Assessing nonlinearity in fish habitat preference of Japanese medaka (Oryzias latipes) using genetic algorithm-optimized habitat prediction models

Abstract

The present study assessed nonlinearity in habitat preferences of Japanese medaka (Oryzias latipes) using genetic algorithm-optimized fuzzy habitat preference models incorporating the environmental factors of water depth (depth), current velocity (velocity), lateral cover ratio (cover), and percent vegetation coverage (vegetation). A linear relationship appeared between habitat preferences for cover and vegetation, which suggest the independent relationship between two factors. The habitat preference for velocity was found to have a nonlinear relationship with those for cover and vegetation, which appeared as different preference curves evaluated by the single-factor model. The habitat preference curves were affected by interactions between the environmental factors, especially those with nonlinear relationships, which resulted in different predictions of spatial distribution of the fish. Based on the present results, the use of a multiplex modeling approach, as applied in this study, would be appropriate to achieve better prediction accuracy, thus leading to more reliable habitat evaluation.