Consideration of fuzziness: Is it necessary in modelling fish habitat preference of Japanese medaka (Oryzias latipes)?

Ecological Modelling

Volumn 220, Issue 21, 2009, Pages 2877-2884

  Fukuda, Shinji ^a  

^a KYUSHU UNIVERSITY   (Japan)

Author keywords

ECEM 07; Fish habitat; Fuzzification; Fuzzy logic; Fuzzy set theory; Genetic algorithm; Habitat prediction model

Indexed keywords

CATEGORY MODELS; CURRENT VELOCITY; ECEM 07; FIELD SURVEYS; FISH HABITAT; FUZZIFICATION; FUZZIFICATIONS; FUZZY INPUT; HABITAT PREDICTION MODEL; HABITAT PREFERENCE CURVES; HABITAT PREFERENCES; HABITAT USE; INITIAL CONDITIONS; JAPANESE MEDAKA; NATURAL CONDITIONS; ORYZIAS LATIPES; PHYSICAL ENVIRONMENTS; SMOOTH CURVES; STANDARD DEVIATION; VEGETATION COVERAGE; WATER DEPTH;

CANALS; FISH; FUZZY LOGIC; FUZZY SETS; GENETIC ALGORITHMS; HYDRAULIC STRUCTURES; MATHEMATICAL MODELS; MEATS; OCEAN HABITATS;

ECOLOGY;

CYPRINID; ECOSYSTEM MODELING; FIELD SURVEY; FUZZY MATHEMATICS; GENETIC ALGORITHM; HABITAT SELECTION; HABITAT USE; WATER DEPTH;

ASIA; EURASIA; FAR EAST; JAPAN;

ORYZIAS; ORYZIAS LATIPES;

EID: 70350063850     PISSN: 03043800     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ecolmodel.2008.12.025     Document Type: Article

References (42)

