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郭飞龙, 徐刚标, 牟虹霖, 李赞. 伯乐树潜在地理分布时空格局模拟[J]. 植物科学学报, 2020, 38(2): 185-194. DOI: 10.11913/PSJ.2095-0837.2020.20185
引用本文: 郭飞龙, 徐刚标, 牟虹霖, 李赞. 伯乐树潜在地理分布时空格局模拟[J]. 植物科学学报, 2020, 38(2): 185-194. DOI: 10.11913/PSJ.2095-0837.2020.20185
Guo Fei-Long, Xu Gang-Biao, Mou Hong-Lin, Li Zan. Simulation of potential spatiotemporal population dynamics of Bretschneidera sinensis Hemsl. based on MaxEnt model[J]. Plant Science Journal, 2020, 38(2): 185-194. DOI: 10.11913/PSJ.2095-0837.2020.20185
Citation: Guo Fei-Long, Xu Gang-Biao, Mou Hong-Lin, Li Zan. Simulation of potential spatiotemporal population dynamics of Bretschneidera sinensis Hemsl. based on MaxEnt model[J]. Plant Science Journal, 2020, 38(2): 185-194. DOI: 10.11913/PSJ.2095-0837.2020.20185

伯乐树潜在地理分布时空格局模拟

Simulation of potential spatiotemporal population dynamics of Bretschneidera sinensis Hemsl. based on MaxEnt model

  • 摘要: 以伯乐树(Bretschneidera sinensis Hemsl.)为研究对象,基于现有的151个伯乐树居群分布点及12个气候变量,运用MaxEnt模型和GIS技术,模拟末次盛冰期、全新世中期、当前、未来(RCP 2.6、RCP 4.5、RCP 6.0和RCP 8.5)气候情景下的伯乐树潜在地理分布格局;采用受试者工作特征曲线(ROC)下的面积(AUC值),评价模拟的精度;综合分析测试增益、气候变量贡献率及置换重要值,探讨制约伯乐树地理分布的主导气候变量;基于分布面积比(Na)、生境变化程度(Ne),比较伯乐树在不同气候情景下的地理分布动态。ROC曲线结果显示,7种不同气候情景下的训练集与测试集AUC值均大于0.99,表明模型模拟精度极高。测试增益、气候变量贡献率及置换重要值显示,昼夜温差月均值、等温性和最干季度降水量是伯乐树潜在地理分布的限制因子。不同气候情景下伯乐树地理分布动态暗示,金佛山、大瑶山可能是伯乐树冰期多个微型避难所;末次盛冰期以来,伯乐树地理分布经历了扩张过程;未来不同气候情景下,其地理分布范围可能会发生不同程度(25%~47%)的收缩,其中RCP 8.5情境下,伯乐树居群生境破碎化最为严重。开展伯乐树资源调查、收集和遗传管理的国际合作,在气候适宜地区建立迁地保育林,是有效防止伯乐树遗传资源丢失的重要措施。

     

    Abstract: To model the potential distribution of Bretschneidera sinensis Hemsl. and compare the spatiotemporal population dynamics of B. sinensis under Last Glacial Maximum, Mid Holocene, Current, and Future periods (RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5), 151 existing populations of this species and 12 climate variables were selected based on the MaxEnt model and GIS. Using the receiver-operating characteristic curve (ROC), we analyzed the credibility of the MaxEnt model. To identify the climate variables restricting the distribution of this species, we used training gain, percentage contribution, and permutation importance of the climate variables. The spatiotemporal population dynamics of B. sinensis were compared using the distribution area ratio (Na) and extent of habitat change (Ne), respectively. The area under the ROC showed that the AUC values of the training data and test data under the seven different climate scenarios were higher than 0.99. This indicated that the simulation accuracy of the MaxEnt model was extremely high. Training gain, climate variable contribution, and permutation importance revealed that the geographical distribution of B. sinensis was limited by mean diurnal range (mean of monthly), isothermality, and precipitation of the driest quarter. The spatiotemporal population dynamics of B. sinensis from the Last Glacial Maximum to Mid Holocene indicated that the Jinfoshan and Dayaoshan mountains likely provided micro-glacial refugia for this species, after which geographical distribution experienced an expansion. In the future, the potential geographical distribution of B. sinensis may diminish from 25%-46%, depending on changes in greenhouse gas emissions, with the range of B. sinensis being the smallest and most fragmented under RCP 8.5. Implementing international cooperation in the investigation, collection, and genetic management of this species, and establishing ex-situ forest gene conservation in climate friendly areas could be important ways to protect and restore this species.

     

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