Acoustic Data from Vessels of Opportunity

MESOPP

Acoustic data from vessels of opportunity

Deliverable Lead: CSIRO Reference: MESOPP-18-0009 Dissemination Level: Public Issue: 1. 0 Date: 2018, Dec. 03

Horizon 2020. Grant agreement No 692173 Public Acoustic data from vessels of opportunity

MESOPP-18-0009 Public V1.0 2018,Dec.03

Chronology Issues

Issue Date Reason for change Author 1.0 21/11/2018 First version of the document K. Haris R. Kloser 23/11/2018 Second version of the document P. Lehodey (comments on first version) 27/11/2018 Third version of the document S. Fielding (comments on first version) 30/11/2018 Fourth version of the document P. Lehodey (comments on second version) 30/11/2018 Finalized issue 1.0 K. Haris R. Kloser

Distribution

Company Means of distribution Names CLS Notification

Citation

This report can be cited as follow:

Haris K., Kloser R. (2018). Acoustic data from vessels of opportunity. Report from the EU-H2020 MESOPP project, MESOPP-18-0009: 39 pp. www.mesopp.eu/documents/

FORM Acoustic data from vessels of opportunity

MESOPP-18-0009 Public V1.0 2018,Dec.03

List of Contents

1. Introduction ...... 1 1.1. Acoustic data to biomass ...... 2 1.2. Ecosystem models ...... 3 1.3. This report...... 4

2. Processed acoustic data portals ...... 4

3. Processed acoustic data description and quality flags ...... 5

4. Overview of bioacoustic metrics ...... 9

5. Effects of data processing routines and quality checking methods ...... 12 5.1. Calibration...... 12 5.2. Effect of motion correction ...... 13 5.3. Data quality and effect of filters ...... 14 5.4. Effect of secondary corrections ...... 17

6. Limitation of single frequency data ...... 21

7. Existing resolution of the NetCDF and limitations ...... 24

8. Conclusions ...... 25

9. References ...... 26

Appendix A - Importance of calibration parameters ...... 29

Appendix B - Secondary corrections for sound speed and absorption ...... 30

Appendix C - Matlab function for visualisation ...... 32

FORM Acoustic data from vessels of opportunity

MESOPP-18-0009 Public V1.0 2018,Dec.03

List of tables and figures

List of tables:

Table 1. Description of variables present in a NetCDF file...... 8 Table 2. A brief overview of bioacoustic metrics used in the literature...... 11

List of figures:

Figure 1. Example of how bioacoustics data is collected by transmitting a pulse of sound in the water that reflects off the species to produce an echogram, www.imos.org.au...... 1 Figure 2. Visualisation of 38 kHz acoustic data (푆푣) collected by FV Rehua during a 4 day transit from New Zealand to Australia in August 2010. Image courtesy: Tim Ryan, CSIRO...... 2 Figure 3. Flowchart showing how acoustic data is normally converted to fish biomass...... 3 Figure 4. Map showing spatial coverage of processed bioacoustic transects as of 13 November 2018. (a) MESOPP project. (b) IMOS...... 5 Figure 5. Generic overview of the data processing sequence in the context of data variables present in a NetCDF file. Reproduced from Ryan et al. (2015)...... 6 Figure 6. Resolution of a NetCDF file containing processed acoustic data...... 6 Figure 7. Organization of variables present in a NetCDF file...... 7 Figure 8. A broad classification of DSL studies...... 9 Figure 9. Example of the fishing vessel (Antarctic Discovery) 38 kHz acoustic data covering the Pacific Ocean. The Longhurst oceanic biogeographical provinces are superimposed as white lines. The center part of the south Pacific highlight oligotrophic regions with low predicted productivity (blue), which is observed in general by the low mesopelagic (400–800 m depth) backscatter strength (small magenta circles) (Haris and Kloser, 2017)...... 10 Figure 10. Calibrated 퐺0 (blue) and 푆푎 corr (red) values for (a) FV Rehua and (b) Austral Leader between 2005–2015. Reproduced from Downie et al. (2018)...... 12 Figure 11. Effect of motion correction on raw 38 kHz 푆푣 data collected by RV Southern Surveyor on 15 October 2013. Note the strong non-linear range dependent effect...... 13 Figure 12. Voyage track of the data presented in Figure 13. Map generated using Ocean Data View (Schlitzer, 2018)...... 14 Figure 13. Combined effects of data processing filters on raw 38 kHz 푆푣 data collected by FV San Tongariro. (a) Calibrated raw data. (b) Final processed data including secondary corrections described in the following section...... 15 Figure 14. Data quality flags associated with the data presented in Figure 13...... 16 Figure 15. Voyage track of the data presented in Figure 16...... 17 Figure 16. Examples of calculated sound speed and absorption coefficient values along the 38 kHz transect from SW Indian Ocean to Mauritius. The absorption coefficients were calculated using two equations as shown in the figure...... 18

FORM Acoustic data from vessels of opportunity

MESOPP-18-0009 Public V1.0 2018,Dec.03

Figure 17. Boxplots of calculated sound speed and absorption coefficient values presented in Figure 16...... 19 Figure 18. The difference in 38 kHz 푆푣 before and after secondary corrections. Related percentage corrections are shown in the boxplots. Note the increasing correction with range...... 20 Figure 19. Voyage track of the data presented in Figure 20. Map generated using Ocean Data View (Schlitzer, 2018)...... 21 Figure 20. Multi-frequency echograms highlighting different scattering layers and functional groups...... 22 Figure 21. Example of multi-frequency metrics (green = 120 kHz; black = 70 kHz; red = 38 kHz; blue = 18 kHz). Average backscatter data in epipelagic (20–200 m), upper mesopelagic (200–400 m), and lower mesopelagic (400–800 m) zone...... 23 Figure 22. Sampling range limitation of high-frequency (120 kHz) data...... 23 Figure 23. Example echogram showing fish schools at a fine resolution. The grid lines represent existing resolution of the NetCDF file...... 24

FORM Acoustic data from vessels of opportunity

MESOPP-18-0009 Public V1.0 2018,Dec.03

Overview

The underlying concept of MESOPP is the creation of a collaborative network and associated e- infrastructure (marine ecosystem information system) between European and Australian research teams/institutes sharing similar interests in the Southern Ocean and Antarctica, its marine ecosystem functioning and the rapid changes occurring with the climate warming and the exploitation of marine resources.

In the past 30 years, facing global knowledge issues, lacking data, addressing huge modelling challenges, we observed the successful world organisation of meteorology. These past 15 years, Europe has kick-started and demonstrated similar successful structuring of the operational oceanography fostered by the Copernicus initiative (http://marine.copernicus.eu/), today worldwide used and recognised, fully anticipated and integrated in Global Ocean Observing System (GOOS, http://www.goosocean.org/), Integration ocean observation system (IOSS, https://ioos.noaa.gov/), Southern Ocean Observing System (SOOS, http://www.soos.aq/), the international global ocean data assimilation experiment (GODAE, https://www.godae-oceanview.org/), and integrated marine biogeochemistry and ecosystem research (IMBER).

A major R&D strategic challenge is to connect the marine ecosystem community across the fields of meteorology, climate, oceanography, and biology. Lack of data, development of accurate high-end models, global coverage and need for exchange are issues that need to be overcome.

The objective of the MESOPP project is to meet this challenge and is threefold:

1. Make an inventory of science challenges, stakes, and existing policies and develop tools to federate and structure the community;

2. Start to organise the related marine ecosystem community between EU and Australia through two implementation actions:

 the specification and prototyping of an international e-infrastructure for marine ecosystems data  the development of best practices, R&D governance in relation with existing policy instruments

3. Propose a R&D roadmap to support a large international cooperation on marine ecosystems based on an e-infrastructure with additional countries such as USA, New Zealand, Canada (in the Frame of the Galway statement), Brazil and all active countries already involved in large organisations such as IMBER, CCAMLR or IMOS.

While MESOPP will focus on the enhancement of collaborations by eliminating various obstacles in establishing a common methodology and a connected network of databases of acoustic data for the estimation of micronekton biomass and validation of models, it will also contribute to a better predictive understanding of the SO based on furthering the knowledge base on key functional groups of micronekton and processes which determine ecosystem dynamics from physics to large oceanic predators. This first project and associated implementation (science network and specification of an infrastructure) should constitute the nucleus of a larger international program of acoustic monitoring and micronekton modelling to be integrated into the general framework of ocean observation following a roadmap that will be prepared during the project.

