Best Practices for Implementing 

Acoustic Technologies to 

Improve Reef Fish Ecosystem Surveys 

Report from the 2017 GCFI Workshop 

NOAA Technical Memorandum NMFS-F/SPO-192 

April 2019 

Best Practices for Implementing Acoustic Technologies

to Improve Reef Fish Ecosystem Surveys 

Report from the 2017 GCFI Acoustics Workshop

William L. Michaels1, Benjamin Binder2, Kevin Boswell2, Laurent M. Chérubin3, David A. Demer4, 

Toby Jarvis5, Frank Reier Knudsen6, Carolina Lang7, Sergio Lillo7, Jorge Enrique Paramo7, Patrick 

J. Sullivan8, J. Christopher Taylor9 and Charles H. Thompson10

1 NOAA, National Marine Fisheries Service, Office of Science and Technology, 

Silver Spring, Maryland, USA 2

Florida International University, Department of Marine Biology, North Miami, Florida, USA

3 Florida Atlantic University, Harbor Branch Institute of Oceanography, Boca Raton, Florida, USA 4 National Oceanic and Atmospheric Administration, National Marine Fisheries Service, Southwest 

Fisheries Science Center, San Diego, California, USA 5 Echoview Software Pty Ltd, Hobart, Tasmania, AUSTRALIA

6 Kongsberg Maritime AS, Bergen, NORWAY 7 University of Magdalena, Santa Marta, COLOMBIA

8 Cornell University, Department of Natural Resources, Ithaca, New York, USA

9 NOAA National Ocean Service, National Centers for Coastal Ocean Science, 

Beaufort, North Carolina, USA

10 NOAA National Marine Fisheries Service, Southeast Fisheries Science Center, 

Pascagoula, Mississippi, USA

April 2019 

NOAA Technical Memorandum NMFS-F/SPO-192

U.S. Department

Of Commerce

National Oceanic and

Atmospheric Administration

National Marine

Fisheries Service

Wilbur L. Ross, Jr.

Secretary of Commerce

Neil A. Jacobs, PhD

Acting Under Secretary of Commerce

for Oceans and Atmosphere

and NOAA Administrator

Christopher W. Oliver

Assistant Administrator

for Fisheries
Recommended citation:

Michaels, W.L., B. Binder, K. Boswell, L.M. Chérubin, D.A. Demer, T. Jarvis, F.R. Knudsen, C. Lang, 

J.E. Paramo, P.J. Sullivan, S. Lillo, J.C. Taylor, C.H. Thompson. 2019. Best Practices for Implementing 

Acoustic Technologies to Improve Reef Fish Ecosystem Surveys: Report from the 2017 GCFI 

Acoustics Workshop. NOAA Tech. Memo. NMFS-F/SPO-192, 161 p. 

Available online at https://spo.nmfs.noaa.gov/tech-memos

The National Marine Fisheries Service (NMFS, or NOAA Fisheries) does not approve, recommend, or 

endorse any proprietary product or proprietary material mentioned in the publication. No reference 

shall be made to NMFS, or to this publication furnished by NMFS, in any advertising or sales promotion 

that would indicate or imply that NMFS approves, recommends, or endorses any proprietary product or 

proprietary material mentioned herein, or which has as its purpose an intent to cause directly or 

indirectly the advertised product to be used or purchased because of this NMFS publication.
CONTENTS 

Acknowledgements....................................................................................................................................... 1

Reconocimiento ............................................................................................................................................ 2

Executive Summary....................................................................................................................................... 3

Resúmen Ejecutivo........................................................................................................................................ 4

1 Introduction (W.L. Michaels) ............................................................................................................ 5

1.1 Background ................................................................................................................................... 5

1.2 Priorities for Building Scientific Capacity in the Region................................................................ 6

1.3 Terms of Reference for the GCFI Acoustic Symposium and Workshop......................................12

2 Suggested Sources for Understanding Acoustic Theory (K. Boswell and D.A. Demer)...................16

3 Statistical Design (P.J. Sullivan).......................................................................................................17

3.1 Basic Summary Statistics.............................................................................................................18

3.2 Cluster Sampling .........................................................................................................................19

3.3 Estimation Methods....................................................................................................................20

3.4 Example Application to Reveal Implications of Sampling Design Assumptions .........................22

3.5 Summary of survey design considerations.................................................................................32

4 Acoustic Survey Operations (D.A. Demer and K. Boswell)..............................................................34

4.1 Pole or hull mounting or towbodies...........................................................................................34

4.2 Power requirements and grounding...........................................................................................37

4.3 Survey and vessel speed considerations.....................................................................................37

4.4 Noise sources and interference from other systems..................................................................38

4.5 GPS and motion sensors and other ancillary data......................................................................38

4.6 Echosounder Calibration.............................................................................................................39

5 Direct and Indirect Biological Data (C.H. Thompson) .....................................................................41

5.1 Camera systems..........................................................................................................................41

5.2 Species composition ...................................................................................................................42

5.3 Size composition .........................................................................................................................43

5.4 Diver surveys...............................................................................................................................44

5.5 Physical sampling - Nets, traps, hook and line............................................................................44

6 Scientific Echosounder System (F.R. Knudsen)...............................................................................45

6.1 Transducers................................................................................................................................. 45

6.2 Frequency....................................................................................................................................50
6.3 Transmit Signal............................................................................................................................51

6.4 Power..........................................................................................................................................52

6.5 Pulse Length................................................................................................................................53

6.6 Pulse Repetition Rate..................................................................................................................54

6.7 Recording Range .........................................................................................................................55

6.8 Summary considerations on echosounder selection and settings.............................................55

7 Echosounder Data Processing (T. Jarvis).........................................................................................56

7.1 Overview .....................................................................................................................................56

7.2 Data exploration .........................................................................................................................58

7.3 Data calibration...........................................................................................................................59

7.4 Data cleaning ..............................................................................................................................63

7.5 Target detection and tracking.....................................................................................................67

7.6 Target classification ....................................................................................................................71

7.7 Characterization..........................................................................................................................73

8 Acoustic Data Interpretation and Abundance Estimation (D.A. Demer)........................................76

8.1 Target Strength Estimation.........................................................................................................76

8.2 Biomass Estimation.....................................................................................................................76

8.3 Uncertainty Estimation ...............................................................................................................77

9 Case Study: Fishery Acoustic Surveys to Locate, Assess and Monitor Reef Fish Spawning 

Aggregations (B. Binder, J.C. Taylor and K. Boswell)...................................................................................78

9.1 Background ................................................................................................................................. 78

9.2 Approaches................................................................................................................................. 79

9.3 Searching for Aggregations in the Florida Keys National Marine Sanctuary ..............................79

9.4 Estimating Abundance of Goliath Grouper in the Southeast Florida .........................................79

9.5 Multibeam Echosounders for Estimating Aggregation Abundance............................................83

9.6 Implications for Fisheries Management ..................................................................................... 84

10 Case Study: Stationary Acoustic Measurement Instruments and Methods 

(J.C. Taylor and K. Boswell) .........................................................................................................................85

10.1 Detecting and tracking movements of reef organisms in complex habitats..............................86

10.2 Predator-prey interactions..........................................................................................................88

11 Case Study: Modelled Day-Night Biases in Spatial Structure of Jack Mackerel 

(Trachurus murphyi) in Chile (J.E. Paramo, C. Lang, S. Lillo) .......................................................................92

11.1 Introduction ................................................................................................................................92

11.2 Materials and Methods...............................................................................................................92
11.3 Results.........................................................................................................................................93

11.4 Discussion ....................................................................................................................................97

12 Case Study: Passive Acoustic Monitoring in Coral Reef Ecosystems (L.M. Cherubin) ....................99

12.1 Background ................................................................................................................................. 99

12.2 Reef Habitat Soundscape............................................................................................................99

12.3 Reef-Specific Sounds and Larval Settlement.............................................................................100

12.4 Reef Health Acoustic Signature................................................................................................. 100

12.5 Sound Production in Fish Spawning Aggregations....................................................................102

12.6 Persistent Presence Robotic Approach for Detection and Characterization of Fish Spawning 

Aggregations.........................................................................................................................................105

13 Conclusion (W.L. Michaels)...........................................................................................................110

13.1 Priorities for Building Capacity with Technologies ...................................................................110

13.2 Lessons Learned and Recommendations..................................................................................112

14 Glossary .........................................................................................................................................114

15 Literature Cited .............................................................................................................................117

16 Appendices....................................................................................................................................135

16.1 Appendix A. Symposium Terms of Reference and Abstracts....................................................135

16.2 Appendix B. Workshop Terms of Reference and Agenda.........................................................148

16.3 Appendix C. Participant Lists.....................................................................................................152

16.4 Appendix D. Results from the Workshop Breakout Sessions. ..................................................157
1 

ACKNOWLEDGEMENTS 

This symposium, workshop and resulting report was made possible through the Gulf and Caribbean 

Fisheries Institute (GCFI) Ocean Innovation Strategic Initiative grant supported by the National Oceanic 

Atmospheric Administration (NOAA). Robert Glazer (GCFI Executor Director) is acknowledged for the 

grant submission with the goal of improving scientific information using innovative technologies for the 

sustainability of living marine resources in the Gulf of Mexico and Caribbean regions. Financial support 

of this grant was provided by the NOAA Fisheries’ Office of Science and Technology. In addition to the 

GCFI-NOAA partnership, we are grateful for the financial and technical support from Kongsberg Simrad 

and Echoview Pty Ltd, who were critical in the success of this acoustic training workshop.

The steering committee comprised of internationally recognized experts (Tonny Algrøy, Kevin Boswell, 

Jeff Condiotty, David Demer, Toby Jarvis, Frank Reier Knudsen, William Michaels, Patrick Sullivan, Chris 

Taylor, Charles Thompson) are recognized for developing the framework of this symposium and 

workshop. Alejandro Acosta and Robert Glazer were key organizers for the 70th annual GCFI Conference 

that provided the international forum for this concurrent acoustic symposium and workshop held in 

Merida, Mexico during November 4-10, 2017. Experts from Kongsberg Simrad (Tonny Algrøy, Jeff 

Condiotty, Lars Nonboe Anderson, and Leonardo Jose Figueroa Argueta) provided logistical support with 

acoustic instrumentation, and Luis Rodriquez organized the boat charter for the acoustic training. Emilia 

Cortes Gomez and Leonardo Jose Figueroa Argueta helped with English-Spanish translations for the 

workshop. Most importantly, the combined efforts of the instructors, support staff, and participants 

contributed to the overall success of the workshop that we hope will result in long-lasting collaborative 

expertise to improve the sustainability and health of reef-fish ecosystems in the region. 
2 

RECONOCIMIENTO 

Este simposio, el taller y el informe final han sido posibles gracias a la contribución e iniciativa 

estratégica de Innovación Oceánica del Instituto de Pesquerías del Golfo y el Caribe (GCFI) patrocinada 

por la Administración Nacional de la Atmósfera Oceánica (NOAA). Hacemos un reconocimiento a Robert 

Glazer (Director Ejecutivo de GCFI) por la presentación del proyecto, con el objetivo de enriquecer la 

información científica utilizando tecnologías innovadoras para la sostenibilidad de los recursos marinos 

vivos en las regiones del Golfo de México y el Caribe. Este Proyecto fué patrocinado por la Oficina de 

Ciencia y Tecnología de Pesquerías de NOAA. Además de la asociación GCFI-NOAA, estamos agradecidos 

con Kongsberg Simrad y Echoview Pty Ltda, por el apoyo financiero y técnico que fueron fundamentales 

para el éxito de éste taller de capacitación acústica.

El comité directivo compuesto por expertos reconocidos internacionalmente (Tonny Algrøy, Kevin 

Boswell, Jeff Condiotty, David Demer, Toby Jarvis, Frank Reier Knudsen, William Michaels, Patrick 

Sullivan, Chris Taylor, Charles Thompson) son reconocidos por desarrollar el marco de este simposio y 

taller. Alejandro Acosta y Robert Glazer quienes fueron los organizadores clave de la 70 Conferencia 

anual de GCFI que proporcionó el foro internacional para éste simposio y el taller de acústica simultáneo 

celebrado en Mérida, México, del 4 al 10 de noviembre de 2017. A la experiencia de Kongsberg Simrad 

(Tonny Algrøy, Jeff Condiotty, Lars Nonboe Anderson, y Leonardo José Figueroa Argueta) quienes 

proporcionaron apoyo logístico con instrumentación acústica, y a Luis Rodríguez por facilitar el barco 

utilizado durante el entrenamiento acústico. A Emilia Cortes Gómez y Leonardo José Figueroa Argueta 

quienes brindaron asistencia en la traducción del inglés al español en el material de capacitación y 

durante el taller. Lo más importante es que los esfuerzos combinados de los instructores, el personal de 

apoyo y los participantes contribuyeron al éxito general del taller y al informe final que tendrán un 

impacto perdurable en la construcción conjunta de conocimiento y experiencia que permitan mejorar 

los ecosistemas de peces de arrecife en la región.
3 

Best Practices for Implementing Acoustic Technologies to Improve Reef Fish 

Ecosystem Surveys: Report from the 2017 GCFI Acoustics Workshop.

EXECUTIVE SUMMARY 

Approximately 330 participants from 30 countries attended the 70th Gulf and Caribbean Fisheries 

Institute (GCFI) conference held in Merida, Mexico during 4-10 November 2017. The conference 

featured a symposium on Acoustic Technologies to Improve Reef Fish Ecosystem Surveys, which

provided presentations on the various acoustic technologies used to monitor reef fish ecosystems. An 

acoustic workshop was also held to build scientific capacity and foster collaborative acoustic expertise, 

in order to enhance research and survey operations in reef-fish ecosystems. Recognizing the 

connectivity of marine populations across the geopolitical jurisdictions of the wider Caribbean region, 

National Oceanic and Atmospheric Administration (NOAA) supported the GCFI Ocean Innovation 

Strategic Initiative grant to build scientific capacity in the region for the sustainability of living marine 

resources. 

The acoustic workshop was conducted over three days with collaboration between GCFI, NOAA, 

Kongsberg-Simrad, Echoview and the South East Acoustic Consortium (SEAC). On the first day 

(November 4th), 33 participants and instructors from 10 countries departed from city of Progreso aboard 

the boat Isla Mujeres for training on the use of three versions of the Simrad EK80 wideband

echosounder: EK80 WBTs (designed for ships), EK80 WBAT (designed for autonomous platforms), and 

portable EK80 EcoSounder. Acoustic data was collected on a reef site approximately 18 miles offshore. 

During the second day (November 5th), participants received an overview of echosounder data 

processing and analytical methods using Echoview software, with a focus on reef environments. 

Participants learned how to load, calibrate and clean their acoustic data, how to detect, track and 

classify reef fish, and how to quantify their distribution and abundance. The third day of the acoustic 

workshop (November 9th) brought together the diverse perspectives of 57 participants from 13 

countries to address the operational challenges of conducting acoustic surveys of reef-fish ecosystems. 

Case studies were presented by experts on various topics relevant to conducting acoustic operations in 

reef-fish habitats, such as statistical survey design, sensor selection and configuration, and interpreting 

acoustic data. Participants worked to prioritize management and operational objectives that can feasibly

be achieved with acoustic technologies in the region, and developed the framework for the technical 

report. 

This report provides guidance on the best practices for active- and passive-acoustic operations to 

enhance the understanding of reef-fish ecosystems. The acoustic workshop forged collaborative 

partnerships and provided direction for future training workshops on the integration of acoustic and 

optical technologies. These collaborative efforts are critical for building the next generation of experts, 

whose role will be to optimize survey operations with technologies for improving the sustainability of 

living-marine-resources in reef-fish ecosystems with connectivity across the geopolitical jurisdictions of 

the region. 

 
4 

Buenas Prácticas para la Implentacíon de Technologías Acústicas para el Mejoramiento de 

Análisis en los Ecosistemas de Peces de Arrecife: Informe del Taller de Acústica de GCFI 2017. 

RESÚMEN EJECUTIVO 

Aproximadamente 330 participantes provenientes de 30 paises asistieron a la 70 Conferencia del 

Instituto Pesquero del Golfo y el Caribe (GCFI) celebrada en Mérida, México, del 4 al 10 de noviembre de 

2017. En esta conferencia GCFI, presentó el simposio sobre Tecnologías Acústicas para el mejoramiento 

de estimaciones de ecosistemas de peces de arrecife, realizando 12 presentaciones sobre las diversas 

tecnologías acústicas utilizadas para monitorear los ecosistemas de peces de arrecife. También se realizó 

un taller de acústica para mejorar la capacidad científica y de las operaciones de investigación y 

levantamiento en ecosistemas de peces de arrecife.

Con el reconocimiento de la conectividad de las poblaciones marinas en las jurisdicciones geopolíticas 

de la región del Gran Caribe, la Administración Nacional Oceánica y Atmosférica (NOAA) patrocinó el 

proyecto e iniciativa estratégica de Innovación Oceánica del Instituto Pesquero del Golfo y el Caribe 

(GCFI) para incrementar la capacidad científica y sostenibilidad de los recursos marinos vivos en la 

región. Este taller de acústica se realizó durante tres días con esfuerzos conjuntos de GCFI, NOAA, 

Kongsberg-Simrad, Echoview y SouthEast Acoustic Consortium (SEAC). El primer día 4 de noviembre, 33 

participantes e instructores de 10 países salieron de la ciudad de Progreso a bordo del bote Isla Mujeres 

para recibir capacitación sobre el uso de tres versiones de la ecosonda de banda ancha Simrad EK80: 

EK80 WBTs (diseñada para plataformas autónomas) y el EK80 EcoSounder portátil. Se recolectó 

información acústica en un arrecife ubicado aproximadamente a 18 millas de la costa. Durante el 

segundo día, 5 de noviembre, los participantes recibieron una descripción general del procesamiento de 

datos acústicos y métodos análiticos, usando el software Echoview para procesar y analizar los datos 

recopilados de las ecosondas científicas del sitio de estudio y otros estudios en el ambiente de arrecife. 

Los participantes aprendieron las características del software Echoview para producir estimaciones de la 

abundancia de peces de arrecife y la descripción de su hábitat. El tercer día del taller celebrado el 9 de 

noviembre reunió las diversas perspectivas de 57 participantes de 13 países para abordar los desafíos 

operacionales de la realización de estudios de ecosistemas de peces de arrecifes. Los expertos 

presentaron diversos casos de estudio sobre diferentes temas relevantes para la ejecución de 

operaciones acústicas en hábitats de peces de arrecife, tales como el diseño de la muestra estadística, la 

selección y configuración de los sensores y la interpretación de los datos acústicos. Los participantes 

trabajaron para priorizar los objetivos operativos y de gestión que fueran alcanzables con las tecnologías 

acústicas en la región, y poder desarrollar el marco para el informe técnico.

Los resultados y recomendaciones de éste taller se presentan aquí como una guía sobre las mejores 

prácticas para realizar investigaciones y operaciones de análisis en ecosistemas de peces de arrecife. Las 

recomendaciones también proporcionan orientación para futuros talleres de capacitación sobre la 

integración de tecnologías acústicas y ópticas para mejorar las operaciones de recolección de 

información que permita apoyar decisiones de gestión administrativa sobre la sostenibilidad de los 

recursos de peces de arrecife en la región. La conclusión general de esfuerzos tales como éste taller son 

críticos para construir la futura generación de expertos, quienes de manera conjunta optimizarán las 

operaciones de recolección de información con tecnologías que permitan mejorar la sostenibilidad de 

los recursos marinos vivos que tienen conectividad en las jurisdicciones geopolíticas de la región.

 
5 

1 INTRODUCTION (W.L. MICHAELS) 

1.1 BACKGROUND 

The Gulf and Caribbean Fisheries Institute (GCFI)

founded about seven decades ago promotes the 

exchange of information for the conservation and 

management of marine resources in the Gulf of 

Mexico and Caribbean region. The GCFI provides an 

annual conference as an international forum for

scientists, managers, students and stakeholders from various sectors and countries to ensure balance 

representation and perspectives on current issues relevant to the use and sustainability of marine 

resources in the region. Fisheries resources and supporting reef habitats have significant socioeconomic 

importance for the coastal communities in this region, but the stock assessments of commercially and 

recreationally important species are challenged by the complexity of life history and habitat use 

patterns, along with the difficulty of sampling reef habitats. The wider Caribbean region is comprised of 

42 geopolitical jurisdictions; therefore, it is recognized that marine conservation requires collaborative 

efforts to assess the connectivity of living marine resources across geopolitical jurisdictions. 

For this reason, GCFI partnered with the National Oceanic and Atmospheric Administration (NOAA) to 

address data-limited stock assessments in the region. The GCFI-NOAA Data-limited Assessment Strategic 

Initiative recently completed a series of workshops that provided advice on data-limited assessment 

methods (Cummings et al., 2014), strategies for improving fishery-independent sampling (Cummings et

al., 2015), and recommendations on optimizing between fishery-dependent and fishery-independent 

sampling in the region (Cummings et al., 2017). The partnerships developed from these workshops 

emphasized that collaborative science is critical for

resource management. 

These workshops provided the framework for the 

existing GCFI-NOAA Ocean Innovation Strategic 

Initiative to develop innovative approaches to 

improve survey efficiencies, analytical tools, and 

scientific capacity to address the data-limited 

assessments in the region. Based on 

recommendations from the data-limited workshops 

and a recent GCFI questionnaire survey (described in the next section 1.2), priority should be given to 

developing the next generation of experts in the application of ocean technology to improve 

transboundary stock assessments. Therefore, the GCFI-NOAA Ocean Innovation Strategic Initiative 

partnered with the SouthEast Acoustic Consortium (SEAC), Kongsberg-Simrad and Echoview to organize

a symposium and workshop on the use of Acoustic Technologies to Improve Reef Fish Ecosystem 

Surveys during the 70th Annual GCFI conference in Merida, Mexico over 4-10 November 2017. 

Marine conservation requires collaborative 

efforts to assess the connectivity of living 

marine resources across geopolitical 

jurisdictions.

The next generation of experts in the 

application of ocean technology will 

enhance integrated ecosystem surveys to 

improve transboundary stock assessments.
6 

1.2 PRIORITIES FOR BUILDING SCIENTIFIC CAPACITY IN THE REGION 

The GCFI-NOAA Ocean Innovation Strategic Initiative strives to develop innovative approaches to 

improve survey efficiencies, analytical tools, and scientific capacity to address the data-limited 

assessments in the Gulf of Mexico and Caribbean region. To guide this initiative and the framework for 

its workshops, a questionnaire survey was conducted among the GCFI community comprised of 

scientists, managers, and stakeholders to obtain broader perspectives on the mission priorities and 

requirements in the region (Figure 1-1).

The survey results provided perspectives from various sectors of 14 countries (Figure 1.2) on priorities 

and requirements to improve scientific information for the conservation and management of living 

marine resources, with recognition of the connectivity of marine populations across the many 

geopolitical jurisdictions in this region.

There are unique challenges in collecting scientific information due to the complexity of life history and 

habitat use patterns of marine populations in the Gulf of Mexico and Caribbean region. Furthermore, 

much of the essential habitats in this region, such as coral reef habitats, are difficult to sample. For these 

reasons, the questionnaire survey was designed to identify which technologies are the most promising 

to advance the mission priorities relevant to the sustainability of living marine resources. Approximately 

half of the survey respondents considered the need to improve fishery independent surveys, ecosystem 

research and marine protected areas as important mission priorities (Figure 1-3), while protecting 

endangered species and fish spawning aggregations were also identified as priorities in this region. Each 

of these mission priorities require improvements in stock and habitat assessments which are generally 

considered to be data-limited in this region.

Government

Agencies

Nongovernment 

Organization

Academic Institution

Graduate 

Students

Fishing Industry

Industry & Sales

AFFILIATION OF SURVEY RESPONDENTS (N = 45)

Figure 1-1. The pre-workshop survey respondents (n=45) represented various sectors 

interested in improving scientific capacity for the sustainability of living marine resources in 

the Gulf of Mexico and Caribbean region.
7 

Bahamas Belize

Bermuda

Bonaire

Colombia

Guatemala

Cayman

Grenada

Montserrat

Mexico

Puerto Rico Norway

USVI

USA

COUNTRY OF SURVEY RESPONDENTS (N = 45)

Figure 1-2. The survey respondents provided perspectives from various countries working on 

improvements in scientific information for the conservation and management of living marine resources 

in the region.

0% 20% 40% 60% 80%

Product Sales

Fishing Engineering

Socioeconomics

Fishery Dependent Surveys

Bathymetric Surveys

Marine Spatial Planning

Climate Change

Stock Assessment

Habitat Assessment

Marine Protected Areas

Conservation

Fishery Independent Surveys

Ecosystem Research

Protected Species

Management

Mission Priorities

Figure 1-3. Approximately half of the survey respondents (n=45) considered fishery independent surveys, ecosystem research 

and marine protected areas as important mission priorities, while protecting endangered species and fish spawning 

aggregations were also important in the region. Stock and habitat assessments were key requirements for the conservation 

management in the region. 
8 

The survey results suggest the integration of 

acoustic and optic sampling technologies can

enhance survey operations in the reef fish 

habitats of the region (Figure 1-4). The survey 

results showed interest and experience in a wide 

range of active acoustic technologies for seafloor 

and watercolumn operations, and this provided 

the framework of developing the acoustic workshop. The use of optical, acoustical and environmental 

sensing technologies appears to be the highest priority for the region, and about half of the 

respondents, who attended the workshop, had some experience with these technologies. The survey 

results also showed a high interest and experience in underwater camera systems as expected for reef 

fish surveys in this region, therefore next year’s GCFI workshop will focus on optical technologies. The 

special session provided a wider forum to address advances in active and passive acoustics, and acoustic 

telemetry (refer to abstracts in Appendix A). 

Integration of acoustic and optic sampling 

technologies can enhance survey operations 

in reef fish habitats.

0 10 20 30 40

Other Optical Technology

Other Acoustics

Electronic Reporting & Monitoring

Seafloor Acoustics

Passive Acoustics

Biotechnology

Acoustic Telemetry

Watercolumn Acoustics

Environmental Sensors

Underwater Cameras

Technology Experience and Interests

Experience Interest

Figure 1-4. Nearly all of the survey respondents (n=45) expressed interest in using acoustic and optical technologies to 

improve scientific capability for the sustainability of living marine resources in the Gulf of Mexico and Caribbean region. 

About 25% of the respondents had some combination of experience with optical, acoustical and environmental sensing 

technologies. 

In this region, there is a high reliance on fishery dependent data collections as reflected with data 

collections from fishing vessels and shore-side sampling (Figure 1-5). Access to research vessels and 

chartered vessels is limited; therefore, the need to optimize fishery independent sampling with remote 

sensing technologies is critical for resolving data-limited assessments in this region. The survey also 

indicated that labor intensive SCUBA diving is commonly used for data collection in coral reef habitats, 

while the interest in alternative platforms with integrated optical and acoustic technologies can improve 

the efficiency of sampling these habitats with increased spatial and temporal resolution. 
9 

Figure 1-5. More than half of the respondents (n=45) had experience sampling from research and fishing vessels as well as 

Scuba diving operations. There was a high degree of interest in using alternative sampling platforms to enhance survey 

operations. The survey also showed considerable reliance in this region on fishery dependent data collections from fishing 

vessels and shore-side sampling.

The operational sampling priorities in the 

region equal emphasis the scientific 

information on marine populations and 

their habitats (Figure 1-6). While there is 

considerable effort to collect scientific 

data on the abundance and distribution 

of demersal and pelagic fish populations, 

there are also operational priorities for 

bathymetric mapping, seafloor and habitat characterization and oceanographic features. As reflected by 

the survey results, there is considerable interest in enhanced survey operations that use acoustic, optic 

and environmental sampling technologies to provide synoptic scientific information with improved 

spatial and temporal resolution for integrated ecosystem assessments. There is recognition that 

technologies can enable synoptic sampling of both biotic (biomass) and abiotic (bathymetry and 

environmental) features which are required for the delineation of marine protected areas and 

protection of spawning aggregations, each of which are some of the management priorities in this 

region. 

0 10 20 30 40

Drifting Platforms

Aerial Platforms

Autonomous Platforms

Chartered Ships

Towed Platforms

Moored Platforms

SCUBA Diving

Research Ships

Shore-side Sampling

Fishing Boats

Sampling Platform Experience and Interests

Experience Interest

Enhanced survey operations using acoustic, optic, 

and environmental sampling technologies provide 

synoptic science information to improve spatial and 

temporal resolution for ecosystem assessments.
10

Figure 1-6. The respondents identified a variety of operational priorities ranging for demersal and pelagic fish surveys, to 

bathymetric mapping and habitat characterization. Overall, there was an interest in collecting synoptic scientific information 

on biological, geophysical and oceanographic features to address various management priorities in the region such as fish 

stock assessments, protection of spawning aggregations, and delineation of marine protected areas. 

The pre-workshop survey emphasized efforts and interests in utilizing a variety of acoustic technologies 

such as active and passive acoustic systems, including telemetry of acoustic tags (Figure 1-7). It is 

noteworthy that half of the respondents attended the acoustic workshop, and most of these 

participants had some experience using scientific echosounders or other acoustic systems. Most of the 

respondents that had little or no experience with acoustic systems emphasized a priority on the ease of 

use in the operating acoustic systems, while the respondents who had some experience with acoustics 

emphasized the priority to obtain absolute abundance estimates (Figure 1-8). This reflected that those 

who obtained some initial training with acoustic systems were no longer concerned with ease of 

operational use of acoustic systems. Although there was interest in obtaining absolute abundance from 

acoustic technology, there was recognition that relative abundance estimates were equally importance, 

especially for operational objectives like monitoring spawning aggregations of grouper-snapper 

complexes. There was interests in low cost acoustic systems to increase its availability and use during 

survey operations in the wider Caribbean, but not at the expense of sacrificing the quality and integrity 

of the scientific data. 

0 10 20 30 40

Marine Protected Areas

Oceanographic Features

Spawning Aggregations

Distributional Studies

Seafloor Bathymetry

Behaviorial Studies

Pelagic Fish Surveys

Habitat Characterization

Demersal or Reef Fish Surveys

Operational Sampling Priorities

Experience Interest
11

0 10 20 30 40

Imaging Sonar

Side Scan

Telemetry & Passive

Multibeam

Echosounder

Experience and Interest with Acoustic Systems

Experience Interest

Figure 1-7. The pre-workshop survey showed interest and experience with various active and passive acoustic systems, 

including the use of telemetry of acoustic tags. More than half of the respondents had some experience with scientific 

echosounders.

0 10 20 30 40

Low Cost

Relative Estimates

Absolute Estimates

Ease of Use

Acoustic System Operational Priorities

Series1 Series2 Series3 Series4

High

Low

High Low

High

Figure 1-8. The respondents that had little or no experience with acoustic systems emphasized higher interest in acoustic 

systems that had ease of use, while the respondents that had some degree of experience with acoustic systems had more 

interest in obtaining absolute abundance estimates. Overall, there was recognition that relative abundance estimates are 

useful for a variety of operational objectives such as locating and monitoring spawning aggregations. 

The pre-workshop survey emphasized the need for acoustic training as the highest priority in the region 

(Figure 1-9). Recommendations also requested a special session at a GCFI conference on the recent 

advances in acoustic technologies, and the need for developing a technical report on the best practices 

for conducting acoustic surveys in reef fish ecosystem. 
12

0 10 20 30 40

Exhibits

Best Practice

Special Session

Training

Acoustic Technology Recommendations

Series1 Series2 Series3 Series4

High

High

Low

Low

Figure 1-9. The 2016 respondents place an equally high priority on the acoustic special session and workshops to provide 

acoustic training and establish best practices for conducting acoustic surveys in the region. As expected, the 2017 

respondents who will participate during the GCFI acoustic training placed a higher priority in this training.

There is consensus that the integrity of utilizing technologies to enhance survey operations is 

strengthened through training and collaborative network of experts to assure standard calibration and 

operational procedures are implemented. The results of this pre-workshop survey provided the 

framework for the acoustic symposium and concurrent acoustic workshop as reported herein. The GCFINOAA Ocean Innovation Initiative partnered with the SouthEast Acoustics Consortium (SEAC), Simrad, 

and Echoview to organize this acoustic technology symposium and workshop to promote collaboration 

among the academic, government, and private sectors to build a pool of acoustic expertise in the Gulf of 

Mexico and Caribbean regions to improve stock and ecosystem assessments. 

1.3 TERMS OF REFERENCE FOR THE GCFI ACOUSTIC SYMPOSIUM AND WORKSHOP 

Approximately 330 participants from 30 

countries attended this GCFI conference which 

featured the symposium entitled “Acoustic 

Technologies for Surveying Reef Fish 

Ecosystems.” This symposium showcased twelve 

selected presentations that provided 

perspectives on the current state of the science, 

applications, and recommendations for 

integrating active and passive acoustic 

technologies to enhance reef fish ecosystem surveys. The abstracts for this symposium can be found in 

Appendix A. Acoustic measurements were shown to have applicability to estimate reef fish abundance, 

map distributions, delineate spawning aggregations, observe behavior, and characterize community 

structure. In addition to remote sensing of the biological community, presentations showed acoustics to

provide seafloor classification and bathymetry of their habitats. This symposium demonstrated the 

advances of scientists who strived to integrate sampling operations with active acoustic (echosounders) 

and passive acoustic (hydrophones and acoustic tags) instruments to locate and monitor spawning 

Training workshops and working groups will 

develop our future collaborative network of 

experts to ensure technologies are effectively 

implemented to enhance ecosystem surveys.
13

aggregations, monitor aquatic ecosystem integrity, and investigate the impact of ocean noise on marine 

resources. This session served to communicate how integrating acoustic technologies into reliable and 

sustained survey and observation systems will provide socioeconomic benefits from the scientific gains.

A three-day workshop entitled “Acoustic Technologies to Improve Reef Fish Ecosystem Surveys” was 

held during this GCFI conference to build scientific capacity and acoustic expertise to increase acoustic 

expertise and collaborative efforts for enhancing research and surveys in reef-fish ecosystems, and to 

build scientific capacity for assuring the sustainability of living marine resources in the Gulf of Mexico 

and Caribbean region. 

This workshop and competitive scholarships for eight graduate students were funded by the GCFI-NOAA 

Ocean Innovation Strategic Initiative grant, Kongsberg-Simrad and Echoview companies. Acoustic 

expertise, planning and logistic support was provided by Kongsberg-Simrad, Echoview and the SouthEast 

Acoustic Consortium (SEAC). The terms of reference and agenda of this workshop can be found in 

Appendix B. 

The first day of the workshop on November 4th involved 33 participants from 10 countries, including 

eight scholarship awardees. The participant list is available in Appendix C. The participants departed 

from the city of Progreso aboard the boat MV Isla Mujeres for training on the use of three versions of 

the Simrad EK80 wideband echosounder: EK80 WBTs (for ships), EK80 WBAT (for autonomous 

platforms), and portable EK80 EcoSounder. Acoustic data was collected on reef fish and reef habitat 

approximately 18 miles offshore. The Echosounder System section of this report provides the key 

principles and best practice highlights of this first day of training. The following group photograph of the 

participants was taken aboard the boat Isla Mujeres during the first day of training (Figure 1-10). 

Figure 1-10. Group photograph of workshop participants on board the MV Isla Mujeres.

During the second day (November 5th), participants received an overview of echosounder data 

processing and analytical methods using Echoview software, with a focus on reef environments (Figure

1-11). Participants learned how to load, calibrate and clean their acoustic data, how to detect, track and 

classify reef fish, and how to quantify their distribution and abundance. Refer to the Echosounder Data 

Processing section of this report for further information from the second day of training.
14

Figure 1-11. Workshop participants learned analytical tools and best practices in echosounder data processing. 

The third day of the acoustic workshop held on November 9th brought together diverse perspectives of 

57 participants from 13 countries to address the operational challenges of conducting acoustic reef fish 

ecosystem surveys (Figure 1-12). Case studies were presented by experts on various topics to consider 

when conducting acoustic operations in reef fish habitats, such as statistical survey design. Participants 

also worked in breakout sessions to prioritize management objectives and resources to achieve 

operational objectives. This workshop provided the framework for developing a technical report to 

provide guidance on the best practices for conducting acoustic reef fish ecosystem surveys in the region.

Figure 1-12. Breakout groups discussed management drivers and operational challenges to implementing acoustics in fishery 

ecosystem assessments.

Recommendations were also provided for future workshops and training on the integration of acoustic 

and optical technologies, with the goal of improving the scientific information available for the 

sustainable management of reef-fish resources in the Gulf of Mexico and Caribbean region. It is also 

recommended that opportunities are provided during the next year to make equipment and expertise 

available in the region to provide additional case studies on conducting acoustic studies in reef-fish 

habitat. This will promote collaborations and build expertise to help resolve data-limited situations in a 

region where there is considerable connectivity of marine resources across the many geopolitical 

jurisdictions of the region.

The workshop provided training in the calibration, operation, and post-processing of data from scientific 

echosounders, with hands-on use of the next-generation EK80 wideband acoustic system. Presentations 

covered acoustic technologies and methods to improve reef-fish surveys, such as recent advances in 

equipment, operational objectives, survey design, and data analysis. Case studies examined the 
15

challenges and lessons learned while conducting acoustic surveys in the Gulf of Mexico and Caribbean 

region. Participation from all attendees, including presenters and participants from the training 

workshop, stock assessors, managers, and stakeholders, provided diverse perspectives to establish 

priorities and best practices for conducting acoustic surveys in reef fish ecosystems. The results and 

recommendation from this workshop are reported herein as guidance on the best practices for 

conducting acoustic operations to improve reef fish ecosystem surveys and research to address datalimited situations in this region. Additionally, this workshop builds an international network of 

collaborative experts to be considered as a working group, who will forge scientific capacity building 

efforts for the sustainability of living marine resources in the Gulf of Mexico and Caribbean region. 
16

2 SUGGESTED SOURCES FOR UNDERSTANDING ACOUSTIC THEORY (K. BOSWELL AND 

D.A. DEMER) 

This report outlines considerations for best practices in designing and implementing acoustic surveys to 

inform fishery and ecosystem management in coral reef ecosystem. The interested reader will note that 

there are many resources available in print and online where background information on the theoretical 

and practical applications of acoustics can be located. Acoustical oceanography is a relatively young 

field, and owes its early development to the measurements from Colladon and Sturm (1827). It was not 

until the onset of World War II, did the field of underwater acoustics begin to rapidly expand (Laskey, 

1977). Substantial investment in the development of acoustic methodologies has followed within the 

many decades since and the use of underwater acoustics to examine processes in the oceans, for 

biological and physical processes, in addition to developments in communication and exploration has 

greatly expanded. It is our intention to point interested readers to several accessible sources which can 

serve as the basis of reference for developing a foundational understanding of underwater acoustics. 

For readers who are interested in the technical aspects of underwater acoustics, several excellent texts 

exist, and we recommend an initial exploration of the following: Urick (1983), Medwin and Clay (1998) 

and Lurton (2002). For those with interests in the application of acoustics for the quantification of 

fisheries resources, we guide the readers to the following: Johannesson and Mitson (1983) Gunderson 

(1993) and Simmonds and MacLennan (2005). 

There also exist several resources online that 

offer practical context towards the operational 

aspects of the use of acoustics in fisheries

science. Perhaps the most notable (“Acoustics 

Unpacked” http://www.acousticsunpacked.org/) 

was derived from a standard operation 

procedure developed to standardize surveys for 

Great Lakes stock assessment efforts (ParkerStetter et al., 2009). The FAO supported an early effort to outline best practices for performing acoustic 

surveys in European waters (Bazigos, 1981). An important consideration for all users of acoustic 

technologies who are interested in quantifying biological resources is the successful and frequent 

completion of a system calibration, and a recent effort was made to summarize the best practices for a 

variety of contemporary acoustic devices (Demer et al., 2015). Finally, common terms and expressions 

encountered in describing acoustically sensed distributions of animals with underwater acoustics can be 

found in the first table in MacLennan et al. (2002), and we encourage all interested readers to become 

familiar with these terms and their interdependencies. 

Online and digital resources are available to 

provide background, terms and definitions in 

proper use of underwater acoustics for fishery 

ecosystem assessments.
17

3 STATISTICAL DESIGN (P.J. SULLIVAN) 

Acoustic surveys have a variety of objectives ranging from observations of animal behavior, predatorprey and biotic-abiotic interactions, to marine resource assessments. In all cases, the survey results are 

for the survey area and time period. For assessment surveys, define the sampling area with 

consideration to the potential distribution of the target species at the time of the survey, the acceptable 

estimation uncertainty, and ultimately the availability of ship time and other survey resources. When 

possible, efficiently and accurately conduct assessment surveys of aggregated monospecific stocks, e.g., 

when the target species is spawning.

A critical first step in the sampling design is to 

understand the spatial distribution of the target 

organisms (Gunderson, 1993), when the survey is to 

be conducted. Often, this knowledge is gained 

iteratively from the literature, characterizing 

potential habitat (e.g., Zwolinski et al., 2011; Demer 

et al., 2012), and adaptive sampling. Initial surveys 

should be considered exploratory, to identify the 

geographic, bathymetric, and oceanographic limits 

of the target population, and its diel and seasonal 

behaviors. Then, use this information to ensure that 

the survey area spans the entire stock, so the 

estimated population biomass is unbiased. 

For the biomass estimate to be usefully precise, ensure that the acoustic and trawl samples are 

sufficient in number and location relative to the population size and distribution. Again, this 

optimization may require knowledge gained from an exploratory survey. In general, however, allocate 

the available sampling effort mostly to the regions with highest animal densities. Without prior 

information about the stock distribution, adaptively move, extend, and add transects to span the animal 

distributions, and vary transect spacing inversely 

to the regional animal density. 

