Characterizing the Spatial Distribution of Eragrostis Curvula (Weeping Lovegrass) in New Jersey (United States of America) Using Logistic Regression

Abstract

The increasing spread of invasive plants has become a critical driver of global environmental change. Once established, invasive species are often impossible to eradicate. Therefore, predicting the spread has become a key element in fighting invasive species. In this study, we examined the efficiency of a logistic regression model as a tool to identify the spatial occurrence of an invasive plant species. We used Eragrostis curvula (Weeping Lovegrass) as the dependent variable. The independent variables included temperature, precipitation, soil types, and the road network. We randomly selected 68 georeferenced points to test the goodness of fit of the logistic regression model to predict the presence of E. curvula. We validated the model by selecting an additional 68 random points. Results showed that the probability to successfully predict the presence of E. Curvula was 82.35%. The overall predictive accuracy of the model for the presence or absence of E. Curvula was 80.88%. Additional tests including the Chi-square test, the Hosmer–Lemeshow (HL) test, and the area under the curve (AUC) values, all indicated that the model was the best fit. Our results showed that E. curvula was associated with the identified variables. This study suggests that the logistic regression model can be a useful tool in the identification of invasive species in New Jersey.