Association of Leopard Cat Occurrence with Environmental Factors in Chungnam Province, South Korea

SIMPLE SUMMARY: Understanding how environmental factors influence wildlife species is important for effective management. This study was conducted to address leopard cats’ distribution according to various environmental factors across Chungnam Province, South Korea, using two analytical approaches: classical statistical (i.e., logistic regression) and machine learning (i.e., boosted regression trees) methods. Results identified that higher leopard cat distribution was observed in the areas with lower elevation, closer to roads and water sources, and lower human population densities. The results also show that two methods can be used in a complementary manner for effective wildlife management. ABSTRACT: This study was conducted to investigate the association of leopard cat (Prionailurus bengalensis) occurrences and environmental factors in Chungnam Province, South Korea, using two different analytical approaches for binomial responses: boosted regression trees and logistic regression. The extensive field survey data collected through the Chungnam Biotope Project were used to model construction and analysis. Five major influential factors identified by the boosted regression tree analysis were elevation, distance to road, distance to water channel/body, slope and population density. Logistic regression analysis indicated that distance to forest, population density, distance to water, and diameter class of the forest were the significant explanatory variables. The results showed that the leopard cats prefer the areas with higher accessibility of food resources (e.g., abundance and catchability) and avoid the areas adjacent to human-populated areas. The results also implied that boosted regression and logistic regression models could be used in a complementary manner for evaluating wildlife distribution and management.