This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Allan JD (1995) Stream Ecology’ structure and function of running waters’. Chapman & hall, 388 pp
Allen TFH, Starr TB (1982) Hierarchy. The University of Chicago Press, 310 pp
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. Freshwater Biology, 16, 67–91
Brosse S, Lek S, Townsend CR (2001) Abundance, diversity, and structure of freshwater invertebrates and fish communities: an artificial neural network approach. New Zealand Journal of Marine and Freshwater Research, 35, 135–145
Calow P, Petts GE (1994) The Rivers Handbook ‘hydrological and ecological principles’. Blackwell Scientific Publications, 523 pp
Carpenter GA, Grossberg S (1987) ART2: self-organization of stable category recognition codes for analog input patterns. Applied Optics, 26, 4919–4930
Chon TS, Park YS, Moon KH, Cha EY (1996) Patternizing communities by using an artificial neural network. Ecol. Model., 90, 69–78
Chon TS, Kwak IS, Park YS (2000a) Pattern recognition of long-term ecological data in community changes by using artificial neural networks: Benthic macroinvertebrates and chironomids in a polluted stream. Korean J. Ecol., 23, 89–100
Chon TS, Park YS, Cha EY (2000b) Patterning of community changes in benthic macroinvertebrates collected from urbanized streams for the short time prediction by temporal artificial neural networks. In: Lek, S. and Guegan, J.F. (Eds.), Artificial Neuronal Networks: Application to Ecology and Evolution. Springer-Verlag, Berlin, pp. 99–114
Chon TS, Park YS, Park JH (2000c) Determining temporal pattern of community dynamics by using unsupervised learning algorithms. Ecol. Model., 132, 151–166
Chon TS, Kwak IS, Park YS, Kim TH, Kim YS (2001) Patterning and short-term predictions of benthic macroinvertebrate community dynamics by using a recurrent artificial neural network. Ecol. Model., 146. (In Press)
Cummins KW (1974) Structure and function of stream ecosystems. Bioscience, 24, 631–641
Cummins KW, Petersen RC, Howard FO, Wuycheck JC, Holt VI (1973) The utilization of leaf litter by stream detritovores. Ecology, 54, 336–345
Dimopoulos Y, Bourret P, Lek S (1995) Use of some sensitivity criteria for choosing networks with good generalization ability. Neural Processing Letters, 2, 1–4
Elizondo DA, McClendon RW, Hoongenboom G (1994) Neural network models for predicting flowering and physiological maturity of soybean. Transactions of the ASAE, 37, 981–988
Elman JL (1990) Finding structure in time. Cognitive Science, 14, 179–211
Fonseca JC, Marques JC, Paiva AA, Freitas AM, Madeira VMC, Jørgensen SE (2000) Nuclear DNA in the determination of weighing factors to estimate exergy from organisms biomass. Ecol. Model., 126, 179–189
Foody GM (1999) Applications of the self-organising feature map neural network in community data analysis. Ecol. Model., 120, 97–107
Giles CL, Kuhn GM, Williams RJ (1994) Dynamic recurrent neural networks: theory and applications. IEEE Transactions on Neural Networks, 5 153–156
Giraudel JL, Aurelle D, Berrebi P, Lek S (2000) Application of the self-organising mapping and fuzzy clustering to microsatellite data: How to detect genetic structure in brown trout (Salmo trutta) populations. In: Lek, S. and Guegan, J.F. (Eds.), Artificial Neuronal Networks: Application to Ecology and Evolution. Springer-Verlag, Berlin, pp. 187–202
Grossberg S (1969) On the production and release of chemical transmitters and related topics in the cellular control. J. Theor. Biol., 22, 325–364
Grossberg S (1982) Studies of Mind and Brain: Neural Principals of Learning, Perception, Development, Cognition, and Motor Control. Reidel Press, Boston
Hauer FR, Lamberti GA (1996) Methods in Stream Ecology. Academic Press, 674 pp
Hawkes HA (1979) Invertebrates as indicators of river water quality. In: James, A. and Evision, L. (Eds.), Biological indicators of water quality. John Wiley and Sons, Chishester, Great Britain, pp. 2.1–2.45
Haykin S (1994) Neural Networks. Macmillian College Publishing Company, 696 pp
Hecht-Nielsen R (1987) Counter propagation networks. Proc. of the Int. Conf. On Neural networks, II, 19–32, IEEE Press, New York, June 1987
Hecht-Nielsen R (1990) Neurocomputing. Addison-Wesley, New York, 433 pp
Hellawell JM (1986) Biological indicators of freshwater pollution and environmental management. Elsevier, London, 546 pp.
