Inversion of Acoustic Zooplankton Measurement for Adaptive Physical-Biological Ocean Forecast
by
Bertrand Renard
alumnus of Ecole Normale Superieure de Cachan, Agr6gation in Civil Engineering, Technological and Energetical Equipments, 2001
Submitted to the Departments of Ocean Engineering and Material Science and Engineering in Partial Fulfillment of the Requirements for the Degree of
Master of Science in Ocean Engineering MASSACHUSETTS INSTiTUTE at the OF TECHNOLOGY Massachusetts Institute of Technology June 2003 AUG 2 5 2003
C 2003 Bertrand Renard LIBRAR IES All rights reserved
The author hereby grants MIT permission to reproduce and to distribute publicly paper and electronic copies of this thesis document in whole or in part.
A u th or ...... Bertrand Renard Department of aE ,003
Certified b y ...... fik Schmidt Professor and Department Head, a Engineering, Chairman, Dept Committee on Graduate Students, Graduat dmissions Officer, d") , I ', ,, Thesis Supervisor
Accepted by ...... Michael Triantafyllou Professor of Ocean Engineering Chairman, Departmental Committee on Graduate Studies Room 14-0551 77 Massachusetts Avenue Cambridge, MA 02139 Ph: 617.253.2800 MITLibraries Email: [email protected] Document Services http://Iibraries.mit.edu/docs
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The images contained in this document are of the best quality available. This research is performed as part of the Poseidon project, at the Massachusetts Institute of Technology department of Ocean Engineering, in cooperation with the Harvard University department of Earth and Planetary Sciences. Funding for this research was provided by: The National Science Fundation (NSF), via Information Technology Research (ITR),
A
and by the US Department of Commerce (DOC), via the National Oceanic and Atmospheric Administration (NOAA) and the National Sea Grant program (Sea Grant) as part of the Poseidon project.
Principal Investigators: Profs. Nicholas M. Patrikalakis and Henrik Schmidt, Department of Ocean Engineering, Massachusetts Institute of Technology
and Profs. Allan R. Robinson and Jim McCarthy, Department of Earth and Planetary Science, Harvard University
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2 Abstract
The Poseidon project is aimed at bringing multi disciplinary oceanographic data together on an Information Technology backbone in real-time, for improved understanding and forecasting. In this framework zooplankton acoustic backscatter is needed for better biology understanding, and can in turn benefit from the input of physical and biological models. Zooplankton backscatter models are subdivided in three categories: fluid-like, hard elastic shells, and gas bladder animals. Zooplankton species neither dominant in number, size or biomass can overwhelm part of the acoustic target strength spectrum, implying a necessary species-specific measurement. Furthermore, the too few high frequencies sampled by available sonars leave the acoustic inversion widely underdetermined. Real data inversion from WHOI's BiomaperlI has provided plankton population estimation comparable to what nets data and counting had recorded. Multiple species acoustic inversion has been demonstrated with the fluid-like and the elastic-shelled model. Purely acoustic field data inversion would require unjustifiable assumptions and lead to unbearable levels of uncertainty, which have always been reduced by cameras and labor-intensive direct tows. While other methods remain necessary to validate large-scale acoustic data, the Poseidon project's adaptive modeling, sampling, and the automatic input of biological information as part of data assimilation could significantly reduce acoustic uncertainty. Other issues addressed are acoustic inversion simulation behavior with various target sizes, the inversion's probabilistic validation, multiple species inversion, bubbles detection, application on WHIIG's BiomaperlI data, sources of error and adaptive modeling.
Keywords Zooplankton acoustics, bioacoustics, model inversion, adaptive sampling.
3 Foreword
I want to pay a special tribute to my parents for the best gift of all: life.
My warmest gratitude goes to my advisor who was always smiling and focused although he had to squeeze me in his tight schedule as department head.
For their invaluable criticism, teaching and advice on my research I would like to thank Dr. Andone Lavery, Dr. Tim Stanton, Dr. Peter Wiebe, Prof. James J. McCarthy, Dr. Van Holliday, Prof. Nicholas Patrikalakis, Dr. Pierre Lermusiaux, Dr. Constantinos Evangelinos, Dr. Michele Zanolin, Gareth Lawson, Luiz Souza, Joshua Wilson, Patricia Moreno, Ding Wang and Da Guo.
For their help and support: Sabina Rataj, Geoffrey Fox, Kathy de Zengotita, Eda Daniel and Mary Mullowney
For their cheering support, allow me to mention my Ocean Engineering friends: Nick (also known as Prof. Nicholas C. Makris), Dr. Purnima Ratilal, Dr. Monica Montanari, Yi-san Lai, Irena Veljkovic, Wenyu Luo, Tianrun Chen, Hwee Min Charles Low, Sunwoong Lee, Travis Poole, Joe Edwards, Andrea Kraay, Jennifer Watson, Ian Ingram;
The friends who helped me create the Club Francophone: Olivier Grunberg, Fr6d&ric Latour, Yannick Foing, Geraldine Kim, Prof. Johann Sadock, Wesley Farfan; and my other friends: Moshe Alamaro, Patricia Sampson, Raihan Khan, Dr. Pavel Hradecky, Adam Saffer, Todd Garvin, Oliver Pfeil.
4 Table of content
ABSTRACT...... 3 Key w o rds ...... 3
FO REW O RD ...... 4 INTRO DU CTIO N ...... 7 1. ZOOPLANKTON ACOUSTICS BACKGROUND ...... 9
1.1 CHALLENGES OF ZOOPLANKTON BIOMASS ASSESSMENT ...... 9 1.11 Introduction...... 9
1.12 Net tows...... 10
1.13 Video camera counting ...... 11
1.2 CHARACTERISTICS OF ACOUSTIC BACKSCATTER ...... 12 1.21 Zooplankton ...... 12
1.22 Acoustic sensors...... 14
1.23 Relevant acoustical-biologicalparameters ...... 16
1.3 ACOUSTIC BACKSCATTER OF BIOLOGICAL TARGETS: ...... 18 1.31 Fluid-like animals...... 19
1.32 Hard elastic shelled and gas bladdered animals...... 22
1.33 Backscatter addition and considerations...... 23
2. M ETH O D S ...... 25
2.1 FROM TARGET TO BACKSCATTER: MEASUREMENT MODELS ...... 25 2.11 Empirical methods ...... 25 2.12 Model-based methods ...... 26
2.13 Acoustics adaptive modeling...... 27
2.2 THE COMPUTATIONAL MEASUREMENT MODEL ...... 28 2.21 Physical basis...... 28
2.22 Geometry of the instrumentalsonars...... 29
2.23 Towing method and data presentation...... 31
2.3 ACOUSTIC INVERSION ...... 34 2.31 Least squares minimum norm inversion ...... 34
2.32 Newton polynomials implementation...... 35
2.33 Multiple models inversion...... 36
5 3. RESU LTS ...... 37 3.1 SINGLE SPECIES SIMULATION ...... 37 3.11 Fluid-like animals: euphausiids, copepods, krill...... 37 3.12 Standard deviation robustness...... 40 3.13 Plankton radius andpolynom ial order influence ...... 42
3.2 M ULTIPLE SPECIES SIMULATION...... 45 3.21 Euphausiids-pteropods...... 45 3.22 Influence of relativeprecision ...... 47
3.3 N OISY FIELD DATA INVERSION ...... 49 3.31 Data inversion setup ...... 49 3 .3 2 R esult...... 5 0 3.33 Populationsize distribution check...... 52
4. DISCU SSIO N ...... 54
4.1 BACKSCATTER ERROR MODELS...... 54 4.11 Multiple targets interference and Dopplerdispersion...... 54 4.12 Bubbles, sand and internal waves...... 55 4.13 Other sources of error...... 56
4.2 A DAPTIVITY...... 57 4.21 Body of water ...... 58 4.22 Physics ...... 59 4.23 Biology ...... 60
4.3 FUTURE W ORK ...... 62 4.31 Computationalmethod assessment...... 62 4.32 Poseidon backbone connection...... 63 4.33 Measurem ent strategy and applications...... 64
C O NCLU SIO N ...... 65 BIBLIO G R A PH Y ...... 66
6 Introduction
Understanding the oceans and their biodiversity is critical to sustaining their ecology and immense global economic value, yet our knowledge of such an important topic is very limited and divided in separate fields. A new goal for oceanographers is to perform real-time interdisciplinary ocean prediction. The monitoring of physical, acoustical, chemical and biological parameters will allow scientific progress and better ocean management. The Poseidon project is designed as an interdisciplinary communication tool to improve scientific understanding. Progress is expected in the information technology backbone of the Poseidon project. Motivation for this research also includes strategic knowledge of bioluminescence at the surface and other side-effect plants and animals may have on the ocean water, with applications in pollution control, outfalls, spills, harmful algal blooms containment, resource exploitation and management, particularly fisheries, and oil platforms application. Physical and Biological modeling will benefit from data assimilation and the adaptivity of sampling and modeling. The holy grail of this research is to forecast physical and biological parameters using these three very important tools as the artificial intelligence of the system: data assimilation, the real-time melding of new observation into predictive dynamical models; adaptive modeling, modifying or replacing the models as some state variables change; and adaptive sampling, which consists of observing at the right place and time to help reduce uncertainties. These combined tools are intended to help the different fields of interest exchange information and could lead to an enhanced scientific productivity. For this research project a patch of coastal ocean in the bay of Maine has been chosen for its importance and the availability of previous research data in the area. Indeed Georges Bank serves as breeding grounds for many species of north Atlantic fish. Zooplankton is an essential link in the food chain, as feed for larvae, fish and even the largest marine mammals (whales). Effective food chain understanding and fisheries management requires wild stocks monitoring, which is only possible with plankton biomass knowledge.
