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Use of classification tree methods to study the habitat requirements of tench (Tinca tinca) (L., 1758) | ||
| Caspian Journal of Environmental Sciences | ||
| مقاله 6، دوره 8، شماره 1، فروردین 2010، صفحه 55-63 اصل مقاله (272.72 K) | ||
| نویسندگان | ||
| R. Zarkami* 1؛ p. Guethlas2؛ N. De Pauw3 | ||
| 1Department of Environmental Sciences, Faculty of Natural Resources, University of Guilan, P.O. Box 1144, Sowmeh Sara, Guilan, Iran. | ||
| 2Department of Applied Ecology, Ghent University, J. Plateaustraat 22, B-9000 Gent | ||
| 3Department of Applied Ecology, Ghent University, J. Plateaustraat 22, B-9000 Gent. * Corresponding author's E-mail: rzarkami2002@yahoo.co.uk | ||
| چکیده | ||
| Classification trees (J48) were induced to predict the habitat requirements of tench (Tinca tinca). 306 datasets were used for the given fish during 8 years in the river basins in Flanders (Belgium). The input variables consisted of the structural-habitat (width, depth, gradient slope and distance from the source) and physic chemical (pH, dissolved oxygen, water temperature and electric conductivity), and the output ones were the abundance and presence/absence of tench. To find the best performance model, a three-fold cross validation was applied on the entire dataset. In order to evaluate the model stability, the dataset were remixed in 5 times, obtaining in total 15 different model training and validation events. The effect of pruning on the reliability and model complexity was tested in each subset. The performance evaluation was based on a combination of the number of Correctly Classified Instances (CCI) and Kappa statistic. The results showed that the predictive performance evaluation was suitable, confirming the reliability of classification trees methods. The overall average of CCI and Kappa for the prediction of tench was obtained 75.8% and 0.53. When analyzing the ecological relevance of classification trees, it seemed that the structural-habitat variables were important predictors compared to physic chemical variables. REFERENCES Belpaire, C., Smolders, R., Vanden A.I., Ercken, D., Breine, J., Van Thuyne, G. and Ollevier, F. (2000) An Index of Biotic Integrity characterizing fish populations and the ecological quality of Flandrian water bodies, Hydrobiologia. 434, 17-33. Breiman, L., Friedman, J.H., Olshen, R.A. and Stone, C.J. (1984) Classification and regression trees, Pacific Grove, Wadsworth. Brosse, S., Guegan, J.F., Tourenq, J.N. and Lek, S. (1999) The use of artificial neural network to assess fish abundance and spatial occupancy in the littoral zone of a mesotrophic lake, Ecol. 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(2002) Development and application of predictive river ecosystem models based on classification trees and artificial neural networks. Ecological informatics, Understanding ecology by biologically inspired computation. (ed. Recknagel), Springer, Berlin: 432 p. Goethals, P. L. M. (2005) Data driven development of predictive ecological models for benthic macroinvertebrates in rivers. PhD thesis, Ghent University, Belgium, 377 p. Gonzalez, G., Maze, R.A., Dominguez, J. and Pena, J. C. (2000) Trophic ecology of Tinca Tinca in two different habitats in North-West of Spain, Cybium. 24, 123-138. Gray, R.H. and Daule, D.D. (2001) Some life history characteristics of cyprinids in the Handford reach, mid-Columbia River, North. Sci. 75, 122-136. Grenouillet, G.l., Pont, D. and Seip, K.L. (2002) Abundance and species richness as a function of food resources and vegetation structure: juvenile fish assemblages in rivers, Ecography. 25, 641-650. Harig, A.L. and Bain, M.B. (1998) Defining and restoring biological integrity in wilderness Lakes, Ecol. Appl. Sci. 8, 71-87. Kaastra, I. and Boyd, M. S. (1995) Forecasting future trading volume using neural networks, Journal of Future Markets. 15, 953-970. Kerle, F., Zollner, F., Kappus, B., Marx, W. and Giesecke, J. (2001) Fish habitat and vegetation modelling in floodplains with CASIMIR. CFR project report 13, IWS, University of Stuttgart, 75 p. Manel, S., Williams, H.C. and Ormerod, S.J. (2001) Evaluating presence-absence models in Ecology: the need to account for prevalence, J. Appl. Ecol. 38, 921-931. Olden, J.D. and Jackson, D.A. (2002) A comparison of statistical approaches for modeling fish species distributions, Freshwater Biol. 47, 1976-1995. Quinlan, J.R. (1986) Induction of decision trees, Mach. Lear. 1(1), 81-106. Quinlan, J.R. (1993) C4.5: program for machine learning. Morgan Kaufmann publishers, San Francisco, 302 p. Pereze Regadera, J.J., Gallardo, J.M., Ceballos, E.G. and Garcia, J.C.E. (1994) Model development for determination of final preferenda in freshwater species application in tench (Tinca tinca). Polish Arch. Hyd. 42, 27-34. Ricciardi, A. & Rasmussen J.B. (1999) Extinction rates of North American freshwater fauna, Cons. Biol. 13, 1220- 1222. Richter, B.D., Braun, D.P., Mendelson, M.A. and Master, L.L. (1997) Threats to imperiled freshwater fauna, Cons. Biol. 11, 1081-1093. Rowe, D.K. (2004) Potential effects of tench (Tinca tinca) in New Zealand freshwater ecosystems, NIWA Project: BOP04221. Vainikka, (2003) Tench, Tinca tinca L. www.cc.jyu.fi/~ansvain/suutari/inde x.html Yilmaz, F. (2002) Reproductive biology of the tench (Tinca tinca) (L., 1758) inhabiting Porsuk Dam Lake (Kutahya, Turkey). Fish. Res. 55, 313- 317. Witten, J.H., and Frank, E. (2000) Data mining: practical machine learning tools and techniques with Java implementations, San Francisco: Morgan Kaufman Publishers, 369 p. | ||
| کلیدواژهها | ||
| tench (Tinca tinca)؛ classification tree models (J48)؛ physical؛ chemical variables؛ structural؛ habitat variables؛ Flanders river basins | ||
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