ABSTRACT: A free-drifting submersible holography system, 'Holosub', was deployed in East Sound, Washington as part of a collaborative field study. Analysis of more than 20 000 holograms recorded during 2 slow profiling ascents (that avoided disturbance to the parameters measured) provided the size and spatial distributions of particles, the orientation of diatom chains, and the mean shear strain and turbulence dissipation rate profiles. This information was complemented by concurrently measured parameters including density, chlorophyll a concentration and optical scattering, and discrete water sampling for species characterization. Key findings were: (1) a prominent thin layer of mostly non-motile small particles, many of them chlorophyll a rich, formed in a region of near-zero shear and low dissipation rates, and high stratification; (2) multiple particle concentration peaks formed at several depths with low shear and dissipation rates; (3) Chaetoceros socialis colonies had several volume fraction peaks near the pycnocline, with the largest coinciding with the thin layer and some smaller peaks coinciding with small particle peaks; (4) zooplankton avoided many regions with elevated C. socialis and/or particle concentrations; (5) diatom chains had nearly horizontal alignment (zero angle) in the thin layer, with low angles also observed in other low shear/dissipation regions, and random orientations in several local shear/dissipation maxima regions; and (6) comparisons between particle counts from on-board cell cytometry, microscopic digital holography, and light microscopy of discrete samples showed similarities in trends but also discrepancies. In addition to these specific findings, this study demonstrates the great potential of digital holography for in situ studies of biophysical interactions and particle dynamics in oceanic flows. KEY WORDS: Holography · Submersible · Shear · Dissipation · Biophysical interactions Mar Ecol Prog Ser 473: 2013 marine plankton, termed 'thin layers' (Derenbach et al. 1979) , often found in regions of physical discontinuities such as pycnoclines (McManus et al. 2005) . Studies, e.g. those by Dekshenieks et al. (2001) , Sullivan et al. (2010b) and Durham & Stocker (2012) , have shown that these thin layers have vertical depths typically varying from a few centimeters to a few meters, while their horizontal extent can be in kilometers and their temporal extent can vary from an hour to several weeks. Heterogeneities in the distribution of plankton in the water column, especially thin layers, can have a significant influence on ecosystem ecology by controlling patterns of primary productivity, trophic transfer, fish larvae survival, etc. There are numerous studies focused on understanding the formation of heterogeneities in the water column, and mechanisms responsible for their stability/instability. A common approach is to postulate the formation of thin layers in regions where 'convergent' forces that thin a phytoplankton layer exceed the 'divergent' forces that broaden the layer (Osborn 1998 , Stacey et al. 2007 . Convergent forces that contribute to formation and maintenance mechanisms include straining by shear (Birch et al. 2008) , buoyancy-driven settling of particles along a pycnocline (Franks, 1992) and active swimming by phytoplankton (Durham et al. 2009 , Sullivan et al. 2010b ). In addition to physical factors, phytoplankton can accumulate in layers in response to the availability of nutrients, presence of predators or for reproduction (Leising 2001) . The primary divergent process is turbulent diffusion (Wang & Goodman 2010) caused by a variety of physical processes such as winds, non-linear internal waves and convective overturns. Field studies on the interplay of these biophysical factors have been presented by e.g. Alldredge et al. (2002), who performed simultaneous measurements of abundances of marine snow aggregates, phytoplankton and zooplankton along with various physical parameters. This study not only showed broad correlation between the presence of thin layers of marine snow or phytoplankton and pycnoclines, but also provided evidence for the preferential aggregation of zooplankton with respect to marine snow. Numerous in situ instruments and deployment techniques have been developed to sample the water column at fine scales. For example, 'slow-drop' vertical profiling, where the sampling platform is both decoupled from ship motion and ballasted to be slightly negatively buoyant, can achieve vertical sampling resolution at centimeter scales (e.g. Donaghay et al. 1992 , Cowles et al. 1998 . Multi-frequency acoustic profiling sensors have been developed to locate large concentrations of zooplankton at fine scales (Holliday et al. 1998 , Benoit-Bird et al. 2010, and spatial variability of the volume scattering function (VSF) has been used to identify high concentrations of particulates and organisms (Twardowski et al. 1999 , Sullivan et al. 2005 , 2010b . Acoustic Doppler Current Profilers (ADCPs) have been commonly used to measure mean shear rates in the water column (e.g. McManus et al. 2005) , but ADCPs have a highest depth resolution of ~1 m, which could be insufficient to study how the flow physics affects the formation of particle layers. The present study is motivated by the fact that a more complete understanding of population dynamics, plankton behavior and species composition requires identification and quantification of the 3-dimensional (3D) spatial distribution of various particles and organisms throughout the water column, in a non-intrusive manner. Underwater video imaging (Davis et al. 1992 ) has been used to make direct observations of marine life and their abundances with depth, but it lacks the ability to maintain focus over a significant thickness, thus providing information only in a small volume of fluid. The current work utilizes free stream holography, which has the ability to maintain high lateral resolution in a sample volume with substantial thickness. This advantage enables tracking of organisms and the determination of the 3D spatial distribution of different organisms and particles at high resolution, and in an undisturbed volume. Thus, a submersible with an on-board holographic optical setup would serve as an ideal tool to study the water column. Several submersible holography systems have been developed, starting with the holographic velocimeter which measured size and settling rates of particulate matter (Carder 1979) . Pulsed laser based submersible holographic systems, which recorded holograms on high resolution film, have since been developed by Katz et al. (1984) , O'Hern et al. (1988) , Katz et al. (1999) , Malkiel et al. (1999 Malkiel et al. ( , 2004 and Watson et al. (2001) . Though use of film enabled these researchers to study large sample volumes without compromising resolution, these systems involved cumbersome instrumentation, and long processing and data analysis times. However, the results in Katz et al. (1984), and Malkiel et al. (2004) resolved relationships among spatial distributions of multiple species that could not have been studied by any other means. While switching to digital holography involves a compromise in resolution, the advantages in live transmission of holograms and a much larger data acquisition capacity outweigh its 30 Talapatra et al.: Holography in ocean water column disadvantage. Furthermore, the emergence of compact diode-pumped solid-state lasers has reduced the size of the optical setup to a small fraction of the original systems. The first in situ implementation of digital holography was by Owen & Zozulya (2000) , who used a low power continuous diode laser and a charge-coupled device camera, followed by Jericho et al. (2006) who recorded organisms trapped in an isolation chamber, the pulsed laser based system built by Sun et al. (2007) for measuring size distributions of copepods, and the sediment measurement system developed by Graham & Smith (2010) . In all of these cases, the results were based on analyzing a small number of particles/organisms. This paper fo cuses on data obtained during a recent deployment in East Sound, Washington, USA of the Holosub -a free drifting, submersible, ballast-controlled, multicamera, digital holography system (Pfitsch et al. 2005 (Pfitsch et al. , 2007 . We start with a brief description of the instrumentation and the deployment details, followed by an explanation of key data analysis steps. Results are