30 years of advances in active bioacoustics: A personal perspective

Methods in Oceanography 1–2 (2012) 49–77

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30 years of advances in active bioacoustics: A personal perspective Timothy K. Stanton ∗ Woods Hole Oceanographic Institution, Woods Hole, MA 02543-1053, United States

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Article history: Received 16 March 2012 Received in revised form 25 May 2012 Accepted 14 July 2012 Available online 1 September 2012

abstract The area of active bioacoustics involves the use of sound to study distributions of fish and zooplankton in aquatic environments. There have been significant advances in this area over the past 30 years, covering many categories, spanning technology and modeling. These advances, as witnessed throughout my career, are reviewed. Issues with past and current approaches are discussed as well as projections into the future. © 2012 Published by Elsevier B.V.

1. Introduction The principles of operation of an acoustic echosounder have remained the same since its invention in the early 1900s (Fig. 1). A burst of sound is transmitted into the water through a combination of a power amplifier and electromechanical transducer. The sound travels through the water and scatters off of various heterogeneities in the water, such as fish and zooplankton. The portion of the sound scattered back to the transducer, the ‘‘echo’’, is then converted back into voltage and recorded for analysis. Use of such a system to study marine organisms in their aquatic environment is generally referred to as ‘‘active bioacoustics’’ where ‘‘active’’ refers to the fact that the system transmits an acoustic signal into the water. Analysis includes interpreting the echo in terms of meaningful biological quantities such as length, numerical density, and species of the organisms. This is in contrast to the area of ‘‘passive bioacoustics’’ which involves a system that does not transmit an acoustic signal into the water and is used strictly to listen to sounds created by the organisms. Acoustics is used to study marine organisms because sound can travel such great distances (depending upon the frequency, tens and hundreds of meters to many kilometers). Thus, synoptic information concerning marine organisms can be obtained rapidly with acoustics. This is in contrast

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2211-1220/$ – see front matter © 2012 Published by Elsevier B.V. doi:10.1016/j.mio.2012.07.002

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Fig. 1. Cartoon sketch of single beam echosounder, beampattern (exaggerated width), and echo voltage. Source: From Clay (1983).

Fig. 2. Gray-scale paper chart recording of data collected in 1981 at Trout Lake, Wisconsin, by Lars Rudstam, a former student of Clarence Clay, using a portable, battery-powered, 70 kHz Simrad EYM echosounder deployed from a small boat. The data were also stored on an analog tape recorder and processed by Rudstam and Stanton in separate studies using the Powell/Stanton prototype digital echo processor. Those analyses are reviewed in Stanton and Clay (1986).

to the use of nets which are considered ‘‘point’’ samples. The challenge lies in interpreting the acoustic data in terms of biologically meaningful parameters. There is a wide range of technologies and interpretation methods that have been developed to aid in the use of sound to study marine organisms. Because of the many uncertainties associated with acoustic methods, acoustic sampling generally needs to be conducted in concert with direct sampling methods such as nets. In essence, the acoustic data can spatially ‘‘connect’’ samples collected with the nets. The technologies and interpretation methods associated with echosounders have advanced significantly since I entered this field in 1980 (Table 1). At that time, all systems contained analog electronics and were noisy, deployment modes were generally limited to the ship’s hull or a towbody, narrowband gated sine waves (single frequency) were used in combination with a single beam transducer, real-time displays were limited to gray-scale paper charts (Fig. 2), data were recorded on analog tape recorders, and the echo data were interpreted using simplistic models (either a regression equation or an oversimplified analytical model). Today (c. 2012), systems are digital all the way down to near the transducer which significantly reduces noise and increases dynamic range, there are various deployment modes available including autonomous underwater vehicles (AUVs) and fixed stations for long-term observations, multiple frequencies and multi-beams (including split-beam) are available, narrowband and broadband linear frequency modulated signals (chirps) are used, calibrated data are displayed in real time in color on a computer monitor, data are recorded digitally on various forms of computer-based storage media

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Table 1 Direct path echosounders. Characteristics and qualities, past and present. 1980

2012

Sonar hardware

Analog Single beam Single frequency Gated sine wave transmit signal (narrowband) Issues: noisy, limited dynamic range, limitations in interpretation from single beam at single frequency

Digital Single-, split-, multi-beam Single- and multi-frequency, broadband Gated sine wave and linear frequency modulated

Modality of operation

Ship-based (hull-mounted or towbody)

Ship-based (hull-mounted or towbody)

Short-term deployment Human controlled Issues: only used for short-term (days/weeks) deployments

Cable- or battery-powered (fixed observatory or AUV) Short-or long-term deployment Human controlled or autonomous

Post-processing and software

Hard-wired processor Mostly researcher-derived software

Programmable processor Mostly commercial software

Display (real-time) and data storage

Paper display (black and white, uncalibrated) Analog tape recorder (serial access)

Computer monitor display (color, calibrated) Digital, computer-based storage medium (random access)

Echo counting

Single beam output—various statistical approaches Single beam output

Split-beam output—direct approach

Echo integration Scattering models-fish

Resonance frequency (physics-based) High frequency (regression-based)

Scattering models-zooplankton

Regression-based or physics-based sphere model

Single beam output Resonance frequency (physics-based) High frequency (regression- and physics-based) Physics-based models specific to boundary conditions and shape

(for later advanced processing, display, and analysis), and interpretation methods involve a range of sophisticated models grounded in data specific to the species. In this paper, I review the advances that I have witnessed (and, in some cases, contributed to) in my career in active bioacoustics. The focus will be on active systems involving the ‘‘direct path’’ scattering geometry which involves a system looking directly beneath (or nearly so) the ship and has also been the principal focus of the field. However, there will be some discussion on long-range systems that look horizontally, but remain principally in development. I also present my perspective on various qualitative aspects of the field including the current issues, where the field might be in 20 years, and what some of the ultimate limitations of the field are. Thus, this paper will span roughly 50 years of the past, present, and projected future. Because of the large span of material, coverage of each topic is necessarily brief and incomplete. Details can be obtained from references given below, including the monographs by Medwin (2005), Medwin and Clay (1998), and Simmonds and MacLennan (2005). Inline Supplementary Video 1 can be found online at http://dx.doi.org/10.1016/j.mio.2012.07.002. 2. A bit about myself I was trained in both theoretical and experimental physics. My first papers from my research in graduate school (Physics) at Brown University involved laboratory measurements (Stanton and Beyer, 1978) and instrumentation development (Stanton and Beyer, 1979) in underwater acoustics. From

