Contribution to the taxonomic knowledge of Ampharetidae (Annelida) from Antarctica with the description of Amage giacomobovei sp. nov.

Thanks to newly collected material from the Terra Nova Bay area (Ross Sea, Antarctica), we discuss the taxonomy of the ampharetid genera Amage Malmgren, 1866 and Amythas Benham, 1921. A new species of Amage, A. giacomobovei sp. nov., is described based on morpho-anatomical data. This is the second new species described from an area which appears to be rich in ampharetids, a coastal embayment at ~500 m depth near the Italian “Mario Zucchelli” research station. The new species is characterized by having 16 abdominal uncinigers and four pairs of branchiae that readily distinguish it from its congeners. Tubes of A. giacomobovei sp. nov. are also characteristic in showing a large amount of embedded sponge spicules, suggesting a possible close association to spicule mats. Based on the amended diagnoses of the two genera, Amage septemdecima Schüller & Jirkov, 2013 is transferred to the genus Amythas. Finally, to simplify the task of ampharetid genera recognition for untrained people, we provide a dichotomic key for ampharetid genera found in Antarctica and a checklist of species occurring in Terra Nova Bay.

Machine Learning Based Predictions of Dissolved Oxygen in a Small Coastal Embayment

Coastal dissolved oxygen (DO) concentrations have a profound impact on nearshore ecosystems and, in recent years, there has been an increased prevalance of low DO hypoxic events that negatively impact nearshore organisms. Even with advanced numerical models, accurate prediction of coastal DO variability is challenging and computationally expensive. Here, we apply machine learning techniques in order to reconstruct and predict nearshore DO concentrations in a small coastal embayment while using a comprehensive set of nearshore and offshore measurements and easily measured input (training) parameters. We show that both random forest regression (RFR) and support vector regression (SVR) models accurately reproduce both the offshore DO and nearshore DO with extremely high accuracy. In general, RFR consistently peformed slightly better than SVR, the latter of which was more difficult to tune and took longer to train. Although each of the nearshore datasets were able to accurately predict DO values using training data from the same site, the model only had moderate success when using training data from one site to predict DO at another site, which was likely due to the the complexities in the underlying dynamics across the sites. We also show that high accuracy can be achieved with relatively little training data, highlighting a potential application for correcting time series with missing DO data due to quality control or sensor issues. This work establishes the ability of machine learning models to accurately reproduce DO concentrations in both offshore and nearshore coastal waters, with important implications for the ability to detect and indirectly measure coastal hypoxic events in near real-time. Future work should explore the ability of machine learning models in order to accurately forecast hypoxic events.