FORM Acoustic data from vessels of opportunity

MESOPP-18-0009 Public V1.0 2018,Dec.03

Terms, Symbols, and Units

The terminology used in this report mostly follows Maclennan et al. (2002) and Demer et al. (2015). All symbols signifying variables are italicized. Any symbol for a variable (푥) that is not logarithmically transformed is in lower case.

Any symbol for a logarithmically transformed variable, e.g. 푋 = 10 log10(푥⁄푥푟푒푓), with units of decibels referred to 푥푟푒푓 (dB re 푥푟푒푓) is capitalized.

Term Symbol Unit Description Range 푟 m The direct-path distance between objects, e.g. the transducer and the target.

On-axis gain 퐺0 dB re 1 10 log10(푔0), where 푔0 is the gain on the centre of transducer beam axis (0, 0).

Equivalent two-way beam 훹 dB re 1 sr 10 log10(휓), where 휓 is the solid angle angle at the apex of the ideal conical beam which would produce the same echo-integral as the real transducer when the targets are randomly distributed in space.

푠푎 correction factor 푆푎 corr dB re 1 Difference in energy of the nominal and actual received pulses.

2 Backscattering cross- 휎푏푠 m The area of an acoustic target section effectively backscattering acoustic power.

2 Target strength 푇푆 dB re 1 m 10 log10(휎푏푠).

2 -3 Volume backscattering 푠푣 m m The sum of 휎푏푠 per unit of water coefficient volume.

2 -3 Volume backscattering 푆푣 dB re 1 m m 10 log10(푠푣). strength

2 -2 Area backscattering 푠푎 m m The integral of 푠푣 over a range of coefficient depths.

2 -2 Area backscattering 푆푎 dB re 1 m m 10 log10(푠푎). strength

FORM Acoustic data from vessels of opportunity

MESOPP-18-0009 Public V1.0 2018,Dec.03

1. Introduction

Vessels of opportunity programs collect underway active bioacoustic data while transiting ocean basins (Kloser et al., 2009). These acoustic snapshots provide an indicator of abundance, distribution, and behaviour of mesopelagic communities (macro-zooplankton and micronekton communities ~2 to 20 cm in length including small fish, crustaceans, squids and gelatinous organisms) (Figure 1). Mesopelagic communities are mid-water prey at 200–1000 m depth and based on their biomass, make the largest daily animal movement on earth. They transfer energy from primary producers to higher predators, and actively transport carbon from the surface waters to the deep-ocean by linking epipelagic and deep-water food chains. The mesopelagic communities living in the twilight zone of the ocean have been identified as one of the least investigated components of open ocean ecosystems (St. John et al., 2016). Knowledge of the structure and function of biota in the deep ocean is necessary to assist in managing human impacts, and especially to predict the behaviour of oceans in a changing climate using ecosystem models (Lehodey et al., 2008; Fulton et al., 2011; MESOPP-17-0002, 2017). Ecosystem models need observations on the distribution and abundance of mesopelagic functional groups at shelf and basin scale to validate predictions. Vessels of opportunity programs are improving this situation with several major international efforts to sample the world's oceans. The mapping will complement established observing systems measuring physical, chemical, and biological environment of the ocean. The main goal of vessels of opportunity programs is to develop a reliable predictive capacity by combining observation and modelling for studying ecosystem dynamics at short and long-term scales. This will help to advance scientific knowledge of marine food chains and manage marine ecosystems sustainably.

Figure 1. Example of how bioacoustics data is collected by transmitting a pulse of sound in the water that reflects off the species to produce an echogram, www.imos.org.au.

FORM Acoustic data from vessels of opportunity

MESOPP-18-0009 Public V1.0 2018,Dec.03

1.1. Acoustic data to biomass

Bioacoustic data sets are useful to estimate the distribution and biomass of mesopelagic communities at regional and global scale, using a combination of existing or future developments (Handegard et al., 2013). Processed acoustic backscatter maps (Figure 2) reveal large-scale spatio-temporal patterns in pelagic sound scattering layers and diel vertical migration, a proxy for open ocean biomass. However, acoustic estimation of biomass poses several challenges mainly due to size distribution of organisms, species composition, and frequency-dependent acoustic backscattering (e.g. resonance scattering) (Davison et al., 2015).

Australia New Zealand

Figure 2. Visualisation of 38 kHz acoustic data (푆푣) collected by FV Rehua during a 4 day transit from New Zealand to Australia in August 2010. Image courtesy: Tim Ryan, CSIRO.

2 -3 Converting bioacoustic data i.e. volume backscattering coefficient 푠푣 (m m ) [linear form of volume 2 -3 backscattering strength 푆푣 (dB re 1 m m ) in Figure 2] to biomass is a multi-step procedure (Figure 3). 푠푣 is assumed to be the linear sum of backscatter from individual targets within the sampling volume. In suitable circumstances it is proportional to the density of organisms, and the primary measurement for estimating biomass from acoustics. 푠푣 values are vertically integrated over a measurement range 2 -2 (푟1 to 푟2) to calculate area backscattering coefficient 푠푎 (m m ) along the vessel track. Scatterer areal density (number m-2) i.e. the number of organisms (e.g. fish) within the measurement range is 2 calculated by dividing 푠푎 by the backscattering cross-section 휎푏푠 (m ) of a representative single fish. Biomass of fish (kg m-2) can be estimated by multiplying this scatterer areal density by the weight 푊 (kg) of a single fish. This requires separation of acoustic data by species composition, location, and 휎푏푠 distribution. Mean weight can be derived from observed weights (using nets) or if necessary from a length to weight regression. Similarly, 휎푏푠 are obtained from in situ measurements and/or 휎푏푠 to length regressions. Biomass calculations from these equations will be biased if weight and target strength 푇푆 2 [10 log10(휎푏푠), dB re 1 m ] of the organism are uncertain (assuming accurate calibration). For that reason, in situ and/or modelled 푇푆 must be calculated with the goal of obtaining a representative distribution. The 푇푆 of an organism is primarily dependent on its morphology and composition with high backscatter from organisms with gas inclusions. Of note is the effect of resonance scattering from fish with gas-filled swimbladders and siphonophores with gas-filled pneumatophores (Ona, 1999; Kloser et al., 2016). For fish the 푇푆 is also strongly affected by its orientation relative to the sound beam (often referred to as aspect). The aspect depends on behaviour such as vertical migration and

FORM Acoustic data from vessels of opportunity

MESOPP-18-0009 Public V1.0 2018,Dec.03

swimming. In addition, pressure changes during vertical migration can affect 푇푆. Given these uncertainties, estimates of global mesopelagic fish biomass vary significantly between 1 and 20 Gt (Proud et al., 2018b).

echogram

Biological scattering Range

Seafloor

(m2 m-3) = Volume backscattering coefficient

(m2 m-2) = Area backscattering coefficient =

Scatterer areal density (number m-2) =

Biomass (kg m-2) =

Figure 3. Flowchart showing how acoustic data is normally converted to fish biomass.

1.2. Ecosystem models

Ecosystem models are important tools for providing a better understanding of ecosystem function (MESOPP-17-0002, 2017). They can be used to bridge the gap in mesopelagic biomass estimation by predicting the biomass of different functional groups for a given area, depth layer and time period. These groupings are mainly based on size, taxonomy, depth or ecosystem function. Bioacoustic data sets can be potentially used to complement the mesopelagic component of ecosystem models (Lehodey et al., 2014), but require an understanding of data processing routines and dominant sources of uncertainties in the data.

FORM Acoustic data from vessels of opportunity

MESOPP-18-0009 Public V1.0 2018,Dec.03

1.3. This report

A previous deliverable in the MESOPP project (MESOPP-18-0003, 2018) explained the acoustic data processing routines and quality checking methods. The report detailed processing steps required to convert raw acoustic data to quality-controlled calibrated acoustic data for use in the MESOPP project. The main objectives of this report are to provide ecosystem modellers with a brief overview of: o Variables contained in the processed acoustic data to assist interpretation of bioacoustic data. o Bioacoustic metrics used in literature to study mesopelagic zone. o Effects of data processing routines on the metrics derived for the use in ecosystem models. o Potential limitations of the data for uptake in ecosystem models.

