the acoustic backscatter cross section ($TS = \frac{σ_b}{4π}, dB re m²$) (Foote et al., 1992), or the acoustic shadowing coefficient $β = \frac{s_A - \hat{s}_A}{s_A}$, where $s_A$ is true areal backscatter ($s_A = 4π1852² \int_{z1}^{z2} s_v dz, m² nmi⁻²$) without shadowing and $\hat{s}_A$ is the measured areal backscatter, potentially with shadowing (Zhao and Ona, 2003). The seabed is most commonly used as the reference target (e.g. Toresen, 1991; Foote et al., 1992; Zhao and Ona, 2003; Uumati et al., 2010), but
a calibration sphere could be used when stationary, or the sea surface (Utne and Ona, 2006) works for upward-looking transducers.

Zhao and Ona (2003) built on Foote et al. (1992) to provide methods to estimate the level of acoustic shadowing and subsequently correct for it. For this response, we assume an aggregation with homogeneous density and extinction cross sections. We leave the cases of inhomogeneous densities and extinction cross sections for the reader to pursue. The areal backscatter of a reference target without an intervening aggregation ($s_{ARo}$) and with an intervening aggregation ($s_{ARf}$) are used to estimate the shadow coefficient ($\hat{\beta}$) by

$$ \hat{\beta} = \frac{s_{ARo} - s_{ARf}}{s_{ARo}} = K \frac{\sigma_e}{\sigma_b} \hat{s}_{A'} $$

where the constant $K = \frac{2}{1852^2}$ accounts for 2-way travel and conversion from SI units to nautical mile squared (1 nmi = 1852 m). This equation eliminates the need to estimate $\rho$ within the aggregation, but still requires estimates of $\sigma_e$ and $\sigma_b$. Foote et al. (1992) derived a method to estimate the ratio of $\sigma_e$ and $\sigma_b$ ($\gamma = \frac{\sigma_e}{\sigma_b}$ using Zhao and Ona (2003) notation) by using the coefficients of the regression between $s_{ARo}$ and $s_{ARf}$

$$ s_{ARo} = \alpha' + \beta's_{ARf}. $$

$\alpha'$ and $\beta'$ are used to define $\gamma$ as

$$ \gamma = \frac{\sigma_e}{\sigma_b} = \frac{-1852^2 \hat{\beta}'}{2 \hat{\alpha}'}, $$

where $\hat{\beta}'$ and $\hat{\alpha}'$ are the estimated regression coefficients derived from survey data. Higher values of $\gamma$ indicate greater acoustic shadowing. Zhao and Ona (2003) provide a correction factor (CF) for acoustic shadowing:

$$ CF = \frac{1}{K\gamma\hat{s}_A} \cdot \ln\left(\frac{1}{1 - K\gamma\hat{s}_A}\right) $$

Table 1 and Figure 1 show that at $\gamma$ values less than 3, $\hat{s}_A$ values need to be greater than 100,000 m$^2$ nmi$^{-2}$ to have correction factors greater than 10%. The R-code to generate Table 1 and Figure 1 is provided at the end of the response.

Table 1. Measured $s_A$ (x1000 m2 nmi-2) ($\hat{s}_A$), and correction factors (CF) at $\gamma = 1, 2$, and 3. The bold and underlined values represent the 10% correction factor where $\hat{s}_A$ values less than that require less than 10% correction and $\hat{s}_A$ values above that require greater than 10% correction. NAN represents CF values that are invalid (see Foote (1990) and Zhao and Ona (2003) for causes). The R-code to generate this table is provided at the end of this document.