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1978 to 1980, I was at the Submarine Signal Division of Raytheon Company where I designed and ocean-tested advanced multi-beam sonar systems. I then went to the University of Wisconsin in 1980 where I began a long and productive relationship with Clarence S. Clay in bioacoustics. In addition to our scientific interactions, we also downhill skied and played music together (he on euphonium and I on piano or trombone). From 1988 to the present, I have continued my bioacoustics research at the Woods Hole Oceanographic Institution. From my start in this field in 1980 to the present, I have always been intrigued by the use of sound as a remote sensing tool to infer important characteristics of marine organisms. It is an incredible challenge to begin with a recording of echo voltage versus time and convert that data into biological parameters. It has also been very rewarding to develop a new approach, whether it is theoretical or experimental, to extract new information from the signal or to improve on existing methods. Overall, my career in bioacoustics has involved a combination of theoretical modeling, laboratory experimentation, instrumentation development, and at-sea experiments. My first paper in bioacoustics involved a numerical study of errors in echo integration due to transducer motion (Stanton, 1982). I believe that my modeling of scattering by marine organisms, which began in the paper Stanton (1988) concerning modeling elongated zooplankton as a finite-length cylinder and is summarized in Stanton and Chu (2000) and Stanton (2009), has made the most impact to date. Key papers in the development were the deformed finite cylinder model (Stanton, 1989a) which has been used in modeling scattering by fish and elongated zooplankton, application of the distorted wave Born approximation (DWBA) to fluid-like zooplankton such as euphausiids (Chu et al., 1993; Stanton et al., 1993, 1998), and the observations (and associated modeling) of the importance of grouping zooplankton into gross anatomical groups according to their different material properties (Stanton et al., 1994; Wiebe et al., 1996; Lavery et al., 2007). The modeling was inspired and grounded to a large extent by my laboratory experiments of scattering by marine organisms. These experiments helped to identify dominant scattering mechanisms of the organisms whose shapes and material property profiles were complex which, in turn, served as a basis for the modeling. An important element of the laboratory experiments was the use of broadband sound which led to an important ‘‘by-product’’ of broadband signal processing methods for use in bioacoustics. These methods are first described in detail in the laboratory measurement paper by Chu and Stanton (1998). Later key papers in this area involved new calibration methods of broadband systems (Stanton and Chu, 2008) and a new broadband ocean instrument for resonance classification of fish (Stanton et al., 2010, 2012). Another key element of the research involved echo statistics, since the scattering is inherently stochastic. This led to a series of papers in the 1980s which describes the echo statistics concerning both the seafloor and marine organisms (see the review in Stanton and Clay, 1986). Key results in those studies involved estimating numerical density of marine organisms and roughness of the seafloor from the shape parameters of the echo amplitude probability density function (PDF). Papers were also written on echo statistics, using similar formulations, concerning the sea surface (Stanton, 1985a) and sea ice (Stanton et al., 1986). Echo statistics were ingrained in many later papers, such as concerning the randomly rough elastic cylinder (Stanton and Chu, 1992), randomly rough deformed finite fluid cylinder in application to zooplankton (Stanton et al., 1998), randomly oriented marine organisms (Stanton et al., 2004) and became a major area of focus very recently in modeling echo statistics concerning multiple scatterers or patches of scatterers in the sonar beam (Stanton and Chu, 2010; Chu and Stanton, 2010). Last, but not least, my work could not have been possible without the inspiration and guidance of Clarence (‘‘Clay’’) Clay and Van Holliday as well as the collaboration with many, as documented by the many co-authors on my papers, especially Dezhang Chu (Fig. 3). 3. Some definitions There are two principal quantities that are analyzed in active bioacoustics: target strength, TS, and volume backscattering strength, Sv . The target strength describes the efficiency with which a single

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Fig. 3. Photo of scientific party in the ocean bioacoustic experiment, Echofront85, in the summer of 1985. The cruise, which took place about 100 miles offshore at the Gulf-Stream boundary, had just been completed and had returned to the Duke University Marine Laboratory. The cruise involved a combination of the Holliday/Pieper 21-frequency system, the Clay/Stanton/Jech multiple frequency system (several independent single-frequency systems connected together), and the Powell/Stanton realtime sonar echo processor. Also, on this cruise Stanton conducted his first laboratory experiment of acoustic scattering by freshly caught organisms involving various fish and zooplankton species (including euphausiids, siphonophores, pterapods, mychtophid, and hatchet fish). The laboratory experiment involved a 5-foot-diameter tank filled with water on-board the ship, with laboratory pulse-echo equipment. Although that work was never published, it inspired the laboratory experiments started later by Stanton at the Woods Hole Oceanographic Institution. Front row, L-R: Steve Brandt, U, John Magnuson, U, U. Middle row, L-R: Tim Stanton (striped shirt), Paul Jacobson, Van Holliday (with sunglasses), Bob Eastwood, Clarence Clay, U, U, Gary Hitchcock. Back row, L-R: Mike Jech (with sunglasses, his head is shown between Jacobson and Holliday), U, Richard Nash (standing just to the right of the pole), Ben Abernathy (blocking Rick Pieper’s right shoulder), Rick Pieper (no shirt), U, U, U. U = unidentified. Photo provided by Mike Jech.

object scatters sound and is given on a logarithmic scale. It is defined in terms of the backscattering amplitude fbs and differential backscattering cross section as TS = 10 log |fbs |2

= 10 log σbs

(1)

where σbs = |fbs | . The units of fbs and TS are m and dB, respectively. The backscattering amplitude is expressed in terms of the backscattered pressure, ps , measured at the echosounder transducer as 2

ps = P0

eikr r

fbs

(2)

where P0 is the√pressure of the incident field at the scatterer, r is the range from the echosounder to the scatterer, i = −1, and k is the acoustic wavenumber (=2π /λ where λ is the acoustic wavelength). Interestingly, although these are definitions of fundamental quantities, there has been confusion and associated errors in this field. The definition of backscattering cross section sometimes comes with a factor of 4π (MacLennan et al., 2002). Once defined in the context of TS in Eq. (1), there is no problem. However, there have been errors in the literature of 11 dB (=10 log 4π ; i.e., a factor of 12.6 error) in biomass estimates when the 4π is not correctly accounted for. For example, if the backscattering cross

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section is defined with a factor of 4π and then the 4π is not subsequently removed in the TS − σbs definition, then the error of 10 log 4π will occur. The volume backscattering strength is the target strength per unit volume (one cubic meter) of scatterers averaged (before the logarithm is taken) over an ensemble of statistically independent pings. For a collection of scatterers with a numerical density nij and average backscattering cross (ij)

section σ bs of each ith size class and jth taxon, the volume backscattering strength, in dB, is Sv = 10 log



(ij)

nij σ bs .

(3)

i,j

The terms TS and Sv are determined from data through use of the sonar equation (Urick, 1983) where geometrical spreading and system parameters such as source level and receiver sensitivity are accounted for. TS is derived from echo envelope data and Sv is determined from echo integration. Echo integration involves calculating the echo energy in a range bin (typically 1 m or less) by squaring the echo voltage and accumulating the sum of those squared values throughout the bin. The statistics of TS and Sv are normally analyzed—principally the mean values (averaged before the logarithms are taken), but also the statistical distribution of the values (both before and after the logarithms are taken). Sometimes the volume backscattering strength is integrated over a range of depths (on a linear scale before the logarithm is taken) to calculate the area backscattering strength. 4. Hardware—electronics Electronics in this field has experienced a profound change as systems that were originally 100% analog have evolved, through the revolution in digital electronics, to being mostly digital. 4.1. 1980 Although digital electronics were making their way into various hardware systems in the 1980s, many systems, including echosounders, were initially all analog. Most settings were ‘‘hardwired’’ (i.e., fixed) with the only options to change settings, such as time varying gain and master gain, being available through several analog knobs on the front panel. Thus, the systems had relatively little flexibility in use. Also, the signals in cables connecting the transmitter/receiver electronics with the transducer were also analog. Because of this, there were significant issues concerning noise in the cable. The analog cables behaved electrically like an antenna, picking up electromagnetic interference which contaminated the sonar echoes, hence limiting the detection range. This was particularly an issue with towed systems that required long cables. Because of these many limitations, there were many digital systems in the developmental stage in the early 1980s as discussed later (Fig. 4). 4.2. 2012 Now, the systems are fully digital except where analog circuitry is required (such as analog preamps at the transducer). The electronics contain DSP’s (digital signal processor) which makes them extremely flexible and compact. In essence, the electronics are programmable and software-based, which allows users to change settings via computer control. In addition, signals traveling along cables between the transducer and electronics can be digital, which greatly reduces noise issues, especially for long cables. 5. Hardware—systems and signals Echosounder hardware and associated signals have evolved from use of one single beam transducer emitting a single frequency, gated sine wave signal, to split- or multi-beam systems spanning multiple frequencies. In some developments, broadband signals are used along with pulse-compression processing.

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Fig. 4. Prototype digital sonar echo processor (Powell and Stanton, 1983). (a) 1981 Apple-II+ personal computer (left) integrated with processor (right). Two floppy disk drives are shown. With two, data could be recorded continuously—when one disk filled up, the system automatically began writing to the other disk while the first disk was replaced with a blank one. (b) Echo processor. The analog board contained a low-noise preamplifier with a gain switchable via manual dual-in-line (DIP) switches as well as an analog-to-digital converter (ADC). The solid state components on the digital board performed basic operations such as add, shift, and multiply. The digital board fed into a computer interface board (not shown) which we designed and built and was, in turn, controlled by assembly language routines which we wrote. We also wrote our own disk operating system to greatly reduce the time it took to save data files onto the disks (60 ms vs. 3 s). These photos were taken by a University of Wisconsin photographer and are appearing for the first time in this paper.