2. Processed acoustic data portals

Calibrated (Demer et al., 2015) and quality-controlled acoustic data sets are archived online (Figure 4) as Network Common Data Form (NetCDF) files. These files follow standardised naming conventions and metadata content defined by the Climate and Forecast (CF), and International Council for the Exploration of the Sea (ICES) (MESOPP-17-0001, 2017; Haris et al., 2018a). The file structure is designed according to well-defined rules for facilitating large-scale analyses and uptake in ecosystem models. As a part of the MESOPP project, processed acoustic data have been made available through the MESOPP Central Information System (CIS) at www.mesopp.eu/data/catalogue/. British Antarctic Survey (BAS), Commonwealth Scientific and Industrial Research Organisation (CSIRO) and French institutes Centre National de la Recherche Scientifique (CNRS)/ Institut de Recherche pour le Développement (IRD)/ Institut français de recherche pour l'exploitation de la mer (IFREMER) provided the reference data. These data sets were post-processed using a combination of in-house tools and commercially available software. The MESOPP reference datasets comprise of 67 transects collected from 6 vessels (both research and fishing), covering the South Atlantic, South Indian, and South Pacific Ocean sectors of the Southern Ocean (Figure 4). As a part of the existing industry collaboration, the Integrated Marine Observing System (IMOS) Bio- Acoustic Ships of Opportunity (BASOOP) sub-facility has been delivering processed water column acoustic data (Figure 4). Since 2010, ~ 411,036 km of data from 18 vessels were processed and made available to the public at https://portal.aodn.org.au/. 70% of total data holdings were collected by 12 commercial fishing vessels. The volume of processed bioacoustic data archived under the vessels of opportunity program is expanding with an improved spatial and temporal coverage (Figure 4, as of 13 November 2018). The majority of archived data are single-frequency (38 kHz) echosounder observations, but also include multi-frequency (18, 70, 120, and 200 kHz) data, highlighting different scattering layers and functional groups. The 18 and 38 kHz data provide an observational range of > 1000 m and cover the deep scattering layer (DSL) and diel vertical migration, key features for validating mesopelagic component of ecosystem models. Both MESOPP and IMOS acoustic data give wide spatial and temporal coverage of scattering layers in the Southern Ocean for use in the MESOPP project. Key uses of the data would be to interpret scattering layers into biological units (such as dominant functional groups, biomass, and size) using ecological and acoustic observational models (MESOPP-18-0006, 2018).

FORM Acoustic data from vessels of opportunity

MESOPP-18-0009 Public V1.0 2018,Dec.03

(a) MESOPP

(b) IMOS

Figure 4. Map showing spatial coverage of processed bioacoustic transects as of 13 November 2018. (a) MESOPP project. (b) IMOS.

3. Processed acoustic data description and quality flags

Acoustic data quality from different vessels can vary significantly due to noise and/or signal attenuation. The ‘signal’, as noted by Simmonds and MacLennan (2005), is the component of the echosounder measurement corresponding to transmitted sound backscattered onto the transducer surface and ‘noise’ can be defined as all other contributions to the acoustic energy received (De Robertis and Higginbottom, 2007). The raw echosounder data collected under vessels of opportunity programs were post-processed to remove noise and improve data quality using a sequence of data processing filters (Ryan et al., 2015; MESOPP-18-0003, 2018). The flowchart below (Figure 5) provides a generic overview of the processing sequence in the context of data variables present in a NetCDF file.

FORM Acoustic data from vessels of opportunity

MESOPP-18-0009 Public V1.0 2018,Dec.03

Calibrated raw data •Sv_unfilt

Motion correction and •uncorrected_Sv filtering

Secondary corrections for absorption and sound •abs_corrected_sv speed variation

Processed data •Sv

Figure 5. Generic overview of the data processing sequence in the context of data variables present in a NetCDF file. Reproduced from Ryan et al. (2015).

The processed data for each transect are stored in echo-integration cells with a resolution of 1 km in distance and 10 m in depth (Figure 6). This output resolution was chosen as a compromise between large data volume and having a biologically meaningful resolution at ocean-basin scale. The resolution of acoustic data is much higher than the scale typically output by regional-scale ecosystem models (MESOPP-18-0001, 2018). To scale these outputs to the same spatial and temporal resolution, processed bioacoustic data can be integrated over desired depth and/or distance ranges. The NetCDF files may also contain quality flags, derived metrics, and auxiliary data variables to assist interpretation of bioacoustic data (Figure 7). A detailed description of each variable that may be present in a NetCDF file is explained in Table 1.

Distance

풔풗 10 m 1000 m

Echo-integration cell

Depth

Figure 6. Resolution of a NetCDF file containing processed acoustic data.

FORM Acoustic data from vessels of opportunity

MESOPP-18-0009 Public V1.0 2018,Dec.03

NetCDF

Dimensions Global attributes

Variables

Coordinate variables & Primary data variables & Auxiliary data variable & attributes attributes attributes

LATITUDE Sv_unfilt day

LONGITUDE uncorrected_Sv CARS_temperature

DEPTH uncorrected_Sv_pcnt_good CARS_salinity

TIME abs_corrected_sv CARS_oxygen

Final data product Sv CARS_nitrate

Sv_pcnt_good CARS_phosphate

epipelagic CARS_silicate

upper_mesopelagic temperature Metrics lower_mesopelagic salinity

mean_height npp Data quality flags mean_depth sound_speed

signal_noise absorption

background_noise

Figure 7. Organization of variables present in a NetCDF file.

FORM Acoustic data from vessels of opportunity

MESOPP-18-0009 Public V1.0 2018,Dec.03

Table 1. Description of variables present in a NetCDF file.

Components Description Global attributes The global attribute section of a NetCDF file contains metadata that describes the overall contents of the file and allows for data discovery. They are categorised based on the following information: o Project o Metadata record o Cruise o Ship o Transect o Instrument o Calibration o Data acquisition o Data processing o Dataset o Data Variables The NetCDF variables include coordinate variables, actual measurements by an echosounder, metrics derived from the primary measurements, and environmental parameters as highlighted below. Coordinate variables The commonest use of coordinate variables is to locate the data in space and time. LATITUDE Specified in decimal degrees relative to the WGS84 coordinate reference system. LONGITUDE Specified in decimal degrees relative to the WGS84 coordinate reference system. DEPTH Measures the depth below the sea surface that is positive in downward direction. TIME Represented numerically as an interval (e.g. number of days or hours) from a reference time (since 1950- 01-01 00:00:00 UTC). Primary data variables Contains actual measurements by an echosounder and derived metrics. -1 Sv_unfilt ‘Unprocessed’ mean volume backscattering coefficient 푠푣 (m ) values. These are an echo-integration of as-acquired ‘raw’ (but calibrated) acoustic water column data.

uncorrected_Sv ‘Filtered’ mean 푠푣 values. These are an echo-integration of ‘filtered’ and ‘motion’ correction applied (if motion data is available) acoustic water column data. uncorrected_Sv_pcnt_good Percentage of ‘uncorrected_Sv’data retained before secondary corrections.

abs_corrected_sv ‘Filtered and secondary corrections applied’ mean 푠푣 values.

Sv ‘Processed’ mean 푠푣 values. This is the final data product.

Sv_pcnt_good Percentage of 푠푣 data retained at the end of post-processing.

epipelagic 푠푣 values averaged between 20–200 m depth and converted to decibel.

upper_mesopelagic 푠푣 values averaged between 200–400 m depth and converted to decibel.

lower_mesopelagic 푠푣 values averaged between 400–800 m depth and converted to decibel. mean_height Mean height (m) values for each echo-integration cell. mean_depth Mean depth (m) values for each echo-integration cell. signal_noise Signal-to-noise-ratio for each echo-integration cell. background_noise Background noise values for each ping axis interval. Auxiliary data variables Auxiliary data variables contain derived products such as climatology and satellite data. day Information of diurnal sun cycle for each ping axis interval. CARS_temperature Climatology temperature values for each echo-integration cell, derived from CSIRO Atlas of Regional Seas (CARS, http://www.marine.csiro.au/~dunn/cars2009/). CARS_salinity Climatology salinity values for each echo-integration cell, derived from CARS. CARS_oxygen Climatology oxygen values for each echo-integration cell, derived from CARS. CARS_ nitrate Climatology nitrate values for each echo-integration cell, derived from CARS. CARS_phosphate Climatology phosphate values for each echo-integration cell, derived from CARS. CARS_silicate Climatology silicate values for each echo-integration cell, derived from CARS. temperature Inferred temperature values for each echo-integration cell, derived from synthetic temperature and salinity (synTS) model (Ridgway and Dunn, 2010). If synTS is not covering the transect region, CARS_temperature values are substituted. salinity Inferred salinity values for each echo-integration cell, derived from synTS. If synTS is not covering the transect region, CARS_salinity values are substituted. npp Ocean net primary production (NPP) values for each ping axis interval (averaged for the previous 12 months with reference to the transect start date). Source: Vertically Generalized Production Model (VGPM, https://www.science.oregonstate.edu/ocean.productivity/). sound_speed Sound speed (m/s) in water, calculated for each echo-integration cell. absorption Absorption coefficient (dB/m) of sound in water, calculated for each echo-integration cell.