7 The framework of this research is the ITR-Poseidon project, intended to gain multi disciplinary understanding of a coastal ocean patch. The goal of this research is to provide the POseidon project with the adequate acoustic measurements of zooplankton. The general framework of data assimilation in the Poseidon research must therefore be grasped to see why certain choices are made. The highlights of the Poseidon project can be summarized as interdisciplinary ocean forecasting: In order to understand the complex coupled physics, biology and acoustics of the oceans, oceanographers are driving research towards Ocean prediction, analogous to atmospheric weather prediction but including biological and chemical as well as physical features. Prediction has been initiated in the Harvard Ocean Prediction System (HOPS), which provides ground material for this project. The ultimate goal is to enable ocean prediction with real-time objective adaptive sampling, assimilation and autonomous adaptive modeling. The research will be based upon a series of Observation System Simulation Experiments (OSSE), Adaptive Sampling and Adaptive Modeling in the distributed digital environment, data driven simulations via data assimilation, simulation driven adaptive sampling and modeling of the ocean, using distributed computing infrastructure to integrate the disciplines and optimize the system, providing feedback from acoustics to physical and biological oceanography, establishing interesting features, setting up optimal attributes sets corresponding to features. The motivation and goals of this research include automated adaptive modeling and sampling, increased scientific access, communication and productivity, better understanding of physical biological and acoustics phenomena, better ocean management.
8 1. Zooplankton acoustics background
This chapter attempts to explain why acoustics and biology are inextricably tied when dealing with zooplankton, and what are some of the challenges the researchers face in this field, in order to have the understanding necessary to proceed with acoustics.
1.1 Challenges of zooplankton biomass assessment
Because the oceans represent the majority of our planets' surface, scientists have tried to estimate in numbers what the productivity and biomass are at all levels of the food chain. Although we would be satisfied with precise biomass assessments without having all the insight into specific populations and behaviors, we will see that eventually one cannot estimate the first goal without some understanding of the latter, whether as a biologist or as an acoustician.
1.11 Introduction
Phytoplankton levels can be estimated with chlorophyll concentration at the ocean's surface seen by satellites. Though this observation does not provide species distinction or vertical distribution, it is a fast, easy and accurate means for primary productivity assessment at the surface. In the case of zooplankton undetected by satellites, animals must be approached closer with cameras, sonars and net tows. Because electromagnetic waves are absorbed within meters underwater, and net tows do not result in quantitative data on scales of horizontal patchiness without exceptional cost and counting effort, if at all", acoustics is the most effective and practical means of sensing or communicating on a large-scale. Other types of measurements are still simultaneously performed to verify or complete the acoustics measurement. Acoustic compression waves cannot strictly replace electromagnetic waves for our purpose because zooplankton is mostly transparent to
9 these waves. It is however possible to know more about the internal structure, and species recognition is a potential that researchers have been exploring for several decades. In the quest for zooplankton biomass assessment, only size and species composition distinction allow accurate acoustic data processing. Of course, size bins and species have the potential to be used by more precise species-specific plankton growth biology models not available at present. Acoustic methods have therefore been called upon in the Poseidon project to provide efficient plankton sensing. This purpose must comply with the constraints of the Poseidon framework: Plankton estimation must include mean and variance values, for the uncertainty is needed for successful assimilation in other models. Acoustics models benefit in turn from physical information on the environment. The nowcast resulting from the assimilation taking into account biology models and a priori information is expected to gain precision by constraining the models or limiting the number of variables.
1.12 Nettows
Nets and pumps are the common practical means for direct zooplankton sampling. Systems to open and close nets can divide a watercolumn sample into a few separate samples integrated over complementary depths. Patchiness in plants and animals at sea has lead biologists to use either uniform sampling or stratified sampling." The samples obtained are prepared, processed according to probabilistic laws and observed under a microscope. In fact, the validity of acoustics measurements can only be compared to other means of direct plankton sampling. These include camera snapshots and net tows. Net avoidance by zooplankton proven from the different measurements with or without sunshine brings a strong bias. Larger nets were tried, up to 1 0m 2 but with the same avoidance as smaller nets. This led to the belief that the larger zooplanktons with sight and fast swimming capabilities were starting to move away as far as 6m ahead of the net. Other solutions tried to reduce net avoidance include higher towing velocities, but with the side effect of crushing some of the animals in the nets.
10 Further inquiries may include acoustic and visual stimuli provided by an advancing net opening. A wide surface of openings with or without nets could contribute to preventing plankton to escape or obtaining known patterns of escape routes. In comparison with the effective diameter of a wind turbine, a reduced volume sampled model, with an effective sampled surface smaller than the real surface of the net opening is will not provide an easy answer because avoidance depends on the specie (ability to see) and size. Avoidance applies naturally to camera systems as well.
1.13 Video camera counting
The Video Plankton Recorder used at Woods Hole Oceanographic Institution (WHOI) consists of a video camera and a strobe light aimed toward each other. Zooplankton shadows in the field of view of the video system are sent up to the towing vessel. The VPR is part of the Biomaper II detailed further in this thesis. Sampling has always been limited by ship time and processing resources instead of the number of samples necessary to determine spatial or temporal variability." Again, the validity of acoustics measurements depends on other means of direct plankton sampling. But whether camera snapshots or net tows are used, computer-automated species naming only has an 80% effectiveness, and even when the count is done by biology staff with 100% effectiveness, after hours of qualified biologist time spent on visualizing the snapshots or microscope slides, a large number of unrecognizable specimens and body parts end up in the column "unknown". Avoidance of nets is believed to occur because of visual stimuli. Evidence of this comes from the strong avoidance in daylight, while nighttime measurements provide much more biomass present in the water column. The new promising technology is flashing. The blinding of plankton in the area in front of nets or cameras would hopefully prevent the animals to avoid the net, but could have the unexpected effect of attracting instead of repulsing. Axial and side sampling at various distances from the axes of flashes may provide valuable information about the effectiveness of the blinding attempt.
11 1.2 Characteristicsof acoustic backscatter
Biological ocean acoustics were used early after WWII, but focus was on larger animals. Species distinction has remained the holy grail of plankton acoustics. Not only is it needed for better biology understanding, but a certain level of distinction among zooplanktons is also necessary to interpret the acoustic backscatter measurements at all. The two main parameters of the acoustic backscatter are acoustic brightness and color. Brightness is the strength of scattering back toward the sound source, measured in dB. Color determines the variation of brightness with frequency, and gives the "central" or main frequency of the backscatter spectrum. Both of these features can be viewed in a graph plotting the Target Strength response as a function of frequency: this is the backscatter spectrum of a single specie and size class. More information is available from the raw acoustic data: Doppler is the change in frequency between the sent and received signal, which is due to relative motion between source/receiver and target. This effect can only be used when frequencies used are separated enough compared to the target velocity, which is the case for plankton acoustics where only few frequencies have been used on any single measurement device. Observations of swimming speeds within migrating layers were reported by Heywood (1996). Imaging is possible when sending narrow beams to obtain angular resolution rather than average brightness information over a solid angle. Even with just one angle sampled, single animal distinction is possible at close range from the transducer.
1.21 Zooplankton
Plankton designates the ocean life forms that are carried by the currents; this broad category is subdivided in phytoplankton, the primary producers on most of the planet's surface, and zooplankton, both herbivores and carnivores. Phytoplankton may have buoyancy control, and many zooplankton species have propulsion means for
12 grazing, but not sufficient to escape ocean currents long enough for a horizontal migration. Daily movements are an important phenomenon for the zooplankton. Phytoplankton migrates vertically as a direct response to sunlight, or finds itself trapped in dense layers at the pycnocline, strongest density gradient often a few tens of meter below the surface. Most zooplankton species will graze at night in the photic zone (upper twenty to fifty meters), and migrate to greater depths during the day to avoid visual predation by fish, and especially if they don't use visual cues to find prey. A few species reverse their daily migration and spend the night deeper than the day, which reduces the risk of being eaten by the non-visual predators.
Heterotrophic N roKaryotes Viruses Export LOOP Cell debris Grazers
Dissolved organic organic InnuL trients matter N,) P, Fe...
Primary producers
figl: The ocean lower trophic level food web, source Jim McCarthy, Harvard The most abundant zooplankton species in the bay of Maine on the northeast coast of North America are Euphausiids and Copepods. Their body is exclusively made of flesh with a light outer skin, they typically account for 80% of the biomass.
...... 1 7 N _ ......
fig2: Euphausiids and Copepods respectively, source: Tim Stanton, WHOI
13 1.22 Acoustic sensors
The Acoustic Doppler Current Profilers (ADCP) has become a routine instrument for physical oceanographers as ships mounted or moored instruments. Their benefits are high sensivity and digital recording, but with neither angular and temporal resolution nor stable calibration. (Temporal resolution is necessary to separate objects at different distances.) TAPS Tracor Acoustic Profiling System is the acronym for a family of instruments manufactured by TRACOR to study the size and extent of populations of very small marine life. The TAPS sensors can be used in several modes, including a "cast" mode, wherein the instrument is lowered through the water column from a ship, making measurements as it goes down and again as it is retrieved. This is the mode used when the TAPS is used on a CTD (Conductivity, Temperature and Depth) instrument. fig3&4: source: Van Holliday, TRACOR In another mode of deployment, the TAPS can be used on r net systems such as the MOCNESS (Multiple Opening Closing Net Environmental Sampling System). Sampling operations, MOCNESS tows are made according to standard 1.1 " r Transducer MARMAP procedures, (i.e., oblique from surface to within five meters of bottom or to a maximum depth of 200m while maintaining a constant wire angle throughout the tow. The TAPS has been deployed on a Sea Soar towed body in the Arabian Sea and on Georges Bank. When used on the Sea Soar body, the TAPS collects data in a "to-yo"
14 fashion, a mix of the towed and cast modes of deployment. Typically, the Sea Soar is used for data collection when broad scale information is desired for a large area. The TAPS is in a constant improvement process, with an increase in the number of frequencies.
