Environmental Analysis Health and Toxicology

The Korean Society of Environmental Health and Toxicology (환경독성보건학회)
권호 표지

Mathematical Evaluation of Response Behaviors of Indicator Organisms to Toxic Materials

지표생물의 독성물질 반응 행동에 대한 수리적 평가

  • Chon, Tae-Soo (Department of Biological Sciences, Pusan National University) ;
  • Ji, Chang-Woo (Department of Biological Sciences, Pusan National University)
  • Published : 2008.12.30
Download PDF
⟨ Previous Next ⟩

Abstract

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.

Keywords

References

  1. Alados CL, Escos JM and Emlen JM. Fractal structure of sequential behaviour patterns: an indicator of stress, Anim Behav 1996; 51: 437-443 https://doi.org/10.1006/anbe.1996.0040
  2. Alt W. Correlation analysis of two-dimensional locomotion paths. In: Alt, W., Hoffmann, G. (Eds.), Biological motion, Lecture Notes in Biomathematics 1990: 254-268
  3. Alt W and Hoffmann G. Biological motion, Lecture Notes in Biomathematics 1990
  4. Alarm MK and Maughan OE. The effects of malathion, diazinon, and various concentrations of zinc, copper, nickel, lead, iron, and mercury on fish, Biol Trace Elem Res 1992; 34: 225-236 https://doi.org/10.1007/BF02783678
  5. Bell WJ and Kramer E. Search and anemotactic orientation of cockroach, J of Insect Physiol 1979; 25: 631- 640 https://doi.org/10.1016/0022-1910(79)90112-4
  6. Bell WJ and Kramer E. Sex pheromone-stimulated orientation of the American Cockroach on a servosphere apparatus, J of Chemical Ecol 1980; 6: 287-295 https://doi.org/10.1007/BF01402908
  7. Bell WJ and Tobin RT. Orientation to sex pheromone in the American cockroach: analysis of chemo-orientation mechanisms, J of Insect Physiol 1981; 27: 501-508 https://doi.org/10.1016/0022-1910(81)90036-6
  8. Berg HC. Random walks in biology, Trends Neurosci 1983
  9. Blubaum-Gronau E, Hoffmann M, Spieser OH and Krebs F. The 'Koblenz behavioral fish test', an automated biotest based on the video processing system $BehaviQunat^{\circledR}$ for monitoring of water quality, Schrifenreihe des Vereins fur Boden-, Wasser- und Lufthygiene 1994; 93: 87-117
  10. Borcherding J. Ten years of practical experience with the Dreissena-Monitor, a biological early warning system for continuous water quality monitoring, Hydrobiologia 2006; 556: 417-426 https://doi.org/10.1007/s10750-005-1203-4
  11. Burridge LE, Haya K, Waddy SL and Wade J. The lethality of anti-sea lice formulations $Salmosan^{\circledR}$ (Azamethiphos) and $Excis^{\circledR}$ (Cypermethrin) to stage IV and adult lobsters (Homarus americanus) during repeated short-term exposures, Aquaculture 2000; 182: 27-35 https://doi.org/10.1016/S0044-8486(99)00251-3
  12. Busvin RJ. A critical review of the techniques for testing insecticides, Commonwealth Agric Bureau Lond 1971;263-288
  13. Ceron JJ, Ferrnado MD, Sancho E, Gutierrez-Panizo C and Andreu-Monliner E. Effects of diazinon exposure on cholinesterase activity in different tissues of European eel (Anguilla anguilla), Ecotoxicol and Enviro Safety 1996; 35: 222-225
  14. Chon TS, Park YS, Moon KH and Cha EY. Patternizing communities by using an artificial neural network, Ecological Modeling 1996; 90: 69-78 https://doi.org/10.1016/0304-3800(95)00148-4
  15. Chon TS, Park YS and Ross MH. Activity of German cockroach, Blattella germanica (L.) (Orthoptera : Blattellidae), at different microhabitats in semi-natural conditions when treated with sublethal doses of pesticides, J of Asia-Pacific Entomol 1 1998; 99-107 https://doi.org/10.1016/S1226-8615(08)60011-4
