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Creators/Authors contains: "Orenstein, Eric C."

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  1. Abstract

    In recent years, harmful algal blooms (HABs) have increased in their severity and extent in many parts of the world and pose serious threats to local aquaculture, fisheries, and public health. In many cases, the mechanisms triggering and regulating HAB events remain poorly understood. Using underwater microscopy and Residual Neural Network (ResNet‐18) to taxonomically classify imaged organisms, we developed a daily abundance record of four potentially harmful algae (Akashiwo sanguinea,Chattonellaspp.,Dinophysisspp., andLingulodinium polyedra) and major grazer groups (ciliates, copepod nauplii, and copepods) from August 2017 to November 2020 at Scripps Institution of Oceanography pier, a coastal location in the Southern California Bight. Random Forest algorithms were used to identify the optimal combination of environmental and ecological variables that produced the most accurate abundance predictions for each taxon. We developed models with high prediction accuracy forA. sanguinea(),Chattonellaspp. (), andL. polyedra(), whereas models forDinophysisspp. showed lower prediction accuracy (). Offshore nutricline depth and indices describing climate variability, including El Niño Southern Oscillation, Pacific Decadal Oscillation, and North Pacific Gyre Oscillation, that influence regional‐scale ocean circulation patterns and environmental conditions, were key predictor variables for these HAB taxa. These metrics of regional‐scale processes were generally better predictors of HAB taxa abundances at this coastal location than the in situ environmental measurements. Ciliate abundance was an important predictor ofChattonellaandDinophysisspp., but not ofA. sanguineaandL. polyedra. Our findings indicate that combining regional and local environmental factors with microzooplankton populations dynamics can improve real‐time HAB abundance forecasts.

     
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  2. Abstract

    The large data sets provided byin situoptical microscopes are allowing us to answer longstanding questions about the dynamics of planktonic ecosystems. To deal with the influx of information, while facilitating ecological insights, the design of these instruments increasingly must consider the data: storage standards, human annotation, and automated classification. In that context, we detail the design of the Scripps Plankton Camera (SPC) system, anin situmicroscopic imaging system. Broadly speaking, the SPC consists of three units: (1) an underwater, free‐space, dark‐field imaging microscope; (2) a server‐based management system for data storage and analysis; and (3) a web‐based user interface for real‐time data browsing and annotation. Combined, these components facilitate observations and insights into the diverse planktonic ecosystem. Here, we detail the basic design of the SPC and briefly present several preliminary, machine‐learning‐enabled studies illustrating its utility and efficacy.

     
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  3. Abstract

    Modern in situ digital imaging systems collect vast numbers of images of marine organisms and suspended particles. Automated methods to classify objects in these images – largely supervised machine learning techniques – are now used to deal with this onslaught of biological data. Though such techniques can minimize the human cost of analyzing the data, they also have important limitations. In training automated classifiers, we implicitly program them with an inflexible understanding of the environment they are observing. When the relationship between the classifier and the population changes, the computer's performance degrades, potentially decreasing the accuracy of the estimate of community composition. This limitation of automated classifiers is known as “dataset shift.” Here, we describe techniques for addressing dataset shift. We then apply them to the output of a binary deep neural network searching for diatom chains in data generated by the Scripps Plankton Camera System (SPCS) on the Scripps Pier. In particular, we describe a supervised quantification approach to adjust a classifier's output using a small number of human corrected images to estimate the system error in a time frame of interest. This method yielded an 80% improvement in mean absolute error over the raw classifier output on a set of 41 independent samples from the SPCS. The technique can be extended to adjust the output of multi‐category classifiers and other in situ observing systems.

     
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  4. Abstract

    Plankton imaging systems supported by automated classification and analysis have improved ecologists' ability to observe aquatic ecosystems. Today, we are on the cusp of reliably tracking plankton populations with a suite of lab‐based and in situ tools, collecting imaging data at unprecedentedly fine spatial and temporal scales. But these data have potential well beyond examining the abundances of different taxa; the individual images themselves contain a wealth of information on functional traits. Here, we outline traits that could be measured from image data, suggest machine learning and computer vision approaches to extract functional trait information from the images, and discuss promising avenues for novel studies. The approaches we discuss are data agnostic and are broadly applicable to imagery of other aquatic or terrestrial organisms.

     
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