presented and analyzed with the goal of elucidating some of the underlying mechanisms responsible for heterogeneities in the distributions of particles and organisms in the water column. MATERIALS AND METHODS A detailed description of the instrumentation associated with the Holosub has been provided by Pfitsch et al. (2005) , and is summarized briefly here. An illustration of this system, as de ployed, is provided in Fig. 1 , and its internal optical setup is sketched in Fig. 2 . A 10 cm long cylinder-shaped sample volume is located between the 2 streamlined vertical towers, whose shape and height (55 cm, with the sample volume located 12 cm below the top) are designed to minimize the flow disturbance in the imaged sample volume. Draginducing elements are at ta ched to the towers, at the same elevation as the transparent viewing windows, in order to reduce the relative motion between the platform and the local currents while the submersible is drifting. Streamlined shells cover the main body of the submersible, to minimize the drag of the parts of the device that are relatively far from the windows. In the 'profiling mode' (not discussed here), the drag elements and streamlined shells are removed, as the submersible is rapidly lowered and raised through the water column. A communication line connecting the submersible to a host computer located on a boat consists of a single mode fiber optic cable, which 473: 29-51, 2013 transmits data in 4 channels, each at a different wave length. One of these channels is used for controlling the 2 cameras, laser, ballast pump, etc. The other 3 channels are used for transmitting the CTD output and the digital holograms from the 2 cameras. All electrical signals are converted into optical signals prior to transmission and then converted back, either on the surface for storage, display and analysis, or inside the Holosub for implementation of user commands. The optical fiber can either be released from the boat, as was done during these experiments, or released from a spool located on the platform. The latter reduces the drag on the Holosub by increasing the slack of the fiber optic cable. We did not utilize this option during this study, owing to convenience and the low current velocities encountered during the deployments. The submersible is powered by Lithium ion batteries, which typically support 1.5 h of operation. The Holosub has an onboard ballast control mechanism, consisting of a positive displacement pump, associated drivers and a rubber tube, which is located inside a sealed air chamber under the platform. Pumping of seawater in and out of the tube modifies its buoyancy, thereby controlling its depth and descent/ascent rates. The CTD data are used for monitoring the system depth in real time, thereby allowing the operator to remotely control its vertical motion. The density data acquired by this CTD were unusable since the sample water was not pumped from the vicinity of the sample volume. This shortcoming will be rectified in future deployments. However, the depth was recorded accurately, as veri fied by comparing its data to measurements performed by an acoustic doppler velocimeter (ADV) that was attached to the platform during some of the deployments. As discussed in the next sub-section, profiles of density and optical scattering from particles were measured by other simultaneously de ployed instruments. The optical setup used during the present deployments (Fig. 2) consists of 2 in-line holography systems with different magnifications that propagate in opposite directions. In-line holography involves illumination of the sample volume with a collimated laser beam, and recording the interference pattern of light scattered by the particles in the sample volume, with the undisturbed part of the beam. The recorded holograms are transferred to a computer where they are numerically reconstructed using the FresnelKirchoff formula (Katz & Sheng 2010) , plane by plane, to obtain a series of in-focus images of particles in the sample volume. In the present configuration, light from a pulsed (50 ns duration), diodepumped, 660 nm neodymium-doped yttrium lithium fluoride (Nd:YLF) laser is split into 2 beams, which cross the same sample volume from opposite directions. Polarizing beam splitters and half wave plates ensure that these overlapping beams have orthogonal polarizations in order to avoid interference. The holograms are recorded at 14.7 frames s −1 by a pair of 2048 × 2048 pixel digital cameras, i.e. the data acquisition rate is 117.6 MB s −1 . Camera A records holograms at a resolution of 3.9 µm pixel −1 with a field of view of 8 × 8 mm, which is embedded within the 11.5 × 11.5 mm field of view of camera B, which records holograms at a resolution of 5.7 µm pixel −1 . In all, 6 TB, i.e. 1.5 million holograms, were recorded over the 2 wk deployment, covering 7.4 h of net profiling time at different locations, times and flow conditions. The Holosub was deployed during May 2010 in EastSound -a fjord on Orcas Island, Washingtonas part of a collaborative field experiment with researchers from WET labs, the University of Rhode Island (URI), National Oceanic and Atmospheric Administration (NOAA) and Naval Research Lab. Three surface vessels and numerous instruments were deployed simultaneously, only a few of which are discussed in this paper. An aircraft operated by NOAA collected daily light detection and ranging (LIDAR) data (Churnside & Donaghay 2009) nearly synoptically over all of East Sound, providing locations of thin layers in real time, and relaying their positions to surface boats. Researchers from WET labs deployed a vertical profiler simultaneously with the Holosub, from the same boat. This profiler was equipped with a SeaBird SBE-49 CTD, a WET labs ac-s, an ECO-BB3, a LISST-100 and the Multi-Angle Scattering Optical Tool (MASCOT) VSF device (Twardowski et al. 1999) . The ac-s is a hyper-spectral (> 80 wavelengths) light absorption and attenuation meter. The ECO-BB3 is a 3 wavelength backscattering sensor (470, 532, 650 nm) at one angle (124°). The LISST-100 is a laser diffractometer that measures the near-forward VSF and particle size distribution (PSD) based on Mie theory inversion, and the MASCOT measures the in-situ VSF of monochromatic light (658 nm) from 10° to 170°, at 10° intervals. On the URI research vessel, data from the high-resolution profiler were used to identify depths for subsequent collection of discrete water samples, using a Ruttner design sampler (KC Denmark). Each sample was split into multiple aliquots, and analyzed, on 32 Talapatra et al.: Holography in ocean water column board the URI vessel, as follows: (1) ~750 ml was gent ly sieved (20 µm pore), and examined live on a Zeiss microscope with phase contrast optics. A checklist of the phytoplankton species present was compiled, qualitative observations were made on the morpho logy and life history of indivi dual taxa and a color video record was made of each slide. This information was used to aid in the identification of phytoplankton imaged by the cinematic, in-line microscopic holography system. (2) A CytoSense scanning flow cytometer was used to count individual particles (unicellular organisms and colonies) and characterize their size, shape, pigment content and scattering. Both the original and the concentrated samples described above were characterized with the flow cytometer. (3) A cinematic in-line microscopic holography system was used to record holograms in a sample volume of 1.75 × 1.75 × 10 mm 3 located within a 1 × 1 × 5 cm 3 lucite cuvette, at a resolution of 1.71 µm pixel −1 , and an acquisition rate of 250 Hz. In conjunction with the on-board light microscopy of living material, these images assisted in identification of the predominant taxa present in Holosub images. This device facilitated observations of the swimming behavior of different organisms. The on-board holography system also enabled counting of particles with a minimum size of 4 µm. (4) 125 ml of whole water was preserved in 1% formaldehyde and 1% glacial acetic acid and returned to Rhode Island, where individual cells in selected samples were quantified. Phytoplankton cells > 8 µm were counted in a Sedgewick Rafter chamber on a Nikon Eclipse E800 microscope using a 10× objective. The results presented in this paper are based on analysis of data acquired in the afternoon of May 14, while the Holosub was drifting, during 2 slow (2 cm s −1 average) ascents, separated