Abstract
Patterning temporal development of community is an important topic in ecosystem management as of late. Especially in aquatic ecosystems, where communities are easily affected by disturbances caused by various natural and anthropogenic agents, it is important to know how communities would develop in response to changes in water quality. They would develop either progressively with further disturbances, or regressively in recovery from pollution (Sladecek 1979; Hellawell 1986). Methods for characterizing ’changes’ in communities are needed in terms of predicting the future development of the community, detecting mechanism of community differentiation, and assessing ecological status of the target ecosystem.
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References
Boudjema G, Chau NP (1996) Revealing dynamics of ecological systems from natural recordings. Ecol Model 91:15-23
Bunn, SE, Edward DH, Loneragan NR (1986) Spatial and temporal variation in the macroinvertebrate fauna of streams of the northern jarrah forest, Western Australia: Community structure. Freshwat Biol 16:67-91
Chon T-S, Park YS, Moon KH, Cha EY (1996) Patternizing communities by using an artificial neuronal network. Ecol Model 90:69-78
Elizondo DA, McClendon RW, Hoongenboom G (1994) Neuronal network models for predicting flowering and physiological maturity of soybean. Transactions of the ASAE 37(3):981-988
Elman JL (1990) Finding structure in time. Cognitive Science 14:179-211
Giles CL, Kuhn GM, Williams RJ (1994) Dynamic recurrent neuronal networks: Theory and applications. IEEE Transactions on Neuronal Networks 5:153-156
Haykin S (1994) Neuronal networks. Macmillian College Publishing Company, New York
Hecht-Nielsen R (1990) Neurocomputing. Addison-Wesley, New York
Hellawell JM (1986) Biological indicators of freshwater pollution and environmental management. Elsevier, London
Huntingford C, Cox PM (1996) Use of statistical and neuronal network techniques to detect how sto-matal conductance responds to changes in the local environment. Ecol Model 97:217-246
Kang DH, Chon T-S, Park YS (1995) Monthly changes in benthic macroinvertebrate communities in different saprobities in the Suyong and Soktae streams of the Suyong River. Kor J Ecol 18:157-177
Kung SY (1993) Digital neuronal networks. Prentice Hall, Englewood Cliffs, New Jersey
Kwon T-S, Chon T-S (1993) Ecological studies on benthic macroinvertebrates in the Suyong River. III. Water quality estimations using chemical and biological indices. Kor J Lim 26:105-128
Legendre P (1987) Constrained clustering. In: Legendre P, Legendre L (eds) Developments in numerical ecology. Springer-Verlag, Berlin, pp 289-307
Legendre P, Legendre L (eds) (1987) Developments in Numerical Ecology. Springer-Verlag, Berlin
Legendre P, Dallot S, Legendre L (1985) Succession of species within a community: Chronological clustering, with applications to marine and freshwater zooplankton. Am Nat 125:257-288
Lek S, Delacoste M, Baran P, Dimopoulos I, Lauga J, Aulagnier S (1996) Application of neuronal networks to modelling nonlinear relationships in ecology. Ecol Model 90:39-52
Levine ER, Kimes DS, Sigillito VG (1996) Classifying soil structure using neuronal networks. Ecol Model 92:101-108
Ludwig JA, Reynolds JF (1988) Statistical ecology: A primer of methods and computing. John Wiley and Sons, New York
Quinn MA, Halbert SE, Williams III L (1991) Spatial and temporal changes in aphid (Homoptera: Aphididae) species assemblages collected with suction traps in Idaho. J Econ Entomol 84:1710-1716
Recknagel F, French M, Harkonen, P, Yabunaka K-I (1997) Artificial neuronal network approach for modelling and prediction of algal blooms. Eco Model 96:11-28
Rumelhart DE, Hinton GE, Williams RJ (1986) Learning internal representations by error propagation. In: Rumelhart DE, McCelland JL (eds) Parallel distributed processing: Explorations in the micro-structure of cognition, vol I: Foundations. MIT Press, Cambridge, pp 318-362
Sladecek V (1979) Continental systems for the assessment of river water quality. In: James A, Evison L (eds) Biological indicators of water quality. John Wiley & Sons, Chichester, pp 31-32
Stankovski V, Debeljak M, Bratko I, Adamic M (1998) Modelling the population dynamics of red deer (Cervus elaphus L.) with regard to forest development. Ecol Model 108:143-153
Tan SS, Smeins FE (1996) Predicting grassland community changes with an artificial neuronal network model. Ecol Model 84:91-97
Tuma A, Haasis H-D, Rentz O (1996) A comparison of fuzzy expert systems, neuronal networks and neuro-fuzzy approaches controlling energy and material flows. Ecol Model 85:93-98
Turchin P, Taylor AD (1992) Complex dynamics in ecological time series. Ecology 73(1):289-305
Waibel, A., Hanazawa, T., Hinton, G., Shikano, K.G. and Lang, K.J. (1989) Phoneme recognition using time delay neuronal networks. IEEE Trans Acoustics, Speech and Signal Process 37:328-339
Wasserman PD (1989) Neuronal computing: Theory and practice. Van Nostrand Reinhold, New York
Woodiwiss FS (1964) The biological systems of stream classification used by the Trent River Board. Chemistry and Industry 14:443-447
Wray J, Green GGR (1994) Calculation of the Volterra kernels of non-linear dynamic systems using an artificial neuronal network. Biol Cybern 71:187-195
Yoon BJ, Chon TS (1996) Community analysis in chironomids and biological assessment of water qualities in the Suyong and Soktae streams of the Suyong River. Kor J Lim 29(4):275-289
Zurada JM (1992) Introduction to artificial neuronal systems. West Publishing Company, New York
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Chon, TS., Park, YS., Cha, E.Y. (2000). Patterning of Community Changes in Benthic Macroinvertebrates Collected from Urbanized Streams for the Short Time Prediction by Temporal Artificial Neuronal Networks. In: Lek, S., Guégan, JF. (eds) Artificial Neuronal Networks. Environmental Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-57030-8_7
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DOI: https://doi.org/10.1007/978-3-642-57030-8_7
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