• The Korean Journal of Ecology
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• 제20권5호
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• pp.329-337
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• 1997

Study of population density and biomass of soil microarthropods in soil, herbaceous leaf litter and wood leaf litter was conducted at 4 sites with different flora in Mt. Jumbong, a nature reserve area, in Korea from Aug. 1994 to May 1996. Total 47, 849 individuals of soil microarthropods in soil were collected and identified into 6 different classes, and 16 orders. The composition, densities, and dominant group of soil microarthropods were different among 4 sampling sites. Collembola was the most abundant group with 50.7% at South-facing slope and 50.6% at the North-facing slope. But Acari was the most abundant group with 49.8% at Altitude 900m site and 47.7% at Altitude 1100m site. Two group ranged 87-95% of total individuals. A/C ratio was less than 1.0 at 4 sampling sites during the all sampling seasons. As a result of biomass evaluation, rato of Acari was decreased but ratio of Diplopoda, coleoptera, Araneae, Diptera and Chilopoda was increased. About 70% of total orders and densities were found in less than 5cm soil depth. The composition, densities, and dominant group of soil microarthropods were different among soil, herbaceous litter and wood litter. Collembola was the most abundant group in herbaceous and wood litter. A/C ratio was less than 1.0 in litter during the sampling seasons. As a result of biomass evaluation in litter, ration of Araneae and Collembola was very high, but ratio of Acari was low.

• Fisheries and Aquatic Sciences
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• 제10권2호
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• pp.86-92
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• 2007

Adult Dungeness crabs are restricted primarily in the lower part of Glacier Bay, Alaska, but the interaction of larval dispersion and adult distribution is unknown. To understand the larval occurrence in the upper part of Glacier Bay, Dungeness crab larvae, sea surface temperature (SST), and sea surface salinity (SSS) were collected at 16 near-shore and 12 mid-channel stations in Glacier Bay, southeastern Alaska during six sampling periods from March through August 2000. Each station was visited from one to five times during the entire sampling period. Geographic Information System (GIS) was used to contour SST and SSS distribution in Glacier Bay. Seven to 27 stations were visited during each sampling period. Most larvae (85% were zoeae I) occurred during May 31 to June 14, 2000. Larval density varied from none to $51.4\;100\;m^{-3}$ between stations. A few later stage larvae occurred during later sampling periods. Overall, no relationship between larval densities, and SSS, and SST existed. Larvae occurring in the upper bay were probably transported by tidal currents from the lower bay; adult Dungeness crabs in Glacier Bay have a relatively high density near the mouth of the bay but decrease sharply around 40km north of the mouth. The lack of adult crabs in the upper 60km of the bay may be related to lower salinity, resulting in sharp haloclines, or colder temperatures which are not conducive to survival or growth of either larvae or adults.

• Communications for Statistical Applications and Methods
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• 제16권3호
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• pp.479-485
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• 2009

When a part of data is unobserved the marginal likelihood of parameters given the observed data often involves analytically intractable high dimensional integral and hence it is hard to find the maximum likelihood estimate of the parameters. Simulated maximum likelihood(SML) method which estimates the marginal likelihood via Monte Carlo importance sampling and optimize the estimated marginal likelihood has been used in many applications. A key issue in SML is to find a good proposal density from which Monte Carlo samples are generated. The optimal proposal density is the conditional density of the unobserved data given the parameters and the observed data, and attempts have been given to find a good approximation to the optimal proposal density. Algorithms which adaptively improve the proposal density have been widely used due to its simplicity and efficiency. In this paper, we describe a fully adaptive algorithm which has been used by some practitioners but has not been well recognized in statistical literature, and evaluate its estimation performance and robustness via a simulation study. The simulation study shows a great improvement in the order of magnitudes in the mean squared error, compared to non-adaptive or partially adaptive SML methods. Also, it is shown that the fully adaptive SML is robust in a sense that it is insensitive to the starting points in the optimization routine.

