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

The present study aims to clarify the necessity and effectiveness of considering fuzziness in modelling fish habitat preference, and the advantages which would be achieved by considering it. For this purpose, genetic algorithm (GA) optimized habitat preference models under three different levels of fuzzification were compared with regard to prediction ability of the habitat use of Japanese medaka (Oryzias latipes) dwelling in agricultural canals in Japan. Field surveys were conducted in agricultural canals in Japan to establish a relationship between fish habitat preference and physical environments of water depth, current velocity, lateral cover ratio and percent vegetation coverage. The habitat preference models employed for testing the fuzzy-based approach were category model, fuzzy habitat preference model, and fuzzy habitat preference model with fuzzy inputs. All the models were developed at 50 different initial conditions. The effectiveness of the fuzzification in fish habitat modelling was assessed by comparing mean square error and standard deviation of the models, and fluctuation in habitat preference curves evaluated by each model. As a result, the effect of fuzzification appeared as smoother curves and was found to reduce fluctuation in habitat preference curves in proportion to the level of fuzzification. The smooth curves would be appropriate for expressing uncertainty in habitat preference of the fish, by which fuzzy habitat preference model with fuzzy input achieve the best prediction ability among the models. In conclusion, the present study revealed that there are two advantages of fuzzification: reducing fluctuations in habitat preference evaluation and improving prediction ability of the model. Therefore, the consideration of fuzziness would be appropriate for representing fish habitat preference under natural conditions.

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