Plant Ecology and Evolution 146(3): 261-271, doi: 10.5091/plecevo.2013.783
Predicting the distribution of potential natural vegetation based on species functional groups in fragmented and species-rich forests
expand article infoZhi-Dong Zhang, Runguo Zang, Matteo Convertino
Open Access
Background and aims – Potential natural vegetation (PNV) can provide a basic reference in guiding the restoration activities for damaged landscapes. Our aim was to find a practicable approach of reconstructing PNV based on functional groups (FGs) in species rich and fragmented forest region. Methods – A total of 149 sample plots (20 × 20 m) were laid and investigated systematically by 1 × 1 km or 1 × 2 km grids across the 482 km 2 forest region in Bawangling nature reserve, Hainan Island, China. The 579 woody plant species found were aggregated into eight functional groups (FGs) according to successional status and potential maximum height. The ecological niche model (MaxEnt) was adopted in predicting the potential distribution of the FGs. The relationship between occurrence probability of FGs and environmental factors was determined as a function of the predicted response curves. The PNV was produced by the overlay of maps for the potential distribution of the FGs, while referring the FGs interactions. Key results – The predicted pioneer FGs scattered widely in the whole landscape, while climax FGs mainly distributed in the central and south part of the study region. Pioneer FGs could better withstand environmental change and occurred in warmer and drier sites, while climax FGs are distributed in cooler and wetter conditions. The PNV could be reconstructed by considering interactions among the FGs. Sixteen main functional patch types were reconstructed in the tropical forest landscape, among which twelve were dominated by climax FGs, accounting for 72.7% of the area, and only four were dominated by pioneer FGs, and accounting for 18.8% of the area. Conclusion – We produced successful prediction of the observed PNV by applying a niche-based model (MaxEnt) based on FGs in fragmented and species rich forest region. This is helpful for predicting how PNV will change to natural and anthropic stressors.