서식지 선호도 및 품질 평가 모형의 신뢰성 개선: 한국 고라니의 경우

Abstract

Purpose: Predicting the occurrence of wild species at the landscape level is a crucial task for environmental managers and conservation biologists. Modeling and evaluating wildlife habitat quality based on the Geographic Information Systems (GIS) technique provides new opportunities for enhancing model predictability.<BR>Methods: We developed a GIS-based habitat suitability index (HSI) model for Korean water deer (Hydropotes inermis argyropus) in Chungnam province, Korea, where they are a significant cause of damage to vegetables and crops. The model was based primarily on logistic regression analysis and was used to assess the impact of multiple variables, such as landscape patterns and structures, topographic characteristics, and human disturbance, on habitat suitability for the species. After developing the model, we employed it to produce a habitat-suitability map.<BR>Results: The HSI model yielded a p-value of 0.339 (χ² = 9.033) and a 74.0% correct prediction rate for the overall predicted data. The model also demonstrated that 42.4% of the province is covered in poor-quality habitat, while Korean water deer prefer fair-quality habitats (mean HSI = 0.50). In addition, the observation probability increased when HSI values rose, meaning that the model has good predictive power.<BR>Conclusion: Associations between landscape patterns and habitat requirements could be utilized to build accurate, easy-to-apply, predictive models for the habitats of wild species. The HSI model and habitat-suitability map provide solid foundations on which to build effective wildlife management and Agri-Environment policies with local governments to conserve endangered species.