Potential geographical distribution and its multi-factor analysis of Pinus massoniana in China based on the maxent model

Abstract

Pinus massoniana, an important timber, producing, and silvicultural species in southern China, exhibits high adaptability and wide distribution. This study utilizes the Maximum Entropy Model (MaxEnt), a species distribution model based on the theory of maximum entropy, to forecast the potential suitable distribution areas of P. massoniana in China under four climate change scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) for both present and future (2080) conditions. The research integrates and analyzes the effects of various environmental factors, including topography, soil, and population, on the distribution of P. massoniana. Additionally, a geographical detector is employed to assess the interaction between different environmental factors and their contribution to the variation in suitability zones.The findings indicate that the MaxEnt model accurately predicts the potential distribution areas of P. massoniana, with AUC values exceeding 0.94. Precipitation in the driest month (BIO14), population density (POP), and annual precipitation (BIO12) emerge as the main factors influencing the current distribution of P. massoniana. Notably, BIO14 has the greatest impact on the species' distribution (43%), followed by POP (32.7%). Furthermore, lower BIO14 values correspond to higher probabilities of pine distribution, while higher POP values correlate with increased pine distribution probabilities. The potential distribution of P. massoniana is primarily concentrated in southern China under current climatic conditions, encompassing a total suitable survival zone of 25.24 × 105 km2, accounting for 26.29% of China's total area. Among the regions, Guangxi exhibits the largest suitable area for survival, reaching 28.9 × 104 km2, implying that the environmental characteristics of Guangxi are conducive to P. massoniana's survival. Under future climate scenarios, the overall distribution pattern of the potential range of P. massoniana remains similar to the present one, with an increasing trend in area. Notably, the SSP3-7.0 emissions scenario shows the most significant increase in area, totaling 4.71 × 104 km2, suggesting that this particular scenario is more favorable for the distribution of P. massoniana. This study provides valuable scientific insights for the management, conservation, and rational site selection of P. massoniana.