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  1. Variability in community composition is often attributed to underlying differences in physical environments. However, predator–prey interactions can play an equally important role in structuring communities. Although environmental differences select for different species assemblages, less is known about their impacts on trait compositions. We develop a trait-based analysis of plankton communities of the southern California Current System across multiple trophic levels, from bacteria to mesozooplankton, and over a gradient of environmental conditions, from the oligotrophic open ocean to coastal upwelling. Across a factor of four differences in total community biomass, we observe similarities in the size structure along the environmental gradient, with the most pronounced departures from proportional variations in the biomasses found in the largest protists (> 40 lm). Differences in the trait distributions emerge within a small range of size groups: greater biomass contribution of larger autotrophs (> 10 lm) is observed only for the upwelling region. 
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  2. 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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  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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