FORM Acoustic data from vessels of opportunity

MESOPP-18-0009 Public V1.0 2018,Dec.03

4. Overview of bioacoustic metrics

Vessels of opportunity bioacoustic sampling methods have clear advantages as well as limitations (Kloser et al., 2009). Their usefulness in resource assessment and ecosystem monitoring is established (Melvin et al., 2016). Application of these techniques to other important species and functional groups are needed to complement ecosystem models, and more broadly to support ecosystem-based management (Trenkel et al., 2011). The first step of this challenge would be to derive useful acoustics- based ‘indicator’ and ‘metric’ for ecosystem-based management. Rochet and Trenkel (2009) have proposed the following definitions for indicator and metric: o Indicator: a variable that quantifies how well an ecosystem (fishery in the original definition) is managed in relation to specified objectives. o Metric: a variable that summarizes a process or pattern of interest in an exploited ecosystem.

The main difference between an indicator and a metric is that the former requires the definition of reference points, i.e. absolute reference values, to interpret measured indicator values, and the latter does not. In this context, calibrated 푠푣 data is a basic acoustic ‘metric’ for ecosystem studies because in underwater active acoustics we fundamentally measure voltage (pressure) and time for deriving 푠푣 values. If the uncertainties are well characterized, this 푠푣 variable can be used to estimate biomass (Figure 3) as an ‘indicator’ for ecosystem-based management (Kloser et al., 2015) . Acoustic studies to derive key deep scattering layer (DSL) metrics can be broadly classified into two categories (Figure 8). DSL features across the open-ocean vary widely in depth and backscattering intensity, evidencing their interrelationship with environmental parameters such as light, oxygen concentration, temperature, and primary production (Figure 9) e.g. (Bianchi et al., 2013; Escobar- Flores et al., 2013; Aksnes et al., 2017; Proud et al., 2017). Integration of acoustic observations into ecosystem models is a part of the MESOPP project. Irrespective of this uptake, the acoustic snapshots (Figure 9) that show patch structures, fine-scale depth distribution, and diel vertical migration is already assisting to understand the open ocean ecosystem and its spatio-temporal variability (Table 2, and references therein).

regional scale twilight zone state and trend composition or structure of layers for extracting key metrics acoustic data based DSL studies global biogeography and response of layers to patterns covariates such as light, temperature, primary production etc.

Figure 8. A broad classification of DSL studies.

FORM Acoustic data from vessels of opportunity

MESOPP-18-0009 Public V1.0 2018,Dec.03

Primary production for 2015 (mg C m-2 day-1) 100 200 300 400 500 600 700 800 900 1000

30Ο E 60Ο E 90Ο E 120Ο E 150Ο E 180Ο E 210Ο E 240Ο E 270Ο E 300Ο E 330Ο E 360Ο E 45Ο N

30Ο N

15Ο N

0Ο

15Ο S

30Ο S

45Ο S

60Ο S

75Ο S

2 -3 Panama City, Panama Volume backscattering strength Sν (dB re 1 m m ) 259Ο E

2015-12-07 2015-12-08 2015-12-09 2015-12-10 2015-12-11 2015-12-12 240Ο E

2015-12-13 2015-12-14 2015-12-15 2015-12-16 221Ο E 195 395 595 795 995

1195 Depth(m) 2015-12-17 2015-12-18 2015-12-19 2015-12-20 2015-12-21 200Ο E

-50 2015-12-22 2015-12-23 2015-12-24 2015-12-25 Nelson, New Zealand -60

-70

-80

2015-12-26 2015-12-27 2015-12-28 2015-12-29 2015-12-30 2015-12-31 Time (UTC) Figure 9. Example of the fishing vessel (Antarctic Discovery) 38 kHz acoustic data covering the Pacific Ocean. The Longhurst oceanic biogeographical provinces are superimposed as white lines. The center part of the south Pacific highlight oligotrophic regions with low predicted productivity (blue), which is observed in general by the low mesopelagic (400–800 m depth) backscatter strength (small magenta circles) (Haris and Kloser, 2017).

FORM Acoustic data from vessels of opportunity

MESOPP-18-0009 Public V1.0 2018,Dec.03

Table 2. A brief overview of bioacoustic metrics used in the literature.

Purpose Region Metrics Reference Scattering layer Pacific Ocean DSL daytime depth Tont (1976) patterns

Temporal variability of Monterey Bay 푆푣 Urmy et al. (2012) scattering layers (using 푆푎 a moored system) Centre of mass Inertia Proportion occupied Equivalent area Index of aggregation Number of layers Scattering layer Gulf of California Layer depth Cade and Benoit-Bird detection method Layer width (2014) Internal structure over time Scattering layer South-west Indian Layer depth Proud et al. (2015) extraction method Ocean and Tasman Sea Layer depth range Layer thickness Layer duration

푆푣 over entire layer

Range of 푆푣 in the layer Standard deviation of 푆푣 in the layer Vertical velocity Large-scale patterns in North Atlantic Ocean, DSL daytime depth Klevjer et al. (2016) scattering layers Indian Ocean, East Migration proportion Pacific, and West Pacific Net vertical transport

Distribution of South-west Indian Vertical 푆푣 profile Boersch-Supan et al. scattering layers Ocean (2017) Biogeography of Global Ocean Layer depth Proud et al. (2017)

mesopelagic zone 푆푣 Fine-scale depth Global Ocean Layer depth Proud et al. (2018a) structure of scattering Layer thickness layers 푆푣 (between 0 and 1200 m)

Spatial and temporal New Zealand sector of 푆푣 Escobar-Flores et al. patterns in scattering the Southern Ocean (2018) layers

FORM Acoustic data from vessels of opportunity

MESOPP-18-0009 Public V1.0 2018,Dec.03

5. Effects of data processing routines and quality checking methods

None of the fishing vessels collecting bioacoustic data under the vessels of opportunity program were purposely built for collecting high-quality acoustic data. In addition to calibration uncertainties (Demer et al., 2015), the range of weather extremes and vessel design can affect the data quality that could cause large biases in 푠푣 and resulting biomass. To reduce these biases, a sequence of data processing filters and secondary correction methods were applied to the raw data as shown in Figure 5 (Ryan et al., 2015; MESOPP-18-0003, 2018). In this section, we briefly highlight the effect of data processing methods applied to the vessels of opportunity data. If these data processing methods are not standardized it may have a potential impact on the ‘metrics’ derived for the use in ecosystem models.

5.1. Calibration

Echosounder calibration is a fundamental requirement in quantitative aquatic biomass surveys. Calibration of acoustic systems involves primary calculations of two performance parameters: (1) the transducer on-axis gain 퐺0 (dB re 1), and (2) the equivalent two-way beam angle 훹 (dB re 1 sr) of the transducer beam pattern. The overall on-axis performance of echosounders can be evaluated by the established sphere calibration method (Demer et al., 2015). This method provides calibrated 퐺0 values and 푠푎 correction factor 푆푎 corr (dB re 1), commonly used to monitor the on-axis performance of hull-mounted echosounders (see Appendix A for importance of calibration parameters in biomass estimation). However, the performance of transducers and associated electronic components may degrade gradually or abruptly. The transducers are also vulnerable to mechanical damage and ageing effects (Knudsen, 2009). For example (Figure 10), calibration results for the FV Rehua demonstrate reasonable repeatability of transducer measurements with 퐺0 values between 25.40.2 dB. In contrast, the Austral Leader indicate gradual degradation of system performance (possibly ageing effect) over a six year period with ~1.3 dB decrease in calibrated 퐺0 values (Downie et al., 2018). This would result in a ~81% decrease in the estimated biomass if the 퐺0 value calculated in 2009 is applied for analysing 2015 data sets. Therefore, it is important to quantify such changes routinely to apply suitable corrections required during the post-processing of data for biomass or relative metric estimation.

