兴安落叶松地理分布对气候变化响应的模拟

以温暖指数、寒冷指数、湿润指数、1月最低温度、7月最高温度和年降水量等环境变量为因子，比较分析了广义线性模型（GLM）、逐步回归广义线性模型（SGLM）、广义加法模型（GAM）和分类回归树模型（CART）对兴安落叶松地理分布的模拟效果，其中GAM模型的模拟效果最好.在此基础上，应用GAM模型，结合未来气候变化情景模拟了兴安落叶松分布.结果表明，在SRES-A₂排放方案下，2020年兴安落叶松适宜分布面积将减少58.1%，2050年将减少99.7％；在SRES-B₂排放方案下，2020年兴安落叶松适宜分布面积将减少66.4%，2050年将减少97.9％.两种排放方案下，到2100年，兴安落叶松适宜分布区将从我国完全消失.

Responses of Larix gmelinii geographical distribution to future climate change: A simulation study

With warmth index, coldness index, humidity index, mean annual precipitation, minimum temperature in January, and maximum temperature in July as environmental variables, and by using Generalized Linear Model (GLM), Stepwise Generalized Linear Model (SGLM), Generalized Additive Model (GAM), and Classification and Regression Tree (CART), this paper simulated the geographical distribution of Larix gemelinii under the conditions of future climate change. Cohen's Kappa and the area under the Receiver Operating Characteristic curve were used to evaluate the performance of the models, and the most suitable model was selected to predict the geographical distribution. The results showed that all the test models except GLM could simulate the geographical distribution of L. gmelinii very well, and GAM performed best. Climate change would result in a reduction in the suitable area of L. gmelinii by 58.1% under SRES-A2 scenario and by 66.4% under SRES-B2 scenario in 2020. The suitable area of L. gmelinii would be further reduced by 99.7% under SRES-A2 scenario and by 97.9% under SRES-B2 scenario in 2050, and completely disappeared under both scenarios in 2100.