Hopfield JJ (1982) Neural Networks and Physical Systems with Emergent Collective Computational Abilities. Proc. Natl. Acad. Sci. USA, Vol. 79, 2554–2558, April
Huntingford C, Cox PM (1996) Use of statistical and neural network techniques to detect how stomatal conductance responds to changes in the local environment. Ecol. Model., 97, 217–246
Hynes HBN (1960) The biology of polluted waters. Liverpool Univ. Press. London, 202 pp
Jørgensen SE (1992) Parameters, ecological constraints and exergy. Ecol. Model., 62, 163–170
Jørgensen SE (1994) Review and comparison of goal functions in system ecology. WIE MILIEU, 44, 11–20
Jørgensen SE (1995) Exergy and ecological buffer capacities as measures of ecosystem health. Ecosys. Health, 1, 150–160
Jørgensen SE (1997) Integration of ecosystem theories: A pattern, 2nd edition. Kluwer, Dordrecht, 400 pp
Jørgensen SE, Nielsen SN, Mejer HF (1995) Emergy, environ exergy and ecological modeling. Ecol. Model., 77, 99–109
Kamgar-Parsi B, Gualtieri JA, Devancy JE, Kamgar-Parsi B (1990) Clustering with neural networks. Biol. Cybern., 63, 201–208
Kang DH, Chon TS, Park YS (1995) Monthly changes in benthic macroinvertebrate communities in different saprobities in the Suyong and Soktae streams of the Suyong river. Korean J. Ecol., 18, 157–177
Kohonen T (1989) Self-organization and associative memory. Springer-Verlag, Berlin, 312 pp
Kung SY (1993) Digital Neural Networks. Prentice Hall, Englewood Cliffs, New Jersey, 444 pp
Kwon TS, Chon TS (1993) Ecological studies on benthic macroinvertebrates in the Suyong River. III. Water quality estimations using chemical and biological indices. Kor. J. Limnol., 26, 105–128
Legendre P, Legendre L (1987) Developments in numerical ecology. Springer-Verlag, Berlin 585 pp
Legendre P (1987) Constrained clustering. In: Legendre, P. and Legendre, L. (Eds.), Developments in numerical ecology. Springer-Verlag, Berlin. Germany, 289–307 pp
Legendre P, Dallot S, Legendre L (1985) Sucession of species within a community: chronological clustering, with applications to marine and freshwater zooplankton. Am. Nat., 125, 257–288
Lek S, Guegan JF (1999) Artificial neural networks as a tool in ecological modelling, an introduction. Ecol. Model., 120, 65–73
Lek S, Guegan JF (2000) Neuronal Networks: Algorithms and Architectures for Ecologists and Evolutionary Ecologists. In: Lek, S. and Guegan, J.F. (Eds.), Artificial Neuronal Networks: Application to Ecology and Evolution. Springer-Verlag, Berlin, pp. 3–27
Lek S, Delacoste M, Baran P, Dimopoulos I, Lauga J, Aulagnier S (1996) Application of neural networks to modelling nonlinear relationships in ecology. Ecol. Model., 90, 39–52
Levine ER, Kimes DS, Sigillito VG (1996) Classifying soil structure using neural networks. Ecol. Model., 92, 101–108
Lippmann RP (1987) An Introduction to computing with neural nets. IEEE ASSP Magazine, April. pp. 4–22
Lohninger H, Stanc F (1992) Comparing the performance of neural networks to well-established methods of multivariate data analysis: the classification of mass spectral data. Fresenius J. Anal. Chem., 344, 186–189
Ludwig JA, Reynolds JF (1988) Statistical ecology: a primer on methods and computing. John Wiley and Sons, New York, 329 pp
McCulloch WS, Pitts W (1943) A logical calculus of the ideas imminent in nervous activity, Bulletin of Mathematical Biophysics, 5, 115–133
Melssen WJ, Smits JRM, Rolf GH, Kateman G (1993) Two-dimensional mapping of IR spectra using a parallel implemented self-organising feature map. Chemometrics and Intelligent Laboratory Systems, 18, 195–204
Norusis MJ (1986) SPSS/PC+ advanced statistics. SPSS inc., Chicago
O’Neill RN, DeAngelis DL, Waide JB, Allen TFH (1986) A hierarchical concept of ecosystems. Princeton University Press, Princeton, 253 pp