(a) FV Rehua (b) Austral Leader

(dB re 1) re (dB

axis gain gain axis

(dB re 1) re (dB Calibrated transducer on transducer Calibrated

Year of calibration

Figure 10. Calibrated 퐺0 (blue) and 푆푎 corr (red) values for (a) FV Rehua and (b) Austral Leader between 2005–2015. Reproduced from Downie et al. (2018).

FORM Acoustic data from vessels of opportunity

MESOPP-18-0009 Public V1.0 2018,Dec.03

5.2. Effect of motion correction

Transducer motion reduces the received signal (Stanton, 1982) and increases inter-ping variability. When vessel-motion data were available at a suitable sampling rate (≥5 Hz), transducer motion effects were corrected using the (Dunford, 2005) filter, before application of semi-autonomous filters (Figure 5). Depending on the nature of weather condition, motion effects on the data can result in a range dependent biomass/푆푣 metric correction of ~ 10% at 400 m and ~40% at 800 m depth (Figure 11). If motion correction is not applied, it can negatively bias (or underestimate) biomass results.

Raw data

Motion corrected data

Depth Depth (m) dB re 1 1 m re dB

Difference (raw – motion corrected)

Distance (nmi)

Figure 11. Effect of motion correction on raw 38 kHz 푆푣 data collected by RV Southern Surveyor on 15 October 2013. Note the strong non-linear range dependent effect.

FORM Acoustic data from vessels of opportunity

MESOPP-18-0009 Public V1.0 2018,Dec.03

5.3. Data quality and effect of filters

Motion-corrected data are then subject to a sequence of data processing filters that are designed to mitigate the effects of three types of noise: impulsive (less than one ping), transient (multiple pings), and background (hours or longer). Another filter is also applied to identify signals that are attenuated by air bubbles beneath the transducer (Ryan et al., 2015). If noise is not removed, it can be mistaken for biological signal and positively bias biomass results. In general, these filters have improved the quality of bioacoustic data to an acceptable level enabling posting of 170 transects from 18 vessels to the IMOS portal. A caution is that while filters may correct for first-order effects, sources of bias and error may remain in the retained data (Ryan et al., 2015). As an example, data collected by FV San Tongariro in Tasman Sea is presented (Figure 12). Figure 13 shows the comparison between raw and final processed echograms, highlighting strong transient noise in the data. The raw data quality of this transect was extremely poor, and despite the appearance of the final product (Figure 13b) signal-to-noise-ratio was not considered to be high enough as compared to other transects with high data quality. This suggests caution is needed when applying filters where overfiltering and subsequent resampling to coarse resolution may produce a ‘clean’ echogram but potentially has had significant biological signal removed in the process. For these reasons, we have refrained publication of this data under the IMOS program, and the decision was made by evaluating the quality flags associated with the data (Figure 14). There is a need for further work to better quantify the error potential for retained data (Ryan et al., 2015). Storing data quality flags in the NetCDF file is designed to assist ecosystem modellers to make an independent assessment of the data quality.

35˚S

40˚S

45˚S

50˚S OceanDataView 150˚E 160˚E 170˚E Figure 12. Voyage track of the data presented in Figure 13. Map generated using Ocean Data View (Schlitzer, 2018).

FORM Acoustic data from vessels of opportunity

MESOPP-18-0009 Public V1.0 2018,Dec.03

(a) Calibrated raw data (dB re 1 m2 m-3)

(b) Final processed data (dB re 1 m2 m-3)

Figure 13. Combined effects of data processing filters on raw 38 kHz 푆푣 data collected by FV San Tongariro. (a) Calibrated raw data. (b) Final processed data including secondary corrections described in the following section.

FORM Acoustic data from vessels of opportunity

MESOPP-18-0009 Public V1.0 2018,Dec.03

(a) Signal-to-noise-ratio Signal-to-noise-ratio (dB)

(b) Percentage of 푺풗 data retained at the end of post-processing Percentage accepted (%)

Figure 14. Data quality flags associated with the data presented in Figure 13.

FORM Acoustic data from vessels of opportunity

MESOPP-18-0009 Public V1.0 2018,Dec.03

5.4. Effect of secondary corrections

Following the filter and scrutiny stages, quality-controlled 푆푣 data were echo-integrated in coarse resolution cells as shown in Figure 6.These data were first processed using a ‘single nominal value’ for sound speed (1500 m/sec) and absorption coefficient (9.74 dB/km) (e.g. 38 kHz). But, open ocean transects (Figure 15) may pass through very different water masses, and a secondary post-processing correction was required to account for changes in sound speed and absorption (shown in Figure 16) (see Appendix B for more details on secondary correction). Temperature and salinity data for these calculations were sourced from CARS (http://www.marine.csiro.au/~dunn/cars2009/), but can also be derived from oceanographic reanalysis and ocean circulation models. Absorption coefficient values were calculated using the currently used two equations of Francois and Garrison (1982) and Doonan et al. (2003) (Figure 17). The difference between variable uncorrected_Sv and abs_corrected_sv (Figure 5) is calculated (uncorrected_Sv  abs_corrected_sv) to demonstrate the effect of secondary corrections. The choice of absorption equation used for secondary correction has a significant effect on the processed 푆푣 data (Figure 18). This step introduces a range dependent correction that can differ substantially based on the absorption of sound in seawater equation used [see Figure 5 in Doonan et al. (2003)]. It is important to note that the percentage of correction is applicable to the example transect only and depend on the nominal sound speed and absorption values. Other transects (e.g. Southern Ocean) have different correction factors that are related to regional changes in temperature and salinity values. It is recommend that open ocean bioacoustic data be corrected for the Francois and Garrison (1982) absorption equation until more evidence is available to select another formula. The equation used for sound absorption calculation is mentioned in the global attribute section of NetCDF file as ‘data_processing_absorption_description’ and ‘history’. Similarly, the equation used for sound speed calculation is mentioned in ‘data_processing_soundspeed_description’ and ‘history’.

Figure 15. Voyage track of the data presented in Figure 16.

FORM Acoustic data from vessels of opportunity

MESOPP-18-0009 Public V1.0 2018,Dec.03

(dB re 1 m2 m-3)

Calculated sound speed (m/sec): (Mackenzie, 1981)

Calculated sound absorption coefficient (dB/km): (Francois and Garrison, 1982)

Calculated sound absorption coefficient (dB/km): (Doonan et al., 2003)

Figure 16. Examples of calculated sound speed and absorption coefficient values along the 38 kHz transect from SW Indian Ocean to Mauritius. The absorption coefficients were calculated using two equations as shown in the figure.

FORM Acoustic data from vessels of opportunity

MESOPP-18-0009 Public V1.0 2018,Dec.03

(Mackenzie, 1981)

(Francois and Garrison, 1982)

(Doonan et al., 2003)

Figure 17. Boxplots of calculated sound speed and absorption coefficient values presented in Figure 16.

FORM Acoustic data from vessels of opportunity

MESOPP-18-0009 Public V1.0 2018,Dec.03

Difference in (dB re 1 m2 m-3) due to secondary corrections (before – after) (Francois and Garrison, 1982)

(Doonan et al., 2003)

(Francois and Garrison, 1982) (Doonan et al., 2003)

Figure 18. The difference in 38 kHz 푆푣 before and after secondary corrections. Related percentage corrections are shown in the boxplots. Note the increasing correction with range.

