Prediction ability and sensitivity of artificial intelligence-based habitat preference models for predicting spatial distribution of Japanese medaka (Oryzias latipes)

Model application niche analysis: Assessing the transferability and generalizability of ecological models

The use of models by ecologists and environmental managers, to inform environmental management and decision-making, has grown exponentially in the past 50 years. Due to logistical, economical, and theoretical benefits, model users frequently transfer preexisting models to new sites where data are scarce. Modelers have made significant progress in understanding how to improve model generalizability during model development. However, models are always imperfect representations of systems and are constrained by the contextual frameworks used during their development. Thus, model users need better ways to evaluate the possibility of unintentional misapplication when transferring models to new sites. We propose a method of describing a model's application niche for use during the model selection process. Using this method, model users synthesize information from databases, past studies, and/or past model transfers to create model performance curves and heat maps. We demonstrated this method using an empirical model developed to predict the ecological condition of plant communities in riverine wetlands of the Appalachian Highland physiographic region, U.S.A. We assessed this model's transferability and generalizability across (1) riverine wetlands in the contiguous U.S.A., (2) wetland types in the Appalachian Highland physiographic region, and (3) wetland types in the contiguous U.S.A. With this methodology and a discussion of its critical steps, we set the stage for further inquiries into the development of consistent and transparent practices for model selection when transferring a model.

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

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.

DO ABSENCE DATA MATTER WHEN MODELLING FISH HABITAT PREFERENCE USING A GENETIC TAKAGI-SUGENO FUZZY MODEL?

Information on species distributions is of key importance when designing management plans for a target species or ecosystem. This paper illustrates the effects of absence data on fish habitat prediction and habitat preference evaluation using a genetic Takagi-Sugeno fuzzy model. Three independent data sets were prepared from a series of fish habitat surveys conducted in an agricultural canal in Japan. To quantify the effects of absence data, two kinds of abundance data (entire data and presence data) were used for developing a fuzzy habitat preference model (FHPM). As a result, habitat preference curves (HPCs) obtained from presence data resulted in similar HPCs between the three data sets, while those obtained from entire data slightly differed according to the data sets. The higher generalization ability of the FHPMs obtained from presence data supports the usefulness of presence data for better extracting the habitat preference information of a target species from field observation data.