A study on microzooplankton community from the coastal waters of eastern Arabian Sea: emphasis on the dominance of heterotrophic dinoflagellates.

We studied the community composition of microzooplankton (MZP) from the surface waters off Candolim, Goa. The MZP communities were examined for the year 2013, covering different seasons and four stations (Near-shore: G3 & G4, offshore: G5 & G6). A total of 30 species belonging to 24 genera were recorded, which include loricate ciliates (LC: 14 species of 13 genera), aloricate ciliates (ALC: 5 species of 3 genera), heterotrophic dinoflagellates (HDS: 11 species of 8 genera), and copepod nauplii. The MZP abundance in the coastal waters varied spatially irrespective of different seasons, with higher abundance in the offshore stations (G5 & G6, 38-127 cells L-1) and lower abundance in the near-shore stations (G3 & G4, 20-97 cells L-1). The MZP community composition showed the dominance of HDS (16-85%) in the near-shore stations during most of the seasons and inferiority during NEM (16-18%). Moreover, all the coastal waters (near and offshore) were dominated by HDS (58-85%) during spring inter-monsoon. The dominant species of HDS were Dinophysis apicata, Dinophysis caudata, Prorocentrum micans, Protoperidinium breve, Protoperidinium latistriatum, and Protoperidinium granii. The statistical analysis (Canonical correspondence analysis and Spearman's rank correlation) depicts that the MZP abundance and community composition were mainly controlled by salinity (r = 0.4-0.7). Whereas the dominance of HDS in the coastal waters could be the reason for its mixotrophic nature and diverse feeding mechanism. Thus, a strong positive correlation between the HDS and LC (r = 0.73-0.92) showed the feeding ability of HDS in their relative community.

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.

Light stress induced DMS(P) production in Skeletonema costatum: An experimental approach and field observation.

Dimethylsulphide is a dominant biogenic sulphur anti-greenhouse gas produced by marine phytoplankton. A non-axenic culture of Skeletonema costatum was studied to comprehend the effects of different growth stages and light stress on DMSP/DMS production. The intracellular DMSP concentration increased during late exponential to mid-stationary phase and attained a maximum (0.59 pg S cell-1) during the stationary phase, indicating more contribution from actively dividing smaller cells. Likewise, exposure to first light after a 12-hour dark phase caused stress, invariably leading to elevated levels of DMS (~9 fold). These observations were upheld by additional laboratory and field experiments, and a field time-series observation, which recorded higher DMS concentrations during exposure to first light after a dark cycle and during early mornings, respectively. While our study depicts the variable DMSP and DMS concentrations during different growth stages of S. costatum, it gives new information on the effect of light stress on DMS production.

Antioxidant responses in gills and digestive gland of oyster Crassostrea madrasensis (Preston) under lead exposure.

Crassostrea are ecologically and economically important bivalves and provide a good livelihood for coastal regions of many countries, including India. This study aims at evaluating the response of the antioxidant defense system in oyster Crassostrea madrasensis against lead (Pb) exposure under laboratory conditions. Antioxidant enzymes such as superoxide dismutase (SOD), catalase (CAT), and glutathione-s-transferase (GST) and oxidative damage parameter lipid peroxidation (LPO) were measured in the gills and digestive glands of oysters exposed to 1-50µg/l of Pb (NO3)2 over a period of 8 days. LPO index increased progressively with increase in Pb concentration (1, 10, 25 and 50µg/l) in both tissues, gills and digestive gland. Although CAT and SOD activities induced together in the initial phase (upto 6th day), their activities decreased at a later stage of the experiment. However, GST activity increased on 8th day in both the tissues at concentration 10, 25 and 50µg/l indicates the compensatory defense mechanism against oxidative stress. The induced antioxidant responses recorded at 25 and 50µg/l of Pb concentrations suggest the presence of Pb-induced oxidative stress at these concentrations. The results of this work also indicate that LPO, SOD, and GST could be used as biomarkers to assess the impact of Pb on the C. madrasensis. This study concludes that any high level of dissolved Pb concentration (>10µg/l) in surrounding seawater could be harmful to the physiology of the C. madrasensis.

Concentrations of Heavy Metals in Commercially Important Oysters from Goa, Central-West Coast of India.

The major beds of oyster along the central-west coast of India are exposed to different anthropogenic activities and are severely exploited for human consumption. In this viewpoint, tissues of oyster Crassostrea madrasensis, C. gryphoides and Saccostrea cucullata were analyzed for Cu, Ni, Cd and Pb concentrations (dry weight) from Chicalim Bay, Nerul Creek and Chapora Bay in pre-monsoon, monsoon and post-monsoon seasons. A higher concentration of Cu (134.4-2167.9 mg kg-1) and Cd (7.1-88.5 mg kg-1) was found, which is greater than the recommended limits in all the three species (and sites). Moreover, significant (p < 0.05) variations were observed for all the metals concentrations among the species, seasons and sites. The high concentrations of Cd and Cu in tissues of edible oyster pose a threat to human health. Therefore, continuous monitoring, people awareness and a stringent government policy should be implemented to mitigate the metal pollution along the studied sites.