FORM Acoustic data from vessels of opportunity

MESOPP-18-0009 Public V1.0 2018,Dec.03

6. Limitation of single frequency data

The majority of archived bioacoustic data are single-frequency (38 kHz) echosounder observations (Figure 4). This frequency provides an adequate range for mapping the mesopelagic zone, but has a high selectivity of gas-bladdered organisms due to resonance scattering e.g. (Kloser et al., 2016). There has been a focus on organisms with gas-bladders that depending on frequency can make up the dominant component of the scattering. The acoustic snapshots (see Figure 19 and Figure 20) display the distribution of strongest scatterers (perhaps the smallest fish with gas bladder), and not necessarily biomass (Davison et al., 2015). A way forward to solve this problem would be to acquire multi-frequency data (Korneliussen et al., 2008) for improving the identification of different scattering groups. With the advent of multi- frequency systems, acoustic methods have been used to classify dominant organisms into gas-filled or fluid-filled categories e.g. (Kloser et al., 2002; Fielding et al., 2012; Peña et al., 2014; Béhagle et al., 2017; Proud et al., 2018b). This methodology would help to segment acoustic observations into different scattering (or functional) groups that can be compared with the ecosystem model predictions for validation (MESOPP-18-0006, 2018). Lack of accurate taxonomic information about the insonified organisms is one of the major limitations of acoustics. Therefore, multi-frequency methods normally compare 푆푣 data (Figure 21) collected at different frequencies to determine characteristics of biological backscatters. The rationale is that the acoustic properties of individual species are known to vary with the operating frequencies (Jech and Michaels, 2006). Despite uncertainties with calibration, species identification, and sampling range limitation of high-frequency data (Figure 22), the multi-frequency methods provide novel insights for studying spatial distribution and fine-scale structure of mesopelagic communities. It seems also possible to derive some relationships between these different frequencies that appear strongly correlated, especially in the epipelagic layer (Figure 21). Such relationships would then offer a method to compare and eventually combine different bioacoustics data sets acquired at different frequencies. Though it might not provide direct biomass abundance estimates, this approach could provide a first useful proxy to investigate relative spatio-temporal variability of the bioacoustics signals.

40˚S

45˚S

50˚S

55˚S Ocean Data View Data Ocean 130˚E 140˚E 150˚E 160˚E Figure 19. Voyage track of the data presented in Figure 20. Map generated using Ocean Data View (Schlitzer, 2018).

FORM Acoustic data from vessels of opportunity

MESOPP-18-0009 Public V1.0 2018,Dec.03

(a) 18 kHz 18 kHz (dB re 1 m2 m-3)

(b) 38 kHz

38 kHz (dB re 1 m2 m-3)

(d) 120 kHz 120 kHz (dB re 1 m2 m-3)

Figure 20. Multi-frequency echograms highlighting different scattering layers and functional groups.

FORM Acoustic data from vessels of opportunity

MESOPP-18-0009 Public V1.0 2018,Dec.03

Epipelagic zone (20200 m) 18 kHz 38 kHz 70 kHz 120 kHz

Upper mesopelagic zone (200400 m)

)

2 (dB re 1 m 1 re (dB

Lower mesopelagic zone (400800 m)

Distance (1 km)

Figure 21. Example of multi-frequency metrics (green = 120 kHz; black = 70 kHz; red = 38 kHz; blue = 18 kHz). Average backscatter data in epipelagic (20– 200 m), upper mesopelagic (200–400 m), and lower mesopelagic (400–800 m) zone.

120 kHz raw (dB re 1 m2 m-3)

Figure 22. Sampling range limitation of high-frequency (120 kHz) data.

FORM Acoustic data from vessels of opportunity

MESOPP-18-0009 Public V1.0 2018,Dec.03

7. Existing resolution of the NetCDF and limitations

The existing 1 km horizontal resolution of the NetCDF file (Figure 6) has limitations to detect and analyse fish schools (shoals or aggregations) observed at a fine resolution (Figure 23). These schools are echo-integrated to coarse resolution in the NetCDF file, limiting individual school analysis. Studies of school distribution and behaviour, together with multi-frequency methods can mutually complement the process of partitioning acoustic data into different scattering (or functional) groups. Analysis of detected schools can provide a set of parameters e.g. (Woodd-Walker et al., 2003; Cabreira et al., 2009; Korneliussen et al., 2009) that are useful for classifying schools by species and length. Different processing routines (ideally at a higher resolution) are needed to export morphometric and energetic parameters of detected fish schools. This can be considered as a way forward for obtaining additional metrics from the data.

Raw data

Depth (m) Depth dB re m 1

Distance (m)

158 m

53 m

Figure 23. Example echogram showing fish schools at a fine resolution. The grid lines represent existing resolution of the NetCDF file.

FORM Acoustic data from vessels of opportunity

MESOPP-18-0009 Public V1.0 2018,Dec.03

8. Conclusions

Vessels of opportunity bioacoustic methods are useful for cost-effective mapping of mesopelagic communities at regional and global scale, but the conversion of acoustic data (푠푣) to biomass is often complex, mainly due to frequency-dependent acoustic scattering of different organisms (MESOPP-18- 0006, 2018; Proud et al., 2018b). Ground-truthing of basin scale bioacoustic data with respect to species composition (e.g. using multi-frequency methods) and acoustic properties of the fish present (e.g. using in situ 푇푆 measurements) is important for credible estimates of biomass. This would help to better understand uncertainties in the data prior to validating the mesopelagic component of ecosystem models. Nets are often used to assist interpretation of bioacoustic data to biomass. Targeted and/or depth-stratified trawling can provide vital biological data, such as species composition, lengths, and weights needed for biomass estimates (Sutton et al., 2018). However, nets have known catchability issues (for example destruction of gelatinous organisms and avoidance by more agile species) that could bias interpretations. Vessel mounted acoustic and net sampling methods of mesopelagic communities can be refined using a profiling multi-frequency acoustic optical system e.g. (Kloser et al., 2016; Marouchos et al., 2016). This sampling method provides visually verified in situ 푇푆 of organisms, helping to resolve uncertainty in global mesopelagic biomass estimation. When interpreting the acoustic data it is important to understand the corrections applied and in this report we highlight the effect of vessel calibration, transducer motion correction, and secondary corrections for sound speed and absorption variation. The motion and secondary corrections have been shown to be range dependant that can greatly influence the lower mesopelagic (400–800 m depth) derived metrics by >50%. In order to mitigate bias in interpretation and to facilitate large-scale uptake of bioacoustic data in ecosystem models, it is recommended that: (1) The echosounder should be calibrated routinely to establish traceable calibration history and continued expected performance for each vessel. (2) The data processing methods (including user-defined filter parameters) need to be well specified and tested with reference data sets to standardize between data providers. (3) The naming conventions and metadata content of the processed data should be consistently followed to generate a globally consistent bioacoustic database.

FORM Acoustic data from vessels of opportunity

MESOPP-18-0009 Public V1.0 2018,Dec.03

9. References

Aksnes, D. L., Røstad, A., Kaartvedt, S., Martinez, U., Duarte, C. M., and Irigoien, X. 2017. Light penetration structures the deep acoustic scattering layers in the global ocean. Science advances, 3: e1602468. Béhagle, N., Cotté, C., Lebourges-Dhaussy, A., Roudaut, G., Duhamel, G., Brehmer, P., Josse, E., et al. 2017. Acoustic distribution of discriminated micronektonic organisms from a bi-frequency processing: the case study of eastern Kerguelen oceanic waters. Progress in Oceanography, 156: 276-289. Bianchi, D., Galbraith, E. D., Carozza, D. A., Mislan, K., and Stock, C. A. 2013. Intensification of open- ocean oxygen depletion by vertically migrating animals. Nature Geoscience, 6: 545. Bodholt, H. 2002. The effect of water temperature and salinity on echo sounder measurements. In ICES Symposium on Acoustics in Fisheries 2002-Montpellier, pp. 1–7. Boersch-Supan, P. H., Rogers, A. D., and Brierley, A. S. 2017. The distribution of pelagic sound scattering layers across the southwest Indian Ocean. Deep Sea Research Part II: Topical Studies in Oceanography, 136: 108-121. Cabreira, A. G., Tripode, M., and Madirolas, A. 2009. Artificial neural networks for fish-species identification. ICES Journal of Marine Science, 66: 1119-1129. Cade, D. E., and Benoit-Bird , K. J. 2014. An automatic and quantitative approach to the detection and tracking of acoustic scattering layers. Limnology and Oceanography: Methods, 12: 742-756. Davison, P. C., Koslow, J. A., and Kloser, R. J. 2015. Acoustic biomass estimation of mesopelagic fish: backscattering from individuals, populations, and communities. ICES Journal of Marine Science, 72: 1413-1424. 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: 1282- 1291. Demer, D., Berger, L., Bernasconi, M., Bethke, E., Boswell, K., Chu, D., Domokos, R., et al. 2015. Calibration of acoustic instruments. ICES Cooperative Research Report, 326: 133. Doonan, I. J., Coombs, R. F., and McClatchie, S. 2003. The absorption of sound in seawater in relation to the estimation of deep-water fish biomass. ICES Journal of Marine Science, 60: 1047-1055. Downie, R. A., Kloser, R. J., and Ryan, T. E. 2018. An analysis of IMOS Bio-Acoustic Ships Of Opportunity (BASOOP) Programs Calibration data 2010 – 2017. CSIRO internal report. Dunford, A. J. 2005. Correcting echo-integration data for transducer motion. The Journal of the Acoustical Society of America, 118: 2121-2123. Escobar-Flores, P., O’Driscoll, R. L., and Montgomery, J. C. 2013. Acoustic characterization of pelagic fish distribution across the South Pacific Ocean. Marine Ecology Progress Series, 490: 169-183. Escobar-Flores, P. C., Driscoll, R. L., and Montgomery, J. C. 2018. Spatial and temporal distribution patterns of acoustic backscatter in the New Zealand sector of the Southern Ocean. Marine Ecology Progress Series, 592: 19-35. Fielding, S., Watkins, J. L., Collins, M. A., Enderlein, P., and Venables, H. J. 2012. Acoustic determination of the distribution of fish and krill across the Scotia Sea in spring 2006, summer 2008 and autumn 2009. Deep Sea Research Part II: Topical Studies in Oceanography, 59: 173-188. Francois, R. E., and Garrison, G. R. 1982. Sound absorption based on ocean measurements. Part II: Boric acid contribution and equation for total absorption. The Journal of the Acoustical Society of America, 72: 1879-1890. Fulton, E. A., Link, J. S., Kaplan, I. C., Savina-Rolland, M., Johnson, P., Ainsworth, C., Horne, P., et al. 2011. Lessons in modelling and management of marine ecosystems: the Atlantis experience. Fish and Fisheries, 12: 171-188.

