Abundance versus presence/absence data for modelling fish habitat preference with a genetic Takagi–Sugeno fuzzy system

Abstract

This study compared the accuracy of fuzzy habitat preference models (FHPMs) and habitat preference curves (HPCs) obtained from the FHPMs in order to assess the effect of two types of data [log-transformed fish population density (LOG) and presence-absence (P/A) data] on the habitat preference evaluation of Japanese medaka (Oryzias latipes). Three independent data sets were prepared for each type of data. The results differed according to the data sets and the types of data used. The HPCs showed a similar trend, whilst the degrees of preference were different. The model accuracy also differed according to the data sets used. Although almost no statistical difference was observed, on average, the P/A-based models showed a better performance according to the threshold-independent performance measures, whilst the LOG-based models showed better performance in predicting absence of the fish. These results can be explained partly from the different shapes of HPCs. This case study of Japanese medaka demonstrated the effect of different types of data on habitat preference evaluation. Further studies should build on the present finding and evaluate the effects of data characteristics such as the size of data sets and the prevalence for better understanding and reliable assessment of the habitat for target species.