The Biomaper-JI (BIOlogical, Multi-frequency Acoustical, Physical and Environmental Recorder) is a multi-sensing device. The acoustic transducers transmit volume backscattering data at five frequencies (43, 120, 200, 420 and 1000 kHz). The system multiplexes through its 10 transducers (5 pointing upwards, 5 pointing downwards), and achieves a ping-rate of -0.3pings/s. The Biomaper is towed up and down in the water column.
Tow Cable Environmental Sensors Acoustic transducers
VPR
Bio-Optical Sensors Fiber Optics Telemetry Housing
Shock Welded Mounts Aluminum Frame Digital Echo Sounder
fig5: the BIOMAPER II, source: Peter Wiebe, WHOI
The Biomaper was designed as a multidisciplinary sensor because the complexity of zooplankton sensing requires to involve as much knowledge as we can: Physical properties of the water are hence recorded as the Biomaper II is towed up and down to
15 sample all parts of the watercolumn. All the data collected by the Biomaper is sent in real time to its towing vessel through the cable. Researchers at WHOI are already considering the design of a future third generation of multidisciplinary sampling device, the Biomaper III and are studying the features and characteristics it may have. Fixed mooring site buoys were used one decade ago with two frequencies to observe acoustic backscatter as a function of time, both by Holliday (TRACOR) and Wiebe (WHOI). Records of wind, cloud cover, sea state plus echo sounder records at 3m complete the measurements of the ancillary collection program." While it can be interesting to see a daily or weekly evolution, the measurement is too localized to be meaningful in itself. The very nature of the Poseidon project, where data is assimilated, is likely to give a new meaning to isolated measurements because maps of physical ocean properties will ensure that local measurement results are extrapolated at the right geographic scale. These measurements could provide, at a more affordable price, an efficient way to implement adaptive sampling as a fixed or drifting solution: one form of adaptive sampling consists in selecting a criterion to grade the relative significance of a measurement locations with respect to the performance and uncertainty of sonar performance. Sufficiently slow and large scale areas of similar properties could be efficiently sampled with just a few devices The decision to select moored or drifting devices depends primarily on the on the typical behavior of the bodies of water.
1.23 Relevant acoustical-biological parameters
Species: The acoustic scattering signature of an animal depends on its physical makeup, morphology, and orientation. Models distinguish three kinds of zooplankton morphology: " fluid-like animals, * animals with hard elastic shells and " animals with gas inclusions. Size, or more precisely animal radius or length, is a major factor. Empirical formulas give one from the other, and the aspect ratio, ratio of length over width is often
16 used. In figure 6, we can see that life forms occupy the whole range of sizes. While vireo-, bacterio- and myco-plankton do not contribute significantly to biomass, the phytoplankton can be measured by satellite and is at the lowest range of possible acoustic measurement. Protozoans are scavengers that belong to the microbial loop below zooplankton in the food chain. The zooplankton we are interested to detect is for the large part mesoplankton with size order of fractions of or few millimeters. The concentration of zooplankton is the number of animals per cubic meter [m 3]. Other ways to quantify plankton exist, such as total dry weight, area or volume per unit volume. All are species- specific.
PLANKTON FEMTO PICO NANO MICRO MESO MACRC MEGA 00- 04- t.0- 70 02 -20 m 2. - 1 200cm 02 uv 2.0 urn 20 8M 700 Um _20__con 200 CM MM CM IDM1 M 0 NEKTON 2-7 2-20 2- 2-20
VIRIo- PLANKTON eACTERIO- PLANKTON
MYCO - PLANKTON PHY TO - PLANKTON
PROTOZOANS
METAZOANS
NE KTON
=j I ~i -i L L -I- I -r 16* 10 70 SZ 10 163 10-2 16-0 70 7I
SIZE (m)
fig6: plankton life forms and size ranges, source Jim McCarthy, Harvard
Frequency: without any Doppler effect, the backscatter from a group of animals occurs at exactly the same frequency as that of the signal sent by the active sonar. The strength of the return signal depends heavily on this frequency. The acoustic frequency signature or backscatter spectrum is the main feature used for the recognition of populations of different species. For mesoplankton a few mm long, the corresponding acoustic frequencies used by the Biomaper II are in the hundreds of kHz, much higher
17 than what off-the-shelf fishing sonars can offer. Size bins are used for the inversion.
f Labsorption/ 2
43kHz 33mm 500m 120kHz 11mm 150m 200kHz 7.2mm I00m 420kHz 3.4mm 50m 1MHz 1.4mm 15m
fig7: BiomaperII sampling frequencies 5 and corresponding absorption lengths l/e. 2
Orientation: the orientation or orientation probability distribution is a key factor in the TS of elongated animals, simply because the acoustic beam pattern of any elongated object has a strong angle dependence. The calibration of the instrument at sea in real conditions is subject to the reliability and precision of the net tows or cameras sampling methods. Even if the quantity and size distribution we have in the other sampling methods are biased, the knowledge of what species are present at all is valuable a priori information that allows to choose the right acoustics model.
1.3 Acoustic backscatterof biologicaltargets:
In order to measure zooplankton densities and species, we must first understand the acoustic response of the plankters to an incident (plane) pressure wave. For each category of species and size, the Target Strength (TS) must be known as a function of frequency f and sound speed c. All other relevant parameters such as sunlight (for migration and plankton body properties), oxygen (for backscatter and plankton migration), flows and temperatures will enter the game through adaptivity. Zooplankton studies are based on their anatomical similarities, leading to the distinction of three major groups from an acoustics perspective:
18 " fluid-like (Decapod shrimps, Euphausiids, Salps), * hard elastic shelled (Gastropods) and " gas-bearing (Siphonophores) animals. 19 Fluid-like animals have been modeled as homogeneous bent cylinders with a density and sound speed ratio between body fluid and surrounding waters. Hard elastic shelled animals behave like monopoles with waves traveling in and along the shell. Gas bladder animals feature a strong resonance in frequency.
1.31 Fluid-like animals
The fluid-like animals are essentially composed of flesh. Density and sound speed contrasts generate the physical gradients responsible for the backscatter. These contrasts are in fact corrected (increased values) to take into account the soft outer shell. Secondary parameters controlling the backscatter are acoustic frequency, size11, shape, orientation and roughness. The fluid-like animals were first transformed by the models into fluid spheres (Anderson 1950), then a directional term was added (Greenlaw, 1977) as well as empirical factors for mean tilt angle and tilt angle distribution (Kristensen and Dalen, 1986). Recent theoretical developments by Stanton showed that the radius of curvature of the animals (1989) and the mean and standard deviation of the angle of incidence (1993) have an important effect on the scattering characteristics of fluid elastic cylinders. The latest models include studies of the small-scale roughness of the shell. Fluid-like animals are the most abundant zooplankton kind; this model is sufficient for small crustaceans including Euphausiids and copepods.8 Models available for fluid-like animals are either approximate or defined for a particular species geometry; these models are still subject to active research. It is important to mention that such models were built on theoretical scattering properties and using mostly preserved zooplankton in the laboratory instead of live animals; their accuracy depends critically upon two parameters that have to be either measured or estimated: the contrasts in sound speed and density between the scatterer and the surrounding water. These parameters are naturally species-dependant; furthermore they are tied to lipids levels and are subject to annual, seasonal and daily cycles. The
19 calibration of any acoustic measurement is subject to the reliability and precision of net tows sampling methods in real conditions at sea. Equation (1) is Stanton's model based on theoretical scattering from bent cylinders . It corresponds to the backscatter from just one animal but with averaged orientation. The backscatter depends mainly on the size and size ratios, acoustic frequency and reflection coefficient through density and sound speed contrast.
TS =1 l0og 0.08 R 2 -exp 8; 2 f'a's2 x cos 4f.a(- 04 j (1) 19, -c 2 c (7r.f a / c + 0.4))) where we have: R: plane/interface reflection coefficient f: acoustic frequency [Hz]
length = D [m] L: plankton class average 0 .134 fr2m D: plankton radius =0.095+0.134L [m] PO: L/D, twice the aspect ratio s: standard deviation of length [m] c: sound velocity [m/s] In figure 8, the Target Strength (TS) plotted as a separate function of frequency and radius is a high-pass filter for both variables. Physically, low frequencies cannot detect the presence of the too small animals. The deep nulls correspond to multiple reflections inside one target hit at broadside. This plot corresponds to TS from just one individual, but averaged over all angles. Model parameters give the choice between a Gaussian or uniform probability distribution of size within a chosen standard deviation. Which means in fact this TS is a random variable and the model an expected value.
20 Ak
T2. c 10
10
10pa
10
10
,plank Ion radius a 102 acoustic frequency f
fig8: Gaussian-length randomly-oriented fluid-like scattering model plotted as a 2D-function of the radius a[m] and the frequency -4(
-50 -
V -60 -c Th C ci~ L. 70F (I)
I- -80 H
-90
-10C 0 0.5 1 1.5 2 2.5 3 k a fqHz] 24 fig9: Stanton's' fluid-like reduced TS for multiple individual plankters12
21 In this second plot of the same model, we see the "deep nulls" are smoothed and disappear when scattering is averaged over many individuals. This plot is more typical of
acoustics, with the product ka = 27r a -wave number times radius- on the X-axis.