  16. Chon TS, Park YS, Moon H and Cha EY. Patternizing communities by using an artificial neural network, Ecological Modeling 1999; 90: 69-78 https://doi.org/10.1016/0304-3800(95)00148-4
  17. Chon TS, Park YS and Park JH. Determining temporal pattern of community dynamics by using unsupervised learning algorithms, Ecological Modeling 2000; 132: 151-166 https://doi.org/10.1016/S0304-3800(00)00312-4
  18. Chon TS, Park YS, Park JY, Choi S-Y, Kim KT and Cho EC. Implementation of computational methods to pattern recognition of movement behavior of Blattella germanica (Blattaria: Blattellidae) treated with $Ca^{2+}$ signal inducing chemicals, Appl Entomol Zool 2004; 39: 79-96 https://doi.org/10.1303/aez.2004.79
  19. Chon TS, Chung NM, Kwak I-S, Kim J-S, Koh S-C, Lee SC, Lee S-K, Leem J-B and Cha EY. Movement behaviour of Medaka (Oryzias latipes) in response to sublethal treatments of diazinon and cholinesterase activity in semi-natural conditions, Environ Monit Assess 2005;101: 1-21
  20. Colgan PW. Quantitative ethology, Wiley, New York. 1978. Daubechie I. The wavelet transform, time frequency localization and signal analysis, IEEE Trans Inform Theor 1990; 36: 961-1005 https://doi.org/10.1109/18.57199
  21. DeAngelis DL and Gross LJ. Individual-based models and approaches in ecology: populations, communities and ecosystems, Chapman and Hall, New York. 1992
  22. Dethier VG, Browne LB and Smith CN. he designation of chemicals in terms of the responses they elicit from insects, J Econ Entomol 1960; 53; 134-136 https://doi.org/10.1093/jee/53.1.134
  23. Dimopoulos I, Chronopoulos J, Chronopoulou-Sereli A and Lek S. Neural network models to study relationships between lead concentration in grasses and permanent urban descriptors in Athens city (Greece), Ecological Modeling 1999; 120: 157-165 https://doi.org/10.1016/S0304-3800(99)00099-X
  24. Drai D, Benjamini Y and Golani I. Statistical discrimination of natural modes of motion in rat exploratory behavior. J of Neuro, Methods 2000; 96: 119-131 https://doi.org/10.1016/S0165-0270(99)00194-6
  25. Dutta H, Marcelino J and Richmonds Ch. Brain acetylcholinesterase activity and optomotor behavior in bluegills, Lepomis macrochirus exposed to different concentrations of diazinon, Arch Intern Physiol Biochim Biophys 1992; 100: 331-334 https://doi.org/10.3109/13813459209000721
  26. Eaton AD, Clesceri LS and Greenberg AE. Standard methods for examination of water and wastewater. 19th Ed. American Public Health Association, Washington, DC. 1995
  27. Ermentrout GB and Edelstein-Keshet L. Cellular automata approaches to biological modeling, J Theor Biol 1993;160: 97-133 https://doi.org/10.1006/jtbi.1993.1007
  28. Forget J, Pavillon JF, Menasria MR and Bocquene G. Mortality and $LC_{50}$ values for several stage of the marine Copepod Tigriopus brevicornis (Muller) exposed to the metal arsenic and cadmium and the pesticides atrazine, carbofuran, dichlorvos, and malathion, Ecotoxicol Environ Saf 1998; 40: 239-244 https://doi.org/10.1006/eesa.1998.1686
  29. Foster SP and Harris MO. Behavioral manipulation methods for insect pest-management, Annual Review of Entomol 1997; 42: 123-146 https://doi.org/10.1146/annurev.ento.42.1.123