Unbiased estimates of abundance require that 

samples be taken under a simple random 

sampling design, or randomly sampled parallel 

transects when using cluster sampling, or in 

situations where no correlative processes are 

likely to cause bias when sampling 

systematically laid out points or transects. The sampling design will influence estimates of mean density 

and global abundance regardless of the statistical approaches used, be it standard estimates of the 

mean and variance assuming independent and identically distributed input or even when a geostatistical 

approach is used that assumes autocorrelation in the observations. Here we demonstrate how certain 

sampling patterns on reefs may over estimate abundance and how that can be remedied by employing 

either a random, stratified or systematic sampling design for data collection.

Consideration of the potential distribution of 

the target species is critical to a robust survey 

design and estimate of density or abundance.
18

3.1 BASIC SUMMARY STATISTICS 

It is useful to review some of the basic summary statistics that we use to characterize populations. These 

include:

where x is a measure of density (e.g. number of fish per unit area) and n is the sample size. These basic 

statistics rely on an unbiased sample taken through Simple Random Sampling. In Figure 3-1 an idealized 

map of relative changes in fish density is provided simulating a situation where fish are gathering around 

some attractor, like a reef. If the goal is to 

estimate the average density of fish for the 

region, or to take that density per unit area and 

scale up to the total area, then simple random 

sampling and the equations above will provide an 

unbiased estimate (Figure 3-1). The sample 

locations here were generated using a uniform 

distribution over easting and then northing (as 

might be applied to a Universal Transverse 

Mercator projection of a region). To get an unbiased sample each region must have an equal probability 

of being sampled. There is a tendency for many professionals to spend more time in the areas where 

fish density is highest. Of course, fishers who target fish aggregations do this too. However, in surveys 

for determining an overall mean, it is just as important to know where the fish are not and thus to gain a 

representative sample.

In surveys for determining an overall mean, 

it is just as important to know where the fish 

are not and thus to gain a representive 

sample.
19

3.2 CLUSTER SAMPLING 

In the case of gathering acoustic 

estimates, it is often easier to take 

the samples along a transect rather 

than at specific isolated points. In 

that case, the observations taken 

along each transect are not 

independent of one another in terms 

of statistical sampling. There is a 

correlation in how they are taken. 

There may also be a correlation in 

what is observed. To keep this 

correlation from sneaking into the 

estimate and potentially biasing the 

results, one can treat each transect 

as an observation (as long as the 

transects are laid out in a parallel 

simple random or systematic design). 

In such instances we can calculated a 

weighted estimate of the mean 

where the weights are 

determined by the length of the 

transect and the effective sample 

size is then the total number of 

transects (and not the total 

number of observations along each transect summed over all the transects). This method, outlined 

below, can be found in several statistical survey design textbooks including Elementary Survey Sampling

7th Edition by Scheaffer et al. (2012), (although earlier editions will suffice for this estimation method). 

∑∑= =

= T Tn

i

i

n

i

clu i mxz

1 1

 ∑∑= =

= T Tn

i

i

n

i

clu i mxz

1 1

 

( )

T

clu

clu

n

i

i clu i

T

clu

n

s

m

zSE

mzx

n

s T

1 )(

 1

1 2

1

=

− − = ∑=

( )

T

clu

clu

n

i

i clu i

T

clu

n

s

m

zSE

mzx

n

s T

1 )(

 1

1 2

1

=

− − = ∑=

where xi is the number of fish summed over transect i and the mi is the number of sample units in 

transect i (basically the length of the transect). The term nT represents the total number of transects and 

thus is the effective sample size while zclu represent fish density per unit area (where unit area is 

determined in the specification of m). This sampling design had been used to estimate mysid (opossum 

shrimp) densities in Lake Ontario (Canada and USA) by researchers at Cornell University as shown below

(Figure 3-2). 

x

y

 95 

 100 

 100 

 100 

 105 

 105 

 110 

 110 

 110 

 110 

 115 

 115 

 115 

 120 

 125 

 130 

 135 

 140 

 145 150 

 155 

 155 

 160 

 160 

 165 

 165 

 170 

 175 

 180 

 185 190 

100 200 300 400 500 600 700 800

100 200 300 400 500 600

 

Figure 3-1. A map of fish abundance over a hypothetical sampling domain.

Points represent locations of observations. Interpolated contours show

magnitude of abundance values (green for low values to white for high values).

Random sampling on an autocorrelated process, such as fish density on a sea

mount, results in an unbiased estimate of mean density. Samples locations

need to unbiasedly represent both areas of high fish density as well as areas of

low fish density if the mean is to be used to expand to total abundance or

average mean density over the area. 
20

-79 -78 -77 -76

43.0 43.5 44.0 44.5

Longitude

Latitude

Figure 3-2. Changes in relative density of mysid shrimp along acoustic transects in Lake Ontario. Samples are taken along a 

transect, so a cluster sampling approach is appropriate for statistical analysis. The transects are then the effective sample

unit and the averages for each transect are weighted to compute the global mean.

3.3 ESTIMATION METHODS 

There are a number of approaches that one can use to estimate abundance (assuming an appropriate 

sampling design), but each method carries assumptions that must be recognized. The different 

estimation methods reflect, in some sense, different paradigms for how we view the world. The two 

methods discussed previously only assume that the data are taken through a random sampling design. 

These are called design-based approaches. However, one may wish to apply a model such as, for 

example, a linear or nonlinear regression model or a geostatistical spatial correlation model. These 

would be referred to as model-based 

estimates. In such instances one further 

assumes that the model correctly represents 

what is going on in the system. Such an 

assumption may seem reasonable, but care 

should be taken in recognizing the significant 

constraints (or perhaps expansiveness of the 

belief) this engenders. 

Consider the overly simple, extreme case, shown below where fish abundance increases linearly from 

west to east (with no variation). A simple random sample may be taken, as shown by the circles overlaid 

on the line. An estimate of the mean and standard error will appropriately and unbiasedly estimate the 

actual mean density and characterize how that mean will vary under repeated random sampling of the 

system. The global abundance can similarly be calculated by an area expansion. But, what if we applied a 

linear regression to this data and calculated the mean density over the whole region of equivalently 

total abundance (Figure 3-3). The estimate of the mean fish density that we would get would be very 

similar to that using the earlier method, but the variance would be zero! This is because our belief in 

what the model line provides us is very high and of course in this over simplified simulation it is perfect. 

There are a number of approaches that one can use 

to estimate abundance (assuming an appropriate 

sampling design), but each method carries with it 

assumptions that must be recognized.
21

How often are our assumptions perfect? The resulting estimates are shown in the accompanying table

(Table 3-1). 

West 

Abundance

0 20 40 60 80 100

Figure 3-3. Abundance of an organism calculated along a series of transects distributed west to east in a sampling domain. 

Estimated mean and standard error are presented for random sampling and linear model estimation methods, providing a

hypothetical linear trend in fish abundance. Randomly sampled points result in a mean and variance that are consistent 

under repeated sampling. Assuming a linear model imposes information (which may or may not be valid) on the estimate, in 

this case, the linear trend is assumed to be true results in an exact estimate with zero variance. How often is such an 

assumption valid?

Table 3-1. Estimated mean and standard error assuming random sampling and linear model of an acoustic survey using 

parallel transects.

Parallel Transect Survey Mean SE

Mean – Random Sampling 51.2 6.0

Linear Model 50.5 0.0

Another model-based method is a geostatistical method known as kriging, where the autocorrelation 

between points is modeled and this model is used to derive a weighted estimate for each point in the 

system (X in the case of the figure below, but more generally the spatially interpolated estimates of all 

of the points in the region). People often erroneously assume that a good survey design is not needed 

when a geostatistical method such as the one described here is applied. But, that is not the case. 

Consider trying to estimate the average elevation of a mountain range by sampling only the elevations 

at the peaks (Figure 3-4). Even a geostatistical interpolator would not be able to accurately estimate the 

elevations in the valleys with such information. 
22

2 4 6 8 1 0

2 4 6 8 10

1.05

0.21

-0.51

-1.01

-0.23

0.08

-0.27

-0.48

0.13

0.06

0.26

0.62

0.35

-0.13

-0.17

-0.63

-0.29

-0.11

0.4

0.75

0.35

0.87

1.31

0.61

-0.02

-0.46

-0.53

-1.39

-0.3

0.15

0.02

0.16

0.41

0.12

0.21

-0.43

-0.6

-1.28

-0.92

-0.67

-0.13

0.15

0.46

 0

0.23

-0.53

-0.37

-1.67

-1.41

-0.36

0.12

-0.29

-0.46

0.07

0.14

-1.16

-1.01

-1.21

-1.47

-1.93

0.09

0.14

0.15

0.41

-0.21

-0.75

-1.12

-1.22

-2.27

-2.64

1.13

0.88

0.32

0.41

-0.38

-0.59

-0.8

-1.31

-1.57

-2.34

0.81

0.67

-0.12

0.24

-0.6

-0.43

-0.71

-0.54

-1.21

-1.9

0.76

0.72

0.68

-0.28

-0.14

-0.26

-0.69

0.16

-0.73

-1.15

1

2 3

4

5

X

Figure 3-4. An example domain with each cell showing the value of response and corresponding color (magenta to cyan). 

Samples of the distribution are taken at 1, 2, 3, 4 and 5. The samples are used to estimate the value at X. Hypothetical 

variation from the mean in fish density. Geostatistical methods, such as kriging, spatially interpolate, in other words 

estimate, the value at a point X based on samples, taken at locations 1 through 5 say, whose values are used to determine a 

weighted average, where the weights are an inverse function of the distance to the point being estimated while accounting 

for correlation between sample points (i.e. samples 1-3) that are close together.

λ kK

1

1

* ˆ

−

=

=

= ∑

n

i

si W

s

W

i

λ

λ kK

1

1

* ˆ

−

=

=

= ∑

n

i

si W

s

W

i

λ

The formula above describes the estimate of fish density W at some new location s* using a weighted 

average of the observations Ws taken at other locations si. The weights λ are a function of the 

correlations k between the observed points and the point to be estimated and the inverse of the 

correlations K between each observation and each other observations. The correlations themselves are 

typically characterized as a function of the distance between points. 

3.4 EXAMPLE APPLICATION TO REVEAL IMPLICATIONS OF SAMPLING DESIGN ASSUMPTIONS 

Consider the survey experiment conducted by Kevin Boswell and Allison White (Florida International 

University) on several reefs off the east coast of Florida to explore this problem. Note that two survey 

designs were employed. One is a parallel transect design along the lines of what we’ve discussed earlier. 

The second is what might variously be called a “flower” or “star” survey design, which is often employed 

but may lead to biased estimates as we shall see (Figure 3-5). 
23

Figure 3-5. Example survey designs in study area MG111 demonstrate acoustic data collected using a parallel transect design 

in contrast to a “flower”, also known as “star”, design (acknowledgements to Kevin Boswell FIU for use of this data). Red line 

represents the “flower” or “star” pattern centered on the reef. Green is systematic parallel lines also centered over the reef.

Consider an application to the MG111 site. Below is shown a bubble plot that indicates changes in 

relative fish density along each transect. Further note, the higher density area seen in the center of the 

survey region (Figure 3-6). We shall now go through a series of estimators that make different 

assumptions about how the data were collected relative to how the estimates are calculated. First, let’s 

just apply a simple mean and standard error calculation to each set of samples. The observations are 

provided as the loge of the Nautical Area Backscattering Coefficient (NASC) often denoted as sA. 

We note that the simple mean is not appropriate because the individual points were not taken via 

simple random sampling. Nevertheless, we see that the two means are different. We further note that 

the standard error for the flower design is higher. This is not so much due to the design itself, but the 

reduced sample size (number of transects) used in this particular experiment. Still the comparison is 

instructive.

Now consider an application of the cluster sampling method, described above, to the parallel 

transect data. Note that this application is appropriate as the data represent random or 

systematic parallel transects and the number of transects and the number of observations per 

transect are appropriately accounted for.
24

-80.027 -80.025 -80.023

26.9765 26.9775 26.9785 26.97

Longitude

Latitude

 

Figure 3-6. Bubble plot showing variation in fish density for study area MG111 shown in the previous Figure 2-5. Size of 

symbols is proportional to magnitude of abundance. Note the higher concentration (over sampling) of observations taken on 

the high density areas using the “flower” design (red) in comparison to the parallel (blue) survey design. 

Table 3-2. Comparison of means and standard errors of fish density from two survey designs over a reef (MG111 study area) 

described above. Note the (biased) higher mean associated with the “flower” design. The different in standard error is due to 

sampling intensity. 

Density (Log NASC) Mean SE

Parallel Transect Survey 3.72 0.05

Flower Transect Survey 4.91 0.21

Note, perhaps not surprisingly (as mathematically, the calculations of the average are equivalent), that 

the cluster sampling estimate matches that for the simple mean. The standard error estimate is now 

appropriate and represents how the estimate itself would vary under repeated sampling. Let’s now 

apply a geostatistical model-based estimation procedure to the parallel transect data. First, we must 

estimate the variogram (spatial correlation) model (Figure 3-7).

Table 3-3. Mean and standard error for three sampling designs to estimate abundance of fish over a reef (MG111 study 

area). Here a statistically more appropriate cluster sampling analysis has been applied. The mean is identical to that derived 

assuming the data are from a random sample, however the standard error of the estimate has increased and is now more 

realistically represents the correlation in how the samples were taken, namely along transects. 

Density (Log NASC) Mean SE

Parallel Transect Sampling 3.72 0.05

Flower Transect Sampling 4.91 0.21

Cluster Sampling 3.72 0.11
25

distance

semivariance

1

2

3

4

5

6

0.0005 0.0010 0.0015

Figure 3-7. Sample variogram modeling of the spatial autocorrelation between points with distance of the parallel transect 

design has been applied to the transect observations taken from the MG111 reef area.

The variogram model can be used to estimate, through geostatistical spatial interpolation as described 

earlier, density (in log NASC) for each location in the region (Figure 3-8). Similarly, we can estimate 

variance of the predictions for each location in the region.
26

-80.028 -80.027 -80.026 -80.025 -80.024 -80.023 -80.022

26.9765 26.9775 26.9785 26.9

Longitude

Latitutde

 

Log(NASC)

0

0.33

0.67

1

1.33

1.67

2

2.33

2.67

3

-80.028 -80.027 -80.026 -80.025 -80.024 -80.023 -80.022

26.9765 26.9775 26.9785 26.97

Longitude

Latitutde

Variance

0.3

0.32

0.34

0.37

0.39

0.41

0.43

0.46

0.48

0.5

 

Figure 3-8. The variogram model was used to conduct a geostatistical analysis as applied to the data collected using the 

parallel transect design. Spatial interpolation using a kriging system and the spatial autocorrelation of sampled values from 

the parallel survey design of the MG111 site are shown for predicted density (log NASC) values (top figure) and variance in 

predictions (bottom figure).
27

Table 3-4. Mean and standard error for the survey designs over a reef described above using various forms of sampling 

statistics from the surveys. The geostatistical estimates on the data collected from the cluster transect design are provided 

for comparison. Note the similarity to the mean assuming random sampling and the mean under cluster sampling. The 

standard error is quite lower because, as discussed earlier, we are imposing some information (in this case a variogram 

model) to globally summarize our findings. 

Density (Log NASC) Mean SE

Parallel Transect Sampling 3.72 0.05

Flower Transect Sampling 4.91 0.21

Cluster Sampling 3.72 0.11

Geostat Parallel 3.70 0.02*

Again, note the similarity in the estimates of mean log relative density (Table 3-4. Mean and standard 

error for the survey designs over a reef described above using various forms of sampling statistics from 

the surveys. The geostatistical estimates on the data collected from the cluster transect design are 

provided for comparison. Note the similarity to the mean assuming random sampling and the mean 

under cluster sampling. The standard error is quite lower because, as discussed earlier, we are imposing 

some information (in this case a variogram model) to globally summarize our findings., where the mean 

and standard error for the survey designs over a reef is described above using various forms of sampling 

statistics from the surveys. The geostatistical estimates on the data collected from the cluster transect 

design are provided for comparison. Note the similarity to the mean assuming random sampling and the 

mean under cluster sampling. The standard error is quite lower because, as discussed earlier, we are 

imposing some information (in this case a variogram model) to globally summarize our findings. Note 

the standard error is lower than that based on the simple mean or cluster sampling. That is in part due 

to the model-based assumption, is also in part due to the fact that when we use the estimated 

variogram parameters to characterize the correlation we used those estimates without error, but is 

mainly due to not accounting for the spatial correlation in how the estimates are derived. Here I simply 

averaged the variances. There are better ways to do this, but for now we can compare within methods 

in a relative way. To see examples of how to include error from various intermediate steps in 

determining the overall variation using a hierarchical Bayesian approach to acoustic data please refer to 

the work of Sullivan and Rudstam (2016). Further note, the relative uniformity of the variance in the 

predictions in the variance figure due to the uniform coverage of the sampling design.

Consider now a geostatistical approach applied to the flower survey design. Both the spatial mean 

estimates and the spatial variances are shown (Figure 3-9. Spatial interpolation of the MG111 site for 

density (Log NASC) value using a kriging system and the spatial autocorrelation of sampled values using 

the flower survey design. Note the design misses observing fish away from the center of the survey, and 

the variance in the predictions increases as we move away from the center of the survey.), in which

spatial interpolation of the MG111 site for density (Log NASC) value is derived using a kriging system and 

the spatial autocorrelation of sampled values using the flower survey design. Note the design misses 

observing fish away from the center of the survey, and the variance in the predictions increases as we 

move away from the center of the survey
28

-80.028 -80.027 -80.026 -80.025 -80.024 -80.023 -80.022

26.9765 26.9775 26.9785 26.9

Longitude

Latitutde

 

Log(NASC)

0

0.33

0.67

1

1.33

1.67

2

2.33

2.67

3

-80.028 -80.027 -80.026 -80.025 -80.024 -80.023 -80.022

26.9765 26.9775 26.9785 26.97

Longitude

Latitute

Variance

0.3

0.32

0.34

0.37

0.39

0.41

0.43

0.46

0.48

0.5

 

Figure 3-9. Spatial interpolation of the MG111 site for density (Log NASC) value using a kriging system and the spatial 

autocorrelation of sampled values using the flower survey design. Note the design misses observing fish away from the 

center of the survey, and the variance in the predictions increases as we move away from the center of the survey. 

Note that the flower design is again biased high. The standard error is low for the same reasons as given 

above, but are still comparable in a relative sense between methods (Table 3-5). Further note, how the 

variance increases in the spatial variance figure as the prediction locations move away from the central 

area where the observations were taken. 
29

Finally, consider two sets of predictions (parallel and flower) using a Generalized Additive Model (GAM). 

The GAM creates a nonparametric smoothed regression curve through the observations to derive an 

overall estimate (Table 2-6). We shall show first the application to the parallel survey design and then to 

the flower survey design.

Table 3-5. Mean and standard error for the survey designs over a reef described above using various forms of sampling from 

the surveys. The GAM applied to the parallel transect data again gives estimates of the mean that are similar to those 

derived using other methods. The standard error is lower because of the structure added by the model. One should 

recognize that assuming a model, while sometimes appropriate, does impose information on the estimates. 

Density (Log NASC) Mean SE

Parallel Transect Sampling 3.72 0.05

Flower Transect Sampling 4.91 0.21

Cluster Sampling 3.72 0.11

Geostat Parallel 3.70 0.02*

Geostat Flower 4.14 0.03*

GAM Parallel 3.70 0.003**
30

-80.027 -80.026 -80.025 -80.024 -80.023

26.9765 26.9775 26.9785

2

3

4

5

6

7

 

Longitude

Latitute

-80.027 -80.026 -80.025 -80.024 -80.023

26.9765 26.9775 26.9785

0.05

0.10

0.15

0.20

0.25

 

Longitude

Latitute

Figure 3-10. Spatial interpolation of the MG111 site for Log (NASC) value using a generalized additive model (GAM and 

sampled values from the parallel survey design. Predictions resulting from a GAM applied to the “flower” transect data. The 

predicted density (log NASC) values (top figure) and variance in predictions (bottom figure) are shown here. Note that poor 

sampling design coverages leads to poor spatial estimates. 
31

-80.027 -80.026 -80.025 -80.024 -80.023

26.9765 26.9775 26.9785

-4

-2

0

2

4

6

8

10

 

Longitude

Latitute

-80.027 -80.026 -80.025 -80.024 -80.023

26.9765 26.9775 26.9785

2

4

6

8

10

12

Figure 3-11. Spatial interpolation of the MG111 site for Log (NASC) value using a generalized additive model (GAM and 

sampled values from the flower survey design. The predicted density (log NASC) values (top figure) and variance in density 

(bottom figure) from a GAM applied to the “flower” transect data. Note predictions resulting from a GAM applied to the 

“flower” transect data show poor sampling design coverages leads to poor spatial estimates while variance increases as the 

sample size decrease. 
32

Table 3-6. Mean and standard error for the survey designs over a reef described above using various forms of statistical 

sampling and model-based estimates from the surveys. The GAM applied to the “flower” transect data results in bias 

estimates of the mean, however this time the biases are in the opposite direction and the GAM model slopes to zero on the 

edges. The standard error is lower because of the structure added by the model.

Density (Log NASC) Mean SE

Parallel Transect Sampling 3.72 0.05

Flower Transect Sampling 4.91 0.21

Cluster Sampling 3.72 0.11

Geostat Parallel 3.70 0.02*

Geostat Flower 4.14 0.03*

GAM Parallel 3.70 0.003**

GAM Flower 2.54 0.003**

Note again the general consistency in the estimates based on the parallel survey design. We see some 

departure from the flower mean in the GAM estimate as the smoothed fit appears to taper off to zero at 

the edge of the range (Figure 3-10 and Figure 2-11). Again, please note that the standard errors as 

computed for both the geostatistical and GAM methods represent the square root of the simple 

averages of the prediction variances under each method (Table 3-6). Simple averaging does not take 

into account that the predictions themselves are autocorrelated in space and while this spatial 

correlation should be adjusted for in the estimates of the standard errors when doing a global estimate, 

we did not perform that calculation here.

3.5 SUMMARY OF SURVEY DESIGN CONSIDERATIONS 

Coral reef ecosystems present a complex 

seascape to design a robust survey for assessing

abundance of reef fishes. To summarize: survey 

design matters and should provide representative 

sampling across the domain, rather than 

concentrating only on your perceived area of 

interest (e.g., a reef). Different estimation 

methods have their strengths and weaknesses, 

however model-based estimation methods make a broader set of assumptions and those assumptions 

impose information that may be erroneous and lead to biases. To determine the best estimation 

method to employ and how to appropriately design a survey consider what the objectives are for the 

study. Is it to estimate density or abundance? Is it to study behavior? These two objectives might lead to 

very different sampling designs and analysis methods.

Finally, keep in mind the estimation of the variance and standard errors. As demonstrated in the caveats 

given in the discussions above, determining the variance of the estimates can sometimes be tricky. 

Nevertheless, simple exercises like plotting the spatial prediction variances and looking at relative 

changes in variance under alternative survey design assumptions can be informative and highlight how a 

survey or an analysis might be improved. 

In the end, even a sophisticated data analysis package cannot compensate for poor collection methods 

or poor survey design. A sampling design that allows equal representation of all elements of the system 

Survey design matters, and should provide 

representative sampling across the domain, 

rather than concentrating only on your 

perceived area of interest.
33

is important. It is just as important to collect information from areas with low fish density as it is from 

areas with high. More focused sampling designs that include higher sampling of high density areas can 

be achieved through pre-stratifying sample allocation. One can put more samples into what has, prior to 

the survey, been defined as a high density area as long as samples have also been included in low 

density areas. Stratification must take place before the survey, not after, to achieve unbiased estimates. 
34

4 ACOUSTIC SURVEY OPERATIONS (D.A. DEMER AND K. BOSWELL) 

The survey operations and survey design are driven by the mission priorities and operational objectives, 

and will likely include the integration of various sampling technologies (Section 13.1 provides a summary 

of mission priorities and operational objectives). Survey platforms come in many configurations from 

fully instrumented fisheries survey vessels (FSV) to basic vessels of opportunity where transducers have 

to be mounted from the vessel by the operators. In the latter case, depending on the goals of the 

survey, resources available, and environmental conditions, transducers may be mounted from a rigid 

pole or affixed to a towbody. As presented in the Section 10 and 12 case studies, acoustic technology

can also be deployed on stationary and autonomous platforms. In all cases the operators must be 

aware of the proper installation of both the transducers and the transceivers. In this section, we focus 

primarily on the deployment of echosounders on 

vessels for the purpose of conducting a spatial 

survey to estimate the abundance of a reef fish 

population. Sections 9-12 in this report provide 

discussions on deployment strategies to achieve 

various operational objectives, while this section

summarizes some of the basic considerations for 

conducting acoustic survey operations.

Survey operations and survey design are 

driven by the mission priorities and 

operational objectives, and will likely 

include the integration of various 

technologies.

4.1 POLE OR HULL MOUNTING OR TOWBODIES 

When hull-mounted transducers are not available on the survey vessel (Figure 4.1), the two main 

approaches to deploy a transducer is with a secure pole deployment (Figures 4.2, 4-3, 4-4) or mounted 

within a towed body (Figure 4-5). The simplest pole mount options include a single transducer mounted 

to a flange at the end of a pole (Figures 4-3, 4-4). 

Depth of the transducers should be determined 

by the sea conditions and draft of the vessel that 

may generate wake and bubbles. When multiple 

transducers are needed, a mounting plate or 

fared body can be used to house transducers to 

reduce drag and eliminate cavitation at the 

transducer face (example images). In both cases, 

care needs to be taken to ensure that cables are 

protected and secured within the pole, or along 

the outside of the pole to reduce the potential for 

cables to vibrate (i.e., strumming) when the 

vessel is under way. It has been previously 

demonstrated that cables can weaken overtime 

when strumming is not eliminated. A good rule of thumb is to secure the cables every 25-50 cm. 

When the only option is to put the transducers on a towed body (for example, when the vessel does not 

permit permanently affixing mounting brackets or cannot structurally support a reinforced mount), a 

towbody may be used. Several commercial towbody options exist if resources are available, however a 

simple structural design can also suffice (Figure 4-5). When a towbody is used, the echosounder cables 
35

also need to be secured to prevent physical damage from strain and pinching/cutting. Often when using 

a towbody, the speed of the vessel will determine the depth at which the towbody flies in the water 

column. Consequently, when the vessel slows or performs a turn the towbody will sink. Thus, it is 

important to be mindful of the water column depth and any potential underwater hazards when 

adopting this survey approach. 

Figure 4-1. A hull-mounted five-frequency transducer array (far to near: Simrad ES70-7C, ES18-11, ES200-7C, ES38B, ES120-

7C). The array is temporarily attached to the hull using angle steel that is bolted through the fiberglass. The cables are routed 

through a standpipe with a 3” diameter ball valve to the echosounder transceivers. The laminated HDPE fairing redirects 

bubbles away from the transducers.

Figure 4-2. A retractable bow mount for deploying a down-projecting 200-kHz split-beam transducer (orange cylinder), sideprojecting 120-kHz split-beam transducer (orange boat shape), and two 500-kHz multi-beam transducers (black, only portside is fully visible). A sensor package mounted on top of the apparatus (black box) provides attitude, heading, heave, 

position, and velocity information. To deploy, the transducer array is pushed forward, rotated down into the water, and 

stabilizing struts attach to the gunwale on each side of the bow.
36

Figure 4-3. A portable four-frequency transducer array (top-bottom: Simrad ES70-7C, ES38-12, ES120-7C, and ES200-7C). To 

deploy, the array in lowered into the water such that the transducers are > 1-m deep and projecting downward, and 

stabilizing lines are ratcheted tightly, fore and aft. 

Figure 4-4. A transom-mounted retractable pole with 120-kHz split-beam transducer (Simrad ES120-7F). For deployment, the 

pole is lowered into the water and locked with a pin such that the transducer is deeper than the propeller, to reduce bubble 

noise, and project downward.
37

Figure 4-5. A simple towbody towed from the side of a research ship. The simple frame holds a single transducer. The rear 

tail stabilizes the towbody while underway.

4.2 POWER REQUIREMENTS AND GROUNDING 

When installing an echosounder system onto a new vessel, there are multiple elements which need to 

be considered to ensure proper installation that will yield high-quality data. First and foremost among 

these is access to a clean and stable power source. In many cases, a 12 VDC power supply will often 

provide the cleanest power aboard a survey vessel. On smaller vessels, an inverter-generator can 

provide consistent clean power for many hours before refueling is required. In the absence of a DC 

power source, some vessels may provide AC power regulated at 120 V (~60 Hz), which may impart 

electrical noise within the acoustic data. 

Often a proper ground will help to eliminate sources of electrical noise, which are generally related to 

the vessel’s electrical system; however, the effectiveness will be a product of the power source and the 

characteristics of each vessel. If grounding to the vessel is not effective, a grounding strap deployed 

overboard (immersed in seawater) can be effective to eliminate some of the observed electrical noise.

4.3 SURVEY AND VESSEL SPEED CONSIDERATIONS 

The speed of the vessel during an acoustic survey will generally be dependent on sea state conditions, 

the selected deployment approach of the transducers (i.e. towbody, pole mount, hull mounted) and the 

vessel configuration. In both a pole mounted and towbody deployment, normal operating speeds ~7-11

km hr-1 (~4-6 knots) should result in high-quality data. In these two deployment approaches, greater 

speeds can result in increased bubble noise and cavitation at the transducer face if the transducers are 
38

not properly enclosed in a faring. Often, a vessel with hull mounted transducers can operate at speeds 

nearly twice that of pole-mounted or towbody systems.

4.4 NOISE SOURCES AND INTERFERENCE FROM OTHER SYSTEMS 

A variety of noise sources can be detected when installing an echosounder system aboard a survey 

vessel, regardless of the vessel class or purpose. Aside from the electrical noise mentioned in the 

previous section, the most common source of noise observed in an echosounder is the operation of 

other sensors installed on the ship. For example, the ship’s depth finder which can overlap in frequency 

bandwidth with the installed echosounder can create persistent pulses observed as high-intensity 

responses radiating vertically through the echogram. Other sensors can also interfere with the 

echosounder systems and should be examined carefully through a systematic approach powering on/off 

devices to determine the prominent sources of interference. Depending on which sensors are identified 

as interfering, accommodations may be possible such as the use of an external trigger for multiplexing

(i.e., transmitting at alternative times), or powering them down during acoustic operations. 

4.5 GPS AND MOTION SENSORS AND OTHER ANCILLARY DATA 

Precise position data is needed to add spatial context to acoustic data and permit the quantitative 

analysis for assessment efforts. At minimum, a WAAS-enabled Global Positioning System (GPS) should 

be available and connected directly to the echosounder to write position data to recorded acoustic data 

files. Most consumer-level GPS systems available today are WAAS-enabled and capable of outputting an 

ASCII format readable by the echosounders (e.g., National Marine Electronics Association format; 

GPGGA, HDT, etc.) through a serial communication protocol. It is important to take note of the correct 

port settings (e.g., baud rate, parity, etc.) to ensure successful port communication. In addition to vessel 

position, motion sensors can be useful to correct the acoustic data for the motion of the survey vessel 

and, by extension, the transducers. In general, this option is most effective for transducers that are 

mounted to the vessel (i.e., pole or hull mounted) and referenced to the vessel’s center of gravity. A 

correctly configured and calibrated motion sensor will permit the integration of heading, pitch, roll and 

heave into the recorded acoustic data file. 

Providing measured or estimated sound-speed measurements for the acoustic data is critical for 

accurately determining range of the target from the transducer. Similarly, temperature, salinity, pH and 

depth or pressure influences the rate of absorption of transmitted sound, especially at higher 

frequencies. Acquisition software from all manufacturers will require values for sound speed and 

absorption or environmental parameters from which the software will calculate the sound values. Most 

manufacturers for scientific echosounders require a single surface value. A surface conductivitytemperature-depth probe is most useful here. Full water column profiles can provide important 

information on water chemistry and potential stratification in temperature or salinity that could explain 

the distribution of biological organisms, or the transmission of sound through the watercolumn.
39

4.6 ECHOSOUNDER CALIBRATION 

Calibrating the scientific echosounder (i.e. quantifying its operating parameters) is a fundamental 

requirement for surveys that collect acoustic measures for deriving quantitative abundance estimates. 

A report by the International Council for the Exploration of the Sea (ICES) Working Group on Fisheries 

Acoustics, Science and Technology (WGFAST)

provides a recent update to the practice for 

calibrating a variety of sonars and echosounder 

systems (Demer et al., 2015). Further details on the 

echosounder configuration and calibration settings 

are also described in Section 6 on the Scientific 

Echosounder System. Here we outline the steps for 

calibrating echosounders and direct the reader to the 

report for additional recommendations for other 

sonars. 

Each survey or project should include a calibration each time the system is installed on a vessel or 

platform, or when a survey is conducted in a new temperature, salinity or depth regime. The purpose of 

the calibration is to establish appropriate gain and transducer beam pattern parameters accounting for 

power sources, which allows for monitoring of instrument performance, degradation or component 

failure. Depending on the echosounder manufacturer, the acquisition software may contain automated 

routines for conducting a standard sphere calibration. In its most simple form, the calibration procedure 

involves suspending a metal sphere with known acoustic backscatter properties in the acoustic beam 

and comparing the theoretical target strength with observed target strength. The spheres are typically 

copper or tungsten carbide with a cobalt binder, with material and size depending upon operating 

frequency (Table 4-1). 

Table 4-1. Recommended calibration spheres for common echosounder frequencies. Spheres are made from copper (Cu) or 

tungsten carbide (WC).

Frequency (kHz) Material and diameter

38 WC/38.1mm, Cu/60mm

70 WC/38.1mm, Cu/32.0mm

120 WC/38.1mm, Cu/23.0mm

200 WC/38.1mm, Cu/13.7mm

Calibrating the scientific echosounder is a 

fundamental requirement for surveys 

that collect acoustic measures for 

deriving quantitative abundance 

estimates.
40

The sphere is suspended under the transducer using monofilaments and, if currents are present, a 

weight may be used to reduce unwanted movement (Figure 4-6). If the transducer is mounted on a 

pole, a single line suspended from a fishing rod is sufficient. If the transducers are mounted in the hull 

of the ship, three or more monofilament lines are needed to move the calibration sphere into the 

appropriate position in each transducer beam. 

Figure 4-6. Three-line method for suspending a sphere under a hull-mounted transducer (from Demer et al. 2015).

Choosing a calibration location is as important as selecting the calibration sphere. The calibration is 

conducted using the same echosounder parameters as planned for the survey, including transmit 

power, pulse length and operating frequencies. Water temperature, salinity and pH measurements are 

used to calculate sound absorption and sound speed. The range to the sphere should be sufficient so 

that it is in the far-field of the transducer – usually greater than 10 meters. The watercolumn should be 

well mixed, with minimal fish, plankton or other targets that could interfere with the backscatter from 

the sphere. Temperature and salinity measurements through the watercolumn can be collected using a 

CTD or similar sensor to measure sounds speed. These measurements are used in the echosounder 

equations, but also for estimating the theoretical target strength of the sphere. NOAA’s Southwest 

Fisheries Science Center Advanced Survey Technologies Program provides a web application for 

estimating theoretical target strength for spheres (https://swfscdata.nmfs.noaa.gov/AST/SphereTS/). 

The sphere is lowered to a desired depth and maintained at a constant range from the transducer. 

Raising and lowering lines or moving the fishing rod with single monofilament moves the sphere slowly 

through the transducer beam to place the sphere on the acoustic axis. Depending on the acquisition 

software, the location of the sphere in the acoustic beam should be visible for real-time adjustment on 

split-beam systems. Record several hundred pings while on the acoustic axis. The sphere can then be 

moved slowly through the acoustic beam to measure target strength and characterize the shape of the 

acoustic beam off axis. Repeat the procedure for each transducer frequency. At the conclusion, the 

acquisition software may use the calibration results to automatically update the properties of the 

transducer during data logging. It is highly recommended to record all the output from the calibration 

routine so that it can be confirmed or used in the analysis and interpretation of the data.
41

5 DIRECT AND INDIRECT BIOLOGICAL DATA (C.H. THOMPSON) 

To support the analyses and interpretation of acoustic data described in Sections 7 and 8, it is necessary 

to estimate the composition of fish species and/or the size distribution of the targets responsible for the 

acoustic signals being analyzed. Direct and indirect biological sampling is often necessary to validate the 

composition of acoustic backscatter measurements. There are situations where many species are 

present, and it may be difficult to apportion the acoustic scattering among species. In other situations, 

species can be segregated either by depth or geographic location or can be distinguished by properties 

of the acoustic scattering they produce. Multifrequency analysis and visualization of the echograms may 

also distinguish frequency dependence difference between species (e.g., Jech and Michaels, 2006), and 

wideband acoustic systems hold promise for improvements in acoustic species classification. At the time 

of this writing an ICES publication on target classification is in progress that summarizes methods of 

target classification (refer to http://www.ices.dk/community/groups/Pages/WGTC.aspx).

This section discusses methods for collecting physical or visual 

samples of the fish anticipated to be present (responsible for 

the acoustic signals) in data from an echosounder survey. 

These methods include physical sampling with nets, traps, or 

hook and line, and visual sampling by divers or camera 

systems. Biases inherent in any such sampling method should 

be minimized or accounted for to avoid misinterpretation of 

the echosounder data. Biases in species composition and size 

distributions can arise because of biases in susceptibility of fish 

present to the sampling gear used (catchability), behavioral 

response of the fish, mismatch between the sampling volume 

of the method compared to that of the echosounder, and for 

other reasons. An assessment of advantages and 

disadvantages of visual survey tools is reported in Yoklavich et al. (2012). 

Similar principles should be applied to analysis of these data as those discussed by MacLennan and 

Simmonds (1992) for analysis of trawl samples supporting a traditional acoustic-trawl survey to 

determine the species and size composition of the local surveyed population. To estimate these 

compositions, if individual sample sites produce large numbers of samples, then species and size 

distributions from the sites can be averaged together. If, however, samples are sparse, then it is best to 

combine samples from all sites into a single distribution.

5.1 CAMERA SYSTEMS 

Optical and acoustic sampling technologies are often 

complimentary, and camera systems are well suited 

to providing information on species and size 

composition of reef fish. Significant research has 

been performed on using underwater video systems 

for observing fish populations, including methods of 

estimating abundance. A good overview of methods 

Optical and acoustic technologies are often 

complimentary, and camera systems are 

well suited to providing information on 

species and composition of reef fish.
42

and systems, and a wealth of literature references (Cappo et al., 2006; Somerton and Gledhill, 2005; 

Rooper et al., 2011). In relatively shallow and clear water, light levels during daytime are frequently 

sufficient to negate the need for auxiliary artificial lighting (Figure 5-1). 

Figure 5-1. The camera system shown at left, recently developed at NOAA’s Southeast Fisheries Science Center, Mississippi 

Laboratories, provides underwater video in a 360° circle around the camera system as well as a hemispherical overhead 

view. Identical units can be stacked vertically as in right image to provide synchronized and calibrated stereo video.

5.2 SPECIES COMPOSITION 

Inexpensive underwater camera systems can be dropped to the seafloor, left to record for a period of 

time and then retrieved, are sufficient for sampling species composition. Biases using such stationary 

cameras arise due to the limited sample volume and time, which are small compared to the area 

typically covered in an acoustic survey. Cameras on the seafloor generally cannot provide good 

information on acoustic targets that are in the water column well above the seafloor, thus care should 

be taken to not misattribute the source of acoustic scattering to species that do not inhabit the volume 

sampled by the echosounder. Mobile camera systems, either towed or deployed on a remotely 

operated vehicle (ROV) or autonomous underwater vehicle (AUV), are capable of sampling a larger 

volume than stationary- or drop-camera systems but introduce additional biases due to behavior of the 

fish in response to the moving platform (Laidig et al., 2012). Some fish may avoid the platform while 

others may be attracted to it. Reactions may be species dependent and also dependent on light level, 

noise generated by the platform, how accustomed the population is to vessel traffic, and other 

variables. The variety of responses can make accurate estimates of species composition difficult and is a 

topic of current research in NOAA’s Fisheries Untrawlable Habitat Strategic Initiative. Figure 5-2 shows a 

generic depiction of how the observed density of fish or fish schools might vary with range from the 

camera sampling platform. Near the camera system, the density may be dependent on attraction or 

avoidance reactions; at greater ranges, the ability to identify species can be degraded by image quality 

(system-dependent resolution, depth of field and environmental effects such as turbidity); and at some 

further range, even detection becomes impossible. 
43

Figure 5-2. General dependence of observed fish density as a function of range from an underwater camera system. The 

shape of the curve may depend on camera location and geometry as well as fish behavior, and may be different from the 

undisturbed or naturally occurring densities.

5.3 SIZE COMPOSITION 

Abundance estimates that incorporate size- or agecomposition data can improve the accuracy and 

precision of stock assessments, especially for datalimited assessments common to reef-fish

ecosystems. For this reason, calibrated stereocamera systems are increasingly used for improved 

identification and length measurements of fish in reef habitats. Stereo pairs of cameras with relative 

orientations that are precisely determined through calibration can accurately measure the 3D location 

of points in the camera view and thus the length of observed fish (Figure 5-3). The accuracy of the 

measurement depends on the geometry and image resolution of the camera system and on the range 

to, and aspect angle of, the measured length. Limited information on fish sizes can be obtained using 

parallel laser beams, referred to as ‘laser calipers’ with a known distance between the beams. Fish 

lengths are estimated by simple proportionality when they pass in front of the lasers and the laser spots 

can be observed on the fish’s body. In practice, this method provides fewer length measurements than a 

stereo camera system.

Calibrated stereo-camera systems are being 

increasingly used for improved 

identification and length measurements of 

fish in reef habitats.
44

Figure 5-3. Example of stereo image of a white grunt. Calibrated stereo images allow for measurements of fish size 

composition but also descriptions of the reef relief and other habitat metrics.