5.1. 1980 Echosounders consisted of a transducer which served as both a transmitter and receiver and produced a single (transmit/receive) beam at a single frequency (Fig. 1). The transducer may have been composed of multiple active elements, but all elements were electrically connected to form a single, unsteered beam in a direction normal to the flat face of the transducer. The beamwidth was typically 5–10°. The frequency was typically 38, 120, or 200 kHz—one frequency per echosounder. This was actually a ‘‘band-limited’’ or narrowband signal, as it was produced via transmitting a gated sine wave with a duration of the order of several tenths of a millisecond. The bandwidths were typically

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Fig. 5. Sketch of vertical plane multi-beam sonar used to image aggregations of fish. A reconstructed image of a fish school is shown (Gerlotto et al., 2003).

5%–10% of the center frequency of the system (e.g., a 200 kHz signal spanning 195–205 kHz). The range resolution is given by the equation:

1r = c τ /2 (range resolution—gated sine wave)

(4)

where c is the speed of sound in water (nominally 1500 m/s) and τ is the duration of the sine wave. For the gate durations used, this range resolution was typically several tens of centimeters. 5.2. 2012 5.2.1. Split- and multi-beam; multi-frequency; and broadband systems Through a series of developments, transducers and associated electronics are now commercially available to produce a split- or multi-beam in addition to a single beam (Fig. 5). Furthermore, commercial echosounders have the option to operate several transducers (typically up to six), each at a different frequency so that multiple frequencies can be used in the same measurement. In addition, some transducers have been designed to produce a broadband signal with roughly one octave bandwidth (e.g., 100–200 kHz). Development of the multiple frequency systems was inspired, in part, by research prototypes (Holliday et al., 1989; Wiebe et al., 2002). Broadband echosounder systems have only recently become available (Ross and Lawson, 2009; Stanton et al., 2010, 2012; Lavery et al., 2010a,b). Calibration of the transducers (while fully integrated into the system) has become standardized through use of a standard spherical target (narrowband: Foote, 1982; broadband: Stanton and Chu, 2008). The split-beam approach was developed so that TS could be measured directly. It can also be used for target tracking. Another technology, the dual beam approach, could also measure TS directly, but ultimately the split-beam approach prevailed due to its advantages over the dual beam (Ehrenberg, 1979). The multi-beam is used to produce 3-D images of patches of biological scatterers. These systems typically have many narrow 1° beams to insonify much of the water beneath the ship in up to a 150° swath in a plane perpendicular to the direction of the ship (Gerlotto et al., 1999; Mayer et al., 2002; Trenkel et al., 2008). Multi-frequency and broadband systems are used to exploit the frequency dependence of the scattering by the organisms for various types of classification as discussed below.

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5.2.2. Chirp signals and pulse-compression processing While most narrowband systems still transmit a gated sine wave, at least one commercial system transmits a narrowband linear frequency modulated signal (‘‘chirp’’) which is post-processed through pulse-compression processing (Ehrenberg and Torkelson, 2000). Also, the broadband systems transmit a similar signal, but spanning a much larger range of frequencies. Whether it be a narrowband or broadband signal, pulse-compression processing can significantly improve the signal-to-noise ratio (SNR) and range resolution of the system. The pulse-compression processing is normally done in realtime so that a high-resolution image is displayed in real-time. The processing is done through the equation: cpr (t ) = kcp vr (t ) ⊗ vrep (t ) pulse-compression processing

(5)

where cpr (t ) is the pulse-compressed received voltage signal, t is time, kcp is a normalization constant, vr (t ) is the raw, unprocessed received voltage, vrep (t ) is the ‘‘replica’’ signal, and ‘‘⊗’’ is the crosscorrelation operator. Mathematically, the pulse-compression processing is conducted through cross correlating the received voltage with a replica signal. This is similar to matched filter processing, where the replica signal is identical to the received signal of interest (Turin, 1960; Chu and Stanton, 1998). In practice, the applied voltage signal may be used as the replica, but with some degradation of performance. The improvements in the SNR and the range resolution due to pulse compression processing are:

∆(SNR) = 10 log (2τ BW ) (dB) improved SNR 1r =

1 2

c (BW )−1

range resolution—exploiting bandwidth

(6) (7)

where τ is the duration of the chirp and BW is the bandwidth of the signal. These improvements can be realized not only for broadband signals, but also for a narrowband signal provided full use of the band is made with the signal such as through a chirp signal. Also, an interesting aspect of this type of processing, as can be seen from Eq. (6), is that the SNR can be improved by increasing the duration of the transmitted signal. However, in contrast to the use of gated sine waves where an increase in duration corresponds directly to a degradation (a larger value) of range resolution (Eq. (4)), the range resolution after pulse-compression processing is independent of transmit signal duration (Eq. (7)). The resolution is improved (i.e., changes to a smaller value) by an increase in bandwidth. To summarize, through pulse-compression processing of a signal, the SNR can be increased through an increase in signal duration and bandwidth and the range resolution can be improved to a smaller value through an increase in bandwidth. For example, for the case of a narrowband 200 kHz signal with a 10 kHz bandwidth, a chirp signal is applied spanning 195–205 kHz and that is 10 ms long. Normally, a 10 ms-long gated sine wave would have a range resolution of 7.5 m (using Eq. (4)). However, after pulse-compression processing, the range resolution is 7.5 cm (using Eq. (7)). In addition, the improvement in SNR over a gated sine wave is 23 dB (using Eq. (6)). For a genuinely broadband signal where bandwidths are comparable to an octave, the results are even more striking. For example, in an operational broadband transducer with a usable frequency range of 30–105 kHz, pulse-compression processing of a 2 ms-long signal is temporally compressed to 11.4 µs long (Fig. 6). This compression corresponds to improving the range resolution, which was originally 1.5 m to 3 cm, while at the same time improving the SNR. Note that the value of 11.4 µs was determined numerically and differs slightly from the 13.3 µs calculated from (BW )−1 . Also, once the non-uniform transducer response is accounted for, as well as an imperfect (operational) replica signal, this value increases to 35 µs, resulting in the 3 cm range resolution (Stanton and Chu, 2008). In another example involving an operational broadband system, for the low frequency channel used for resonance classification where the bandwidth is about 6 kHz (centered around 3 kHz), the range resolution is about 20 cm (Stanton et al., 2010). This resolution is finer than that of a traditional gated sine wave system at 200 kHz, even though the wavelengths are 100 times greater. Note that the value of 20 cm accounts for a non-uniform transducer response and imperfect replica as in the previous example.

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Fig. 6. Effects of pulse-compression of a broadband signal. The original 2 ms-long signal in (a) has a frequency range of 30–105 kHz and is compressed via pulse-compression processing (matched filter processing in this case) to being 11.4 µs long in (c). The spectra of the signals are preserved throughout the process ((b) and (d)). Source: From Stanton and Chu (2008).

6. Software Along with the hardware evolution, software evolved just as rapidly over the past 30 years. We went from ‘‘home-brew’’ software in the 1980s to commercial software that could be downloaded from the internet in 2012. The internet didn’t exist in 1980! 6.1. 1980 Mainframe computers had dominated the computer market up to this point and personal computers were just beginning to emerge. On the mainframe computers, software was written in various versions of Fortran with standard math functions and graphics packages available. In these days, computers were actually used to compute (vs. e-mail, preparation of documents, surfing the web, etc., in 2012)! The disadvantage was the accessibility of the computer. After data were provided to the computer (via reel-to-reel tape), the software that was punched on a stack of cards was fed into the computer. Writing software was clearly a cumbersome task, especially since it involved punching one computer card for every line of code (each character was denoted via a hole punched in the card). One syntax error could cost you a day in time! The emergence of personal computers helped address the cumbersome aspect of the mainframes. However, the computers came ‘‘bare’’ in that there was a very basic operating system and not much more. Users spent much time writing all software from scratch, as the software was not developed yet as it had been with the mainframes. For example, in my own simulations (such as in Stanton, 1982), I

