Abstract
In the field of agriculture, development of evaluation techniques for environmental changes is urgently required for the purpose of finding a balance between growth in agricultural productivity and environmental considerations. The habitat evaluation procedures (HEP) constitute one technique for habitat assessment. While HEP is widely applied to estimate both habitat quality and quantity in an environment, it appears to be necessary to develop an accurate habitat prediction model in order to evaluate environments precisely. In fact, habitat selection by fish is affected by complicated interaction between multiple environmental factors, which makes it difficult to relate physical environments to habitat preference. In the present study, we utilize artificial neural networks (ANNs), which are commonly applied to model complex systems, to predict the habitat selection of Japanese medaka (Oryzias latipes) in agricultural canals. Considering the essential vagueness of fish behavior, fuzzy membership functions are introduced into the input layer, which advances ANN to fuzzy neural network (FNN). In addition, symmetric triangular fuzzy numbers are employed to account for uncertainty in measurement errors and dispersions of physical environment. The FNN model precisely predicts the habitat preference of Japanese medaka in an agricultural canal, and the results show a good agreement between the calculated and observed habitat suitability indices (HSI). Finally, the habitat quality of two different reaches at the same point in time is compared using HEP, with a view of suitable habitat for Japanese medaka.
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References
Buckley JJ, Hayashi Y (1994) Fuzzy neural networks: A survey. Fuzzy Set Syst 66:1–13
Crance JH (1987) Habitat suitability index curves for paddlefish, developed by the delphi technique. N Am J Fish Manage 7:123–130
Fukuda S, Hiramatsu K, Mori M, Shikasho S (2005) Mathematical characterization of fuzziness in fish habitat preference of Japanese medaka (Oryzias latipes) in agricultural canal. Trans JSIDRE 239:43–49 (in Japanese with English abstract)
Fukuda S, Hiramatsu K, Mori M, Shikasho S (2006) Numerical quantification of the significance of aquatic vegetation affecting spatial distribution of Japanese medaka (Oryzias latipes) in an agricultural canal. Landscape Ecol Eng 2:65–80. DOI 10.1007/s11355-006-0030-8
Garson GD (1998) Neural networks: An introductory guide for social scientists. Sage, London
Goh ATC (1995) Back-propagation neural networks for modeling complex systems. Artif Intell Eng 9(3):143–151
Guey JC, Boisclair D, Rioux D, Leclerc M, Lapointe M, Legendre P (2000) Development and validation of numerical habitat models for juveniles of Atlantic salmon (Salmo salar). Can J Fish Aquat Sci 57:2065–2075
Hata K (2002) Perspectives for fish protection in Japanese paddy field irrigation systems. JARQ 36(4):211–218
Hiramatsu K, Shikasho S, Mori K (1995) Application of multi-layered perceptron model to the estimation of chlorinity variation in a tidal river. Trans JSIDRE 178:83–92 (in Japanese with English abstract)
Hiramatsu K, Shikasho S (2004) GA-based model optimization for preference intensity of Japanese medaka fish (Oryzias latipes) to streamflow environments. Paddy Water Environ 2:135–143. DOI 10.1007/s10333-004-0052-5.
Hubert WA, Rahel FJ (1989) Relations of physical habitat to abundance of four nongame fishes in high-plains streams: A test of habitat suitability index models. N Am J Fish Manage 9:332–340
Jakober MJ, McMahon TE, Thurow RF (2000) Diel habitat partitioning by bull charr and cutthroat trout during fall and winter in Rocky Mountain streams. Environ Biol Fish 59:79–89
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, Japan (in Japanese with English description)
Nykänen M, Huusko A (2003) Size-related changes in habitat selection by larval grayling (Thymallus thymallus L.). Ecol Freshw Fish 12:127–133
Oohira Y, Nakano Y, Yuge K (2005) Environmental restoration target of irrigation and drainage channels based on the observation of the aquatic animals. Sci Bull Fac Agr Kyushu Univ 60(2):233–251 (in Japanese with English abstract)
Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagation errors. Nature 323:533–536
Rumelhart DE, McClelland JL (1986) Parallel distributed processing: Explorations in the microstructure of cognition: vol. 1: Foundation. MIT Press, Cambridge, UK
Sato H (2005) Toward preservation of the multi-functional roles of paddy field irrigation. Paddy Water Environ 3:1–3
Tanaka M (1999) Influence of different aquatic habitats on distribution and population density of Misgurnus anguillicaudatus in paddy fields. Jpn J Ichthyol 46(2):75–81 (in Japanese with English abstract)
U.S. Fish and Wildlife Service (1980a) Habitat evaluation procedures (HEP): Ecological service manual 102. Washington, DC, USA
U.S. Fish and Wildlife Service (1980b) Standards for the development of habitat suitability index models: Ecological service manual 103. Washington, DC, USA
Wolter C, Bischoff A (2001) Seasonal changes of fish diversity in the main channel of the large lowland river Oder. Regul Rivers Res Manage 17:595–608. DOI 10.1002/rrr.645
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The authors gratefully thank Mr. Katsuichiro Abe for his intensive assistance and help in the field survey.
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Fukuda, S., Hiramatsu, K. & 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, 119–124 (2006). https://doi.org/10.1007/s10333-006-0039-5
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DOI: https://doi.org/10.1007/s10333-006-0039-5