Right after the 2007 Hebei Spirit Oil Spill phytoplankton ecosystems were investigated for 11 years based on the seasonal monitoring of the composition and abundance of phytoplankton species. Comparable time-series data from the 1989 Exxon Valdez or the 2010 Deepwater Horizon Oil Spill sites were not available. It was suggested that the ecological healthiness of phytoplankton ecosystems at EVOS sites had recovered after 10 years following the oil spill based on chlorophyll concentrations even though these concentrations only represented phytoplankton communities in most cases. Chlorophyll concentrations can only reflect limited aspects of highly complex phytoplankton ecosystems. During the last 11 years following the 2017 HSOS, extreme variabilities were met in the seasonally averaged ratios of diatoms to phototrophic flagellates including dinoflagellates based on the microscopic cell countings. Summer phytoplankton communities exhibited some cyclic interannual changes in dominant groups every 2-4 years. During the early years (2008-2010) cryptophytes or raphidophytes (Chattonella spp.) dominated alternately each year, which was repeated again in 2014, 2015 and 2017. Two thecate dinoflagellates, Tripos fusus and Tripos furca, together accounted for 52.5% and 50.0% of all organisms in the summers of 2011 and 2012, respectively, which was repeated again in 2018. Summer occurrence and dominance by the phototrophic flagellates including HABs (Harmful Algal Blooms) species as well as their interannual variabilities in the oil spill sites could be utilized as markers for the stable and long-term management of healthy ecosystems. For this type of scientific ecosystem management monitoring of chlorophyll concentrations may sometimes be insufficient to gain a proper and comprehensive understanding of phytoplankton communities located in areas where oil spills have occurred and harmed the ecosystem.

This acoustic experiment noted that fish species in Chilam-Gijang marine ranching area were more densely distributed in the pelagic zone during nighttime than daytime. In each season, the gill nets caught 15 different fish species and the estimated average target strengths were -44.0 dB and -44.4 dB for autumn and winter surveys, respectively. The estimated autumn fish biomass were 7.7 tons and 26.0 tons during daytime and nighttime, respectively. Winter biomass was 2.27 tons and 30.97 tons during daytime and nighttime, respectively. Different fish species form schools that exhibit different movements and behaviors, and thereby occupy varying water layers. These results explained the estimated fish biomass, and variation with seasons and time of the surveys around artificial reefs in Chilam Bay, Korea.

The present study obtained universal mathematical models of all elements and characteristics regarding hook and line fishing systems. To describe the hook and line fishing systems on site we used three kinds of coordinate systems: the earth based coordinate system, natural coordinate system, and flow (velocity) coordinate system. Mathematical models presented in this article allow us to define the shape of the fishing gear, the tension of the rope at different points, hydrodynamic resistance, diameter of the hook's wire, immersion depth of the fishing hooks, distance from hooks to the ground and the required lifting force of the floats. These models allow for the performance of computer simulations regarding any kinds of hook and line gears in still water or water where flow occurs.

The purpose of this study is to identify factors influencing fish production of shallow-sea aquaculture in South Korea. This study employed the two-way fixed effect and random effect models based on the panel models and also the difference between GMM and system GMM models based on the dynamic panel models using the amount of fish farming production, the number of stocked fry, the number of cultured fish, the amount of inputted feed, the farming area, the number of workers, and the sales price data from 2010 to 2017. First, the two-way fixed effect model of the panel models was selected by panel characteristics, time characteristics and Hausman tests and also the model was statistically significant. As a result of the two-way fixed effect model, the number of stocked fry, the amount of inputted feed, and the number of workers were identified as factors that increase the fish production of shallow-sea aquaculture. However, the number of cultured fish and the sales price were analyzed as factors that reduce the fish production of shallow-sea aquaculture. Second, the system GMM model of the dynamic panel models was selected by Hansen test and Arellano-Bond test in order to identify whether or not the over-discrimination condition is appropriate. Based on the system GMM model, the number of stocked fry, the amount of inputted feed, the number of workers in this year and 1 year ago, the number of cultured fish 2 years ago, and the sale price 3 years ago were analyzed as factors that increase the fish production of shallow-sea aquaculture. However, the amount of fish farming production 1, 2, 3 years ago, the farming area in this year, and the number of cultured fish in this year and 1 year ago were identified as factors that reduce the fish production of shallow-sea aquaculture. In conclusion, this study suggests that it is desirable to control the amount of stocked fry rather than to expand the farming area for fish farming in shallow-sea aquaculture, so as to keep the sale price at a certain level by maintaining the appropriate amount of fish production.