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With recent advances in Machine Learning techniques based on Deep Neural Networks (DNNs), automated plankton image classification is becoming increasingly popular within the marine ecological sciences. Yet, while the most advanced methods can achieve human-level performance on the classification of everyday images, plankton image data possess properties that frequently require a final manual validation step. On the one hand, this is due to morphological properties manifesting in high intra-class and low inter-class variability, and, on the other hand is due to spatial-temporal changes in the composition and structure of the plankton community. Composition changes enforce a frequent updating of the classifier model via training with new user-generated training datasets. Here, we present a Dynamic Optimization Cycle (DOC), a processing pipeline that systematizes and streamlines the model adaptation process via an automatic updating of the training dataset based on manual-validation results. We find that frequent adaptation using the DOC pipeline yields strong maintenance of performance with respect to precision, recall and prediction of community composition, compared to more limited adaptation schemes. The DOC is therefore particularly useful when analyzing plankton at novel locations or time periods, where community differences are likely to occur. In order to enable an easy implementation of the DOC pipeline, we provide an end-to-end application with graphical user interface, as well as an initial dataset of training images. The DOC pipeline thus allows for high-throughput plankton classification and quick and systematized model adaptation, thus providing the means for highly-accelerated plankton analysis.

Interactions between autotrophic and heterotrophic bacteria are fundamental for marine biogeochemical cycling. How global warming will affect the dynamics of these essential microbial players is not fully understood. The aims of this study were to identify the major groups of heterotrophic bacteria present in a Synechococcus culture originally isolated from the Red Sea and assess their joint responses to experimental warming within the metabolic ecology framework. A co-culture of Synechococcus sp. RS9907 and their associated heterotrophic bacteria, after determining their taxonomic affiliation by 16S rRNA gene sequencing, was acclimated and maintained in the lab at different temperatures (24–34°C). The abundance and cellular properties of Synechococcus and the three dominant heterotrophic bacterial groups (pertaining to the genera Paracoccus, Marinobacter, and Muricauda) were monitored by flow cytometry. The activation energy of Synechococcus, which grew at 0.94–1.38 d–1, was very similar (0.34 ± 0.02 eV) to the value hypothesized by the metabolic theory of ecology (MTE) for autotrophs (0.32 eV), while the values of the three heterotrophic bacteria ranged from 0.16 to 1.15 eV and were negatively correlated with their corresponding specific growth rates (2.38–24.4 d–1). The corresponding carrying capacities did not always follow the inverse relationship with temperature predicted by MTE, nor did we observe a consistent response of bacterial cell size and temperature. Our results show that the responses to future ocean warming of autotrophic and heterotrophic bacteria in microbial consortia might not be well described by theoretical universal rules.

The temperature-size Rule (TSR) states that there is a negative relationship between ambient temperature and body size. This rule has been independently evaluated for different phases of the life cycle in multicellular eukaryotes, but mostly for the average population in unicellular organisms. We acclimated two model marine cyanobacterial strains (Prochlorococcus marinus MIT9301 and Synechococcus sp. RS9907) to a gradient of temperatures and measured the changes in population age-structure and cell size along their division cycle. Both strains displayed temperature-dependent diel changes in cell size, and as a result, the relationship between temperature and average cell size varied along the day. We computed the mean cell size of new-born cells in order to test the prediction of the TSR on a single-growth stage. Our work reconciles previous inconsistent results when testing the TSR on unicellular organisms, and shows that when a single-growth stage is considered the predicted negative response to temperature is revealed.