by 40 min, hereafter referred to as Ascents I and II, whose time-histories are shown in Fig. 3 . We only utilized data recorded while the vehicle was climbing slowly through the water column in order to minimize the hydrodynamic impact of the platform on the flow/fluid within the sample volume. A total of 19 200 and 21 260 holograms were recorded by both cameras during Ascents I and II, respectively, along with their corresponding depths. Only a subset of these data was analyzed, as explained below. To identify particles to the best of our ability, we used the higher resolution, 3.9 µm pixel −1 holograms, with 8 × 8 mm field of view. There were 9600 and 10 630 higher resolution holograms recorded during Ascents I and II, respectively. For quantitative analysis, particles were divided into 4 broad categories: (1) 'small particles' in the 10−200 µm size range, where the classification 'small' is purely based on the Holosub resolution, (2) the typically large, spherical colonies of the diatom Chaetoceros socialis, (3) straight or helical diatom chains, and (4) zooplankton, including copepods and nauplii. It must be noted that for calculating the concentration profiles of small particles, diatom chains and zooplankton, the volumes in front and behind the large C. socialis colonies, where individual particles might be obscured, were excluded from the sample volume. In order to prevent counting the same particle more than once and reduce computation costs, 1 in 5 holograms were used for analysis of particle distributions, which gave an average sampling rate of 1 hologram per 6.55 mm traversed by the Holosub. As a result, a total of 1920 and 2126 holograms were analyzed for Ascents I and II, respectively, in this analysis. The following steps were utilized to quantify the in situ distributions of the different particle categories, the in situ shear strain and dissipation rates, and the cell concentrations in the collected samples: Calculating in-focus images In-focus images of particles >100 µm were generated by reconstructing each hologram over the entire 10 cm sample volume thickness, in steps of 1 mm, dividing the 2048 × 2048 pixel reconstructed planes into 128 × 128 pixel windows, and then choosing the Mar Ecol Prog Ser 473: 2013 location that brought the largest object in each window into focus. For most windows, a simple method to detect the location of in-focus images was to select the plane with the maximum number of pixels with intensities below a specified threshold. Subsequently, edge detection was also performed selectively when minimization of intensity failed. This hybrid method brought most of the large particles into focus, providing a quick and effective way to qualitatively analyze the large sample volume. Diatom chains, both linear and helical, as well as zooplankton consisting of copepods and nauplii, were identified visually from these in-focus images. Small (10−200 µm) particle statistics These were extracted by reconstructing each hologram in steps of 0.1 mm. The images were then box band-pass filtered, with a 3 × 3 pixel low-pass filtering used to reduce the laser speckle noise, and a 40 × 40 pixel high-pass filtering was used to remove the large particles from the images. The images were then collapsed onto a single plane by selecting the lowest intensity for each pixel. Image segmentation was subsequently performed to define the boundaries of each particle, measure their size and aspect ratio, and discard the elongated diatom chains. Particles with mean diameters <10 µm were also discarded, since distinguishing real particles from noise was unreliable below this scale at this magnification. To extract statistics for Chaetoceros socialis colonies, the holograms were reconstructed, in steps of 2 mm, and then low-pass filtered using 5 × 5 pixel box filters to remove small particles. After image segmentation, followed by filtering based on aspect ratio and size, the cross-sectional areas of the colonies were recorded, which appeared as 'blobs' in the intensity-combined images. The mean diameter of each colony, computed from the area, was used to estimate its volume, using a simplifying assumption of spherical colonies. Adding all such volumes in a specific hologram, and dividing it by the sample volume (6.4 ml) provided an estimate for the C. socialis volume fraction. The Holosub data, along with the high resolution holograms recorded by the on-board holographic microscope, were also utilized to estimate the average number of cells in a C. socialis colony. Then, based on the concentration of colonies in situ, the concentration of C. socialis single cells in the water column was obtained. Fig. 4a −c shows sample in-focus images of C. socialis colonies recorded at different depths, while Fig. 5 shows an in-focus colony recorded by the on-board high resolution microscopic holography system. First, the original reconstructed Holosub images of in-focus C. socialis colonies were box band-pass filtered to remove the background noise, histogram equalized, and thresholded. Next, the area occupied by the chains and the total size of each colony was mea- sured. The typical dimensions of individual cells (7 ± 2 µm), as well as the void between cells in the chains, were estimated from the high resolution microscopic holograms, e.g. the one presented in Fig. 5b , where individual cells are clearly discernable. A sample infocus C. socialis colony, from the Holosub data, and the corresponding processed image, used in the cellcount, are illustrated in Fig. 4d ,e. The inset shows a sample sub-section of the colony for which individual cells can be identified and counted, numbering 6 for the illustrated sample. Several such sample images, both from the in situ (Fig. 4 ) and the on-board (Fig. 5 ) holography data, were used to calibrate the parameters relating the cross sectional area of processed images with the corresponding number of cells. The high magnification on-board data provide particularly clear images of individual cells that could be counted and measured (for shape and size), and even used for distinguishing between the C. socialis cells and other particles that are mixed with the colony. One such particle is highlighted in the magnified image in Fig. 5b . Then, the total number of cells per colony was calculated based on the area of its processed image. For instance, the estimated number of cells in the colony shown in Fig. 4a is 2150 ± 490. An uncertainty of ~23% was calculated by varying the image enhancement parameters and repeating the analysis for several samples. Fig. 4c shows a large, dense colony found in the thin layer. Here, due to the presence of colonies that occupy substantial thickness, and the resulting degradation of the reference beam, it was frequently difficult to focus on an entire colony in the same plane. Consequently, the internal structure of colonies did not come as clearly into focus as the images at other depths, where colony numbers were lower. For this colony, we estimated the number of cells per colony based on its size and data obtained for other samples. It must be noted that this method provides only a crude cell count, since the cell density in the larger colonies was likely higher than what was calculated, leading to an underestimation of the number of cells. Despite this limitation, the aforementioned framework constitutes a unique approach to obtain the in situ abundance of cells in C. socialis colonies, accurate at least to an order of magnitude. For the data acquired on board by the 250 frames s −1 , high resolution microscopic holography system for the collected samples, 200 holograms were selected for analysis (resulting in a net sample volume of 6 ml), using a sampling frequency of 4 holograms s −1 to minimize the likelihood of counting the same object multiple times. An automated particle count was performed by reconstructing each hologram in steps of 20 µm, low-pass filtering the reconstructed images using a 3 × 3 pixel box filter, collapsing them onto a single plane by selecting the lowest intensity for each pixel, and then performing image segmentation to identify the shape (aspect ratio) and size of each object. As a second step, colonies or chains consisting of multiple cells were identified in collapsed images by direct observations, and the constituent number of individual cells was counted visually using images of in-focus colonies/chains, thereby providing a complete cell count. The latter was used to