5.4 DIVER SURVEYS 

Similar to stationary and mobile camera systems, diver surveys are conducted using point census or strip 

transects, and by towing divers behind a surface vessel. Methods for estimating species composition by 

scuba divers are well documented. SCUBA visual methods range from transects, point census and 

quadrats (Bortone et al., 1986; Bortone et al., 1991; Sale and Douglas, 1981; Bohnsack and Bannerot, 

1986; Buckley and Hueckel, 1989). In general, no visual method eliminates all bias (Thresher and Gunn, 

1986; Greene and Alevizon, 1989; Murphy and Jenkins, 2010). Diver surveys can under-sample or oversample species, and produce estimates of abundance, size, and species composition that vary with diver 

experience, method of sampling, visibility, sampling duration and fish behavior. 

5.5 PHYSICAL SAMPLING - NETS, TRAPS, HOOK AND LINE 

Compared to visual methods, most physical sampling methods exhibit greater selectivity for species and 

fish size (Bacheler et al., 2013). Both hook size (Campbell, 2014; Ralston, 1990) and mesh size in traps 

and nets (Millar and Fryer 1999, Mahon et al 2001) are known to bias estimates of species and size 

composition. Other variables such as the length of time a trap is deployed (Bacheler et al., 2013), habitat 

(Streich et al., 2018) and fish density (Gobert, 1998) can also result in sampling biases.

In summary, there are considerable options for 

direct and indirect biological sampling to help 

validate the acoustic measurements, species 

composition and density estimates. However, the 

acoustic operations and complimentary biological 

sampling must consider statistical survey design to 

provide representative samples of the population 

for unbiased density estimates as described in 

Section 3 on Statistical Design. 

Acoustic operations and complimentary 

biological sampling must consider 

statistical survey design to provide 

representative samples of the population 

for unbiased density estimates.
45

6 SCIENTIFIC ECHOSOUNDER SYSTEM (F.R. KNUDSEN) 

There is recognition that scientific echosounders can be used for a variety of operational objectives, 

such as quantitative abundance estimates of organisms in the watercolumn (described in Sections 7 and 

8). The scientific echosounder and other sonar systems can also be used for seafloor mapping and 

habitat characterization. The ICES report from Anderson et al. (2007) provides a comprehensive

overview of using acoustic systems for seafloor characterization. The scope of this training workshop

focused mainly on watercolumn data collection procedures for conducting fish acoustic survey 

operations. Participants received shipboard training on the principles of scientific echosounder systems 

and operations using two wideband echosounder systems: the Simrad EK80 and autonomous Simrad 

WBAT. Based on the questionnaire survey results (Section 1), high priority was given to the need to 

develop expertise on the operation of scientific echosounders to improve reef fish ecosystem research 

and surveys in the region. 

Part of the survey planning is correct choice 

of echosounder hardware and settings. 

During the survey, frequent observation of 

the echosounder screen is important to tune 

settings so that high quality data are 

recorded. This will reduce the data analysis 

effort and secure sound biological estimates. 

This section focuses on the principles and 

operational consideration for the scientific 

echosounder system, with recognition that 

other acoustic systems have applicability for 

improving reef fish surveys.

6.1 TRANSDUCERS 

Scientific echosounder transducers typically cover frequencies from 18 to 500 kHz, and have narrow 

acoustic beams. A modern transducer can generate both continuous wave (CW) and broad band (chirp) 

pulses. Examples of different transducers are found in Figure 6-1 and Figure 6-2. Low frequencies are 

primarily used to obtain long ranges like 1000 m and more. Low frequency transducers are physically 

large and heavy, therefore not suited for portable use and installation on smaller vessels. For operation 

from smaller platforms, transducers operating at frequencies above 70 kHz are recommended unless a 

wider beam can be accepted. Wider beam transducers are physically smaller (Figure 6-1). Scientific 

transducers are preferably split-beam providing accurate sizing and positioning of single targets in the 

acoustic beam (Bodholt, 1991). 
46

Figure 6-1. Transducers come in different sizes and shapes depending on frequency and beam width. For the same beam 

width, transducers become smaller as frequency increases and wider beam transducers are physically smaller. Most 

transducers generate circular acoustic beams, but some generate elliptical beams. Mind that lower frequency transducers 

are not suited for portable use due to size and weight. The largest transducer in the picture (top right) is 18 kHz, has a 

diameter of 63 cm and weighs 85 kg.

Transducer performance changes with temperature, and they must be kept out of direct sunlight and 

high temperatures. Before use, the transducer should be acclimated to the actual water temperature. 

Make sure to enter the correct water temperature and salinity in the echosounder settings. Further, 

transducer cables are vulnerable. Do not lift the transducer by the cable or step on them. Do attach 

them properly so they are not vibrating in the water during mobile surveys, otherwise the conductors in 

the cable will soon become work hardened and then break. To check echosounder functioning and 

performance, regular calibrations are recommended. 

Figure 6-2. The Simrad ES38-18/200-18 omni-transducer combines the 38 kHz and 200 kHz frequencies which can be utilized 

to improve target discrimination and identification. Due to the relatively wide beam width (18° at both frequencies) the 

transducer is small (416x172x94 mm) and with a weight of 7.3 kg, it is suitable for portable use. The boat shape is chosen for 

good hydrodynamic performance.
47

In addition to size and weight, choice of transducer will primarily depend on the required beam width 

and frequency, but side lobes and near-field should also be considered. The acoustic beam can be 

described as in Figure 6-3. 

Figure 6-3. Illustration of the acoustic beam. At the acoustic axis the sound intensity is highest and it drops off at increasing 

angles from the axis. The beam width is defined as the combined angle to each side of the acoustic axis where the sound 

intensity is r. 

Choice of beam width is a compromise between sampling volume and resolution of single targets in the 

water column and near the bottom. Transducer beam widths ranges roughly from 3 to 30 degrees, while 

most transducers for fish assessment have beam widths of 5-18 degrees. A large sampling volume (wide 

beam) is good because it will detect more fish, but will compromise resolution of single targets e.g. 

needed for fish sizing. Using a 7 degrees circular beam as an example, the beam footprint will have a 

diameter of 1.2 m at 10 m and 12 m at 100 m (Figure 6-4). If two or more fish in the acoustic beam are 

at the same range from the transducer, they will merge into one echo and will be excluded as single 

targets (Bodholt and Solli, 1992). However, they are not excluded in the calculation of the total 

abundance (Bodholt, 1990). To improve the horizontal resolution of single targets, a narrower beam is 

needed. 
48

Figure 6-4. Illustration of a 7° beam width acoustic beam (not to scale). The beam diameter at 10 m is 1.2 m and 12 m at 100 

m. If two or more fish are at the same range from the transducer, they will not be detected as single targets, but will 

contribute in the calculation of total abundance. Using the beam width half angle, the beam diameter at different ranges is 

found by the above formula.

A narrow beam is also better for resolving fish near the bottom. If the echo from the bottom is returned 

before the echo from a fish, the fish echo will be masked in the bottom echo and will not be detected 

(Figure 6-5). The resolution of fish near the bottom must also take into account the bottom dead zone 

(described below). An alternative to the single-beam echosounder is the multibeam. Quantitative 

multibeams for fish assessment are available like the Simrad ME70. A multibeam is typically shaped like 

a swath consisting of many narrow single beams. The swath is normally oriented athwartship. A 

multibeam will secure both a large sampling volume and good resolution of single targets in the water 

column and near the bottom. There is sound energy outside the half power angle. A narrow beam 

width will produce beam patterns like in Figure 6-6, consisting of a main lobe and side lobes. During 

transducer mounting, care should be taken so that the side lobes are not directed towards the hull of 

the vessel or any other structure. A continuous line at a fixed range in the echogram indicates echoes 

from side lobes. It could also be a reflection from the water surface caused by transducer back radiation 

or other multi-pathing. Adjusting transducer position and depth will reduce or remove these artifacts.
49

Figure 6-5. Illustration of wide (left) and narrow beam (right) with bottom profile. The wide beam will hit the bottom first 

and the fish will be masked in the bottom echo and not detected. The narrow beam will hit the fish before the bottom and 

the fish will be masked in the bottom echo and not detected. The narrow beam will hit the fish before the bottom and the 

fish will be detected.

Figure 6-6. Illustration of the acoustic beam pattern with main lobe and side lobes. The sound intensity in the side lobes is 

much lower than in the main lobe, but they can produce “false” echoes.

The transducer nearfield is a zone in front of the transducer where data is unreliable (Lockwood and 

Willette, 1973) and must be avoided in analysis. The nearfield can be approximated by the formula:
50

NNNN = ππππ

4ββ2

where, NF=near field [m], λ =wavelength [m], and β=-3 dB beam width [radians]. The near field at 

common frequencies for 7 degrees transducers is given in Table 6-1. 

Table 6-1. Transducer nearfield approximation for selected 7 degrees transducer frequencies. The nearfield must be 

discarded in data analysis. 

Frequency [kHz] Near field [m]

38 2.1

70 1.2

120 0.7

200 0.4

6.2 FREQUENCY 

Choice of frequency depends on the required range and size of targets to be detected. When comparing 

data between frequencies, the maximum range that can be used is given by the maximum detection 

range of the highest frequency. Detection range for a -32 dB fish and bottom for different frequencies is 

given in Table 6-2. 

Table 6-2. Detection range for fish (TS= -32 dB re. 1m2) and bottom (Sb=-30 dB/m2) for common 7 degrees echo sounder 

transducers.

Frequency [kHz] 38 70 120 200 333

Fish [m] 1000 570 400 310 138

Bottom [m] 2700 1270 750 550 262

For detection of smaller targets like zooplankton, a high frequency is needed. This is due to the 

relationship between the target echo strength and the wavelength of the acoustic pulse. The 

wavelength is given by the formula: 

= cc

ff

where, λ= wavelength [m], c=speed of sound in water [m/s], and f=frequency [Hz]. A general rule is that 

the target of interest should be larger than the wavelength of the echosounder operating frequency. 

When the target is smaller than the wavelength, reflection is weak but increases steeply as target size 

approaches the wavelength (Rayleigh scattering). When the target is larger than the wavelength, it will 

reflect sound as a planar surface (geometrical scattering) and be relatively strong and stable with 

frequency. Targets with gas inclusions, like swimbladder fish, are resonant when they have a size similar 

to the wavelength (Horne and Jech, 2005). In Table 6-3, echosounder frequencies and corresponding 

wavelengths are given. The best frequencies for fish are the lowest because they will have little 

interference from smaller targets. At 70 kHz, the wavelength is 2 cm thus giving a weak echo from 

targets smaller than 2 cm. At 333 kHz, the wavelength is 5 mm, and targets larger than 5 mm will have a 

relatively strong echo. 
51

Table 6-3. Common echosounder operating frequencies and the corresponding wavelength. A general rule is that the target 

of interest should be larger than the wavelength.

Frequency [kHz] Wavelength, λ [mm]

18 83

38 39

70 21

120 13

200 8

333 5

A combination of frequencies (CW or chirp) is often used to improve target identification and species 

discrimination (Korneliussen and Ona, 2004; Jech and Michaels, 2006; Lavery et al., 2007; Bassett et al., 

2017). 

6.3 TRANSMIT SIGNAL 

The transmit signal is described by its frequency, duration and amplitude. Frequency is number of 

periods per second. One period is illustrated in Figure 6-7. The wavelength of the period at different 

frequencies is given in Table 6-3. To generate a discrete frequency, a minimum of 6-7 periods are 

needed. This defines the shortest pulse length at different frequencies. Therefore, the shortest pulses 

available are frequency dependent (Table 6-4). The sound pulse can be described by both its length (m) 

and duration (s). 

Figure 6-7. Illustration of one period of a sound pulse. The wavelength of the period is inversely related to the frequency of 

the transmit signal. At least 6-7 periods are needed to generate discrete frequencies. The amplitude of the period is the 

strength of transmitted sound.

In a CW pulse, the wavelength is constant while in a chirp pulse the wavelength decreases or increases 

causing a frequency up-sweep or down-sweep. CW pulses have durations from less than 100 

microseconds (μs) to many milliseconds (ms). Short pulses are used when a good vertical resolution is 

needed, and long pulses are used when longer ranges are needed. When combining several 

echosounder frequencies, it is recommended to use the same pulse duration at all frequencies. To get 

comparable data, the acoustic beams should also have the same width and overlap as much as possible 

(Korneliussen et al., 2004). Available pulse durations for the Simrad EK80 echosounder in CW for 

different frequencies is given in Table 6-4. The pulse duration varies from 64 μs to 8192 μs. This 
52

corresponds to pulse lengths from 96 mm to 12.3 m. A standard pulse duration for larger surveys is 1024 

μs (1 ms) corresponding to a pulse length of 1.5 m. The currently shortest chirp pulse is 512 μs. A 

frequency modulated (FM) pulse is also available in the Simrad EK80, transmits across a bandwidth 

depending on the frequency and transducers. It is a current topic of research in fisheries acoustics 

discussed in other reports (Demer et al., 2017). 

Table 6-4. The x-axis is pulse duration in μs and the y-axis echosounder operating frequency in kHz. “X” means that the pulse 

duration is available for the given frequency.

Frequency 64 128 256 512 1024 2048 4096 8192

18 kHz X X X X X

38 kHz X X X X X

70 kHz X X X X X

120 kHz X X X X X

200 kHz X X X X X

333 kHz X X X X X

The amplitude of the transmit signal is relevant in relation to environmental impact, and regulators 

might request that the sound pressure level of the echosounder is reported. The average sound 

pressure level at 1 m can be calculated as follows:

where, SPLrms=average sound pressure level [dB re 1μPa@1m], G=transducer gain, P=electrical transmit 

power [W]. The transducer gain is found from calibration of the echosounder or in transducer data 

sheets. The electrical transmit power is user selected in the echosounder operational software. 

6.4 POWER 

A high transmit power is mostly needed at the lower frequencies (<70 kHz) to obtain long ranges. 

Transmit power at low frequencies are often more than 1 kW. Such high transmit powers are not 

recommended for frequencies relevant to reef studies. At the higher echosounder frequencies (>100 

kHz) the sine wave of the pulse will be distorted at high transmit power into a saw tooth shape pulse 

rendering the data useless for scientific purposes. This sine wave distortion is called a non-linear effect 

(Tichy et al., 2003) There is a recommended maximum transmit power at different frequencies 

(Korneliussen et al., 2004) to avoid non-linear effects (Table 6-5).

Table 6-5. Maximum recommended transmit power at different frequencies.

Frequency [kHz] Max power [W]

18 2000

38 2000

70 750

120 250

200 150

333 50

P

SPLrms = G + 10 log ( ) + 181.76 4π
53

6.5 PULSE LENGTH 

Traditionally echosounders transmit CW pulses. Choice of pulse length is a compromise between the 

required range and vertical resolution. A long pulse will give a longer range, but will compromise vertical 

resolution. For a CW pulse the vertical resolution is given by:

VVVV = cccc

2

where, VR=vertical resolution [m], c=speed of sound in water [m/s], and τ= pulse duration [s]. 

Figure 6-8. Illustration of the acoustic beam and sound pulse with duration of 1 ms and pulse length of 1.5 m. 

Assuming pulse duration of 1 ms and sound speed of 1500 m/s, the pulse length will be 1.5 m (Figure 

6-8) and the vertical resolution 75 cm. Two fish must be separated with more than 75 cm in range to be 

detected as individual targets. If they are closer than 75 cm, their echoes merge into one larger echo. A 

vertical resolution of 75 cm is likely too poor for reef studies and shorter pulses are recommended. Try 

the shortest pulse available at the relevant frequencies. Since most reefs are not deep, choice of pulse 

length can be based on the required vertical resolution. If several frequencies are used simultaneously, 

it is recommended that they all have the same pulse duration.

Resolving fish near bottom must also take into consideration the pulse length (valid for CW pulses). The 

bottom dead zone (Ona and Mitson 1996) for a flat bottom is illustrated in Figure 6-9. 
54

Figure 6-9. Illustration of bottom dead zone. The bottom dead zone is given by the angle of the target in the beam and the 

pulse duration (valid for CW pulses).

For chirp pulses the vertical resolution is not dependent on pulse duration, but on the band width of the 

pulse according to the formula:

VVVV = cc

2 × BBBB

where, VR=vertical resolution [m], c=speed of sound in water [m/s], and BW=band width [Hz]. A 200 kHz 

chirp transducer can sweep from 150-250 kHz covering a band width of 100 kHz, in case the vertical 

resolution is 7.5 mm. Using chirp pulses, long pulse duration can be used without compromising vertical 

resolution. 

6.6 PULSE REPETITION RATE 

The factor limiting pulse repetition rate (PRR) is the water depth according to the formula:

= cc

2xxxx

where, PRR=pulse repetition rate [Hz], c=speed of sound in water [m/s], and d=water depth [m]. At a 

water depth of 750 m, the maximum PRR is 1 pulse per second, or 1 Hz. At 75 m it will be 10 Hz. When 

data are recorded, a recording range (see below) that is greater than the water depth will become the 

factor which limits PRR. For a standard biomass assessment survey, a PRR of 1 Hz might be sufficient. A 

higher PRR is relevant for tracking the direction and speed of single targets.

Some combinations of PRR and water depth may result in a “ghost bottom” appearing on the echogram 

and thus in the water column data. This is caused by pulses that have already travelled to the bottom 

and back to the transducer reflecting from the sea surface, travelling to the bottom, and back to the 

transducer again. It is important to recognize when this is occurring, and adjust PRR to avoid data 

contamination caused by these multiple reflections. 
55

6.7 RECORDING RANGE 

Scientific echosounders store data for 

later processing and replay. The 

recording range must be somewhat 

longer than the maximum water depth 

of interest. In addition, it is 

recommended to record some files with 

the echosounder in passive mode. Passive recordings are used to identify and remove noise sources. The 

recording range for passive recordings should be a least 300 m. Echosounder settings must be the same 

for passive as for normal (active) operation. 

6.8 SUMMARY CONSIDERATIONS ON ECHOSOUNDER SELECTION AND SETTINGS 

The intent of this section is to provide a good understanding of the prerequisites for scientific

echosounder operations to assure good data quality, correct data interpretation and reliable 

quantitative estimates. Some key operational principles and recommendations to consider when 

conducting acoustic fish reef surveys are:

1. Beam width. A narrow beam will improve resolution of fish both in the water column and near the 

bottom. A wider beam will see more fish and provide more representative data for the fish 

distribution.

2. Acclimate transducer to the actual water temperature before calibration and survey. Enter the 

correct temperature and salinity in the echosounder settings.

3. Adjust transducer during mounting to avoid echoes from side-lobes and other false echoes. 

4. Attach transducer cable properly to prevent vibrations.

5. Always know your transducer near-field. Data in the near-field are unreliable. 

6. A low frequency is best for detecting fish while higher frequencies are better for detecting 

zooplankton. Consider a transducer combining a low and a high frequency.

7. Know your wavelength. When targets are smaller than the wavelength of the transmit pulse, 

measurements are unreliable. 

8. Reduce transmit power. A high power is primarily needed to obtain long ranges.

9. Use short pulse length to improve target resolution. Long pulses are primarily for long ranges.

10. The pulse repetition rate is dictated by the water depth, but aim for a high rather than a low rate. A 

rate of 2-3 pulses per second is fine for most surveys.

11. Make sure your recording range is always longer than the range of interest. 

12. Observe the echosounder screen during survey and adjust settings to optimize data quality. 

Part of the survey planning is correct choice of 

echosounder hardware and settings.
56

7 ECHOSOUNDER DATA PROCESSING (T. JARVIS) 

7.1 OVERVIEW 

In order to work effectively with single-beam echosounder 

(SBES) data and/or multibeam echosounder (MBES) data, a 

data analyst needs to be familiar with a range of topics 

across a number of different disciplines:

1. Echosounder theory: This is the knowledge that 

underpins the use of echosounders in studies of aquatic 

ecosystems. It requires an understanding of both 

underwater sound (a physics-based discipline) and 

electronics (an engineering discipline). See Sections 2, 6 

and 14. 

2. Digital-signal-processing (DSP) theory: This is the 

knowledge that underpins the processing of digital 

samples, such as those obtained from an echosounder or 

a digital camera. There are many DSP techniques in use 

in other disciplines (e.g. photography, telecommunications) that may prove useful for echosounder 

data processing, but have yet to be adopted. Wider reading on the general subject will undoubtedly 

lead to a range of new ideas for echosounder data-processing applications (e.g. Peña, 2016).

3. Data-processing-software specifics: Some sort of software is required to visualize and process 

echosounder data. A good understanding of the specific structure, terminology and design 

components of one or more of the available software tools is essential for effective data 

visualization and efficient and accurate data processing.

4. Data-processing workflow: It is possible to identify a common sequence of broad-level processing 

steps (a workflow) that can be applied to the data from any echosounder (SBES or MBES) for any 

ecological application (e.g. coastal reef-fish studies, open-ocean fish-stock surveys, fish passage in 

rivers, plankton studies, etc.). This workflow serves as a useful checklist to ensure that the necessary 

processing steps are carried out in the appropriate order. Depending on the aims of the study, the 

end products from the echosounder workflow are typically combined with the products from other 

instruments (e.g. trawls, video cameras, CTDs, current meters, etc.) and analyzed with an 

appropriate spatial and/or statistical model (see sections 3 and 8). The term “post-processing” is 

commonly used to describe the fact that the “raw” echosounder data being considered (e.g. 

received electric power, derived from the received voltage) has already undergone significant “preprocessing” by the echosounder system (analog-to-digital conversion, bandpass filtering, 

downsampling, etc.). 

The following subsections describe the echosounder data-processing workflow in more detail. Examples 

are provided from an echosounder dataset collected by NOAA Ship Nancy Foster at the Tortugas Banks 

in the Florida Keys Marine Sanctuary in Aug 2017 (Figure 7-1). This dataset contains examples of fish 

(e.g. jacks, snappers, angelfish, groupers, triggerfish, surgeon, barracuda), plankton and coral reef 

detected simultaneously with hull-mounted, vertically oriented SBES and MBES systems (Simrad EK80 at 

38, 120 and 200 kHz; RESON SeaBat 7125 at 200 kHz).
57

 

Figure 7-1. Overview of the example echosounder dataset collected from NOAA Ship Nancy Foster in Aug 2017 (see Section 

7.1). [A] Survey location at the Tortugas Banks in the Florida Keys Marine Sanctuary. [B] 4D view of the data (looking north) 

showing a section of the SBES ping curtain and 7 pings of the MBES data (every 40th ping is shown). Water depth along the 

survey track was ~55 m. The 3D bottom surface was generated from a previous MBES survey.
58

7.2 DATA EXPLORATION 

Data exploration is the preliminary process of visualizing and inspecting the data to establish its broad 

characteristics prior to detailed processing.

Visualization

Once the required data files have been located and their contents read into a suitable software 

environment, the stored measurements can be visualized in the form of echograms, graphs, tables, 

maps and 4D displays (Figure 7-2). 

Inspection

Thorough inspection of the data is encouraged as a first step to ascertain:

• Where and when the data was collected.

• Issues such as gaps in the data, errors or offsets in the ping locations and times, data 

thresholds, transducer ringdown, time-varied-gain (TVG) errors etc.

• How the data was collected in terms of transducer orientation, pulse duration, power, 

frequency, recording range etc. 

• What sorts of noise and backscatter features are present (see Sections 4.4 and 7.4), and how 

they relate to each other spatially and temporally. 

• The distribution of TS and Sv values for different components of the echograms.

This information will help to identify any issues with the data and inform the appropriate approaches to 

take for the remaining workflow steps.
59

Figure 7-2. Some of the ways in which echosounder data can be visualized and inspected. [A] The raw data files being 

considered and the readable information within. [B] Survey track. [C] Table of GPS fixes. [D] Multibeam Sv echogram showing 

a single ping from the SeaBat 7125 MBES. [E, G, I] Single-beam Sv echograms showing multiple pings from each of the three 

EK80 SBES systems (38, 120 and 200 kHz). [F, H, J] Sv profile of the ping indicated by the vertical dotted line on the echogram 

to the left. The survey track, echograms and ping profiles are all synchronized to indicate the same moment in time.

7.3 DATA CALIBRATION 

Data calibration is the process of establishing:

1. The correct real-world location and extent of each sample in terms of latitude, longitude, time, 

range and depth.

2. The correct backscattering-strength value of each sample.

Note the distinction between data calibration, which we are describing here, and instrument calibration 

(described in Sections 4.6 and 7.7). 

Latitude, longitude and time

Latitude, longitude and time are measured with a GPS receiver at an appropriate rate (e.g. 1 Hz) and are 

commonly (but not always) recorded in the echosounder data file (Figure 7-2). The GPS times can then 

be matched to the echosounder ping times to ascertain the latitude and longitude of the platform at the 

time of each ping. If the XYZ (3D) offset of each transducer from the GPS is known (Figure 7-3), it is also 

possible to ascertain the latitude and longitude of the transducer at the time of each ping.

GPS times are typically Universal Time Coordinated (UTC), while ping times depend on the system clock 

of the echosounder computer. It is good practice to synchronize the system clock to the GPS clock to 

avoid complexities with time offsets. A single offset can then be applied during post-processing to adjust 

both the GPS and ping times to the local time zone if desired.
60

Sample range

Sample range (r in m) is derived based on the water sound speed (cw in m s-1) and the echosounder’s 

measurements of time for each sample (th), the latter of which are highly precise and accurate for 

scientific-grade echosounder systems. Simmonds and MacLennan (2005, p116-117) discuss how to 

factor target extent and receiver delay (tdel) into the calculation of target range (Figure 7-4). Demer et al. 

(2015, p42) recommend that cw should be estimated as the harmonic mean over the propagation path.

Sample depth

Sample depth (d in m) is a trigonometric function of sample range, transducer depth and transducer 

orientation (pointing direction) (Figure 7-3). It is therefore important to know the depth of the 

transducer and its pointing direction (azimuth and elevation in degrees).

For mobile platforms whose depth changes significantly from moment to moment (e.g. heave for a 

surface vessel, diving or ascending for an underwater vehicle), it is important to make measurements of 

those changes in depth at an appropriate rate (e.g. 10 Hz) and incorporate them in the calculation of 

sample depth. 

For target-tracking applications, it is also important to know the rotation of the transducer (around the 

beam axis) in relation to the platform, and to measure any changes in platform heading from moment to 

moment (Figure 7-3). 

Sample backscattering strength

The equations to convert the raw echosounder measurements to TS and Sv vary from instrument to 

instrument, but they all represent a rearrangement of the general echosounder equation (Figure 7-4). 

The Simrad EK80 equations for narrowband data are described by Demer et al. (2015, p61), and are 

effectively identical to those shown in Figure 7-4. The SeaBat 7125 is not generally recommended for 

quantitative use (but see Dunlop et al., 2018; and references therein).
61

Figure 7-3. Factors contributing to the calculation of latitude, longitude, time and depth for a given echosounder sample. In 

this example the echosounder platform is a mobile surface vessel, but the principles can be extended to other mobile and 

stationary platforms such as towed bodies, autonomous underwater vehicles, probes, moorings and landers. The text box 

represents Echoview 8 settings.
62

 

Figure 7-4. The echosounder equation. [A] In its general form (also referred to as the “sonar equation”, e.g. Rudstam et al. 

2012, p6). [B] Solved for point targets (see Figure 7-6). [C] Solved for volume targets (see Figure 7-6). Equations B and C are 

rearranged to make EReflected the subject (i.e. the acoustic properties of a target such as a single fish or an aggregation of fish). 

In these examples, EReceived and ESent are presented in terms of electric power (W or dB re 1 W), which can be calculated from 

the received voltage (volts, V) as V × current (amperes, A), or V2/impedance (ohms, Ω) (see Demer et al., 2015). 
63

7.4 DATA CLEANING 

Data cleaning is the process of removing or mitigating unwanted components of the data, where 

“unwanted” is subjective and depends on the aims of the study. The voltage measurements made by the 

echosounder receiver represent the sum of backscatter (often referred to as “signal”) and noise (i.e. 

anything else that contributes to the received voltage).

Unwanted components can include:

1. Samples from certain times, locations or ranges. 

2. Backscatter from targets that are not part of the study.

3. Stochastic variation in the signal.

4. Acoustic and electrical noise.

5. Reduced backscatter due to pulse/echo attenuation or transducer motion.

6. Measurements with a low signal-to-noise ratio.

7. Non-linear effects.

Unwanted samples

Samples collected at unwanted times, locations or ranges during a survey can be quickly identified and 

removed during a first pass of the data. For example, it may be desirable to disregard pings recorded 

during the day when animal targets are dispersed or beyond the range of the echosounder, or pings 

collected during transit between transects.

Unwanted targets

Backscatter from unwanted targets is sometimes referred to as “reverberation” (e.g., Simmonds and

MacLennan, 2005). For example, backscatter from near-surface bubbles, zooplankton and the bottom 

(Figure 7-5) might be considered reverberation in a study of reef-fish distribution and abundance. 

Backscatter from near-surface bubbles in data collected from a surface vessel is typically dealt with by 

creating a 2D line that identifies the maximum range of the bubbles in each ping, and then excluding 

from further analysis all samples between the transducer and this line (Figure 7-5 shows examples of 2D 

lines for delineating various phenomena, although near-surface bubbles are not apparent in this 

dataset). Similarly, backscatter from unwanted targets such as zooplankton and the bottom can be 

excluded from analysis once they have been detected (see Section 7.5 and Figure 7-5).

Stochastic variation in the measurements

For effectively unknown or unknowable reasons, repeated echosounder measurements of the same 

volume of water will yield a different value, even if the conditions remain essentially unchanged. This 

variation will typically be small and random (i.e., normally distributed), hence the term “stochastic” 

(which is used to describe something that is randomly determined).

This variation may not be an issue when characterizing the data at a single frequency. For example, the 

sample-mean Sv of a cross-section through an aggregation of fish would be calculated on the basis of 

100s or even 1000s of samples, such that the variation would be mitigated by central-limit theorem. 

However, when wishing to compare backscatter measurements between frequencies (e.g. for 

multifrequency classification analysis; see Section 7.6), it is generally recommended that steps are first 
64

taken to reduce the variation at each frequency. This can be achieved by either smoothing or resampling 

the data.

Acoustic and electrical noise

Acoustic noise in the echosounder signal 

can arise from phenomena such as pulses 

and echoes from other echosounders, 

engine noise, breaking waves, collapsing air 

bubbles (cavitation) and vocalizing animals. 

Electrical noise can arise from grounding 

issues, inverters, transducer cables running 

next to each other, and so on (see Sections 

4 and 6). In addition, the transducer elements can continue to vibrate for a short period of time after 

transmission (commonly referred to as “ringdown”). Figure 7-5 shows examples of noise in the SBES 

data from Tortugas Banks.

Various algorithms are reported in the literature for removing many of the acoustic and electrical noise 

phenomena commonly found in echosounder data (e.g. De Robertis and Higginbottom, 2007; Ryan et 

al., 2015; Peña, 2016). While these algorithms can be effective in many cases, it is recommended that all 

possible avenues are pursued for the collection of noise-free data in the first place.

Ringdown may contribute to the signal over 40 m of range or more for older transducers consisting of 

tonpilz ceramic elements. Newer transducers with composite ceramic elements may only ring for a few 

meters or less. Ringdown can be removed by creating a 2D “exclusion” line as for near-surface bubbles. 

The range of this line is generally determined by eye as the maximum range at which the signal is 

significantly affected, although it is also possible to conceive of automated algorithms to perform this 

calculation.

Reduced backscatter

In addition to spreading loss and absorption (which are factored into the calculation of sample 

backscattering strength; see Section 7.3), the energy in the pulse and/or echo can be significantly 

attenuated by highly reflective and/or highly absorbent targets along the propagation path. Common 

scenarios are air bubbles in front of the transducer at short range, and dense schools of fish. With a 

mobile surface vessel, close-range air bubbles can be caused by breaking waves (which can aerate the 

water column to considerable depth in very bad weather) and/or the design of the hull (which can cause 

bubbles to be swept down and across the transducers as the vessel moves). Turbulence in high-flow 

environments such as rivers and tidal channels can also entrain air bubbles in front of the transducer.

Ryan et al. (2015) describe an algorithm for identifying and removing pings whose sample values are 

significantly weaker (due to bubble attenuation) than those nearby (“ping dropouts”). While we might 

expect the level of attenuation for any given ping to vary from 0 to 100 %, the Ryan et al. (2015) 

algorithm removes only those pings above a given attenuation threshold.

Transducer motion can also cause up to a twofold reduction in measured backscatter if the pointing 

direction changes between transmission and reception (Stanton, 1982). Dunford (2005) describes a 

single correction function for a wide range of circular transducers based on vessel motion (pitch and roll) 

In order to work effectively with single-beam 

echosounder data and/or multibeam echosounder 

data, a data analyst needs to be familiar with a 

range of topics across a number of different 

disciplines.
65

and the transducer beam pattern. Pitch and roll measurements are made with a motion reference unit

(MRU) and must be made at a rate sufficient to capture the change in pointing direction between 

transmission and reception (~10-100 Hz).

Signal-to-noise ratio

If an estimate of the noise contribution to the measurement can be provided for any given sample, then 

the signal-to-noise ratio for that sample can be calculated (where signal = measurement – noise). It is 

then possible to filter out samples whose ratio is lower than a desired quality threshold.

The background-noise-removal algorithm described by De Robertis and Higginbottom (2007) 

incorporates such a threshold (which in this case describes the ratio of signal to background noise, but 

not any other noise sources). In this paper, the authors explain how the optimum threshold depends on 

“the equipment used, the operational settings, and the extent to which data are averaged”. A more 

detailed appraisal of the broader topic can be found in Kieser et al. (2005).

Non-linear effects

At very close range the multiple curved wavefronts generated by the spaced transducer elements result 

in a non-linear relationship between the sound pressure and the particle velocity (Simmonds and

MacLennan, 2005, p39). This close range is known as the “near field” or “Fresnel zone” (as opposed to 

the “far field” or “Fraunhofer zone”), where any backscatter measurements will be variable and 

untrustworthy. The effective near-field range can be calculated for a given transducer via a simple 

equation (see Section 6.1). This range can be used to create a 2D “exclusion” line as for near-surface 

bubbles.

A number of other conditions can lead to further non-linear effects in which the propagating sound 

waves no longer obey simple rules (see Simmonds and MacLennan, 2005, p35; and references therein). 

Although these effects can be significant, they have rarely been considered in published data-processing

workflows. 
66

 

Figure 7-5. EK80 narrowband SBES echograms with nominal center frequencies at 38 kHz (left), 120 kHz (middle) and 200 kHz 

(right), showing some of the concepts discussed in Sections 7.3 and 7.4. 
67

7.5 TARGET DETECTION AND TRACKING 

Target detection is the process of delineating targets (Figure 7-6) in the cleaned echograms. Once 

delineated, targets can be characterized (e.g. depth, size, backscattering strength etc.; see Sections 7.6 

and 7.7), filtered as required (Section 7.4) and (where possible) tracked through space and time over 

multiple pings (Section 7.5). 

Figure 7-6. The relationship between targets defined in a physical context (e.g. a single fish, a single aggregation of fish, the 

bottom) and targets defined in the context of an SBES (point targets, P, and extended targets, E; after Lurton, 2002). Point 

targets arise when the physical target is considerably smaller than the sampling volume, such that the echoes from each 

facet of the target reach the transducer at essentially the same time and from the same direction. Extended targets can be 

further divided into volume targets (Ev) and surface targets (Es). Notice how a single fish and a single aggregation of fish can 

both be either P or Ev depending on the situation. Care must therefore be taken when using echosounder data to infer how, 

or even if, an individual animal such as a fish is aggregated in an ecological sense (see Parrish and Hamner, 1997). 

Point targets

In SBES data, point targets (Figure 7-6) are most commonly detected on a ping-by-ping basis by looking 

for peaks of a certain shape and amplitude in the TS ping profile (Figure 7-7 [B]). For MBES data, where 

the target cross-section can be discerned across multiple beams in each ping, the approach is to 

delineate contiguous clusters of above-threshold samples in a single ping (see Figure 7-7 [E] and Dunlop 

et al., 2018) in the same way that the cross-section of a volume target (such as an aggregation of fish) is 

delineated in SBES data across multiple pings (see Figure 7-7 and “Volume targets” below).

The most commonly used SBES point-target-detection algorithm is the “standard phase deviation 

algorithm” described by Soule et al. (1996). While this algorithm is almost universally adopted, it is 
68

important to note that it is “not foolproof and will often accept multiples” (i.e. multiple echoes) “when 

resolution densities are exceeded”, explaining why “large inconsistencies are evident in in situ TS results 

reported for pelagic fish” (Soule et al., 1996).

While there is merit in exploring alternative point-target-detection algorithms for SBES data (as 

discussed in Soule et al. 1996), no further studies appear to be published in this regard. However, 

algorithms have been proposed for identifying situations in which the point-target-detection algorithm 

is likely to have failed, i.e. when it has detected multiple echoes (e.g. Sawada et al., 1993; Demer et al., 

1999).

Point targets can be tracked if they are detected repeatedly over multiple pings (Figure 7-7 [A] and [C]). 

The most commonly used tracking algorithm is the “multiple-target tracking” algorithm described by 

Blackman (1986), although other techniques have also been described (e.g. Hedgepeth et al., 2000; Xie, 

2000; Handegard et al., 2012).

Figure 7-7. Point targets in SBES and MBES data. [A] SBES raw TSu (uncompensated TS) samples echogram (zoomed view). [B] 

TSc (compensated TS) profile of ping 1876 from echogram A. [C] SBES TSc point-targets detected from echogram A. [D] MBES 

raw Sv echogram (uncalibrated) from the same point in time as SBES ping 1876 (zoomed view). [E] MBES Sv point targets 

detected from echogram D. Note targets 4 and 5 in the MBES data that are not apparent in the SBES data.

Volume targets

Volume targets (Figure 7-6) are detected in SBES data by delineating contiguous clusters of abovethreshold samples across multiple pings (identical in concept to point-target detection in individual 

MBES pings; see Figure 7-7 [E] and “Point targets” above). The result is a 2D polygon (region) that 

describes the intersection of the target cross-section with the survey track (Figure 7-8). For MBES data, 

the volume-target cross-sections are delineated in each ping as for MBES point targets (Figure 7-7 [E]) 
69

and combined across pings on the basis of proximity (assuming no target movement between pings), 

resulting in a 3D object that describes the entire target (or portion thereof if not completely insonified 

within the MBES swath).

The most commonly used SBES volume-target detection algorithm is the “SHAPES” algorithm described 

by Coetzee (2000). The term “school detection” is commonly used, but note that a school is a specific 

biological phenomenon (see e.g. Parrish and Hamner, 1997) while the SHAPES algorithm is simply a tool 

for delineating contiguous clusters of samples (and hence can be used to detect shapes in the echogram 

caused by other phenomena, such as bubble clouds and plumes). Once the 2D cross-section is created, it 

is necessary to correct its dimensions to account for the beam pattern (Diner, 2001). The corrected 

dimensions can then be used to derive a range of metrics that can be used to say something about the

nature of the target and its ecology (e.g. Nero and Magnuson, 1989; Scalabrin et al., 1996; Reid et al., 

2000; Lawson et al., 2001). Although beam-pattern corrections are technically required for target crosssections in both SBES and MBES data, it is perhaps surprising to note that they are not commonly 

applied when processing MBES data.

Figure 7-8. An example of a volume target in SBES data, likely representing a cross-section through an aggregation of jacks or 

snappers. The 2D region delineates a contiguous cluster of Sv samples, with each sample satisfying the volume-target 

condition (see Figure 7-6). 
70

The bottom

The bottom (which is an example of a surface target; see Figure 7-7) is typically detected on a ping-byping basis in SBES data by looking for a peak of a certain shape and amplitude in the Sv ping profile 

(Figure 7-9 [A] and [B]). The result is a 2D line that describes the location and range/depth of the bottom 

in each ping (Figure 7-9[A]). For MBES data, the SBES approach is applied to each beam in each ping 

(Figure 7-9 [C] and [D]) and the resulting detections used to generate a 3D bottom surface (Figure 7-1

[B]).

Bottom-detection algorithms vary in their level of sophistication, from simply identifying the 

range/depth of the maximum Sv sample in each ping, through to identifying the range/depth at which 

the split-beam angles (when available) cross through zero (see Demer et al. 2009 and references 

therein). The aim with SBES data is typically to identify the bottom depth directly below the transducer 

at any given instant, but split-beam angle measurements also make it possible to estimate the slope and 

roughness of the bottom within the beam footprint (Demer et al., 2009). Nevertheless, an accurate SBES 

bottom-detection algorithm that is robust across all situations remains one of the “Holy Grails” of 

quantitative echosounding.

An added complication is the so-called “dead zone”, which arises due to the curved wave front of the 

transmitted pulse. A number of algorithms have been described for determining the extent of the dead 

zone (see Demer et al., 2009; Tušer et al., 2013; and references therein), which can then be delineated 

on an echogram via one or more 2D lines.

Targets on the bottom

Targets attached to the bottom such as kelp, seagrass and shipwrecks may act as either volume or 

surface targets depending on their acoustic properties. They are typically detected in a similar way to 

the bottom, by looking for a particular point (e.g. a strong gradient) in the Sv ping profile (Figure 7-9) and 

generating a 2D line. 

Other density discontinuities and boundaries

It is possible to detect phenomena such as thermoclines, pycnoclines and oxyclines if the density change 

is sufficient, or if they cause an accumulation of biotic or abiotic targets. As for the bottom and targets 

on the bottom, it may be possible to detect the extent of a cline based on diagnostic features within the 

Sv ping profile. For example, Bertrand et al. (2010) found that the oxycline depth off Peru was the point 

at which 98% of the Sv cumulative sum was reached.
71

Figure 7-9. Examples of the bottom (a surface target) in SBES and MBES data. [A] SBES raw Sv echogram of a number of pings 

zoomed in to the area around the bottom. The black line shows the bottom detection. [B] Sv profile of one ping in the 

echogram to the left. As the sound pulse reaches the bottom, the Sv increases rapidly to an extremely high level (0 dB re. 1 

m2 m-3) before decreasing again at a slower rate. [C] MBES raw Sv echogram of a number of beams in a single ping, zoomed in 

to the area around the bottom. The turquoise dot in each beam shows the bottom detection. [D] Sv profile of one beam in 

the echogram to the left. The profile is similar to panel [B] but with more variation from sample to sample (note also that 

this is not calibrated backscatter, hence the unrealistically high Sv values).