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wrote my own code to calculate Bessel functions. The simulations used the language ‘‘Forth’’, a stackoriented program, that was one level above assembly language. It was very time consuming to write. Since there were no graphics packages, I wrote my own such as to display plots for simulations or the real-time display in the echo processor I co-developed, as described later (Powell and Stanton, 1983). My software controlled each pixel on the display to draw axes, numbers, and data values. Another issue is that this field was so new, being a computer programmer was not established as a career path, thus there were few ‘‘experts’’ in software to consult. 6.2. 2012 Now, commercially available software is ubiquitous and highly evolved. The software is so sophisticated, that one can install and run it themselves without the assistance of a computer expert. Users still write their own software, but have easy access to established graphics and math routines. Software not only is used to process data, but also to control hardware (see discussions on ‘‘programmable hardware’’). Software is available both online (via ‘‘downloads’’) or through a physical medium such as a CD. 7. Deployment modes and associated instrument platforms Modes of deployment and associated instrument platforms have evolved over the past three decades, in part, because of the shrinking of electronics and reduction in power requirements, but also because of engineering developments of new platforms (observatories and AUV’s) (Edson et al., 2002; Curtin and Bellingham, 2001). 7.1. 1980 Deployment of systems was generally limited to being hull-mounted on the ship or towed from the ship. In each of these cases, electrical power was supplied by the ship. In this period, electronics generally consumed relatively much electrical power, hence requiring a significant source of power such as what could be provided by the ship. 7.2. 2012 There were several major developments that significantly affected the types of modes of deployment available for echosounders: (1) miniaturization of electronics and reduction of power requirements, (2) development of batteries with increased ratios of energy to volume, and (3) development of AUV’s for scientific use (which exploited #1 and #2). As a result, in addition to the hull-mounted and towbody deployments, echosounders can also be deployed on AUVs (Fernandes et al., 2003) and long-term observatories (Trevorrow, 2005; Pawlowicz and McClure, 2010). In each of these latter cases, batteries are generally used, although some observatories close to shore are also cabled with shore-based power. 8. Display and recording of data 8.1. 1980 In 1980, personal computers were in their infancy and, as discussed above, echosounder hardware was entirely analog. This had significant implications in the display and recording of data. The realtime displays were the so-called paper charts which involved a constantly and quickly scrolling device that marked the paper along the depth dimension while, at the same time, the paper was constantly slowly scrolling along the other dimension (which represented time at a fixed station, or distance along a transect for a ship moving at a constant speed). The marks indicated the presence of an echo. The displays were uncalibrated, in black and white, and had a very small dynamic range (Fig. 2). Thus,

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Fig. 7. False-color echogram reconstructed during post-processing and shown in relation to latitude and longitude. Source: From Wiebe et al. (1997).

it was important to maintain a watch on the display and to adjust the display sensitivity according to when the overall echo levels changed. In addition to the scientific limitations of the paper recorders, they frequently gave off an unpleasant odor and needed to be vented! Memory devices were also generally in analog form, as digital memory was very expensive. Data were normally recorded on analog tape, either through use of a scientific reel-to-reel recorder or sometimes on cassette tape. There were a number of significant issues with this approach: (1) tapes are inherently a serial access medium. In order to access data, the entire portion of the tape preceding the desired data must first be passed through to access the data of interest. (2) There is no indication on the tape of the precise time at which the data were recorded. Thus, it was necessary to relate a measurement of how much tape had passed through the heads to time. There was inherent error in this process, thus time was not known to the desired precision. (3) Since the data were stored in analog form, there was a drift in sensitivity of the tape as a function of use. Thus, if a tape were reprocessed several times, after each time the tape was used, the echo levels were reduced slightly. (4) Analog data on tapes had a relatively small dynamic range (in amplitude of signal), thus limiting the quality of data recorded. (5) The analog tapes were also limited to audio frequencies. Thus, recordings were limited to base-banded signals whose carrier frequency was at an audio frequency. Or, sometimes the echo envelope was recorded. Thus, there was never an option to record the full original waveform for the high frequency systems.

8.2. 2012 The great advances in electronics over the past 30 years have addressed the above issues with correspondingly great improvements. Displays are now presented on a computer monitor, provide much information, and are very flexible. For example, calibration can be used in a false-color display to show, in real-time, calibrated TS or Sv over a wide dynamic range. The displays are flexible so that multiple channels (such as from multiple frequencies) can be displayed in real time. Post-processed displays of single- and multi-beam data provide great new insight (Figs. 5, 7). In addition, the monitors are quiet and do not emit an odor like the original paper recorders! Memory devices are all dense with high speed random access and have completely replaced tape storage media. They come in the form of, for example, internal and external hard drives which now

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exceed 1 TB of memory. The cost of memory is relatively low and not a major consideration in data acquisition systems. 9. Processing of data 9.1. 1980 9.1.1. Hardware Processing of data was done almost entirely from recorded data, hence ‘‘post-processing’’, although real-time processors were being developed in the 1970s and early 1980s. The processing was performed either with a dedicated processor or via computer using digitized data. The dedicated processors were hardware systems that were generally not programmable. A key challenge was the fact that, without real-time processing, all data (which were stored on analog tapes) had to be played back at the same rate at which it was stored. Imagine collecting 200 h of data and taking at least another 200 h after the experiment to replay the data for processing! This problem helped to drive the development of real-time processors. As the microcomputers and personal computers became widely available in the 1970s and early 1980s, there was the emergence of prototype hardware processors that were programmable and permitted real-time processing (Thorne, 1977; Kanemori and Ehrenberg, 1978; Powell and Stanton, 1983; the review of the Bergen echo integrator in Foote et al., 1991). However, memory was very expensive then and posed a significant challenge. For example, in the echo processor that Powell and I developed, there was a total of about 10,000 bytes (8-bits) of random access memory (RAM) available on-board the personal computer (Fig. 4). Because of that, and what would be considered in 2012 as a slow clock speed (1 MHz), a combination of assembly language and another low level macro language (‘‘Forth’’, as described above) was used so that the personal computer could keep up with the rate at which the processed data were being acquired (Stanton and Powell, 1982). Furthermore, in order for data to be stored onto a permanent storage medium (5 1/4′′ floppy disks and, later, Bernoulli disks) fast enough in between pings so that data would not be lost, the disk operating system (DOS) of the computer was bypassed and we wrote the data directly to disk with assembly language. This reduced what had been a 3 s operation for storing a data file into a 60 ms operation. In spite of the challenges, the performance of the system was maximized and yielded useful results, such as the analysis of combining echo integration and echo statistics to estimate the numerical density of fish (Fig. 8). 9.1.2. Determining TS and Sv from single beam data Regardless of which processor was used, data were generally processed to obtain echo envelope time series and volume scattering strength. From the time series, peaks were picked so that TS could be estimated through various indirect techniques. Sv was always determined from echo integration methods. The challenge involved in determining TS was that the fish or zooplankton were randomly located in the single beam of the echosounder. The value of the echo voltage is proportional to the product of the backscattering amplitude of the organism and the value of the beampattern. However, with the organism randomly located in the beam, the beampattern value is, in turn, random. Hence the component of variability in the echo associated with these (random) beampattern effects needed to be removed from the data. Once removed, the remaining echo variability is due solely to the organism. Various approaches were used to remove beampattern effects, including the Craig and Forbes (1969) algorithm, the deconvolution approach (Ehrenberg, 1972; Clay, 1983; Stanton and Clay, 1986), and a method using maximum likelihood (Hedgepeth et al., 1999; Moszynski, 2002). While these nonparametric (i.e., no assumption of scattering statistics) methods are based on the same principles, the developments were made for improved efficiency and accuracy (Rudstam et al., 1988; reviewed in Ehrenberg, 1989). The end result of the post-processing was calibrated echograms of Sv at one frequency and distributions of TS. Contours of Sv were frequently hand drawn!

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Fig. 8. Echo peak histograms versus depth, as determined from the output of the sonar echo processor developed by Powell and Stanton (1983). The transition from non-Rayleigh to Rayleigh statistics near 65 m depth was used to estimate the numerical density of fish at that depth which, in turn, allowed for density estimates for the entire water column using the echo integration values. The data were collected and recorded on analog tape by University of Wisconsin researchers in Lake Michigan in the early 1980s. Source: From Stanton (1985b).

