The National Institute of Forest Science (NIFoS) of the Korea Forest Service is conducting research on climate suitability using machine learning to predict and swiftly respond to large-scale outbreaks of invasive insect species.

Invasive insects can typically be eradicated in the early stages of introduction due to their limited distribution. However, once they adapt to the domestic climate and spread, effective control becomes difficult, requiring continuous management.

Recent climate change has been expanding pest distribution and increasing the number of generations per year. This has raised concern about the introduction and settlement of new subtropical invasive pests, highlighting the need for a proactive response system.

NIFoS researchers analyzed ecological characteristics and climate data of about 200 invasive insects, including Cryptotermes domesticus and Anoplophora horsfieldii by using machine learning. The analysis predicted that Anoplophora horsfieldii could potentially settle in certain southern coastal areas of South Korea, whereas Cryptotermes domesticus was found to have a lower likelihood of settlement.

The climate suitability data through this research will be used to quantitatively identify common traits among species known for massive outbreaks and to predict the settlement of potential invasive species in Korea. NIFoS plans to further improve prediction accuracy by integrating artificial intelligence (AI) technologies into the current model.