<table><thead><tr><th>σ̂<sub>A</sub></th><th>CF:γ=1</th><th>CF:γ=2</th><th>CF:γ=3</th></tr></thead><tbody><tr><td>1</td><td>1.000</td><td>1.001</td><td>1.001</td></tr><tr><td>5</td><td>1.001</td><td>1.003</td><td>1.004</td></tr><tr><td>10</td><td>1.003</td><td>1.006</td><td>1.009</td></tr><tr><td>15</td><td>1.004</td><td>1.009</td><td>1.013</td></tr><tr><td>20</td><td>1.006</td><td>1.012</td><td>1.018</td></tr><tr><td>25</td><td>1.007</td><td>1.015</td><td>1.023</td></tr><tr><td>30</td><td>1.009</td><td>1.018</td><td>1.027</td></tr><tr><td>35</td><td>1.010</td><td>1.021</td><td>1.032</td></tr><tr><td>40</td><td>1.012</td><td>1.024</td><td>1.037</td></tr><tr><td>45</td><td>1.013</td><td>1.027</td><td>1.042</td></tr></tbody></table>
<table><tr><td>50</td><td>1.015</td><td>1.030</td><td>1.046</td></tr><tr><td>55</td><td>1.016</td><td>1.034</td><td>1.051</td></tr><tr><td>60</td><td>1.018</td><td>1.037</td><td>1.056</td></tr><tr><td>65</td><td>1.019</td><td>1.040</td><td>1.062</td></tr><tr><td>70</td><td>1.021</td><td>1.043</td><td>1.067</td></tr><tr><td>75</td><td>1.023</td><td>1.046</td><td>1.072</td></tr><tr><td>80</td><td>1.024</td><td>1.050</td><td>1.077</td></tr><tr><td>85</td><td>1.026</td><td>1.053</td><td>1.083</td></tr><tr><td>90</td><td>1.027</td><td>1.056</td><td>1.088</td></tr><tr><td>95</td><td>1.029</td><td>1.060</td><td>1.094</td></tr><tr><td>100</td><td>1.030</td><td>1.063</td><td><b>1.099</b></td></tr><tr><td>150</td><td>1.046</td><td><b>1.099</b></td><td>1.160</td></tr><tr><td>200</td><td>1.063</td><td>1.139</td><td>1.231</td></tr><tr><td>250</td><td>1.081</td><td>1.182</td><td>1.315</td></tr><tr><td>300</td><td><b>1.099</b></td><td>1.231</td><td>1.418</td></tr><tr><td>350</td><td>1.118</td><td>1.285</td><td>1.547</td></tr><tr><td>400</td><td>1.139</td><td>1.347</td><td>1.719</td></tr><tr><td>450</td><td>1.160</td><td>1.418</td><td>1.966</td></tr><tr><td>500</td><td>1.182</td><td>1.500</td><td>2.374</td></tr><tr><td>550</td><td>1.206</td><td>1.599</td><td>3.402</td></tr><tr><td>600</td><td>1.231</td><td>1.719</td><td>NaN</td></tr><tr><td>650</td><td>1.257</td><td>1.872</td><td>NaN</td></tr><tr><td>700</td><td>1.285</td><td>2.076</td><td>NaN</td></tr><tr><td>750</td><td>1.315</td><td>2.374</td><td>NaN</td></tr><tr><td>800</td><td>1.347</td><td>2.897</td><td>NaN</td></tr><tr><td>850</td><td>1.381</td><td>4.784</td><td>NaN</td></tr><tr><td>900</td><td>1.418</td><td>NaN</td><td>NaN</td></tr><tr><td>950</td><td>1.457</td><td>NaN</td><td>NaN</td></tr><tr><td>1000</td><td>1.500</td><td>NaN</td><td>NaN</td></tr></table>
<figure><img src="image_6.png" /><figcaption>Figure 1. Correction factor (CF) as a function of (s_A) for three different y values. The horizontal dash-dot is at the 10% correction level. The R-code to generate this figure is provided at the end of this document.</figcaption></figure>

A limitation to using a reference target, such as the seabed echo, is that measurements of that seabed echo must be obtained without the intervening aggregations. This may be difficult under survey conditions, so additional resources may be required to survey the seabed.

## Corrections for Acoustic Shadowing Without a Reference Target

In instances where the seabed echo is not recorded, e.g. when the target species is located where the water depth is much deeper than the depth of the target species, using the seabed as a reference target is not possible.

In the absence of a reference target, the correction factor developed by Zhao and Ona (2003) uses $\gamma$ as a proxy for animal density and extinction coefficient, thus estimates of $\gamma$ may be used to indicate the magnitude of correction. Foote et al. (1992) provide ranges of $\gamma$ for measurements of Atlantic herring at 38 kHz found in their study as well as from the literature from 1.17 to 3.3 (Foote et al., 1992). For these $\gamma$ values, correction factors can range from approximately 3 to 10% for aggregations with $\hat{s}_A$ of 100,000 m$^2$ nmi$^{-2}$ or from about 14 to 70% for aggregations with $\hat{s}_A$ of 400,000 m$^2$ nmi$^{-2}$ (Table 1). Thus, it is up to the analyst to decide what level of estimation they are comfortable with.