Pao YH (1989) Adaptive pattern recognition and neural networks. Addison-Wesley Publishing Company, Inc., New York, 309 pp
Park YS, Kwak IS, Cha EY, Lek S, Chon TS (2001a) Relational patterning on different hierarchical levels in communities of benthic macroinvertebrates in an urbanized stream using an artificial neural network. J. Asia-Pacific Entomol. (Submitted)
Park YS, Kwak IS, Chon TS, Kim JK, Jørgensen SE (2001b) Implementation of artificial neural networks in patterning and prediction of exergy in response to temporal dynamics of benthic macroinvertebrate communities in streams. Ecol. Model., 146. (In Press)
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 KI (1997) Artificial neural network approach for modelling and prediction of algal blooms. Eco. Model., 96, 11–28
Recknagel F, Wilson H (2000) Elucidation and prediction of aquatic ecosystems by artificial neuronal networks, In: Lek, S. and Guegan, J.F. (Eds.), Artificial Neuronal Networks: Application to Ecology and Evolution. Springer-Verlag, Berlin, pp. 143–155
Rumelhart DE, Hinton GE, Williams RJ (1986) Learning internal representations by error propagation. In: Rumelhart, D.E. and McCelland, J.L. (Eds.), Parallel distributed processing: Explorations in the microstructure of cognition, Vol. I: Foundations, MIT Press, Cambridge, pp. 318–362
Salvador R, Piňol J, Tarantola S, Pla E (2001) Global sensitivity analysis and scale effects of a fire propagation model used over Mediterranean shrublands. Ecol. Model., 136, 175–189
Scardi M (2000) Neuronal network models of phytoplankton primary production, In: Lek. S. and Guegan, J.F. (Eds.), Artificial Neuronal Networks: Application to Ecology and Evolution. Springer-Verlag, Berlin, pp. 116–129
Sladecek V (1979) Continental systems for the assessment of river water quality. In: James, A. and Evison, L. (Eds.), Biological Indicators of Water Quality. John Wiley & Sons, Chichester, pp. 3.1–3.32
Spellerberg IF (1991) Monitoring Ecological Change. Cambridge University Press, 334 pp
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 neural network model. Ecol. Model., 84, 91–97
Tittizer TT, Koth P (1979) Possibilities and limitations of biological methods of water analysis. In: James, A. and Evison, L. (Eds.), Biological Indicators of Water Quality. John Wiley and Sons, Chichester, Great Britain, pp. 4.1–4.21
Tuma A, Haasis HD, Rentz O (1996) A comparison of fuzzy expert systems, neural networks and neuro-fuzzy approaches controlling energy and material flows. Ecol. Model., 85, 93–98
Wasserman PD (1989) Neural computing: Theory and practice. Van Nostrand Reinhold, New York
Welch EB, Lindel T (1992) Ecological effects of wastewater ‘Applied limnology and pollutant effects’. Chapman & Hall, 425 pp
Williams RJ, Zipser D (1989) A learning algorithm for continually running fully recurrent neural networks. Neural Computation, 1, 270–280
Wray J, Green GGR (1994) Calculation of the Volterra kernels of non-linear dynamic systems using an artificial neural 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. Limnol., 29(4), 275–289
Zar JH (1984) Biostatistical Analysis. Prentice-Hall International, Inc, New Jersey, 718 pp
Zurada JM (1992) Introduction to artificial neural systems. West Publishing Company. New York, 683 p
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Chon, T.S., Park, Y.S., Kwak, I.S., Cha, E.Y. (2006). Non-linear Approach to Grouping, Dynamics and Organizational Informatics of Benthic Macroinvertebrate Communities in Streams by Artificial Neural Networks. In: Recknagel, F. (eds) Ecological Informatics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-28426-5_10
Download citation
DOI: https://doi.org/10.1007/3-540-28426-5_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28383-6
Online ISBN: 978-3-540-28426-0
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)