  30. Gerhardt A, Carlsson A, Ressemann C and Stich KP. New online biomonitoring system for Gammarus pulex (L.) (Crustacea): In situ test below a copper effluent in south Sweden, Evnrion Sci Technol. 1998; 32: 150-156 https://doi.org/10.1021/es970442j
  31. Gerhardt A. Janssens de Bistoven L and Penders E. Quality control of drinking water from the river Rhine with multispecies freshwater biomonitor, Aqua Ecosystem Health & Management 2003; 6: 159-166 https://doi.org/10.1080/14634980301466
  32. Gerhardt A, Janssens de Bistoven L and Soares AM. Effect of acid mine drainage and acidity on the activity of Choroterpes picteti (Ephemeroptera: Leptophlebiidae), Environ Contam and Toxicol 2005; 48: 450-458 https://doi.org/10.1007/s00244-003-0222-2
  33. Grimm V and Railsback SF. Individual-Based Modeling and Ecology, Princeton University Press: Princeton, NJ, 2005
  34. Gunatilaka A and Diehl P. A Brief Review of Chemical and Biological Continuous Monitoring of Rivers in Europe and Asia. In: 'Biomonitors and Biomarkers as Indicators of Environmental Change, Volume II', Eds. Butterworth, Gunatilaka & Gonsebatt, Plenum Press, New York. 2000
  35. Haykin S. Neural Networks. Macmillian College Publishing Company, New York. 1994
  36. Hecht-Nielsen, R. Neurocomputing. Addison-Wisley, Sydney. 1990
  37. Hodgson E and Levi PE. Pesticides: an important but underused model for the environmental health sciences, Environ Health Perspect 1996; 104: 97-106 https://doi.org/10.2307/3432700
  38. Hogeweg P. Cellular automata as a paradigm for ecological modeling, Appl Math Comp 1998; 27: 81-100 https://doi.org/10.1016/0096-3003(88)90100-2
  39. Ji CW, Lee SH, Kwak I-S, Cha EY, Lee S-K and Chon T-S. Computational analysis of movement behaviors of medaka (Oryzias latipes) after the treatments of copper by using fractal dimension and artificial neural networks. In: Kungolos AG, Brebbia CA, Samaras CP, Popov V (Eds.), Environ. Toxicol., WIT Press, Southampton and Mykonos 2006; 93-107
  40. Ji CW, Lee SH, Choi K-H, Kwak I-S, Lee SG, Cha EY, Lee S-K and Chon T-S. Monitoring of movement behaviors of chironomid larvae after exposure to diazinon by using fractal dimension and Self-Organizing Map, Ecological Dynamics 2008; 2: 1-12
  41. Johnson AR, Milne BT and Wiens JA. Diffusion in fractal landscapes: simulations and experimental studies of tenebrionid beetle movements, Ecology 1992; 73: 1968-1983 https://doi.org/10.2307/1941448
  42. Kim C-K, Kwak I-S, Cha E-Y and Chon T-S. Implementation of wavelets and artificial neural networks to detection of toxic response behavior of chironomids (Chironomidae: Diptera) for water quality monitoring, Ecological Modelling 2006; 195: 61-71 https://doi.org/10.1016/j.ecolmodel.2005.11.010
  43. Kohonen T. Self-organized formation of topologically correct feature maps, Biol Cybern, 1982
  44. Kohonen, T. Self-organizing maps, Springer, Berlin, 2001
  45. Kramer KJM, Jenner HA and De Zwart D. The valve movement response of mussels: a tool in biological monitoring, Hydrobiologia 1989; 433-443
  46. Kwak I-S, Chon T-S, Kang HM, Chung NI, Kim JS, Koh SC, Lee SK and Kim, YS. Pattern recognition of the movement tracks of medaka (Oryzias latipes) in response to sub-lethal treatments of an insecticide by using artificial neural networks, Environ Pollution 2002; 120:671-681 https://doi.org/10.1016/S0269-7491(02)00183-5
  47. Lek S, Delacoste M, Baran P, Dimopoulos I, Lauga J and Aulagnier S. Application of neural networks to modelling nonlinear relationships in Ecology, Ecological Modelling 1996; 90: 39-52 https://doi.org/10.1016/0304-3800(95)00142-5