compare the holographic and light microscopy data. The absolute velocity in the local flow could not be determined since the Holosub was drifting slowly, but the instantaneous vertical velocity gradients . In focus images of (a) Chaetoceros socialis colony, and (b) part of the same colony, magnified (dotted circle: particle that is not C. socialis). These data were recorded by the on-board microscopic holographic system using samples collected at 4.5 m Mar Ecol Prog Ser 473: 2013 across the sample volume could be measured. To calculate the velocity relative to the platform at each point, we utilized each of the 9600 and 10 630 low resolution holograms recorded during Ascents I and II, respectively. Each of these holograms were reconstructed in steps of 0.1 mm over a 1.5 cm thick section located at the center of the sample volume, and the images were intensity collapsed onto a single plane. These collapsed images were band-pass filtered to retain only the small particles, and enhanced by histogram equalization. To obtain the local velocity distribution, consecutive images of the 11.5 × 11.5 mm field of view were then divided into interrogation windows of 256 × 256 pixels, and cross-correlated, following standard procedures utilized in Particle Image Velocimetry (PIV) (Adrian & Westerweel 2011). With 50% overlap between interrogation windows, the vector spacing was 0.7 mm, and each instantaneous vector field contained M × N velocity vectors, where M and N varied from 9 to 14. This range was caused by the depth-dependent large mean displacement between consecutive frames, implying that some interrogation windows close to the edge of the field of view did not contain particles that appeared in both frames of the PIV pair. To obtain the characteristic shear, each velocity vector field was divided into an upper and lower half, and the characteristic shear strain rate was calculated from the difference between spatially averaged velocity vectors in the 2 halves. A running average, consisting of 40 consecutively recorded holograms centered around the specified depth, i.e. an average over 2.7 s, or 3 cm in depth, was used to obtain each of the points in the vertical velocity gradient profile, ∂u/∂z, where u is the horizontal velocity and z the vertical coordinate. In order to facilitate a comparison of the vertical distribution of turbulence level, these data were also used to calculate the turbulent dissipation rate (Pope 2000), which is given by ε = 2 νS ij S ij . Here ν is the kinematic viscosi ty and S ij is defined as S ij = 0.5 (∂u i /∂x j + ∂u j /∂x i ), where x i , i ∈ [1, 2, 3], de note the 3 spatial coordinates, and u i the corresponding velocity component. Obtaining an estimate for the dissipation rate from planar velocity dis tributions requires iso tropy assumptions to account for the impact of out-of-plane velocity gradients, as discussed in Luznik et al. (2007) . Assuming isotropy, the turbulence dissipation rate estimate is given by ε = 15/2ν<(∂u/∂z) 2 >. To perform these measurements, the calculations were based on a much thinner sample thickness of 2 mm, and an interrogation window size of 2 × 2 mm. This scale was just above (~1.25 times) the Kolmogorov scale under the present flow conditions, which was sufficient for fully resolving the relevant small-scale velocity gradients (Doron et al. 2001) . The local velocity gradients were calculated from the velocity difference between neighboring interrogation windows. A finer scale for these thin samples could not be used since each interrogation window should contain 5 to 10 particles to obtain meaningful data. Each instantaneous velocity distribution provided 56 to 80 data points, where the variable range was once again caused by the depthdependent large mean displacement between consecutive frames. Averaging over all the points in an instantaneous distribution, and over 40 consecutive realizations provided a profile of the turbulence dissipation rate. Although, such a database cannot be considered as converged turbulence statistics, it nonetheless provided a profile of the characteristic level of turbulence. Some of the organisms with sparse distributions throughout the water column are shown in Fig. 6 . Heterotrophic dinoflagellates of the genus Noctiluca, shown in Fig. 6a ,b, were found intermittently throughout the water column in small numbers. Appendicularians ( Fig. 6f ) with a size range of 0.5 to 8 mm were also observed in small numbers at different depths. Other zooplankton, such as copepods and nauplii, shown in Fig. 6c −e, had non-uniform distributions throughout the water column, as discussed in the following sections. Organisms such as ciliates and certain diatom species, shown in Fig. 6g −i, were observed a few times. RESULTS Optical scattering data acquired by the MASCOT were utilized to identify the presence of a thin layer and other patches of elevated particle concentration at the time and location of the Holosub ascents. During these tests, the MASCOT downcasts extended to a depth of ~8 m, while the Holosub recorded data down to 18 m. Since the Holosub was drifting, the distance between these systems varied from several tens of meters to ~100 m based on the length of the optical fiber. Fig. 7a −h shows sample profiles of particulate scattering at a wavelength of 532 nm (b p -532). As described in Twardowski et al. (2001) , b p , the particle scattering coefficient, is calculated from the volume scattering function β(θ) (sr ), which is defined as β(θ) = dI(θ)/E dV. Here, dI(θ) is the scattered intensity in the direction of polar angle θ from the volume dV and E is the incident irradiance. Integrating the scattering in all directions provides the total scattering coefficient, b (m −1 ), namely . In seawater, the scattering co effi cient can be divided into its water and particulate components, i. ) from the density of the seawater. For convenience, we will refer to σ t as density in the rest of the text. This series of profiles indicates the existence of a 0.2 to 1 m deep layer located within a pycnocline, at depths ranging from 3.8 to 4.8 m, with a particulate scattering signature that is substantially higher than that in the rest of the water column. In the cases when the layer is broad, it consists of 2 or more thinner peaks. We begin by comparing the MAS-COT optical profiles to those of the Chaetoceros socialis and small particle concentrations, both of which should contribute to the chlorophyll a and scattering signatures. Fig. 8a shows the vertical profile of the volume fraction occupied by the C. so cialis colonies during As cent II, where each point represents the averaged volume fraction over the instantaneous sample volume of 0.8 × 0.8 × 10 cm 3 , and the line represents a 10 point running average. Fig. 8b shows the profile of the mean diameter of C. socialis colonies, while Fig. 8c shows the spatially averaged C. socialis cell concentrations, calculated as discus sed in the previous section. Several C. socialis maxima are observed in the depth range of 3.8 to 4.9 m, with the volume fraction, colony size and cell counts all peaking at 4.4 m (dashed line in Fig. 8 ). At this depth, some of the instantaneous volume fractions ex ceed 1%, and the mean colony diameters exceed 1.3 mm. Similar numbers have been reported before, e.g. by Alldredge et al. (2002), who observed peak volume fractions of ~0.5% for marine snow aggregates with diameters > 0.5 mm. Fig. 9d -f compares the vertical profiles of the holographically measured small particle concentration and Chaetoceros socialis volume fraction, to those of b p -532, density and chlorophyll a concentration recorded by the MASCOT, during a profile which was temporally closest to Ascent II. It must be noted that due to the (10 to 100 m) distance between sensors, it would be presumptuous to assume that the same layer would appear at the exact same depth at the 2 sites, and the profile comparisons should be interpreted with this in mind. Fig. 9d shows a particle concentration peak at a depth of 4.4 m, whose location matches the lower of the 2 optical signature peaks shown in Fig. 9e ,f, the largest C. socialis peak and the top of the pycnocline. At depths of 3.8 and 4.7 m, there are peaks in the optical signatures, which coincide with peaks in C. socialis volume fraction, though there are no small particle concentration maxima at these depths. Several additional distinct particle maxima appear below the pycnocline, which have been labeled, for