FORM Acoustic data from vessels of opportunity

MESOPP-18-0009 Public V1.0 2018,Dec.03

Handegard, N. O., Buisson, L. d., Brehmer, P., Chalmers, S. J., Robertis, A., Huse, G., Kloser, R., et al. 2013. Towards an acoustic-based coupled observation and modelling system for monitoring and predicting ecosystem dynamics of the open ocean. Fish and Fisheries, 14: 605-615. Haris, K., and Kloser, R. J. 2017. Latest bioacoustic tracks from FV Antarctic Discovery provide unique data to IMOS. In IMOS Marine Matters, p. 9. Haris, K., Kloser, R. J., and Ryan, T. E. 2018a. IMOS SOOP-BA NetCDF Conventions. Version, 2.2. 42 pp. Haris, K., Kloser, R. J., Ryan, T. E., Malan, J., and Handling editor: David, D. 2018b. Deep-water calibration of echosounders used for biomass surveys and species identification. ICES Journal of Marine Science, 75: 1117-1130. Jech, J. M., and Michaels, W. L. 2006. A multifrequency method to classify and evaluate fisheries acoustics data. Canadian Journal of Fisheries and Aquatic Sciences, 63: 2225-2235. Klevjer, T. A., Irigoien, X., Røstad, A., Fraile-Nuez, E., Benítez-Barrios, V. M., and Kaartvedt, S. 2016. Large scale patterns in vertical distribution and behaviour of mesopelagic scattering layers. Scientific Reports, 6: 19873. Kloser, R. J., Ryan, T., Sakov, P., Williams, A., and Koslow, J. A. 2002. Species identification in deep water using multiple acoustic frequencies. Canadian Journal of Fisheries and Aquatic Sciences, 59: 1065-1077. Kloser, R. J., Ryan, T. E., Keith, G., and Gershwin, L. 2016. Deep-scattering layer, gas-bladder density, and size estimates using a two-frequency acoustic and optical probe. ICES Journal of Marine Science, 73: 2037-2048. Kloser, R. J., Ryan, T. E., Young, J. W., and Lewis, M. E. 2009. Acoustic observations of micronekton fish on the scale of an ocean basin: potential and challenges. ICES Journal of Marine Science, 66: 998-1006. Kloser, R. J., Sutton, C., Krusic-Golub, K., and Ryan, T. E. 2015. Indicators of recovery for orange roughy (Hoplostethus atlanticus) in eastern Australian waters fished from 1987. Fisheries research, 167: 225-235. Knudsen, H. P. 2009. Long-term evaluation of scientific-echosounder performance. ICES Journal of Marine Science: Journal du Conseil, 66: 1335-1340. Korneliussen, R. J., Diner, N., Ona, E., Berger, L., and Fernandes, P. G. 2008. Proposals for the collection of multifrequency acoustic data. ICES Journal of Marine Science, 65: 982-994. Korneliussen, R. J., Heggelund, Y., Eliassen, I. K., and Johansen, G. O. 2009. Acoustic species identification of schooling fish. ICES Journal of Marine Science, 66: 1111-1118. Lehodey, P., Conchon, A., Sennia, I., Domokos, R., Calmettes, B., Jouanno, J., Hernandez, O., et al. 2014. Optimization and evaluation of a micronekton model with acoustic data. ICES Journal of Marine Science. Lehodey, P., Senina, I., and Murtugudde, R. 2008. A spatial ecosystem and populations dynamics model (SEAPODYM)–Modeling of tuna and tuna-like populations. Progress in Oceanography, 78: 304- 318. 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: 365-369. Marouchos, A., Sherlock, M., Kloser, R., Ryan, T., and Cordell, J. 2016. A profiling acoustic and optical system (pAOS) for pelagic studies; Prototype development and testing. In OCEANS 2016- Shanghai, pp. 1-6. IEEE. Melvin, G. D., Kloser, R., and Honkalehto, T. 2016. The adaptation of acoustic data from commercial fishing vessels in resource assessment and ecosystem monitoring. Fisheries research, 178: 13- 25. MESOPP-17-0001. 2017. NetCDF structure for acoustic data in MESOPP project. MESOPP-17-0002. 2017. Documentation of the models, approaches for their standardization and protocols to be used for model inter-comparison in MESOPP.

FORM Acoustic data from vessels of opportunity

MESOPP-18-0009 Public V1.0 2018,Dec.03

MESOPP-18-0001. 2018. Documentation of the definition of taxonomic/functional/energetic group in MESOPP models in relation to existing acoustic information. MESOPP-18-0003. 2018. Report of acoustic processing routines and quality checking methods. MESOPP-18-0006. 2018. Acoustic observation model: linking ecological models to acoustic signals. Ona, E. 1999. Methodology for target strength measurements. ICES Cooperative Research Report, 235: 59. Peña, M., Olivar, M. P., Balbín, R., López-Jurado, J. L., Iglesias, M., and Miquel, J. 2014. Acoustic detection of mesopelagic fishes in scattering layers of the Balearic Sea (western Mediterranean). Canadian Journal of Fisheries and Aquatic Sciences, 71: 1186-1197. Proud, R., Cox, M. J., and Brierley, A. S. 2017. Biogeography of the global ocean’s mesopelagic zone. Current Biology, 27: 113-119. Proud, R., Cox, M. J., Le Guen, C., and Brierley, A. S. 2018a. Fine-scale depth structure of pelagic communities throughout the global ocean based on acoustic sound scattering layers. Marine Ecology Progress Series, 598: 35-48. Proud, R., Cox , M. J., Wotherspoon, S., and Brierley, A. S. 2015. A method for identifying Sound Scattering Layers and extracting key characteristics. Methods in Ecology and Evolution, 6: 1190-1198. Proud, R., Handegard, N. O., Kloser, R. J., Cox, M. J., Brierley, A. S., and Handling editor: David, D. 2018b. From siphonophores to deep scattering layers: uncertainty ranges for the estimation of global mesopelagic fish biomass. ICES Journal of Marine Science: fsy037-fsy037. Ridgway, K. R., and Dunn, J. R. 2010. Using satellite altimetry to correct mean temperature and salinity fields derived from Argo floats in the ocean regions around Australia. Deep Sea Research Part I: Oceanographic Research Papers, 57: 1137-1151. Rochet, M.-J., and Trenkel, V. M. 2009. Why and how could indicators be used in an ecosystem approach to fisheries management? In The Future of Fisheries Science in North America, pp. 209-226. Springer. 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: 2482-2493. Schlitzer, R. 2018. Ocean Data View, https://odv.awi.de. Simmonds, J., and MacLennan, D. N. 2005. Fisheries acoustics: theory and practice, Blackwell Science. 456 pp. St. John, M. A., Borja, A., Chust, G., Heath, M., Grigorov, I., Mariani, P., Martin, A. P., et al. 2016. A Dark Hole in Our Understanding of Marine Ecosystems and Their Services: Perspectives from the Mesopelagic Community. Frontiers in Marine Science, 3. Stanton, T. K. 1982. Effects of transducer motion on echo-integration techniques. The Journal of the Acoustical Society of America, 72: 947-949. Sutton, C., Kloser, R. J., and Gershwin, L. 2018. Micronekton in southeastern Australian and the Southern Ocean; A collation of the biomass, abundance, diversity and distribution data from CSIRO’s historical mesopelagic depth stratified net samples. CSIRO, Australia. Tont, S. A. 1976. Deep scattering layers: patterns in the Pacific. Rep Calif Coop Ocean Fish Invest, 18: 112-117. Trenkel, V. M., Ressler, P. H., Jech, M., Giannoulaki, M., and Taylor, C. 2011. Underwater acoustics for ecosystem-based management: state of the science and proposals for ecosystem indicators. Marine Ecology Progress Series, 442: 285-301. Urmy, S. S., Horne, J. K., and Barbee, D. H. 2012. Measuring the vertical distributional variability of pelagic fauna in Monterey Bay. ICES Journal of Marine Science, 69: 184-196. Woodd-Walker, R. S., Watkins, J. L., and Brierley, A. S. 2003. Identification of Southern Ocean acoustic targets using aggregation backscatter and shape characteristics. ICES Journal of Marine Science, 60: 641-649.

