Integrated Prediction of Potato Cyst Nematodes’ (Globodera rostochiensis and Globodera pallida) Potential Distribution and Risk Regions: Using China as a Case Study

Abstract Global potato trade has resulted in the spread of potato cyst nematodes (PCNs) worldwide, which causes significant agricultural and economic loss. Predicting the potential habitats and risk regions for PCNs is critical for management and biosecurity strategies. However, building such prediction models is challenged by the uncertainty of the occurrence data. This research aimed to mitigate the effect of the deficiency of data and build a reliable prediction model of PCNs. The model proposed a combination of fuzzy logic and Maxent modelling enabling the forecasting of the integrated distribution of PCNs. Firstly, the niche similarity between two PCN species was tested by a fuzzy generalised linear model. Then, an integrated dataset was employed to calibrate and evaluate the Maxent model. Results showed that the model constructed on the integrated dataset possessed higher accuracy (Boyce index 0.917) compared to that of individual datasets. After verifying the prediction with the recent incursions in China, the prediction was in accord with actual presence records, which provided further evidence to prove the accuracy of this model. The prediction illustrated that 39% of the land surface in China was suitable for PCNs. The high-risk regions occupied more than half of the cultivated lands, including 66% of the potato-producing areas. In conclusion, the proposed modelling procedure with an integrated dataset can provide an informative reference for countries facing the uncertainty of PCNs’ occurrence to conduct an adequate risk assessment. The integrated prediction result can support policymakers in simultaneously managing both PCN species.