7.6 TARGET CLASSIFICATION 

Target classification is the process of differentiating and categorizing the detected targets. It represents 

another “Holy Grail” of quantitative acoustics (Horne, 2000) because the derivation of ecologically 

relevant categories (taxonomic group, body size, life-history stage, bottom type etc.) from the indirect 

measurements made by an echosounder (voltage, time and phase/bearing) is not a straightforward task. 

Point targets

The characteristics of SBES-derived point targets that could be used to classify them include their 

distribution, volume number density (ρv), TS as a function of frequency (TS(f)), TS ping-profile 

characteristics (length, symmetry etc.) and ping-to-ping characteristics. The most common approach has 

been to classify point targets on the basis of TS at a given frequency, although other target

characteristics have been explored (e.g. Stanton and Clay, 1986; Vray et al., 1990; Burwen and

Fleischman, 1998; Spampinato et al., 2010). The focus to date has been on narrowband data, but we can 

expect greater emphasis on wideband data into the future (e.g. Stanton et al., 2010).

Although TS is less likely to be available for MBES-derived point targets due to the difficulties associated 

with calibration (see Demer et al., 2015, p86-94), a range of additional characteristics can be extracted 

from the target cross-sections in each ping (length, height, perimeter etc.) and used to provide a basis 

for classification. Note, however, that the cross-section geometry should be corrected for the beam 

pattern to avoid range effects. 
72

Volume targets

Volume targets representing aggregations of fish (Figure 7-6) have formed the focus of much of the SBES 

target-classification literature (e.g. Reid et al., 2000; Fernandes et al., 2005; Jech and Michaels, 2006;

Korneliussen et al., 2016). Reid et al. (2000, p17) grouped the characteristics of volume targets as 

follows:

1. Positional: Latitude, longitude, time and depth

2. Morphometric: Length, height, perimeter, compactness etc.

3. Energetic: Sv statistics, e.g. mean, minimum, skewness etc.

4. Environmental: Water depth, temperature, currents etc.

Early SBES studies explored relatively sophisticated classification techniques for volume targets, such as 

forward and inverse modelling (see Martin et al., 1996; and references therein), echo statistics (e.g. 

Stanton and Clay, 1986) and artificial neural networks (e.g. Haralabous and Georgakarakos, 1996). Since 

this time, most studies have focused on more straightforward analysis of the frequency response (Sv(f)), 

quantified in one of the following ways: 

1. The difference in Sv at different frequencies (ΔSv, ΔMVBS or dB difference; e.g. Kang et al., 2002)

2. The sum of Sv at different frequencies (ΣSv; e.g. Ballon et al., 2011)

3. The statistics of ΔSv (e.g. De Robertis et al., 2010)

As with SBES point targets, the focus to date for SBES volume targets has been on narrowband data, but 

we can expect greater emphasis on wideband data into the future (e.g. Jech et al., 2017).

Fewer studies have focused on classification of MBES-derived volume targets in the water column (see 

Colbo et al., 2014; and references therein). The characteristics of these targets can be grouped in the 

same way as for SBES targets (see above), but with less of an emphasis on the energetic parameters and 

more on the morphometric ones due to the different instrument characteristics.

The bottom

The characteristics of the bottom echo that can be used to classify it include depth, backscatter (Sb) and 

(more generally) ping-profile, beam-to-beam and ping-to-ping characteristics (see Anderson et al., 2007; 

and references therein). As with point and volume targets in the water column, the strength of SBES 

data typically lies in the ability to yield calibrated backscatter, while MBES data provides better spatial 

coverage.

The general data-processing sequence for SBES bottom classification involves: 

1. Bottom detection: Identifying the range/depth of the bottom in each ping. 

2. Feature extraction: Deriving quantities from the bottom-backscatter samples in each ping or 

group of pings (interval), e.g. water depth, maximum Sv, time taken to rise to the maximum Sv

value etc.

3. Statistical analysis (multivariate analysis and clustering): Defining which features are 

responsible for most of the variation across pings or intervals, and clustering these pings or 

intervals into those with the most similar features.
73

Perhaps the most commonly extracted features are the backscattering energy in the tail of the first 

bottom echo and the backscattering energy in the entire second bottom echo (referred to as E1 and E2 

respectively; Chivers et al., 1990), although many more features have been variously devised and used 

(e.g. Hamilton, 2001).

There are two general approaches for MBES bottom classification (see Brown and Blondel, 2008; and 

references therein):

1. Geoacoustic: This aims to solve the so-called inverse problem by matching the Sv or Sb pingprofile shapes to those expected from different bottom types.

2. Feature-based: This considers individual or multiple features (water depth, angular response 

etc.) as described for SBES data (see above). 

As for point- and volume-target classification, the perennial challenge for bottom classification is to 

robustly match the echosounder-derived features to the ecological features of interest (which for the 

bottom include hard/soft, rough/smooth, rocky/sandy, etc.). 

7.7 CHARACTERIZATION 

Characterization is the process of calculating metrics from the calibrated, cleaned and classified data to 

describe:

1. The distribution, density, abundance, biomass and acoustic properties of the detected and 

classified water-column targets.

2. The water depth and the acoustic properties of the bottom.

3. The extent of the dead zone.

4. The performance of the echosounder (referred to as instrument calibration). 

Point targets

Point-target volume number density is estimated by echo counting (Ehrenberg and Lytle, 1972), from 

which target abundance and biomass can be calculated (see “Volume targets” below). For mobile SBES 

surveys, in which sequential pings are grouped into time-, ping- or distance-based intervals, a number of 

echo-counting models have been described (see Kieser and Ehrenberg, 1990; and references therein):

1. Cone model

Volume number density, ρv (number m-3) = Σ point-target detections / Σ 3dB beam volumes

2. Trace model

Volume number density, ρv (number m-3) = Σ tracks / Simple wedge volume

3. Wedge model

Volume number density, ρv (number m-3) = Σ tracks / Complex wedge volume

4. Acoustic-detection-volume model

Volume number density, ρv (number m-3) = Σ point-target detections / Σ TS beam volumes

Although the acoustic-detection-volume model is the most sophisticated, most studies have employed 

either the cone or the trace model, perhaps because these are easier to compute and/or are available in 

existing software. While both the cone and trace models should yield similar results, the added 
74

complexity and uncertainty introduced by detecting tracks means that the cone model (which doesn’t 

require targets to be tracked) provides perhaps the best balance of simplicity and accuracy.

Echo counting is rarely performed for mobile MBES surveys (see e.g. Dunlop et al., 2018). For stationary 

SBES and MBES surveys, target tracking enables target flux to be measured (number of targets passing a 

fixed-point during a given time interval) without the need to explicitly consider sampling volume. 

However, the cone model can be applied on a ping-by-ping basis to monitor the change in volume 

number density over time.

Volume targets

The volume number density of individuals within a volume target is calculated by echo integration 

(Ehrenberg and Lytle, 1972), from which the abundance and biomass of those individuals can then be 

calculated. Although the concept of echo integration is simple (i.e. the total amount of backscattering 

energy in a given volume of water is the sum of the backscattering energy from all of the individual 

targets in that volume), it took considerable work in the 1970s and 80s to show that this assumption of

linearity could be robustly applied in practice (see Foote, 1983; and references therein). 

The echo-integration equations are similarly simple. Working with the common metrics (sv, Sv, sa and sA) 

for describing the backscattering energy of a given group of samples (analysis domain), it is possible to 

calculate the density, abundance and biomass of individuals as follows:

1. Density

Volume number density, ρv (number m-3) = sv/<σbs> or 10(Sv-<TS>)/10 or (sa/<σbs>)Δz or

sA/<σbs>4π18522

Δz

Area number density, ρa (number m-2 re. Δz) = sa/<σbs> or sa/10<TS>/10

Area number density, ρA (number nmi-2 re. Δz) = sA/(4π<σbs>) or sA/(4π10<TS>/10) 

Volume mass density, mv (kg m-3) = ρv x <mi>

Area mass density, ma (kg m-2 re. Δz) = ρa x <mi> 

2. Abundance for a given water volume (V) or area (A)

nb (number) = <ρv> x V or <ρa> x A

3. Biomass for a given water volume (V) or area (A)

mb (kg) = <mv> x V or <ma> x A

where:

• sv is the mean volume-backscattering coefficient (m2

 m-3) of the samples in the analysis domain 

(where sv = 10Sv/10

, and Sv is defined below)

• Sv is the mean volume-backscattering strength (dB re. 1 m2

 m-3) of the samples in the analysis 

domain

• sa is the area-backscattering coefficient (ABC; m2

 m-2 re. Δz) of the samples in the analysis 

domain

• sA is the nautical area-scattering coefficient (NASC; m2 nmi-2 re. Δz) of the samples in the analysis 

domain

• <σbs> is the backscattering cross-section (m2

) of a representative individual in the analysis 

domain

• <TS> is the target strength (dB re. 1 m2

) of a representative individual in the analysis domain
75

• Δz is the average range extent (m) of each ping in the analysis domain

• <mi> is the mass (kg) of a representative individual

These equations show that if you wish to know the density of individuals, then the goal of the 

echosounder data-processing workflow is to provide accurate measurements of the backscattering 

energy due solely to the individuals of interest in a given volume of water (expressed as either sv, Sv, sa

or sA). The ability to achieve this depends on the successful completion of each workflow step in turn 

(i.e. calibration, cleaning, detection and classification).

These equations also show that another critical value is required, namely the backscattering energy 

from a representative individual in each volume (as either σbs or TS, where <...> denotes 

“representative”). This is perhaps the most challenging task in quantitative acoustics (see Ona, 1999; 

and references therein).

Accurate measurements of sv, Sv, sa or sA can only be obtained from a calibrated echosounder, i.e. one 

that is stable from ping to ping and whose performance characteristics can be quantified (see Demer et 

al., 2015; Section 4.6 and Section 6 on the Scientific Echosounder System). Currently there are more 

SBES systems available fitting this description than MBES systems, but this may change in the future (see 

e.g. Andersen et al., 2007; Mosca et al., 2016).

The bottom

Once the bottom has been detected as a 2D line or 3D surface (Figure 7-9), it can be characterized in 

terms of latitude, longitude, depth and time. As described in Section 7.6, it is also possible to 

characterize the bottom in terms of its backscatter features. These features can be used to classify the 

bottom, illustrating that the data-processing workflow can also work in an iterative and non-linear way.

The echosounder system

Detections of a target with known acoustic properties (e.g. a 38.1 mm-diameter tungsten-carbide 

sphere) can be used to estimate some of the parameters in the echosounder equations for TS and Sv, 

most notably on-axis gain and the sa correction factor (Fig. 7-4 [B] and [C]). This is typically referred to as 

“calibration” (see Section 4.6; Demer et al., 2015; and references therein), but note the distinction 

between this (which is a procedure performed to characterize the performance of the echosounder, i.e. 

instrument calibration) and data calibration (Section 7.3). As for any other echosounder-based 

procedure, a calibration experiment involves data collection (Section 4.6) and data processing (Section 

7).
76

8 ACOUSTIC DATA INTERPRETATION AND ABUNDANCE ESTIMATION (D.A. DEMER) 

Stock abundance may be estimated by echo-integration analysis of echosounder data collected along 

transects that span the target stock distributions. Basically, animal density is estimated by summing the 

echoes received from all of the sampled animals, and dividing that by the echo representative of an 

average individual. The abundance is estimated by multiplying the estimated density by the sampled 

area. This section summarizes the steps of an echo-integration survey analysis.

8.1 TARGET STRENGTH ESTIMATION 

To estimate the mean backscattering cross-sectional areas (σbs, m2; MacLennan et al., 2002) for 

individual fish of the dominant species within each trawl catch or camera image, the length distributions 

from the catches or images are input to models of target strength, TS = 10log(σbs) versus fish length. The 

TS models may be obtained from the literature, or derived from TS and length measurements made 

from in situ or ex situ fish. 

8.2 BIOMASS ESTIMATION 

Animal densities are estimated by dividing the nautical area scattering coefficients (sA; m2 nmi-2) 

(MacLennan et al., 2002) for each species by their respective length-weighted average σbs (Simmonds 

and MacLennan, 2005; Demer et al., 2012). The biomass densities within each acoustic transect are 

averaged to comprise the sample unit (Simmonds and Fryer, 1996). 

Because each species does not generally span the entire survey area (Zwolinski et al., 2014), their 

natural patchiness is delineated by statistically-independent, stationary, post-sampling strata 

(Johannesson and Mitson, 1983; Simmonds et al., 1992). Each stratum should have: 1) at least three 

transects, all of them have approximately equal spacing, 2) fewer than three consecutive transects with 

zero-biomass density, and 3) bounding transects with zero-biomass density.

The mean biomass density of each stratum is calculated by a transect-length weighted average of the 

transect-mean densities (Demer et al., 2012; Zwolinski et al., 2012), which is equivalent to the arithmetic 

mean of all individual samples in the stratum.

An estimate of abundance is obtained by multiplying the average estimated density in the stratum by

the stratum area (Demer et al., 2012). The variance is calculated through non-parametric bootstrap of 

the transect-mean densities. The total abundance in the survey area is the sum of abundances in all 

strata. The total variance is the sum of the variance in each stratum.
77

8.3 UNCERTAINTY ESTIMATION 

Acoustic survey estimates have random and systematic components of measurement and sampling 

uncertainty (Demer, 2004). Random measurement and sampling error, or precision, is reduced by 

averaging independent measures. Because there are usually numerous measurements comprising a 

sampling unit, e.g., a transect, measurement error is generally negligible. On the other hand, random 

sampling error, typically summarized by the coefficient of variation (CV), depends on the distribution of 

the animals sampled and the number of independent sampling units, e.g. transects.

Provided that each stratum has spatially independent transect means (i.e., densities on nearby transects 

are not correlated), random-sampling estimators provide unbiased estimates of variance. Transectmean densities are treated as replicate samples, and the variance is calculated for each post-sampling 

stratum using non-parametric bootstrap resampling (Efron, 1981). 

The 95%-confidence intervals for the mean biomass densities are estimated as the 0.025 and 0.975 

percentiles of the distribution of 1000 bootstrap survey-mean biomass densities. The bootstrap 

estimates are constructed by resampling with replacement, the transects within the strata (Efron, 1981). 

Coefficient of variation (CV) values are obtained by dividing the bootstrapped standard errors by the 

estimated means (Efron, 1981).

Often, more insidious than random error 

is the systematic estimation uncertainty, 

which varies in time and space. Examples 

are variable biases due to diel and 

seasonal migratory behaviors and 

environmental changes, which affect 

animal distributions and their 

detectability; and the accuracies of echo 

classification and target strength estimation, which change according to the species present, and their 

ontogeny and demographics. Measurement and sampling biases are non-random, so their effects must 

be either negligible, or their estimates must be considered in the accounting for uncertainty. These 

variable biases may be positive or negative (e.g., Demer, 2004; Simmonds and MacLennan, 2005). If they 

are not negligible, and their variable magnitudes are unknown or not accounted, then the estimates 

cannot be statistically evaluated for change.

It is essential to first identify and mitigate all 

significant components of measurement and 

sampling bias, then sample the animals of interest 

such that the CV is small enough to detect 

meaningful change.

To evaluate change in a time-series of survey estimates, it is therefore essential to first identify and 

mitigate all significant components of measurement and sampling bias, then sample the animals of 

interest such that the CV is small enough to detect meaningful change. The requisite sampling for a 

targeted level of uncertainty may be learned from past survey results, estimated from simulated 

sampling of a hypothesized animal distribution, or both. 
78

9 CASE STUDY: FISHERY ACOUSTIC SURVEYS TO LOCATE, ASSESS AND MONITOR REEF 

FISH SPAWNING AGGREGATIONS (B. BINDER, J.C. TAYLOR AND K. BOSWELL) 

9.1 BACKGROUND 

The formation of fish spawning aggregations 

(FSAs) is a vital part of the life cycle of many reef 

fishes, and represents the sole reproductive 

opportunity for many aggregating species 

(Domeier and Colin, 1997). Additionally, FSA’s 

play a key role in promoting overall reef health 

(Archer et al., 2014), and stimulate increases in 

reef fish abundance and diversity (Heyman et 

al., 2005; Schärer et al., 2009). Unfortunately, 

spatial and temporal knowledge of these 

resources is predominately limited to 

experienced anglers, which has led to many 

cases of decline and extirpation after extended 

periods of heavy fishing (Sadovy De Mitcheson 

et al., 2008). However, increases in participation by resource users in the design of fisheries 

management strategies have led to the discovery and protection of several spawning aggregations 

throughout the Unites States and Greater Caribbean (Burton, 1998; Lindeman et al., 2000; Nemeth et 

al., 2006 and 2012; Zeigler and Hunt, 2012). For example, the commercial fishing community was 

directly responsible for identifying the decline of goliath grouper catches in south Florida; and the 

subsequent management actions that resulted in the complete moratorium on their harvest in 1990 

(SAFMC, 1990; Lindeman et al., 2000; Ferreira et al., 2014). Following the closure, additional resource 

user driven survey initiatives were developed to monitor the recovery of the species, and have provided 

evidence of a measurable recovery throughout their range in Florida; including the reformation of 

multiple spawning aggregations on the east and west coasts of Florida (Koenig and Coleman, 2009; CassCalay and Schmidt, 2009; Mann et al., 2009; Collins, 2014). Additionally, a user-driven initiative to 

protect a failing snapper fishery in the Dry Tortugas led to the development of the Tortugas South 

Ecological Reserve in 2001. The fishery has seen a significant recovery since inception, with direct 

observations of spawning individuals, and as many as ~4000 aggregated snapper (Burton et al., 2005;

Zeigler and Hunt, 2012).

The cumulative body of knowledge from targeted research as well as resource users accumulated over 

generations of fishing and their contributions as ‘citizen scientists’ has improved our understanding of 

the spatial and temporal dynamics of aggregation activity but is lacking in the peer reviewed literature 

(Lindeman et al., 2000; Koenig and Coleman, 2009). But surveys to confirm the location and timing of 

aggregations and to assess trends in spawner abundance has relied primarily on visual surveys which are 

inefficient in covering large areas, inherently risky in deep environments, and may not be able to provide 

robust estimates of abundance in very dense aggregations. Fishery acoustics provides advantages to inwater visual surveys in the ability to cover large areas relatively quickly providing an index of biomass 

from the acoustic backscatter, and pinpointing the location of aggregations. Here, we provide case 
79

studies of acoustic applications for locating, assessing abundance and monitoring trends in reef fish 

spawning aggregations.

9.2 APPROACHES 

Numerous reports have found reef fish spawning aggregations located in close proximity to prominent 

geomorphological features such as promontories, seamounts, or outer reef ridges. But these features 

may extend several kilometers, leading to inefficiencies in searching for or monitoring by scuba divers. 

To examine the changes in aggregation density and abundance at spawning sites, hydroacoustic and 

diver visual surveys were conducted over consecutive 2-3 day periods around peak lunar phases (new 

and full moon) throughout the spawning season of the target species. The suite of devices implemented 

in the hydroacoustic surveys have included multiple frequencies to differentiate fish aggregations from 

aggregations of other taxa such as plankton (e.g., 38, 70 and 120 kHz). Multibeam sonars are also used 

to simultaneously map the seafloor to provide further inference on the habitat use by aggregations.

Watercolumn backscatter from multibeam echosounders can also provide indicators for the presence of 

large aggregations. Survey design, as described earlier in this report can involve systematic parallel 

transects, as well as “flower” shapes, with important assumptions to consider as described in the 

sampling design section of this report. Acoustic backscatter can provide estimates of abundance, but 

require groundtruthing to validate species presence. Roving and drifting visual surveys with divers or 

remotely operated vehicles are used to ground truth estimates derived from acoustic surveys. Detailed 

discussions on survey design and estimating abundance are in previous sections.

9.3 SEARCHING FOR AGGREGATIONS IN THE FLORIDA KEYS NATIONAL MARINE SANCTUARY 

Beginning in 2009, NOAA began a project focused on locating and examining the status of FSAs in the 

Florida Keys National Marine Sanctuary. Partnering with commercial fisherman from the region, we first 

investigated historically known to aggregation sites in the Florida Keys. These reports were provided as 

geographic coordinates, or points on a paper map or nautical chart, with descriptions of the bathymetry 

and sometimes geomorphology of the seafloor. The precision of the geographic location varied 

considerably. Initially, large-scale acoustic surveys were conducted to develop bathymetric profiles for 

the study areas that could be used to predict the precise locations of aggregation activity. We then used 

an exploratory survey method using parallel lines, a grid or transects, or clover leafs around 

geomorphological features such as promontories, to pin-point areas of high biomass using a split-beam 

echosounder. When we detected high acoustic backscatter in the watercolumn, we adapted the surveys 

with a tighter clover leaf to pinpoint the location of the aggregation and quickly deployed divers on 

these hotspots to confirm the presence of target spawning species, quantify their abundance, and 

document spawning activity (Figure 9-1). Using this approach, aggregations of black grouper, mutton 

snapper, and gray snapper were detected near historically reported aggregations sites, confirming 

association with unique bathymetric features. The large scale acoustic surveys also documented 

additional aggregations other species that were not the focus of management, but pointed to evidence 

of multi-species spawning aggregation sites.

9.4 ESTIMATING ABUNDANCE OF GOLIATH GROUPER IN THE SOUTHEAST FLORIDA 

Building on what we learned in the Florida Keys, an extension of the original project was undertaken 

along the East coast of South Florida. Using information gathered from a network of commercial and 
80

recreational resource users, we developed a comprehensive database of historical FSA reports that were 

considered in a wide-scale re-zoning effort in the Southeast Florida Coral Reef Initiative regions (Figure 

9-2 and Figure 9-3). 

Figure 9-1. Real-time monitoring of acoustic data acquisition allows for the rapid deployment of divers to visually confirm 

the presence of aggregating fish. Once an area of elevated backscatter was pin-pointed using the echogram (Left), divers 

were deployed. In the provided example, divers were rapidly deployed on an area of interest and confirmed the presence of 

a large aggregation of gray snapper (Right) that had eluded researchers in preceding months.
81

Figure 9-2. Information gathered from a network of commercial and recreational resource users was used to develop a 

comprehensive geospatial database of historical FSA reports that were considered in a wide-scale re-zoning effort through 

the Southeast Florida Coral Reef Initiative.
82

Figure 9-3. Following information gathering, systematic survey transects were established centered on a known wreck where 

the goliath grouper aggregate.

Using techniques described in the abundance estimation section in this report, abundance estimates 

were made for each survey leading to better understanding of the timing of the goliath aggregations. 

Despite our efforts, data pertaining to the precise location and timing of FSAs was sparse, making a 

dedicated multi-species acoustic survey effort impractical. We used acoustic surveys to estimate 

abundance of a reef fish community and the target species, goliath grouper near Jupiter, Florida (Figure 

9-4). 

Figure 9-4. An echogram (left) showing an aggregation of large individual targets over structure on the seafloor. Right image 

shows the same group of goliath grouper observed by divers.
83

9.5 MULTIBEAM ECHOSOUNDERS FOR ESTIMATING AGGREGATION ABUNDANCE 

Calibrated splitbeam echosounders provide the best means for estimating target strength of individuals 

and measuring backscatter for estimating abundance of aggregations. But the splitbeam echosounder 

typically uses a narrow transducer beam resulting in a narrow slice through the watercolumn. By 

example, a typical transducer beam has a 7-degree opening angle, resulting in swath at the seafloor of 

about 12% of depth (e.g., 12 m wide at 100 m depth). A wider transducer beam angle increases the 

search volume, but also increases the acoustic deadzone and occlusion of individuals close to the reef 

interface. Studies in coral reefs have successfully used multibeam echosounders to increase the swath 

and search volume by orders of magnitude. Multibeam echosounders used for hydrographic and 

seafloor mapping surveys can also collect backscatter from objects in the watercolumn. A typical 

multibeam sonar has a beam fan angle as large as 140 degrees, or 10 to 20 times a typical splitbeam 

echosounder. Instead of a slice through part of the aggregation, the multibeam swath can image the 

three-dimensional volume and shape of the aggregation. Multibeam echosounders are not generally 

calibrated, so data from these systems will not provide useful measures of backscatter to estimate 

abundance. Instead, the sequence of cross sections at each multibeam ping can be combined to 

estimate the volume of the aggregation. Densities calculated using the splitbeam echosounder can then 

be scaled up to estimates of total abundance in the aggregation.

Taylor and Ebert (2012) used watercolumn backscatter from multibeam echosounders collected during a 

seafloor mapping survey of El Seco, a feature near Vieques Island, Puerto Rico and the Flower Garden 

Banks National Marine Sanctuary in the Northwest Gulf of Mexico to locate and measure the size, shape 

and abundance of fish in large schools and aggregations. The survey evaluated the combination of 

splitbeam and multibeam echosounders and demonstrated the ability to increase search volume to 

detect relatively small aggregations of reef fish, providing precise estimates of abundance as validated 

by video from a remotely operated vehicle. The approach also documented unusually large school of 

reef fish at West Flower Garden Banks (Figure 9-5). Repeated surveys further reported a consistent 

hotspot of fish biomass in that area. 

Figure 9-5. Example of a large fish (Atlantic creole fish) aggregation mapped using both multibeam and splitbeam 

echosounder surveyed over the coral reef cap in Flower Garden Banks National Marine Sanctuary. The blue-orange-red 

curtain represents backscatter and cross-section of the aggregation from the splitbeam echosounder. The grey shaded 

volume represents the 3-dimensional shape of the aggregation resolved using a multibeam echosounder, with a single ping 

multibeam fan shown. Multibeam derived bathymetry is shown from pink (shallow) to green (deep). Data source: 

Unpublished, JC Taylor and EF Ebert, NOAA National Centers for Coastal Ocean Science.
84

9.6 IMPLICATIONS FOR FISHERIES MANAGEMENT 

FSAs are important to the reproductive success and continuity of reproducing species, and contributes 

to the enrichment of genetic diversity (Sadovy De Mitcheson et al., 2008). The implications for 

developing an FSA monitoring program that incorporates the local community and fisheries acoustics 

are considerable. Indeed, in regions where invested fisherman have assisted scientists in managing and 

studying FSAs, there are numerous cases of recovery following decades of heavy fishing pressure 

(Burton et al., 2005; Heyman et al., 2005; Schärer et al., 2009). Thus, by integrating local knowledge with 

the existing literature, and implementing a wide-scale quantitative acoustic survey approach, we 

promote a methodology to address the spatial and temporal aspects of FSAs that can be used as a basis 

to apply a similar non-invasive monitoring methodology in other regions deficient in their knowledge of 

FSAs. 
85

10 CASE STUDY: STATIONARY ACOUSTIC MEASUREMENT INSTRUMENTS AND 

METHODS (J.C. TAYLOR AND K. BOSWELL) 

Ecological processes on coral reefs occur over a continuum of 

spatial and temporal scales. Elsewhere in this report, authors 

have described several advantages provided by fisheries 

acoustics, especially where surveys can cover large areas 

relatively quickly at very fine spatial resolution. But these 

surveys usually provide a single snap-shot of the distribution of 

biological organisms. This raises some challenges in interpreting 

habitat use at finer scales in the complex mosaic of habitats. For 

example, in coral reefs, several species groups migrate from the 

reef onto adjacent habitats during dusk to feed on benthic 

organisms emerging from soft sediments. Observing these 

behaviors is made difficult by reduced light levels and the 

chance that divers as observers may disrupt the behavior of 

interest. Marine organisms also use the watercolumn as habitat, 

and migrate vertically to feed on pelagic organisms queued by 

light levels, temperature or other environmental influences. These vertical movements could also be 

related to interactions with the underlying benthic habitats. Observations from stationary platforms can 

elucidate these very fine-scale individual movement and behaviors over time scales of seconds to years. 

In this case study, we present areas of research with management applications using stationary 

platforms and fishery acoustic systems to: 

1. Detect and observe movements of reef organisms in complex reef habitats, 

2. Observe predator-prey interactions on coral reefs. 

As described previously in this report, split-beam echosounders transmit and collect backscatter from 

the watercolumn multiple times per second. Splitbeam echosounders can be deployed on a mooring 

looking upward or buoy looking downward (Figure 10-1). A single point target can be located in splitbeam during sequential pings, providing “tracks” depicting movement of the organism through the 

acoustic beam. These tracks can be 

placed in real-world space to position the 

target relative to the location of the 

acoustic system and other features in the 

environment or habitats. Large schools 

of fish or layers of plankton can be 

analyzed as volume targets to track 

vertical or horizontal movement of groups of animals. Multibeam sonars and their imaging sonar 

relatives provide a wider field of view compared to the narrow beam of the splitbeam echosounders. 

Imaging sonars like Dual-frequency Identification Sonars (DIDSON, http://www.Soundmetrics.com) and 

ARIS imaging sonars operate at very high frequencies (1-3 MHZ) to produce video-like images in the view 

field (Belcher et al., 2002). These sonars can resolve size and shapes of groups of organisms and body 

shape and movement of individuals. In the early application of these systems, they were employed on 

stationary platforms to observe anadromous fish during spawning migrations up rivers, reporting counts 

Observations from stationary platforms can 

elucidate very fine-scale individual movement and 

behaviors over time scales of seconds to years.
86

and timing of migration as well as sizes of species like salmon. In coral reefs, they can provide high 

resolution images of individuals as well as images showing the complexity of the habitats (Figure 10-1). 

Figure 10-1. Example of a stationary deployment for imaging sonars (left panel showing DIDSON and Kongsberg M3), a diver 

deploying an imaging sonar and video camera (middle panel), and an upward facing split-beam echosounder (right panel) 

deployed near Aquarius Reef Base in the Florida Keys. 

10.1 DETECTING AND TRACKING MOVEMENTS OF REEF ORGANISMS IN COMPLEX HABITATS 

Several species of reef fish make regular migrations between reefs and adjacent habitats. A well-known 

example involves groups of smaller snappers (Lutjanidae) and grunts (Haemulidae) that use the reefs 

during the day as refuge from predation (Hobson, 1972; Helfman, 1984). Around dusk, individuals or 

groups of fish migrate off the reef and feed over 

adjacent sediment, likely consuming invertebrates 

emerging from the soft sediment. It is thought that the 

migration at dusk is queued to light levels that may 

inhibit predators from detecting the smaller fishes. The 

migration may be selective for certain habitat types 

adjacent to the reefs, such as macroalgae or other 

vegetation, rather than unvegetated sand. Movements 

of animals over the seascape is studied using various 

forms of tag and telemetry systems where individuals 

are implanted with transmitters that report location on 

deployed receivers scattered around the seascape 

(Pittman et al., 2014). This lagrangian-type observatory 

has made important discoveries of animal movements 

across seascapes, but may not be able to estimate rates 

of movement in terms of number of individuals or fine 

temporal scales of movement across habitat transition 

zones due to limits in the number of tags that can be 

deployed. Observations using stationary acoustic 

imaging platforms can provide measurements of the 

sizes and movement rates of individuals to provide 

insights into the connectivity of coral and adjacent 

habitats. An acoustic imaging system can be set at the 

interface between a coral reef and adjacent sediment or 

Figure 10-2. Figure 4 2. An example image of a reef 

shark captured by a DIDSON imaging sonar from 

McCauley et al. (2016). The body shape and

movement (in the acoustic video) allow for

measurement of individual lengths.
87

vegetated habitats. Measurements of fish movement between habitats can provide measures of flux of 

biomass across boundaries. The key challenge is to select a representative location for the observation 

that will provide meaningful and interpretable data. 

Top predators like sharks or groupers are often elusive during certain life stages or are transient and 

move through coral reef habitats at night. Detection of these large animals requires well-placed 

observing systems that minimize interference with behaviors such as the use of lights or other devices. 

Imaging sonars have been used to detect and enumerate reef sharks in remote habitats (McCauley et 

al., 2016). Timing of habitat use by shark species can be tracked using autonomous platforms with 

sonars or echosounders deployed for extended periods. Individuals can be enumerated to provide an 

index of abundance and with length information, biomass estimates can also be derived. 

Rare and elusive species of reef fish pose challenges to enumeration and assessments to guide 

management and species conservation. By example, goliath groupers (Epinephelus itajara) are IUCN 

listed species whose harvest is severely restricted throughout much of its range in the western Atlantic 

Ocean. Juvenile stages of goliath groupers use mangrove root systems as part of their nursery habitats 

before moving into deeper reef habitats as adults (Koenig et al., 2007). The highly complex structured 

habitats of the mangroves are challenging to survey to understand habitat use by the fish and 

enumerate and assess rare or endangered species. Imaging sonars used like acoustic cameras have been 

deployed successfully to detect and estimate abundance of juvenile goliaths in mangrove habitats in 

southern Florida, USA (Frias-Torres and Luo, 2009). In examples provided by Frias-Torres and Luo (2009), 

the DIDSON imaging sonar was able to detect individuals that were not seen in videos due to poor water 

clarity (Figure 10-3). 

Figure 10-3. Example video image of goliath grouper deep in a mangrove habitat (A). Corresponding imaging sonar image 

(DIDSON) of same fish showing individuals that were not detected by video. Example source: Frias-Torres and Luo, 2009. 
88

10.2 PREDATOR-PREY INTERACTIONS 

When conditions are favorable, imaging sonar data can be directly coupled with video data from 

underwater cameras to provide species identification and to help identify members of the biological 

community in addition to the 

interactions among individuals and 

between species. For example, Boswell 

et al. (unpub.) and Catano et al. (2016) 

have examined the effects of large 

predators on herbivory rates in coral reef 

ecosystems of the Florida Keys. Coupling 

underwater video data with imaging 

sonar data allowed direct quantification of the rates of herbivory relative to the threat of predation

(Figure 10-4 and Figure 10-5). The high-resolution data that are provided from imaging sonars offers 

researchers the ability to directly quantify interaction rates, organismal size, speed and direction 

(Boswell et al., 2010; Handegard et al., 2012). 

Figure 10-4. Example of shoaling prey (left panel) response to predator (right panel) in a reef ecosystem. Prey are identified 

by green ‘+’ symbols and predator is identified by a red ‘+’ symbol. The prey were observed rapidly exiting the field of view 

prior to the predator entering. The difference between the two images is approximately 3 sec. Data source (Boswell 

unpublished). 

Stationary deployments can offer direct insight into the interactions between predators and prey (e.g., 

schooling behavior and predator disturbance; Figure 10-4 and Figure 10-5; Handegard at al., 2012; Price 

et al., 2014; Rieucau et al., 2011). At present, this approach allows researchers to quantify important 

ecosystem processes at the local scale. These data may be useful for parameterizing ecosystem models 

and understanding rates of energy transfer and changes in interaction rates as a function of ecosystem 

character end stability.

Imaging sonar data can be directly coupled with 

video data from underwater cameras to provide 

species identification and observe predator prey 

interactions at the scale of action.
89

Figure 10-5. Sequence of imaging sonar data illustrating a solitary barracuda (Sphyraena barracuda, 48 cm) identified by red 

‘+’, interacting with a school of juvenile menhaden (Brevoortia tyrannus). Data source (Boswell unpublished).

While imaging sonars can provide fine scale perspective of interactions across complex habitats, upward 

looking deployments can provide unique perspectives of processes that occur in the water column and 

above the seabed (Figure 10-6). Traditionally, these observations are conducted with upward looking 

echosounders to quantify biological scatterers and examine their vertical distribution patterns across 

time and possibly related to oceanographic processes. This perspective can be very useful in ecosystems 

with complex habitats, where boundary effects can prohibit examination of organisms within a couple 

meters of the sea bed. For example, an upward looking echosounder (configured with a wide-angle

transducer,) can provide distributional data of nekton within 1 m of the sea bed. In contrast, traditional 

downward looking echosounders would likely be unable to detect fish near the complex habitats due to 

the acoustic dead zone. This approach has been implemented across a suite of ecosystems and habitats, 

ranging from shallow reefs to deep-water open ocean systems. As seen in Figure 10-6, high resolution 

data can be gathered to quantify nekton interactions within the water column across time. This is 

particularly important, when considering the interest in tracking changes in the biological community 

across time. For example, seasonal pulses of large planktonic forage fish schools moving into reef 

ecosystems can be observed and quantified to study pulses of biological material and nutrients into 

these systems.
90

Figure 10-6. Example echogram from a bottom mounted split-beam echosounder (70 kHz Simrad EK80) illustrating a school 

of white grunt (Haemulon plumieri) interacting with a barracuda (Sphyraena barracuda) near the Aquarius reef base In the 

Florida Keys.

Stationary methodologies are constrained by logistical and practical concerns. Perhaps one of the most 

important considerations is matching the spatial and temporal scale of the observation relative to the 

hypotheses examined. Stationary systems can be deployed to the seafloor with tethered cables to a 

surface acquisition computer. Because ecological processes occur over various temporal and spatial 

scales, thus the duration and periodicity of observation need to be determined prior to sampling. 

Controllers and computers encased in submersible housings can be used to operate sonars on a duty 

cycle and extend data collection periods to ensure data are collected at relevant intervals to capture the 

process of interest. Power supply can be provided through submersible batteries, with necessary 

limitations to endurance of the deployment. Establishing a duty cycle will allow users to extend the 

deployment relative to power requirements and sample rate needed to address the specific hypothesis. 

Other important considerations for conducting stationary acoustic observations are the location of 

deployment and environmental conditions. When conducting stationary observations, it is important to 

be aware of the potential environmental variation (e.g., prevailing current direction and strength, sea 

state, noise sources, habitat stability, etc.), all of which can influence data quality. For example, when 

deploying a stationary imaging sonar on the seabed to examine the distribution of nekton associated 

with the habitat, it is imperative to ensure the sonar tilt/elevation angle is appropriate for the data that 

are desired (Figure 10-7). For upward looking echosounders, wave/wind induced noise at the air-sea 

interface can impart significant artifacts in the acoustic record, and depending on the depth and 

intensity of the event, air bubbles can be detected many meters below the surface and potentially 

occlude signals from nekton in the upper water column (Figure 10-8). 
91

Figure 10-7. Example echograms derived from a horizontally 

aligned split-beam echsounder (A-C; 400kHz BioSonics DTX) and 

imaging sonar (Right panels; DIDSON) illustrating the effect of 

transducer angle on acoustic responses in shallow waters (~2 m 

depth). Panels A–C (left) illustrate the effect of substrate and 

surface reverberation when aimed (A) +4◦ upward from 

horizontal, 15 m range; (B) +1◦ upwards from horizontal, 30 m 

range; and (C) −4◦ downwards from horizontal 13 m range. 

Effects can be observed in both the echosounder and the 

imaging sonar data, where the +4◦ condition indicates surface 

detection between 6 and 10 m range; +1 Indicates slight bottom 

grazing from 7–9 m range; and −4◦ shows extensive bottom 

grazing from 3.5 to 9 m range. The horizontal lines on the 

echograms represent 5 m range intervals. Figure adapted from 

Boswell et al. (2007).

Figure 10-8. Example echograms from a stationary upward looking echosounder (Simrad 70kHz EK80) illustrating two 

conditions of sea state from a deployment near the Aquarius reef base in the Florida Keys. In the left panel the seastate is 

very calm, seas at 0m, whereas as in contrast, the right panel illustrates sea states ~1.5 m. Data source (Boswell 

unpublished).

We have described a few examples of how the use of stationary sonars can be implemented for studying 

fine-scale behavioral interactions. Well-placed stationary echosounders can be used like turnstile gates 

to observe and quantify fish movement between adjacent habitats. Imaging sonars can be used like 

acoustic cameras to observe fish within complex habitats in poor water clarity or low light situations. 

Lastly, observations from imaging sonars can provide unique insights into prey behaviors during 

predation events. In a carefully designed observational study, stationary deployments offer advantages 

by reducing the potential for disturbance and permitting observations across a longer temporal scale 

from seconds to years.
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11 CASE STUDY: MODELLED DAY-NIGHT BIASES IN SPATIAL STRUCTURE OF JACK 

MACKEREL (TRACHURUS MURPHYI) IN CHILE (J.E. PARAMO, C. LANG, S. LILLO) 

11.1 INTRODUCTION 

The Jack mackerel (Trachurus murphyi) (Nichols, 1920) is 

a pelagic species widely distributed in the Southeastern 

Pacific Ocean, from Ecuador (1o30’S) to the south of Chile 

(55oS), reaching the coasts of New Zealand, Tasmania and 

Australia to the west. This area is known as the jack 

mackerel belt (Serra, 1991; Grechina, 1992; Vasquez et al, 

2013). The fishery of jack mackerel is developed mainly in 

the coasts of Chile and Peru. In Chile it has a great 

economic significance since it is one of the main fisheries 

resources, reaching landing levels between 1.6 and 1.3 

million tons between 2001 and 2006 (Sernapesca, 2011). 

The center-south industrial fleet contributed with 48% of these levels, which includes the ocean sector 

off the EEZ. Numerous studies have been developed around this resource, about biology, spatial 

distribution, population dynamics and migration. Serra (1991) describes the existence of a seasonal 

migration of the Chilean sub-population of T. murphyi related to feeding and spawning processes 

(Quiñones et al., 1997; Miranda et al., 1998). Towards the Austral winter in southern of Chile (30 ° -40 ° 

S) a feeding area is identified where congregate adults and regularly occurs juvenile recruitment of 2 to 

3 years (Arcos et al., 2001; Sepulveda et al., 2003), during this period jack mackerel has been evaluated 

with hydroacoustic methods (Cordova et al., 2002) in order to estimate the biomass of the resource in 

the area.