Model assumptions include uniform and unique fluid body properties, with corrected sound-speed and density ratios. The targets are supposed to be randomly oriented, which is slightly inconsistent with the knowledge of daily migrations and has been observed to be non-random 5 . Linear addition of the backscatter contributions from all targets without shadowing is also assumed. Backscatter from elongated cylinder-like animals is much stronger at broadside, where the modeling of the scattering is more precise; therefore the contribution from less predictable end fire backscatter is less relevant which contributes to the overall excellent results the fluid-like model has proven to offer.
1.32 Hard elastic shelled and gas bladdered animals
The scattering behavior of "small" animals with hard elastic shells is that of an acoustic monopole when ka Animals with gas inclusions produce backscatter both from their bubble and from their tissues. The gas inclusion has by far the strongest acoustic response to an incident wave: all other scattering mechanisms account for 5dB less backscatter power than the 22 bubble-reflected wave. This difference is large enough to look upon other effects as secondary; nonetheless tissue response has also been modeled. The gas sphere, filled with very low density and sound velocity fluid, is subject to a resonance that augments the scattering significantly when ka<1. For most cases, the combination of bubble size and swimming depth causes the resonance to be below 50kHz." The bubble is somewhat spherical and always modeled as such, but its real shape can modify the equivalent radius measured because it alters the cross-section as well as the angle of incidence of the pressure wave on a significant percentage of the bubble. Random orientation of multiple animals contributes to reduce this default when averaging. 1.33 Backscatter addition and considerations As a conclusion to this section 1.3, it must be stated that TS is only proportional to animal concentration for a given species, size and acoustic frequency. TS is very frequency-sensitive and acoustic backscatter may be dominated for a single frequency by animals neither dominant in number, volume, area, weight nor length.18 This makes it quite difficult to directly link the echo energy to the biomass of the animals.1 4 As a result, attempts for single frequency devices to correlate backscatter with any single geometric quantification of plankton have met with little success, and research doesn't have the choice but to work on multi-frequency species differentiation. Now that the basis for acoustic inversion has been laid, assessing the presence of different species is needed. Figure 10 shows the relative scattering strength of the major species and physical phenomena relevant to high acoustic frequencies. 23 -20- Swim-bladdered fish -30 - , IS Siphonophore -70 Copepod 0- 100 - T-microstructure Krill S-microstructure 11 1-12 3 4 a 10a 10 10 10 10 10 10 1 Frequency (Hz) fig 10: the most important biological and physical ocean acoustics scatterers, source: Andone Lavery, WHOI Siphonophore and swim bladdered fish display a narrow peak of scattering: the resonance of a gas bladder. We recognize Pteropod, Copepod and Krill (= Euphausiid) as high-pass fluid-like plankton. Notice that the smaller Copepod tend to have weaker acoustic response to insonification, and must be clearly identified in order to assess accurately the biomass. The only way to assess size and biomass among fluid-like animals is by finding the value of ka where the asymptotes cross. Temperature and salinity microstructure represent significant noise, especially the latter one, which provides a TS above that of fluid-like animals for frequencies below 1MHz. This noise is a problem especially is it prevents a clear detection of the acoustic transition between Rayleigh and geometric scattering - rising and horizontal asymptots for fluid-like scatterers. Information about currents and inhomogeneities of the bodies of water available through data assimilation may prove to be very helpful in that regard. 24 2. Methods 2.1 From target to backscatter: measurement models The models or forward models allow acousticians to predict TS from information on the target. The measurement models must tackle the inverse problem: how to obtain information on the plankton population from the acoustic TS 18? The use of a model-based method allows for physical interpretation and improvement of the inverse technique. Moreover, it allows us to take advantage of adaptive modeling with the use of computational resources. The simplest inversions have been achieved in geographic locations and seasons where and when one targeted specie could be so widely dominant that models did not have to worry much about species differentiation. Even in these simple conditions, the goal of the studies is to measure both abundance and animal length distribution, two or more unknown parameters that require the use of multi-frequencies devices. 2.11 Empirical methods Determination of animal length leading to species differentiation has been achieved with relatively little effort in areas of low species diversity. Madureiras et al. (1993) used two frequency observations, 38 and 120 kHz. By plotting the mean volume scatter at frequency one against frequency two, it was possible to distinguish three areas for three present species. Visual comparison with the same plots constructed from the Stanton et al. model offered population estimation. Brierley et al. (1998) used the technique to consider a range of five species using three measuring frequencies and discriminant function analysis for automated classification. Agreement between the classification and net observations had about 75% concordance. Of course this visual methods do not work as soon as the number of frequencies exceeds three, and the trend is to augment the number of transducers and measuring frequencies in an effort to better recognize the different scattering groups present. Note 25 that because of pricing and designing challenges, even the most comprehensive multidisciplinary zooplankton sensing devices only have half a dozen sensors, while TAPS, the only commercially available off-the shelf tool possesses two frequencies. Biomass assessment from concentration and length distribution is then achieved by turning all animals into spheres of equivalent volume. The analysis of the biomass spectrum has been measured in Equivalent Spherical Radius where all animals were assumed to be fluid-like and spherically shaped. Whatever the method used, contributions from each individual within a population, and from each population add up linearly. This approximation is quite reasonable given the weakness of the scattering, hence eliminating cross-scattering return in practice. The acoustic backscatter cross section is therefore assumed to be linear relative to concentration and population type and class. 2.12 Model-based methods The simplest model inversion comes for fluid-like animals with two frequencies and the formula (2): a4 =2(r -R (2) where: a is the equivalent spherical radius representative of the fluid animal's size, r is the ratio of frequencies fHI/fLo, and R the ratio of scattering cross-sections THI/ALO- a can be turned into the length L using a=0.095+0.134L This inversion model has three clear advantages: first, it is explicit, which makes it possible to plot and interpret physically. Second, the error models are easily obtained for the parameters appearing in the formula. The third advantage of this particular model using two frequencies is that it cancels out all empirical constants present in the true frequency-dependant model. As can be seen in figure 10, the backscatter spectrum from the three main types of animal has a different shape. In tens and hundreds of Hertz, gas-bearing, fluid-like and 26 elastic-shelled animals are predicted by the models to respectively have a linear behavior above resonance, a cosine with null spacing of 130 to 370 kHz, and a cosine with null spacing between 60 and 100 Hz spectra. Martin et al. introduced two classifiers based on the physical scattering models18 : The Model Parameterization Classifier (MPC) first correlated the receiver echo spectrum against the bladder model.1 8 If the sum of the square of the residuals was less than a determined value, the echo was classed as being from a gas bladder animal. If not, correlation with the fluid-like model spectrum was performed and so on.' 8 The Empirical Orthogonal Function Classifier (EOFC) is another computational method for species recognition that uses the eigenvector and eigenvalues of the covariance matrix to find the dominant mode.1 8 Both techniques proved themselves worthy with single targets within a laboratory tank but the sonar and transducers need improvements before they can be used for real time in situ classification. 2.13 Acoustics adaptive modeling The acoustics inversion is an attempt to reverse the scattering process and guess from the backscatter spectrum what the zooplankton population, or most explicitly population size and species distribution, initially was. In reaching this goal, we must keep in mind how this measurement fits into the big picture of data assimilation and adaptivity at the heart of the ITR-Poseidon research project. The acoustics inversion provides a result, expected value of zooplankton size and species distribution, and an estimation of the error of this inversion, just as important as the result itself because any model or sample estimate must come along with an uncertainty level to allow data assimilation, and because adaptive sampling will be aimed at reducing this uncertainty by adjusting the real-time sensing tools in any possible way. 27 Assimilation and adaptivity Adaptive Uncertainty sampling Target strength Acoustics Population spectrum inversion distribution Data Adaptive assimilation modeling Physical and Biological metadata figi 1: a graphical summary of the roles of assimilation and adaptivity with plankton acoustics In turn, information stored in the interdisciplinary database will contribute to improve acoustics methods directly and indirectly. Direct improvement happens by providing up-to-date physical and biological propagation and scattering coefficients as well as the appropriate model to be used. Indirect improvement has a role to play when the assimilation of acoustical and biological information with their respective error estimations will converge to provide the end user with a unique information expected to be the best of both worlds. 2.2 The computationalmeasurement model 2.21 Physical basis It is assumed at backscatter from different targets simply adds up at the receiver. We have the relation a(f) = N x o(f1), where -(fi) is the total scattering cross-section 28 for the given frequency fi, -(f1 ) is the cross-section of a single individual and Ni the number of animals per m 3 The population of animals is then broken down into classes, each class having a narrow range of radius (centered on aj) and a unique behavior c(f 1 ,a1 ) at the frequency fi. A typical size class is distributed for all simulations evenly on a log scale between .05 and 5mm.22 The total backscatter for the diverse population adds up with one or multiple frequencies to: a(f )=Za(f , ak)xNk (3) k Utoti = a(fk,aI)xNI (4) k I Classes are not just different classes of radius, but can also represent different species. Different classes must have a different TS behavior on the studied frequencies, otherwise the matrix is singular and the inversion cannot be performed. The model itself is provided by Stanton and allows species separation. This method is only valid for weakly scattering bodies, which is the case here, and is valid for all angles of orientation.8 One of the approximation also assumes that material properties are uniform inside the animal, which explains why the measured average properties are not the best for the scattering inversion, and why the degree of variability (inhomogeneities) is also a key issue.8 Assumptions on the L/a ratio and the probability distribution in size around the center of the size bin are also required and hidden in the explicit formulation given in Stanton's equation (l)model. 