Geographical boundary and climatic analysis of Pinus tabulaeformis in China: Insights on its afforestation

• Geographical distribution and climatic niche of Chinese red pine is simulated using MaxEnt.
• Priority afforestation area for Chinese red pine is screened out using GlobCover 2009 data.
• Future climate change will have a positive effect on afforestation projects of Chinese red pine.
• The workflow used in this study is suitable for many afforestation tree species around world.

Pinus tabulaeformis, as an endemic species in China, is one of the main tree species for afforestation. For rational cultivation planning and conservation, it is necessary to understand the geographical boundaries and the ecological characteristics of P. tabulaeformis, and to explore its priority afforestation areas. In this study, maximum entropy modeling (MaxEnt) was used to identify and prioritize suitable habitats of P. tabulaeformis, based on 13 climatic variables and GlobCover 2009 data. The results show that the MaxEnt model performs better than random prediction, with an average test area under the curve (AUC) value of 0.93 (0.91–0.94). Precipitation of wettest month (PWM), annual biotemperature (ABT), mean temperature of the coldest month (MTCM), annual mean temperature (AMT), precipitation of driest month (PDM), coldness index (CI), annual precipitation (AP), and mean temperature of the warmest month (MTWM) are identified as dominant variables which explain 94.6% of variability the geographical distribution of P. tabulaeformis. Climatic conditions of P. tabulaeformis in the core area of its distribution are as follows: PWM, 100–246 mm; ABT, 3.0–4.7 °C; MTCM, −20.2 to −1.1 °C; AMT, 2.9–14.7 °C; PDM, 2–11 mm; CI, −49.4–0 °C; AP, 431–1122 mm; and MTWM, 19.6–31.2 °C. The suitable areas for afforestation are 2.6 × 104 km2 (patches with an area >1 km2), 1.6 × 104 km2 (patches with an area >10 km2), and 1.2 × 104 km2 (patches with an area >100 km2), which are mainly located in north Shaanxi, south Ningxia, and the middle of Gansu and Liaoning provinces with serious landscape fragmentation caused by human agricultural activities. Our simulation results can improve our understanding of the geographical and ecological characteristics of P. tabulaeformis and provide prediction of priority areas for afforestation of this species under current and future climate change scenarios in China.