In the other hand, this species shows a gregarious behavior, commonly found in dense schools with a 

high commercial interest. In this sense, Soria, 1994; Gerlotto, 1996; Bertrand, 2004 suggest that spatial 

structures and density suffer alterations, varying cyclically according to physiological (feeding and 

reproduction) and environmental factors, presence or absence of predators, as well as changes in light 

intensity during the 24-hour cycle. According to Bertrand et al. (2004), mackerel behavior varies 

substantially between diel periods, forming deeper small schools during the light period, while in the 

night the pattern differ strongly and distributed in surface waters. This spatial organization changes with 

a slight decrease in occupancy of space by aggregations and increase in acoustic density within 

measurement unit area. The objective of this work is to characterize the jack mackerel structures during 

the day-night periods and their implications of differences in the estimation of biomass during the 

austral winter in the Southeast Pacific Ocean. 

11.2 MATERIALS AND METHODS 

The survey was carried out in oceanic waters off Chile (35°S – 42°S) in 2002 and the sampling survey 

used was systematic with transects perpendicular to the coast separated by 20 nautical miles (n.mi). The 
93

elementary distance sampling unit (EDSU) was 0.5 n.mi. fragment of track. The density of Jack mackerel 

was obtained from the nautical scattering coefficient (NASC; m2

/n.mi.2) of a scientific echosounder 

SIMRAD EK500 with a transducer of 38 kHz, using the target strength equation of Jack mackerel, TS = 

20log L – 68.91 (Lillo et al., 1996). Generalized Additive Models (GAM) (Hastie and Tibshirani, 1990) were 

used in order to find the relationship between biomass with diel periodicity. GAM is a modern statistic 

tool which allow to fit models according to ecological theory (Katsanevakis and Maravelias, 2009). The 

biomass estimation approach of Jack mackerel of acoustics data was likelihood-based geostatistics. The 

method estimates the effective stock area using a generalized linear spatial model on presence/absence 

data and the logit link function (Roa-Ureta and Niklitschek, 2007). 

11.3 RESULTS 

Jack mackerel showed a marked diel pattern in acoustics biomass (P = 0.00) with high values of biomass 

found at night hours (18:00 to 6:00 hours) and lowest values during daylight hours (Figure 11-1). 

According to layers in the water column (Figure 11-2), higher biomass were related to 1:00 to 6:00 hours 

in the 0-50 and 50-100 layers. Higher biomass related to deeper layers were found at 17:00 hour in the 

100-150 layer and between 11:00 to 13:00 hours in the 150-200 layer (Figure 11-2). On the day, schools 

of jack mackerel were formed in compact higher densities, while at night, these schools were joined to 

form large aggregations in extensive layers. However, at dawn the schools were separating and 

deepening to form again the compact higher densities (Figure 11-3). 

Figure 11-1. Modelling of functional relationships between biomass with diel periodicity.
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Figure 11-2. Modelling of functional relationships between biomass with diel periodicity by layers.
95

Figure 11-3. Fish school behavior of jack mackerel during day, night and dawn.

The diel behavior of fish schools were assessed by variograms (Figure 11-4 in order to characterize the 

spatial structure of fish density. In general, variograms showed differences in the day-night spatial 

structure. The day variogram showed a higher nugget effect than night. The percentage of variance 

unexplained by the sampling design (sill/nugget) was 62.64 in the day variogram and 34.25% at night 

variogram and 32.70% in the day-night variogram (Table 11-1). An intense schooling behavior during the 

day is likely to be responsible for this increased small-scale variability. The night variogram were 

characterized by a smaller range of autocorrelation than day variogram (Table 11-1). These features may 

be caused by differences in schooling behavior which exists in jack mackerel with respect to day and 

night. 

On the day, schools of Jack mackerel were formed in compact higher densities that were distributed in a 

more extensive aggregation (variogram range 20.31 n.mi.), but occupying minor area of spatial 

distribution (30831.11 n.mi.2

). While at night, these schools were joined to form large aggregations in 

extensive higher densities layers (variogram range 12.84 n.mi.), but occupying more area of spatial 

distribution (46615.07 n.mi.2

) (Table 11-1). The biomass estimation was different at day, night and daynight, the night estimation was double compared to day (Table 11-1).
96

Figure 11-4. Variograms for biomass of jack mackerel showing differences in the day, night and day-night spatial structure.
97

Table 11-1. Maximum likelihood estimation of jack mackerel during day, night and day-night.

Quantity/Parameter Day Night Day-Night

Number of null observations 834 724 1558

Number of positive observations 1186 1937 3123

Minimum distance (n.mi.) 20.00 20.00 20.00

Maximum distance (n.mi.) 481.66 481.66 496.79

Box-Cox transformation, lambda 0.20 0.10 0.10

Nugget 5.02 1.18 1.50

Sill 3.00 2.26 3.09

% Sill 62.64 34.25 32.70

Range (n.mi.) 20.31 12.84 7.84

Distribution area of stock (n.mi.2) 30831.11 46615.07 70073.41

Mean biomass (ton/n.mi.2) 34.64 42.73 35.63

C.I. low mean (ton/n.mi.2) 31.45 38.04 33.63

C.I. up mean (ton/n.mi.2) 37.84 47.42 37.63

Probability of observing the stock 0.62 0.84 0.78

Biomass (ton) 1068076.90 1991935.13 2496775.86

C.I. low Biomass (ton) 969.53 1773.42 2356.38

C.I. up biomass (ton) 1166.62 2210.45 2637.18

Variance 2.66 5.72 1.05

Standard deviation 1.63 2.39 1.02

CV (%) 4.71 5.60 2.87

11.4 DISCUSSION 

Since the early 90s, the importance of fish school behavior has been recognized as critical in fisheries 

acoustics (Anon., 1993), for various reasons such as avoidance of predation, physiological facilitation, 

feeding, mating, etc. (Pitcher and Parrish, 1993; Parrish and Turchin, 1997) as well as effects of fishing 

pressure, which takes advantage of the fish schooling behavior to increase catches (Fréon and Misund, 

1999). Studies have been developed due to its migratory nature of jack mackerel to neritic and 

epipelagic environments to feed, as confirmed by Medina and Arancibia (1992) for the north of Chile. 

This shows a pattern that determines the seasonal availability of the resource in the coastal and ocean 

fisheries. In fact, jack mackerel is an opportunistic consumer, foraging mainly on macro-zooplankton and 

micronekton (Konchina, 1981). Therefore, that feeding behavior is a determining factor of jack mackerel 

distribution (Quiñones et al., 1997; Grechina, 1998), due to the predator-prey relationships that can 

affect its diel vertical migration (Bertrand et al., 2004). In terms of spatial occupation, jack mackerel is 

more aggregated during the night than during the day, related to their nocturnal active foraging 

behavior (Bertrand et al., 2004). Indeed, the night variogram were characterized by a smaller range of 

autocorrelation than day variogram. These features may be caused by differences in schooling behavior 

which exists in jack mackerel with respect to day and night. On the day, schools of Jack mackerel were 

formed in compact higher densities that were distributed in a more extensive aggregation (variogram 

range 20.31 n.mi.), but occupying minor area of spatial distribution (30831.11 n.mi.2

). While at night, 
98

these schools were joined to form large aggregations in extensive higher densities layers (variogram 

range 12.84 n.mi.), but occupying more area of spatial distribution (46615.07 n.mi.2

). This behavior is 

according to a model for Jack mackerel in relation to their biotic and abiotic environment (Bertrand et 

al., 2006). In this sense, the Jack mackerel and their prey (mainly euphausiids and mesopelagic fish) 

perform vertical migration in which during the day the prey are generally distributed between 250 and 

400 m, out of reach of Jack mackerel. Then, the prey become available at dusk, when they migrate 

toward the surface (Bertrand et al., 2006). This fish school behavior is typical of a diel vertical migration 

(Type I) in which fish move up in the water column at the onset of night, and down with the onset of day 

(Neilson and Perry, 1990). Therefore, the lower day time biomass could be associated with reductions in 

the volume of gas in the swim bladders 

of Jack mackerel due to deep schools. 

This can be explained because Target 

Strength (TS) of Jack mackerel during the 

day could be uncertain because multiple 

targets in dense deepest school are 

passing the single target detector, although fish schools are easily separated on echograms. The TS by 

night are more reliable with fewer multiple targets and the biomass estimates will be more precise due 

to the less clumped spatial distribution (Knudsen et al., 2009). These differences in the form of school 

aggregation have strong implications for the estimation of biomass in the interpolation process by 

kriging. Finally, the major difference in biomass observed between day-time and night-time suggest that 

hydro-acoustic surveys for the assessment of fish biomass should be conducted at night.

Differences in the form of school aggregation have 

strong implications for the estimation of biomass.
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12 CASE STUDY: PASSIVE ACOUSTIC MONITORING IN CORAL REEF ECOSYSTEMS (L.M. 

CHERUBIN) 

12.1 BACKGROUND 

Ambient noise in coral reef ecosystems is largely composed of sounds generated by abiotic sources such 

as wind and waves, and biotic sounds produced by various marine organisms (Kerman, 1984; Deane and 

Stokes, 2010). However, a third sound source, generated by underwater anthropogenic activities is

increasingly becoming part of many marine soundscapes, especially in coastal environments (Scharwz, 

1985; Slabbekoorn and Bouton, 2008; Lobel et al., 2010). Together, these sources combine to create the 

acoustic signature of an environment. Such signatures, or soundscapes, provide a set of acoustic cues 

that can influence many aspects of a marine organism’s behavior, including mating, feeding activity, 

predator or prey detection, orientation and territory’s

defense (Montgomery et al., 2006; Schwarz, 1985; Lobel et 

al., 2010; Parmentier et al., 2010; Simpson et al., 2010; 

Bertucci et al., 2015). Data regarding the nature of marine 

soundscapes now exists for many locations globally, including 

sites within the Pacific (Andrew et al., 2002; McCauley and 

Cato, 2010a, b; Chapman and Price, 2011; Freeman and 

Freeman, 2016; Bertucci et al., 2015), Atlantic (Axelrod et al., 

1965; Urick et al., 1972; Butler et al., 2016) and Indian 

Oceans (Cato, 1967; McCauley, 2011; Parsons et al., 2013), 

allowing preliminary descriptions of variation in acoustic 

activity between and within environments to be made. 

12.2 REEF HABITAT SOUNDSCAPE 

Reef sounds vary on a daily basis, by moon phases, and by seasons. For example, in Australian waters, 

the combined vocal activity of fishes and crustaceans is most intense at dusk, with an increase of 20 dB 

above the mean ambient noise level (Cato, 1992; Cato, 2002). However, the biotic noise signature can 

also vary over longer temporal scales due to interspecific differences in vocal behavior, with the vocal 

activity of some marine organisms increasing during certain seasons (McCauley et al., 2010, McCauley, 

2001; McCauley, 2012), while that of others appears to remain consistent throughout the year (Radford 

et al., 2010, 2014; Nedelec et al., 2015). Localized acoustic variability between adjacent habitats, i.e. 

those separated by less than 1 km, has only recently been highlighted (Bertucci et al., 2015). On coral 

reefs for instance, how the soundscape of an inner reef crest differs from that of a barrier reef, and how 

temporally variable these soundscapes are, is largely unknown. However, recent evidence suggests that 

spectral differences between spatially associated reef habitats can be largely due to variation in the 

sonic activity of marine organisms, i.e. soniferous fishes and snapping shrimps (Radford et al., 2014). 

Another recent study comparing the soundscapes of a temperate urchin-dominated rocky reef to a 

sandy beach identified significantly higher sound amplitude in frequencies between 800 Hz and 2500 Hz 

on the rocky reef as well as diel variations in the temporal and spectral composition of these 

soundscapes (Radford et al., 2010). Different, but spatially associated, marine habitats (including inner, 

barrier, fringing reefs, mangrove and reef pass) surrounding Moorea Island, French Polynesia were 
100

shown to exhibit differences in the temporal and spectral acoustic features (Bertucci et al., 2015). While 

habitats close to the shore showed no significant diel variation in sound intensities, sound levels 

increased at the pass during the night and 

barrier reef during the day. Because many 

coral reef-associated species have highly 

specialized habitat requirements, biotic 

variation between habitat types can create 

unique acoustic signatures that could 

provide key information for larval marine 

organisms during settlement.

Noise signature can also vary over longer temporal 

scales due to interspecific differences in vocal 

behavior, with the vocal activity of some marine 

organisms increasing during certain seasons

12.3 REEF-SPECIFIC SOUNDS AND LARVAL SETTLEMENT 

Indeed, most recently, the soundscape has garnered attention as an additional sensory cue for larvae 

(Montgomery et al., 2006; Simpson et al., 2008; Stanley et al., 2012). The acoustic characteristics of the 

marine environment have the potential to provide rich sensory information to settling organisms, 

reflecting both the presence and quality of the adult habitat over relatively broader spatial scales (e.g., 

meters to kilometers) than localized chemical and substrate cues (Radford et al., 2014; Lillis et al., 

2014; Piercy et al., 2014). Acoustic characteristics have been implicated in the orientation and settlement 

of larval fishes, crustaceans, and molluscs (Simpson et al., 2004; Stanley et al., 2012; Lillis et al.,

2013; Lillis et al., 2015), and a variety of marine invertebrates are known to be sensitive to the water- and 

substrate-borne vibrations (i.e., particle motions) generated by sound waves (Budelmann, 

1989; Budelmann, 1992). Simpson et al., (2004) experimented the effect of sound played by underwater 

speakers on settlement-stage reef fish at Lizard Island, Great Barrier Reef, Australia. They saw a 67% 

increase in the traps with broadcast reef noise. Lillis et al. (2016) reported that sounds at the loudest reef 

generated significantly higher coral larvae settlement during trials compared to the quietest site (a 29.5 

% increase). Their results suggest that soundscapes could be an important influence on coral settlement 

patterns and that acoustic cues associated with reef habitat may be related to larval settlement. 

Parmentier et al. (2015) demonstrated the species-specific influence of sound on coral-reef fish larvae

behavior. They showed that coral-reef fish larvae react to habitat sound by being either attracted or 

repelled. Moreover, the strategy used and the habitat chosen varied between species within families, 

highlighting the importance of conducting studies of sound influence on behavior at the species level. 

Coastal habitat noise heterogeneity appears to be used adaptively by different fish species. 

12.4 REEF HEALTH ACOUSTIC SIGNATURE 

As more studies link the ecological processes of larval recruitment and soundscape production, it will 

become increasingly important to monitor and conserve coastal soundscapes. Soundscape ecology – the 

study of sounds that emanate from a land-scape – is a growing field whose roots lie in terrestrial ecology 

(Pijanowski et al., 2011), but now include many studies in marine ecosystems (Harris and Radford, 

2014). This field of science merges aspects of psychology, behavior, humanities, and ecology to examine 

how soundscapes (i.e., all sounds emanating from a specific landscape) vary over space and through 

time, how anthropogenically generated and naturally generated sounds interact, and how best to 

monitor and conserve soundscapes for their intrinsic and ecological value (Pijanowski et al., 2011).

Unfortunately, habitat degradation, whether by anthropogenic influences or natural disturbance, 
101

disproportionately affects near-shore environments (Vitousek et al., 1997; Limburg, 1999; Watanabe et 

al., 2002; Lotze and Milewski, 2004), where the nursery habitats of many marine organisms occur. 

Marine habitat restoration and restoration ecology are becoming indispensable tools not only to repair 

damaged environments, but also to test ecological theories (Peterson and Lipcius, 2003; Halpern et al., 

2007). Butler et al. (2016) have document the sound loss associated with the degradation of sponge 

habitat in South Florida. Over the past two decades, the hard-bottom communities of Florida Bay have 

experienced large sponge die-off events (Butler et al., 1995; Stevely et al., 2011), eradicating nearly all 

sponges, including the structurally dominant loggerhead sponge S. vesparium, from large portions of the 

central and lower bay. The widespread loss of shelter for snapping shrimp has likely led to a loss of 

shrimp populations within sponge die-off areas, and thus the loss of the biological cacophony produced 

by the shrimp. This change was evident by comparing recordings of hard-bottom communities within 

the sponge die-off area to recordings of hard-bottom outside the die-off area. Mangrove, healthy hardbottom, and restored hard- bottom habitats had higher soundscape spectra levels than seagrass and 

degraded hard-bottom whether at noon or dusk during new or full moons. There were also higher 

numbers of snapping shrimp snaps in mangrove, healthy hard-bottom, and restored hard-bottom 

habitats than in degraded hard- bottom and seagrass beds, especially during the prominent dusk 

snapping shrimp chorus. Butler et al. (2016) further demonstrated that near-shore tropical habitats have 

unique soundscapes that are diminished by habitat degradation, but can be reestablished by habitat 

restoration, at least in the case of sponge-dominated hard-bottom. Freeman and Freeman (2016) 

showed that oceanographic habitat grouped along a principal component defined by an acoustic sliding 

scale: from protected or more remote sites at which lower frequencies were more dominant, to 

degraded sites which produced soundscapes dominated by higher frequency sound. Freeman and 

Freeman (2016) indicated that this acoustic sliding scale enables rapid, inexpensive and spatially 

integrative remote sensing of the ecological state of coral reefs. Such quantitative methods could be 

used to ecologically assess vast areas of reef habitat autonomously in near real-time and could be 

important for remote in situ detection and characterization of subtle but significant ecological changes 

brought about by climate change and other more localized anthropogenic impacts. 
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12.5 SOUND PRODUCTION IN FISH SPAWNING AGGREGATIONS 

Studies have shown that more than 800 fish species can produce sounds for diverse purposes (Kaatz, 

2002; Rountree et al., 2006). Among the soniferous fishes are some of the most abundant and important 

commercial fish species, including many codfishes, drum fishes, grunts, groupers, snappers, jacks, and 

catfishes (Rountree et al., 2006). Some invertebrates with important fisheries also produce sounds, 

including mussels (Mytilus edulis), sea urchins (Fish, 1964), white shrimp (Penaeus setiferus, Berk, 1998), 

spiny lobsters (Moulton, 1957; Fish, 1964; Patek, 2002), American lobster (Homarus americanus, Fish, 

1966; Henninger and Watson, 2005), and perhaps squid (Iversen et al, 1963). Most of the sounds are 

emitted at low frequencies (Ladich, 2004), usually below 1000Hz. However, some pulses can reach 8kHz

(Zelick e al., 1999; Tavolga et al., 2012) or present more complex characteristics (Vasconcelos et al., 

2011). In addition, these emissions are typically broadband short-duration signals (Figure 12-1). Fish 

generate sounds through several mechanisms, which depend on the species and a variety of 

circumstances, such as courtship, threats or defending territory (Kaysumian, 2008). Passive acoustics has 

been used for over 60 years in fish biology and fisheries survey (see Fish et al., 1952 and Fish and 

Mowbray, 1970 for review) and is used routinely today to determine habitat use, delineate and monitor 

spawning areas, and study the behavior of fishes (Hawkins, 1986; Rountree et al., 2003a, b, 2006).
103

Figure 12-1. Grouper courtship associated sound spectrograms. (a) Red Hind tonal call (E. guttatus). (b) Nassau grouper tonal 

call (E. striatus). (c) Black grouper tonal call (M. bonaci). (d) Yellowfin grouper pulse calls (M. Venenosa). (e) Yellowfin 

grouper tonal call (M. venenosa).

Mature adults of many fish species swim long distances and gather in high densities for mass spawning 

at precise locations and times (Domeier and Colin, 1997). Worldwide depletion of large predatory fishes 

has already caused top-down changes in coral reef ecosystems and biodiversity loss (Mumby et al., 

2006). Moreover, most known fish spawning aggregations (FSA) sites are shared by many species at 

different times (Heyman and Kjerfve., 2008) and as such, represent breeding hotspots requiring some 

form of protection (Erisman et al. 2017). It is critical that their role in the persistence of marine 
104

populations be elucidated. FSAs share common features such as high density of large body-sized 

individuals, strong site fidelity, temporal predictability and geomorphological attributes, (i.e. shelf-break, 

capes) (Claro and Lindeman, 2003; Kobara and Heyman, 2010; Kobara et al., 2013). Once located, they 

are easily over-exploited and depleted (Sadovy, 1997; Sala et al., 2001; ICRS, 2004). Despite numerous 

historical records of Caribbean-wide FSAs (Smith, 1972; Eklund et al., 2000) only a few are documented 

to date and many remain unprotected (Sadovy et al., 2008).

Sound production in a number of species is known to be associated with courtship, territoriality, or 

reproduction, warranting the use of passive acoustics to locate spawning aggregations (Luczkovich et al., 

1999, 2008; Walters et al., 2009; Rowell et al., 2011) and determine temporal spawning behavior and 

habitat use by different species (Locascio and Mann, 2008; Mann et al., 2009, 2010; Nelson et al., 2011;

Schärer et al., 2012). The existing FSAs in the Caribbean Sea, Gulf of Mexico and the Bahamas Region 

(i.e., the Intra-America Seas) are where a number of vocalizing grouper species such as the Nassau 

(Epinephelus striatus), yellowfin (Mycteroperca venenosa), red hind (Epinephelus guttatus) and black 

grouper (Mycteroperca bonaci) (Figure 12-1), among others, aggregate to spawn (Nemeth, 2005; Rowell 

et al., 2015). Most of these species spawn during the winter and spring months (December to May) in 

the northern hemisphere (Nemeth, 2012). The timing of spawning is usually cued to the moon and 

daylight, but also to water temperatures and local current conditions (Nemeth, 2009). Because 

remaining FSAs often occur at remote locations, are most active at dusk and are in water depths 

between 30 and 80 m, near the shelf break, spawning activities and fish population are challenging to 

observe, and thus to monitor (Kobara et al., 2013).

While many of these sites are known to fishers and represent areas of intensive harvest, not all fish 

spawning locations have been documented. As such, there may be significant number of unreported 

FSAs, which, if located, could provide a better estimate of the status of certain populations of grouper 

species such as Nassau, Warsaw (Hyporthodus nigritus), Black, Red Hind, Goliath (Epinephelus Itajara) 

and others. Data on the FSA dynamics of these species is critical to the management of these stocks, 

which involve the South Atlantic, Gulf of Mexico and Caribbean Fishery Management Councils (SAFMC, 

GMFMC, CFMC), as well as local or state entities such as the Puerto Rico Department of Natural and 

Environmental Resources (PR-DNER), USVI Department of Planning and Natural Resources (DPNR), 

Florida Fish and Wildlife Conservation Commission (FWC). Determination of the timing, duration and 

intensity of spawning will be of direct utility for the design and evaluation of management actions, stock 

assessment and effective conservation measures. 

Passive acoustic monitoring (PAM) is thus a fisheries-independent approach that can provide in-situ

observations of soniferous fishes, such as groupers (Mann et al., 2010; Rowell et al., 2011 and 2015; 

Schärer et al., 2012, 2014; Wall et al., 2014, 2017). Additionally, PAMs can be relatively non-intrusive 

and provide data on grouper behavior and distribution, critical for understanding their biology and 

ecology. As particular grouper populations begin to recover from overfishing, new or previously lost 

aggregations may reform, also making this technology particularly relevant for surveying and evaluating 

the recovery of groupers. To date, fisheries monitoring efforts using PAMs have primarily used an 

Eulerian approach; recordings are made from fixed stations at known FSAs (Rowell et al., 2012). 
105

12.6 PERSISTENT PRESENCE ROBOTIC APPROACH FOR DETECTION AND CHARACTERIZATION OF FISH 

SPAWNING AGGREGATIONS 

FSAs are known to be spatially dynamic and can shift outside the range of fixed stations in a relatively 

short period. As such, more mobile approaches with PAMs are required to best encapsulate FSA 

dynamics. For example, the use of autonomous platforms such as buoyancy-driven gliders or wavegliders that are equipped with PAM systems can be programmed more accurately to encompass FSA 

spatial extents as well as scout regions of the shelf edge in the exploration of unknown FSAs. Wall et al. 

(2014) used Slocum gliders, buoyancy driven autonomous underwater glider (AUG) to conduct a largescale spatial mapping across the West Florida shelf of Red Grouper (E. Morio) sound production. A 

similar survey was conducted with the same technology along the southeast U.S. (Wall et al., 2017). This 

survey was conducted during winter when fishery-independent survey data were lacking from 

traditional ship-based approaches (due to prolonged periods of inclement weather) and covered the 

winter-spawning dynamics of multiple species managed by the SAFMC. According to the SAFMC, the 

importance of increasing collection/detection and interpretation of acoustic signatures of managed 

species is long overdue in the South Atlantic Bight. 

These surveys were conducted with low power 

acoustic recorders (DSG - Loggerhead 

Instruments; www.loggerheadintruments.com), 

which are self-contained acquisition-only devices 

that are not integrated to their host, and do not 

allow for onboard processing and analysis. 

Therefore, these devices are not capable of 

characterizing a FSA in real-time, nor can they 

provide information such as the species 

composition of FSA aggregates, precise location 

and timing, population size and the fish behavior or distance from the glider. But automated data 

collection means that surveys can take place at times and in places where it would be too expensive or 

dangerous to send human observers (Marques et al., 2013). 

Chérubin et al. (2018) conceived a real-time detection and classification PAM system that can be 

integrated on any glider. Their glider of choice was the SV3 wave glider (WG) because of its continuous 

real-time transmission and positioning capabilities, which are crucial to the localization of FSAs that are 

most of the time ephemeral events. The SV3 wave glider is a self-propelled, unmanned persistent 

mobile data-gathering platform that harvests both solar and wave energy for propulsion and power. A 

simple, Web-based interface, called WGMS transmits control system and sensor data from the WG to 

shore and commands back from shore to the WG during a mission. It also provides a precise and 

intelligent navigation web interface. Two-way transmission via cellular network or Iridium satellite 

provides real-time navigational, operational, and sensor control as well as real- or near-real-time data 

reporting (Greene et al., 2014). The PAM system consists of a SIMRAD NSS7 Evo2 echosounder with 

structurescan sonar and with frequency modulation (CHIRP) sonarhub. Sonar screen movies are 

recorded for sound detection validation. The sonarhub is mounted on the aft of the WG. (2) An onboard 

AST4000 pressure sensor. (3) A Turner C3 Fluorometer, which measures CDOM, Chlorophyll-a, and 

backscattering fluorescence. (4) Hydrophones. (5) A fish sounds detection and classification algorithm. 

The PAM records 10s audio files every 30 seconds. Each audio file is analyzed by the detection algorithm 

The use of autonomous platforms such as 

buoyancy-driven gliders or wave-gliders that 

are equipped with PAM systems can be 

programmed more accurately to encompass 

FSA spatial extents as well as scout regions of 

the shelf edge in the exploration of unknown 

FSAs.
106

and if there is a detection, a 3 second snippet that contains the sound detected is produced by the 

software.

The PAM computer operates in real-time the fish acoustic detection algorithm research (FADAR) 

program, an automated identification scheme for fish vocalizations based on the auditory analysis for 

feature extraction followed by a machine-learning algorithm for classification (Ibrahim et al., 2018).

Experimental results showed that the overall percentage of identification using the best combination of 

the selected feature extractor Weighted Mel Frequency Cepstral Coefficients and sparse classifier 

achieved 82.7 % accuracy overall, although the accuracy varies per species. E. gutattus and M. venenosa

were the most successfully classified species, while E. striatus was slightly lower than the previous two 

and M. bonaci had the lowest accuracy rate of all. The algorithm was initially developed in MATLAB and 

was then converted into a C executable, which is embedded on the PAM computer of the tow-body 

package.

During a U.S. Virgin Islands survey in February 2017, the target species was red hind which aggregate to 

spawn around the full moon from December to February at the Red Hind Bank, on the southern shelf of 

St. Thomas and just west of Grammanik Bank (Figure 12-2). The aggregation usually peaks in January 

and spawning can occur from 0 to 4 days before the full moon (Nemeth, 2005). Result from the survey 

showed a scattered distribution of red hind grouper CAS and most of them were localized inside the Red 

Hind Bank MCD and a few near the Grammanik Bank (Fig. 2X). Nassau and yellowfin grouper were also 

recorded, in particular at Grammanik Bank where Nassau groupers are known to aggregate for spawning 

(Fig. 10.4b). This pattern was previously documented with hydrophones from fixed sites combined with 

acoustic telemetry of tagged Nassau grouper (Rowell et al., 2015). 

A similar survey along Puerto-Rico’s west coast known FSA sites within the MCDs at Abrir la Sierra (ALS) 

and Bajo de Sico (BDS), located along the shelf edge in the Mona Passage. ALS has FSA sites of red hind 

grouper at a depth of 30 m, which occur from December to March and peak 7-9 days after the full moon 

(Rowell et al., 2012). BDS is a submerged seamount approximately 27 km west of Puerto Rico, 

surrounded by depths of over 250 m to the southeast near the Puerto-Rican insular shelf and over 1000 

m to the north. This site, where Nassau groupers aggregate to spawn was documented in 2012 and 

intensively studied with hydrophone by Schärer et al. (2012b). BDS is also a spawning site for black 

grouper (Schärer et al., 2014; Sanchez et al., 2017). Results from the glider survey confirmed the 

presence of CAS for red hind and Nassau grouper with species segregation between ALS and BSD. Red 

hind sounds were detected only near ALS, though at two distinct locations (Fig. 3X), whereas Nassau 

grouper sounds were detected only at BDS, although at two separate locations, which provides new 

information for this site (Figure 12-3).

In August 2016, the wave glider was deployed from aboard the NOAA ship Nancy Foster, near Riley’s 

Hump (RH) in the FKNMS, which is a FSA site for at least two species of snappers (e.g. Cubera snapper 

(Lutjanus cyanopterus) and mutton snapper (Lutjanus analis) in summer months and one species of 

grouper (black grouper) in winter months (Locascio et al., 2016; Sanchez et al., 2017). Although no 

grouper CAS were identified during the summer survey in the Dry Tortugas, numerous red grouper

(Epinephelus morio), squirrel fish (Holocentrus spp), and grouper alarm calls were identified in addition 

to other unidentified marine sounds, near the documented FSA site (Figure 12-4).
107

These data also provide insights into diurnal, environmental, and spatial soundscape variability at the 

scale of hours to day and between different spawning sites around the Caribbean and in the Gulf of 

Mexico. 

Figure 12-2. Glider surveys fish detection in St. Thomas, U.S. Virgin Islands, 7-15 February 2017. Shaded areas show marine 

conservation areas. Brown dots show the glider path. Purple circles are specific monitoring or known FSA sites that were 

targeted with the wave glider. Red (red hind), green (Nassau grouper) and yellow (yellowfin grouper) dots show fish 

detections. Dates and times along the glider track are also indicated.
108

Figure 12-3. Glider tracks, fish detection and environmental parameters along Puerto-Rico’s western shelf. Colored dots 

show salinity (psu) on the left plot and turbidity (NTU) on the right plot. Left plot shows the lower part the right plot, south 

of Abrir la Sierra only. Red (red hind), green (Nassau grouper) and yellow (yellowfin grouper) dots show fish detections. 

Dates and times along the glider track are also indicated.
109

Figure 12-4. Fish call spectrograms recorded with the wave glider PAM at Riley’s Hump. (a) Red grouper (lower left 

rectangles) and unknown calls. (b) Grouper alarms calls. (c) Squirrel fish. (d) Unknown calls.

The WG PAM is a new concept that includes simultaneous measurements of soundscape, environmental 

data but also sonar data. At, this stage the sonar on the WG does not provide quantitative information 

nor does the acoustic information. However, recent progress in sonar data classification, as exemplified 

in this report, could provide complementary information to CAS at FSAs. Namely, the development of 

broadband frequency response models and associated target strength for individual and group of fish 

for large adult fish such as groupers and snappers would provide a mechanism to validate fish detection 

and ultimately assess fish abundance. Correlation between CAS numbers and fish abundance were 

shown to be significant at grouper FSAs when repeated at regular intervals (Rowell et al., 2012). It

appears then, that such correlation could also be established between sonar and passive acoustic data, 

significantly increasing the degree of confidence in the biomass assessment. Such approach for biomass 

estimation is currently use in the tuna fisheries. Sonar buoys are used to provide accurate biomass 

quantification that are used to direct the fishing effort. We believe that the integration of sonar 

measurements and PAM on the WG and persistent presence platforms in general, in addition to fixed 

monitoring stations will become one of the most promising approach to monitor reef ecosystem health 

in the future.
110

13 CONCLUSION (W.L. MICHAELS) 

13.1 PRIORITIES FOR BUILDING CAPACITY WITH TECHNOLOGIES 

The workshop was designed with the recognition that 

management and operational objectives drive the 

implementation of acoustic technologies to improve reef 

fish ecosystem surveys and research. Acoustic surveys and 

research in reef fish ecosystems present unique challenges, 

such as the complexity of reef fish life-history patterns and 

difficulties with sampling reef fish habitat. Recent advances 

in acoustic technologies and alternative platforms for 

deploying integrated sensing arrays have become more 

available for improving reef fish ecosystem surveys, yet a 

technical report providing guidance on the best practices 

for conducting acoustic operations in reef fish habitats is 

lacking. This workshop brought together diverse 

perspectives from acoustic experts, scientists, managers, 

and stakeholders who strive to improve scientific 

information for the sustainability of reef fish and their 

habitats. The third day of the workshop was devoted to 

establish the framework for this report on the best practices for using acoustic technologies to conduct 

reef fish ecosystem surveys. During the third day of the workshop, participants addressed the following 

trigger questions: 

1. What are your top three management objectives pertinent to reef fish ecosystems?

2. What are your top three operational challenges relevant your management objectives? 

3. How have you been addressing these challenges?

4. What additional resources might help you achieve your operational objectives?

Before the workshop, the participants provided some 

initial feedback on these questions (Appendix D). 

During the third day of the workshop, presenters 

provided overviews and case studies on the principles 

and key considerations for conducting acoustic 

surveys operations in reef fish habitats. The 

presentations from both the acoustic symposium and 

the workshop emphasized the applicability of 

acoustic technologies for a range of management 

priorities and operational objectives. This increased the perspectives and exchange of information 

among the participants, and breakout sessions were conducted to collectively discuss and prioritize the

responses to the four trigger questions above. The overall intent of these trigger questions is to highlight 

the lessons learned and recommendations on the best practices for conducting acoustic surveys and 

research in reef fish ecosystems. 

There is recognition on the importance of 

networking and training to build expertise 

with technologies, and creative approaches 

for validating acoustic estimates when 

sampling is limited.
111

During the workshop breakout sessions, participants collectively discussed these questions by 

management objective categories, and prioritized the following management objectives for the region 

(Table 13-1). The working group considered the priorities for utilizing acoustic technology were to 

improve abundance estimates from long-term surveys in support of stock assessments, locating and 

monitoring fish spawning aggregations, to improve spatial coverage of monitoring to understand 

distributional and migration patterns and connectivity between stocks, and use of essential habitats.

Table 13-1. Priorities for marine resource management objectives in the region summarized from workshop breakout 

sessions.

Rank Priorities for marine resource management objectives in the region.

1 (n=28) Abundance and biomass estimates of marine populations

2 (n=21) Marine resource use and anthropogenic impacts

3 (n=20) Distribution, spatial variation and connectivity of marine populations

4 (n=10) Survey design and operational efficiencies to support assessments

5 (n=6) Delineation and conservation of marine protected areas (MPAs)

6 (n=5) Spawning aggregation location and monitoring

7 (n=4) Essential habitat identification and characterization

8 (n=2) Ecosystem health and environmental impacts

The working group considered validation of acoustic backscatter estimates by species and length for 

abundance estimates the greatest challenge when conducting acoustic operations (Table 13-2). The 

second challenge is the spatial uncertainty in sampling operations, especially when there is limited 

availability boat time. Other challenges include the difficulty of sampling near bottom due to the 

acoustic deadzone, and the steep learning curve involved with becoming proficient with acoustic 

instrumentation and methods. 

Table 13-2. Priorities for operational challenges related to management objectives summarized during workshop breakout 

sessions.

Rank Priorities for operational challenges relevant to management objectives.

1 (n=19) Lack of expertise and collaboration in acoustic technology

2 (n=14) Funding constraints and access to acoustic equipment

3 (n=10) Detection of marine organisms near seafloor and reef structure

4 (n=7) Improve standard operating procedures (SOPs) to reduce data processing time

5 (n=6) Validation of species composition and length data from acoustic surveys

6 (n=6) Acoustic classification of seabed and habitat

7 (n=5) Uncertainty in estimates due to behavioral and environmental variation

8 (n=5) Need simplified methods to engage stakeholders and citizen science

9 (n=4) Spatial variation in distribution, connectivity and habitat use

10 (n=1) Lack of enforcement relevant to harvest regulations

Several researchers have utilized other acoustic technologies, such as telemetry tags and passive 

acoustics like fish sounds, to collect data and monitor fish aggregations (Table 13-3). Optical 

technologies are widely used to collect information from reef habitats. There is recognition on the 

importance of networking and training to build expertise with technologies, and creative approaches for 

validating acoustic estimates when sampling is limited, such as correlations with landings catch data. 
112

Furthermore, cameras and side-looking acoustics has been used to address the acoustic deadzone near 

bottom.

Table 13-3. Priorities for operational challenges related to management objectives summarized during workshop breakout 

sessions.

Rank Priorities for addressing operational challenges.

1 (n=26) Building pool of acoustic expertise through training and collaborations

2 (n=12) Integrated sampling and survey design efficiencies by coupling technologies

3 (n=10) Best practice guidance on acoustic data collection and post-processing

4 (n=5) Guidance on acoustic methods, tools and analysis

5 (n=6) Acoustic equipment loan or lease, and technical support from vendor

6 (n=4) Address lack of baseline data and literature searches on acoustic operations

7 (n=1) Cooperative research among stakeholders, fishers and citizen science

The working group considered training and networking the key solution for building regional expertise 

and collaborations for improving reef fish ecosystem surveys in the region. Access to acoustic 

instrumentation or upgrades to existing acoustic instrumentation, including multifrequency capabilities, 

is considered the second priority. Building collaborative partnerships is recognized as a means for 

funding support, and complimentary sampling is necessary for interdisciplinary research and survey 

operations (Table 13-4). 

Table 13-4. Priorities for addressing operational challenges summarized from workshop breakout sessions.

Rank Priorities for resources to achieve operational objectives in the region.

1 (n=35) Build acoustic expertise in the region through training and collaborative studies

2 (n=25) Improve equipment accessibility with loans, lease and collaborative efforts

3 (n=14) Optimize survey design and sampling efficiencies with integrated technology

4 (n=12) Develop long-term monitoring programs and time series

5 (n=9) Funding opportunities with collaborative partnership and cooperative research

6 (n=6) Collaborative efforts on research and developing analytical methods

7 (n=3) Increasing boat and platform sampling opportunities, and citizen science

8 (n=2) Cooperative research and sampling with fishers

13.2 LESSONS LEARNED AND RECOMMENDATIONS 

There was consensus from the steering committee and participants that the acoustic workshop was 

successful in establishing the necessary technical guidance on the best practices for conducting acoustic 

surveys in reef fish ecosystems. The scientific community and stakeholders in the Gulf and Caribbean 

region, a region comprised of 42 geopolitical jurisdictions, recognized that cost-effective technologies 

such as acoustic technology can enhance research and survey operations in reef fish ecosystems 

(Section 1). The integration of acoustical and optical technologies was considered a priority for 

addressing data-limited assessments relevant to the challenges of sampling reef fish habitats. Section 2 

and 4 highlighted the principles and logistical considerations for conducting acoustic surveys, while 

experts provided hands-on training at-sea with the configuration, calibration and operation of state-ofthe-art EK80 scientific echosounder (Section 6). The importance of collecting biological data for species 
113

composition and interpretation of the acoustic data was scribed in Section 5. In the classroom setting, 

on-hands training in the use of Echoview software demonstrated post-processing and analytical

procedures for deriving quantitative estimates (Section 7). Technical guidance on the interpretation of 

the acoustic data was also provided for deriving species-specific target strength and biomass estimates, 

as well as interpretation of uncertainties in the estimates. Critically important, is the need to establish a 

statistically sound survey design. As described in Section 3, a survey must be established based on 

knowledge of the distributional patterns to assure the survey design captures a target population such 

as reef fish in a standardized manner to provide unbiased biomass estimates. For deriving overall mean 

biomass estimates over time, the survey design must not be biased towards an area of known 

aggregations because it is equally important to know where fish are not to gain a representative sample. 

Section 3 on Statistical Design provides an overview of statistical approaches for deriving unbiased 

abundance estimates depending on the survey design, and with each method carries assumptions that 

must be recognized. The case studies (Sections 9-12) emphasis the importance of the best practices 

described in this report; and recommend that the integration of various types of technologies ranging 

from active and passive acoustics, optical technologies, and alternative sampling platforms can best 

achieve the cross-functional mission and operational objectives. 

The acoustic workshop forged collaborative partnerships and provided direction for future training 

workshops on the integration of acoustic and optical technologies. There was consensus that next year’s 

GCFI workshop should be focused on the integration of optic (stereo imagery) and acoustic (EK80 

wideband) technologies to improve reef fish ecosystem surveys. As described in Sections 1 and 13.1, 

these collaborative efforts are critical for building the next generation of experts, whose role will be to 

optimize survey operations with technologies for improving the sustainability of living-marine-resources 

in reef-fish ecosystems with connectivity across the geopolitical jurisdictions of the region. 
114

14 GLOSSARY 

• Backscatter: Sound energy from the echosounder pulse reflected from a target back to the 

transducer and measured by the receiver. 

• Backscattering strength: Metrics of backscatter expressed in terms of intensity (i; W m-2), where 

1 W = 1 J s-1, 1 J = 1 N m, and 1 N is the force needed to accelerate 1 kg of mass at 1 m s-1 s-1. In 

the context of an echosounder, the sound intensity is considered in terms of [a] the incident 

intensity (iinc, the energy from the echosounder pulse striking the target), and [b] the 

backscattered intensity (ibs, the level of incident intensity backscattered by the target). See 

Demer et al. (2015), Jackson and Richardson (2007) and references therein for a complete 

definition and derivation of the terms described below.

