Ensemble modelling of dominant tree communities for smart afforestation planning in China

Abstract

Abstract Context Southern and central China harbor some of the most diverse and ecologically significant forest ecosystems in China, making them priority regions for biodiversity conservation and afforestation planning. Within this broader context, the Qinling Mountains serve as a representative case study, as they are recognized as a global biodiversity hotspot and one of China’s most important temperate broad-leaved forest regions. Understanding the spatial distribution of dominant tree communities in this area is essential for biodiversity conservation and ecosystem management in the face of increasing anthropogenic pressures and climate change. Objectives This study aimed to (1) identify the potential distribution and richness patterns of dominant tree communities in the Qinling Mountains and adjacent regions, and (2) assess their vegetation associations and key environmental drivers to inform afforestation and conservation planning; and (3) Explore the potential species richness and endemicity to identify the most suitable areas for afforestation of target communities or group of the dominant tree communities. Methods We applied ensemble stacked species distribution models (S-SDMs) using ecological niche modeling to predict the potential distribution of target dominant tree communities across China, capturing diverse ecological regions including the Qinling region. Key environmental variables included climatic, edaphic, and anthropogenic factors. Vegetation associations were explored using non-metric multidimensional scaling (NMDS) analysis based on environmental gradients and field data collected from the Qinling Mountains and surrounding areas. Results The S-SDMs showed high predictive performance (AUC > 0.8, TSS > 0.5). Key predictors of species distribution included mean diurnal temperature range (Bio2), human influence, precipitation seasonality (Bio15), and organic soil carbon. Richness and endemicity hotspots were concentrated in southeast and south-central China, particularly in Hainan and Taiwan Islands. NMDS analysis revealed three distinct tree associations structured along environmental gradients, notably human disturbance, precipitation, and soil characteristics. Spatial prioritization highlighted provinces such as Henan, Anhui, Shandong, and Jiangsu as key areas for afforestation by dominant tree communities. Conclusions The findings along with the developed interactive map offer valuable insights for landscape-scale biodiversity conservation, afforestation efforts, and ecosystem restoration, supporting the achievement of global Sustainable Development Goals (SDGs). Incorporating climate change modeling into future analyses will be critical to ensuring the long-term resilience and success of restoration initiatives. Graphical abstract