Phytoplankton Hotspot Prediction With an Unsupervised Spatial Community Model

Many interesting natural phenomena are sparsely distributed and discrete. Locating the hotspots of such sparsely distributed phenomena is often difficult because their density gradient is likely to be very noisy. We present a novel approach to this search problem, where we model the co-occurrence relations between a robot's observations with a Bayesian nonparametric topic model. This approach makes it possible to produce a robust estimate of the spatial distribution of the target, even in the absence of direct target observations. We apply the proposed approach to the problem of finding the spatial locations of the hotspots of a specific phytoplankton taxon in the ocean. We use classified image data from Imaging FlowCytobot (IFCB), which automatically measures individual microscopic cells and colonies of cells. Given these individual taxon-specific observations, we learn a phytoplankton community model that characterizes the co-occurrence relations between taxa. We present experiments with simulated robot missions drawn from real observation data collected during a research cruise traversing the US Atlantic coast. Our results show that the proposed approach outperforms nearest neighbor and k-means based methods for predicting the spatial distribution of hotspots from in-situ observations.Comment: To appear in ICRA 2017, Singapor

Informatics solutions for large ocean optics datasets

Ocean Optics XXI, Glasgow, Scotland October 8-12 2012Lack of observations that span the wide range of critical space and time scales continues to limit many aspects of oceanography. As ocean observatories and observing networks mature, the role for optical technologies and approaches in helping to overcome this limitation continues to grow. As a result the quantity and complexity of data produced is increasing at a pace that threatens to overwhelm the capacity of individual researchers who must cope with large high-resolution datasets, complex, multi-stage analyses, and the challenges of preserving sufficient metadata and provenance information to ensure reproducibility and avoid costly reprocessing or data loss. We have developed approaches to address these new challenges in the context of a case study involving very large numbers (~1 billion) of images collected at coastal observatories by Imaging FlowCytobot, an automated submersible flow cytometer that produces high resolution images of plankton and other microscopic particles at rates up to 10 Hz for months to years. By developing partnerships amongst oceanographers generating and using such data and computer scientists focused on improving science outcomes, we have prototyped a replicable system. It provides simple and ubiquitous access to observational data and products via web services in standard formats; accelerates image processing by enabling algorithms developed with desktop applications to be rapidly deployed and evaluated on shared, high-performance servers; and improves data integrity by replacing error-prone manual data management processes with generalized, automated services. The informatics system is currently in operation for multiple Imaging FlowCytobot datasets and being tested with other types of ocean imagery.This research was supported by grants from the Gordon and Betty Moore Foundation, NSF, NASA, and ONR (NOPP)

Distance maps to estimate cell volume from two-dimensional plankton images

Author Posting. © Association for the Sciences of Limnology and Oceanography, 2012. This article is posted here by permission of Association for the Sciences of Limnology and Oceanography for personal use, not for redistribution. The definitive version was published in Limnology and Oceanography: Methods 10 (2012): 278-288, doi:10.4319/lom.2012.10.278.We describe and evaluate an algorithm that uses a distance map to automatically calculate the biovolume of a planktonic organism from its two-dimensional boundary. Compared with existing approaches, this algorithm dramatically increases the speed and accuracy of biomass estimates from plankton images, and is thus especially suited for use with automated cell imaging technologies that produce large quantities of data. The algorithm operates on a two-dimensional image processed to identify organism boundaries. First, the distance of each interior pixel to the nearest boundary is calculated; next these same distances are assumed to apply for projection in the third dimension; and finally the resulting volume is adjusted by a multiplicative factor assuming locally circular cross-sections in the third dimension. Other cross-sectional shape factors can be applied as needed. In this way, the simple, computationally efficient, volume calculation can be refined to include taxon-specific shape information if available. We show that compared to traditional manual microscopic analysis, the distance map algorithm is unbiased and accurate (mean difference = -0.25%, standard deviation = 17%) for a range of cell morphologies, including those with concave boundaries that deviate from simple geometric shapes and whose volumes are not well represented by a solid of revolution around a single axis. Automated calculation of cell volumes can now be implemented with a combination of this new distance map algorithm for complex shapes and the solid of revolution approach for simple shapes, with an automated decision criterion to choose the appropriate approach for each image.This research was supported by grants (to HMS) from the Gordon and Betty Moore Foundation and NASA’s Ocean Biology and Biogeochemistry program, and a Woods Hole Oceanographic Institution Summer Student Fellow award (to EAM)