2.22 Geometry of the instrumental sonars The Biomaper II used at WHOI and in this thesis sends pings upward and downward at five frequencies. The sound is not sent continuously but as a series of pulses every third of a second. The time integration of the signal returned over small At provides a separate layered measurement of the watercolumn and it's layered zooplankton population in 1.5 or 2m heights. Absorption is taken into account linearly as a function of distance. An extinction length occurs as close as 15m (half traveling length) for the highest frequency (1MHz) and can be calculated when linear absorption added to the 29 weak backscatter reflection reaches a certain threshold. The absorption a=a(z) could be a function of z if a better approximation than a constant is needed, especially to take into account the intensity loss for a wave traveling through dense plankton layers. The integrated beam pattern, intrinsic to the geometries of the source and receiver (piston and disc), is part of the calibration constant used in the codes processing the Biomaper's raw data. This constant also includes calibration from basin experimentation. With measurement data, compensation must but be made for the dependence of the system acoustic response on depth." Source and receiver beampatterns are part of the hidden Biomaper calibrations. Because these beampatterns have very strong directivity with their narrow cones geometry, a plane wave assumption in the insonified region is a fair assumption. The half-power beam-width is 1.5' for all Biomaper frequencies except at 43 kHz where it is 3'. The spherical wavefront is assumed to be plane with the condition 4r2 AR, hence the scattering models created for plane waves are applicable and the horizontal layers sampled separately. The lowest frequency of the Biomaper was designed (diameter and power) to function at 38kHz. It turns out that 43kHz provide a much better dynamic pressure resonance; therefore the transducer is now used at this higher frequency. This illustrates why broadband sonar cannot be used in plankton acoustics: these weak scatterers reflect sound poorly and the high frequencies are absorbed within tens of meters at the hundreds- of-Hertz high frequencies, so a lot of power - as much as reasonable and technically feasible - is sent with a resonance at a very few number of frequencies. Because the resonance depends on the geometry of the transducers, one specific transducer must be designed for each frequency sampled. In principle, horizontal beaming reduces the risk of coherent or specular reflection from a pycnocline 2, but it is more practical to do vertical plankton abundance measurement for two reasons: The vertical concentration profile provides at once values that can be extrapolated up to hundreds of meters around. Secondly, towing an instrumental device is more practical than having to stop regularly for vertical measurements at sea. 30 In the near field, the raw acoustic data allows to count individual targets when they are sufficiently separated by their travel time. But this possibility remains a dim prospect compared to the huge task of biomass assessment on the entire column. 2.23 Towing method and data presentation The BIOMAPER-II acoustic data used in this thesis were collected on May 28, 2001, in Laubeuf Fjord, which is an extension of Marguerite Bay. The latter is found on the west side of the Western Antarctic Peninsula, and is the study area of the Southern Ocean GLOBEC program. path plankton beam fig 12: behavior pattern of the Biomaper II when towed The data is presented ping by ping, with date, latitude, longitude, frequency, starting depth, ending depth and depth intervals information for each row of data. At 43 and 120 kHz, data were collected in 1.5m bins (i.e. integrated over depth interval of 1.5m). At 200 and 420 kHz, depth bins were im in size. This experimental data was chosen for its monospecies layer of plankton clearly observed. Although the Biomaper possesses a transducer at 1 MHz, this latter malfunctioned during that experiment which leaves us with data from four usable frequencies. It is but one illustration of the difficulties and impediments occurring when conducting field experimentation. While lines correspond to pings, columns of data give volume 31 backscattering measurements at the starting depth, depth interval by depth interval. For example it means at 120 kHz that the first data column gives volume scattering measurements from 1-2.5m, then from 2.5-4m, etc. Similarly, at 200 kHz, the first column shows 1-2m, the second 2-3m, etc. Data was only collected as far as extinction length permits, depending on frequency, above and below the Biomaper that was towed at linearly varying depth. Data is therefore not available for locations either within the very near field of the system or at ranges greater than those reached by the transducers. Data were thresholded for system noise, integrated over three pings and the dynamic range of the system was -100 to -40 dB. Measurements less than 1*1010 were automatically set to zero. The 43 kHz transducers have a nominal half-power beam-width of 3 degrees, while all other transducers have half-power beam-widths of 1.5 degrees which is sufficiently narrow to apply the plane wave assumptions used by the scattering models. 32 Laubeuf Fjord measurements at 43, 120, 200 and 400 kHz -40 - 100 200 - 300 -50 - 400 147.85 147.9 147.95 148 148.05 148.1 148.15 148.2 100 -60 - 200 300 400 (I- -70- 147.85 147.9 147.95 148 148.05 148.1 148.15 148.2 -:A 100 200 300 80 400 500 147.85 147.9 147.95 148 148.05 148.1 148.15 148.2 -90- 100 300 400 145 1100 500 147.85 147.9 147.95 148 148.05 148.1 148.15 148.2 fig 13: TS measurements at four frequencies, source: BiomaperII, WHOI We can clearly observe recurrent measurements between the depths of 50 and loom, where net collections were 95% krill (Euphausiid, a kind of shrimp). The two deeper measurements are attributable to fishes. The very surface layer -first 20m- gives very strong TS certainly because of bubbles and significant surface waves mixing, but also possibly because of the bio-diversity and -density occurring at the surface. The biological acoustic response close to the surface is probably related to small pelagic fishes as well as larvae and plankton. 33 The ping-by-ping measurement solution provides a record of the range through time gone by since the last ping, but maximizing the number of measurements through a maximal number of pings with the best intentions can be inadequate: in some cases of flat ocean floor and mirror-like ocean surface, too many reflections on the surface and the bottom have led to arrivals posterior to a second ping. This creates "shadow" or "ghost" measurements that have to be diagnosed on the spot in order to change ping separation and obtain usable data during the rest of the cruise. The scientists can augment ping time separation to avoid these cross-measurement between pings whenever they are identified. 2.3 Acoustic inversion 2.31 Least squares minimum norm inversion Zooplankton has been accurately modeled, allowing an acoustic scattering prediction given a known population. We want to solve the inverse problem: going from the measurement of the TS at a set of frequencies back to the plankton population. Once we achieve that reconstruction, we can feed the result and its uncertainty for data assimilation. The uncertainty of the estimate is also useful to perform adaptive sampling. Physical and biological information should be fed to the model or inverse model for adaptive modeling, the real time adaptation of models and model parameters. The acoustics inversion therefore must be designed from the beginning to make possible or facilitate the use of adaptive modeling, sampling and data assimilation. The principle of the reconstruction is to use the forward model we know and perform a computational inversion. This is achieved by equating the measured TS to a TS made of the model applied to the unknown population we are looking for. TS measured() = TS(f;,ai) * Nreconstructed (5) Instead of numbers (concentration, in other words number of zooplankters per m3 or 1 000m 3) at different size classes, the population is chosen to be a Newton nested 34 polynomial (7), subdivided into a Vandermonde matrix R with arbitrary radii values and an unknown coefficients vector C. A few matrix transpositions and inversions provide the unknown coefficients according to a method of least squares and minimum norm solution (8). Nreconstructed is modeled as a Newton polynomial. TSmeasured(f) )=TS*R *C (6) 2 r r,2 ... TSmeasure (fi )=TS*1 (1K C =((TS * R) T * (TS* R * (TS * R) T * Ntrue (8) This is a very common inversion method; it can be efficiently implemented with software and provides a least square minimal norm inversion on an orthogonal basis of solutions (hence the unicity and minimal norm proof). 2.32 Newton polynomials implementation The polynomial modeling, subdividing the population values in each size bin, comparable to a bar chart, by a polynomial function, allows us to have more size bins than inversion unknowns, and will force the population to be somewhat smoothly, or at least continuously varying, which can be expected from a natural population distribution. Practically it means less unknown coefficients in the unknown C vector, and/or more size classes available. Newton nested polynomials are convenient to use in computational methods because of the recurrent loop present in the nested property. The Vandermonde matrix R displaying increasing powers of the arbitrary coefficients ri can be inversed at will as long as none of the ri is null. 35 2.33 Multiple species inversion The inversion principle can be extended to several species in the following way: The target strength model matrix is now made of two or more models side by side, and the population becomes a superposition of population matrices. These populations can simply be of one or more values; they can also be subdivided into a polynomial construction, allowing to use more size classes than the number of unknown polynomial coefficients. The inversion principle and matrix calculus then obeys the same rules. TS measured(f) = TS(fJ, a1) * N ,econstr,,,ed(5) The TS model is decomposed into appropriate species =[Ts, TSS2]* LNSI (9) The species are made of single or multiple values whenever Rsi is unity or the identity matrix, but can in the more general case be polynomials =[TsSI TSs 2 ]* sI Ls] (10) Following this parallel combination of two different scattering models, the same inversion (8) applies. C = ((TS * R) T * (TS * R))' * (TS * R) T * Ntr,, (8) Because the active sonar measurements are a superposition of all backscatter response, at all frequencies, a variety of physical and biological ocean sound scatterers apply and the right models have to be chosen. The frequency separation is automatic and the Doppler effects are ignored, but the appropriate choice of models is a crucial decision where adaptive modeling applies. 36 3. Results 3.1 Single species simulation 3.11 Fluid-like animals: euphausiids, copepods, krill This is an example of reconstruction: a simulated population (blue) is measured without noise; the reconstruction is plotted (dashed green) as a polynomial of lower degree. The 50 size classes are spread in a logarithmic scale, with widths of the order of 0.1mm. The Y-axis shows the number of individuals in the volume insonified, say the 1m3 unit volume and you obtain a concentration of plankton. One hundred (100) measuring frequencies and fifty size bins are used in these simulations. 