• Point-target backscattering

 Backscattering cross-section (σbs; m2

): The ratio of ibs and iinc, i.e. (ibs r210αr/10)/iinc, where 

ibs = ([σiinc/10 αr/10]/4πr

2

)F, σ (m2) is the scattering cross-section of the target (the area of 

the target that intercepts the incident sound pulse), α (dB m-1) is the absorption 

coefficient, r (m) is the distance (range) between the target and the measurement 

position (i.e. the transducer) and F is a factor (0-1) describing the reflectivity of the 

target. It follows from the equations above that a perfectly reflecting (F = 1) sphere of 

radius 2 m would yield a σ value of 12.6 m2

 (π22, i.e. the area of a circle) and a σbs value 

of 1 m2 (when solved for any values of iinc, α and r). Traditional definitions of σbs consider 

ibs at a reference range of 1 m from the target (r0 = 1 m); it is necessary to specify a 

reference range because ibs decreases as a function of r

2 (due to spreading loss). An r0

value of 1 m is implicit in the σbs equation above because we are using SI units with 1 m 

as the unit of distance, and because the range dependence of ibs is removed by 

multiplying by r

2

. A more logical term for σbs might be point backscattering coefficient

(sp).

 Target strength (TS; dB re 1 m2

): 10log10(σbs). Fish and zooplankton will have σ values 

that are significantly less than 12.6 m2

, and F values less than 1, so their σbs values will 

be significantly less than 1 m2 and their TS values will be negative (because 10log10(1) = 

0, 10log10(0.5) = -3, 10log10(0.1) = -10, etc.). TSc and TSu denote whether or not the 

measurements have been compensated for the off-axis location of the target in the 

beam (see Figure 7-4 [B]). A more logical term for TS might be point backscattering 

strength (Sp).

• Volume-target backscattering

 Volume backscattering coefficient (sv; m2

 m-3): The sum of the backscattering crosssections from the targets within a given volume of water (V), normalized to 1 m3

, i.e. 

Σσbs / V.

 Volume backscattering strength (Sv; dB re 1 m2

 m-3): 10log10(sv).

• Surface-target backscattering

 Bottom backscattering coefficient (sb, dimensionless i.e. 0-1): ...

 Bottom backscattering strength (Sb; dB re 1): ...

• Beam pattern: The intensity of the sound pulse decreases away from the beam axis, in the same 

way that the light from a torch/flashlight fades towards the edges of the beam. The beam 

pattern is the shape of the graph describing sound intensity vs. off-axis distance.
115

• Broadband: See “wideband”.

• Depth (d in m): Vertical distance below the water surface.

• Echo: See “backscatter”.

• Echosounder: An active-sonar instrument consisting of transceiver, transducer and computer 

(for transceiver control and data recording). Echosounders can be categorized in a variety of 

ways, the simplest of which is to group them based on the number of transmit and/or receive 

beams into single-beam echosounders (SBES) and multibeam echosounders (MBES). Note, 

however, that the terminology around active-sonar instruments is diverse and is often 

market/application specific (e.g. scientific/recreational, fishery/hydrographic etc.), and does not 

always subscribe to the simple dichotomy defined above.

• Multiple echo: Backscatter due to closely-spaced targets, such that the individual targets cannot 

be resolved in time (hence in range) in the data. With narrowband echoes, targets need to be at 

least half a pulse length (cwτ/2) apart in range for the echoes from each target to be completely 

separated in time. With wideband echoes, the minimum range separation is a function of 

bandwidth.

• Narrowband: A pulse consisting of a narrow range of frequencies (<10% of the center 

frequency). Note that narrowband is often referred to as “continuous wave” (CW), which is not 

strictly true since a continuous (sinusoidal) wave represents only a single frequency (which in 

practice is effectively impossible to generate with a transducer).

• Noise: All contributions to the signal other than backscatter. Noise can be either acoustic or 

electrical in origin. Variations in amplitude and duration can be used to categorize the noise, for 

example:

• Background noise: Low-amplitude, long-duration (100s to 1000s of pings) acoustic noise.

• Impulse noise: High-amplitude, short-duration (<1 ping) electrical or acoustic noise.

• Transient noise: High-amplitude, medium-duration (1 to 10s of pings) electrical or acoustic 

noise.

• Ping: See “pulse”.

• Pulse: A short burst of sound energy generated (transmitted) by a transducer. Note that once 

the pulse is backscattered by a target, it is referred to as an echo.

• Range (r; m): Distance from the transducer face to the target along the beam axis.

• Reflection: See “scattering”.

• Sample: The echosounder receiver measures voltage over time as an analog (continuous) signal. 

This continuous signal is broken up (digitized) into short periods of time (time gates) by the 

receiver’s analog-to-digital converter (ADC). The voltage representing each time gate is referred 

to as a sample. 

• Scattering: The redirection of sound energy when it interacts with a target.

• Signal: The component of the value recorded by the echosounder receiver that is due to

backscatter.

• Target: An object with a density sufficiently different to the surrounding medium, such that it 

causes the sound energy from the echosounder pulse to be scattered in different directions. The 

definition of an individual object and an individual target depends on the context (Figure 7-6). In 

a physical context, we might define an individual target as a single gas bubble, a single fish, a 

component of a single fish (e.g. swimbladder, cranium etc.), a cluster of bubbles or an 

aggregation of fish. In an acoustic context, an individual target is defined based on the 
116

relationship between the properties of the object and the sampling volume and frequency 

content of the echosounder pulse as follows:

• Point target: A target significantly smaller than the sampling volume, such that the 

backscatter arrives at the transducer face effectively from a single direction. Its 

backscattering strength, expressed as σbs (see above), is therefore a property of the target

(i.e. independent of its range or the sampling volume). Other terms commonly used to 

describe this scenario include “single targets”, “single echoes”, “single-target echoes”, 

“single scatterers”, “single-fish echoes”, “resolved scatterers” and “echo pulses”. Care must 

be taken, however, when using these terms. For example, “single target” is commonly used 

to mean “single fish acting as a point target”, but unless explicitly defined it could logically 

refer to a range of scenarios (e.g. “single component of a fish acting as a point target”, 

“single aggregation of fish acting as a point target” or even “single aggregation of fish acting 

as a volume target”). 

• Volume target: A target whose volume is larger than the sampling volume, such that its 

backscattering strength, expressed as sv (see above), will depend on the geometric 

intersection of the target with the sound beam.

• Surface target: A target whose surface extends beyond the sampling volume, such that its 

backscattering strength, expressed as sb (see above), will depend on the geometric 

intersection of the surface with the sound beam.

• Transducer: An instrument that converts (transduces) voltage to sound pressure, and vice versa. 

• Wideband: A pulse consisting of a wide range of frequencies (>10% of the center frequency). 

This is sometimes referred to as a “chirp” or a “frequency-modulated” (FM) pulse.
117

15 LITERATURE CITED 

Anderson, J.T., D.V. Holliday, R. Kloser, D. Reid, Y. Simrad, G.J. Brown, R. Chapman, R. Coggan, R. Kieser, 

W.L. Michaels, A. Orlowski, J. Preston, J. Simmonds, A. Stepnowski. 2007. Acoustic seabed classification 

of marine physical and biological landscapes. ICES Cooperative Research Report No. 286. 183 pp.

http://www.ices.dk/sites/pub/Publication%20Reports/Cooperative%20Research%20Report%20(CRR)/cr

r286/CRR286.pdf

Andrew R., B. Howe, J. Mercer. 2002. Ocean ambient sound: Comparing the 1960s with the 1990s for a 

receiver off the California coast. Acoustics Research Letters Online. 3: 65–70. doi: 10.1121/1.1461915. 

https://asa.scitation.org/doi/10.1121/1.1461915

Anonymous, 1993. The spatial organization of aquatic populations as observed using hydroacoustic 

methods. Aquat. Liv. Res. 6: 171-174.

Archer S.K., J.E. Allgeier, B.X. Semmens, S.A. Heppell, C.V. Pattengill-Semmens, A.D. Rosemond, P.G. 

Bush, C.M. McCoy, B.C. Johnson, C.A. Layman. 2014. Hot moments in spawning aggregations: 

implications for ecosystem-scale nutrient cycling. Coral Reefs. 10.1007/s00338-014-1208-4.

http://www.reef.org/db/publications/9765

Arcos, D., Cubillos, L., Núñez, S. 2001. The jack mackerel fishery and El Niño 1997-98 effects off Chile. 

Prog. Oceanog. 49: 597-617.

Axelrod, E.H., B.A. Schoomer, W.A.V. Winkle. 1965. Vertical Directionality of Ambient Noise in the Deep 

Ocean at a Site near Bermuda. Journal of the Acoustical Society of America. 37: 77–83.

https://doi.org/10.1121/1.1909312

Bacheler, N. M., V. Bartolino, and M. Reichert. 2013. Influence of soak time and fish accumulation on 

catches of reef fishes in a multispecies trap survey. Fish. Bull. 111:218-232.

Bacheler, N.M., C. Schobernd, Z. Schobernd, S. Mitchell, D. Berrane, G. Kellison, and M. Reichert. 2013. 

Comparison of trap and underwater video gears for indexing reef fish presence and abundance in the 

southeast United States. Fisheries Research 143 (2013) 81-88.

Ballón, M., A. Bertrand, A. Lebourges-Dhaussy, M. Gutiérrez, P. Ayón, D. Grados, and F. Gerlotto. 2011. 

Is there enough zooplankton to feed forage fish populations off Peru? An acoustic (positive) 

answer. Progress in Oceanography, 91(4): 360-381.

Bassett, C., De Robertis, A. and Wilson, C. 2017. Broadband echosounder measurements of the 

frequency response of fishes and euphausiids in the Gulf of Alaska. ICES J. Mar. Sci. DOI · 

10.1093/icesjms/fsx204

Bazigos, G.P., 1981. A manual on acoustic surveys. Sampling methods for acoustic surveys. CECAF. ECAF 

Ser, 80(17), p.137. http://www.fao.org/docrep/003/X6602E/x6602e00.HTM

Berk, I.M., 1998. Sound production by white shrimp (Panaeus setiferus), analysis of another crustaceanlike sound from the Gulf of Mexico, and applications for passive sonar in the shrimp industry. Journal of 

Shellfish Research, 17:1497-1500.
118

Bertrand A, Barbieri MA, Cordova J, Hernandez C, Gomez F, Leiva F. 2004. Diel vertical behaviour, 

predator prey relationships, and occupation of space by jack mackerel (Trachurus murphyi) off Chile. 

ICES J. Mar. Sci. 61: 1105–1112.

Bertrand, A., Ballon, M. and Chaigneau, A. 2010. Acoustic observation of living organisms reveals the 

upper limit of the oxygen minimum zone. PLoS One, 5(4), p.e10330.

Bertrand, A., Barbieri, M.A., Gerlotto, F., Leiva, F., Córdova, J. 2006. Determinism and plasticity of fish 

schooling behaviour as exemplified by the South Pacific jack mackerel Trachurus murphyi. Mar. Ecol.

Prog. Ser. 311: 145-156.

Bertucci, F., Parmentier, E., Berten, L., Brooker, R.M. and Lecchini, D. 2015. Temporal and Spatial 

Comparisons of Underwater Sound Signatures of Different Reef Habitats in Moorea Island, French 

Polynesia. Ferse, S.C.A., Ed., PLOS ONE, Public Library of Science (PLoS), 10, 

e0135733. https://doi.org/10.1371/journal.pone.0135733.

Blackman, S.S. 1986. Multiple-target tracking with radar applications. Dedham, MA, Artech House, Inc., 

1986, 463 p.

Bodholt, H. 1990. Fish density derived from echo-integration and in-situ target strength measurements. 

ICES C.M. 1990/B:11. Sess. R.

Bodholt, H. 1991. Split-beam transducer for target-strength measurement. Scandinavian Cooperation 

Meeting in Acoustics XIII. Ed. by H. Hobæk, Department of Physics. Science Technical Report, 227 

(University of Bergen, Norway), p. 73.

Bodholt, H., and Solli, H. 1992. Application of the split-beam technique for in-situ, target-strength 

measurements. In World Fisheries Congress, Athens. 21 pp.

Bohnsack, J.A. and S.P. Bannerot. 1986. A stationary visual census technique for quantitatively assessing 

community structure of coral reef fishes. U.S. Dept. Commerce, NOAA Tech. Report NMFS 41. 15p.

Bortone, S.A., R.W. Hastings, and J.L. Oglesby. 1986. Quantification of reef fish assemblages: A 

comparison of several in situ methods. Northeast Gulf Science 8(1): 1-22.

Bortone, S.A., T.M. Martin, and C.M. Bundrick. 1991. Visual census of reef fish assemblages: A 

comparison of slate, audio, and video recording devices. Northeast Gulf Science 12(1): 17-23.

Brown, C.J. and Blondel, P. 2009. Developments in the application of multibeam sonar backscatter for 

seafloor habitat mapping. Applied Acoustics, 70(10), pp.1242-1247.

Buckley, R.M., and G.J. Hueckel. 1989. Analysis of visual transects for fish assessment on artificial reefs. 

Bulletin of Marine Science 36: 47-63.

Budelmann, B-U. 1989. Hydrodynamic receptor systems in invertebrates. In: Coombs, S., P. Görner, H. 

Münz. eds. The Mechanosensory Lateral Line. New York: Springer. 607-631.

Budelmann, B-U. 1992. Hearing in nonarthropod invertebrates. In: Webster DB, Fay RR, Popper AN, eds. 

The evolutionary biology of hearing. New York: Springer-Verlag. 141-155.
119

Burton, M.L. 1998. The effect of spawning season closures on landings of two reef associated species. In: 

Proceedings of the 50th Gulf and Caribbean Fisheries Institute. p 1–23

Burton, M.L., Brennan, K.J., Muñoz, R.C., Parker Jr R.O. 2005. Preliminary evidence of increased 

spawning aggregations of mutton snapper (Lutjanus analis) at Riley’s Hump two years after 

establishment of the Tortugas South Ecological Reserve. Fish Bull 410:404–410.

Burwen, D.L. and Fleischman, S.J., 1998. Evaluation of side-aspect target strength and pulse width as 

potential hydroacoustic discriminators of fish species in rivers. Canadian Journal of Fisheries and Aquatic 

Sciences, 55(11), pp.2492-2502.

Butler IV, M.J., Hunt, J.H., Herrnkind, W.F., Childress, M.J., Bertelsen, R., Sharp, W., Matthews, T., Field, 

J.M., Marshall, H.G., 1995. Cascading disturbances in Florida Bay, USA: cyanobacteria blooms, sponge 

mortality, and implications for juvenile spiny lobsters Panulirus argus. Mar. Ecol. Prog. Ser. 129, 119–

125. 

Butler, J., Stanley, J.A. and Butler IV, M.J. 2016. Underwater Soundscapes in near-Shore Tropical Habitats 

and the Effects of Environmental Degradation and Habitat Restoration. Journal of Experimental Marine 

Biology and Ecology, 479, 89–96

Campbell, M., A. Pollack, W. Driggers, and E. Hoffmayer. 2014. Estimation of Hook Selectivity of Red 

Snapper and Vermilion Snapper from Fishery-Independent Surveys of Natural Reefs in the Northern Gulf 

of Mexico. Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science, 6:1, 260-273.

Cappo, M., Harvey, E., Shortis, M. 2006. Counting and measuring fish with baited video techniques-an 

overview. AFSB Conference and Workshop: Cutting-edge Technologies in Fish and Fisheries Science. 

Cass-Calay, S., Schmidt T. 2009. Monitoring changes in the catch rates and abundance of juvenile goliath 

grouper using the ENP creel survey, 1973–2006. Endanger Species Res. 7: 183–193.

Cato, D.H. 1976. Ambient sea noise in waters near Australia. Journal of the Acoustical Society of 

America, 60: 320–328.

Cato, D.H. 2002. Australian research in ambient sea noise. Acoustics Australia, 30: 13–20.

Cato, D.H. 1992. The biological contribution to the ambient noise in waters near Australia. Australian 

Acoustical Society, 20: 76–80.

Cavaleri, L.G., Marenzi, R.C., Andrade, Á., Medeiros, R.P., Hostim-Silva, M. 2006.) Local Ecological 

Knowledge on the Goliath Grouper Epinephelus itajara (Teleostei : Serranidae) in Southern Brazil. 

Neotrop Ichthyol 4:441–450

Chapman N.R., Price, A. 2011. Low frequency deep ocean ambient noise trend in the Northeast Pacific 

Ocean. The Journal of the Acoustical Society of America, 129: EL161–EL165. pmid:21568369

Chérubin, L.M., F. Dalgleish, A. Ibrahim, M. Schärer Umpierre, R. Nemeth, R. Appeldoorn, B. Ouyang, A. 

Dalgleish. 2018. Implementation of a Passive Acoustic Monitoring System on a SV3 Wave Glider and 

Applications. Proceedings of the 70th Gulf and Caribbean Fisheries Institute, 5-11 November 2017, 

Merida Mexico.
120

Chivers, R.C., Emerson, N., and Burns D.R., 1990. New acoustic processing for underway surveying. 

Hydrological Journal, 56: 9–17.

Claro, R. and K.C. Lindeman. 2003. Spawning aggregation sites of snapper and grouper species 

(Lutjanidae and Serranidae) on the insular shelf of Cuba. Gulf Carib Res. 14 (2): 91-106. 

Coetzee, J., 2000. Use of a shoal analysis and patch estimation system (SHAPES) to characterise sardine 

schools. Aquatic Living Resources, 13(1), pp.1-10.

Colbo, K., Ross, T., Brown, C. and Weber, T. 2014. A review of oceanographic applications of water 

column data from multibeam echosounders. Estuarine, coastal and shelf science, 145, pp.41-56.

Colladon, J.D. and Sturm, F.K. 1827. The Compression of Liquids (in French). Ann. Chim. Phys., 36, 

pp.236-257.

Collins, A. 2014. An investigation into the habitat, behavior and opportunistic feeding strategies of the 

protected Goliath grouper (Epinephelus itajara). University of South Florida. PhD Dissertation. 171 pp. 

http://scholarcommons.usf.edu/cgi/viewcontent.cgi?article=6198andcontext=etd

Conti, S.G., and Demer, D.A. 2003. Wide-bandwidth acoustical characterization of anchovy and sardine 

from reverberation measurements in an echoic tank. Ices Journal of Marine Science, 60: 617-624.

Conti, S.G., Demer, D.A., Soule, M.A., Conti, J.H.E. 2005. An improved multiple-frequency method for 

measuring in-situ target strengths, ICES Journal of Marine Science, 62(8):1636-1646.

Córdova, J., Barbieri, M.A., Espejo, M. 2004. Evaluación Hidroacústica de jurel. En: Evaluación 

hidroacústica de jurel en la ZEE, año 2004. Instituto de Fomento Pesquero, Santiago, Chile. 

Cummings, N. J., M. Karnauskas, A. Rios, W. J. Harford, R. Trumble, R. Glazer, A. Acosta, and W. L. 

Michaels (editors). 2017. Report of a GCFI Workshop: Best practices and trade-offs for selecting 

between fishery-dependent and fishery-independent sampling in data-limited regions. Gulf and 

Caribbean Fisheries Institute Conference, Panama City, Panama, November 9-13, 2015. NMFS-SEFSC771, 26p. doi: 10.7289/V5/TM-SEFSC-711.

Cummings, N. J., M. Karnauskas, W. Harford, W. L. Michaels, and A. Acosta (editors). 2015. Report of a 

GCFI workshop: Strategies for improving fishery-dependent data for use in data-limited stock 

assessments in the wider Caribbean region. Gulf and Caribbean Fisheries Institute Conference, Christ 

Church, Barbados, November 3–7, 2014. NMFS-SEFSC-681, 25p. doi: 10.7289/V5BK19BN.

Cummings, N. J., M. Karnauskas, W. L. Michaels, and A. Acosta (editors). 2014. Report of a GCFI 

workshop. Evaluation of current status and application of data-limited stock assessment methods in the 

larger Caribbean region. Gulf and Caribbean Fisheries Institute Conference, Corpus Christi, Texas, 

November 4–8, 2013. NOAA Tech. Memo. NMFS-SEFSC-661, 24 p. doi: 10.7289/V5DN4304.

Cutter, Jr, G.R., and Demer, D.A. 2014. Seabed classification using surface backscattering strength versus 

acoustic frequency and incidence angle measured with vertical, split-beam echosounders, ICES Journal 

of Marine Science, 71(4): 882-894.

De Robertis, A. and Higginbottom, I. 2007. A post-processing technique to estimate the signal-to-noise 

ratio and remove echosounder background noise. ICES Journal of Marine Science, 64(6): 1282-1291.
121

De Robertis, A., McKelvey, D.R. and Ressler, P.H. 2010. Development and application of an empirical 

multifrequency method for backscatter classification. Canadian Journal of Fisheries and Aquatic 

Sciences, 67(9), pp.1459-1474.

Deane G.B., Stokes M.D. 2010. Model calculations of the underwater noise of breaking waves and 

comparison with experiment. The Journal of the Acoustical Society of America, 127: 3394–3410. 

pmid:20550239.

Demer, D.A., Andersen, L.N., Bassett, C., Berger, L., Chu, D., Condiotty, J., Cutter Jr., G.R., Hutton, B., 

Korneliussen, R., Le Bouffant, N., Macaulay, G., Michaels, W.L., Murfin, D., Pobitzer, A., Renfree, J.S., 

Sessions, T.S., Steirhoff, K.L., Thompson, C.H. 2017. 2016 USA–Norway EK80 Workshop Report:

Evaluation of a wideband echosounder for fisheries and marine ecosystem science. ICES Cooperative 

Research Report No. 81 pp.

Demer, D.A. 2012. 2007 survey of rockfishes in the Southern California Bight using the collaborative 

optical-acoustic survey technique, NOAA-TM-NMFS-SWFSC 498, 110 pp.

Demer, D.A., Berger, L., Bernasconi, M., Bethke, E., Boswell, K., Chu, D., Domokos, R., Dunford, A., 

Fässler, S., Gauthier, S. and Hufnagle, L.T. 2015. Calibration of acoustic instruments. ICES Cooperative 

Research Report No. 326. 133 pp. 

Demer, D.A., Cutter, G.R., Renfree, J.S., and Butler, J.L. 2009. A statistical-spectral method for echo 

classification, ICES Journal of Marine Science, 66: 1081–1090.

Demer, D.A., Soule, M.A., and Hewitt, R.P. 1999. A multi-frequency method for improved accuracy and 

precision of in situ target strength measurements, Journal of the Acoustical Society of America, 105(4):

2359-2376.

Demer, D.A., Zwolinski, J.P., Byers, K.A., Cutter, G.R., Renfree, J.S., Sessions, T.S., Macewicz, B.J. 2012. 

Prediction and confirmation of seasonal migration of Pacific sardine (Sardinops sagax) in the California 

Current Ecosystem, Fishery Bulletin, 110: 52-70.

Diner, N. 2001. Correction on school geometry and density: approach based on acoustic image 

simulation. Aquatic Living Resources, 14(4): 211-222.

Domeier, M.L. and P.L. Colin. 1997. Tropical reef fish spawning and aggregations: defined and reviewed. 

Bull. Mar. Sci. 60(3): 698-726.

Dunford, A.J. 2005. Correcting echo-integration data for transducer motion. The Journal of the 

Acoustical Society of America, 118(4): 2121-2123.

Dunlop, K.M., Jarvis, T., Benoit-Bird, K.J., Waluk, C.M., Caress, D.W., Thomas, H. and Smith, K.L., 2018. 

Detection and characterisation of deep-sea benthopelagic animals from an autonomous underwater 

vehicle with a multibeam echosounder: a proof of concept and description of data-processing 

methods. Deep Sea Research Part I: Oceanographic Research Papers.

Efron, B. 1981. Nonparametric standard errors and confidence intervals. The Canadian Journal of 

Statistics, 9: 139-172.
122

Ehrenberg, J.E. and Lytle, D.W. 1972. Acoustic techniques for estimating fish abundance. IEEE 

Transactions on Geoscience Electronics, 10(3), 138-145.

Eklund, A.M., D.B. McClennal, and D.E. Harper. 2000. Black grouper aggregations in relation to protected 

areas within the Florida Keys National Marine Sanctuary. Bull Mar Sci. 66(3), 721–728.

Erisman B., W. Heyman, S. Kobara, T. Ezer, S. Pittman, O. Aburto-Oropeza, and R.S. Nemeth. 2017. Fish 

spawning aggregations: where well-placed management actions can yield big benefits for fisheries and 

conservation. Fish and Fisheries 18(1): 128–144. 

Fernandes et al. 2005. Species Identification Methods from Acoustic Multi-frequency Information. 

Contract number Q5RS-2001-02054. Final Report.

Ferreira, H.M., Reuss-Strenzel, G.M., Alves, J. A., Schiavetti, A. 2014. Local ecological knowledge of the 

artisanal fishers on Epinephelus itajara (Lichtenstein, 1822) (Teleostei: Epinephelidae) on Ilhéus coast--

Bahia State, Brazil. J Ethnobiol Ethnomed 10: 51. 

Fish, M.P. 1964. Biological sources of sustained ambient noise. Pages 175-194 in W.N. Tavolga, ed. 

Marine bioacoustics. Pergamon Press, NY.

Fish, M.P. 1966. Sound production in the American lobster Homarus americanus. Crustaceana, 11:105.

Fish, M.P., A.S. Kelsey, Jr., and W.H. Mowbray. 1952. Studies on the production of underwater sound by 

North Atlantic coastal fishes. Journal of Marine Research, 11: 180-193.

Fish, M.P., and W.H. Mowbray. 1970. Sounds of western North Atlantic fishes. John Hopkins Press, 

Baltimore, Maryland.

Foote, K.G. 1983. Linearity of fisheries acoustics, with addition theorems. The Journal of the Acoustical 

Society of America, 73(6): 1932-1940.

Freeman, L.A. and Freeman, S.E. 2016. Rapidly Obtained Ecosystem Indicators from Coral Reef 

Soundscapes. Marine Ecology Progress Series, 561, 69–82.

Fréon P., Misund, O.A. 1999. Dynamics of Pelagic Fish Distribution and Behaviour: Effects on Fisheries 

and Stock Assessment. Fishing News Books, Blackwell Science, London.

Fry Jr., D.H., and Roedel, P.M. 1949. Tagging experiments on the Pacific mackerel (Pneumatophorus 

diego) 73.

Gerhardinger, L.C., Hostim-Silva, M., Medeiros, R.P., Matarezi, J., Bertoncini, Á.A., Freitas, M.O., Ferreira 

B.P. 2009. Fishers resource mapping and goliath grouper Epinephelus itajara (Serranidae) conservation 

in Brazil. Neotrop Ichthyol 7: 93–102. 

Gerlotto, F. 1996. Gregariousness and school behaviour of pelagic fish: impact of the acoustics 

evaluation and fisheries. Proceedings of Acoustics Seminar Akustikan 2. p. 233-251.

Gobert, B. 1998. Density-dependent size selectivity in Antillean fish traps. Fisheries Research 38: 159-

167.
123

Grechina, A. 1992. Historia de investigación y aspectos básicos de la ecología del jurel (Trachurus 

symmetricus murphyi) en alta mar del Pacífico Sur. In: H. Arancibia and L. Cubillos (eds.). Documento 

Técnico. Instituto de Investigación Pesquera, Talcahuano, 1(2): 1-47.

Grechina, A. 1998. Historia de investigaciones y aspectos básicos de la ecología del jurel Trachurus 

symmetricus murphyi (Nichols) en alta mar del pacífico Sur. In: Biología y ecología del jurel en aguas 

chilenas, pp. 11e34. Ed. by D. Arcos. Instituto de Investigación Pesquera, Concepción, Chile. 212 pp.

Greene, L.E., and W.S. Alvizon. 1989. Comparative accuracies of visual assessment methods for coral 

reef fishes. Bulletin of Marine Science 44(2): 899-912.

Gunderson, D. R. 1993. Surveys of Fisheries Resources, John Wiley and Sons, Inc., New York, NY, 248 pp.

Halpern, B.S., Silliman, B.R., Olden, J.D., Bruno, J.P., Bertness, M.D. 2007. Incorporating positive 

interactions in aquatic restoration and conservation. Front. Ecol. Environ. 5, 153–160.

Hamilton, L.J. 2001. Acoustic seabed classification systems (No. DSTO-TN-0401). Defence Science and

Technology Organisation Victoria (Australia) Aeronautical and Maritime Research Lab. 

Handegard, N.O., Boswell, K.M., Loannou, C.C., Leblanc, S.P., Tjøstheim, D.B. and Couzin, I.D. 2012. The 

dynamics of coordinated group hunting and collective information transfer among schooling 

prey. Current Biology, 22(13): 1213-1217.

Haralabous, J. and Georgakarakos, S. 1996. Artificial neural networks as a tool for species identification 

of fish schools. ICES Journal of Marine Science, 53(2): 173-180.

Harris, S.A., Radford, C.A., 2014. Marine soundscape ecology. Inter-noise and Noise- Con Congress and 

Conference Proceedings. Institute of Noise Control Engineering, pp. 5003–5011.

Hastie, T.J., Tibshirani, R.J. 1990. Generalized Additive Models. Chapman and Hall, London.

Hawkins, A.D., 2003. The use of passive acoustics to identify a haddock spawning area. Pages 49-53 in 

R.A. Rountree, C. Goudey, and t. Hawkins, eds. Listening to fish: proceedings of the international 

workshop on the applications of passive acoustics to fisheries. 8-10 April 2002. Dedham, MA. MIT Sea 

Grant Technical Report MITSG 03-2.

Hedgepeth, J.B., Fuhriman, D., Cronkite, G.M., Xie, Y. and Mulligan, T.J. 2000. A tracking transducer for 

following fish movement in shallow water and at close range. Aquatic Living Resources, 13(5): 305-311.

Henninger, H.P., and W.H.II. Watson. 2005. Mechanisms underlying the production o carapace 

vibrations and associated waterborne sounds in the American lobster, Homarus americanus. The journal 

of experimental Biology, 208: 3421-3429.

Heyman, W.D. and B. Kjerfve. 2008. Characterization of transient multi-species reef fish spawning 

aggregations at Gladden Spit, Belize. Bulletin of Marine Science 83(3): 531–551.

Heyman, W.D., Kjerfve, B., Graham, R.T., Rhodes, K.L., Garbutt, L. 2005. Spawning aggregations of 

Lutjanus cyanopterus (Cuvier) on the Belize Barrier Reef over a 6 year period. J. Fish. Biol. 67: 83–101. 
124

Horne, J.K. and Jech, M.J. 2005. Models, measures, and visualizations of fish backscatter. In: Sounds in 

the Sea. From Ocean Acoustics to Acoustical Oceanography. Medwin, H. (ed.). Cambridge University 

Press. ISBN-10 521 82950 X.

Horne, J.K., 2000. Acoustic approaches to remote species identification: a review. Fisheries 

Oceanography, 9(4): 356-371.

Ibrahim, A.K., L.M. Chérubin, H. Zhuang, M.T. Schärer Umpierre, F. Dalgleish, N. Erdol, B. Ouynag, A, 

Dalgleish. 2018. An approach for automatic classification of grouper vocalizations with passive acoustic 

monitoring. J. Acous. Soc. Ame., 143:2, 666-676.

Iversen, R.T.B., P.J. Perkins, and R.D. Dionne. 1963. An indication of underwater sound production by 

squid. Nature, 199 (4890): 250-251.

Jackson, D. and Richardson, M. 2007. High-frequency seafloor acoustics. Springer Science and Business 

Media.

Jech, J. M., Lawson, G. L., and Lavery, A.C. 2017. Wideband (15–260 kHz) acoustic volume backscattering 

spectra of Northern krill (Meganyctiphanes norvegica) and butterfish (Peprilus triacanthus). ICES Journal 

of Marine Science, 74(8): 2249-2261. doi:10.1093/icesjms/fsx050.

Jech, J.M., W.L. Michaels. 2006. A multifrequency method to classify and evaluate fisheries acoustics 

data. Can. J. Fish. Aq. Sci., 63(10): 2225-2235. https://doi.org/10.1139/f06-126.

Johannesson, K.A. and Mitson, R.B., 1983. Fisheries acoustics: a practical manual for aquatic biomass 

estimation. Food and Agriculture Organization of the United Nations. 249 pp. 

http://www.worldcat.org/title/fisheries-acoustics-a-practical-manual-for-aquatic-biomassestimation/oclc/10740789

Kaatz, I.M. 2002. Multiple sound-producing mechanisms in teleost fish and hypotheses regarding their 

behavioural significance. Bioacoustics 12: 230–233. 

Kang, M., Furusawa, M. and Miyashita, K. 2002. Effective and accurate use of difference in mean volume 

backscattering strength to identify fish and plankton. ICES Journal of Marine Science, 59(4): 794-804.

Kasumyan A. O. 2008. Sounds and sound production in fish. J. Ichthyol. 48: 981–1030. 

Katsanevakis, S., Maravelias, CH.D. 2009. Bathymetric distribution of demersal fish in the Aegean and 

Ionian Seas based on generalized additive modeling. Fish. Sci. 75: 13-23.

Kerman, BR. 1984. Underwater sound generation by breaking wind waves. The Journal of the Acoustical 

Society of America, 75: 149-165.

Kieser, R. and Ehrenberg, J.E. 1990. An unbiased, stochastic echo-counting model. Rapports et ProcèsVerbaux des Réunions, Conseil International pour l’Exploration de la Mer, 189, pp.65-72.

Kieser, R., Reynisson, P. and Mulligan, T.J. 2005. Definition of signal-to-noise ratio and its critical role in 

split-beam measurements. ICES Journal of Marine Science, 62(1): 123-130.

Knudsen, F.R., Hawkins, A., McAllen, R., Sand, O. 2009. Diel interactions between sprat and mackerel in a 

marine lough and their effects upon acoustic measurements of fish abundance. Fish. Res. 100: 140-147.
125

Kobara, S, W.D. Heyman, S. J. Pittman, R. S. Nemeth. 2013. Biogeography of transient reef fish spawning 

aggregations in the Caribbean: a synthesis for future research and Management. Ocean. Mar. Biol. Ann. 

Rev. 51: 281–326.

Kobara, S. and Heyman, W.D. 2010. Sea bottom geomorphology of multi-species spawning aggregation 

sites in Belize. Marine Ecology Progress Series, 405: 231–242.

Koenig, C.C., Coleman, F.C. 2009. Population density, demographics, and predation effects of adult 

goliath grouper (Epinephelus itajara). Final Rep MARFIN. 

Konchina, Y.V. 1981. The Peruvian jack mackerel, Trachurus symmetricus murphyi, a facultative predator 

in the coastal upwelling ecosystem. J. Ichthyol. 21: 46-59.

Korneliussen, R.J. and Ona, E. 2004. Combined algorithms for detection of acoustic categories. ICES 

CM2004/R:38. 

Korneliussen, R.J., Diner, N., Ona, E. and Fernandes, P.G., 2004/R. Recommendations for the collection 

of multi-frequency acoustic data. ICES ASC, 36. 

Korneliussen, R.J., Heggelund, Y., Macaulay, G.J., Patel, D., Johnsen, E. and Eliassen, I.K. 2016. Acoustic 

identification of marine species using a feature library. Methods in Oceanography, 17: 187-205.

Ladich F. 2004. Sound production and acoustic communication. In The Senses of Fish. Springer, Berlin, 

2004, pp. 210–230. 

Laidig, T.E., L.M. Krigsman, and M.M Yoklavich. 2012. Reactions of fishes to two underwater survey 

tools, a manned submersible and a remotely operated vehicle. Fishery Bulletin 111: 54-67. 

Lavery, A.C., Wiebe, P. H., Stanton, T. K., Lawson, G. L., Benfield, M. C. and Copley, N. 2007. Determining 

dominant scatterers of sound in mixed zooplankton populations. J. Acoust. Soc. Am 122 (6), 3304-3326.

Lawson, G.L., Barange, M. and Fréon, P., 2001. Species identification of pelagic fish schools on the South 

African continental shelf using acoustic descriptors and ancillary information. ICES Journal of Marine 

Science, 58(1): 275-287.

Lillis, A., Bohnenstiehl, D.R., Eggleston, D.B. 2015. Soundscape manipulation enhances larval recruitment 

of a reef-building mollusk. PeerJ. 3:e999

Lillis, A., Bohnenstiehl, D., Peters, J.W., and Eggleston, D. 2016. Variation in Habitat Soundscape 

Characteristics Influences Settlement of a Reef-Building Coral. PeerJ. 4, 

e2557. https://doi.org/10.7717/peerj.2557

Lillis, A., Eggleston, D.B., Bohnenstiehl, D.R. 2014. Estuarine soundscapes: distinct acoustic 

characteristics of oyster reefs compared to soft-bottom habitats. Mar. Ecol. Prog. Ser. 505: 1–17.

Lillis, A., Eggleston, D.B., Bohnenstiel, D.R. 2013. Oyster larvae settle in response to habitat-associated 

underwater sounds. PLoS ONE 8 (10), e79337.

Lillo, S., Cordoba, J., Paillaman, A. 1996. Target-strength measurements of hake and jack mackerel. ICES J. 

Mar. Sci. 53: 267-271.
126

Limburg, K.E., 1999. Estuaries, ecology, and economic decisions: an example of perceptual barriers and 

challenges to understanding. Ecol. Econ. 30: 185–188.

Lindeman, K., Pugliese, R., Waugh, G., Ault, J. 2000. Development patterns within a multispecies reef 

fishery: Management applications for essential fish habitats and protected areas. Bull. Mar. Sci. 66: 929–

956. 

Lobel, P.S., Kaatz, I.M., Rice, A.N. 2010. Acoustical behavior of coral reef fishes. In: Cole KS, editor. 

Reproduction and Sexuality in Marine Fishes: Patterns and Processes. University of California Press. pp. 

307–386.

Locascio, J.V., Mann, D.A. 2008. Diel patterns of fish sound production in Charlotte Harbor, FL. Trans. 

Am. Fish. Soc. 137: 606−615. 

Locascio, J.V., and M.L. Burton. 2016. A passive acoustic survey of fish sound production at Riley’s Hump 

within Tortugas South Ecological Reserve; implications regarding spawning and habitat use. Fish. Bull. 

114(1): 103-116.

Lockwood, J.C. and Willette, J.G. 1973. High speed method for computing the exact solution for the 

pressure variations in the near field of a baffled piston. J. Acoust. Soc. Am. 53: 735-741.

Lotze, H.K., Milewski, I. 2004. Two centuries of multiple human impacts and successive changes in a 

North Atlantic food web. Ecol. Appl. 14 (5): 1428–1447.

Luczkovich, J.J., Pullinger, R.C., Johnson, S.E., Sprague, M.W. 2008. Identifying sciaenid critical spawning 

habitats by the use of passive acoustics. Trans. Am. Fish. Soc. 137: 576−605. 

Luczkovich, J.J., Sprague, M.W., Johnson, S.E., Pullinger, R.C. 1999. Delimiting spawning areas of 

weakfish Cynoscion regalis (family Sciaenidae) in Pamlico Sound, North Carolina using passive acoustic 

surveys. Bioacoustics 10: 143−160.

Lurton, X. 2002. An introduction to underwater acoustics: principles and applications. Springer Science 

and Business Media.

MacLennan D.N. and J. Simmonds. 1992. Fisheries Acoustics. Chapman and Hall Fish and Fisheries Series 

5.

MacLennan, D.N., Fernandes, P.G. and Dalen, J. 2002. A consistent approach to definitions and symbols 

in fisheries acoustics. ICES Journal of Marine Science, 59(2): 365-369. 

https://doi.org/10.1006/jmsc.2001.1158

Mahon, R. and W. Hunte. 2001. Trap mesh selectivity and the management of reef fishes. Fish and 

Fisheries, 2: 356-375.

Mann, D., Locascio, J., Schärer, M., Nemeth, M., Appeldoorn, R. 2010. Sound production by red hind 

Epinephelus guttatus in spatially segregated spawning aggregations. Aquat. Biol. 10: 149−154. 

Mann, D.A., Locascio, J.V., Coleman, F.C., Koenig, C.C. 2009. Goliath grouper Epinephelus itajara sound 

production and movement patterns on aggregation sites. Endang. Species Res. 7: 229−236. 
127

Marques, T.A., Thomas, L., Martin, S.W., Mellinger, D.K., Ward, J.A., Moretti, D.J., Harris, D., Tyack, P.L. 

2013. Estimating animal population density using passive acoustics. Biol. Rev. 88: 287-309.

Martin, L.V., Stanton, T.K., Wiebe, P.H. and Lynch, J.F. 1996. Acoustic classification of zooplankton. ICES 

Journal of Marine Science, 53(2): 217-224.

McCauley, R.D., Cato, D.H. 2010. Patterns of fish calling in a nearshore environment in the Great Barrier 

Reef. Philosophical Transactions of the Royal Society B: Biological Sciences. 355: 1289-1293.

McCauley, R.D. 2012. Fish Choruses from the Kimberley, seasonal and lunar links as determined by long 

term sea noise monitoring. In McMinn T, editor. Conference Proceedings of Acoustics 2012 Fremantle, 

November 21–23. Fremantle, Western Australia: Acoustical Society of Australia.

McCauley, R.D. 2001. In Biological Sea Noise in Northern Australia: Patterns of Fish Calling. Vol. pp. 

James Cook University. 

McCauley, R.D. 2011. Woodside Kimberley sea noise logger program, Sept-2006 to June-2009: Whales, 

fish and man-made noise. Report produced for Woodside Energy Ltd. 

McCauley, R.D., D.H. Cato. 2001. Separation of the acoustic "sound space" by nearshore fishes in the 

Great Barrier Reef, Australia. The Journal of the Acoustical Society of America. 109: 2391. 

https://doi.org/10.1121/1.4744432

Medina, M., Arancibia, H. 1992. Interacciones tróficas entre el jurel (Trachurus murphyi) y la caballa 

(Scomber japonicus) en el ecosistema pelágico de la zona norte de Chile. Invest. Cient. Tecnol., Ser. Cienc. 