9.2. 2012 Fixed hardware systems and prototype programmable systems have given way to commercial programmable hardware for real-time processing with high clock speeds (>2 GHz). Echograms of Sv are obtainable in real-time displays in addition to post-processing. Very importantly, TS is determined directly through use of split-beam processing rather than through the indirect methods cited above (Ehrenberg, 1979; Foote et al., 1986). Although determined directly, TS remains a stochastic quantity, even for a single organism, and a TS distribution is still determined. Based on this distribution, TS is frequently expressed as a mean quantity. With many systems being multi-frequency, color contours (computer generated) of Sv vs. depth and transect distance (or time at a fixed station) are made for each frequency. The multi-frequency and broadband data are used to create echo spectra (discrete or continuous) (Fig. 9). Synthetic echograms are also created to illustrate differences in echo levels across frequencies. Interpretation of these frequency dependencies derived from these different approaches are discussed below. In addition to the above processing associated with a single (or split) narrow beam, multi-beam images are constructed during post-processing (Fig. 5). Through advanced post- processing software, images of entire aggregations of organisms can be visualized, rotated, and analyzed, such as in terms of school morphometry (Paramo et al., 2007, 2010). 10. Interpretation of data In the absence of quantitative methods for interpretation, acoustic echoes are used to indicate the presence and absence of biological sound scatterers. There have been significant advances made toward translating the echoes into meaningful biological parameters such as numerical density, size,

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Fig. 9. Using resonance classification to spectrally resolve two major size classes of collocated swimbladder-bearing fish that are otherwise not spatially resolvable. The approach relies on a broadband acoustic system, 1–6 kHz, that excites the resonances of the fish swimbladders (Stanton et al., 2010). The lower and upper frequency resonance peaks correspond to larger and smaller fish, respectively. Source: From Stanton et al. (2012).

species, and spatial variability of the fish or zooplankton. Scattering models for TS and fbs in Eqs. (1) and (2) are central in these interpretation methods. Development of advanced scattering models has enabled the field to exploit multi-frequency and broadband data for advanced interpretation of the acoustic data. For example, inversions of multifrequency and broadband data, using scattering models, has led to accurate estimates of size and numerical density of fish and zooplankton (Holliday and Pieper, 1995; Lawson et al., 2008; Stanton et al., 2010, 2012; Lavery et al., 2010a). These inversions were inspired, to a large extent, by the multifrequency inverse theory presented in Holliday (1977a). Synthetic echograms based on the differences in scattering levels across the different frequencies have also been developed to classify organisms based on their scattering properties (Warren et al., 2003; Korneliussen et al., 2009a). In addition to scattering-model-based methods, there have been other methods involving statistically characterizing the morphometry of aggregations of organisms as derived from echograms, such as the physical dimensions of the aggregation, distance above the seafloor, texture of the echo data, etc. These methods classify species according to their behavior as inferred from the morphometry (Haralabous and Georgakarakos, 1996; Scalabrin et al., 1996; Paramo et al., 2007; Cox et al., 2010). Initially, the methods involved use of echograms from single-beam echosounders where parameters associated with a 2-D plane were analyzed. As multi-beam systems became available, the methods expanded into characterizing the full 3-D information of the distributions of the organisms. Interpretation of acoustics data can be further improved by combining data from multiple acoustic technologies. For example, multi-beam and multi-frequency systems provide complementary information regarding the morphometry and scattering response of an aggregation of organisms, respectively. When those systems are used simultaneously and the respective acoustic inferences are combined, there is potential for reducing uncertainties in the interpretation of the data compared with using either technology alone (Korneliussen et al., 2009b). Regardless of the interpretation method, concomitant use of nets or other direct sampling methods is imperative. Acoustical methods are inherently under-determined. Through use of direct sampling of the organisms, although sampling a far smaller volume than that of the acoustics system, the acoustical interpretation method can be constrained to fewer unknown parameters. Since the focus of my research involving interpretation methods has been in the development of scattering models, the following sections will focus in that area.

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10.1. 1980 Scattering models were generally quite primitive, with some exceptions. For fish, there were models for the two regimes—resonance scattering by the swimbladder and high frequency (well above resonance). For zooplankton, there were also two types of models, not according to frequency regime, but rather of different approaches, one based on an analytical model and the other with an empirical formula. 10.1.1. Fish—resonance scattering model for swimbladder-bearing fish For frequencies near the resonance frequency of the swimbladder, an analytical model was derived assuming that the swimbladder dominated the scattering and was a monopole scatterer (Love, 1978): a2 ρw /ρf



σbs =

2

f02 /(fH )2 + f02 /f 2 − 1



2

resonance scattering model

(8)

where a is the equivalent spherical radius of the swimbladder, ρw and ρf are the mass densities of water and fish flesh, respectively, f is the frequency, f0 is the resonance frequency, and H is related to the damping of the swimbladder. At resonance, H is equal to the quality factor of the resonator which is equal to the ratio of the resonance frequency to the width of the resonance. This theory had been developed over a number of years, beginning with Minnaert (1933) and including Andreeva (1964). The term H is quite complex and includes viscous and thermal damping from the tissue, surface tension, and radiation damping. By the time Love had derived this equation, there had been much activity in determining the damping parameters of the swimbladder, as well as the depth-dependence of the resonance frequency and adaptation time of the swimbladder resonance to rapid changes in depth (reviewed in Diachok, 2005). For common sized fish, the resonance frequency occurs in the upper 100’s Hz to lower kHz. Thus the frequencies at which the model applies are generally below 10 kHz. 10.1.2. Fish—high frequency scattering model for swimbladder-bearing fish For frequencies well above 10 kHz, a regression equation was used for fish: TS = A log L + C

high frequency regression equation

(9)

where L is the length of the fish and A and C are empirically-determined parameters. In the early uses of this equation, the equation was generally fitted to fish from a large range of species and sizes. As a result, the variance was significant. The term A was generally near the value of 20, representing the L2 dependence of scattering. 10.1.3. Zooplankton—analytical models The first attempts toward analytically modeling zooplankton scattering involved use of the exact modal series solution to the fluid sphere (Anderson, 1950; Pieper and Holliday, 1984). The form was: fbs =

∞ i 

k m=0

bm (−1)m

modal series solution

(10)

where bm is the modal series coefficient. Holliday and Pieper determined that in order for the theory to fit multi-frequency backscatter data, the series needed to be truncated to include only the first two terms. This model was applicable for all frequencies and sizes of zooplankton. However, it approximated all zooplankton to resemble a sphere, with no orientation dependence of the scattering. Furthermore, it treated all zooplankton as having the same material properties (fluid-like), regardless of whether they are composed principally of tissue, or also have a hard shell or contain gas. For applications to copepods, which is what inspired this model, the fluid assumption was valid.

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Because of the complexity of the model, efforts were made to formulate a simplified version of the modal series so that the overall levels were maintained at all frequencies, but not necessarily the structure, such as nulls. Johnson formulated the so-called ‘‘high-pass’’ model, where the terminology makes the analogy that the scattering function resembles a high pass electrical filter (‘‘passing’’ higher frequencies and rejecting lower frequencies) (Johnson, 1977). This formulation was generalized in Stanton (1989b) to describe a range of shapes—sphere, straight finite cylinder, bent finite cylinder, and prolate spheroid in the general expression: (≪)



σbs = σbs G 1 +

σbs(≪)

 −1

σbs(≫) R2 F

(≪)

high-pass model

(11) (≫)

where σbs is the ka ≪ 1 limit for the case of a fluid object, σbs is the ka ≫ 1 limit for the case of a rigid/fixed object, G is a heuristic function to produce nulls and/or peaks in the curve, F is a heuristic function to account for deviations in σbs when the object is irregular and/or lossy, R is the planewave/plane-interface reflection coefficient, and a is the radius of the sphere, cylindrical radius of the cylinder, or length of the semi-minor axis of the prolate spheroid.

10.1.4. Zooplankton—empirical model There were also attempts to fit a regression curve between biomass and volume backscattering. This was based solely on comparisons between echo data and net tows. These curves generally include contributions from all species and sizes of zooplankton and had a significant variance. In a study by Wiebe et al. (1996), this high variance for a single regression equation description of a mixed population of zooplankton is demonstrated, as well as significant improvements once species-specific models are incorporated as discussed later.

10.1.5. Comparison with nets Nets generally had a single cod end, although multiple net systems were under development and in use by some. With only a single cod end, the organisms could only be sampled at a constant depth or through a range of depths via an oblique tow. As a result, there was a limited amount of net data that could be used in a given study for adequate direct comparison with acoustics data.

10.2. 2012 There was significant development since 1980 on the models, especially in the high-frequency regime with fish and all major types of zooplankton.