## Software

Echoview currently does not have a dedicated module/virtual variable to identify and correct acoustic extinction by fish schools, but the user can build their own process using their virtual variables to do this.

BEI (Bergen Echo Integrator) that is not used anymore did have a processing module to correct for extinction of herring. There is no similar implementation for LSSS, although the code for BEI is available so that implementing correction for extinction would be relatively easy.
## Recommendations

1. Inspect historical data for the prevalence of aggregations that may be affected by acoustic shadowing (e.g. $\hat{s}_A$ values greater than 100,000 m² nmi⁻²), and develop metrics to estimate the magnitude of the effects. Metrics include percentage of aggregations with acoustic shadowing, magnitude of acoustic shadowing, and effects on abundance estimates.

2. If acoustic shadowing is determined to be significant, devote resources to develop survey protocols and collecting additional data.

## References

Foote, K. G. 1983. Linearity of fisheries acoustics, with addition theorems. Journal of the Acoustical Society of America, 73(6): 1932-1940. doi.org/10.1121/1.389583.

Foote, K. G. 1990. Correcting acoustic measurements of scatterer density for extinction. Journal of the Acoustical Society of America, 88(3): 1543-1546, doi.org/10.1121/1.400312.

Foote, K. G. 1999. Extinction cross-section of Norwegian spring-spawning herring. ICES Journal of Marine Science, 56: 606-612, doi.org/10.1006/jmsc.1999.0479

Foote, K. G., Ona, E., Toresen, R. 1992. Determining the extinction cross section of aggregating fish. Journal of the Acoustical Society of America, 91(4):1983-1989. doi.org/10.1121/1.403682.

Raveau, M., and Feuillade, C., 2015. Sound extinction by fish schools: Forward scattering theory and data analysis. Journal of the Acoustical Society of America, 137:539, https://doi.org/10.1121/1.4906172

Rottingen, I. 1976. On the relation between echo intensity and fish density. FiskDir. Skr.Ser.HavUnders., 16: 301-314.

Stanton, T. K. 1983. Multiple scattering with applications to fish-echo processing. Journal of the Acoustical Society of America, 73(4): 1164-1169. doi.org/10.1121/1.389287.

Toresen, R. 1991. Absorption of acoustic energy in dense herring schools studied by the attenuation in the bottom echo signal. Fisheries Research, 10(3-4): 317-327. doi.org/10.1016/0165-7836(91)90083-R

Utne, K. R., Ona, E. 2006. Acoustic extinction in dense herring layers, measured from a bottom-mounted transducer. ICES CM 2006/I:10.

Uumati, M., Ona, E., Hjellvik, V. 2010., "Correction for shadowing within sardine schools," OCEANS 2010 MTS/IEEE SEATTLE, 2010, pp. 1-8, doi: 10.1109/OCEANS.2010.5664456.

Zhao, X., Ona, E. 2003. Estimation and compensation models for the shadowing effects in dense fish aggregations. ICES Journal of Marine Science. 60: 155-163. doi.org/10.1006/jmsc.2002.1319

## R-Code

# Thesaurus: Acoustic-Shadow.R with 18 functions.

1. calculate acoustic shadow correction factors using Zhao and Ona (2003)

2. "Estimation and compensation models for the shadowing effect in dense fish aggregations", ICES JMS, 60:155-163.

3. # jech

4. source('Acoustic-Shadow.R')

5. startwith clean slate

6. rm(list=ls(all=TRUE))

7. generate areal backscatter values. These simulate those used in Fig. 2 of

8. Zhao and Ona
```R
sA = c(seq(0, 100, by=5), seq(150, 1000, by=50))
sA[1] = 1
```

# scale to typical NASC values of aggregations
sA = sA*1000
# the K constant
K = 2/1852^2

# output plot to a .png file with 300 dpi resolution
png('test.png', bg='white', res=300, width=5, height=4, units='in')
# gamma value, ratio of sigma-e and sigma-b, sigma-e/sigma-b
d = 1

# the correction factor (C), equation 27
cf = (1/(K*d*sA))*log(1/(1-K*d*sA))
plot(sA/1000, cf, pch=20, ylim=c(1,2), lty=1, type='l',
      xlab=expression(paste(hat(s)[A], ' (x1000', m^2, '',nmi^-2, ')')),
      ylab=expression(italic('CF')))
abline(h=1.1, lty=4)

