국립공원에 서식하는 생물종의 분포모델 평가

Abstract

This study aims to provide useful information for the conservation and management of Brachydiplax chalybea flavovittata (Ris, 1911) (Insecta: Odonata), which is a climate-sensitive biological indicator species, and to develop a more accurate and optimal distribution model that aligns with the research objectives. To achieve this, we intend to evaluate two factors that influence the prediction accuracy of the distribution model. In order to compare the prediction accuracy of the models based on biased location data in a specific region and based on the multicollinearity of environmental variables known as bioclimatic variables, the confirmed habitat locations of B. chalybea flavovittata were collected from the GBIF database, and 19 biological climate variables used as environmental variables were collected from the Worldclim database. The bias of the location data was examined using four types of data selected with a certain distance between the location data. The multicollinearity of the environmental variables was assessed using the Variation Inflation Factor (VIF). Preprocessed data were applied to seven species distribution models, and the prediction accuracy of the models was compared and analyzed using three evaluation metrics (Kappa, TSS, AUC). Among machine learning and regression models, seven distribution models were used for comparison. The machine learning models included Boosted Regression Trees (BRT), Random Forest (RF), Support Vector Machine (SVM), Classification and Regression Trees (CART), and MaxEnt, while the regression models included Generalized Linear Models (GLM) and Multivariate Adaptive Regression Splines (MARS). Overall, the prediction accuracy of the models was better explained by the Random Forest (RF) model, which is a machine learning method that better captures the complex relationship between species and environmental variables, compared to other models. The bias of location data, which is more commonly surveyed in easily accessible areas, affected the prediction accuracy in all models, while multicollinearity varied depending on the model. This study is expected to serve as fundamental guidance for developing an optimal species distribution model that aligns with the research objectives of species conservation and it can be utilized as foundational data for effective management of wildlife habitat in national parks.