• Journal of the Korean Statistical Society
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• 제27권4호
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• pp.385-396
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• 1998

A computing method of fractional Bayes factor (FBF) for a point null hypothesis is explained. We propose alternative form of FBF that is the product of density ratio and a quantity using the generalized Savage-Dickey density ratio method. When it is difficult to compute the alternative form of FBF analytically, each term of the proposed form can be estimated by MCMC method. Finally, two examples are given.

• The Korean Journal of Ecology
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• 제20권5호
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• pp.355-365
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• 1997

Macrozoobenthic community structure was studied on the mud-tidalflat around Mokpo coastal area, Korea, from September 1995 to May 1996. Ten sampling stations were chosen along the 1km transect with a 100 m intervals. Triple macrozoobenthos sampling were conducted with a stainless box core sampler (size $13{\times}22{\times}30$ cm) at each station on the seasonal basis. A total of 119 species of macrobenthos was sampled with a mean density 2,357 $ind./m^2$ and biomass of 180. 94 $g/m^2$ during the study period. Of these species, there were 51 species of mollusce (43%), 36 of crustaceans (30%), 27 of polychaetes (23%). Molluscs were density-dominant faunal group with a mean density of 1,804 $ind./m^2$, comprising of 75% of the total density of the benthic animals. It was followed by polychaetes with 347 $ind./m^2$ (15% of the total density). Molluscs were biomass-dominant faunal group with a mean biomass of 147.51 $g/m^2$ (82% of the total biomass). The number of species, abundance and biomass showed increasing trends toward the lower intertidal stations from the higher stations. Major dominant species were Musculista senhousia (bivalve), Capitellidae spp. (polychaetes) with a density of 1,640 $ind./m^2$ and 179 $ind./m^2$ , respectively. The density of M. senhousia was more than 63% of the total density of benthic organism on the tidal area. Small gastropod Cerithideopsilla umumriensis was another dominant epifaunal species between st.1 and 2 with a mean density of 100 $ind./m^2$. Cluster analysis showed that the benthic community could be divided into three stational groups, that is, upper, middle and lower groups by the exposure duration.

• 한국응용곤충학회지
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• 제40권3호
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• pp.197-202
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• 2001

제주지역의 온주밀감 과수원에서 귤응애(Panonychus citri (McGregor)) 밀도에 대해 잎당 응애수를 2년에 걸쳐 조사하였다. 이항표본조사법은 잎당 귤응애의 밀도와(m)와 귤응애가 T마리보다 많이 존재하는 잎의 비율($P_{T}$)과의 관례를 기본으로 하며, T는 경험적 이항분포모형 [$ln(m)-{\alpha}+{\beta}ln(-ln(1-P_{T}))$]에서의 tally threshold로서 본 연구에서는 1, 3, 5, 7을 사용하였다. 표본단위 수의 증가는 T와 관계없이 이항분포 모형의 정확도에 영향이 거의 없었던 반면에 T는 값이 증가함에 따라 표본수를 증가시켜도 정확도가 낮아졌다. 이항분포모형의 정확도는 T=1일 때 가장 높았으며, 최적의 tally threshold인 것으로 나타났다. 또한, 이항표본조사의 유효성을 조사하기 위하여 독립된 표본을 추출, 조사하였으며, 그 결과 온주밀감원에서 귤응애 밀도추정에는 T=1인 경우가 적합한 것으로 판단되었다. 또한, 귤응애 밀도를 분류하기 위한 이항표본조사과정을 개발하여 action threshold가 귤응애 밀도가 잎당 2마리일 때의 이항표본조사 프로그램을 작성하였다.