# calculate and plot C for gamma values 2 & 3
for (d in 2:3) {
  tmp = (1/(K*d*sA))*log(1/(1-K*d*sA))
  lines(sA/1000, tmp, pch=20, lty=d)
  cf = cbind(cf, tmp)
}

legend(0, 2, lty=c(1,2,3), box.col='white',
        legend=c(expression(paste(gamma,'=1')),
                   expression(paste(gamma,'=2')),
                   expression(paste(gamma,'=3'))))
dev.off()

# print the values
cat('Measured sA', '\t\t', 'CF:d=1', '\t\t', 'CF:d=2', '\t\t',
     'CF:d=3', '\n')

for (i in 1:length(sA)) {
  cat sprintf('%of', sA[i]/1000), '\t\t',
    sprintf('%3f, cf[i,1]), '\t\t',
    sprintf('%3f, cf[i,2]), '\t\t',
    sprintf('%3f, cf[i,3]), '\t\t',
    '\n')
}
Annex 5: Report on WGAST Resource Surveys

<table><thead><tr><td>Country</td><td>Institute</td><td>Survey name</td><td>Target stock(s)</td><td>Species [latin names]</td><td>Area</td><td>Survey date [month(s)]</td><td>Output(s)</td></tr></thead><tbody><tr><td>Australia</td><td>CSIRO</td><td>NZ orange roughy</td><td>Orange roughy</td><td>Hoplostethus atlanticus</td><td>New Zealand Chatham Rise</td><td>June/July 2021</td><td>Biomass estimate</td></tr><tr><td>Australia</td><td>CSIRO</td><td>Southern Ocean Time Series (SOTS)</td><td>Mesopelagics</td><td>Multiple</td><td>Southern Ocean</td><td>April 2021, April 2021, May 2022</td><td>Census of mesopelagic species using optics and acoustics, behavioural observations in response to platform</td></tr><tr><td>Australia</td><td>CSIRO</td><td>Cascade Plateau orange roughy surveys</td><td>Orange roughy</td><td>Hoplostethus atlanticus</td><td>Southeast Tasmanian, Australia</td><td>June 2021, June 2022</td><td>Observations of fish schools, distribution and bheavious, biomass estimates of spawning aggregations</td></tr><tr><td>Australia</td><td>CSIRO</td><td>Tasmanian West Coast Blue grenadier surveys</td><td>Blue grenadier</td><td>Macruronus novaezelandiae</td><td>West Coast, Tasmania, Australia</td><td>June-August 2020, 2021, 2022</td><td>Biomass estimates based on opportunistic transect surveys carried out during fish processing time. Season-long detection of schools to produce metrics of abundance</td></tr><tr><td>Australia</td><td>CSIRO</td><td>IMOS Bioacoustics sub-Facility</td><td>Pelagic and mesopelagics to 1000 m</td><td>Multiple</td><td>Indian Ocean, Southern Ocean, Tasman Sea, Pacific Ocean</td><td>2020-2022 opportunistic collection of bioacoustic data from 10 vessels</td><td>NetCDF of quality controlled echointegration data of ocean-basin scale acoustic backscatter posted to a public repository (https://port.aodn.org.au/search?uuid=8edf509b-1481-48fd-b9c5-b95b42247f82)</td></tr><tr><td>Brazil</td><td>Insititut de Recherche pour le Développement (IRD) Universidade Federal de Pernambuco (UFPE)</td><td>ABRAÇOS 2 (Acoustic Along the Brazilian Coast) http://dx.doi.org/10.17600/17004100</td><td>Ecosystem</td><td>Multiple</td><td>Northeast Brazil</td><td>April-May 2017</td><td>Comprehensive three-dimensional characterization of the demersal and pelagic ecosystems in Northeast Brazil</td></tr></tbody></table>