convenience, as Layers #1 to #4. Peaks in C. socialis volume fraction can be observed to coincide with Layers #1 and #2. Though multiple small peaks in b p -532 and chlorophyll a profiles are also observed at similar depths, they do not match the particle concentration maxima. The same comparison for Ascent I, recorded forty minutes earlier, is presented in Fig. 9a -c. Although the particle concentration peaks are located at different depths (Fig. 9a) , the overall picture is similar to the one corresponding to the later ascent. Both the particles and C. socialis profiles have multiple peaks, with their respective largest ones coinciding with each other within the (MASCOT) pycnocline. Each profile has another (non-coinciding) large peak just above the pycnocline. The 2 lar gest small particle concentration maxima at 4.0 and 4.7 m coincide with b p -532 peaks, but only the lower (pyc nocline) peak has corresponding maxima in C. socialis and chlorophyll a concentrations. The smaller peaks in par ticle and C. socialis concentration at depths of 6.1 and 7.3 m also have corresponding optical signature peaks at similar (but not exactly the same) depths. While Fig. 7 indicates the existence of a 0.2 to 1 m deep persistent layer in the vicinity of the pycnocline, the Holosub data provide independent evidence for the presence of a small particle and Chaetoceros socialis colony layer at approximately the same depth, and at a distance of ~50 m away. Data recor ded at 14:43 h by a profiler deployed by the URI group 500 m away (data not shown here) also indicated the presence of a distinct layer with high particulate scattering and chlorophyll a concentration loc ated at a depth of 4.3 m in the pycnocline. The spatial-temporal coherence of the layer loca ted at the depth of 4.4 m during Ascent II and 4.7 m during Ascent I suggests that this phenomenon is in deed a thin layer. Coincidence of high particle, C. socialis and chlorophyll a concentration peaks in this thin layer indicates that it contains an abundance of phytoplankton. The smaller peaks in particle/C. socialis concentration below the pycnocline during both ascents, including those identified as Layers #1 to #4 (Fig. 9d) , have limited spatial and/or temporal ex tents that fall below the values typically associated with thin layers (Durham & Stocker 2012). Though not identified as 'thin layers', the local population dynamics and interactions with physical parameters provide insights into the generation and maintenance of inhomogeneities in the water column. Fig. 10a−d compares the depth profiles of the small particle concentrations superimposed on the Chaetoceros socialis volume fraction, with the characteristic shear strain and turbulent dissipation rates, during Ascents I and II. It must be noted that for both ascents, the C. socialis colonies dominated the field of view above a depth of 3.8 m, while the small particle concentration was 39 Fig. 9 . Profiles of Ascents I (top) and II (bottom): (a,d) holographically measured particle concentration (black line) and Chaetoceros socialis volume fraction (gray); (b,e) particle scattering signature at 532 nm (black line and gray dots) superimposed on density (thin gray line); and (c,f) chlorophyll a concentration; particle scattering and chlorophyll a recorded by the MultiAngle Scattering Optical Tool (MASCOT) during the deployment that started at 14:40 h. Unlabelled dashed lines: depths of some additional concentration peaks. Dots: independent measurements; solid curves represent their 10-point running average Mar : 29-51, 2013 low, preventing us from obtaining reliable images with uniformly distributed particles to perform PIV analysis, and thus calculate the shear and dissipation rates (Fig. 10b,d ). A comparison of the Ascent II vertical profiles, shown in Fig. 10c,d , indicates that the thin layer, located at the upper part of a strong pycnocline (Fig. 9d -f ), coincides with a near-zero shear strain rate region. This region seems to be stably stratified, as the low values of turbulent dissipation indicate. Although we do not have data on density and velocity gradient profiles at the same location to properly estimate the Richardson number (Ri = (g/ρ)(∂ρ/∂z) (∂u/∂z) −2 ; Woods 1969), MASCOT density and the holographic velocity gradients were used to obtain a rough estimateof Ri = 15 at z = 4.4 m, well above the critical value of 0.25 required for stable stratification. The Chaetoceros socialis volume fraction peak at 3.8 m corresponds to a region of high shear strain, but the stratification and low dissipation rate there again suggest flow stability, consistent with the roughly estimated Richardson number (1.7) there. Below the primary pycnocline, Layers #1, 2 and 3 of Ascent II, with the first 2 coinciding with C. socialis peaks, form in narrow regions of local shear strain rate and turbulence minima. We cannot determine, based on the available data, whether the flow is local ly stratified. Only the deepest layer (#4) is located in a region of a local shear strain and dissipation rate maximum. The corresponding comparison for Ascent I, shown in Fig. 10a,b , presents consistent trends, namely that both the small particle and C. socialis concentration peaks preferentially form at depths with low levels of shear and dissipation rates. To demonstrate this point, we use horizontal dashed lines to show that the 4 particle concentration peaks are located in distinct regions with local shear and dissipation rate minima. The spatial and temporal variability in particle fields seems to be correlated with variation in the shear-strain and dissipation rate fields. In order to help with the interpretation of the in situ data, discrete water samples were collected at the URI deployment site at 14:35 h from the surface and at 15:05 h from a depth of ~4.5 m. These samples were analyzed by video microscopy, scanning flow cyto metry, microscopic holography, and later by micro scopic counts of preserved samples. Though the focus is on the sample collected at 4.5 m, the surface sample aids in determining whether the measured trends are persistent. Table 1 shows the particle counts obtained from the flow cytometer and the on-board holographic microscope at 0 and 4.5 m, cell counts based on light microscopy of preserved samples collected at 0 and 4.5 m, and the in situ Holosub data during Ascent II at 4.4 m, i.e. the thin layer. In describing the on-board particle counts, we first compare results obtained by the various instruments using the same collected samples along with species characterization. Then, we compare the on-board to the in situ data. The cytometry counts indicate that while 75 to 80% of the particles < 200 µm do not contain chlorophyll a, most of the larger particles do. For the holographic microscopy data, we provide several results, the first counts continuous colonies/chains as a single 'particle', and the second, counts each cell separately using the estimation method discussed earlier. Consequently, the number of large particles decreases, and that of small particles increases between counts of colonies/chains and estimated single cell counts (Table 1) For both depths, the holographic microscopy particle counts are about 3 times smaller than flow cytometer data in the 4−10 µm size range, 2 times smaller in the 10−20 µm range, and 5 times smaller in the 20−200 µm range. Above 200 µm, the counts are similar. Possible reasons for these discrepancies, e.g. settling of particles in stationary samples, and associated recommendations for future studies are discussed in detail in the 'Discussion'. The total particle counts obtained using microscopic holography (10 130 ml −1 at 0 m and 9740 ml −1 at 4.5 m) are of the same order of magnitude as the cell counts from the light microscopy data (7539 ml −1 at 0 m and 5964 ml −1 at 4.5 m). The latter counts are expected to be lower than the actual cell count, since some fraction of the cells does not survive preservation. A detailed breakdown of the chlorophyll a containing species distributions determined using light micro scopy is shown in Fig. 11 . It is evident that the phytoplankton community with in the observable size (Table 1) are significantly lower than those ac quired by both the onboard holographic microscopy and cytometry data. We attempt to explain these trends and associated implications in the discussion section, including differences in location and resolution issues. Table 1 also provides an estimate of in