高山栎林建群种是研究全球气候变化对东喜马拉雅和横断山区植物物种空间分布影响的理想材料。本项目将对高山栎林5个建群种开展大量的野外调查和采集，利用植物研究与标本管理系统软件构建标准综合标本数据库，并绘制详细的地理分布图。然后利用GIS软件使其与分布区气候数据一一对应，根据多种生态位预测模型分析明确气候要素与高山栎林建群种空间分布的相互关系，探讨影响其分布的主导因子。进而，利用分布区气候参数作为变量，物种空间分布作为因变量，模拟基准气候条件（1961-1990年）下高山栎林建群种潜在地理分布，并与实际的地理分布进行对比，结合模型预测精确性评估参数确定合适空间分布模拟模型。在此基础上，引入非气候环境因素（如土壤理化性质等）建立经验模型，并结合组合预测方法和一致性预测方法对多预测结果进行整合，较精确的预测未来气候变化不同情景条件下高山栎林5个建群种地理分布。最后将预测结果与基准气候条件下的潜在分布进行比较，分析不同物种分布区变化趋势，比较不同物种之间的变化差异。

The constructive species in Quercus section Heterobalanus forest are the ideal material to study influence on the plants spatial distribution by global climate change in the East Himalaya and Hengduan Mountains, a global biodiversity hotspot. Using Botanical Research and Herbarium Management System (BRAHMS) and geographic information system software (ArcGIS v9.x), basing on a lot of plant specimens collection and consultation, the spatial distribution of five constructive species in the forest will be comprehensively investigated, and a thorough specimens database of the species will be established. Basing on the spatial distribution in the database, the relationship between the digitized species geographic information and the corresponding digitized climate data will be determined based on a variety of statistical softwares and ecological niche models. Furthermore, the potential spatial distribution of the five constructive species under benchmark climate conditions (1961-1990) will be simulated by using areal climate parameters as variables, distribution information of species as the dependent variables, and then an appropriate niche models will be determined by comparing the potential distribution with the observed actual distribution and accuracy evaluation parameters of modeling. Then, combined with other environmental factors more than climate parameters, the spatial distribution of the constructive species will be simulated by experience models under different climatic scenarios in the future, and the change will be analysis by comparing with the potential spatial distribution under the benchmark climate conditions. Finally, a more accurate prediction of the constructive species spatial distribution and its change trends will be took out by using ensemble forecasting methods to reduce the uncertainty of the models and consensus approach to integrate different predictive results by different models, and different response to climate change of different constructive species will be analyzed. The result may provide scientific guidance for ecological conservation and utilization of these particular plant resources.