• The Korean Journal of Ecology
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• 제23권2호
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• pp.89-100
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• 2000

On community data. sampled in regular intervals on a long-term basis. artificial neural networks were implemented to extract information on characterizing patterns of community changes. The Adaptive Resonance Theory and Kohonen Network were both utilized in learning benthic macroinvertebrate communities in the Soktae Stream of the Suyong River collected monthly for three years. Initially, by regarding each monthly collection as a separate sample unit, communities were grouped into similar patterns after training with the networks. Subsequently, changes in communities in a sequence of samplings (e.g., two-month, four-month, etc.) were given as input to the networks. After training, it was possible to recognize new data set in line with the sampling procedure. Through the comparative study on benthic macroinvertebrates with these learning processes, patterns of community changes in chironomids diverged while those of the total benthic macro-invertebrates tended to be more stable.

• 생태와환경
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• 제39권1호통권115호
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• pp.52-61
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• 2006

본 연구는 지난 10여년간의 (1994 ${\sim}$ 2003) 주간격의 자료를 이용한 자가조직화 지도 (SOM) 방법으로 낙동강 하류역 (물금: 낙동강 하구언으로부터 27 km 상류지점)에서 동물플랑크톤 군집 동태에 대한 계절별 유형화 분석을 하는데 목적이 있다. 담수생태계내의 먹이망에서 동물플랑크톤 군집의 역할은 매우 중요하나, 다른 군집 구성원들과의 비교 연구는 다소 미진하게 진행되었다. 비선형 모형 알고리즘인 SOM을 동물플랑크톤 군집 역동성과 강 환경 인자들과의 상관관계 파악을 위하여 적용하였다. 육수학적 환경 인자 (수온, 용존산소, pH, 세키투명도, 탁도, 클로로필 a 농도, 유량 등) 들을 동물플랑크톤 군집 구조(윤충류, 지각류 및 요각류)의 계절적 변화 유형파악을 위하여 사용하였다. 학습된 SOM 모형은 육수학적 환경인자와 연관 지어 지도상에 동물플랑크톤을 배치되었다. 동물플랑크톤의 주요 세 군집들은 계절별 변화 유형에 있어서 높은 유사성을 가지고 있었다. 다양한 육수학적 환경인자 중, 수온은 동물플랑크톤 군집 역동성과 매우 높은 연관관계를 나타내었다(특히, 지각류). SOM 모형은 여름기간 증가된 강 유량에 의해서 동물플랑크톤을 매우 저해하는 요인으로 표현되었다. 클로로필 a 농도는 우점한 초식성 동물플랑크톤 활성도에 의해 지도상에서 구획되었다. 본 연구는 비선형 방법을 이용한 육수학적 환경요인과 동물플랑크톤 역동성을 연관 지어 소개하였으며, 이러한 정보는 먹이망이라는 관점에서 볼 때, 강 생태계 관리에 유용한 정보로 활용될 것으로 사료된다.

• Ocean and Polar Research
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• 제25권3호
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• pp.237-247
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• 2003

The temporal dynamics of the meiofauna community in Marian Cove, King George Island were observed from January 22 to October 29 1996. Generally, 14 taxa of metazoan meiofauna were found. Nematodes were dominant comprising 90.12% of the community, harpacticoid 6.55%, and Kinorhynchs 1.54%. Meiofauna abundance increased monthly from January to May 1996, while varying in abundance after August 1996. Overall mean abundance of metazoan meiofauna was $2634ind./10cm^2$ during the study periods, which is about as high as that found in temperate regions. Nematodes were most abundant representing $2399ind./10cm^2$. Mean abundance of harpacticoids, including copepodite and nauplius was $131ind./10cm^2$ by kinorhynchs $(26ind./10cm^2)$. The overall abundance of other identified organisms was $31ind./10cm^2$ Other organisms consisted of a total of 11 taxa including Ostracoda $(6ind./10cm^2)$, Polycheata $(7ind./10cm^2)$, Oligochaeta $(8ind./10cm^2)$, and Bivalvia $(6ind./10cm^2)$. Additionally, protozoan Foraminifera occurred at the study area with a mean abundance of $263ind./10cm^2$. Foraminiferans were second in dominance to nematodes. The dominant taxa such as nematodes, harpacticoids, kinorhynchs and the other tua were trained and extensively scattered in the map through the Kohonen network. The temporal pattern of the community composition was most affected by the abundance dynamics of kinorhynchs and harpacticoids. The neural network model also allowed for simulation of data that was missing during two months of inclement weather. The lowest meiofauna abundance was found in August 1996 during winter. The seasonal changes were likely caused by temperature and salinity changes as a result of meltwater runoff, and the physical impact by passing icebergs.

• Environmental Analysis Health and Toxicology
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• 제23권4호
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• pp.231-245
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• 2008

Various methods for detecting changes in response behaviors of indicator specimens are presented for monitoring effects of toxic treatments. The movement patterns of individuals are quantitatively characterized by statistical (i.e., ANOVA, multivariate analysis) and computational (i.e., fractal dimension, Fourier transform) methods. Extraction of information in complex behavioral data is further illustrated by techniques in ecological informatics. Multi-Layer Perceptron and Self-Organizing Map are applied for detection and patterning of response behaviors of indicator specimens. The recent techniques of Wavelet analysis and line detection by Recurrent Self-Organizing Map are additionally discussed as an efficient tool for checking time-series movement data. Behavioral monitoring could be established as new methodology in integrative ecological assessment, tilling the gap between large-scale (e.g., community structure) and small-scale (e.g., molecular response) measurements.

• 생태와환경
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• 제40권2호
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• pp.346-351
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• 2007

Change of carabid beelte (Coleoptera, Carabidae) diversity and population structures in Woopo Wetlands (Changneung-Gun, Gyeungsangnam-Do, S. Korea) were investigated after flooding events. During the investigation period, 11 species belonging to five genera were identified. Dolichus halensis halensis(Schaller), Chlaenius (Ch.) pallipes Gebler, Ch. (Ilaenchus) naeviger Morawitz, and Pheropsophus (Stenaptinus) jessoensis Morawitz were the predominant species in Woopo Wetlands. Floods occurred twice, August and September in 2004. After the flooding events, species diversity decreased and species assemblage structures changed dramatically. Changes of the diversity and species assemblage structures were more evident in August then in September, because water level was much higher and inundation period was longer than September. A non-linear patterning algorithm of the Self-Organizing Map (SOM) was applied to discover the relationship between flooding events and carabid beetles community dynamics. Although abundance of the majority species decreased after the flooding events, that of the predominant species increased. Further detailed studies on species distribution and emigration patterns will likely bring a new insight in understanding of the adaptation mechanism of carabid beetles in wetlands.