situ Chaetoceros socialis cell concentrations in the thin layer, i.e. 22 000 ± 5000 ml −1 . This value is higher but of the same order of magnitude as the on-board holographic count of all the particles, and is higher than the microscopy based cell counts, which, as discussed above, is affected by preservation. Within the volume occupied by the colonies, the cell concentrations are 2 orders of magnitude higher than the averaged values, reaching 4 × 10 6 ml −1 (Table 1) . The images of predominantly chain-forming diatom species in samples collected at a depth of 4.5 m (Fig. 12) , ~500 m from the Holosub, primarily in clude linear chains belonging to the genus Chaetoceros, and helical chains, primarily of the species C. debilis. We cannot identify the long chains in the Holosub data. During Ascent II, the running average diatom chain lengths generally (but not monotonically) decrease from 1.4 to 0.5 mm with increasing depth (Fig. 13b) , but the observed sizes of individual chains reach to 8 mm. For the entire volume above the pycno cline, the chains are characteristically longer than those at depths below. The number of diatom chains more than doubles as the depth decreases from 9 to 5 m, peaking at 3. high, but at a slightly lower level than the peak, all the way to the surface (Fig. 13c) . Their angles of inclination with the horizontal direction (Fig. 13a) decrease significantly in the vicinity of the thin layer, where, as noted before, the shear strain and turbulence rates are low and the density gradients are high, as illustrated in Fig. 13a −c. The upper part of the broad low inclination region, between 3.8 and 4.4 m, coincides with a region of low turbulence. Diatom chain orientation angle remains low over the entire domain of low shear below the inclination minimum. There are several other narrow regions in the water column where the orientation angle, shear strain and turbulence minima coincide, which are marked by thick dotted lines in Fig. 13 . Interestingly, these regions are also characterized by increased gradients in diatom abundances, which, as discussed in the discussion section, might be related to the dependence of settling velocity on chain orientation. Conversely, some (but not all) depths with local shear and turbulence maxima (marked by thin dashed lines) contain diatom chains with higher inclination angles compared to those in their surrounding areas. To illustrate this variability in orientation, Fig. 14 provides sample in-focus, high resolution Holosub images of diatom chains. The diatoms located in the stably stratified thin layer are aligned almost horizontally (Fig. 14a) , but the inclination increases progressively, and at a depth of 5.74 m (Fig. 14c) , where there is a local maximum in shear strain rate, most of the diatom chains appear to be nearly vertical. For Ascent I, Fig. 15 only provides a comparison be tween the diatom chain orientation and the mean shear that confirms the consistency in alignment trends with the results obtained for the later ascent. The smallest angle and broadest minimum occur in the near zero shear rate region in the pycnocline, and several other minima coincide with local shear troughs. Conversely, the angles peak at depths of local shear maxima. As for the correlation with the dissipation rate (Fig. 10b) , in general, the angle minima occur in regions of low dissipation, but the correspondence to alignment trends is not as distinct since the dissipation is lower everywhere compared to the later ascent. Other (not shown) trends of Ascent I include a distinct maximum in mean chain length (1.5 mm) at the top of the pycnocline, coinciding with the angle minimum, and characteristic concentrations that do not differ significantly from those of the later measurements. It must be noted that measurements of diatom orientations and lengths are based on the 2-dimensional (2D) projection of 3D orientation. Thus, the lengths of the diatom chains are underestimated. Furthermore, nearly horizontal diatom chains may appear vertical if they are inclined primarily out of plane, introducing a bias in the measured angles, as quantified in Fig. 16 , which shows the probability distribution function (PDF) of the 2D projection angle for lines with random 3D orientations. To obtain this distribution, we consider a line oriented in 3D at polar angle θ p (0 < θ p < π) and azimuthal angle ϕ p (0 < ϕ p < 2π). Its 2D projected angle is given by tan −1 (|tanθ p /cosϕ p |). If both θ p and ϕ p are uniformly distributed, i.e. the particle 3D orientation is random, the PDF of tan −1 (|tanθ p /cosϕ p |) has the form shown in Fig. 16 . The measured PDFs of 2D orientation angles of diatom chains are presented as scatter plots in Fig. 17 . The solid lines show the exponentially leastsquare fitted distribution of the original data. To demonstrate how they differ from the random PDF (Fig. 16) , we divide the measured distributions by the random one at each (projected) angle, and then renormalize the result to have a cumulative value (area under the graph) of one. The renormalized ratios are plotted as dashed lines in Fig. 17 , and referred to as 'corrected' PDFs. The first 2 plots show orientation distributions in sample volumes in depth bands of 0.2 m centered around specified values, one denoted in Region I in Fig. 13 , centered around 4.4 m depth, where the angle, shear strain and turbulence are low (Fig. 17a) ; and the other in Region II, cen- (Fig. 17b) , where the angle, strain and turbulence are high. The third PDF (Fig. 17c ) covers a broad depth band of 2 m, centered around 14 m depth, shown as Region III in Fig. 13 , and serves as a reference. Dividing the database into chains shorter and longer than 0.5 mm length, shown in the left and right sides of Fig. 17 , respectively, indicates that while in regions of high shear there is no significant length-dependent difference in orientation, in the rest of the water column, the longer chains have a higher tendency to be aligned horizontally. We reiterate that the lengths are based on 2D projections on the camera plane, due to which some of the chains that are classified as < 0.5 mm might actually be longer. To verify that the chain orientations are indeed non-random in regions of moderate to low shear, we use the Pearson's chi-squared test (Pearson 1900) , which is performed on the null hypothesis that there is no significant difference between the random orientation distribution function (ODF) and the measured PDFs for Ascent II. Table 2 lists the chi-squared statistics for the 3 sample depths shown in Fig. 17 , and the corresponding p-values for data histograms divided to 25 bins. These p-values indicate that at depths of 4.4 m and the broad region surrounding 14 m, the null hypothesis can be rejected, implying that the distributions at these depths are significantly different from the random ODF. On the other hand, at 5.7 m, where there is a local maximum in shear and dissipation, the p-value indicates that the measured distribution is not statistically different from the ODF of randomly aligned diatom chains. One should keep in mind that due to the bias shown in Fig. 16 , the actual distribution of 3D orientation in the low shear region should have a higher tendency towards low angles compared to the measured results, as indicated by the corrected values shown in dashed gray lines in Fig. 17 . Reasons for this preferred alignment are analyzed in the discussion. ) and corresponding pvalues for the comparison between orientation of diatom chains shown in Fig. 18 and random orientation distribution function (ODF) presented in Fig. 17 Mar Ecol Prog Ser 473: 2013 Zooplankton distribution in situ Fig. 19 shows the vertical distribution of zooplankton, consisting of copepods and nauplii with a size range of 0.2 to 1.5 mm, superimposed on the small particle concentration and Chaetoceros socialis volume fraction for the 2 ascents. Each zooplankton concentration data point is a result of averaging over 20 holograms, equivalent to a sampling volume of 130 ml, a volume significantly smaller than those typi cally utilized in zooplankton statistics (tens of liters). Thus, the zooplankton distribution cannot be considered as statistically convergent. However, such a distribution can still indicate trends, which might help elucidate predator-prey interactions (Fig. 18) . In Fig. 18b , it is evident that the thin layer and particle Layers #1 and #4 coincide with troughs in zooplankton concentration, while