Mar. 2: 67-78.

Medwin, H. and Clay, C.S., 1997. Fundamentals of acoustical oceanography. Academic press.

Millar, R. and R. Fryer. 1999. Estimating the size-selection curves of towed gears, traps, nets and hooks. 

Reviews in Fish Biology and Fisheries, 9: 89-116.

Miranda, L., Hernández, A., Sepúlveda, A., Landaeta, M. 1998. Alimentación de jurel y análisis de la 

selectividad en la zona centro-sur de Chile. In: Arcos, D. (Ed.), Biología y ecología del jurel en aguas 

chilenas. Instituto de Investigación Pesquera, Talcahuano, pp. 173–187.

Montgomery, J.C., Jeffs, A., Simpson, S.D., Meekan, M., Tindle, C. 2006. Sound as an orientation cue for 

the pelagic larvae of reef fishes and decapod crustaceans. Adv. Mar. Biol. 51: 143-188. 

Mosca, F., Matte, G., Lerda, O., Naud, F., Charlot, D., Rioblanc, M. and Corbières, C. 2016. Scientific 

potential of a new 3D multibeam echosounder in fisheries and ecosystem research. Fisheries 

Research, 178: 130-141.

Moulton, J.M., 1957. Sound production in the spiny lobster Panulirus argus (Latreille). Biological Bulletin, 

113: 286-295.

Mumby, P.J., C.P. Dahlgren, A.R. Harborne, C.V. Kappe, F. Micheli, D.R. Brumbaugh, K.E. Holmes, J.M. 

Mendes, K.Broad, J.N. Sanchirico, K. Buch, S. Box, R.W. Stoffle, and A.B. Gill. 2006. Fishing, Trophic 

Cascades, and the Process of Grazing on Coral Reefs. Science. 311: 98–101.
128

Murphy, H.M. and G.P. Jenkins. 2010. Observational methods used in marine spatial monitoring of fishes 

and associated habitats: a review. Marine and Freshwater Research 61: 236-252.

Nedelec, S.L., Simpson, S.D., Holderied, M., Radford, A.N., Lecellier, G., Radford, C. et al. 2015. 

Soundscapes and living communities in coral reefs: temporal and spatial variation. Marine Ecology 

Progress Series. 525: 125-135.

Neilson, J.D., Perry, R.I. 1990. Diel vertical migration of marine fishes: an obligate of facultative process. 

Adv. Mar. Biol. 26: 115-168.

Nelson, M.D., Koenig, C.C., Coleman, F.C., Mann, D.A. 2011. Sound production of red grouper 

Epinephelus morio on the West Florida Shelf. Aquat. Biol. 12: 97-108. 

Nemeth, R.S., J. Blondeau, S. Herzlieb, E. Kadison. 2006. Spatial and temporal patterns of movement and 

migration at spawning aggregations of red hind, Epinephelus guttatus, in the U.S. Virgin Islands. Environ 

Biol Fishes 78: 365-381. 

Nemeth, R.S. 2005. Population characteristics of a recovering US Virgin Islands red hind spawning 

aggregation following protection. Mar. Ecol. Prog. Ser. 286: 81−97. 

Nemeth, R.S. 2012. Ecosystem aspects of spawning aggregations. Chapter 2 In Y. Sadovy de Mitcheson 

and P. Colin (eds), Reef Fish Spawning Aggregations: Biology, Research and Management. Springer pp: 

21-56.

Nemeth, R.S. 2009. Chapter 4: Dynamics of reef fish and decapod crustacean spawning aggregations: 

underlying mechanisms, habitat linkages and trophic interactions. In: I. Nagelkerken, ed. Ecological 

connectivity among tropical coastal ecosystems. Springer pp 73-134

Nemeth, R.S., K. Rhodes, E. Kadison, J. Martens. 2012. Conservation and Management of Grouper 

Spawning Aggregation Sites: Adaptive Strategies Based on Fish Movement Patterns. Proceedings of the

65th Gulf Caribbean Fisheries Institute, November 5-9, 2012, Santa Marta, Colombia. 

http://nsgl.gso.uri.edu/flsgp/flsgpw12004/data/papers/65-58.pdf

Nero, R.W. and Magnuson, J.J. 1989. Characterization of patches along transects using high-resolution 

70-kHz integrated acoustic data. Canadian Journal of Fisheries and Aquatic Sciences, 46(12): 2056-2064.

Ona E. and Mitson R.B. 1996. Acoustic sampling and signal processing near the seabed: the deadzone 

revisited. ICES J. Mar. Sci. 53: 677-690.

Ona, E. et al., 1999. Methodology for target strength measurements (with special reference to in situ 

techniques for fish and micro-nekton). Prepared by the Study Group on Target Strength 

Methodology. ICES Cooperative Research Report, 235, p.59.

Parker-Stetter, S.L., Rudstam, L.G., Sullivan, P.J., and Warner, D.M. 2009. Standard operating procedures 

for fisheries acoustic surveys in the Great Lakes. Great Lakes Fish. Comm. Spec. Pub. 09-01.

Parmentier, E., Kéver, L., Casadevall, M., Lecchini, D. 2010. Diversity and complexity in the acoustic 

behaviour of Dacyllus flavicaudus (Pomacentridae). Marine Biology. 157: 2317-2327.
129

Parmentier, E., Berten, L., Rigo, P., Aubrun, F., Nedelec, S.L., Simpson, S.D. and Lecchini, D. 2015. The 

Influence of Various Reef Sounds on Coral-Fish Larvae Behaviour. Journal of Fish Biology, WileyBlackwell, 86, 1507–1518. https://doi.org/10.1111/jfb.12651.

Parrish, J.K. and Hamner, W.M. (eds). 1997. Animal groups in three dimensions: how species aggregate. 

Cambridge University Press.

Parrish, J.K., Turchin, P. 1997. Individual decisions, traffic rules, and emergent patterns in schooling fish. 

In: Parrish, J.K., Hamner, W.M. (Eds.) Animal groups in three dimensions. Cambridge University Press, 

England, pp. 126-142.

Parsons, M., McCauley, R.D., Thomas, F. 2013. The sounds of fish off Cape Naturaliste, Western 

Australia. Acoustics Australia. 41: 58–64.

Patek, S.N. 2002. Squeaking with a sliding joint: mechanisms and motor control of sound production in 

palinurid lobsters. Journal of Experimental Biology, 205: 2375-2385.

Peña, M., 2016. Incrementing data quality of multi-frequency echograms using the Adaptive Wiener 

Filter (AWF) denoising algorithm. Deep Sea Research Part I: Oceanographic Research Papers, 116, pp.14-

21.

Peterson, C.H., Lipcius, R.N., 2003. Conceptual progress towards predicting quantitative ecosystem 

benefits of ecological restorations. Mar. Ecol. Prog. Ser. 264: 297-307. 

Piercy, J.J.B., Codling, E.A., Hill, A.J., Smith, D.J., Simpson, S.D. 2014. Habitat quality affects sound 

production and likely distance of detection on coral reefs. Mar. Ecol. Prog. Ser. 516: 35-47.

Pijanowski, B.C., Farina, A., Gage, S.H., Dumyahn, S.L., Krause, B.L. 2011. What is sound- scape ecology? 

An introduction and overview of an emerging new science. Landsc. Ecol.

Pitcher, T.J., Parrish, J.K., 1993. Functions of shoaling behaviour in teleosts. In: Pitcher, T.J. (Ed.) Behavior 

of teleosts fish. Chapman and Hall, London, pp. 363-439.

Quiñones, R., Serra, R., Núñez, S., Arancibia, H., Córdova, J., Bustos, F. 1997. Relación espacial entre el 

jurel y sus presas en la zona centro-sur de Chile. In: Tarifeño, E. (Ed.), Gestión de sistemas 

oceanográficos del Pacífico oriental. UNESCO COI/INF – 1046, pp. 187-202.

R. A. Rountree, R. G. Gilmore, C. A. Goudey, A. D. Hawkins, J. J. Luczkovich, and D. A. Mann. 2006.

Listening to fish: Applications of passive acoustics to fisheries science. Fisheries 31: 433-446. 

Radford, C.A., Stanley, J.A., Jeffs, A.G. 2014. Adjacent coral reef habitats produce different underwater 

sound signatures. Mar. Ecol. Progr. Ser. 505: 19-28.

Radford, C.A., Stanley, J.A., Tindle, C.T., Montgomery, J.C., Jeffs, A.G. 2010. Localised coastal habitats 

have distinct underwater sound signatures. Mar. Ecol. Progr. Ser. 401: 21-29.

Ralston, S. 1990. Size selection of snappers (Lutjanidae) by hook and line gear. Can. J. Fish. Aquat. Sci. 

47: 696-700.

Reid, D.G. et al. 2000. Report on echo trace classification. ICES Cooperative Research Report, 238, 

pp.115.
130

Roa-Ureta, R., E. Niklitschek. 2007. Biomass estimation from surveys with likelihood-based geostatistics. 

ICES J. Mar. Sci. 64 (9): 1723-1734.

Rooper, C., M. Martin, J.L. Butler, D.T. Jones, and M. Zimmermann. 2011. Estimating species and size 

composition of rockfishes to verify targets in acoustic surveys of untrawlable areas. Fish. Bull., National 

Oceanic and Atmospheric Administration. 110: 317-331.

Rountree, R.A., C. Goudey, T. Hawkins, J. Luczkovich, and D. Mann, 2003. Listening to fish: passive 

acoustic applications in marine fisheries. Sea Grant Digital Oceans. Massachusetts Institute of 

Technology Sea Grant College Program. MITSG 0301. 

Rountree, R.A., C. Goudey, T. Hawkins. (Eds) 2003. Listening to fish: proceeding of the international 

workshop on the applications of passive acoustics to fisheries. 8-10 April 2002. Dedham, MA. MIT Sea 

Grant Technical Report MITSG 03-2. 

Rowell, T., Nemeth, R., Schärer, M., Appeldoorn, R. 2015. Fish sound production and acoustic telemetry 

reveal behaviors and spatial patterns associated with spawning aggregations of two Caribbean groupers. 

Mar. Ecol. Prog. Ser. 518: 239-254. doi: 10.3354/meps11060

Rowell, T.J., Appeldoorn, R.S., Rivera, J.A., Mann, D.A., Kellison, T., Nemeth, M., Schärer-Umpierre, M.T. 

2011. Use of passive acoustics to map grouper spawning aggregations, with emphasis on red hind, 

Epinephelus guttatus, off western Puerto Rico. Proc. Gulf Caribb. Fish. Inst. 63:139-142. 

Rowell, T.J., Schärer, M.T., Appeldoorn, R.S., Nemeth, M.I., Mann, D.A., Rivera, J.A. 2012. Sound 

production as an indicator of red hind density at a spawning aggregation. Mar. Ecol. Prog. Ser. 462: 241-

250.

Rudstam, L.G., Jech, J.M., Parker-Stetter, S.L., Horne, J.K., Sullivan, P.J. and Mason, D.M. 2012. Fisheries 

acoustics. Fisheries techniques, p.40.

Ryan, T.E., Downie, R.A., Kloser, R.J. and Keith, G. 2015. Reducing bias due to noise and attenuation in 

open-ocean echo integration data. ICES Journal of Marine Science, 72(8): 2482-2493.

Sadovy De Mitcheson, Y. A. Cornish, M. Domeier, P. Colin, M. Russel and K.C. Lindeman. 2008. A Global 

Baseline for Spawning Aggregations of Reef Fishes Conservation Biology, Volume 22(5): 1233–1244. 

Sadovy, Y. 1997. The case of the disappearing grouper: Epinephelus striatus (Pisces: Serranidae). J. Fish. 

Biol. 46(6): 961-976.

Sala, E., E. Ballesteros, R.M. Starr. 2001. Rapid decline of Nassau Grouper Spawning Aggregations in 

Belize: Fishery Management and Conservation Needs. Fisheries. 26(10): 23-30. 

Sale, P.F. and W.A. Douglas. 1981. Precision and accuracy of visual census technique for fish 

assemblages on coral reef patches. Environmental Biology of Fishes 6(3/4): 333-339. 

Sanchez, P.J., R.S. Appeldoorn, M.T. Schärer-Umpierre, J.V. Locascio. 2017. Patterns of courtship 

acoustics and geophysical features at spawning sites of black grouper (Mycteroperca bonaci). Fish. Bull. 

115:186-195. doi: 10.7755/FB.115.2.5

Sawada, K., Furusawa, M. and Williamson, N.J. 1993. Conditions for the precise measurement of fish 

target strength in situ. Journal of the Marine Acoustical Society of Japan, 20, pp.73-79.
131

Scalabrin, C., Diner, N., Weill, A., Hillion, A. and Mouchot, M.C. 1996. Narrowband acoustic identification 

of monospecific fish shoals. ICES Journal of Marine Science, 53(2): 181-188.

Schärer, M.T., Nemeth, M.I., Appeldoorn, R.S. 2009. Protecting a multi-species Spawning Aggregation at 

Mona Island, Puerto Rico. Proc. 62nd Gulf. Caribb. Fish. Inst. 252-259

Schärer, M.T., Nemeth, M.I., Mann, D., Locascio, J., Appeldoorn, R.S., Rowell, T.J. 2012. Sound 

production and reproductive behavior of yellowfin grouper, Mycteroperca venenosa (Serranidae), at a 

spawning aggregation. Copeia 1: 136-145.

Schärer, M.T., M.I. Nemeth, T.J. Rowell, and R.S. Appeldoorn, 2014. Sounds associated with the 

reproductive behavior of the black grouper (Mycteroperca bonaci). Marine Biology. 161: 141-147.

Schärer, M.T., T.J. Rowell, M.I. Nemeth, and R.S. Appeldoorn. 2012. Sound production associated with 

reproductive behavior of Nassau grouper Epinephelus striatus at spawning aggregations. Endanger 

Species Res. 19: 29–38. 

Scheaffer, R. L., Mendenhall, W., Ott, L., and Gerow, K. 2012. Elementary Survey Sampling, 7th Edition. 

PWS -Kent, Boston. 

Schwarz, A.L. 1985. The behavior of fishes in their acoustic environment. Environmental Biology of 

Fishes. 13: 3-15.

Sepúlveda, A., Cubillos, L., Arcos, D. 2003. Pesquerías pelágicas chilenas: una síntesis sobre la 

incertidumbre biológica. In: E. Yañez (Ed.) Actividad Pesquera y de Acuicultura en Chile, Escuela de 

Ciencias del Mar, UCV, Valparaíso: 91-107.

Sernapesca. 2011. Anuario Estadístico de Pesca. Servicio Nacional de Pesca y Acuicultura. Ministerio de 

Economía, Fomento y Turismo. Chile. 240 pp.

Serra, R. 1991. Important life history aspects of the Chilean jack mackerel, Trachurus symmetricus 

murphyi. Invest. Pesq. (Chile). 36: 67-83.

Simmonds, E.J., and Fryer, R.J. 1996. Which Are Better, Random or Systematic Acoustic Surveys? A 

Simulation Using North Sea Herring as an Example. ICES Journal of Marine Science, 53: 39-50.

Simmonds, E.J., and MacLennan, D.N. 2005. Fisheries Acoustics: Theory and Practice, Blackwell, Oxford. 

456 pp.

Simpson, S.D., Meekan, M.G., Larsen, N.J., McCauley, R.D., Jeffs, A. 2010. Behavioral plasticity in larval 

reef fish: orientation is influenced by recent acoustic experiences. Behavioral Ecology. 21: 1098-1105.

Simpson, S.D., Meekan, M.G., McCauley, R.D. and Jeffs, A. 2004. Attraction of settlement-stage coral 

reef fishes to reef noise. Mar. Ecol. Progr. Ser. 276: 263-268.

Simpson, S.D., Meekan, M.G., Jeffs, A., Montgomery, J., McCauley, R., 2008. Settlement-stage coral reef 

fish prefer the higher-frequency invertebrate-generated audible component of reef noise. Anim. Behav. 

75: 1861-1868.

Slabbekoorn H, Bouton N. 2008. Soundscape orientation: a new field in need of sound investigation. 

Animal Behavior. 76: e5–e8.
132

Smith, C.L. 1972. A spawning aggregation of Nassau grouper, Epinephelus striatus (Block). Trans. Amer. 

Fish. Soc. 101: 225-261.

Somerton, D.A. and C.T. Gledhill (eds). 2005. Report of the National Marine Fisheries Service Workshop 

on Underwater Video Analysis. U.S. Dep. Commerce, NOAA Tech. Memo. NMFS-F/SPO-68.

Soria, M. 1994. Structure et stabilitd des bancs et agrégations de poissons pélagiques côtiers tropicaux: 

application halieutique. Trav. et Doc. ORSTOM, 125: 285 p.

Soule, M., Hampton, I., and Barange, M. 1996. Potential improvements to current methods of 

recognizing single targets with a split-beam echo-sounder. ICES Journal of Marine Science, 53: 237–243.

Spampinato, C., Giordano, D., Di Salvo, R., Chen-Burger, Y.H.J., Fisher, R.B. and Nadarajan, G. 2010.

October. Automatic fish classification for underwater species behavior understanding. In Proceedings of 

the first ACM international workshop on Analysis and retrieval of tracked events and motion in imagery 

streams (pp. 45-50). ACM.

Stanley, J.A., Radford, C.A., Jeffs, A.G. 2012. Location, location, location: finding a suitable home among

the noise. Proc. R. Soc. B. 279: 3622-3631.

Stanton, T. and Clay, C. 1986. Sonar echo statistics as a remote-sensing tool: Volume and seafloor. IEEE 

Journal of Oceanic Engineering, 11(1): 79-96.

Stanton, T.K. 1982. Effects of transducer motion on echo-integration techniques. The Journal of the 

Acoustical Society of America, 72(3): 947-949.

Stanton, T.K., Chu, D., Jech, J.M. and Irish, J.D. 2010. New broadband methods for resonance 

classification and high-resolution imagery of fish with swimbladders using a modified commercial 

broadband echosounder. ICES Journal of Marine Science, 67(2): 365-378.

Stevely, J.M., Sweat, D.E., Bert, T.M., Sim-Smith, C., Kelly, M. 2011. Sponge mortality at Marathon and 

Long Key, Florida: patterns of species response and population recovery. Proc. Gulf Caribb. Fish. Inst. 63: 

384–400.

Streich, M., M. Ajemian, J. Wetz, G. Stunz. 2018. Habitat-specific performance of vertical line gear in the 

western Gulf of Mexico: A comparison between artificial and natural habitats using a paired video 

approach. Fisheries Research 204: 16-25.

Sullivan, P.J., L.G. Rudstam. 2016. Quantifying acoustic uncertainty using Bayesian hierarchical modeling 

with an application to assessing Mysis relicta population densities in Lake Ontario. ICES J. Mar. Sci. 73(8): 

2104-2111. https://doi.org/10.1093/icesjms/fsw080

Taylor, J.C. and Ebert, E. 2012. Mapping coral reef fish schools and aggregations with high-frequency 

multibeam and split-beam sonars. Proc. Mtgs. Acoust. 17. 

Tavolga, W.N., A.N. Popper, and R.R. Fay. 2012. Hearing and Sound Communication in Fish. Springer 

Science and Business Media, Berlin. 

Thresher, R.E. and J.S. Gunn. 1896. Comparative analysis of visual census techniques for highly mobile, 

reef-associated piscivores (Carangidae). Environmental Biology of Fishes 17(2): 93-116.
133

Tichy, F.E., Solli, H. and Klavenes, H. 2003. Nonlinear effects in a 200 kHz sound beam and the 

consequences for target-strength measurements. ICES J. Mar. Sci. 60: 571-574.

Tušer, M., Balk, H., Mrkvička, T., Frouzová, J., Čech, M., Muška, M., Kubečka, J., 2011: Validation of 

current acoustic dead-zone estimation methods in lakes with strongly sloped bottoms. Limnol. 

Oceanogr. Meth. 9: 507–514.

Urick, R.J., Lund, G.R., Tulko, T.J. 1972. The depth profile of ambient noise in the deep sea north of St. 

Croix, Virgin Islands. White Oak: Naval Ordnance Laboratory.

Urick, R.J., 1983. Principles of Underwater Sound (Peninsula, Los Altos, CA). Google Scholar, p.423.

Vasconcelos, R.O., P. J. Fonseca, M. C. P. Amorim, and F. Ladich. 2011. Representation of complex 

vocalizations in the Lusitanian toadfish auditory system: Evidence of fine temporal, frequency and 

amplitude discrimination. Proc. Biol. Sci. 278: 826-834. 

Vásquez, S., Parada, C., Correa-Ramírez, M., Sepúlveda, A. 2013. The influence of oceanographic 

processes on jack mackerel (Trachurus murphyi) larval distribution and population structure in the 

southeastern Pacific Ocean. ICES J. Mar. Sci. 70(6): 1097-11.

Venables, W. N., and Ripley, B. D. 2002. Modern Applied Statistics with S, Springer-Verlag, New York.

Vitousek, P.M., Mooney, H.A., Lunchenco, J., Melillo, J.M. 1997. Human domination of the Earth's 

ecosystems. Science 277: 494–499.

Vray, D., Gimenez, G. and Person, R. 1990. Attempt at classification of echo-sounder signals based on 

the linear discriminant function of Fisher. Rapports et Proces-Verbaux des Réunions du Conseil 

International pour l’Exploration de la Mer, 189: 388-393.

Wall, C.C., P. Simard, M. Lindemuth, C. Lembke, D.F. Naar, C. Hu, B.B. Barnes, F.E. Muller-Karger, and 

D.A. Mann. 2014. Temporal and spatial mapping of Red Grouper Epinephelus morio sound production. 

Journal of Fish Biology 85: 1469-1487.

Wall, C.C. David A. Mann, C. Lembke, C.s Taylor, R. He, T. Kellison. 2017. Mapping the Soundscape Off 

the Southeastern USA by Using Passive Acoustic Glider Technology, Marine and Coastal Fisheries, 9:1, 

23-37, DOI: 10.1080/19425120.2016.1255685 

Walters, S., Lowerre-Barbieri, S., Bickford, J., Mann, D. 2009. Using a passive acoustic survey to identify 

spotted seatrout spawning sites and associated habitat in Tampa Bay, Florida. Trans. Am. Fish. Soc. 138: 

88-98.

Watanabe, M., Sekine, M., Hamada, E., Ukita, M., Imai, T. 2002. Monitoring of shallow sea environment 

by using snapping shrimps. Water Sci. Technol. 46: 419–424.

Waugh, G.T., Gauvin, J.R., Gregory, D.R.J., Lamberte, A.B. 1990. Fishery Management Plan for the 

Snapper Grouper Fishery of the South Atlantic Region - Amendment 2. 

Wilson, D.C., Raakjær, J., Degnbol, P. 2006. Local ecological knowledge and practical fisheries 

management in the tropics: a policy brief. Mar. Policy 30: 794-801. 
134

Xie, Y., 2000. A range-dependent echo-association algorithm and its application in split-beam sonar 

tracking of migratory salmon in the Fraser River watershed. IEEE journal of oceanic engineering, 25(3): 

387-398.

Yoklavich, M., J. Reynolds, and D. Rosen. 2015. A Comparative assessment of underwater visual survey 

tools: Results of a workshop and user questionnaire. U.S. Dep. Commerce, NOAA Tech. Memo. NOAATM-NMFS-SWFSC-547, June 2015.Andersen, L.N., Berg, S., Gammelsaeter, O.B. and Lunde, E.B. 2006. 

New scientific multibeam systems (ME70 and MS70) for fishery research applications. Journal of the 

Acoustical Society of America, 120(5): 3017-3017. https://doi.org/10.1121/1.4787077

Zeigler, T., Hunt, J. 2012. Implementing the Dry Tortugas National Park Research Natural Area Science 

Plan The 5-Year Report. 

Zelick, R., D.A. Mann, and A.N. Popper. 1999. Acoustic communication in fish and frogs. In Comparative 

Hearing: Fish and Amphibians. Springer, Berlin. pp. 363-411. 

Zwolinski, J.P., Demer, D.A., Cutter Jr., G.R., Stierhoff, K.L., and Macewicz, B.J. 2014. Building on fisheries 

acoustics for marine ecosystem surveys. Oceanography 27(4): 68–79.

Zwolinski, J.P., Emmett, R.L., and Demer, D.A. 2011. Predicting habitat to optimize sampling of Pacific 

sardine (Sardinops sagax), ICES Journal of Marine Science, 68: 867–879.
135

16 APPENDICES 

16.1 APPENDIX A. SYMPOSIUM TERMS OF REFERENCE AND ABSTRACTS 

Symposium on Acoustic Technologies for Surveying Reef Fish Ecosystems

Terms of Reference: The Gulf and Caribbean Fisheries Institute (GCFI) and SouthEast Acoustic 

Consortium (SEAC) will held a special session entitled “Acoustic Technologies for Surveying Reef Fish 

Ecosystems.” Given the complexity of the life history and habitat of reef fish, the difficulties of sampling 

reef ecosystems have resulted in data-limited assessments in the Gulf of Mexico and Caribbean regions. 

Ongoing advances in both active and passive underwater acoustic technologies have brought a variety 

of tools to scientists for improving surveys and experimental research in reef habitats that can address a 

variety of operational research objectives. For example, active acoustics instruments (echosounders) 

provide measurements to estimate reef fish abundance, map distributions, delineate spawning 

aggregations, observe behavior, and characterize community structure. In addition to remote sensing of 

the biological community, active acoustics can also provide seafloor classification and bathymetry of 

their habitats. As scientists strive for integrated sampling operations, passive acoustic instruments 

(hydrophones and acoustic tags) are also used to locate and monitor spawning aggregations, monitor 

aquatic ecosystem integrity, and investigate the impact of ocean noise on marine resources. 

The objective of this special session is to provide the current state-of-the-science, challenges, 

applications, and recommendations on the best practices for integrating active and passive acoustic 

technologies into reef fish and ecosystem surveys to provide high quality and timely scientific 

information for the management of living marine resources. This session also serves to communicate 

how integrating acoustic technologies into reliable and sustained survey and observation systems will 

provide socioeconomic benefits from the scientific gains. Abstracts for this special session should be 

submitted by the abstract submission deadline in the GCFI announcement.

The following abstracts were accepted during the Joint GCFI-SEAC Symposium on Acoustic Technologies 

to Improve Reef Fish Ecosystem Surveys.
136

Developing a strategic initiative to transition technologies into

operations for improving reef fish ecosystem surveys

Desarrollar una iniciativa estratégica para las tecnologías de transición en operaciones para mejorar 

las encuestas de ecosistemas de peces de arrecife.

Développer une initiative stratégique visant à transformer les technologies en opérations visant à 

améliorer les enquêtes sur les écosystèmes de poissons de récifs. 

WILLIAM L. MICHAELS

NOAA Office of Science and Technology

1315 E West Hwy, Silver Spring MD 20910 United States

ABSTRACT

Scientific information is often data-limited for the conservation and management of reef fish and their 

habitats due to the difficulties in sampling these complex ecosystems. Advances in the integration of 

sensor, platform, communication, and processing technologies have great potential for improving the 

quality, quantity, and timeliness of scientific information. Management priorities and information gaps 

should drive technology investments, and a change in the mindset of the corporate culture is often 

required with this strategic planning. In the evaluation of technologies, proven feasibility, calibrations, 

and stability in measurements are critical considerations when transitioning innovative technologies into 

sustained and reliable survey operations. For these reasons, evaluation of the technology’s performance 

metrics and cost-benefits is necessary before investing in the transition phase. Ultimately, the transition 

from traditional sampling gear to technology requires acceptance by the scientific and management 

community as well as the stakeholders. Rapidly evolving technology presents challenges for survey 

operations, data management, and maintaining a high level of accuracy and precision in environmental 

monitoring and survey operations that rely on standardized measures to support our long-term time 

series. As organizations strive to augment or enhance survey operations with innovative technologies, 

disruptions should be minimized in existing business practices when the scientific information is used for 

effective policy decisions.
137

Hydroacoustic Tools for Monitoring Shrimp Distribution, Movement, 

and Behavior in Natural and Built Environments

Herramientas Hidroacústicas para Monitorear la Distribución, Movimiento y Comportamiento del 

Camarón en Entornos Naturales y Construidos

Outils Hydroacoustiques pour la Surveillance de la Distribution, du Mouvement et du Comportement 

des Séjours dans des Environnements Naturels et Construits

 

PAT NEALSON*, COLLEEN SULLIVAN, TRACEY STEIG, and SAM JOHNSTON

HTI-Vemco USA, Inc., 711 NE Northlake Way, Seattle Washington 98105 USA.

ABSTRACT

Shrimp and other underwater species have been monitored using both echo sounding and acoustic 

tagging techniques in various types of environments. These efforts have included sampling in a broad 

range of environmental conditions from studies in open systems measuring response to environmental 

disturbances and perturbations to studies in closed systems monitoring species interaction. The same 

basic principles of sound propagation apply to both echo sounding and acoustic telemetry techniques 

for shrimp monitoring. The selection of the most suitable hydroacoustic sampling tool will be based on 

the purpose of the investigation. We will look at strengths and limitations in using echo sounding and 

acoustic telemetry techniques for shrimp monitoring within several types of environments. Results of 

shrimp will be presented from hydroacoustic and acoustic telemetry studies.

KEYWORDS: Hydroacoustics, acoustic telemetry, acoustic tags, shrimp
138

An integrated approach to develop in situ target strength – length relationships for Atlantic Goliath 

Grouper (Epinephelus itajara) on coastal reefs.

Home Idiomas Ingresar a Epistemonikos Búsqueda avanzada Un enfoque integrado para desarrollar in 

situ las relaciones de fuerza de longitud para el garrido de Goliath Atlántico (Epinephelus itajara) en 

los arrecifes costeros.

Une approche intégrée pour développer des relations in situ sur la force et la longueur cible pour le 

mérou de Goliath de l'Atlantique (Epinephelus itajara) sur les récifs côtiers.

BENJAMIN M. BINDER1

, JAMES LOCASCIO2, KEVIN M. BOSWELL1

Florida International University, Miami, FL1

; Mote Marine Laboratory, Sarasota, FL2; Florida International 

University, Miami, FL1

; 

ABSTRACT

Hydroacoustic surveys represent a rapid non-invasive alternative to labor-intensive population 

assessments using traditional fisheries dependent and independent survey methods. While 

hydroacoustic surveys provide a wealth of information that can simultaneously address management 

and ecological questions at enhanced spatiotemporal scales, there are certain limitations that have 

made studying reef associated and diverse fish assemblages exceptionally challenging. Specifically, the 

taxonomic discrimination of a species using acoustic approaches requires a comprehensive 

understanding of their frequency-dependent scattering properties that can be logistically difficult to 

measure. Here we provide data related to this effort, and present an approach to derive in situ target 

strength-length relationships for Atlantic Goliath Grouper statistically derived from the comparison 

between laser-calibrated photogrammetry estimated length distributions and coincident in situ target 

strength distributions collected from spawning aggregations in Jupiter, Florida. With these data, 

improvements in estimates of abundance, density, and biomass can be used to augment information 

needed by resource managers and policy makers to inform management decisions. Additionally, these 

data provide a framework for the development of similar population assessment efforts related to data 

poor species that exhibit conspicuous aggregating behavior, similar to goliath grouper spawning. Lastly, 

considering the advances in available acoustic technologies and the logistical challenges associated with 

acoustically surveying complex habitats, it is imperative that we consider strategic survey designs that 

maximize data quality and scope.
139

Using acoustic and optical methods in fish spatial distribution assessment

Uso de metodos acusticos y opticos para monitorear la distribución espacial de peces

Utilisation de méthodes acoustiques et optiques dans l'évaluation de la distribution spatiale des 

poissons

M. MONTSERRAT LANDERO, IAIN PARNUM BENJAMIN J SAUNDERS AND MILES PARSONS

Centre for Marine Science and Technology, Curtin University Ken Street, Bentley. Perth Western 

Australia 6102 Australia

ABSTRACT

This study combined acoustic (echo-sounder) and optical (stereo baited remote underwater video, 

referred to as stereo-BRUVs) methods to evaluate the distribution and abundance of fish. These 

techniques complement each other, as the echo-sounder can be used to map the location of fish schools 

and individuals over a large area, and the visual data can help identify the species composition of a 

school, which is often non-trivial to attain with acoustic methods alone. The non-extractive and noninvasive characteristics of these techniques are useful, particularly in conservation areas, such as marine 

parks. The objective of this study was to compare the spatial distribution of fish using acoustic and 

optical methods over different benthic habitats in Ningaloo Reef Marine Park, Western Australia. 

Acoustic data was collected using a single-beam echo-sounder at two frequencies 38 and 200 kHz. 

Optical data was collected using stereo-BRUVs, deployed at 656 different sampling points within the 

same region. Relative biomass per species was estimated using the stereo-BRUVs data for each point of 

deployment. Schools and single-targets were extracted and the target strength of the species 

representing the highest proportion of biomass, according to the stereo-BRUVs data was used as a 

preliminary way to convert the backscatter energy into relative biomass. Correlations between the 

relative biomass estimated with both methods were explored. The relative advantages and drawbacks 

of these methods for monitoring fish populations is discussed.
140

Underwater acoustics for ecosystem research: current advances and perspectives in Northeast Brazil 

Acústica subacuática para la investigación de ecosistemas: avances y perspectivas actuales en el 

Nordeste de Brasil

Acoustique sous-marine pour la recherche sur les écosystèmes: avancées et perspectives actuelles 

dans le nord-est du Brésil

ANNE LEBOURGES-DHAUSSY 1

, GARY VARGAS 2, GILDAS ROUDAUT 1, FLAVIA LUCENA-FRÉDOU 2 AND 

ARNAUD BERTRAND 3,2,4

1

Institut de Recherche pour le Développement (IRD), LEMAR UMR 6539 LEMAR 

IFREMER/IRD/CNRS/UBO, Technopole Brest Iroise, 29280 Plouzané, France. 2

Universidade Federal Rural de Pernambuco (UFRPE), Recife, Pernambuco, Brazil

3

Institut de Recherche pour le Développement (IRD), UMR MARBEC 248 IRD/CNRS/IFREMER/UM, 

Avenue Jean Monnet, CS 30171, 34203, Sète, France. 4

Universidade Federal de Pernambuco (UFPE), Recife, Pernambuco, Brazil

ABSTRACT

Recent improvements (e.g. multifrequency, broad-band echosounders) allow to simultaneously 

characterize physical structures (e.g. thin layers, internal waves, eddies) and organisms (from 

zooplankton to whales) patterns of distribution across scales from meters to thousands of kilometers. 

These progresses open a variety of perspectives for understanding complex processes. On this basis we 

are developing in Northern Brazil the ‘Acoustic along the Brazilian Coast’ (ABRAÇOS) project. The main 

objective is to establish a 3D characterization of the abiotic and biotic compartments and their 

interactions in coastal and oceanic ecosystems. The project is based on two multidisciplinary at-sea 

surveys performed on-board the IRD R/V Antea in Sept. – Oct. 2015 and April – May 2017 as well as 

small scale surveys using vessels of opportunity. The study area includes the Northeast coast Brazil and 

an oceanic area including the Archipelagos of Fernando de Noronha, the Atoll das Rocas and oceanic 

seamounts. These campaigns have three specific objectives: (i) characterization of the water masses and 

their dynamics; (ii) ecosystem acoustics with the collection of multifrequency acoustic data (38, 70, 120 

and 200 kHz) coupled with pelagic and bottom trawls, zooplankton net sampling and video images; and 

(iii) biodiversity and trophic structure with the study of benthic, demersal and pelagic biodiversity, 

patterns of distribution, trophic ecology and contamination (mercury). First results provide a new vision

of the ecosystem structure and dynamics and reveal, among other, the importance of gelatinous in both 

coastal and oceanic ecosystems. The perspectives include comprehensive mapping of demersal and 

pelagic patterns of distribution according to the habitat characteristics including coral reef areas.
141

Acoustic assessment of bathymetry, bottom types and characterization of ichthyofaunal community in 

shallower waters of Serrana Key Island, Biosphere Reserve Seaflower, Colombia

Evaluación acústica de la batimetría, tipos de fondo y caracterización de la comunidad ictiofaunal en 

aguas someras de la isla Cayo Serrana, Reserva de la Biosfera Seaflower, Colombia

Évaluation acoustique de la bathymétrie, des types de fond et caractérisation de la communauté 

ichthyofaunale dans les eaux peu profondes de Serrana Key Island, Réserve de la biosphère Seaflower, 

Colombie

SANTIAGO MARTÍNEZ CLAVIJO, JORGE PARAMO, MARCO CORREA AND DANIEL PÉREZ

Escuela Naval de Cadetes Almirante Padilla.Barrio El Bosque Sector Manzanillo. Cartagena Bolivar 57

Colombia.

Universidad del Magdalena, Cra. 32 No. 22-08 Avenida del Ferrocarril.Santa Marta Magdalena 57

Colombia.

Instituto de Investigaciones Marinas y Costeras INVEMAR,Playa Salguero. Santa Marta Magdalena 

Colombia.

Universidad de Bogotá Jorge Tadeo Lozano.Carrera 2 No. 11-68, Edifício Mundo Marino, Rodadero 

Santa Marta Magdalena 57 Colombia.

ABSTRACT

The Biosphere Reserve Seaflower since its declaration in 2000 has become a strategic region of 

conservation and sustainable development for Colombia. Therefore, the objective is to assess by 

hydroacoustics the bathymetry, bottom types and characterize the ichthyofaunal community in shallow 

waters (5-100 m) in Serrana Key, Reserve of the Biosphere Seaflower. The acoustic survey was carry out

onboard in a boat at which a scientific echosounder Biosonics DTX with a 38 kHz transducer was 

installed. In the bottom types analyzes, fine-sand sediments characteristics (-26.0 db) are shown. 

However, other types of bathymetric structures other than sediments with an echo of -35.0 dB, which 

may be a coral structure, are also shown. The depth ranged between 3.62 and 24.98 m (mean 11.39 

±4.17 m). 59 species belong to 13 orders and 23 families were registered, being the more abundant 

Labridae (19%), Pomacentridae (10%) and Serranidae (9%) and with minor frequency Sphyraenidae and 

Scianidae (1%). The species Thalassoma bifasciatum, Mulloidichthys martinicus and Chromis cyanea 

were the more abundant in all sampling stations, while Clepticus parrae, Halichoeres garnoti, Stegastes 

partitus and Gramma loreto were sporadic. However, the fish species Sphyraena barracuda, Pareques 

acuminatus, Equetus punctatus, Mycteroperca venenosa, Ginglymostoma cirrat showed lower relative 

abundances (<1%). The richness, diversity and composition of species were similar in all study area. 

According to similarity analysis two assemblages were found, the first one associated to bottoms with 

patches of coral reef with abundant octocorals and bigger coral heads, the second assemblage 

associated to patches of less complex coral and the infralittoral zone.
142

Coupling Echosounder and Hydrophone Surveys at Spawning Aggregations:

Relationships Between Levels of Fish Sound Production and Density

Combinando Monitoreos entre Ecosondas e Hidrófonos en las Agregaciones Reproductivas: 

Descubriendo Relaciones Entre el Sonido que Producen los Peces y su Densidad

Couplage d'études par echosondeur et par hydrophone dans les frayères: relations entre les niveaux 

de production de son et de densité de poissons

TIMOTHY J. ROWELL, DAVID A. DEMER, OCTAVIO ABURTO-OROPEZA, JUAN JOSÉ COTA-NIETO, JOHN R. 

HYDE AND BRAD E. ERISMAN.

Scripps Institution of Oceanography, UCSD

9500 Gilman Dr, 208, La Jolla California 92093-0208 United States

ABSTRACT

Advancements in the use of acoustic methods to characterize, map, and assess spawning aggregations 

has expanded our understanding of the reproductive biology, life histories, and stock sizes of vulnerable 

species. The versatility of active acoustics (echosounders) has permitted the estimation of fish 

abundances and biomasses in challenging environments, such as reefs and estuaries, while the 

efficiency of passive acoustics (hydrophones) to monitor the sounds produced by aggregating fishes has 

increasingly been embraced to identify spawning areas and periods. However, a logical and desirable 

progression to efficiently and accurately estimate fish abundances from their sounds has been hindered 

by the complexity of fish calling rates and acoustic propagation. In this study, we compared Gulf Corvina 

(Cynoscion othonopterus) sound levels with simultaneous measurements of densities from echosounder 

surveys recorded at a spawning aggregation in the Colorado River Delta, Mexico, to investigate empirical 

relationships between fish sound levels and density. We observed aggregations comprised of more than 

1.5 million fish and elevated sound levels distributed over 25 km of the delta. The relationship between 

sound levels and density varied within surveys but stabilized during the two-hour period of peak 

spawning, resulting in an equation to estimate densities from received sound levels. Our results support 

the inclusion of active acoustics into assessments of spawning stock abundance and indicate that sound 

levels can be used to estimate fish densities when relationships are scaled to the spatial and temporal 

dynamics of spawning activity. Our approach is applicable to other soniferous, aggregating fishes, 

providing an efficient method to assess and monitor reproductive stocks using passive acoustics.
143

Coupling passive acoustic techniques to survey fish spawning habitats in Puerto Rico

Combinando técnicas de acústica pasiva para estudiar hábitats de desoves de peces en Puerto Rico

Couplage des techniques acoustiques passives pour enquêter sur les habitats des poissons 

reproducteurs a Puerto Rico

MICHELLE SCHÄRER-UMPIERRE; EVAN TUOHY AND RICHARD APPELDOORN

HJR Reefscaping. P.O.Box 1442 Boquerón PR 622 United States. University of Puerto Rico. Department 

of Marine Sciences. Mayaguez Puerto Rico 00680-9000 United States

ABSTRACT

Measuring habitat use by reef fishes for spawning aggregations is enhanced with non-invasive passive 

acoustic monitoring. Some advantages that can be applied to cases where species are known to produce 

sound associated with reproduction include a low cost of deployment and recovery of the instruments, 

high sampling frequency, night-time sampling and a record of the soundscape. Some of these pros make 

passive acoustics instrumental to gather occupation data given the logistical constraints of in-situ 

surveys during the short time period this critical habitat is used by some species. Notable disadvantages 

include the technical requirements of signal recognition in large datasets, not being able to verify why 

fish are not being detected, and masking of sounds by other sound sources including anthropogenic. 

