Five species of the genus Pseudodiaptomus, P. inopinus, P. poplesia, P. marinus, P. nihonkaiensis, and P. sp. occur in Korea nearshore waters. Although the four species except for P. sp. have been classified into Lobus and Ramosus groups, two for each group, based on morphological characters, this classification had yet to be confirmed by molecular characters. Here, we determined molecular characters and phylogenetic relationships of the five species in order to evaluate the morphology-based groupings and the species identifications. For this, a 625-bp DNA region of the mitochondrial gene cytochrome oxidase subunit I (mtCOI) was sequenced and compared among the species. Intraspecific variation of the sequences is less than 0.6%, while interspecific variation ranges from 17.6–26.7%, indicating every species, including P. sp., is a genetically distinct, valid species. Phylogenetic trees of the mtCOI DNA reveal that the Lobus-group species including P. inopinus and P. poplesia form a well-supported clade and that P. sp. belongs to this group. On the other hand, the Ramosus group clade consisting of P. marinus and P. nihonkaiensis is not well supported by bootstrap analyses, suggesting that further evaluation of the validity of this group assignment is needed.

Caromiobenellagen. nov., represented by males of two new species of monstrilloid copepods (Copepoda: Monstrillidae), Caromiobenella castoreasp. nov. (type species) and C. polluxeasp. nov., is established based on morphological, molecular, and ecological evidence. The genus is characterized by the following new combination of morphological characters: 1) relatively short cephalothorax, 2) inconspicuous oral papilla located anteriorly, 3) last antennular segment modified into serrate ridges on the inner distal margin with a general absence of branched setae, 4) modified spinous element 2d₂ (elongated, biplumose or both), and 5) two pairs of prominent, crater-like, concave depressions on the anterior dorsum of the cephalothorax. These characters are also shared by several other nominal species hitherto assigned to MonstrillaDana, 1849. Molecular analyses of mitochondrial cytochrome c oxidase subunit I (mtCOI) genes and nuclear 28S ribosomal RNA (28S rRNA) genes indicate both that these species are distinct from each other and that, as a group, they are isolated from other known monstrilloid genera. Furthermore, previous studies have shown that one of these species differs from other species of Monstrilla in utilizing a different group of invertebrate hosts, namely gastropods. Based on this integrated data, the new genus comprises six additional species: C. helgolandica (Claus, 1863) comb. nov., C. serricornis (Sars, 1921) comb. nov., C. arctica (Davis & Green, 1974) comb. nov., C. hamatapex (Grygier & Ohtsuka, 1995) comb. nov., C. pygmaea (Suárez-Morales, 2000) comb. nov., and C. patagonica (Suárez-Morales, Ramírez & Derisio, 2008) comb. nov.

To understand the effects of variable water masses in the northeastern East China Sea (Korea South Sea), planktonic copepods were seasonally sampled. Out of a total of 106 copepod species, 85 were oceanic warm-water species, and the number of species varied in summer, autumn, spring, and winter. The study area was divided into two or three regions according to the degree of influence of the water masses. Canthocalanus pauper, Clausocalanus furcatus, Oithona plumifera, Oncaea venella, Oncaea venusta, and Paracalanus aculeatus showed a positive correlation with water temperature and salinity and were indicator species of warm currents. Calanus sinicus, known as an indicator species of the Yellow Sea Bottom Cold Water, showed a high abundance and occurrence ratio in the western sea of the study area from spring to autumn. Moreover, Acartia pacifica indicated the extension of coastal waters to offshore areas. Several oceanic warm-water species (A. danae, Centropages gracilis, Labidocera acuta, Rhincalanus nasutus, and Temoropia mayumbaensis) were considered indicator species of the Taiwan Warm Current. Our results suggest that the spatiotemporal distribution patterns of indicator species are partly explained by different water masses.

A new Pseudodiaptomus (Copepoda, Calanoida) from Korea supported by molecular data

The calanoid copepod Acartia ohtsukai predominates the estuarine and coastal waters of East Asia during summer. Its occurrence characteristics confer it with good potential as live prey for fish larvae through mass culture. To investigate the effect of temperature and salinity combinations on its egg production rate (EPR), hatching success (HS), and mortality rate, experiments were undertaken and repeated three times for combinations of five temperatures (10, 15, 20, 25, and 30°C) and seven salinities (10, 15, 20, 25, 27, 30, and 33 psu). EPR and HS were highest at temperatures of 25 and 30°C, respectively, with a salinity of 27 psu. Mortality rate was highest at 10°C in almost all salinity gradients, whereas it was lower at water temperature and salinity ranges of 20–30°C and 20–30 psu, respectively. These findings indicate that A. ohtsukai can inhabit wide ranges of water temperatures and salinities, and that the optimized condition for mass culture is a combination of water temperature of 25°C and salinity of 27 psu.

The concentration of chlorophyll-a (Chl-a) is an integrative bio-indicator of aquatic ecosystems and a direct indicator that evaluates the ecological status of water bodies. In this study, we focused on predicting the Chl-a concentration in seawater using machine learning (after replacing missing values). To replace the missing values among marine environment observation data, a comparison experiment was performed using multiple built-in imputation methods (i.e., pmm, cart, rf, norm, norm.nob, norm.boot, and norm.predict) of the mice package in R. The cart method was selected as the most suitable. We generated each regression model using six machine learning algorithms (regression tree, support vector regression (SVR), bagging, random forest, gradient boosting machine (GBM), and extreme gradient boosting (XGBoost)) to predict the Chl-a concentration based on the complete imputed dataset. The prediction performance of the models was evaluated by four evaluation criteria using 10-fold cross-validation tests. XGBoost, an ensemble learning approach, outperformed other models in predicting the Chl-a concentration; SVR, a single model, also showed a good performance. The most important environmental factor in predicting the Chl-a concentration was an organic carbon particulate; however, dissolved oxygen also showed potential. This study was conducted with field observations in the spring and summer in the coastal zone of Korea. There exists a limit in machine learning applications, which excludes temporal and spatial factors. However, extensions to time series forecasting for deep learning or machine learning can lead to meaningful regional and seasonal analysis. It can also improve prediction performance as a result of the long-term data accumulation of field observations of more varied features (such as meteorological and hydrodynamic) besides water quality.