10.2.1. Fish—resonance scattering model for swimbladder-bearing fish There were relatively modest developments in this area during this period. I attribute this to the fact that there was a great expansion of work concentrating in the frequency range of 10’s–100’s kHz, which was well above resonance and required a different kind of model. The use of seismic sources such as in the 1960s–1970s which excited the resonance of the fish swimbladder and led to the development of the resonance model was greatly reduced. Some of the resonance modeling research involved including elastic aspects of the swimbladder (Feuillade and Nero, 1998). Others involved alternative types of formulations to model the scattering, including the deformed cylinder formulation in the Kirchhoff-Ray-Mode (KRM) model (Clay, 1991, 1992; Clay and Horne, 1994), a conformal mapping method (FMM) (Reeder and Stanton, 2004), and a T -matrix method (Feuillade, 2012). These latter approaches explicitly account for the elongation of the swimbladder, rather than use of an eccentricity correction factor as in Love’s sphere-based model.

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Fig. 10. Images of the outer boundary of a fish and its swimbladder using a medical computerized tomography (CT) machine. These data are used for advanced modeling of scattering by fish. Source: From Reeder et al. (2004).

10.2.2. Fish—high frequency scattering model for swimbladder-bearing fish The importance of species-specific scattering was accounted for through various means. For example, the regression equation in Eq. (9) was applied to fish of the same species. As a result, the variance of the regression was greatly reduced and species-specific values of the regression equation coefficients were determined (Foote, 1979; McClatchie et al., 1996). Analytical methods to describe the scattering by the swimbladder were also developed and applied for selected species. For example, the swimbladder shape was digitized and incorporated into various models, most involving the Kirchhoff surface integral: fbs =

i

λ



⃗

′

kˆ inc · nˆ ei2kinc ·⃗r dA

R

(12)

A

where ⃗ kinc is the wavenumber vector of the incident field, ⃗ r ′ is the position vector of the surface, dA ˆ is the differential element of the surface A, n is the unit vector of the outward normal of the surface, and ‘‘ ˆ ’’ denotes a unit vector. This has been applied in many cases, including the KRM model. Besides the Kirchhoff model, there has been the FMM, deformed cylinder model, T -matrix, and others cited above that have attempted to account for the shape of the swimbladder at various scales (Fig. 10).

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Fig. 11. Laboratory tank used to make measurements of acoustic backscattering by various marine organisms, ranging from mm-sized pterapods to 20 cm fish. The frequencies ranged from 24 kHz to 1.2 MHz, using a combination of narrowband and broadband (octave) transducers. The organisms were rotated 0–360°. This setup varied over the 20 years we used it. It was normally used on land, although twice it was taken to sea on the deck of a ship in which the scattering by freshly caught zooplankton was measured. Source: From Stanton et al. (2000).

10.2.3. Fish—high frequency scattering model for fish without swimbladder Although the vast majority of fish scattering research has focused on swimbladder-bearing fish, the important problem of investigating the scattering by fish without swimbladders has been addressed. For example, Gorska and colleagues have developed a deformed cylinder model that accounts for both the flesh and bone of the fish and have compared the results with data (Gorska et al., 2005). Although the levels are lower than for fish with swimbladders, the echoes are detectable. 10.2.4. Zooplankton The diversity of material properties as well as elongation have been accounted for through various developments. This is an area where I focused much of my work, beginning with the paper, Stanton (1988), in which elongated organisms were modeled as a finite-length cylinder. This, and several other modeling papers, were followed by a series of laboratory measurements by myself and various colleagues of acoustic scattering by a wide range of organisms, and made over a wide range of frequencies and orientation (Fig. 11). Although most measurements were conducted on land, some were performed at sea on the deck of a ship using the same apparatus and with freshly caught organisms. These measurements inspired the development of new models, as well as provided a basis for validation of the models. See, for example, the review of the experiments and model developments in Stanton (2009). Three anatomical groups Depending upon the species or gross anatomical group, the material properties of zooplankton can (1) be weakly scattering in which the organism is principally composed of tissue whose material properties are close to that of the surrounding water, (2) contain a hard elastic shell, or (3) contain a gas inclusion. Because of the wide range of material properties, the ratio of biomass to echo energy has been demonstrated in laboratory measurements to vary by up to 19,000:1 over the range of organisms given above. This observation refutes use of a regression equation that assumes a constant

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Fig. 12. Progression of models of the shape of euphausiids for advanced modeling of scattering, as derived by Stanton and colleagues. The progression begins with the finite-length straight cylinder, which replaced the previous sphere model (Stanton, 1988). The progression ends with two classes of models, one in which the full 3-D shape information based on a CT scan (such as in Fig. 10) is incorporated into a full 3-D deterministic acoustic scattering model (Lavery et al., 2002) and the other involving use of a randomly rough boundary superimposed on an otherwise smoothly varying boundary (Stanton et al., 1998).

ratio (Stanton et al., 1994). The resultant models from Stanton et al. (1994) were validated in later field studies such as Wiebe et al. (1996) and Lavery et al. (2007). Details of scattering models of the three groups are summarized in Lavery et al. (2007). Elongation and deformation effects (deformed cylinder formulation) A key development involved accounting for the elongation of organisms as well as their lengthwise deformations (Fig. 12). In essence, the shape is being realistically modeled. This was made possible through development of the deformed cylinder formulation (Stanton, 1989a). The original formulation was based on the exact modal-series solution of an infinitely long cylinder. This assumption limited the deformed cylinder predictions to angles near broadside incidence. In Stanton (1992), the formulation was generalized to being based on any infinite cylinder solution and in Chu et al. (1993) and Stanton et al. (1993), the distorted wave Born approximation (DWBA) was incorporated into it for weakly scattering zooplankton. The deformed cylinder formulation from those developments was shown explicitly in Stanton et al. (1998): fbs =

   J1 (2k2 a cos βtilt )  ⃗ d⃗rpos  , γκ − γρ e2i (ki )2 ·⃗rpos a 4 rpos cos βtilt

k1



(13)

where k1 and k2 are the wavenumbers of the surrounding water and body of scatterer, respectively,

γκ and γρ are related to the density and sound speed contrasts of the scatterer, βtilt is the tilt angle (relative to the incident wave) of the local cross section of the cylinder at point ⃗ rpos , ⃗ rpos is the position vector of the axis of the cylinder, and J1 is the Bessel function of the first kind of order 1.

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This formulation is valid for all frequencies, sizes, and orientations of weakly scattering zooplankton. In Stanton et al. (1998), a randomly rough cylinder was modeled and compared successfully with backscattering data from a euphausiid over all angles of orientation. A key element of that development was the randomly rough exterior that was required to predict backscattering at orientation angles near end-on incidence. The formulation was later applied to scattering by copepods in Stanton and Chu (2000). As shown in Chu et al. (1993) and Stanton et al. (1993), the original DWBA formulation is fully 3-D (in contrast to the 1-D line integral of the deformed cylinder formulation). This integral was exploited by Lavery et al. (2002) in modeling elongated zooplankton as a 3-D volume, rather than the approximation of a deformed cylinder. This development involved incorporating 3-D data from medical computerized tomography (CT) scans of the organism into the 3-D DWBA integral. In summary, significant developments have been made in modeling zooplankton. The models of euphausiids are most reliable at this point, due to the amount of validation with experimental data. Development of models of other organisms in that (weakly scattering) class, such as copepods, as well as gas-bearing and hard elastic shelled organisms has remained limited due, in part, to the lack of data. 10.2.5. Measurements and inferences of model parameters A scattering model, no matter how mathematically complex and rigorous, cannot be effectively used in interpreting data without accurate model parameters. Key parameters include sound speed and density contrasts of tissue, damping coefficients of tissue (associated with gas-bearing fish and zooplankton near resonance), and tilt angle distribution. All of these parameters directly affect the echo level. The echo from tissue-only organisms (e.g., euphausiids) is particularly sensitive to sound speed and density contrasts since those values are close to unity. And, because those values are close to unity, they are correspondingly difficult to measure. The echo is also very sensitive to tilt angle when the acoustic wavelength is much smaller than the length of the organism. The tilt angle is difficult to measure because measurements made in the proximity of the organism will tend to disturb it. Significant progress has been made concerning most of these parameters, with the exception of the damping coefficients due to a general lack of activity in the field in the resonance region of gas-bearing organisms. Measurements of sound speed and density have been made for various zooplankton and fish larvae (Foote et al., 1990; Chu et al., 2000; Chu and Wiebe, 2005; Wiebe et al., 2010; Smith et al., 2010). The measurements, or perhaps better stated as inferences based on measurements, included measuring the speed of sound in a mixture of zooplankton to determine sound speed contrast. The quantities buoyancy, weight, and resistivity of the zooplankton were measured to determine density contrast. Measurements of the tilt angle of organisms have also involved several different methods. For example, the tilt angles of zooplankton have been measured through use of a camera (Lawson et al., 2006). The tilt angles of fish have also been measured with cameras (Huse and Ona, 1996), equating the angle at which the fish is swimming (as determined via acoustic tracking) to the tilt angle (Ona, 2001), and relating the angle directly to the duration of a broadband echo (Stanton et al., 2003). In addition to the above more direct approaches, indirect approaches have involved inferring these parameters through backscatter data and an assumed scattering model (Foote and Traynor, 1988; Chu et al., 1993; Martin Traykovski et al., 1998). 10.2.6. Discriminating between echoes from zooplankton and physical microstructure Zooplankton commonly reside at or near sharp changes in physical properties (density or temperature) of the water. Since the physical microstructure can be a source of scattering, there was much controversy in the 1980s regarding whether the echo was due to the zooplankton or microstructure. It was generally assumed in the early 1980s that the echoes were dominated by zooplankton and, hence, whatever was seen in the echogram was assumed as being due to the presence of zooplankton only. Through a series of model developments grounded with experimental data, it was determined that, although the zooplankton would generally dominate the echoes, there were important conditions under which the microstructure could dominate (Goodman, 1990; Haury et al., 1979; Seim, 1999; Seim et al., 1995; Trevorrow, 1998; Lavery et al., 2003, 2010a,b; Geyer et al.,

