Recurrence of Gonyaulax polygramma bloom in the southeastern Arabian Sea.

Gonyaulax polygramma, a bloom-forming dinoflagellate, has been repeatedly observed along the southeastern Arabian Sea in recent years. During our study in October 2021, a patch of reddish-brown water was observed in the nearshore waters off Kannur (southwest coast of India) and later identified as Gonyaulax polygramma using scanning electron microscopy (SEM) and HPLC-based phytoplankton marker pigments. Gonyaulax polygramma accounted for 99.4% of the phytoplankton abundance at the bloom location, with high concentrations of peridinin and chlorophyll-a at the study site. High concentration of SiO42- was observed at the bloom site, while other nutrients were lower than the previously reported values. The bloom of Gonyaulax polygramma also resulted in high concentrations of dimethylsulfide, an anti-greenhouse gas, at the bloom site. In addition to onsite observation, Sentinel-3 satellite data was also used in the detection and validation of the observed bloom using the NDCI index. From the satellite image, it was evident that the bloom persisted at the mouth of the rivers during the study period. Since the red tide of Gonyaulax polygramma has been observed recurrently in the southeastern Arabian Sea, it is proposed to use satellites to detect and monitor the bloom on a routine basis.

Applications of Machine Learning in Chemical and Biological Oceanography.

Machine learning (ML) refers to computer algorithms that predict a meaningful output or categorize complex systems based on a large amount of data. ML is applied in various areas including natural science, engineering, space exploration, and even gaming development. This review focuses on the use of machine learning in the field of chemical and biological oceanography. In the prediction of global fixed nitrogen levels, partial carbon dioxide pressure, and other chemical properties, the application of ML is a promising tool. Machine learning is also utilized in the field of biological oceanography to detect planktonic forms from various images (i.e., microscopy, FlowCAM, and video recorders), spectrometers, and other signal processing techniques. Moreover, ML successfully classified the mammals using their acoustics, detecting endangered mammalian and fish species in a specific environment. Most importantly, using environmental data, the ML proved to be an effective method for predicting hypoxic conditions and harmful algal bloom events, an essential measurement in terms of environmental monitoring. Furthermore, machine learning was used to construct a number of databases for various species that will be useful to other researchers, and the creation of new algorithms will help the marine research community better comprehend the chemistry and biology of the ocean.