165 7- 160 .1'~ / / N 155 0 0 150 / 145 / ii / 140 / 135 2 3 4 5 6 1 2 3 4 5 6 plankton radius [in]X103 figl4: simulated random degree 10 polynomial population (blue), and degree 4 polynomial inversion (dashed green) 37 comparative given vs reconstructed concentration of euphasiids 160 140 \ 120- S100 80 - U 20 C ~40 20 0.5 1 1.5 2 2.5 3 radius [m] X 10i figl 5: other inversion example and the distribution of errors between the given random population and the reconstruction from a perfect measurement This other reconstruction hints that the precision may be weaker for smaller radii of zooplankton. Indeed the physics of the scattering provides stronger TS in the geometrical acoustics zone (larger ka) while the Rayleigh scattering zone is a high pass and drops quickly as ka becomes smaller. The difference in polynomial degree is here obviously also an error factor. 38 Repartition of mean normalized errors for a hundred randon populations reconstructions 30 25 0 201[ 0 0 C 15 [ 0 E 10 [- C 5 - 0-L 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 mean error fig16: mean errors for a hundred inversions The center of gravity of these mean errors is at 12.6%. The number of inversions with mean errors above 25% is less than 10%, with the highest concentration of mean errors between 5% and 10%. 39 3.12 Standard deviation robustness A test of standard deviation provides us with valuable insight about the transfer function that turns the probability densities of measurements into probability densities of the corresponding numerical inversion. The error is an essential component of assimilation therefore model and inversion error must be quantified. To test the inversion and how it affects variance systematically, a uniform population simulator was chosen with mean 100 individuals in each of fifty size classes, and a Gaussian distribution of standard deviation (Y= 20. All the following plots in section 3.1 provide animal concentration in each size bins vs. radius in mm. After a perfect "measurement" of the fiction population at a hundred (100) frequencies ranging from 10 kHz to 1MHz, the population is approximated by a degree ten polynomial: Example of random population and its reconstruction 200 150 1 100 01 -50 0 1 2 3 4 5 6 figl7: example of random population used for standard deviation test 40 standard deviation of true population 200 1501 1--- -II-I I _ -- 100 1 0 -50' 0 1 2 3 4 5 6 7 x 103 figl8: true population abundance distribution per volume of water in each size bin for variance test inversion Standard deviation after inversion 200 *1~150 - 100 - --- 8 50 (4~4 0 -50 0 1 2 3 4 5 6 7 Fluid-like zooplankton size bins [mnm] figl9: standard deviation of 200 realizations of reconstructed polynomial populations 41 After 200 realizations, we observe in figure 19 that the reconstruction does not bring bias. The important question to tackle is where the standard deviation envelope shape comes from. 3.13 Plankton radius and polynomial order influence In order to clearly determine whether the standard deviation observed after 200 realizations is indeed the transfer function of the computational acoustics inversion for uncertainty, and what part of the error can be attributed to the physics, the choice of polynomial simplification or the inversion mechanics, a much broader range of plankton radii size bins is used, and different degrees for the polynomial are examined in figures 20 and 21. First, we can observe that the inversion is very poor on the edges as can be seen for a= 10 m, which can be linked to the absence of constraints for the polynomials past the edges. The inversion itself is reliable except for the loose ends: polynomials are free on the edges and lead the standard deviation to excessive limits. This skyrocketing uncertainty moves with a choice of broader radii from 6*10- in fig19 to 102 in fig20 and finds itself always shifted to the outer edges of the inversion. The central structure of the standard deviation in figures 19, 20 and 21 -nodes and antinodes- is clearly related to the order of the polynomial: The number and height of the bumps in the general envelope of the standard deviations correspond to polynomial patterns, 2 central maxima in figure 20, 4 in figure 21 while physical scattering reduces precision as for small radii (small ka) and the matrix inversion is unreliable close to the chosen window extremities. Note that the number of nodes observed is expected to be (N-1) if N is the inversion size polynomial degree. Because the radius scale used for these simulations is logarithmic ak = exp log(amax (ami * ,- +log(ami)] for the kth radius out of n, the width of the error bar tops is also logarithmic, which creates a misleading graphical effect. 42 standard deviation of degree 4 reconstructed population 250 200 150 100 50 - 0 -50 -100- 10 10 10 2 fig20: degree four wide radius range inversion standard deviation of degree 6 reconstructed population 300 250 200 150 100 50 0+- .501 -k -- --- L - - I - . .. .. 1 4 6 . 4 10 10*3 102 fig21: degree six wide radius range inversion 43 Figure 22 is an example of simulation with very broad radii in order to observe what happens for small and large radii. The two curves are almost superposed for a 2 x 10- while they diverge more and more rapidly as the radius becomes smaller. wide size range fluid-like reconstruction 90 85- / / / / 75 70[ 65 true reconstructed 601 4 . . a I ,1. . i. 1 - 101 102 plankton equivalent radius fig22: wide radii range inversion; observe the divergence of the curves as a decreases. The physics of the TS make the inversion better for larger plankton radii: zooplankton smaller than .5 or 1mm has a weak scattering strength compared to larger animals, and acoustical sampling uncertainty rises under this threshold. Fifty measuring frequencies were used on the simulated reconstructions, with just 1 kind of zooplankton, while high frequency acoustic sonars used by researchers offer at most 6 frequencies. Because some model parameters need to be fine-tuned and if more species are present in the water, the inversion is much underdetermined in the real world. 44 3.2 Multiple species simulation 3.21 Euphausiids-pteropods co-inversion Gas-bearing and elastic-shelled animals, even when not in great number must be detected simultaneously with the fluid-like plankters to lessen the noise they represent. 19 The assessment of their small number and biomass may be of little interest because irrelevant compared to the often overwhelming biomass of euphausiids and copepods, but their effect on backscatter can be significant at some frequencies. Elastic-shelled animals are here used for the simultaneous inversion with the more common fluid-like zooplankton type. The two species have separate TS models and different size ranges. TS measuredf = T SS2]* 1 S j * (10) 10 RS2_ .Cs2_ From the numerical inversion method in paragraph 2.33, R is chosen to have 250 columns. In this first presentation of a double inversion, Csi attributed to fluid-like animals has 200 parameters, while 50 are attributed to elastic-shelled bearers. We obviously get as a result a much more precise inversion on the species that was given 4/5th of the inversion equations. 45 200 coefficients fluid-like co-inversion -80 -90 -100 -110 -120 -130 I I- -140 / -150 - / / -160 *,/ 17k .5 1 1.5 2 2.5 x 10 fig23: fluid-like acoustics co-inversion with 200 equations 50 parameters elastic-shelled co-inversion 150 1 001F 50 1- / / N / 0 1- /1 N / -50 - -inn i I I 0 1 2 3 4 5 6 X10 fig24: twin elastic-shelled co-inversion with a complementary 50 equations 46 / 3.22 Influence of relative precision It would only seem natural to expect that the ratio reversion for the number of unknowns attributed to each species in figures 23 and.24 vs. figures 25 and 26 would reverse the quality of the inversion. An examination of this assumption in the figures 25 and 26 doesn't quite offer this picture: The fluid-like reconstruction has lost precision, but without transferring this lost information to the elastic-shelled animals. A possible explanation can consist in the fact that the elastic-shelled inversion does not depend critically upon the number of equations, while the fluid-like animals reconstruction does. Another attempt to interpret this unexpected result would be to claim that the parameter reduction and lower quality of the fluid-like inversion has a significant negative impact on the precision of the elastic-shelled inversion. The reciprocity does not hold true. The fact that both inversions are inextricably tied in the simultaneous inversion can certainly account for some crossing uncertainty: the total spectrum measured is made of categories in size and species. If one category is underestimated, the other categories will share the missing TS according to their TS spectrum. 47 50 coefficients fluid-like co-inversion 320 300 280 260 - N - 240 220 200 \ - --- _ - - 180 160 140 11 .5 1 1.5 2 25 3 x 1 ' fig25: fluid-like acoustics co-inversion with 50 equations 200 parameters elastic-shelled co-inversion 1wJ 100 50 0 -50 -100 -150 ...... -200 -250 -300 1 2 3 4 6 x 10 fig26: twin elastic-shelled co-inversion with 200 equations 48 3.3 Noisy field data inversion The purpose of this section is to experiment the acoustic inversion developed and simulated previously on field data collected by WHOI's Biomaper II. In particular, the acoustic inversion aim is to see what response to noise and bubbles could be brought. Would a double- or multiple-model inversion with a model for bubbles or noise help? 3.31 Data inversion setup Data were thresholded for system noise, and the dynamic range of the system was -100 to -40 dB. Measurements less than le-10 were automatically set to zero. None of these data were corrected for calibration offsets, nor were they cleaned up from near- surface measurement. This means that echoes from the bottom and surface bubble layers have not been edited out, and some of the data (at 43 kHz in particular) are somewhat noisy. From paragraph 2.23 fig 13, data at the single depth of 75m is extracted in order to perform the inversion on data where the actual species composition and size distribution have been recorded by means other than acoustics. The x-axis, ranging from zero to two thousand, is the number of pings. Pings occur every AT = 1/3rd of a second for all frequencies. (One frequency cannot disturb measurement at another frequency because they are sufficiently separated.) Because the pings are averaged three by three, and for the vessel moving at V = 4knots = 8kmh-1 , the distance between the measurements plotted is: 8000 3 Ax=VxAT= x-2.22m 3600 3 The total scale of the sampling used is 4.5km long and the horizontal patchiness on the order of a few hundreds of meters. 49 75m depth data cut 10 + 43kHz -- 120kHz 200kHz 10 420kHz ILiI 1017 +/ I i I a +4 + 4A ++ + ++ + + + + 0 200 400 600 BOO 1000 1200 1400 1600 1800 2000 fig27: 2000 multifrequency measurements at 75m depth 3.32 Result An inversion is now performed at this depth using Stanton's fluid-like average- length average-orientation bent cylinder with Euphausiids parameters. 50 75m deep Antartica fluid-like inversion x 10-- 2.5 2 IE (DJ E 0 4500 10- 3375 10-125 1125 Fluir-liike krill rrii rml 102 0 I d l = k Iea stance aong0 e ]~ t tL l-L l fig28: fluid-like inversion on 75m deep data track The inversion has been limited to a narrow radii size range because of the low number of frequencies sampled, in an effort to limit the number of unknowns. In figure 28 we observe recognize the same patchiness scale than in the raw data shown in figure 27, on the order of hundreds of meters. The band of missing data corresponds to the part where the Biomaper dived a little deeper. In this area the highest frequency (420kHz) did not reach the 75m-deep layer and the inversion cannot be performed. The edges of the inversion, at a = 5 x 104 and a = 2 x 10- witness the occurrence of the highest peaks. As was demonstrated in paragraph 3.13, the inversion is not reliable on the outer edges, in particular for a radius of 2mm. It looks like the sharpest peaks for radii larger than 1.3* 10-3 belong to the outer layer where inversion cannot be trusted. These concentration peaks should simply be ignored. Interesting features include the width of the main peak of animal concentration. The shape of the size repartition is certainly smoothed by the polynomial function. 