<table><tr><td></td><td>Universidade Federal Rural de Pernambuco (UFRPE)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Brazil</td><td>Insititut de Recherche pour le Développement (IRD); Universidade Federal de Pernambuco (UFPE); Universidade Federal Rural de Pernambuco (UFRPE)</td><td>AMAZOMIX</td><td>Multiple</td><td>Multiple including Melichtys niger Canthidermis sufflamen Sphyraena barracuda</td><td>Amazon shelf off French Guiana and North-east Brazil</td><td>August-October 2021</td><td>Demersal and pelagic ecosystems and oceanography in Northeast Brazil</td></tr><tr><td>Cabo Verde</td><td>Instituto do Mar (IMar) / Sea Institute</td><td>CABO VERDE ECOSYSTEM SURVEY</td><td></td><td></td><td>Around the Cape Verde islands<br>The survey covered waters from shallow depths (about 20 m depth) to upper slope (about 1000 m depth)</td><td>20 November - 15 December, 2021</td><td>Assess the demersal and pelagic resources of the continental shelf and upper slope by determining their distribution and abundance, while also studying the oceanographic conditions, distribution of microplastics and marine debris, and measure nutrient and contaminant levels in commercial fish</td></tr><tr><td>Cabo Verde</td><td>Instituto do Mar (IMar) / Sea Institute in coloboration with GEOMAR</td><td>MSM106 Wascal Floating University</td><td></td><td></td><td>St. Luzia (Marine Reserve), Nola Seamount and Eddy in the north of São Vicente to the site CVOO</td><td>26 February to 19 March 2022</td><td>Echosystemic approach</td></tr><tr><td>Canada</td><td>DFO Institute of Ocean Sciences</td><td>Joint U.S.-Canada Pacific hake survey</td><td>Pacific hake</td><td>Merluccius productus</td><td>West coast of North America</td><td>June-September 2021</td><td>Index of abundance-at-age (age 2+), age-1 index.</td></tr></table>
<table><tr><td>Canada</td><td>DFO Institute of Ocean Sciences</td><td>Joint U.S.-Canada Pacific hake survey</td><td>Pacific hake</td><td>Merluccius productus</td><td>West coast of North America</td><td>August-September 2020</td><td>Research on Pacific Hake migration</td></tr><tr><td>Canada</td><td>DFO Institute of Ocean Sciences</td><td>La Perouse zooplankton surveys</td><td>Zooplankton</td><td>Multiple</td><td>West coast Vancouver Island</td><td>May and September 2020, 2021</td><td>Index of zooplankton abundance</td></tr><tr><td>Canada</td><td>DFO Pacific Biological Station</td><td>Strait of Georgia juvenile herring survey</td><td>Pacific herring and other pelagics</td><td>Multiple</td><td>Strait of Georgia</td><td>September 2020, 2021</td><td>Index of abundance for juvenile herring and pelagic species</td></tr><tr><td>Canada</td><td>DFO Pacific Biological Station</td><td>Seamounts surveys</td><td>Zooplankton and pelagic species</td><td>Multiple</td><td>Offshore West coast of Canada</td><td>June 2021</td><td>Abundance and distribution of pelagic species in seamounts areas of interest</td></tr><tr><td>Canada</td><td>DFO Institute of Ocean Sciences</td><td>Line P survey</td><td>Zooplankton and pelagic species</td><td>Multiple</td><td>West coast Canada</td><td>May, September, and February 2020, 2021</td><td>Long time-series of zooplankton and fish abundance</td></tr><tr><td>Canada</td><td>Fisheries and Marine Institute of Memorial University/Université Laval/Fisheries and oceans Canada</td><td>ArcticNet and Amundsen Science</td><td>Polar cod, zooplankton, and mesopelagic species</td><td>Boreogadus saida, Benthosema glaciale, and others</td><td>Canadian Arctic and Labrador Sea</td><td>Annual