  48. Lek S and Guegan J-F, Artificial neural networks. Springer-Verlag. Berlin, Heidelberg, New York, 2000
  49. Lemly AD and Smith RJ. A behavioral assay for assessing effects of pollutants of fish chemoreception, Ecotoxicol Environ Saf 1986; 11: 210-218 https://doi.org/10.1016/0147-6513(86)90065-5
  50. Lippmann RP. An Introduction to Computing with Neural Nets, IEEE ASSP Magazine 1987; 4-22
  51. Lorenz EE, Archeski RD, Flerov BA and Kozlovskaya VI. Behavioral indicators of sublethal toxicity in rainbow trout, Arch Environ Contam Toxicol 1990; 19: 380-385 https://doi.org/10.1007/BF01054982
  52. Matsumura F and Beeman RW. Biochemical and physiological effects of chlordimeform, Environ Health Perspect 1976; 14: 71-82 https://doi.org/10.2307/3428364
  53. Moldaenke C. Daphnia Toximeter, BBE und J. Uley Software entwicklung 1997. Nimkerdphol K and Nakagawa M. Effect of sodium hypochlorite on zebrafish swimming behavior estimated by fractal dimension analysis, J Biosci Bioeng 2008; 105:486-492 https://doi.org/10.1263/jbb.105.486
  54. Norton S. Behavioral toxicology: a critical approach. In: Witschi, HR (Ed.), The scientific basis of toxicity assessment. North Holland Biomedical Press, Amsterdam, 1980
  55. OECD Guideline for testing of chemicals (Fish, Acute Toxicity Test), Adopted by the council on 17th, 1992
  56. Oppenheim AV and Schafer RW. Digital signal processing, Prentice Hall, 1975
  57. Park Y-S, Chung N-I, Choi K-H, Cha EY, Lee S-K and Chon T-S. Computational characterization of behavioral response of medaka (Oryzias latipes) treated with diazinon, Aqua Toxicol 2005; 71: 215-228 https://doi.org/10.1016/j.aquatox.2004.11.002
  58. Park YS, Song MY, Park YC, Oh KH, Cho E and Chon TS. Community patterns of benthic macroinvertebrates collected on the national scale in Korea, Ecological Modelling 2007; 203: 26-33 https://doi.org/10.1016/j.ecolmodel.2006.04.032
  59. Rakowski G, Sorensen KA and Bell WJ. Response of dermestid beetles, Dermestes maculates, to puffs of aggregation pheromone extract [Coleoptera: Dermestidae], Entomol Gener 1989; 14: 211-215 https://doi.org/10.1127/entom.gen/14/1989/211
  60. Recknagel F. Ecological Informatics. Springer-Verlag, Berlin, Heidelberg, New York. 2002
  61. Rioul O and Vetterli M. Wavelets and signal processing, IEEE Signal Process 1991; 8: 14-38
  62. Roberts SK. Circadian activity in cockroaches. I-The freerunning rhythm in steady state, J Cell Comp Physiol 1960; 55: 99-110 https://doi.org/10.1002/jcp.1030550112
  63. Roberts SK. Circadian activity rhythms in cockroaches. IIEntrainment and phase-shifting, J Cell Comp Physiol 1962; 59: 175-86 https://doi.org/10.1002/jcp.1030590210
  64. Sancho E, Ferrando MD and Andreu E. Sublethal effects of an organophosphate insecticide on the european eel, Anguilla Anguilla, Ecotoxicol Environ Saf 1997; 36: 57-65 https://doi.org/10.1006/eesa.1996.1488
  65. Saunders DS. Insect Clocks. Pergamon Press, Oxford, 1982
  66. Scardi M and Harding LW. Developing an empirical model of phytoplankton primary production: a neural network case study, Ecological Modelling 1999; 120: 213-223 https://doi.org/10.1016/S0304-3800(99)00103-9
  67. Schal C, Tobin TR, Surber JL, Vogel G, Tourtellot MK, Leban RA, Sizemore R and Bell WJ. Search strategy of sex pheromone-stimulated male German cockroaches, J of Insect Physiol 1983; 29: 575-579 https://doi.org/10.1016/0022-1910(83)90023-9