1. Adriaenssens V., Goethals P.L.M., and De Pauw N. Fuzzy knowledge-based models for prediction of Asellus and Gammarus in watercourses in Flanders (Belgium). Ecol. Model. 195 (2006) 3-10 10.1016/j.ecolmodel.2005.11.043
2. Ahmadi-Nedushan B., St-Hilaire A., Bérubé M., Robichaud E., Thiémonge N., and Bobée B. A review of statistical methods for the evaluation of aquatic habitat suitability for instream flow assessment. River Res. Appl. 22 (2006) 503-523 10.1002/rra.918
3. Bovee, K.D., Lamb, B.L., Bartholow, J.M., Stalnaker, C.B., Taylor, J., Henriksen, J., 1998. Stream habitat analysis using the instream flow incremental methodology. U.S. Geological Survey, Biological Resources Division Information and Technology Report. USGS/BRD-1998-0004.
4. Brosse S., and Lek S. Modelling roach (Rutilus rutilus) microhabitat using linear and nonlinear techniques. Freshwater Biol. 44 (2000) 441-452
5. Burgman M.A., Breininger D.R., Duncan B.W., and Ferson S. Setting reliability bounds on habitat suitability indices. Ecol. Appl. 11 1 (2001) 70-78
6. Downs P.W., and Gregory K.J. River Channel Management: Toward Sustainable Catchment Hydorosystems (2004), Arnold, London
7. Drake J.M., and Lodge D.M. Forecasting potential distributions of nonindigenous species with a genetic algorithm. Fisheries 31 1 (2006) 9-16
8. Fukuda S., Hiramatsu K., Mori M., and Shikasho S. Mathematical Characterization of fuzziness in fish habitat preference of Japanese medaka (Oryzias latipes) in agricultural canal. Trans. JSIDRE 239 (2005) 43-49 (in Japanese with English abstract)
9. Fukuda S., and Hiramatsu K. Prediction ability of resource selection functions for the estimation of fish habitat preference. Trans. JSIDRE 247 (2007) 113-118
10. Fukuda S., and Hiramatsu K. Prediction ability and sensitivity of artificial intelligence-based habitat preference models for predicting spatial distribution of Japanese medaka (Oryzias latipes). Ecol. Model. 215 (2008) 301-313 10.1016/j.ecolmodel.2008.03.022
11. Fukuda S., and Okushima S. Assessing nonlinearity in fish habitat preference of Japanese medaka (Oryzias latipes) using genetic algorithm-optimized habitat prediction models. JARQ-Jpn. Agric. Res. Quart. 42 2 (2008) 97-107
12. Fukuda S., Hiramatsu K., and Mori M. Fuzzy neural network model for habitat prediction and HEP for habitat quality estimation focusing on Japanese medaka (Oryzias latipes) in agricultural canals. Paddy Water Environ. 4 3 (2006) 119-124 10.1007/s10333-006-0039-5
13. Fukuda S., Hiramatsu K., and Okushima S. An information-theoretic approach for the model selection in habitat preference evaluation of Japanese medaka (Oryzias latipes). Bull. Inst. Trop. Agric. Kyushu Univ. 29 (2006) 105-118
14. Fukuda S., Hiramatsu K., Mori M., and Shikasho S. Numerical quantification of the significance of aquatic vegetation affecting spatial distribution of Japanese medaka (Oryzias latipes) in an agricultural canal. Landscape Ecol. Eng. 2 (2006) 65-80 10.1007/s11355-006-0030-8
15. Gevrey M., Dimopoulos I., and Lek S. Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecol. Model. 160 (2003) 249-264
16. Goldberg D.R. Genetic Algorithms in Search, Optimization and Machine Learning (1989), Addison-Wesley
17. Guay J.C., Boisclair D., Rioux D., and Leclerc M. Development and validation of numerical habitat models for juveniles of Atlantic salmon (Salmo salar). Can. J. Fish. Aquat. Sci. 57 (2000) 2065-2075
18. Guay J.C., Boisclair D., Leclerc M., and Lapointe M. Assessment of the transferability of biological habitat models for Atlantic salmon parr (Salmo salar). Can. J. Fish. Aquat. Sci. 60 (2003) 1398-1408 10.1139/F03-120
19. Hiramatsu K., Fukuda S., and Shikasho S. Mathematical modeling of habitat preference of Japanese medaka for instream water environment using fuzzy inference. Trans. JSIDRE 228 (2003) 65-72 (in Japanese with English abstract)
20. Hiramatsu K., and Shikasho S. GA-based model optimization for habitat preference of Japanese Medaka fish (Oryzias latipes) to streamflow environments. Paddy Water Environ. 2 (2004) 135-143 10.1007/s10333-004-0052-5
21. Iwamatsu T. The Integrated Book for the Biology of the Medaka (1997), University Education Press, Okayama, Japan (in Japanese)
22. Jorde K., Schneider M., Peter A., and Zoellner F. Fuzzy based models for the evaluation of fish habitat quality and instream flow assessment. Proceedings of the 2001 International Symposium on Environmental Hydraulics (2001)
23. Lek S. Uncertainty in ecological models. Ecol. Model. 207 (2007) 1-2
24. Lek S., Belaud A., Baran P., Dimopoulos I., and Delacoste M. Role of some environmental variables in trout abundance models using neural networks. Aquat. Living Resour. 9 (1996) 23-29
25. Lek S., Delacoste M., Baran P., Dimopoulos I., Lauga J., and Aulagnier S. Application of neural networks to modelling nonlinear relationships in ecology. Ecol. Model. 90 (1996) 39-52
26. Lechowicz M.J. The sampling characteristics of electivity indices. Oecologia (Berl.) 52 (1982) 22-30
27. Mastrorillo S., Lek S., Dauba F., and Belaud A. The use of artificial neural networks to predict the presence of small-bodied fish in a river. Freshwater Biol. 38 (1997) 237-246
28. Mizumoto M. Realization of PID controls by fuzzy control methods. Fuzzy Sets Syst. 70 (1995) 171-182
29. Morishita, I., Morishita, Y., 1997. Kyosei no shizen-gaku, stream organisms in Japan: how they are affected by Japanese culture and how they express ecological health. Sankaido, Tokyo (in Japanese with English description).
30. Mouton A.S., Schneider M., Depestele J., Goethals P.L.M., and De Pauw N. Fish habitat modelling as a tool for river management. Ecol. Eng. 29 (2007) 305-315 10/1016/j.ecoeng.206.11.002
31. Mouton A.S., Schneider M., Peter A., Holzer G., Müller R., Goethals P.L.M., and De Pauw N. Optimisation of a fuzzy physical habitat model for spawning European grayling (Thymallus thymallus L.) in the Aare river (Thun, Switzerland). Ecol. Model. 215 (2008) 122-132 10.1016/j.ecolmodel.2008.02.028
32. Nykänen M., and Huusko A. Size-related changes in habitat selection by larval grayling (Thymallus thymallus L.). Ecol. Freshwater Fish 12 (2003) 127-133
33. Nykänen M., and Huusko A. Transferability of habitat preference criteria for larval European grayling (Thymallus thymallus). Can. J. Fish. Aquat. Sci. 61 (2004) 185-192 10.1139/F03-156
34. Nykänen M., Huusko A., and Lahti M. Changes in movement, range and habitat preferences of adult grayling from late summer to early winter. J. Fish. Biol. 64 (2004) 1386-1398 10.1111/j.1095-8649.2004.00403
35. Orth D.J., and Maughan O.E. Evaluation of the incremental methodology for recommending instream flows for fishes. Trans. Am. Fish. Soc. 111 (1982) 413-445
36. In: Recknagel F. (Ed). Ecological Informatics: Scope, Techniques and Applications (2006), Springer, Germany
37. Silvert W. Fuzzy indices of environmental conditions. Ecol. Model. 130 (2000) 111-119
38. U.S. Fish and Wildlife Service, 1980. Habitat Evaluation Procedures (HEP): Ecological Service Manual 102. Washington, DC, USA.
39. U.S. Fish and Wildlife Service, 1980. Standards for the Development of Habitat Suitability Index Models: Ecological Service Manual 103. Washington, DC, USA.
40. Vadas R.L., and Orth D.J. Formulation of habitat suitability models for stream fish guilds: do the standard methods work?. Trans. Am. Fish. Soc. 130 (2001) 217-235
41. Van Broekhoven E., Adriaenssens V., De Baets B., and Verdonschot P.F.M. Fuzzy rule-based macroinvertebrate habitat suitability models for running waters. Ecol. Model. 198 (2006) 71-84
42. Yoshioka H. On the effects of environmental factors upon the reproduction of fishes: 2. Effects of short and long day-lengths on Oryzias latipes during spawning season. Bull. Fac. Fish. Hokkaido Univ. 14 3 (1963) 137-151