• Journal of the korean society of oceanography
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• v.37 no.4
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• pp.201-211
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• 2002

We investigated the abundance and biomass of dinoflagellates and factors controlling their abundance in marine planktonic ecosystems in Korean coastal waters. The abundance of photosynthetic (PDNF) and heterotrophic dinoflagellates (HDNF) was in the range of 0.7${\times}$10$^2$ cells/1-14.0${\times}$10$^6$ cells/1 and in the range of 3.0${\times}$10$^2$ cells/1-6.47${\times}$10$^5$ cells/I, respectively. Their biomass was 0.5${\times}$10$^{-1}$-2.56${\times}$10$^4$ ${\mu}gC/I$ and 2.0${\times}$10$^{-1}$-1.5${\times}$10$^{2}$ ${\mu}gC/I$, respectively. In order to find factors controlling their abundance, stepwise regression and best subsets regression analyses were used. We found that during the summer the most important factors controlling PDNF abundance are DO, P, N and S (abiotic factors), and for HDNF, the abundance of zooplankton, ciliates and HF (biotic factors), and that high turbidity may effect the distribution of dinoflagellate species.

• Journal of The Korean Astronomical Society
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• v.7 no.1
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• pp.11-15
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• 1974

An attempt has been made to re-evaluate the photospheric iron abundance by comparing computed, theoretical line profiles with observations. Our resulting abundance is found to be in good agreement with that of Garz and Kock (1969), when the logarithmic abundance of iron is taken to be 7.5.

• Economic and Environmental Geology
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• v.28 no.3
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• pp.199-211
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• 1995

The deep sea camera system could render it possible to obtain the detailed information of the nodule distribution, but difficult to estimate nodule abundance quantitatively. In order to estimate nodule abundance quantitatively from deep seabed photographs, the nodule abundance equation was derived from the box core data obtained in KODOS area(long.: $154^{\circ}{\sim}151^{\circ}W$, lat.: $9^{\circ}{\sim}12^{\circ}N$) during two survey cruises carried out in 1989 and 1990. The regression equation derived by considering extent of burial of nodule to Handa's equation compensates for the abundance error attributable to partial burial of some nodules by sediments. An average long axis and average extent of burial of nodules in photographed area are determined according to the surface textures of nodules, and nodule coverage is calculated by the image analysis method. Average nodule abundance estimated from seabed photographs by using the equation is approximately 92% of the actual average abundance in KODOS area. The measured sampling points by box core or free fall grab are in general very sparse and hence nodule abundance distribution should be interpolated and extrapolated from measured data to uncharacterized areas. The another goal of this study is to depict continuous distribution of nodule abundance in KODOS area by using PC-version of geostatistical model in which several stages are systematically proceeded. Geostatistics was used to analyse spatial structure and distribution of regionalized variable(nodule abundance) within sets of real data. In order to investigate the spatial structure of nodule abundance in KODOS area, experimental variograms were calculated and fitted to a spherical models in isotropy and anisotropy, respectively. The spherical structure models were used to map out distribution of the nodule abundance for isotropic and anisotropic models by using the kriging method. The result from anisotropic model is much more reliable than one of isotropic model. Distribution map of nodule abundance produced by PC-version of geostatistical model indicates that approximately 40% of KODOS area is considered to be promising area(nodule abundance > $5kg/m^2$) for mining in case of anisotropy.

• Economic and Environmental Geology
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• v.30 no.5
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• pp.493-498
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• 1997

The purpose of this study is to develop geostatistical model for evaluating the abundance of deep-sea manganese nodule. The abundance data used in this study were obtained from the KODOS (Korea Deep Ocean Study) area. The variation of nodule abundance was very high within short distance, while sampling methods was very limited. As the distribution of nodule abundance showed non-gaussian, indicator simulation method was used instead of conditional simulation method and/or ordinary kriging. The abundance data were encoded into a series of indicators with 6 cutoff values. They were used to estimate the conditional probability distribution function (cpdf) of the nodule abundance at any unsampled location. The standardized indicator variogram models were obtained according to variogram analysis. This SIS method had the advantage over other traditional techniques such as the turning bands method and ordinary kriging. The estimating values by indicator conditional simulation near high abundance area were more detailed than by ordinary kriging and indicator kriging. They also showed better spatial characteristics of distribution of nodule abundance.

