Modeling the Potential Distribution of Pine Forests Susceptible to 'Sirex Noctilio' Infestations in Mpumalanga, South Africa

Abstract

Reducing the impact of the siricid wasp, 'Sirex noctilio' is crucial for the future productivity and sustainability of commercial pine resources in South Africa. In this study we present a machine learning model that serves as a spatial guide and allows forest managers to focus their existing detection and monitoring efforts on key areas and proactively adopt the most appropriate course of intervention. We implemented the random forest model within a spatial framework to determine which pine forests in Mpumalanga are highly susceptible to 'S. noctilio' infestations. Results indicate that a majority (63%) of pine forest plantations located in Mpumalanga have a high susceptibility (>70%) to 'S. noctilio' infestation. A KHAT value of 0.84 and F measures above 0.87 indicate that the random forest model is a robust classifier that produces accurate results. Additionally, the use of the backward variable selection method enabled us to simplify the random forest modeling process and identify the minimum number of explanatory variables that offer the best discriminatory power and help in the empirical interpretation of the final random forest model. Overall, the results show that pine forests that experience stress caused by evapotranspiration and evaporation followed by rainfalls, especially during the summer months are more susceptible to 'S. noctilio' infestations.