51 3.33 Population size distribution check The inversion examined in figure 28 is now averaged over the entire experimental data, in an effort to compare the inversion result to net collection statistical estimates. We can again discard the 2mm peak of plankton concentration as clarified in 3.13. Although the inversion is only performed on a narrow radii range of optimal precision, the result is displayed on the same scale as that of the biology counting plot. The inversion results have been simply trimmed when turning negative, with no physical meaning. Although animals larger than 30mm are very sparse, their contribution to the total biomass is very significant. But echo from the larger Euphausiids -50mm- is much easier to pick up; it does not require frequencies in the hundreds of kHz and could therefore be detected with sensitive commercial fishing sonar. 52 Plankton length repartition from acoustics inversion B 7 6 0:)5 C0 C0 4. -0 E 2 1 - 1 01 10 20 30 40 50 50 70 80 00 10 20 30 40 50 60 70 60 Krill(Euphausiid) size [mm] fig29: 4500m average size-abundance distribution of computational inversion Plankton length repartition in the 50 to 1D00m depth layer observed B I 7 6 0E 34 U 21 1 01 0 10 20 30 40 s0 60 70 80 KriI (Euphausiid) size [mm] fig30: population distribution from direct net collection, statistical sampling and microscope observations 53 4. Discussion 4.1 Backscatter error models It is fair to say that the physical modeling precision is more than enough, but that the errors and uncertainties regarding many influencing parameters don't allow good measurement. The low number of frequencies is also a factor limiting the reliability of acoustics inversion, because they are widely underdetermined. 4.11 Multiple targets interference and Doppler dispersion All models assume the backscatter of the sum is the sum of the backscatters for animals of same size and shape. This linearity assumption between acoustic backscatter and animal concentration is justified as long as the concentration of targets is reasonably low and the distribution sufficiently random.20 Why are the models and their linear approximation good enough? The shadow zone behind any individual is negligible. Multiple reflections are strongly attenuated because the reflection coefficient R12 appears multiple times. The precision of the model is greater than that of the other zooplankton assessment techniques necessary to calibrate the acoustic instruments. The linear addition assumption is generally true for zooplankton found in somewhat limited numbers in the temperate New England waters, even when swimming in thin layers of much higher plankton and particulate matter concentration floating on a pycnocline. On the contrary, this may be a source of error in tropical upwelling waters where swarms of plankton animals densely aggregate near the surface or in thin layers. As a matter of fact, the model for one animal is different from the model for many, where the variance of animal size has a role to play. A single fixed target provides a returned signal at the same frequency than the incident one. For multiple targets, the mean return frequency only provides information about the motion of the target as a whole. Since we don't expect the animals to behave as 54 a military squad, the relative motions will create dispersion in frequencies: dfmax is proportional to Vmax where Vmax is the maximum velocity of any animal in a motionless flock. Hence in the general case Vmax is the maximum difference between the mean flock velocity vector and the velocity of any individual animal in the flock. The narrowband transducers of the Biomaper are not meant to be extremely frequency sensitive and frequency shifts have been left aside in the data WHOI has provided. 4.12 Bubbles, sand and internal waves Bubbles are always present near the ocean surface. This phenomenon brings strong bias to all measurements. Air bubbles contamination may affect measurements in the highest water layer no deeper than 5m, where it can be a source of concern.' The attitude adopted when faced with this extremely strong backscatter from non-biological sources was to simply discard the data. Indeed, even if an acoustics inversion were using physical models to unmask physical features from a surface layer measurement, the likelihood of holding a quality inversion would still be low because of the extreme diversity of life near the ocean-atmosphere interface. If the surface layer represents a higher level of challenge, there is a bright side to the medal: it is also much easier to observe and access by any other method. On the continental shelf, sand can be suspended by the current and lifted in the watercolumn. Acoustic high frequency sonars clearly pick up sand in their measurements Sand and dissolved organic material suspended in the water column have been reported to look like plankton. Data assimilation of currents and opacity of the water could help automatically detect these abnormalities if three-dimensional knowledge of continental shelf currents and upwelling provide hints on whether measurements are potentially a sand storm or marine snow. The animals are often in greater concentration near a moving isodensity surface called internal wave, which is similar in form, but not directly related to surface waves." Just like typical zooplankton patches, internal waves and solutions are characterized by horizontal lengths of hundreds of meters and vertical lengths of a few meters. The thermo 55 cline and pycnocline, depths of strongest temperature and density gradients, have to be closely watched because the change of density often captures some of the neutrally buoyant plankton and attracts the rest of the marine plankton and life for feeding. The thermal gradient is helpful to validate TSzoopankton > TSgradient. More generally, noise from turbulence can be comparable to the TS of large zooplankton. This noise can be successfully eliminated either with a broadband sonar (distinct behavior with broader frequency range) or spectral analysis using properties like temporal variations in the noise, or the fact that the noise's average doesn't have any spectral structure. 4.13 Other sources of error It also appears in the literature that bio-acoustic measurements up to present date had a fair precision as far as models, instruments and calibration were concerned. In fact, all the methods used have had limited precision, but searchers pointed out that the patchiness in time and space of the population being sampled doesn't allow straightforward improvements of the measurements. In particular the TS has a stochastic nature. 20 Some of the major concerns were due to unwanted species (fish schools etc.) entering the measurement area sometimes for only part of the measurement, weather and time effects on the comportment of the plankton, inconsistency of the plankton population size through diurnal cycle etc. The vertical migration is well observed, but total zooplankton biomass integrated over the watercolumn is consistently different during day versus night times. Assimilation of other parameters (physical and biological) may be an efficient way to improve the measurement methods. The Poseidon project here has been appropriately designed to promote interdisciplinary science and models improvements in that regard. Bioacoustics measurements are indeed not limited by instruments or models, but by the complexity of zooplankton behavior and interdisciplinary implications on zooplankton. Assimilation is expected to tie critical bonds and obtain significant science improvements. Calibration is achieved with metal spheres used as reference targets, to produce a well-defined echo, which leads to a calibration accuracy of about 5%20 56 The fluid-like animals model is extremely sensitive to slight changes in the parameters g and h, because values of plankton body density and sound speed are close to that of the oceanic water. As an example, altering the density contrast g from 1.035 to 1.04 is a 14% increase, significantly altering numerical outputs. Values of g reported in the literature vary from 1.016 to 1.120, while the values of sound speed contrast h range from 1.007 to 1.033.11 According to Peter Wiebe, "acoustics is great at measuring backscatter, but not strictly zooplankton." This refers to the many physical features that are recorded by acoustical measurements, and sometimes look like biological features. This category of physical features includes solitons, internal waves and suspended sand or sediment. Although acoustics is the only measure that samples large volumes fast, Dr. Wiebe finds it difficult to confidently relate acoustic measurement to plankton biomass. He believes progress can be made faster using the Video Plankton Recorder, because this method is also automated and it provides direct counts and accurate sizes. 4.2 Adaptivity Species identification will not be accomplished with acoustics alone, but cannot be accomplished without acoustics. 20 All available information must be integrated in new analytical procedures, which will make probabilistic statements regarding the specific cause of the marks seen on echo sounders and sonars. 20 The objectives of Adaptive Sampling include dynamical "hotspots" sampling, reduction of error variance, reduction of errors for tomorrow, maintaining accurate forecast and accurate synoptic picture, optimization of the sampling as a function of objectives and metrics, automation of the previous points, nonlinear and interdisciplinary sampling impact study, reduction of error in analysis vs. in forecast, minimization of final time errors vs. minimization of time-averaged errors, cost function minimization for the three following: forecasted model errors (ESSE), forecasted significant dynamical events (MS-EVA, pattern recognition) and maximum length of time an area can be left without updating. 57 4.21 Body of water Using data assimilation, the real-time melding of measurements and calculation outputs, metadata can be created, for meaningful use and visualization of information. Adaptive modeling consists in using these metadata and state variables evaluated in real time to address the changing environment and adapt the model parameters or very nature of the models used. Adaptive sampling closes the loop: given our knowledge of the ocean patch that is monitored as a digital ocean, and given the uncertainties that our metadata carry in space and time, a sampling strategy changing in real time as well can be carefully thought in order to minimize the uncertainties. Pumping double-checking or precise depth measurement can be made in an adaptive manner, with a relatively small horizontal patchiness (ranges of hundreds of meters instead of meters)". The specificity of this research on plankton is to use the tools of adaptivity and meta-data to benefit from interdisciplinary information. How do acoustics fit into the big picture? Physical water properties of Gulf Stream vs. cold upwelling northern waters must be accounted for in the model parameters. These different bodies of water and the pycnocline must be documented for sampling depth, vertical daily migrations and extrapolation of the measurements on larger areas. At least two distinct bodies of water coexist on Georges Bank: polar waters and Gulf Stream waters. True enough, mixing is occurring between these bodies, but practically the mixing is occurring with widths of small scale compared to the mainstream and gyres of the two bodies. Practically, knowledge of the water body is determined in 3D by the temperature and salinity. The body of water can help predetermine plankton composition and physical parameters. It is necessary for geographic adaptivity (right choice of measurement locations in the area of study). The correlation of the body of water to nutrients, temperature and the resulting plankton abundance at the surface has been strongly reported and demonstrated. 