survey (summer-autumn) between 2004 and 2022</td><td>Index of abundance (age-0 and age-1+). Target Strength. Long time-series of zooplankton and fish abundance</td></tr><tr><td>Canada</td><td>DFO Maurice Lamontagne Institute</td><td>nGSL herring acoustic survey</td><td>Atlantic herring</td><td>Multiple</td><td>Northern Gulf of St. Lawrence</td><td>July 2019-present; November 2009-present</td><td>Age-stratified herring abundance index</td></tr><tr><td>Canada</td><td>DFO Maurice Lamontagne Institute</td><td>nGSL bottom-trawl multispecies survey</td><td>Redfish, Atlantic cod, herring and capelin</td><td>Multiple</td><td>Northern Gulf of St. Lawrence</td><td>August 2012-present</td><td>Index of abundance</td></tr><tr><td>Canada</td><td>DFO Newfoundland Region</td><td>Northeast coast Newfoundland herring</td><td>Newfoundland NE coast Atlantic herring</td><td>Clupea harengus</td><td>NE coast of NL</td><td>October-November, 2020-2022</td><td>Biomass index</td></tr></table>
<table><tr><td>Canada</td><td>DFO Newfoundland-land Region</td><td>South coast Newfoundland herring</td><td>Newfoundland South coast Atlantic herring</td><td>Clupea harengus</td><td>Placentia and Fortune Bays</td><td>February-March 2020,2022</td><td>Biomass index</td></tr><tr><td>Canada</td><td>DFO Newfoundland-land Region</td><td>Spring capelin</td><td>NAFO Divsions 2J3KL capelin</td><td>Mallotus villosus</td><td>NAFO Division 3L</td><td>May 2020-2022</td><td>Biomass index</td></tr><tr><td>Canada</td><td>DFO Newfoundland-land Region</td><td>Capelin/Arctic Cod Reserach</td><td>NAFO Divsions 2J3KL capelin</td><td>Mallotus villosus / Boregogadus saida</td><td>NAFO Division 2J3K</td><td>Jan 2023</td><td>CW and FM species response curves</td></tr><tr><td>Denmark<br>Germany<br>Ireland<br>Netherlands<br>Norway<br>UK (Scotland)</td><td>DTU-Aqua (DK), Thünen-Institute of Sea Fisheries (GER), Marine Institute Ireland (IRL), WMR-Wageningen Marine Research (NL), IMR - Institute of Marine Research (NOR), Marine Scotland Science (UK-SCO)</td><td>The ICES Coordinated Acoustic Survey in the Skagerrak and Kattegat, the North Sea, West of Scotland and the Malin Shelf area (HERAS)</td><td>North Sea Autumn Spawning Herring NSAS, Western Baltic Spring Spawning Herring WBSS, West of Scotland autumn spawning Herring (VIa N), Malin Shelf Herring (MSHAS); North Sea Sprat (Sub-area 4); Spratin Div3a.</td><td>Clupea harengus, Sprattus sprattus, (Engraulis encrasicolus, Sardina pilchardus)</td><td>Continental shelf of North Sea north of 52°N incl. West of Scotland and Ireland to northern limit of 62°N</td><td>June/July</td><td>Biomass Index<br>Abundanxe Index<br>Age structure<br>Mean weight at age<br>Maturity</td></tr><tr><td>Denmark, Estonia, Finland, Germany, Latvia, Lithuania, Poland, Russia, Sweden</td><td>Danish Technical University, National Institute of Aquatic Resources; University of Tartu, Estonian Marine Institute; Natural Resources Institute Finland;</td><td>BIAS (Baltic International Acoustic Survey)</td><td>Baltic sprat and herring (spr.27.22-32, her.27.20-24, her.27.25-2932)</td><td>Clupea harengus membras, sprattus sprattus</td><td>Baltic Sea (IIIb-d)</td><td>September/October</td><td>Age stratified index of abundance</td></tr></table>
Thünen-Institute
of Baltic Sea
Fisheries;