• Journal of Periodontal and Implant Science
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• v.51 no.5
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• pp.316-328
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• 2021

Purpose: This study aimed to examine the prevalence and abundance of 9 representative periodontal pathogens in the saliva samples of periodontally healthy subjects (PH) and patients with periodontitis who underwent supportive periodontal therapy (SPT). The age-specific distribution of these pathogens in periodontally healthy individuals was also analyzed. Methods: One hundred subjects (aged >35 years) were recruited (50 each in the PH and SPT groups) between August 2016 and April 2019. The prevalence and abundance of periodontal pathogens in the PH group were compared with those in periodontally healthy young subjects (94 subjects; aged <35 years), who were included in our previous study. DNA copy numbers of Aggregatibacter actinomycetemcomitans (Aa), Porphyromonas gingivalis (Pg), Tannerella forsythia (Tf), Treponema denticola (Td), Prevotella intermedia (Pi), Fusobacterium nucleatum (Fn), Campylobacter rectus (Cr), Peptostreptococcus anaerobius (Pa), and Eikenella corrodens (Ec) were analyzed using real-time polymerase chain reaction. Results: The detection frequencies of all pathogens, except Aa, were high in the PH and SPT groups. The ranking order of pathogen DNA copy numbers was similar in both groups. In both groups, Fn had the highest abundance, Aa had the lowest abundance. Additionally, Td was significantly more abundant in men than in women in both groups (P<0.05). Compared with the PH group, the SPT group exhibited significantly lower total bacteria and Fn abundance and higher Pg abundance (P<0.05). The age-specific pathogen distribution analysis revealed a significantly low Aa abundance and high Tf and Cr abundance in the PH group. Conclusions: The clinical parameters and microbial profiles were similar between the SPT and PH groups. However, patients with periodontitis require supportive care to prevent recurrence. As the abundance of some bacteria varied with age, future studies must elucidate the correlation between age-related physiological changes and periodontal bacterial composition.

• Journal of Environmental Science International
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• v.30 no.5
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• pp.379-389
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• 2021

Soil microorganism activity in an agricultural field is affected by various factors including climate conditions, soil chemical properties, and crop cultivation. In this study, we elucidate the correlation between microorganism activity and agricultural environment factors using the dehydrogenase activity (DHA) value, which is one of the indicators of soil microbial activity. As a result, the various factors noted above were related to the DHA value. Annual rainfall, soil Mg2+, bacterial and fungal diversities, types of crops, developmental stages, seasons, and cultivation status were highly correlated with the DHA value. Furthermore, next-generation sequencing (NGS) analysis was used to identify that the type of crop affected soil microbial compositions of both bacteria and fungi. Soil used for soybean cultivation showed the highest relative abundance for Verrucomicrobia, Planctomycetes, and Acidobacteria but Actinobacteria and Firmicutes had the lowest relative abundance. In the case of soil used for potato cultivation, Actinobacteria had the highest relative abundance but Proteobacteria had the lowest relative abundance. Armatimonadetes showed the highest relative abundance in soil used for cabbage cultivation. Among the fungal communities, Mortierellomycota had the highest relative abundance for soybean cultivation but the lowest relative abundance for cabbage cultivation; further, Rozellomycota, Chytridiomycota, and Cercozoa had the highest relative abundance for cabbage cultivation. Basidiomycota had the highest relative abundance for potato cultivation but the lowest relative abundance for soybean cultivation.

• Journal of the korean society of oceanography
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• v.38 no.3
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• pp.111-121
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• 2003

The seasonal succession of zooplankton communities in the coastal area off Incheon, Kyeonggi Bay, was investigated with the samples collected at intervals of 10 to 15 days from January 1999 to December 2000. Total abundance of zooplankton communities showed remarkable seasonal variations, ranged from 1,100 to $120,400{\;}indiv./\textrm{m}^3$, and annual mean abundance was $22,000{\;}indiv./\textrm{m}^3$. There were several times of the total abundance during a year, and the timing ofhigh abundances were about the same in 1999 and 2000. During the study period except summer, the abundance of dinoflagellate Noctiluca scintillans and copepod Acartia hongi contributed to the most part of total zooplankton. Whereas, during summer, smaller copepod Oithona davisae and Paracalanus crassirostris were dominant species. Zooplankton communities in the coastal waters off Incheon showed typical characteristics of coastal-estuarine communities, which were dominated by a few species, and abrupt seasonal variations in abundance. We suggest that the seasonal succession and abundance variations of zooplankton communities were caused by the seasonal variations in water temperature and by the seasonally varying phytoplankton biomass in the study area.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.28 no.6
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• pp.708-719
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• 1995

