Machine-Learning Ensemble Model Prediction of Northward Shift in Potato Cyst Nematodes (Globodera Rostochiensis and G. Pallida) Distribution Under Climate Change Conditions

Potato Cyst Nematodes (PCNs) are a significant threat to agriculture and horticulture, having caused substantial damage in many countries. Predicting the future distribution of PCN species is crucial to implementing effective biosecurity strategies, especially given the impact of climate change on pest species invasion and distribution. Machine-Learning (ML), specifically ensemble models, has emerged as a powerful tool in predicting species distributions due to their ability to learn and make predictions based on complex data sets. Thus, this research utilised advanced machine learning techniques to predict the distribution of PCN species under climate change conditions, providing the initial element for invasion risk assessment. We first used Global Climate Models to generate homogeneous climate predictors to mitigate the variation among predictors. Then five machine learning models were employed to build two groups of ensembles, multi-algorithm ensembles (EMA) and single-algorithm ensembles (ESA), and compared their performances. Results indicated that the distribution range of PCNs would shift northward with a decrease in tropical zones and an increase in northern latitudes. However, the total area of suitable regions will not change significantly, occupying 16-20% of the total land surface (18% under current conditions). Also, in this research, the EMA did not always perform better than the ESA, and the ESA of Artificial Neural Network gave the highest performance while being cost-effective. This research alerts policymakers and practitioners to the risk of PCNs’ incursion into new regions. Additionally, this ML process offers the capability to track changes in the distribution of various species and provides scientifically grounded evidence for formulating long-term biosecurity plans for their control.