Institute of Food
Safety, Animal
Health and Envi-
ronment (BIOR),
Fish Resources
Research Depart-
ment;

Marine Research
Institute

Klaipeda Univer-
sity;

National Marine
Fisheries Re-
search Institute;

AtlantNIRO;

Swedish Univer-
sity of Agricul-
tural Sciences,
Department of
Aquatic Re-
sources

Estonia,
Germany,

Latvia,

Lithuania,

Poland,

Russia,

Sweden

University of
Tartu, Estonian
Marine Institute;

Thünen-Institute
of Baltic Sea
Fisheries;

Institute of Food
Safety, Animal
Health and Envi-
ronment (BIOR),
Fish Resources
Research Depart-
ment;

BASS (Baltic Acoustic Spring Survey)

Baltic sprat
stock
(spr.27.22-32)

Sprattus sprattus

Baltic Sea (IIIb-
d)

May/June

Age stratified index of abundance
<table><tr><td></td><td>Marine Research Institute<br/>Klaipeda University;<br/>National Marine Fisheries Research Institute;<br/>AtlantNIRO;<br/>Swedish University of Agricultural Sciences, Department of Aquatic Resources</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Estonia, Latvia</td><td>University of Tartu, Estonian Marine Institute; Institute of Food Safety, Animal Health and Environment (BIOR)</td><td>GRAHS (Gulf of Riga Acoustic Herring Survey)<br/>Gulf of Riga herring stock (her.27.28.1)</td><td>Clupea harengus membras</td><td>Gulf of Riga in Baltic Sea (ICES SD 28.1)</td><td>July/August</td><td>Age stratified index of abundance</td><td></td><td></td></tr><tr><td>France</td><td>Ifremer</td><td>PELGAS</td><td>Anchovy, sardine</td><td>Engraulis encrasicolus, Sardina Pilchardus,</td><td>Bay of Biscay</td><td>May, annually</td><td>Age stratified index of abundance, distribution</td><td></td></tr><tr><td>France</td><td>Ifremer</td><td>PELGAS</td><td>Horse mackerel, mackerel, sprat, boarfish</td><td>Trachurus trachurus, Sprattus sprattus</td><td>Bay of Biscay</td><td>May, annually</td><td>Abundance index, distribution</td><td></td></tr><tr><td>France</td><td>Ifremer</td><td>PELMED</td><td>Anchovy, sardine,</td><td>Engraulis encrasicolus, Sardina Pilchardus,</td><td>Gulf of Lion</td><td>July, annually</td><td>Age stratified index of abundance</td><td></td></tr><tr><td>France</td><td>Ifremer</td><td>PELMED</td><td>Horse mackerel, sprat</td><td>Trachurus trachurus, Sprattus sprattus</td><td>Gulf of Lion</td><td>July, annually</td><td>Abundance index, distribution</td><td></td></tr></table>
<table><thead><tr><th>France</th><th>Ifremer</th><th>ESSDRIX</th><th>Multiple</th><th>Gulf of Lion</th><th>November</th><th>Technical assessment of abundance index measured by USV compared to Research Vessel</th></tr></thead><tbody><tr><td>Germany</td><td>Thünen-Institute of Sea Fisheries/Thünen-Institute of Baltic Sea Fisheries</td><td>German Acoustic Autumn Survey (GERAS)</td><td>Western Baltic Spring Spawning Herring WBSS, Central Baltic Herring CBH, Baltic Sea Sprat (SD22-32)</td><td>Clupea harengus, Sprattus sprattus, (Engraulis encrasicolus)</td><td>October</td><td>Biomass Index<br>Abundance Index<br>Age structure<br>Mean weight at age<br>Maturity</td></tr><tr><td>Greece</td><td>Hellenic Centre for Marine Research (HCMR)</td><td>MEDIAS (GSA 20 and 22)</td><td>European anchovy and Sardine</td><td>Engraulis encrasicolus<br>Sardina pilchardus</td><td>Jun-Jul (Aegean Sea)<br>Sep-Oct (east. Ionian Sea)</td><td>Index of abundance-at-age</td></tr><tr><td rowspan="2">Iceland</td><td rowspan="2">Marine and freshwater research institute</td><td rowspan="2">International ecosystem survey in Norwegian Sea (IESNS)</td><td rowspan="2">Norwegian spring-spawning herring</td><td rowspan="2">Clupea harengus</td><td>May 2020<br>May 2021<br>May 2022</td><td rowspan="2">Index of abundance</td></tr><tr><td>May 2020<br>May 2021<br>May 2022</td></tr><tr><td>Iceland</td><td>Marine and freshwater research institute</td><td>International ecosystem survey in Norwegian Sea (IESNS)</td><td>Norwegian spring-spawning herring</td><td>Clupea harengus</td><td>May 2020<br>May 2021<br>May 2022</td><td>Index of abundance</td></tr><tr><td>Iceland</td><td>Marine and freshwater research institute</td><td>Herring survey</td><td>Icelandic summer-sp. herring</td><td>Clupea harengus</td><td>Icelandic shelf (in E, S and W)</td><td>November-December and March</td></tr><tr><td>Iceland</td><td>Marine and freshwater research institute</td><td>Capelin autumn survey</td><td>Icelandic capelin</td><td>Mallotus villosus</td><td>Irminger Sea, Greenland Sea, Iceland Sea and Denmark Strait</td><td>Biomass estimate<br>September-October 2020<br>September-October 2021<br>September-October 2022</td></tr></tbody></table>