the Layers #2 and #3 are located in an area of low numbers of zooplankton, just below a zooplankton peak. The multiple peaks with high C. socialis concentrations (3.8 to 4.9 m), in and above the pycnocline, correspond to a zooplankton concentration trough, while at lower depths, 2 of the dis cernable peaks in C. socialis volume fraction also correspond to minima in predator concentration. However, the trends are not as consistent in Fig. 18a . Avoidance still seems to oc cur in the vicinity of the primary particle peaks at 3.8 and 4.7 m along with several of the deeper peaks that are marked by horizontal dashed lines. However, it does not occur at other depths that are marked by dotted lines. Simultaneous in situ measurements in East Sound, Washington, involving the Holosub and hydrographic-optical profilers, as well as analysis of samples using flow cytometry, holographic and light microscopy, enabled us to examine interactions among biological, physical and optical parameters in the water column. To perform this study, we introduced several new data analysis techniques for estimating the concentration of cells in a Chaetoceros socialis colony, the vertical gradients of horizontal velocity, the turbulent dissipation rate, and orientation of diatoms. We also adapted procedures described in Katz & Sheng (2010) to measure the concentration and spatial distribution of particles. Several trends, some of which are new, and others consistent with previous findings, were observed based on multiple deployments of the MASCOT optical profiler, and detailed analysis of 2 datasets recorded during 2 Holosub ascents separated by 40 min, the first containing 7500 holograms (Ascent I), and the second 12 500 holograms (Ascent II). While prior studies, e.g. Alldredge et al. (2002), measured marine snow, phytoplankton, and zooplankton distributions, and turbulent dissipation, they involved use of multiple profilers sampling different volumes, and collection of samples for subsequent microscopic analysis. The turbulence parameters were determined indirectly, e.g. the dissipation rate was estimated by fitting the measured temperature power spectra to the Batchelor's model spectrum (Gibson & Schwarz 1963) . As noted before, Alldredge et al. (2002) already showed the correlation among thin layers with high particle concentration, presence of a pycnocline, and low turbulence levels. They also showed that zooplankton avoid layers containing high concentration of marine snow. Digital hologra- phy and the procedures introduced in this paper enabled us to determine the spatial structure of particle aggregations as well as the (directly measured) local shear and turbulence to which they are exposed to in situ. As a result, we revealed several distinct, finescale correlations among physical parameters, population and orientation of particles. For example, we determined the effect of local shear on the alignment of diatom chains, as well as the scales, number and structure of the large Chaetoceros socialis colonies. These unique results are discussed below in 4 separate sections, each focusing on a different topic. The results demonstrated that a number of layers with elevated concentrations of small particles developed in the water column. A thin layer with substantial temporal and spatial coherence was identified at the upper part of a strong pycnocline. Ad di tional distinct particle peaks located deeper in the water column could not be identified as broad thin layers since they did not have corresponding signatures from the optical sensors. Though a complete time history of these layers and associated physical parameters would be necessary for a full understanding of factors controlling their formation and persistence, analysis of the current dataset identified several trends. The thin layer was associated with a stably stratified shear layer with low dissipation. These observations are consistent with field data of, e.g. Sullivan et al. (2010a) , which showed that a majority of thin layer formation occur in the vicinity of pycnoclines. They also agree with the conclusions of Prairie et al. (2001) that sharper phytoplankton concentration gradients, i.e. narrower layers, cooccur with low mixing and sharp density gradients. Some of the Chaetoceros socialis colony peaks coincided with the broad thin layer as well as several of the particle layers, but the water column contained several C. socialis colony peaks with out small particle maxima at those depths. Both the small particle and C. socialis peaks were preferentially located in regions of low shear strain and dissipation rates, namely, in layers with reductions in both turbulenceinduced erosion and mixing with the sur rounding volume. The physical mechanisms typically attributed to formation of inhomogeneities, summarized in Stacey et al. (2007) , include straining of a phytoplankton patch (Osborn 1998), motility disruption by shear (Durham et al. 2009 ), and buoyancy-induced restoring forces (Franks 1992) . Since the particles within the thin layer consisted predominantly of non-motile diatom cells and particles without chlorophyll a content, motility-based mechanisms were ruled out. Buoyancy effects, namely retention of particles along an isopycnal of equal density, might have contrib uted to the formation of the thin layer, since the thin layer was associated with the predominant pycnocline. It is not obvious how the particle layers located below the pycnocline formed in the first place, and whether the high strain rates bordering them played a role in their formation by shearing an initially homogeneous phytoplankton patch (Birch et al. 2008) . One possible scenario is that a minimum in shear rate encouraged the local formation of Chaetoceros socialis blooms, which in turn acted as a deterrent for grazing by zooplankton, allowing smaller phytoplankton to grow. Indeed, simultaneous peaking of C. socialis and small particle concentrations, along with a low concentration of predators, was observed at several depths with low shear strain and turbulence levels. They occurred during both of the Holosub ascents in the thin layer and in multiple additional transient layers below it. Our results indicated that the diatom chains clearly tended towards a horizontal alignment, except for regions with high shear strain rate. This preference can be explained by Jeffery's (1992) theory of the motion of spheroids in a shear flow. In a low Reynolds number uniform shear flow, spheroids undergo periodic rotations when they are perturbed from a horizontal equilibrium orientation. Their angular velocity ω, and period of rotation T, are given by where t is time, ϕ is the inclination angle, G is the shear strain rate, and r the length ratio of the long to short axes (r >> 1). Eq. (1) reveals that the angular velocity is minimal for ϕ = 0, implying that a large fraction of the rotational period is spent in alignments close to those of the local streamlines, i.e. horizontal. The shape of many marine planktonic diatoms deviates from the spheroids used for developing Eq. (1), and they are exposed to varying shear rates and turbulence levels. Shape effects reduce the periods of rotation, though the functional dependence on G and r remains consistent with that in Eq. (1) (Karp-Boss & Jumars 1998) . Turbulence should presumably randomize the chain orientation. Indeed, in some, but 47 Mar Ecol Prog Ser 473: 29-51, 2013 not all of the regions of high shear and dissipation rates, the orientation distributions became nearly random (Fig. 17b) . Conversely, in regions of low shear and dissipation, the orientation became preferentially horizontal, consistent with the implications of Eq. (1). The statistical significance of the latter observation is confirmed using the Pearson's chi-squared test. Furthermore, except for regions of high shear and turbulence, the longer chains showed a higher tendency to be nearly horizontally aligned compared to shorter chains, also consistent with Eq. (1). Thus, despite the simplifying assumptions, Jeffery's model predicts trends that are consistent with our observations of the in situ orientation of diatom chains. Interestingly, a recent study by Marcos et al. (2011) used experimentation and modeling to show that at a much smaller scale, elongated microbes subjected to a laminar shear are also preferentially aligned along the flow direction. Slow sinking of diatom chains in the water column can also explain the aforementioned