• Title/Summary/Keyword: Patterning community changes

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Pattern Recognition of Long-term Ecological Data in Community Changes by Using Artificial Neural Networks: Benthic Macroinvertebrates and Chironomids in a Polluted Stream

  • Chon, Tae-Soo;Kwak, Inn-Sil;Park, Young-Seuk
    • The Korean Journal of Ecology
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    • v.23 no.2
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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.

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Patterning Zooplankton Dynamics in the Regulated Nakdong River by Means of the Self-Organizing Map (자가조직화 지도 방법을 이용한 조절된 낙동강 내 동물플랑크톤 역동성의 모형화)

  • Kim, Dong-Kyun;Joo, Gea-Jae;Jeong, Kwang-Seuk;Chang, Kwang-Hyson;Kim, Hyun-Woo
    • Korean Journal of Ecology and Environment
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    • v.39 no.1 s.115
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    • pp.52-61
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    • 2006
  • The aim of this study was to analyze the seasonal patterns of zooplankton community dynamics in the lower Nakdong River (Mulgum, RK; river kilometer; 27 km from the estuarine barrage), with a Self-Organizing Map (SOM) based on weekly sampled data collected over ten years(1994 ${\sim}$ 2003). It is well known that zooplankton groups had important role in the food web of freshwater ecosystems, however, less attention has been paid to this group compared with other community constituents. A non-linear patterning algorithm of the SOM was applied to discover the relationship among river environments and zooplankton community dynamics. Limnological variables (water temperature, dissolved oxygen, pH , Secchi transparency, turbidity, chlorophyll a, discharge, etc.) were taken into account to implement patterning seasonal changes of zooplankton community structures (consisting of rotifers, cladocerans and copepods). The trained SOM model allocated zooplankton on the map plane with limnological parameters. Three zooplankton groups had high similarities to one another in their changing seasonal patterns, Among the limnological variables, water temporature was highly related to the zooplankton community dynamics (especially for cladocerans). The SOM model illustrated the suppression of zooplankton due to the increased river discharge, particularly in summer. Chlorophyll a concentrations were separated from zooplankton data set on the map plane, which would intimate the herbivorous activity of dominant grazers. This study introduces the zooplankton dynamics associated with limnological parameters using a nonlinear method, and the information will be useful for managing the river ecosystem, with respect to the food web interactions.

Temporal Dynamics and Patterning of Meiofauna Community by Self-Organizing Artificial Neural Networks

  • Lee, Won-Cheol;Kang, Sung-Ho;Montagna Paul A.;Kwak Inn-Sil
    • Ocean and Polar Research
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    • v.25 no.3
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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.

Mathematical Evaluation of Response Behaviors of Indicator Organisms to Toxic Materials (지표생물의 독성물질 반응 행동에 대한 수리적 평가)

  • Chon, Tae-Soo;Ji, Chang-Woo
    • Environmental Analysis Health and Toxicology
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    • v.23 no.4
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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.

Change of Carabid Beetle (Coleoptera, Carabidae) Diversity and Species Composition after Flooding Events in Woopo Wetlands (우포늪 범람에 의한 먼지벌레류(딱정벌레목, 딱정벌레과)의 다양성과 종조성 변화)

  • Do, Yun-O;Jang, Min-Ho;Kim, Dong-Kyun;Joo, Gea-Jae
    • Korean Journal of Ecology and Environment
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    • v.40 no.2
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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.