FORM Acoustic data from vessels of opportunity

MESOPP-18-0009 Public V1.0 2018,Dec.03

Appendix A - Importance of calibration parameters

According to (Simmonds and MacLennan, 2005) a good calibration practice should determine the performance of the echosounder system to within 7%. The aim should be to develop a routine or protocol for calibration which will achieve this accuracy consistently. Therefore, it is important to note that 1 dB change in 퐺0 and 푆푎 corr represents a twofold 2 dB variation in the measured 푆푣 (see below examples) that would result in 58% change in the estimated biomass if accurate calibration

parameters are not applied.

) )

3 3

- -

m m

2 2

(dB re 1 m 1 re (dB m 1 re (dB

On-axis gain (dB re 1) (dB re 1)

The 훹 of transducers is an important (and apparently the least investigated) calibration parameter in echo-integration based biomass estimation. Incorrect 훹 values contribute to a first-order bias in a biomass estimate as below (Haris et al., 2018b). The bias in biomass estimation with respect to an o ideal 7 transducer is superimposed as circles. Note that 휃−3 푑퐵> the true value decreases estimated biomass and 휃−3 푑퐵< the true value increases estimated biomass.

-18

-19 -47 -38 -30 -22 -20 -14 -7 0 -21 7

16 (dB re 1 sr) 1 re (dB 25 Ψ -22 36 48 62 -23 (%) biomass estimated in Bias

-24 5 5.5 6 6.5 7 7.5 8 8.5 9 –3 dB beamwidth θ (o) -3 dB

FORM Acoustic data from vessels of opportunity

MESOPP-18-0009 Public V1.0 2018,Dec.03

Appendix B - Secondary corrections for sound speed and absorption

2 –3 The volume backscattering strength 푆푣푛 (dB re 1 m m ) for an echo 푃푒푟 (dB re 1 W) received at 푡.푐 nominal range 푟 = 푛 (m) for a given travel time 푡 (s) is given as below where range dependent 푛 2 –1 –1 corrections for nominal sound speed 푐푛 (m s ) and absorption coefficient 훼푛 (dB m ) are needed:

2 2 (푝푒푡휆푛푔 푐푛휏휓푛) 푆 = 푃 + 20 푙표푔 (푟 ) + 2훼 푟 − 10 푙표푔 ( 0 ) − 2푆 푣푛 푒푟 10 푛 푛 푛 10 32휋2 푎 푐표푟푟 Equation 1

where 푝푒푡 (W) is the transmit power, 휆푛 (m) is the nominal wavelength, 푔0 (dimensionless) is the nominal transducer on-axis gain, 휏 (s) is the pulse duration, 휓푛 (sr) is the nominal equivalent two-way 2 –2 beam angle, and 푆푎 푐표푟푟 (dB re 1) is the area backscattering coefficient 푠푎 (m m ) correction factor.

For a given temperature and salinity profile the cumulative average sound speed at range 푎, ∑푎 푐 ∑푎 훼 푐⃛ = 푖=1 푖 (m s–1), and absorption coefficient, 훼⃛ = 푖=1 푖 (dB m–1), need to be calculated at the new 푎 푎 푎 푎 푡.푐⃛푎 푐⃛푎 range, 푟푎 = = 푟푛 (m), to provide the actual range corrected volume backscattering strength 푆푣푎 2 푐푛 (dB re 1 m2 m–3) values:

2 2 (푝푒푡휆푎푔푎푐푎휏휓푎) 푆 = 푃 + 20 푙표푔 (푟 ) + 2훼⃛ 푟 − 10 푙표푔 ( ) − 2푆 푣푎 푒푟 10 푎 푎 푎 10 32휋2 푎 푐표푟푟 Equation 2

where 휆푎 (m) is the new wavelength, 푔푎 (dimensionless) is the new transducer on-axis gain, 푐푎 –1 (m s ) is the sound speed profile, 휓푎 (sr) is the new equivalent two-way beam angle.

–1 For sound speed 푐푡 (m s ) at the transducer face, the 휆푎, 푔푎, and 휓푎 in Equation 2 is (Bodholt, 2002; Demer et al., 2015):

2 4 2 2 2 푐푡 2 2 푐푛 푐푡 휆푎 = 휆푛 ( ) , 푔푎 = 푔0 ( ) , and 휓푎 = 휓푛 ( ) . 푐푛 푐푡 푐푛

Therefore, Equation 2 can be written as:

2 2 (푝푒푡휆푛푔 푐푎휏휓푛) 푆 = 푃 + 20 푙표푔 (푟 ) + 2훼⃛ 푟 − 10 푙표푔 ( 0 ) − 2푆 . 푣푎 푒푟 10 푎 푎 푎 10 32휋2 푎 푐표푟푟 Equation 3

Note that the non-range dependent transducer calibration and gain correction term (푝 휆2 푔2휏휓 ) 10 푙표푔 ( 푒푡 푛 0 푛 ) − 2푆 is constant for both Equation 2 and Equation 3 as the sound speed 10 32휋2 푎 푐표푟푟 related terms in 휆푎, 푔푎, and 휓푎 cancel each other.

Therefore, by rearranging the terms and substituting 푃푒푟 from Equation 1 in Equation 3:

푐⃛푎 푐⃛푎 푐푎 푆푣푎 = 푆푣푛 + 20 푙표푔10 + 2푟푛 (훼⃛푎 − 훼푛) − 10 푙표푔10 . 푐푛 푐푛 푐푛 Equation 4

FORM Acoustic data from vessels of opportunity

MESOPP-18-0009 Public V1.0 2018,Dec.03

Or in linear terms:

2 2푟 푐⃛ 푐⃛ 푛(훼⃛ 푎− 훼 ) 푐 푎 10 푎푐 푛 푛 푠푣푎 = 푠푣푛 ( ) 10 푛 ( ) 푐푛 푐푎 Equation 5

2 -3 where 푠푣푎 and 푠푣푛 are the corrected and uncorrected volume backscattering coefficient (m m ) values respectively.

The secondary corrections detailed here has the undesirable side effect of creating a grid with irregular depths. Hence, the 푠푣푎 values at new ranges 푟푎 need to be interpolated and reported at the specified 푟푛 depth ranges.

FORM Acoustic data from vessels of opportunity

MESOPP-18-0009 Public V1.0 2018,Dec.03

Appendix C - Matlab function for visualisation

A Matlab function to read and visualise IMOS NetCDF files (shown as examples in this report) can be downloaded from the following websites. IMOS: http://imos.org.au/facilities/shipsofopportunity/bioacoustic/balinks/ GitHub: https://github.com/CSIRO-Acoustics/Visualize-acoustic-water-column-data-

FORM