A fluorescence-activated cell sorting subsystem for the Imaging FlowCytobot

© The Author(s), 2016. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Limnology and Oceanography: Methods 15 (2017): 94–102, doi:10.1002/lom3.10145.Recent advances in plankton ecology have brought to light the importance of variability within populations and have suggested that cell-to-cell differences may influence ecosystem-level processes such as species succession and bloom dynamics. Flow cytometric cell sorting has been used to capture individual plankton cells from natural water samples to investigate variability at the single cell level, but the crude taxonomic resolution afforded by the fluorescence and light scattering measurements of conventional flow cytometers necessitates sorting and analyzing many cells that may not be of interest. Addition of imaging to flow cytometry improves classification capability considerably: Imaging FlowCytobot, which has been deployed at the Martha's Vineyard Coastal Observatory since 2006, allows classification of many kinds of nano- and microplankton to the genus or even species level. We present in this paper a modified bench-top Imaging FlowCytobot (IFCB-Sorter) with the capability to sort both single cells and colonies of phytoplankton and microzooplankton from seawater samples. The cells (or subsets selected based on their images) can then be cultured for further manipulation or processed for analyses such as nucleic acid sequencing. The sorting is carried out in two steps: a fluorescence signal triggers imaging and diversion of the sample flow into a commercially available “catcher tube,” and then a solenoid-based flow control system isolates each sorted cell along with 20 μL of fluid.NSF Grant Number: OCE-11300140; WHOI internal support; NSERC through a Post-Graduate Masters awar

Microzooplankton community structure investigated with imaging flow cytometry and automated live-cell staining

© The Author(s), 2016. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Marine Ecology Progress Series 550 (2016): 65-81, doi:10.3354/meps11687.Protozoa play important roles in grazing and nutrient recycling, but quantifying these roles has been hindered by difficulties in collecting, culturing, and observing these often-delicate cells. During long-term deployments at the Martha’s Vineyard Coastal Observatory (Massachusetts, USA), Imaging FlowCytobot (IFCB) has been shown to be useful for studying live cells in situ without the need to culture or preserve. IFCB records images of cells with chlorophyll fluorescence above a trigger threshold, so to date taxonomically resolved analysis of protozoa has presumably been limited to mixotrophs and herbivores which have eaten recently. To overcome this limitation, we have coupled a broad-application ‘live cell’ fluorescent stain with a modified IFCB so that protozoa which do not contain chlorophyll (such as consumers of unpigmented bacteria and other heterotrophs) can also be recorded. Staining IFCB (IFCB-S) revealed higher abundances of grazers than the original IFCB, as well as some cell types not previously detected. Feeding habits of certain morphotypes could be inferred from their fluorescence properties: grazers with stain fluorescence but without chlorophyll cannot be mixotrophs, but could be either starving or feeding on heterotrophs. Comparisons between cell counts for IFCB-S and manual light microscopy of Lugol’s stained samples showed consistently similar or higher counts from IFCB-S. We show how automated classification through the extraction of image features and application of a machine-learning algorithm can be used to evaluate the large high-resolution data sets collected by IFCBs; the results reveal varying seasonal patterns in abundance among groups of protists.This research was supported in part by NSF (grants OCE-1130140, OCE-1434440), NASA (grants NNX11AF07G and NNX13AC98G), the Gordon and Betty Moore Foundation (grants 934 and 2649), and the Woods Hole Oceanographic Institution’s Innovative Technology Program