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2010). These conditions involve regions where the microstructure was very energetic, such as in an internal wave in the open ocean or at the mouth of a freshwater river as it emptied into a saltwater (ocean) environment. 10.2.7. Comparison with nets Multiple opening and closing nets have been further developed and are more routinely in use (Wiebe and Benfield, 2003). With these devices, the organisms can be sampled at multiple depths with one cod end of organisms per depth or multiple sections of the transect at a constant depth. By sampling with these nets, there was much more data which could be compared with the acoustic data, such as patchiness of the organisms at constant depth or depth-dependence of organisms. This provides a better grounding of the acoustic data. For example, consider the case when there is a patch of organisms within a larger layer of organisms and with a species and numerical density different from that of the surrounding layer. The acoustic system can ordinarily detect the patch. Furthermore, a multiple opening and closing net can be directed so that one of its nets is open only when passing through the patch allowing for one-for-one comparison between the species in the patch and their corresponding acoustic signature. 11. Other (long-range sonars; broadband impulsive devices) Although this paper has focused on the devices principally used (i.e., direct path systems using reciprocal transducers), there have been other systems either used in the past, or are currently in development, that have merit and are summarized below. 11.1. Broadband impulsive devices In the 1960s and 1970s, there were experiments performed in which an impulsive device was used to generate broadband sound in the 100’s Hz to low kHz frequency range. Most approaches involved use of an explosive device in which the echo was received with an array of hydrophones (Hersey et al., 1962; Chapman and Marshall, 1966; Holliday, 1972, 1977b; Chapman et al., 1974; Hall, 1981; Hall and Quill, 1983; Thompson and Love, 1996). In some cases, a seismic arcer was used (Holliday, 1977b). An advantage of these systems was the strong signal transmitted into the water and the broad frequency content that could be used for resonance classification of swimbladderbearing fish. There were a number of disadvantages, however, which included safety associated with the explosive devices, repeatability of the transmitted signal, limited repetition rate, and inability to apply broadband signal processing algorithms (such as matched filter processing, since the outgoing signal was not well known). As interest in these frequencies reduced in the 1980s and 1990s, so did use of these approaches. However, interest increased in more recent years, and the impulsive devices were replaced by transducer-based systems in which the transmitted signal was repeatable, signal processing algorithms could be applied, and the repetition rate was high (Nero et al., 2004; Stanton et al., 2010, 2012). 11.2. Long-range sonars There has been work on this subject nearly continuously since Weston published his work from the 1960s (Weston and Revie, 1971). In his work, he detected fish at (horizontal) distances of up to 65 km at 1 kHz. There have been many other studies since, with frequencies ranging from low 100’s Hz to 10’s kHz (Rusby et al., 1973; Revie et al., 1990; Farmer et al., 1999; Trevorrow and Pedersen, 2000; Makris et al., 2006; Jones and Jackson, 2009; Gauss et al., 2009). There is at least one commercial system available in the 20–30 kHz range. A major advantage of this approach is the fact that fish can be viewed synoptically across a large area. This is in sharp contrast to downward looking systems (including high frequency multi-beams) with minimal areal coverage for a given ping and in which substantial areal coverage can only be obtained by moving the ship, whose speed is typically, at most, 10 knots.

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A major disadvantage of the so-called waveguide sonars is the fact that the waveguide (effects of sea surface, seafloor, and water column properties) is strongly variable along the path of propagation of the acoustic signal (i.e., a ‘‘range-dependent waveguide’’). It is not only spatially variable, but also temporally variable, making it very difficult, if not impossible, to adequately model the propagation loss of the signal over great distances. Another disadvantage is that the systems cannot resolve depth. For frequencies required for the acoustic signal to travel long distances, they are near the resonance frequency of swimbladders. As fish migrate diurnally, the resonance frequency of the swimbladder will change significantly. If the frequency of the acoustic signal is near the resonance, then the level of the backscattered signal (TS and Sv ) can change dramatically during migration (quite plausibly, an order of magnitude). Since a long-range system cannot resolve depth, the strong migration-induced changes in signal will be ambiguous—is the change in signal due to vertical migration, horizontal migration (and changes in patch density), or a combination? Because of these challenges, these systems remain principally at the research level until error bars for estimates of Sv are determined. Quantitative applications may include studying fish that remain at fixed depth in a simple waveguide whose acoustical properties are well known. 12. Future Personally, I am just as excited and optimistic about the future of this field as I was in 1980. By 1980, there had already been significant advances in the area, but it was clear that there was room for many more important advances. I believe that the same is true for 2012. Continued advances both in technology and interpretation methods can greatly improve characterization of distributions of marine organisms. Areas that should see growth are listed below: Technology. (1) Multi-beam systems. Making these imaging systems standardly used so that distributions of organisms can be routinely imaged in 3-D. (2) Broadband systems. Development of standardized commercial broadband systems that continuously span a wide range of frequencies (100’s Hz to beyond 1 MHz) so that fish and zooplankton can be directly sized through spectral classification. (3) Signal processing. Incorporation of advanced signal processing methods, such as matched filter processing into all systems, narrow- and broadband. (4) Hardware. Further decrease in power consumption of electronics as well as miniaturization for use in long-term observatories and autonomous vehicles. (5) Long-range sonars. Development of more standardized long-range sonars for large area synoptic surveys. Interpretation methods. (1) Scattering models: (a) Fish. Resonance properties of swimbladder-bearing fish (damping mechanisms, swimbladder size/shape vs. depth, scattering vs frequency and depth) need to be characterized better for improved resonance classification. Scattering properties of nonswimbladder-bearing fish need to be better grounded in data. (b) Zooplankton. Scattering properties of elastic shelled and gas-bearing zooplankton require more modeling and experimental data. (c) For use in multi-beam systems. All high frequency models need to account for oblique angles encountered with multi-beam systems. (2) Multi-frequency inverse methods. Further development of robust methods that will consistently provide reliable results. Key factors include number and range of frequencies, diversity of size and anatomical groups of organisms, and variable signal-to-noise ratio in each of the frequency channels. (3) Patch/school morphometry. With multi-beam systems becoming more commonly used, analysis of new, large sets of morphometry data based on 3-D information will improve the understanding of species- and environment-dependent morphometry. 13. Challenges One challenge involves the fact that the new technology is so complex. In spite of the many problems with the previous analog systems, there were some distinct advantages that have been lost. For example, those systems were generally easier to use—simply turn them on and go. When they began to fail, it was a so-called ‘‘graceful degradation’’ in which data could still be collected, but just with more noise in it. Also, they were easier to troubleshoot: to do so, one would open