  68. Scharstein H. Paths of carabid beetles walking in the absence of orienting stimuli and the time structure of their motor output. In: Alt W and Hoffmann G (Eds.). Biological Motion. Lecture Notes in Biomathematics 89. Springer-Verlag, Berlin, 1989
  69. Son KH, Ji CW, Park Y-M, Cui Y, Wang HZ, Chon T-S and Cha EY. Recurrent Self-Organizing Map implemented to detection of temporal line-movement patterns of Lumbriculus variegates (Oligochaeta: Lumbriculidae) in response to the treatments of heavy metal. In: Kungolos AG, Brebbia CA, Samaras CP, Popov V (Eds.), Environ. Toxicol., WIT Press, Southampton and Mykonos, 2006; 93-107
  70. Sorensen KA and Bell WJ. Orientation responses of an isopod to temporal changes in relative humidity: simulation of a "humid patch" in a "dry habitat", J Insect Physiol 1986; 32: 51-57 https://doi.org/10.1016/0022-1910(86)90157-5
  71. Sovan L and Guegan J. Artificial Neural Networks. Springer-Verlag. Berlin, Heidelberg, New York, 2000
  72. Staaks G. Experimental studies on temperature preference behaviour of juvenile cyprinids, Limnologica 1996; 26:165-177
  73. Staaks G, Kirschbaum F and Willot P. Experimental studies on thermal behaviour and diurnal activity rhythms of juvenile European sturgeon (Acipenser sturio), J Appl Ichthyol 1999; 15: 243-247 https://doi.org/10.1111/j.1439-0426.1999.tb00243.x
  74. Tchernichovski O, Benjamini Y and Golani I. The dynamics of long-term exploration in the rat. Part I. A phase-plane analysis of the relationship between location and velocity, Biol Cybern 1998; 78: 423-432 https://doi.org/10.1007/s004220050446
  75. Tourtellot MK, Collins RD and Bell WJ. The problem of movelength and turn definition in analysis of orientation data, J Theor Biol 1991; 150: 287-297 https://doi.org/10.1016/S0022-5193(05)80428-X
  76. Untersteiner H, Kahapka J and Kaiser H. Behavioural response of the cladoceran Daphnia magna to sublethal Copper stress-validation by image analysis, A Toxicol 2003; 65: 435-442 https://doi.org/10.1016/S0166-445X(03)00157-7
  77. Varsta M, Heikkonen J and Millan JDR. Context learning with the self-organizing map. In Proc. Of Workshop on Self-Organizing Maps, 197-202. Helsinki University of Technology, 1997
  78. Wiens JA, Crist TO and Milne BT. On quantifying insect movements, Environ Entomol 1993; 22: 709-715 https://doi.org/10.1093/ee/22.4.709
  79. Wiens JA, Crist TO, With KA and Milne, BT. Fractal patterns of insect movement in microlandscape mosaics, Ecology 1995; 76: 663-666 https://doi.org/10.2307/1941226
  80. White J, Tobin TR and Bell WJ. Local search in the house fly Musca domestica after feeding on sucrose, J of Insect Physiol 1984; 30: 477-487 https://doi.org/10.1016/0022-1910(84)90028-3
  81. Whitmore AV, Bignell DE. Drinking behaviour in the cockroach Periplaneta americana: an automated method for the measurement of periodicity and uptake, J of Insect Physiol 1990; 36: 103-109 https://doi.org/10.1016/0022-1910(90)90180-N
  82. Wolfram S. Cellular Automata and Complexity: Collected Papers. Addison-Wesley, Reading, Massachusetts. 1994
  83. Ye S and Bell WJ. A simple video position-digitizer animal movement patterns, J Neuro Methods 1991; 37: 215-225 https://doi.org/10.1016/0165-0270(91)90027-W
  84. Zar JH. Bioststistical analysis. Prentice-Hall International, Englewood Cliffs. 1984
  85. Zurada JM. Introduction to artificial neural systems. West Publishing Company, New York, 1992