Diatoms favor their younger daughters

Author Posting. © Association for the Sciences of Limnology and Oceanography, 2012. This article is posted here by permission of Association for the Sciences of Limnology and Oceanography for personal use, not for redistribution. The definitive version was published in Limnology and Oceanography 57 (2012): 1572-1578, doi:10.4319/lo.2012.57.5.1572.We used a time-lapse imaging approach to examine cell division in the marine centric diatom Ditylum brightwellii and observed that daughter cells who inherited their parents' hypothecal frustule half were more likely to divide before their sisters. This is consistent with observations in Escherichia coli of a bias between sister cells, where faster growth in one sister is thought to arise from its inheriting parental material with less oxidative damage. We also observed that hypothecal sisters in D. brightwellii were more likely to inherit a greater proportion of their parents' cellular material, similar to what has been seen in E. coli. We found a statistically significant correlation between the amount of parental material inherited by a hypothecal daughter and its relative division rate, indicating that this extra material inherited by the hypothecal daughter plays a role in its more rapid division. Furthermore, the intercept in this regression was greater than zero, indicating that other factors, such as differences in the quality of inherited material, also play a role. This similarity between two taxonomically distant microbes suggests that favoritism toward one daughter might occur broadly among unicellular organisms that reproduce asexually by binary fission. Such a bias in cell division might be advantageous, given model predictions that show that favoring one daughter at the expense of the other can result in higher population growth rates, increasing the chance that a cell's genotype will survive compared to a model where the daughters divide at equal rates.This research was funded in part by the Woods Hole Oceanographic Institution through an Ocean Life Institute Postdoctoral Scholarship to S.R.L. and by support to R.J.O. and H.M.S. from the Gordon and Betty Moore Foundation

Bio-optical discrimination of diatoms from other phytoplankton in the surface ocean: Evaluation and refinement of a model for the Northwest Atlantic

© The Author(s), 2018. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Remote Sensing of Environment 217 (2018): 126-143, doi:10.1016/j.rse.2018.08.010.Diatoms dominate global silica production and export production in the ocean; they form the base of productive food webs and fisheries. Thus, a remote sensing algorithm to identify diatoms has great potential to describe ecological and biogeochemical trends and fluctuations in the surface ocean. Despite the importance of detecting diatoms from remote sensing and the demand for reliable methods of diatom identification, there has not been a systematic evaluation of algorithms that are being applied to this end. The efficacy of these models remains difficult to constrain in part due to limited datasets for validation. In this study, we test a bio-optical algorithm developed by Sathyendranath et al. (2004) to identify diatom dominance from the relationship between ratios of remote sensing reflectance and chlorophyll concentration. We evaluate and refine the original model with data collected at the Martha's Vineyard Coastal Observatory (MVCO), a near-shore location on the New England shelf. We then validated the refined model with data collected in Harpswell Sound, Maine, a site with greater optical complexity than MVCO. At both sites, despite relatively large changes in diatom fraction (0.8–82% of chlorophyll concentration), the magnitude of variability in optical properties due to the dominance or non-dominance of diatoms is less than the variability induced by other absorbing and scattering constituents of the water. While the original model performance was improved through successive re-parameterizations and re-formulations of the absorption and backscattering coefficients, we show that even a model originally parameterized for the Northwest Atlantic and re-parameterized for sites such as MVCO and Harpswell Sound performs poorly in discriminating diatom-dominance from optical properties.This work was supported by: a Woods Hole Oceanographic Institution Summer Student Fellowship (NSF REU award #1156952) and a Bowdoin College Grua/O'Connell Research Award to SJK; grants to HMS from NASA (Ocean Biology and Biogeochemistry program and Biodiversity and Ecological Forecasting program), NSF (Ocean Sciences), the Gordon and Betty Moore Foundation, the Simons Foundation, and NOAA through the Cooperative Institute for the North Atlantic Region (CINAR) under Cooperative Agreement NA14OAR4320158; and grants to CSR from NASA (Ocean Biology and Biogeochemistry program)