This study is to estimate abundance of the Chesapeake Bay blue crab stock. Using 823 dredge tows which were conducted during the 1991 winter survey, blue crab abundance was estimated on the basis of newly developed methods which account for unequal dredge tow distances, size- and sex-specific heterogeneous overwintering spatial distributions, wintertime exploitation, the time period of quasi-hibernation, and dredge capture efficiency. The estimate of total abundance before correction by gear efficiency was 131.8 million crabs $(95\%\;C.I.\;=\;118.2\;million\;crabs\;to\;145.4\;million\;crabs),$ Dredge capture efficiency was estimated to be 0.474. Thus, the estimate of total abundance was calculated as 278.1 million crabs after correction by the efficiency factor.

• Ocean Science Journal
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• v.43 no.3
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• pp.135-146
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• 2008

The monthly distribution of zooplankton communities in Han River estuary was investigated at two stations from July 1998 to June 1999. Monthly mean abundance of total zooplankton varied remarkably, with the range from 20 $indiv.{\cdot}m^{-3}$ to 19,600 $indiv.{\cdot}m^{-3}$. During the study period, dominant species of zooplankton community were dinoflagellate Noctiluca scintillans, copepods Paracalanus indicus, Paracaanus crassirostris, Acartia hongi, Acartia ohtsukai, and meroplanton cirriped larvae. According to tidal states, relative high abundance occurred at high tide without regard to season. The temporal distribution of abundance implied that the reduced salinity probably limited the zooplankton populations and the fluctuations of salinity were an important factor in the variation of abundance. However, the results of salinity tolerance test shows that the variations in salinity do not directly influence the decrease of abundance. This study shows that the relatively high abundance of zooplankton near high tide seems to be related with the expansion of abundant zooplankton inhabiting Incheon coastal waters through tidal currents.

• Journal of the Korean Institute of Landscape Architecture
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• v.30 no.3
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• pp.73-85
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• 2002

The purpose of this study was to develop prediction models for plant species abundance by stream restoration. Generally the stream plant is affected by stream gemophology. So in this study, the relationship between the vegetation abundance and stream gemophology was developed by multiple regression analysis. The stream characteristics utilized in this study were longitudinal slope, transectional slope, micro-landforms through the longitudinal direction, riparian width and geometric mean diameter and biggest diameter of bed material, and cumulated coarse and fine sand weight portion. The Pyungchang River with mountainous watershed and the Kyungan stream and the Bokha stream in the agricultural region were selected and vegetation species abundance and stream characteristics were documented from the site at 2~3km intervals from the upper stream to the lower. The Models for predicting the vegetation abundance were developed by multiple regression analysis using SPSS statistics package. The linear relationship between the dependant(species abundance) and independant(stream characteristics) variables was tested by a graphical method. Longitudinal and transectional slope had a nonlinear relationship with species abundance. In the next step, the independance between the independant variables was tested and the correlation between independant and dependant variables was tested by the Pearson bivariate correlation test. The selected independant variables were transectional slope, riparian width, and cumulated fine sand weight portion. From the multiple regression analysis, the $R^2$for the Pyungchang river, Kyungan stream, Bokga stream were 0.651, 0.512 and 0.240 respectively. The natural stream configuration in the Pyungchang river had the best result and the lower $R^2$for Kyunan and Bokha stream were due to human impact which disturbed the natural ecosystem. The lowest $R^2$for the Bokha stream was due to the shifting sandy bed. If the stream bed is fugitive, the prediction model may not be valid. Using the multiple regression models, the vegetation abundance could be predicted with stream characteristics such as, transection slope, riaparian width, cumulated fine sand weigth portion, after stream restoration.