入侵植物通常由于具有较强的适应性而能够快速繁殖扩散,影响本土物种的生长繁殖,进而威胁到当地生态安全、景观格局和农业生产等。西藏生态环境非常脆弱,一旦发生恶性物种大面积入侵,生态后果不堪设想。为了探究入侵植物印加孔雀草(Tage...入侵植物通常由于具有较强的适应性而能够快速繁殖扩散,影响本土物种的生长繁殖,进而威胁到当地生态安全、景观格局和农业生产等。西藏生态环境非常脆弱,一旦发生恶性物种大面积入侵,生态后果不堪设想。为了探究入侵植物印加孔雀草(Tagetes minuta L.)对西藏东南生态安全的影响趋势,基于野外实地调查数据,采用最大熵(MaxEnt)模型,应用R语言平台对模型和数据进行优化筛选,探讨影响其地理分布的主要环境因子,并模拟预测了当代及2种气候变化情景(RCP 4.5、RCP 8.5)下,其在西藏的潜在适生区分布情况。结果表明:(1)训练数据集和测试数据集的受试者工作特征曲线下的面积(AUC)均为0.997,模拟效果较好;底层土壤酸碱度、最暖季降水量、土壤有效含水量、最暖月最高温度为影响印加孔雀草分布的主导环境因子,贡献率总和超过90%。(2)加查县、朗县是印加孔雀草分布密集区域,米林县、林芝市区、察隅县、墨脱县等地为入侵高风险地区。(3)中短期(2050年)内印加孔雀草适生面积增加明显,2070年时面积则会减少;印加孔雀草适生区在藏东南地区进一步向东北区域扩张,分布质心由当前的墨脱县域向波密县域转移。总体而言,印加孔雀草分布受土壤环境、温度和降水影响较大,气候变化将使其向西藏东部、南部扩张。研究结果对于西藏自治区制定植物入侵防控管理办法具有重要参考价值。展开更多
The Yarlung Zangbo River Basin is an important populated area in Tibet, and its plant community structure and diversity pattern have attracted the attention of many scholars. In this paper, the distribution pattern of...The Yarlung Zangbo River Basin is an important populated area in Tibet, and its plant community structure and diversity pattern have attracted the attention of many scholars. In this paper, the distribution pattern of plant diversity and the environmental factors impacting it in the middle and upper reaches of the Yarlung Zangbo River are revealed and discussed through sample surveys and climate and habitat data. The results show that the plant communities in the study area can be divided into seven types according to the dominant species: Artemisia minor + Stipa purpurea, Artemisia wellbyi + Festuca ovina, Potentilla fruticosa + Orinus thoroldii, Trikeraia hookeri + Artemisia frigida, Kobresia pygmaea, Sophora moorcroftiana + Artemisia hedinii, and Sophora moorcroftiana + Pennisetum centrasiaticum. Plant diversity decreases with decreasing longitude, increasing latitude, and increasing altitude;and the diversity distribution pattern in the study area has distinct zonal characteristics. Water and heat are the main factors which affect the distribution of vegetation types. The explanation rates of water and heat for the plant diversity distribution pattern were 19.3% and 5.7%, respectively, while the spatial variation explained by these two factors together was 60.8%. Therefore, the coupling effect is obvious.展开更多
文摘入侵植物通常由于具有较强的适应性而能够快速繁殖扩散,影响本土物种的生长繁殖,进而威胁到当地生态安全、景观格局和农业生产等。西藏生态环境非常脆弱,一旦发生恶性物种大面积入侵,生态后果不堪设想。为了探究入侵植物印加孔雀草(Tagetes minuta L.)对西藏东南生态安全的影响趋势,基于野外实地调查数据,采用最大熵(MaxEnt)模型,应用R语言平台对模型和数据进行优化筛选,探讨影响其地理分布的主要环境因子,并模拟预测了当代及2种气候变化情景(RCP 4.5、RCP 8.5)下,其在西藏的潜在适生区分布情况。结果表明:(1)训练数据集和测试数据集的受试者工作特征曲线下的面积(AUC)均为0.997,模拟效果较好;底层土壤酸碱度、最暖季降水量、土壤有效含水量、最暖月最高温度为影响印加孔雀草分布的主导环境因子,贡献率总和超过90%。(2)加查县、朗县是印加孔雀草分布密集区域,米林县、林芝市区、察隅县、墨脱县等地为入侵高风险地区。(3)中短期(2050年)内印加孔雀草适生面积增加明显,2070年时面积则会减少;印加孔雀草适生区在藏东南地区进一步向东北区域扩张,分布质心由当前的墨脱县域向波密县域转移。总体而言,印加孔雀草分布受土壤环境、温度和降水影响较大,气候变化将使其向西藏东部、南部扩张。研究结果对于西藏自治区制定植物入侵防控管理办法具有重要参考价值。
基金The National Key Research and Development Program of China (2016YFC0502006)。
文摘The Yarlung Zangbo River Basin is an important populated area in Tibet, and its plant community structure and diversity pattern have attracted the attention of many scholars. In this paper, the distribution pattern of plant diversity and the environmental factors impacting it in the middle and upper reaches of the Yarlung Zangbo River are revealed and discussed through sample surveys and climate and habitat data. The results show that the plant communities in the study area can be divided into seven types according to the dominant species: Artemisia minor + Stipa purpurea, Artemisia wellbyi + Festuca ovina, Potentilla fruticosa + Orinus thoroldii, Trikeraia hookeri + Artemisia frigida, Kobresia pygmaea, Sophora moorcroftiana + Artemisia hedinii, and Sophora moorcroftiana + Pennisetum centrasiaticum. Plant diversity decreases with decreasing longitude, increasing latitude, and increasing altitude;and the diversity distribution pattern in the study area has distinct zonal characteristics. Water and heat are the main factors which affect the distribution of vegetation types. The explanation rates of water and heat for the plant diversity distribution pattern were 19.3% and 5.7%, respectively, while the spatial variation explained by these two factors together was 60.8%. Therefore, the coupling effect is obvious.
