Prediction of Potential Forest Risk Areas for Phytopythium helicoides in China Under Climate Change Based on Maximum Entropy Modeling
Year: 2026
Type: article
Abstract: Despite the growing threat of Pythium helicoides to forest plantations in China, a nationwide assessment of climatic suitability remains unavailable, limiting the development of preventive strategies. This study applied the Maximum Entropy model combined with geographic information system analysis to predict the potential distribution and suitable habitats of the pathogen across China. The model was constructed using occurrence records from the Global Biodiversity Information Facility and published literature, together with bioclimatic, topographic, and soil variables. Simulations were performed under current and future climate conditions throughout the twenty-first century across low, medium, and high emission scenarios. The model performed reliably, with Area Under the Curve values indicating favorable predictive accuracy across all periods. Habitat suitability was governed primarily by precipitation of the driest month, temperature annual range, and elevation. Under current conditions, highly suitable areas are concentrated in tropical and subtropical monsoon regions, particularly eastern Hainan and Taiwan. Under future scenarios, suitable habitats are projected to shift toward warm temperate regions while contracting overall, with plains, basin floors, and valleys retaining high suitability due to favorable moisture retention. Windward mountain slopes are generally unsuitable, although scattered medium-suitable habitats may form in lower-lying depressions with gentler slopes.
Source: Forests
DOI: 10.3390/f17050626
Authors: Yuzhe Kong, Binbin Jiao, Size Dai, Chun Yang, Qing Chen, Tingting Dai
Institution: Nanjing Forestry University
Language: English
Subfield: Ecological Modeling
Field: Environmental Science
Domain: Physical Sciences
SDGs: Life on land, Climate action
Open Access status: gold
Estimated APC paid: $3,137
Awards: G2419348557, G3892720259, G8520568035
OpenAlex ID: W7162007220