preferential orientation, though the in situ measurements in this study could not establish whether the diatom chains were indeed negatively buoyant. Previous studies (Padisak et al. 2003) showed that rigid cylinders, with dimensions and specific gravity similar to those of diatom chains, maintain their orientation while sinking slowly (Stokes flow) in a quiescent flow. Furthermore, the sinking speed decreased significantly with decreasing orientation angle and increasing aspect ratio. A lower sinking speed presumably implies longer time of flight through a certain region, and consequently a preferential accumulation of chains with low inclination angles in regions of low shear, which is more pronounced for long chains. This reasoning is consistent with the predominantly nearhorizontal orientation near the thin layer as well as in other areas of shear/dissipation minima. However, there was no evidence of accumulation in regions of low shear. In fact, several minima in inclination angle seemed to coincide with or were located just below (~0.1 m) concentration gradients (Fig. 13) , suggesting that further factors were involved, e.g. effects of turbulence on settling rates. Non-random alignment of diatom chains within the water column has critical repercussions to the ambient light field and associated inherent optical properties. When non-spherical particles are preferentially aligned, they can affect the volume scattering function, particularly by an enhancement of backscattering (b b ) (Marcos et al. 2011) . This phenomenon could have repercussions to remote sensing products, where the reflectance is approximately proportional to b b /(a + b b ) (Gordon et al. 1975) , a being the absorption coefficient. Further, overall light transmission through the water column may be affected, potentially changing local phytoplankton growth dynamics. To the best of our knowledge, this study is the first demonstration of preferential alignment by large colonial phytoplankton in a natural environment, along with simultaneous direct measurements of shear strain and dissipation rates. In both ascents, the thin layer with high particle and Chaetoceros socialis colony concentrations in the vicinity of the pycnocline exhibited a low concentration of zooplankton. The zooplankton also seemed to avoid the other layers with elevated particle concentration of Ascent II, and several, but not all, of the particle peaks of Ascent I. Avoidance of high concentration phytoplankton layers by zooplankton is consistent with the findings of Holliday et al. (2003) , who reported that the copepod Acartia tonsa accumulates on either side of chlorophyll a layers, and that mysid shrimp avoids thin layers of Chaetoceros socialis. Also, Fiedler's (1982) sampling based field study showed that macro-zooplankton avoid phytoplankton layers, while field data reported in Sullivan et al. (2010a) indicated that the chlorophyll a biomass in thin layers has a consistently lower rate of depletion compared to the rest of the water column. These observations suggest that at least in some cases, layers with high particle concentration may deter grazing. There are a number of possible reasons for the discrepancies between the particle counts obtained using cytometry and on-board holographic microscopy. For both the surface and 4.5 m deep samples, the flow cytometry results were consistently higher, but of the same order of magnitude as those of holographic microscopy. Since both sets showed similar trends, we assume that the discrepancy was not caused by variations among sub-samples used for the different techniques. Furthermore, since the discrepancy extended to the 20−200 µm range, for which the reconstructed particle images already occupied at least 12 × 12 pixels, and the images were clear and showed distinct shapes, it is unlikely that the discrepancy was caused by resolution limits of the holography system. To verify this statement, we compared 48 Talapatra et al.: Holography in ocean water column the variations in concentration with distance from the hologram plane, which affected the resolution for small particles (Sheng et al. 2006 ). The results, based on analysis of 500 holograms, showed a mean decrease in concentration of ~1% for every millimeter of distance from the hologram plane for particles <10 µm, and a negligible decrease for larger ones. Thus, resolution limits for small particles were also unlikely to cause the discrepancy. A possible explanation for this difference might have been the settling of non-neutrally buoyant particles in the stationary water samples placed in the cuvette, as it lay undisturbed for ~15 min before we recorded the holograms. Recent laboratory observation confirmed that settling of 10 µm particles in stationary samples indeed reduced the number of particles in the middle of the sample volume with time. For future studies, gentle circulation can solve this problem. There are several possible reasons for the discrepancy between the particle counts acquired by the Holosub and those based on the sample collected at 4.5 m. First, the measurements were performed at sites located > 500 m apart. Second, the lower magnification, 3.9 µm pixel −1 , should severely affect the ability to detect particles <10 µm, and to some extent up to 20 µm, especially with increasing distance from the hologram plane. Based on the measured variations in the particle concentration with distance from the hologram plane in the in situ results, using data from 100 holograms, the average decrease in counts for 10−20 µm particles was 0.17% mm −1 . For a 10 cm deep sample, this signal attenuation caused an average loss of data of 13.5%. For particles > 20 µm, the signal attenuation was negligible. Thus, attenuation of signal was a contributor, but not to the level that would explain the discrepancy between the in situ and on-board microscopic analysis. Third, compared to the Holosub, which counted particles in their natural state with minimal disturbance, the sample collection and subsequent analysis might have caused some breakage, especially for the large colonies, chains and brittle detrital particles. Such breakage would increase the particle counts. Indeed, the samples of Chaetoceros socialis colonies examined holographically on board appeared to be significantly smaller than the in situ colonies, and had multiple broken cell linkages, as seen in Fig. 5a . However, unbroken large colonies were also observed in the collected samples, and as a result, the impact of breakage on the counts is not clear. Finally, the onboard samples showed that the space occupied by the large C. socialis colonies also contained many small particles, which were not part of the colony itself. Thus, they were not accounted for in the analysis of in situ data. Once the samples were collected, and the colony became partially fragmented, at least some of these particles became dispersed in the sample volume and were counted separately. The present study has taught us that some of the procedures should be modified in future studies. For example, the resolution of the in situ holograms, at least for one of the views, should be increased to about 1 µm pixel −1 . Also, the water samples used for microscopic observations and taxonomy should be collected at the same time and location as the in situ studies, and during the on-board holographic imaging, the sample must be constantly agitated to prevent settling of particles. Sampling of the water by the CTD should be performed at the same elevation as the sample volume. Having a velocity sensor on board, such as an ADV to measure the platform motion relative to the local flow would also be beneficial. We have already deployed the system together with an ADV, but only when it was used in a profiling mode. Finally, this analysis demonstrates that because of the complex environments inherent to the coastal ocean, an increased understanding on the causes and effects associated with spatial distributions of particles and organisms is greatly enhanced by the simultaneous deployment of multiple complementing instruments that resolve the physics, chemistry and biology in space and time, and resolve particles in their undisturbed, natural state.
al. REF used a submersible holographic device to measure the size and spatial distribution of particles in a 15 m water column.
Characterization of biophysical interactions in the water column using in situ digital holography
Marine Ecology Progress Series
Biology