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up the system while it was in operation and use an oscilloscope probe connected with an analog oscilloscope to find the source of the problem. Now, there are many steps toward using the advanced digital systems, with programming skills required. Systems don’t necessarily degrade gracefully, but sometimes catastrophically such as when the communication protocol has failed between two digital sub-systems. They are very difficult, if not impossible, to troubleshoot because of the all-digital components. Because of all of these differences, the new systems seem to be more of a ‘‘black box’’ with the potential for the user to have less intuition involving its use. For example, it is even difficult to determine if the echo is saturated (such as due to the seafloor) since intermediate tapping points may not be accessible and filters smooth out the clipping effects of the saturation (hence allowing it to go undetected). Nonetheless, when working properly, these advanced systems are incredibly powerful and versatile compared with the previous systems, yielding up to orders of magnitude more information. Another challenge involves the fact that the new interpretation methods are so complex. Along with the complexity is the challenge in understanding the assumptions of the method as well as how to program complicated mathematical formulas or algorithms. As a result, new advanced methods can be misused, resulting in erroneous, misleading results, which is completely contrary to the original intent of the methods. These challenges are common among models of scattering and morphometry, as well as inversion methods. Much like the above discussion of advanced technology, advanced interpretation methods may be treated more like a black box than previous simpler approaches (such as a simple regression equation). And, like with advanced technology, proper informed use of these methods can yield impressive results, yielding much more information and with greater accuracy (and less ambiguity) than with previous methods. There are also challenges with the new software packages. Although there is now a vast and diverse array of software packages available that can get a user quick results, these packages may not be fully tested. The user needs to put new packages through a series of tests to fully trust the use of them, as they would with something they developed themselves. Even with vastly improved technology and interpretation methods, acoustics used alone and without a priori information will remain a tool that produces underdetermined information. For example, there are many thousands of species of marine organisms in the ocean, each with their own scattering parameters. There are not that many channels of information possible with an acoustic system, even with frequencies ranging from 100 Hz to 1 MHz at all multi-beam angles (here a ‘‘channel’’ can be a frequency or beam angle). Thus, acoustics must be used in concert with direct sampling methods so that the number of inferred parameters is reduced to a number comparable to the number of channels of information. Also, there are acoustic approaches that should be emphasized to further reduce the number of inferred parameters. For example, the scattering at high frequencies by elongated organisms depends strongly upon the tilt angle distribution of the organism. By reducing the frequency, such as to the resonance frequency of a swimbladder, the tilt angle distribution is not a factor (Stanton et al., 2010). 14. Key to success in the field of bioacoustics The above focuses principally on technology and associated methods. However, it takes much more than that to be successful in this field. There is no user manual or publication that can replace having the right people and training to conduct the research properly. This is a very complex subject and inherently multi-disciplinary. A big mistake made is to take on the subject alone. For example, for a person trained in acoustics (from a physics or engineering perspective), fundamental mistakes can be made without appropriate knowledge from someone trained in biology. Conversely, a biologist working in this field alone can make fundamental mistakes in the interpretation of the acoustics data. A good ‘‘model’’ of a team involved that of Drs. Holiday and Pieper, as in the above cited papers, where they combined expertise from these different areas to make great strides in bioacoustics. Whether it be for development or operational use, it is important to have a team with diverse training. There is no such thing as a ‘‘turnkey’’ system that provides ‘‘the answer’’. In addition to appropriate team building, it is essential that there be formal education at the undergraduate and graduate level to train

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future researchers. For example, training fisheries biologists in the areas of technology, physics, and engineering is important. 15. Summary/conclusion I have witnessed and participated in significant advances in the field of active bioacoustics over the past three decades. This field remains an exciting one, as the fruits of previous developments are being realized and exploited, and there is much room for advancements well into the future. While there continue to be many changes in the state-of-the-art, one constant is the need for the bioacoustics team that is composed of experts in biology and acoustics. Acknowledgments I thank Dr. Jules Jaffe of the Scripps Institute of Oceanography, La Jolla, CA for inviting me to write this paper. I am also grateful to Dr. J. Mike Jech of the National Marine Fisheries Service, Northeast, Woods Hole, MA for his thoughtful comments on an early draft of the manuscript and Shirley Barkley of the Woods Hole Oceanographic Institution for her assistance in preparing this manuscript. The writing of this manuscript was supported by the US Office of Naval Research grant N00014-1-10-0127. References Anderson, V.C., 1950. Sound scattering from a fluid sphere. Journal of the Acoustical Society of America 22, 426–431. Andreeva, I.B., 1964. Scattering of sound by air bladders of fish in deep sound scattering ocean layers. Soviet Physics-Acoustics 10, 17–20. Chapman, R.P., Bluy, O.Z., Adlington, R.H., Robison, A.E., 1974. Deep scattering layer spectra in the Atlantic and Pacific Oceans and adjacent seas. Journal of the Acoustical Society of America 56, 1722–1734. Chapman, R.P., Marshall, J.R., 1966. Reverberation from deep scattering layers in the western North Atlantic. Journal of the Acoustical Society of America 40, 405–411. Chu, D., Foote, K.G., Stanton, T.K., 1993. Further analysis of target strength measurements of Antarctic krill at 38 kHz and 120 kHz: comparison with deformed cylinder model and inference of orientation distribution. Journal of the Acoustical Society of America 93, 2985–2988. Chu, D., Stanton, T.K., 1998. Application of pulse compression techniques to broadband acoustic scattering by live individual zooplankton. Journal of the Acoustical Society of America 104, 39–55. Chu, D., Stanton, T.K., 2010. Statistics of echoes from a directional sonar beam insonifying finite numbers of single scatterers and patches of scatterers. IEEE Journal of Oceanic Engineering 35, 267–277. Chu, D., Wiebe, P.H., 2005. Measurements of sound-speed and density contrasts of zooplankton in Antarctic waters. ICES Journal of Marine Science 62, 818–831. Chu, D., Wiebe, P.H., Copley, N., 2000. Inference of material properties of zooplankton from acoustic and resistivity measurements. ICES Journal of Marine Science 57, 1128–1142. Clay, C.S., 1983. Deconvolution of the fish scattering PDF from the echo PDF for a single transducer sonar. Journal of the Acoustical Society of America 73, 1989–1994. Clay, C.S., 1991. Low-resolution acoustic scattering models: fluid-filled cylinders and fish with swimbladders. Journal of the Acoustical Society of America 89, 2168–2179. Clay, C.S., 1992. Composite ray-mode approximations for backscattered sound from gas-filled cylinders and swimbladders. Journal of the Acoustical Society of America 92, 2173–2180. Clay, C.S., Horne, J.K., 1994. Acoustic models of fish: the atlantic cod (gadus morhua). Journal of the Acoustical Society of America 96, 1661–1668. Cox, M.J., Warren, J.D., Demer, D.A., Cutter, G.R., Brierley, A.S., 2010. Three-dimensional observations of swarms of Antarctic krill (Euphausia superba) made using a multi-beam echosounder. Deep Sea Research Part II: Topical Studies in Oceanography 57, 508–518. Craig, R.E, Forbes, S.T., 1969. Design of a sonar for fish counting. Fishkeridirektoratets Skrifter Serie Havundersokelser 15, 210–219. Curtin, T.B., Bellingham, J.G. (Eds.), 2001. Autonomous ocean-sampling networks. IEEE Journal of Oceanic Engineering 26 (4), 421–768. (Special issue). Diachok, O., 2005. Bioacoustic absorption spectroscopy: a new approach to monitoring the number and lengths of fish in the Ocean. In: Medwin, H. (Ed.), Sounds in the Sea: From Ocean-Acoustics to Acoustical Oceanography. University Press, Cambridge, UK, pp. 398–410. (Chapter 14). Edson, J.B., Chave, A.D., Dhanak, M., Duennebier, F.K. (Eds.), 2002. Ocean Observatories. IEEE Journal of Oceanic Engineering 27 (2), 145–274. (Special issue). Ehrenberg, J.E, 1972. A method for extracting the fish target strength distribution from acoustic echoes. In: Ocean 72-IEEE International Conference on Engineering in the Ocean Environment, pp. 61–64. Ehrenberg, J.E, 1979. A comparative analysis of in situ methods for directly measuring the acoustic target strength of individual fish. IEEE Journal of Oceanic Engineering OE-4, 141–152.

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