• 한국산림과학회지
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• 제95권5호
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• pp.595-600
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• 2006

The plot design influences the budgets and the precision of forest inventory results. The objective of this study is to determine the efficiency of estimating forest variables such as tree density, basal area, volume, and species richness based on various plot sizes using fixed-area plot sampling in the natural deciduous forest of Pyeong-Chang County, Gang-won Province, Korea. In this study, 108 reference plots were established with a fixed plot size and shape of 0.09 ha ($30m{\times}30m$). In order to determine the optimal plot size for the interest of variables, each sample plot was established using different shapes (square, circle, and rectangle) and was divided into different plot sizes from 100 to $900m^2$. The mean relative difference (MRD) for the sum of the basal area and volume, and tree density per hectare decreased as plot size increased. But the MRD for three variables were only below 13% at the plot size of $500m^2$. Species richness for each reference stand observed ranging from 2 to 15 species, demonstrated highly positive significant relationships with plot size. The minimum plot size for the estimation of tree density, the sum of the BA and volume was determined to be about $400m^2$, whereas the estimation of species richness required a minimum plot size of $500m^2$.

• Journal of the korean society of oceanography
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• 제33권3호
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• pp.113-122
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• 1998

The requisite numbers of sample replicates for the population study of soft-bottom benthos were estimated from survey data on the Songdo tidal flat and subtidal zone in Youngil Bay, Korea. Large numbers of samples were taken; two-hundred-fifty 0.02 m$^2$ box corers and fifty 0.1m$^2$ van Veen grabs were taken on the Songdo tidal flat and in Youngil Bay, respectively. The effect of sampler size on sampling efforts was investigated by pooling the unit samples in pairs, fours, eights, etc. The requisite number of sample replicates (n$_r$) was determined by sample variance (s$^2$) and mean (m) function (n$_r$:s$^2$/P$^2$m$^2$), at P=0.2 level, in which s$^2$ and m were calculated from the counts of individuals collected. For example, seven samples of 0.02 m$^2$ corer for the intertidal and two samples of 0.1 m$^2$ van Veen grab for subtidal fauna were required to estimate the total density of community. The smaller sampler size was more efficient than larger ones when sampling costs were compared on the basis of the total sampling area. The requisite number of sample replicates was also predicted ($\^{n}$n$_r$) by substituting $\^{s}$$^2$ obtained from the regression of s$^2$ against m using the Taylor's power law ($\^{s}$$^2$:am$^b$). The regression line of survey data on s$^2$ and m plotted on log scale was well fitted to the Taylor's power law (r$^2$${\geq}$0.95, p<;0.001) over the whole range of m. The exponent b was, however, varied when it was estimated from m which was categorized into classes by its scale. The fitted exponent b was large when both density class and the sampler size were large. The number of sample replicates, therefore, could be more significantly estimated, if regression coefficients (a and b) would be calculated from sample variance and mean categorized into density classes.

• 한국어류학회지
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• 제20권3호
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• pp.228-232
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• 2008

To investigate a proper sampling method for anchovy eggs and larvae in coastal waters of the South Sea, replicated samplings were made by different towing methods with different sampling gears and compared in terms of abundance and length composition. There was no significant difference in abundance in samples from vertical and oblique tows with a ring net. The abundance by replicated vertical tows with a ring net was not significantly different, but significant difference in abundance among sampling stations were found. The ring net sampled anchovy eggs in significantly greater numbers than collected by a NORPAC net, but both gears were not effective in obtaining quantitative samples of anchovy larvae larger than 3 mm. Therefore, samples by vertical tows with a ring net during the day at various stations is more efficient at estimating the density of anchovy eggs in an area compared to replicated sampling at a single station.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권4호
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• pp.1110-1127
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• 2022

To improve the training efficiency and generalization performance of a support vector machine (SVM) in a large-scale set, an optimal SVM learning method based on adaptive sparse sampling and the granularity shift factor is presented. The proposed method combines sampling optimization with learner optimization. First, an adaptive sparse sampling method based on the potential function density clustering is designed to adaptively obtain sparse sampling samples, which can achieve a reduction in the training sample set and effectively approximate the spatial structure distribution of the original sample set. A granularity shift factor method is then constructed to optimize the SVM decision hyperplane, which fully considers the neighborhood information of each granularity region in the sparse sampling set. Experiments on an artificial dataset and three benchmark datasets show that the proposed method can achieve a relatively higher training efficiency, as well as ensure a good generalization performance of the learner. Finally, the effectiveness of the proposed method is verified.