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TWINSPAN 기법을 이용한 한국의 저서성 대형무척추동물의 유형분류 및 유형분포에 영향을 미치는 환경요인에 대한 판별분석

Classification of Korean Benthic Macroinvertebrate Types Using the TWINSPAN Clustering and Discriminant Analysis of Environmental Factors Affecting the Distribution of the Types

  • 민정기 (경기대학교 바이오융합학부) ;
  • 김동희 (경기대학교 바이오융합학부) ;
  • 문정숙 (국립환경과학원 물환경연구부) ;
  • 김진영 (국립환경과학원 물환경연구부) ;
  • 공동수 (경기대학교 바이오융합학부)
  • Min, Jeong-Ki (Department of Bioconvergence, Kyonggi University) ;
  • Kim, Dong-Hee (Department of Bioconvergence, Kyonggi University) ;
  • Moon, Jeong-Suk (Water Environment Department, National Institude of Environmental Research) ;
  • Kim, Jin-Young (Water Environment Department, National Institude of Environmental Research) ;
  • Kong, Dongsoo (Department of Bioconvergence, Kyonggi University)
  • 투고 : 2018.09.15
  • 심사 : 2018.11.12
  • 발행 : 2018.11.30

초록

Korean benthic macroinvertebrate community from 959 sites and 14 environmental factors that could affect the biological distribution were surveyed and analyzed. The 438 taxa were divided into six groups according to TWINSPAN. Based on the environmental factor values corresponding to each taxon, the average and range of environmental factors for each biological group tended to increase or decrease in eight environmental factors (altitude, slope, current velocity, fraction of fine particles in substrates, turbidity, $BOD_5$, T-N and T-P). As a result of discriminant analysis (hit ratio: 69.6 %), environmental factors that significantly influence the distribution of each biological group were $BOD_5$, altitude, catchment area, T-N, T-P and fraction of fine particles in substrates. The discriminant functions 1 and 2 classified the existing six biological groups (clustered by TWINSPAN) into five (group 1: highland highly sensitive group; group 2: lowland sensitive group, group; 3 and 4: lowland slightly tolerant group; group 5: lowland tolerant group; group 6: lowland highly tolerant group). Most of the benthic macroinvertebrates were identified in the species level. However, a few others were identified in genus or family level, too. Even the organisms belonging to the same genus or family may prefer different environmental factors in the species level, the identification of characteristic of the habitat environment preferred by organism and species level identification of these organisms are utmost. The results of this study may be used as basic information on environmental factors which can be considered in preservation and restoration research for biodiversity.

키워드

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Fig. 1. Normality test and frequency analysis for the individual abundance of Baetis fuscatus; Note octaves mean $log^n_2$, and n is individual abundance (Ind./$m^2$).

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Fig. 2. Normality test and frequency analysis for the individual abundance of Dugesia japonica.

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Fig. 3. Result of TWINSPAN based on adjustment individual abundance of benthic macroinvertebrates (*: Division level, **: Eigenvalue of division level).

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Fig. 4. Distribution patterns of physical factors according to each TWINSPAN group (a) stream order, (b) catchment area, (c) stream width, (d) altitude, (e) slope, (f) depth, (g) current velocity, (h) fraction of fine particles in substrates.

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Fig. 5. Distribution patterns of chemical factors according to each TWINSPAN group (a) water temperature, (b) dissolved oxygen, (c) turbidity, (d) BOD5, (e) T-N, (f) T-P.

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Fig. 6. Normality test for the environmental factors and factors following normality (a) stream order, (b) slope, (c) current velocity, (d) fraction of fine particles in substrates, (e) water temperature, (f) dissolved oxygen.

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Fig. 7. Normality test for the environmental factors and physical factors not following normality (a) catchment area, (b) stream width, (c) altitude, (d) depth.

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Fig. 8. Normality test for the environmental factors and chemical factors not following normality (a) turbidity, (b) BOD5, (c) T-N, (d) T-P.

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Fig. 9. Distribution patterns of 6 groups of benthic macroinvertebrates based on canonical discriminant function 1 and 2.

Table 1. Summary of environmental factors in the sampling sites

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Table 2. Environmental factors used in discriminant analysis

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Table 3. Eigenvalues, Wilks' Lambda and significance of each discriminant function

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Table 4. Five canonical discriminant functions using six discriminant variables

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Table 5. Classification of results based on discriminant analysis

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