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    张春华, 和菊, 孙永玉, 李昆. 基于MaxEnt模型的紫椿适生区预测[J]. 北京林业大学学报, 2017, 39(8): 33-41. DOI: 10.13332/j.1000-1522.20170002
    引用本文: 张春华, 和菊, 孙永玉, 李昆. 基于MaxEnt模型的紫椿适生区预测[J]. 北京林业大学学报, 2017, 39(8): 33-41. DOI: 10.13332/j.1000-1522.20170002
    ZHANG Chun-hua, HE Ju, SUN Yong-yu, LI Kun. Distributional change in suitable areas for Toona sureni based on MaxEnt model[J]. Journal of Beijing Forestry University, 2017, 39(8): 33-41. DOI: 10.13332/j.1000-1522.20170002
    Citation: ZHANG Chun-hua, HE Ju, SUN Yong-yu, LI Kun. Distributional change in suitable areas for Toona sureni based on MaxEnt model[J]. Journal of Beijing Forestry University, 2017, 39(8): 33-41. DOI: 10.13332/j.1000-1522.20170002

    基于MaxEnt模型的紫椿适生区预测

    Distributional change in suitable areas for Toona sureni based on MaxEnt model

    • 摘要: 气候变化通过改变物种的生境进而影响生物多样性。紫椿是一种具有高生态、经济、药用价值的用材树种,在我国与其他香椿属物种一起被称为“中国桃花心木”。了解该物种对生境要求、评价其生境质量、预测其适生区分布有助于紫椿的保护、引种及其人工林的发展。MaxEnt模型的优点在于能利用现存不完整、小样本、离散型分布数据构建物种适生区预测模型,且用受试者工作曲线下面积(AUC)检验预测模型的精度,面积越大精度越高。研究应用紫椿在云南分布数据及MaxEnt软件构建其适生区分布模型,结果表明:适生区分布模型平均训练AUC和平均测试AUC分别为0.959和0.818,说明对紫椿适生区的预测是可靠的;温度季节性变化的标准差、最冷月最低温(℃)、最干季度平均温度(℃)、最冷季度降水量(mm)、年均温变化范围是决定紫椿适生区分布的重要因素。对当代和未来(21世纪50年代,21世纪70年代)气候变暖条件下(RCP2.6情景)的紫椿在云南省和全国适生区面积进行了计算,结果直观、定量反映了气候变化下紫椿适生区的变迁,预测云南省及全国的紫椿适生区随全球变暖而萎缩。

       

      Abstract: Climate change influences biodiversity by altering the habitat of species in ecosystem. Toona sureni is a timber plant with high ecological, economic and medicinal value. It is called "Chinese mahogany" in China with other species in Toona. Understanding the habitat requirements, evaluating habitat quality and predicting the species' potential habitat are significant for protecting T.sureni, as well as for its protection introduction and plantation. Because of the advantages of using presence-only data and performing well with small sample sizes, incomplete data and gaps, MaxEnt model was employed to simulate the habitat suitability distribution, and the area under the receive operating characteristic curve(AUC)was used to examine the model's accuracy, the larger the AUC is, the more accurate the prediction is. So, based on the distribution of T. sureni in Yunnan Province of southwestern China, the MaxEnt model was used to set up its distributional model of potential habitat. The results showed that the mean training AUC and mean test AUC were 0.959 and 0.818, respectively. It is illustrated that the prediction of T.sureni's suitable habitats was reliable. Five variables, namely standard deviation of temperature seasonal change, minimum temperature of the coldest month(℃), mean temperature of the driest quarter(℃), precipitation of the coldest quarter (mm), range of annual temperature were significant factors determining T. sureni 's suitable habitat. Habitat suitability for current and future climate warming(2050s, 2070s) under scenario RCP2.6 in Yunnan Province and China was calculated. The study reports the intuitive and quantitative predictions of climate change on T. sureni species' suitable habitats. The habitat suitability of T.sureni in Yunnan Province and China is predicted to deteriorate with global warming.

       

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