Nassau grouper produce courtship associated sounds during reproductive behaviors that were recorded 

simultaneously with two seasons when 29 individuals had been tagged internally with acoustic tags at 

Bajo de Sico a seamount off western Puerto Rico. Receivers deployed throughout the spawning area 

allowed for the verification of absence of fish when no sounds were detected. The combination of 

passive acoustic monitoring techniques and internal tagging of spawning grouper at this site provided 

evidence of temporal and spatial patterns of diel habitat use continuously during six months. With this 

information, a remotely monitored component of fish spawning aggregations research can help answer 

important site specific questions.
144

Implementation of a passive acoustic monitoring system on a SV3 wave glider and applications

Implementación de un sistema de vigilancia acústica pasiva en un Wave Glider SV3 y aplicaciones

Mise en oeuvre d'un système de surveillance acoustique passive sur un Wave Glider SV3 et 

applications

LAURENT CHERUBIN; FRASER DALGLEISH, ALI IBRAHIM, MICHELLE SCHÄRER UMPIERRRE AND RICHARD 

NEMETH

Harbor Branch oceanographic Institute. Florida Atlantic University. 5600 US 1 North. Fort Pierce FL

34946 United States. Florida Atlantic University Department Computer and Electrical Engineering and 

Computer Science Department Computer and Electrical Engineering and Computer Science. 777 Glades 

Road. Boca Raton FL United States. University of Puerto-Rico, Mayagüez Department of Marine 

Sciences. PR-108 Mayagüez PR 682 Puerto Rico. Center for Marine and Environmental Studies 

University of the Virgin Islands#2 John Brewers Bay St. Thomas 802 Virgin Islands, U.S.

ABSTRACT

Fisheries independent research strives for new technology that can help remotely and unobtrusively 

quantify fish biomass. Some large fish species, such as groupers vocalize during mating. Fish sounds 

provide an innovative approach to assess fish presence and numbers. However, large datasets make the 

detection process by a human ear very tedious and lengthy. We have developed an algorithm based on 

machine learning and voice recognition methods to identify and classify fish sounds. This algorithm 

currently operates on a SV3 Liquid Robotics wave glider, which has been fitted to accommodate a 

passive listening device. Fish sounds detection and classification results, and location, along with 

environmental data are transmitted in real-time to the science crew who can ground truth the detection 

with divers. This passive acoustic monitoring system has been deployed in the US Virgin Islands, PuertoRico, the Florida Keys and on the East Florida shelf. We will provide an overview of the findings made 

with this autonomous monitoring system.
145

Acoustic assessment of zooplankton biomass in the Coast of Magdalena, Colombian Caribbean

Evaluación acústica de la biomasa zooplancton en la costa del Magdalena, Caribe colombiano

Évaluation acoustique de la biomasse du zooplancton dans la côte de Magdalena, Caraïbes 

colombiennes

LINA MARCELA SILVA, JORGE PARAMO AND MARIA ISABEL CRIALES-HERNÁNDEZ

Universidad Industrial de Santander, Facultad de Ciencias Básicas; Escuela de Biología

Bucaramanga Santander 57 Colombia. Universidad del Magdalena, Cra. 32 No. 22-08 Avenida del 

Ferrocarril

Santa Marta Magdalena 57 Colombia; Universidad Industrial de Santander, Facultad de Ciencias Básicas, 

Escuela de Biología. Bucaramanga Santander Colombia

ABSTRACT

Zooplankton is one of the main components of biological communities in marine ecosystems. 

Traditionally they had been caught with net tows in order to be studied. However, in recent years, new 

technologies such as acoustics and optical instruments have allowed newer and deeper insights into 

these organisms. The present study uses a general model to correlate acoustic backscatter with the dry 

weight biomass of zooplankton. Data for the zooplankton biomass were obtained by a vertical trawl net 

in specific locations and the acoustic data were taken using a scientific echosounder Biosonics DTX with 

a transducer of 38 kHz. A linear model was used to correlate hydroacoustic backscatter with biomass. 

The survey was carried out during May 2016 in the Magdalena Department, localized in the Caribbean 

Colombian Coast. The relationship between zooplankton dry weight and acoustic backscatter was 

significant (p <0.001) and explained 54% of variability in dry weight data. The mean mesozooplankton 

dry weight biomass estimated from plankton net tows and acoustic were not significantly different 

(p=0.99). The spatial distribution of zooplankton showed higher aggregations in front of Tayrona 

National Park and Ciénaga Grande of Santa Marta. This work constitutes the first estimation of the 

acoustic conversion factor for the zooplankton in the Colombian Caribbean Sea.
146

Monitoring the Soundscape of Paradise Reef, Cozumel:

A Tool for Assessment and Conservation Planning

Paisaje sonoro submarino del arrecife paraíso en Cozumel:

Una herramienta de evaluación, monitoreo y conservación

Paysage Sonore Sous-marin de Paradise Reef, Cozumel:

Un outil pour l'Évaluation, la surveillance et la Conservation

CYNTHIA PYC AND JONATHAN VALLARTA

JASCO Applied Sciences

402 Wisdom Woods Court, Houston Texas 77094 United States; Protasio Tagle 8-1 San Miguel 

Chapultepec Miguel Hidalgo. Cuidad de Mexico 11850 Mexico

ABSTRACT

Tourism is an essential ecosystem service provided by diverse coral reefs. The economic benefits derived 

from these services sustain coastal cultures and social structure, contributing several billion dollars 

annually to local and national economies. Cozumel is visited by approximately 3 million tourists every 

year, drawn to the island’s coral reefs. Diving and snorkeling tourism in the National Reef Park of 

Cozumel is the main economic driver of Cozumel. Given the relative importance of this ecosystem 

service, maintaining healthy coral reefs is essential. Coral reef biodiversity studies have traditionally 

relied on intensive survey techniques that are costly, infrequent or sporadic, limited to depths accessible 

to human divers, and generally conducted only during daylight. Sound measurement is an emerging 

alternative that uses non-invasive, passive acoustic monitoring (PAM) to measure reef soundscapes and 

biodiversity. JASCO in partnership with the Parque Nacional Arrecifes de Cozumel, deployed an 

Autonomous Multichannel Acoustic Recorder (AMAR) on Paradise Reef in July 2017. The AMAR 

recorded two months of continuous acoustic data. The objectives of the study included characterizing 

the anthropogenic and natural soundscape of this location that is heavily trafficked by cruise ships and 

dive boats, and is home to the iconic Splendid toadfish (Sanopus splendidus). The data collected will 

provide information regarding the volume of anthropogenic noise at this location and the potential for 

effects of on the reef fauna, including masking of biologically important activities, and will facilitate 

identification of additional Splendid toadfish habitat in future deployments. This initial study is 

demonstrating the power of acoustics as a tool for biodiversity assessment, monitoring and 

conservation.
147

Hydroacoustics for the discovery and quantification of

Nassau grouper (Epinephelus striatus) spawning aggregations

Hidroacústica para el descubrimiento y cuantificación de

Nassau grouper (Epinephelus striatus) agregación de desove

Hydroacoustique pour la découverte et la quantification de

Nassau grouper (Epinephelus striatus) agrégation de frai

JACK EGERTON

School of Ocean Sciences, Bangor University, Wales, UK

University of Texas at Austin, Marine Science Institute

Askew St, Menai Bridge Anglesey LL59 5AB United Kingdom

ABSTRACT

Fish spawning aggregations (FSAs) are vital life-history events that need to be monitored to determine 

the health of aggregating populations; this is especially true of the endangered Nassau grouper 

(Epinephelus striatus). Hydroacoustics were used to locate Nassau grouper FSAs at sites on the west end 

of Little Cayman (LCW), and east ends of Grand Cayman (GCE) and Cayman Brac (CBE). Fish abundance 

and biomass at each FSA were estimated via echo integration and FSA extent. Acoustic mean fish 

abundance estimates (±SE) on the FSA at LCW (893 ± 459) did not differ significantly from concurrent 

SCUBA estimates (1150 ± 75). Mean fish densities (number 1000 m-3) were significantly higher at LCW 

(33.13 ± 5.62) than at the other sites (GCE: 7.01 ± 2.1, CBE: 4.61 ± 1.16). We investigate different 

acoustic postprocessing options to obtain target strength (TS), and we examine the different TS to total 

length (TL) formulas available. The SCUBA surveys also provided measures of TL through the use of laser 

callipers allowing development of an in situ TS to TL formula for Nassau grouper at the LCW FSA. 

Application of this formula revealed mean fish TL was significantly higher at LCW (65.4 ± 0.7 cm) than 

GCE (60.7 ± 0.4 cm), but not CBE (61.1 ± 2.5 cm). FSA location examined with reference to seasonal 

marine protected areas (Designated Grouper Spawning Areas) showed FSAs were partially outside these 

areas at GCE and very close to the boundary at CBE. As FSAs often occur at the limits of safe diving 

operations, hydroacoustic technology provides an alternative method to monitor and inform future 

management of aggregating fish species. 
148

16.2 APPENDIX B. WORKSHOP TERMS OF REFERENCE AND AGENDA 

Workshop on Acoustic Technologies to Improve Reef Fish Ecosystem Surveys

Background and Goals: The Gulf and Caribbean Fisheries Institute (GCFI) and SouthEast Acoustic 

Consortium (SEAC), with sponsorship from the National Oceanic and Atmospheric Administration 

(NOAA), Kongsberg-Simrad, and Echoview, will hold a three-day acoustic technology workshop in 

Merida, Mexico during 4-10 November 2017. Although there is no workshop registration fee, you must 

refer to the GCFI website for the GCFI conference information concerning the GCFI conference 

registration fee, venue and accommodations. The goals for this 3-day workshop are to increase acoustic 

expertise and collaborative efforts for enhancing research and surveys in reef-fish ecosystems, and to 

build scientific capacity for assuring the sustainability of living marine resources in the Gulf of Mexico 

and Caribbean region.

Day 1: Workshop Agenda

Scientific echosounder operations

Terms of Reference: Participants will receive an introduction to the principles of scientific echosounder 

systems and operations, and shipboard training using two wideband echosounder systems: Simrad EK80

and the autonomous Simrad WBAT. 

Date and Location: November 4, 2017, participants will meet in the main lobby of the Hyatt Regency 

Merida Hotel at 08:00 AM, and depart by bus from Merida to Progreso in Mexico. Following an 

orientation, participants will depart aboard the boat Isla Mujeres for training. Training aboard the boat 

will be limited to 30 participants (refer to the attached participant list). The boat will return to Progresso 

later in the day, acoustic equipment will be removed from boat, and participants will return to the hotel 

by bus. 

Instructor: Frank Reier Knudsen (Simrad-Kongsberg Maritime) with support staff.

Day 1 (November 4, 2017) Agenda: Training with scientific echosounder operations.

08:00 Depart at 8:00 AM by bus from Hyatt Regency Merida Hotel in Merida. The bus trip to 

the coastal town of Progresso will likely take about 50 minutes.

09:00-09:30 Welcome, introductions and review terms of reference. 

09:30-10:00 Embark aboard the boat Isla Mujeres and conduct safety drills.

10:00-10:30 Principles of Underwater Sound Propagation and Acoustic Technology

• Theory of sound in water (basic sonar equation and terminology)

• Environment, sound speed, spreading absorption

• Transmit-pulse characteristics

10:30-11:00 Overview of Scientific Echosounder Systems

• Echosounder types: single frequency, multi-frequency, broadband and wideband

• Transducer types: single-beam and split-beam

• Echosounder and software configurations

11:00-12:00 Echosounder and software configurations (hands-on exercises)

12:00-13:00 Lunch aboard the boat Isla Mujeres

13:00-14:00 Calibration procedures
149

14:00-16:00 Data-acquisition software (hands-on exercises)

16:00-17:00 Acoustic systems for various platforms

17:00-17:30 Disembark boat, remove scientific equipment from boat and pack for transportation

17:30-19:30 Dinner at restaurant in Progresso

19:30-20:30 Depart by bus from Progreso, return to Merida, and unpack equipment

Day 2: Workshop Agenda

Echosounder data processing using Echoview software

Terms of Reference: Participants will receive an overview of acoustic data processing and analysis 

methods using Echoview software, in a classroom setting. This introduction to Echoview software will 

focus on processing data from scientific echosounders with examples from surveys in coastal and reef 

environments. It will not include training relevant to other acoustic systems, such as multibeam systems, 

that are covered in a more comprehensive 5-day Echoview training course. The instructor will utilize 

presentations and Echoview demonstrations with “follow-me” exercises to provide hands-on training. 

Printed Echoview Learner Guides will be provided, along with electronic training materials and 

temporary Echoview license dongles. Trainees will need to bring a laptop computer running Windows.

Date and Location: November 5, 2017 at the Hyatt Regency Merida Hotel (Room: Regency III).

Instructor: Toby Jarvis (Echoview) with support staff.

Day 2 (November 5, 2017) Agenda: Echosounder data processing using Echoview software.

08:00-08:30 Software installation and introductions

08:30-10:00 Overview

• Echosounder data-processing workflow

• Echoview features

Preliminary data exploration

10:00-10:30 Break

10:30-12:00 Apply calibration settings

Data cleaning: “bad data” regions

Data cleaning: noise-removal algorithms

12:00-13:30 Lunch

13:30-15:00 Seafloor detection: lines and “line pick” algorithms

Point scatterers: single-echo detection (narrowband and wideband) and 

tracking

Volume scatterers: detection of fish aggregations

15:00-15:30 Break

15:30-17:00 Multifrequency classification: narrowband and wideband

Seafloor classification

Density estimation (number and biomass)

• Echo integration: by cells and by regions

• Echo counting

17:00 Adjourn
150

Day 3: Workshop Agenda 

Best practices for acoustic surveys in reef fish ecosystems

Terms of Reference: Presentations will cover acoustic technologies and methods to improve reef-fish 

surveys, such as recent advances in equipment, operational objectives, survey design, and data analysis. 

Case studies will examine the challenges and lessons learned while conducting acoustic surveys in the 

Gulf of Mexico and Caribbean region. Participation from all attendees, including presenters and 

participants from the training workshop, stock assessors, managers, and stakeholders, will provide 

diverse perspectives to establish priorities and best practices for conducting acoustic surveys in reef fish 

ecosystems. A report of this working group will be used to enhance the scientific capacity in this region

and direct future workshops.

Date and Location: November 9, 2017 at the Hyatt Regency Merida Hotel (Room: Regency III). 

Moderators: William Michaels, Chris Taylor and Kevin Boswell

Day 3 (November 9, 2017) Agenda: Best practices for conducting acoustic surveys in reef fish 

ecosystems. 

08:30-08:40 Introductions and review of terms of reference

08:40-08:55 Bill Michaels – Strategies and priorities for building scientific capacity with acoustic 

technologies in the Gulf of Mexico and Caribbean region

08:55-09:15 Lars Nonboe Andersen – Overview of acoustic systems and recent advances in acoustic 

technologies for improving reef fish ecosystem surveys

09:15-09:45 Break-out session to prioritize management objectives and operational challenges

09:45-10:00 Summary from breakout session groups

10:00-10:30 Break

10:30-10:45 Frank Reier Knudsen - Overview of lessons learned from Day 1 training, and 

operational considerations for conducting reef fish surveys.

10:45-11:00 Toby Jarvis – Overview of lessons learned for Echoview post-processing and data 

analysis for conducting reef fish surveys.

11:00-11:20 Jorge Paramo - Case study on acoustic surveys for stock assessments.

11:20-11:40 Dave Demer – Measurement considerations for surveys of reef-fish and their seabed 

habitats.

11:40-12:00 Ben Binder – Integrating acoustic surveys and fish spawning aggregation research: 

challenges and lessons learned.

12:00-13:30 Lunch

13:30-13:50 Patrick Sullivan - Statistical considerations for acoustic survey designs and abundance 

estimation; optimizing data collection for reef fish management.

13:50-14:10 Charles Thompson – Integrated acoustic-optic sampling strategies for improving reef 

fish ecosystem surveys.

14:10-14:30 Chris Taylor - Fishery acoustic-derived metrics and indicators for assessing and 

monitoring performance of Marine Protected Areas (MPAs) in coral reef ecosystems.

14:30-14:50 Laurent Chérubin - Acoustic detection and characterization of fish spawning 

aggregations using a novel, persistent presence robotic approach.

14:50-15:00 Closing discussions and recommendations on case studies.
151

15:00-15:30 Break

15:30-16:00 Breakout sessions on solutions to challenges with acoustic operations on reef fish 

habitats, and recommendations for addressing management objectives.

16:00-16:30 Summary from breakout session groups

16:30-16:50 Concluding discussions on best practice recommendations for acoustic surveys in reef 

fish ecosystems.

16:50-17:00 Writing assignments for the technical report.

17:00 Adjourn.

 
152

16.3 APPENDIX C. PARTICIPANT LISTS 

Participant list for the GCFI-SEAC Workshop on Acoustic Technologies to Improve Reef Fish Ecosystem 

Surveys. Day 1 of the workshop provided training with scientific echosounders, Day 2 provided software 

training, and Day 3 was devoted to establishing best practice guidance for conducting acoustic 

operations to improve reef fish ecosystem surveys. The asterisk (*) indicates which participants served 

as instructors or steering committee members.

Last name First name Affiliation and email Attended

Acosta* Alejandro Florida Fish and Wildlife Conservation Commission, Fish 

Wildlife Research Institute, 2796 Overseas Hwy, Marathon, 

Florida 33050, USA

Email: Alejandro.Acosta@myfwc.com 

Day 3

Algrøy* Tonny Simrad, Kongsberg Maritime, Strandpromenaden 50. NO3183, Horten, Norway

Email: Tonny.Algroy@simrad.com

Day 1,2,3

Andersen* Lars 

Nonboe

Simrad, Kongsberg Maritime, Strandpromenaden 50. NO3183, Horten, Norway

Email: Lars.Nonboe.Andersen@simrad.com

Day 1,2

Arvizu 

Torres

Ximena 

Alejandra

Blue Core A.C., 77710 Playa de Carman, México

Email: e4.ximena@gmail.com

Day 1,2,3

Bassos-Hull Kim Mote Marine Laboratory, 1600 Ken Thompson Pkwy, 

Sarasota, Florida 34236, USA

Email: kbhull@mote.org

Day 3

Barona Tanya Toledo Institute for Development and Environment, Southern 

Hwy, Hopeville, Belize

Email: tbarona@tidebelize.org 

Email: tanyabarona@gmail.com

Day 3

Beyea Rebekah 

'Taylor'

School of Fisheries, Aquaculture and Aquatic Sciences, 

Auburn University, 8300 State Hwy 104, Fairhope, Alabama 

36849, USA

Email: rtbeyea88@gmail.com

Day 3

Biggs Christopher Marine Science Institute, University of Texas at Austin, 750 

Channel View Dr., Port Aransas, Texas 78373, USA

Email: cbiggs@utexas.edu

Day 1,2,3

Binder Benjamin Florida International University, 3000 NE 151 Street, North 

Miami, Florida 33181, USA

Email: bbind002@fiu.edu

Day 1,2,3

Boswell* Kevin Florida International University, 3000 NE 151 Street, North 

Miami, Florida 33181, USA

Email: Kevin.Boswell@fiu.edu

Day 1,2,3

Butkowski Drew Central Caribbean Marine Institute (CCMI), N Coast Rd. E, 

Little Cayman, Cayman Islands

Email: dbutkowski@reefresearch.org

Day 3

Candelmo Alli Central Caribbean Marine Institute (CCMI), N Coast Rd. E, 

Little Cayman, Cayman Islands

Email: acandelmo@reefresearch.org

Day 3
153

Castro 

Gonzalez

Erick 

Richard

La Gobernadora del Departmento de San Andrés, 

Departmento Archpiélogo de San Andrés, Providencia y Santa 

Catalina, Km 26 Via San Luis, San Andres Isla, Colombia

Email: pescastro@gmail.com

Day 3

Chérubin Laurent Florida Atlantic University, Harbor Branch Oceanographic 

Institute, 5600 N US Hwy 1, Fort Pierce, Florida 34946, USA

Email: lcherubin@fau.edu

Day 1,2,3

Coniotty* Jeff Simrad, Kongsberg Maritime,19210 33rd Ave., Lynnwood, 

Washington 98036, USA

Email: Jeff.Condiotty@km.kongsberg.com

Day 1,2,3

Coronado 

Castro

Eva Centro de Investigación y de Estudios del Instituto Politécnico 

Nacional (CINVESTAV), Unidad Mérida, Antigua Carretera a 

Progreso km 6, Cordemex, Loma Bonita Xcumpich, CP. 97310 

Mérida, México

Email: ev.coronado@gmail.com

Day 3

Cruz 

Marrero

Wilmelie Marine Estuarine Environmental Sciences Program, University 

of Maryland Eastern Shore, 11868 College Backbone Rd, 

Princess Anne, Maryland 21853, USA

Email: wcruz-marrero@umes.edu

Day 3

Cuevas 

Flores

Eduardo 

Amir

Departmento de Manejo de Zonas Costeras, Universidad 

Nacional Autónoma de México (UNAM), Av. Universidad 

3000, Copilco Universidad, Coyoacán, Cuidad de México, 

Distrito Federal 04510, México

Email: amir.cuevas@gmail.com

Day 1,2,3

Demer* David NOAA Fisheries, Southwest Fisheries Science Center, 8901 La 

Jolla Shores Dr., La Jolla, California 92037, USA

Email: David.Demer@noaa.gov

Day 1,2,3

Erisman Brad Marine Science Institute, University of Texas at Ausitn, 750 

Channel View Dr., Port Aransas, Texas 78373, USA

Email: berisman@utexas.edu

Day 1,2,3

Euán Avila Jorge Centro de Investigación y de Estudios Avanzados del Instituto 

Politécnico Nacional (CINVESTAV), Departamento de 

Recursos del Mar, Km 6 Antigua carretera a Progreso, C.P. 

97310, Mérida, México

Email: Euan.Jorge@gmail.com 

Email: Jorge.Euan@cinvestav.mx

Day 3

Figueroa 

Albornoz*

Leonardo 

Jose

Simrad, Kongsberg Maritime, La Marigalente 311-31, Boca del 

Río 94298, Veracruz, México

Email: Leonardo.Figueroa@kongsberg.com

Day 1,2,3

Galvez 

Argueta

Guillermo 

Antonio

Fundación para el Ecodesarrollo y la Conservación 

(FundaEco), 25 Calle 2-39 zona 1 01001, Cuidad de 

Guatemala, Guatemala

Email: g.galvez@fundaeco.org.gt

Day 3

Garcia 

Gisholt

Eduardo 

Amir

Centro de Investigación y de Estudios Avanzados del Instituto 

Politécnico Nacional (CINVESTAV), San Pedro Zacatenco, 

07360 Cuidad de México, CDMX, México

Email: egisholt@yahoo.com

Day 3
154

González 

Máynez

Violeta E. Instituto Nacional Pesca y Acuacultura (INAPESCA), Programa 

Pelágicos Menores del golfo de California, Calle 20 No. Sur 

C.P. 85400, Guaymas, Sonora, México

Email: bios.maynez@gmail.com

Email: vmaynez@pg.cibnor.mx

Day 1,2,3

Hamilton Jill Duke University, 2127 Campus Dr., Durham, North Carolina 

27708, USA

Email: Jill.Hamilton@duke.edu

Day 3

Hancock Christopher Simrad, Kongsberg Maritime, Texas, USA

Email: Chris.Hancock@km.kongsberg.com

Day 1

Heyman Will LGL Ecological Research Associates Inc., 4103 Texas Ave. 211, 

Bryan, Texas 77802, USA

Email: HeymanWill@yahoo.com

Day 2,3

Jarvis* Toby Echoview Software Ltd., 1c/38 Montpelier Retreat, Battery 

Point, Tasmania 7004, Australia

Email: Toby.Jarvis@echoview.com

Day 1,2,3

Johnson Bradley Department of Environment, Cayman Islands Environmental 

Centre, 580 N Sound Rd., George Town, Grand Caymans, 

Cayman Islands

Email: Bradley.Johnson@gov.ky

Day 3

Johnson Donald Gulf Coast Research Laboratory, University of Southern 

Mississippi, 703 E Beach Dr., Ocean Springs, Mississippi 

39564, USA

Email: Donald.R.Johnson@usm.edu

Day 3

Keller Elizabeth Louisiana State University, Baton Rouge, Louisiana 70803, 

USA

Email: ekell29@lsu.edu

Day 3

Knudsen* Frank Reier Simrad, Kongsberg Maritime, Strandpromenaden 50. NO3183, Horten, Norway

Email: Frank.Reier.Knudsen@km.kongsberg.com

Day 1,2,3

Landero 

Figueroa

Marcela 

Montserrat

Centre for Marine Science and Technology, Curtin University, 

B301 Hayman Rd., Bentley WA 6102, Australia

Email: m.landerof@postgrad.curtin.edu.au

Email: marcelalandero@hotmail.com

Day 1,2,3

LebourgesDhaussyAnne Institut de Recherche pour le Développement (IRD), LEMAR 

UMR 6539 LEMAR IFREMER/IRD/CNRS/UBO, Technopole 

Brest Iroise, 29280 Plouzané, France

Email: Anne.Lebourges.Dhaussy@ird.fr

Day 3

Lysenko Nina Recursos Costeros y Marinos, Carr. Duarte Km 6V2, Santos 

Dominingo, D.N., Dominican Republic

Email: nina.lysenko@ambiente.gob.do

Day 3

Mayorga 

Martinez

Melissa Instituto de Ciencias Marinas y Pesquerías, Universidad 

Veracruzana, Miguel Hidalgo 607, Río Jamapa, 94290 Boca del 

Río, Veracruz 91000, México

Email: mmayorga0104@gmail.com

Day 1,2,3
155

Michaels* William L. NOAA Fisheries, Office of Science and Technology, Advanced 

Sampling Technology Program, 1315 E West Hwy, Silver 

Spring, Maryland 20910, USA

Email: William.Michaels@noaa.gov

Day 1,2,3

Muhang Celia Toledo Institute for Development and Environment, Southern 

Hwy, Hopeville, Belize

Email: cmahung@tidebelize.org

Day 3

Nealson Patrick HIT-Vemco USA Inc., 711 NE Northlake Way, Seattle, 

Washington 98105, USA

Email: pnealson@htisonar.com

Day 3

Paramo 

Granados

Jorge 

Enrique

University of Magdalena, Carrera 32 # 22-08, Santa Marta, 

Magdalena, Colombia

Email: jparamo@unimagdalena.edu.co

Day 1,2,3

Peraza 

López

Enrique 

Eduardo

Instituto Tecnológico de Mérida y Jefe de Laboratorio de 

Aprovechamiento de Recursos Marinos, Calle 10, Plan de 

Ayala, 97118 Mérida, México

Email: E_Peraza_L@hotmail.com

Day 1,2,3

Pyc Cynthia JASCO Applied Sciences Ltd., 202-32 Troop Ave., Dartmouth, 

Nova Scotia B3B 1Z1, Canada

Email: Cynthia.Pyc@jasco.com

Day 3

Ramirez 

Yela

Silja 

Morgana

Fundación para el Ecodesarrollo y la Conservación 

(FundaEco), 25 Calle 2-39 zona 1 01001, Cuidad de 

Guatemala, Guatemala

Email: s.ramirez@fundaeco.org.gt

Day 3

Rodriguez 

Jimenez

Alfredo INVEMAR Programa Valoración y Aprovechamiento de 

Recursos Marinos - VAR, Calle 25 No. 2-55, Playa 

Salguero,Santa Marta D.T.C.H., Colombia

Email: Alfredo.Rodriguez@invemar.org.co

Day 1,2

Rodríguez 

Medina 

Karla Centro de Investigación y de Estudios del Instituto Politécnico 

Nacional (CINVESTAV), Instituo de Ecologia, A.C., Carretera 

Antigua a Coatepec 351, El Haya, 91070 Xalapa, Veracruz, 

México

Email: karlardriguez@gmail.com

Day 3

Rodríguez 

Gil

Luis 

Alfonso

Instituto Tecnológico de Mérida y Jefe de Laboratorio de 

Aprovechamiento de Recursos Marinos, Calle 10, Plan de 

Ayala, 97118 Mérida, México

Email: luis_rdzgil@hotmail.com

Day 1,2,3

Rowell Timothy Scripps Institution of Oceanography, University of California 

(UCSD), San Diego, California, 92093, USA

Email: trowell@ucsd.edu

Day 1,2,3

Salas 

Márquez

Silva Centro de Investigación y de Estudios Avanzados del Instituto 

Politécnico Nacional (CINVESTAV), Departamento de 

Recursos del Mar, Km 6 Antigua carretera a Progreso, C.P. 

97310, Mérida, México

Email: ssalas@cinvestav.mx

Day 3
156

Sanchez 

Hernandez

Suleyma El Colegio de la Frontera Sur, Departamento de Sistemática y 

Ecología Acuática, Av. Centenario km 5.5, Col. Pacto Obrero, 

77014 Chetumal, Quintana Roo, México

Email: suly.sanz.24@gmail.com

Day 3

Smith Deborah 

'Elizabeth'

University of the Virgin Islands, Kingshill, V.I. 00850, St. Croix, 

U.S. Virgin Islands

Email: elizabethsmith194@gmail.com

Day 1,2,3

Sosa Metri Roberto 

Samuel

Centro de Investigación y de Estudios Avanzados del Instituto 

Politécnico Nacional (CINVESTAV), Cordemex, Loma Bonita 

Xcumpich, 97310 Mérida, México

Email: Roberto.Sosa@cinvestav.mx

Day 3

Stock Brian Scripps Institute of Oceanography, University of California 

(UCSD), San Diego, California, 92093, USA

Email: b1stock@ucsd.edu

Day 3

Sullivan* Patrick Department of Natural Resources, 111B Fernow, Cornell 

University, Ithaca, New York 14850, USA

Email: pjs31@cornell.edu

Day 3

Taylor* Christopher NOAA National Centers for Coastal Ocean Science (NCCOS), 

101 Pivers Island Rd., Beaufort, North Carolina 28516, USA

Email: Chris.Taylor@noaa.gov

Day 1,2,3

Thompson* Charles H. NOAA Fisheries, Southeast Fisheries Science Center, Stennis 

Space Center, Mississppi 39549, USA

Email: Charles.H.Thompson@noaa.gov

Day 1,2,3

Valdez 

Pelayo

Alejandro Instituto Nacional Pesca y Acuacultura (INAPESCA), Programa 

Pelágicos Menores del golfo de California, Calle 20 No. Sur 

C.P. 85400, Guaymas, Sonora, México

Email: avp.valdezpelayo@gmail.com

Day 1,2,3

Vallarta Jonathon JASCO Applied Sciences Ltd., Protasio Tagle #8-1, San Miguel 

Chapultepec, Miguel Hidalgo, 11850 Cuidad de México, 

México

Email: Jonathan.Vallarta@jasco.com

Day 3

Villalobos 

Ortiz

Hector Depto. De Pesquerías y Biología Marina, Instítuto Politécnico 

Nacional - Centro Interdisciplinario de Ciencias Marinas (IPN - 

CICIMAR), Av. Instítuto Politécnico Nacional s/n Col. Playa 

Palo de Santa Rita, La Paz, Baja California Sur., México 23096

Email: hvillalo@ipn.mx

Day 1,2,3

Zayas 

Santiago

Carlos M University of Puerto Rico, Recinto Universitario de Mayaguez 

(UPRM), PR-108, Mayaguez, 00682, Puerto Rico

Email: Carlos.Zayas3@upr.edu

Day 1,2,3
157

16.4 APPENDIX D. RESULTS FROM THE WORKSHOP BREAKOUT SESSIONS 

Before the workshop, participants provided responses to four trigger questions on management 

objectives, operational challenges, and recommended solutions and resource requirements to address 

operational challenges. The responses to these questions were discussed and prioritized in breakout 

sessions during the third day of the workshop, and the results are presented below. 

Table 16-1. Summary of pre-workshop and workshop breakout session results for Question 1: What are your top 

management objectives in reef fish ecosystem management?

Pre-workshop Workshop 

Breakout 

Sessions

Category Question 1: What are your top management 

objectives pertinent to reef fish ecosystems?

1 (n=9) 1 (n=28) Population 

biomass 

estimation

Abundance and biomass estimates (from long-term 

survey operations) [JC,CT,DD]; Population size (taking 

advantage of aggregating behavior) [KB]; Distribution 

and abundance [TJ]

2 (n=21) Resource use 

and 

anthropogenic 

impact

Understand use of resources by fishers and 

ecotourism [KB]; Sustainable fishing [TJ]; Harvest 

culling [TJ]; Monitor fishing pressure and use [CT]; 

Baseline to understand impacts on resources [JC]

3 (n=6) 3 (n=20) Distribution, 

connectivity, 

spatial 

variation

Distribution/migration and connectivity of 

populations with environmental variability [PW]; 

Migratory behavior (connectivity) [DD]; Spatial 

distribution (closed areas, spawning, stock 

identification) [DD]; Temporal variation in distribution 

(diel, seasonal, annual) [DD]; Understand variation of 

bias [KB]; Baseline to understand environmental 

changes in distribution and seasonal patterns [KB];

(n=2) 4 (n=10) Survey 

operational 

efficiencies

Technology supplement to existing methods with 

minimal disruption to assessments [PW]; Do not be 

scared of new technology [KB]; Identification of 

species [JC]; Validation using instruments [JC]; 

Methods for ships of opportunity [JC]

(n=1) 5 (n=6) MPAs Delineation of MPAs [PW]; Conservation (MPAs) 

design performance [TJ]

2 (n=9) 6 (n=5) Spawning 

aggregations

Spawning aggregation location and monitoring 

[JC,CT]; Spatial distribution (closed areas, spawning, 

stock identification) [DD]; Behavior and life history 

patterns [PW]

4 (n=6) 7 (n=4) Essential 

habitat

Habitat classification, use and health [PW]; 

Identification of habitat domain [JC]; Artificial reef 

(decommission of gas/oil rigs) [TJ]

(n=2) 8 (n=2) Ecosystem 

health and 

changes

Ecosystem health and anthropogenic impacts [PW]; 

Trophic rates and ecosystem function [CT]; 

Zooplankton identification and size [CT]

(n=1) FADs Fish Aggregation Devices (FADs) effectiveness [DD]
158

Table 16-2. Summary of pre-workshop and workshop breakout session results for Question 2: What are your top operational 

challenges relevant to your management objectives?

Pre-workshop Workshop 

breakout 

session

Category Question 2: What are your top operational 

challenges relevant to your management objectives?

4 (n=5) 1 (n=19) Lack of 

acoustic 

expertise

Steep learning curve with acoustic methods, sensitive 

instruments, and need for training [PW]; Training and 

empowerment with access to help [KB,TJ]; Lack of 

communication among groups [KB]; Combine 

resources and expertise [CT]

(n=1) 2 (n=14) Funding 

constraints

Costs and funding [PW,JC,TJ]; Access to resources and 

equipment [KB]

3 (n=6) 3 (n=10) Deadzone Depth limitations and deadzone (mostly in shallow 

water, but also deep water) [PW]; Fish near bottom 

difficult to detect with acoustics (deadzone) and video 

[CT,DD]

4 (n=7) Protocols Standard Operating Protocols (SOPs) for data 

acquisition and processing [JC]; Data processing 

bottlenecks [JC]

1 (n=8) 5 (n=6) Species ID Identification of species [DD]; Difficulties with 

validation of acoustic classification, species 

composition and length data [PW]; Aggregations of 

many species and diversity [CT]

6 (n=6) Habitat ID Seabed and habitat classification [DD]

5 (n=5) 7 (n=5) Behaviorial 

and 

environmental 

variability

Effect of fish behavior [DD]; Acoustic measurement 

uncertainty including impact from environmental

variability [PW]

(n=1) 8 (n=5) Equipment Involve stakeholders [JC]; Simplified instrumentation 

and methods for collection by fishers and citizen 

science [PW]; Need acoustic systems that are more 

portable and easier to use [CT]

2 (n=8) 9 (n=4) Spatial 

variation

Spatial uncertainty in abundance estimates and 

habitat use relative to survey design [PW]; 

Understanding connectivity [KB]; Low abundance 

[CT]; Locating target species [TJ]

(n=1) Enforcement Lack of enforcement relevant to harvest regulations 

[KB]

(n=1) Other 

technology

Abundance from passive acoustics and telemetry 

[PW]
159

Table 16-3. Summary of pre-workshop and workshop breakout session results for Question 3: How have you been addressing 

these challenges?

Pre-workshop Workshop 

breakout 

sessions

Category Question 3: How have you been addressing these 

challenges?

3 (n=3) 1 (n=26) Pool of 

expertise

Training and networking to build expertise [PW]; 

Developing networks and collaborations [KB]; Training 

opportunities (internships and partnerships) [KB]; 

Collaborations [DD]

2 (n=8) 2 (n=12) Integrated 

technologies

Integrated sampling (e.g., acoustics and cameras) to 

validation acoustics [PW]; Coupling technologies [DD]; 

Adaptive sampling (prioritize sampling effort, 

observation systems) [JC]; Autonomous and repeated 

observations [DD]; Technology transfer [CT]; 

Underestimate of abundance in acoustic deadzone 

[PW]; Near bottom sampling (e.g., cameras and 

horizontal acoustics) to address deadzone [PW]; 

Deadzone quantification [DD

1 (n=5) Passive 

acoustic 

technology

Use of telemetry and passive acoustics, including fish 

sound library [PW]; Passive acoustics (closer receivers 

to reduce noise) [TJ]; Passive acoustics (receiver range 

for deeper operational capability) [TJ]; Passive acoustics 

(eaten tags by predators) [TJ]; 

(n=1) 3 (n=10) Best practice 

collection 

and postprocessing

Standardize collection of data [JC]; More simplified 

post-processing templates and programming [PW]; 

Automated data processing (sound libraries, 

discrimination analysis) [JC]

(n=1) 4 (n=5) Analytical 

methods

Multifrequency classification with validation [PW]; 

Spectral and statistical classification [DD]; Split-beam 

imaging [DD]; Wideband range resolution [DD]

(n=2) 5 (n=4) Shared 

equipment 

and support

Communicate need to vendors for equipment 

loan/lease [TJ]; FAQs [KB]; Trouble-shooting and 

contacting vendors for equipment issues [TJ]; More 

portable deployment alternatives with simplified 

functionality [PW]; Need portable and low-cost 

echosounders [CT]

6 (n=4) Baseline Address lack of baseline data and literature searches 

[JC]

(n=1) 7 (n=1) Stakeholders Data collection from fishers and citizen science [PW]; 

Talk to fishers for locating targets/areas [TJ]; Need 

cooperative research with commercial/recreational 

stakeholders [CT] Cooperative research [CT]

4 (n=3) Validation Use of catch landings data in area to validate acoustic 

species composition [PW]
160

Table 16-4. Summary of pre-workshop and workshop breakout session results for Question 4: What resources would help 

achieve operational objectives?

Pre-workshop Workshop 

breakout 

sessions

Capacity Question 4: What resources would help achieve 

operational objectives?

1 (n=8) 1 (n=35) Build 

expertise 

through 

training and 

collaborative 

studies

Training/networking to build expertise and 

collaborations [PW]; Training and online help resources 

[TJ]; Internships and assistantships [KB]; Access to 

literature [KB]; Workshops and integrative 

opportunities [KB]; Equipment and training [KB]; 

Training events with actual data collection [CT]; Multidisciplinary training [DD]; Develop network for more 

multinational collaborations [CT]; Email network [CT]

2 (n=7) 2 (n=25) Improve 

equipment 

availability

Access to or update acoustic equipment, including 

multi-frequencies and portable systems [PW];

Equipment lease/loan opportunities; shared pool 

equipment [KB,TJ]; Pool of sharable equipment [CT]; 

Portable low-cost echosounders that are easier to use 

for collaboration [CT]; Equipment and expertise 

availability [DD]

4 (n=4) 3 (n=14) Optimize 

survey design 

and sampling 

efficiencies 

with 

integrated 

technology

Use of complimentary sampling gear (e.g., cameras, 

telemetry) [PW]; Ocean observation system 

(passive/active acoustic) w real-time processing and 

synthesis (JC); Alternative platforms [KB]; Increase 

capacity in remote and under-sampled areas [JC];

Miniature self-contained acoustic-optical sampler [DD];

Develop remote sensing (acoustics) to monitor fishing 

and other impacts [CT]; Stealth and autonomous 

instruments [DD]; Evaluation of time, cost and 

performance [DD]

4 (n=12) Develop longterm 

monitoring/

time series

Shared data pool and meta-analyses [TJ]; Continuous 

time series [JC]; Long-term measurements at fixed 

stations [DD]

3 (n=4) 5 (n=9) Funding 

opportunities

Increase in funding opportunities including through 

partnerships [PW]; Consumer responsible for 

subsidizing the costs [JC]; Regional funds [CT]

6 (n=6) Collaborative 

efforts on 

analysis

Dead-zone reduction [DD]; Data fusion [DD];

Developing new science for new problems [KB]; 

Broadband and wideband experiments to obtain TS 

curves for Caribbean species [PW]

(n=1) 7 (n=3) Boat 

availability

More boat time for survey sampling [PW]; Alternative 

platforms (e.g., fishers) [KB]; Outsourcing vessels of 

opportunities and citizen science [TJ]

8 (n=2) Cooperative 

research with 

fishers

Cooperative research with commercial or recreational 

stakeholders [CT]
161