58 4.22 Physics The parameters g & h, density and sound speed ratios, may be partially provided by the knowledge of the sampled water body, but must be more precisely actualized. This adaptivity can ideally be continuous, with rates of modification well under a day, because the parameters vary as a function of sunshine exposure. Seasonal and diurnal cycles depend strongly on feed availability and physiological processes. We must note that the averaged g and h values that the acoustical model calls for are not the ideal value to be used, but the value must be somewhat larger, to better take into account the external skeleton. We therefore speak of modified average density and sound speed contrasts. Values of g and h can be chosen according to the cross-comparison between acoustic measurements and sampling. When the measure has similar spectrum but with a lower TS, it is probably not a problem of net avoidance from larger plankters but values of g and h too low." Adaptivity of g & h parameters can ideally be continuous: these parameters vary as a function of sunshine over time: seasonal and diurnal cycles depending strongly on feed availability and physiological processes via lipid proportions. The pycnocline and thermocline must be known to look for plankton layers sitting on it and\or avoid specular or coherent reflection on it. Plankton is often concentrated on small patches on top of the thermocline, with typical vertical range of less than 3m and horizontal range from a few to 200m. Plankton biomass is apparently much higher in well-mixed areas, especially near the bottom, possibly because of sand resuspension.2 2 The validity of bioacoustics should at this point be limited to areas where resuspended sediments are known to be absent from the watercolumn.2 2 Physical knowledge of upwelling areas can contribute to warning scientists when the sonar echoes are likely to originate from undesired physical features in the watercolumn. Adaptivity applied to physical features is aimed at automatically determining features like upwelling, vortices, jet filaments, gradients, gyres, meanders, jets, velocity- based fronts, temperature-salinity fronts, shelf-slope fronts, eddies, streams, crests, tidal phenomena, plumes, lenses, currents, meandering, circular fronts and facilitate adaptive sampling. Adaptive modeling and sampling are ultimately meant to be automatic. 59 The full list of "interesting" features must be established and could welcome additions as events of interest are added to the scope of research. The optimal attributes sets corresponding to these features must be defined. 4.23 Biology Further biological knowledge can be useful to either predetermine or compare species composition when running models using a priori seasonal knowledge etc. Higher frequencies lead to the measurement of phytoplankton and possibly a significant response from particulate organic material. Phytoplankton represents noise that must be taken into account in the measurement campaigns (location and height in the water column) and could also be used in the model to be better subtracted, data assimilation could also point out how much phytoplankton is affecting a zooplankton remote sensing: Some of the large phytoplankters interact with zooplankton measurement and could be subtracted to avoid redundancy with total chlorophyll seen by satellites. The ratio of the phytoplankton population larger than, say, 1mm, can be modeled and it's TS spectrum subtracted from acoustic sensing in each water body. Fish behavior research has greatly improved our understanding of target strength and why it is a highly variable feature. Much less is known at this point about plankton behavior, but understanding of TS can be expected to improve in that regard. In particular, orientation is a scattering issue where biology understanding can provides and improve significant information. The orientation probability distribution of elongated fluid-like cylinders like animals is a key parameter that is believed to vary during the day depending on plankton behavior. Migration and feeding are behavior patterns likely to change the distribution of zooplankton orientation. Quantitative comparison of acoustically derived data to pump and net samples has proven to be difficult (Costello et al. 1989). The sampling devices work on different scales and collect data from different volumes of water.2 2 Plankton is perpetually in motion: different water depth are carried by currents of different directions and velocities. 60 No plankton patch is uniform, stable or easily traceable in time. The complexity of the coupled physics and biology of the ocean provide no small challenge and justify the Poseidon projects' attempt to put artificial intelligence at work through adaptivity. Arctic Seasonal blooms: 2 A second primary producers bloom is usually observed in temperate latitudes. Temperate Keep in mind that the abundance of animals observed and reflected in these curves is not the rate of A hh= growth, because predation pressure plays an important role as Tropical a population reducer. J F M A M J J A S O N D AIjae z Herbivores Fig3 1: phytoplankton and zooplankton blooms, source: Jim McCarthy, Harvard Finally, the season could provide valuable information on model parameter values such as species present, expected average size, and sound velocity ratio that comes with lipid contents. Meteorology is a strong factor in phytoplankton growth models, it should directly or indirectly appear in the sampling strategy. The size range targeted depends on the season with/without the presence of juveniles. The "season" can in fact be turned into a state parameter separated from the actual date, because weather and primary productivity may be early or late a couple of weeks from year to year. 61 4.3 Future work 4.31 Computational method assessment This method has several advantages: " Size classes and precision can be adapted at any moment depending on the rapidity requirements, precision need and computing possibilities. * Only the forward model needs to be known; it can be modified, replaced or improved just by modifying the corresponding coefficients in the backscatter cross-section matrix. " Measurements can be used instead of theoretical models: a limited number of measured species-specific backscatter values can replace a physical model for comparison purposes. " Multiple models can be used simultaneously simply by adding or dropping size classes with different characteristics. It is important to emphasize that this features is necessary to take care of the real world superposed measurements and is a good way to wipe out noise from unwanted species and physical features measurement. Again, the inversion will only be able to separate the species as far as their backscatter spectrum shape differs in the sampled frequencies. Now comes the disadvantages side: " The inversion is a black box " The error model is not theoretically and explicitly obtained, but has to come from multiple inversions of virtual populations with known (Gaussian) probabilistic repartitions. In short, the computational inversion allows scalability in the model, the computational power available and the precision we require (though the limitations here are not in the computational power or in the model precision but in the measurements). Measured or empirical values of TS can be used in lieu of formulas. We can make the 62 inversion simultaneously for a few species. On the inconvenience side, the inversion is opaque and error models can only be guessed from multiple trials, without closed-form formula. 4.32 Poseidon backbone connection The adaptive physical-biological-acoustical modelling at the core of the Poseidon project is to be designed after the already existing and effective Harvard Ocean Prediction System (HOPS), system of integrated software for multidisciplinary oceanographic research. Accurate estimation of ocean fields in a timely and reliable manner is the first step. HOPS data are stored in a NetCDF file. A palette file, contour parameters file, coastline data file, isobath file, geosat track file, etc. are also needed. HOPS Plotting packages can then visualize over two hundred fields by providing contours of the desired fields at the given depth and time. Vector fields include geostrophic velocity vectors, total velocity, zonal velocity, zonal geostrophic velocity and meridional velocity. Scalar fields cover temperature, transport stream function, surface heat flux, wind stress, surface pressure, velocity components, density, salinity, zooplankton and chemical concentrations. To present the results in a fashion easy to use remotely -over the internet- and that can remains open to future adaptations, the NetCDF files format has been chosen: useful and popular format to store large scientific data, it provides an interface for array- oriented data access, a library with an implementation of the interface, (in Fortran or C codes), and other interfaces that can be used by other tools (Java interface, toolboxes for MATLAB-5). As a second step, the data can be transferred from NetCDF to ASCII format, and finally Ncview can be used to get a quick look at the NetCDF data. NetCDF files are array-oriented data that can be created, accessed and shared in a form that is self- describing {dimensions, variables, data} and portable. The acoustic data will in the future need to draw physical and environmental parameters from the NetCDF library, and provide its results in this form as well. Matlab is a suitable tool compatible with, or at least convertible to the NetCDF file format. 63 4.33 Measurement strategy and applications The Biomaper designed and custom-built at WHOI has proven to be an efficient multi-disciplinary sampling device. While it is among the leading edge high-frequency acoustics sensing devices, it is complicated to operate and must be towed by a vessel, an expensive and manpower-hungry operation. The Biomaper remains a reference for measurements and multidisciplinary double-checking, but it also is meaningful to envision some simpler captors such as moored or drifting bi-frequency sensors. The use of such sensors has been abandoned for research operations in the last decade, but these sensors truly offer an opportunity for automatic sensing. Depending on what the diagnosis of the bay of Massachusetts uncertainties "hotspots" brings, such affordable sensors could be used in a more automated fashion. 64 Conclusion High frequency ocean acoustics measurements capture any sound velocity or density contrast, ranging from sand, bubbles and internal waves to the shells, bubbles and bodies of zooplankters. This type of measurement, fundamentally more efficient than any other localized zooplankton-sampling tool, is also intrinsically tied to physical features in the water column. The Poseidon project possesses in its very structure a possible response to the challenges offered by interdisciplinary ocean science: The pycnocline and thermocline are physical features that typically concentrate plankton layers and can lead to specular or coherent pressure wave reflection. Mixing (of nutrients as well as generation of small-scale sound velocity gradients), presence of sand in upwelling plumes or bubbles in a surface layer, solitons and mutlireflections between a peaceful sea and a flat sediment bottom are features likely to generate undesired sonar echoes. The definition of the body of water sampled could provide estimates for sound speed and density ratios g & h, further precised by sunshine exposure, seasonal, diurnal and physiological models. Direct species sampling, plankton body lipid proportions and average orientation of the plankton were also identified as valuable for adaptive sampling. Fifty measuring frequencies were used on simulated reconstructions, proven to work with fluid-like or elastic-shelled kinds of zooplankton, while high frequency acoustic sonars used by researchers offer at most 8 frequencies. 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