<table><tr><td>Iceland</td><td>Marine and freshwater research institute</td><td>Capelin winter survey</td><td>Icelandic capelin</td><td>Mallotus villosus</td><td>Icelandic Waters</td><td>January-February 2020<br>January-February 2021<br>January-February 2022</td><td>Biomass estimate</td></tr><tr><td>Iceland</td><td>Marine and freshwater research institute</td><td>International ecosystem summer survey in Nordic Seas (IESSNS)</td><td>Norwegian spring-spawning herring and blue whiting</td><td>Clupea harengus and Micromesistius poutassou</td><td>Nordic Seas</td><td>July 2020<br>July 2021<br>July 2022</td><td>Indices of abundance</td></tr><tr><td>Iceland</td><td>Marine and freshwater research institute</td><td>Icelandic Spring survey</td><td>Krill</td><td>Meganyctiphanes norvegica and Thysanoessa intermis</td><td>Icelandic Waters</td><td>May 2020<br>May 2021<br>May 2022</td><td>Index of abundance</td></tr><tr><td>Iceland</td><td>Marine and freshwater research institute</td><td>Mapping of the seabed</td><td>N/A</td><td>N/A</td><td>Within Iceland's EEZ</td><td>June 2020<br>August 2020<br>June 2021<br>August 2021</td><td>Multibeam bathymetry Backscatter (applies to all surveys)</td></tr><tr><td>Italy</td><td>CNR-National Research Council, IRBIM-Institute for Marine Biological Resources and Biotechnologies</td><td>MEDIAS (FAO GSAs 17 and 18)</td><td>European anchovy and Sardine</td><td>Engraulis encrasicolus<br>Sardina pilchardus</td><td>Western Adriatic Sea</td><td>June-July, annually</td><td>Biomass index<br>Abundance index<br>Age structure<br>Spatial distribution</td></tr><tr><td>Italy</td><td>CNR-National Research Council, IRBIM-Institute for Marine Biological Resources</td><td>MarE-Albania (FAO GSA18)</td><td>European anchovy and Sardine</td><td>Engraulis encrasicolus<br>Sardina pilchardus</td><td>Southeastern Adriatic Sea (Albania)</td><td>May 2021</td><td>Biomass index<br>Abundance index<br>Age structure<br>Spatial distribution</td></tr></table>
<table><tr><td></td><td>and Biotechnologies</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Italy</td><td>CNR-National Research Council, IRBIM-Institute for Marine Biological Resources and Biotechnologies</td><td>ROSSKRILL</td><td>Antarctic krill, crystal krill</td><td>krill (Euphausia superba, Euphausia crystallorophias)</td><td>Ross Sea, Antarctica</td><td>January 2022</td><td>Biomass index<br>Abundance index<br>Spatial distribution</td></tr><tr><td>Ireland</td><td>Marine Institute</td><td>Celtic Sea herring acoustic survey (CSHAS)</td><td>HER, SPR</td><td>Herring (Clupea harengus)<br>Sprat (Sprattus sprattus)</td><td>Celtic Sea, Ireland</td><td>October, annually</td><td>Age stratified index of abundance</td></tr><tr><td>Ireland</td><td>Marine Institute</td><td>International blue whiting spawning stock survey (IBWSS)</td><td>WHB, Mesopelagic spp</td><td>Blue whiting (Micromesistius poutassou)</td><td>W of Ireland &amp; Scotland</td><td>March-April, annually</td><td>Age stratified index of abundance</td></tr><tr><td>Ireland</td><td>Marine Institute</td><td>Western European Shelf Pelagic Acoustic Survey (WESPAS)</td><td>HER, BOC, HOM(W)</td><td>Herring (Clupea harengus), Boarfish (Capros aper), Horse mackerel (Trachurus trachurus)</td><td>W of Ireland &amp; Celtic Sea</td><td>June-July, annually</td><td>Age stratified index of abundance</td></tr><tr><td>Ireland and UK (Scotland)</td><td>Marine Institute and Marine Scotland Science</td><td>6a7bc herring industry survey</td><td>HER</td><td>Herring (Clupea harengus)</td><td>W of Ireland &amp; Scotland</td><td>Aug/Sept (autumn spawning) and Oct/Jan (winter spawning) annually</td><td>Age stratified index of abundance</td></tr><tr><td>Netherlands</td><td>Wageningen Marine Research</td><td>International blue whiting spawning stock survey (IBWSS)</td><td>WHB, Mesopelagic spp</td><td>Blue whiting (Micromesistius poutassou)</td><td>W of Ireland &amp; Scotland</td><td>March-April, annually</td><td>Age stratified index of abundance</td></tr></table>