Abstract
Prediction of the spaces used by animals is an important component of wildlife management, but requires detailed information such as animal visit and occupy in a short span of the target species. Computational simulation is often employed as an effective and economical approach. In this study, the visit and occupy of sika deer (Cervus nippon) during the plant growing season were predicted using a virtual ecological approach. A virtual ecological model was established to predict the visit and occupy of sika deer based on the indices of their food resources. The simulation results were validated against data collected from a camera trapping system. The study was conducted from May to November in 2018 in the northern Kanto region of Japan. The predictive performance of the model using the kernel normalized difference vegetation index (kNDVI) was relatively high in the earlier season, whereas that of the model using landscape structure was relatively low. The predictive performance of the model using combination of the kNDVI and landscape structure was relatively high in the later season. Unfortunately, visit and occupy of sika deer could not predict in November. The use of both models, depending on the month, achieved the best performance to predict the movements of sika deer.