赵金鹏, 王闫利, 陆兴利, 沈沾红, 王明田, 李庆, 王茹琳. 软枣猕猴桃在中国的适生区分析及对未来气候变化的响应[J]. 中国生态农业学报(中英文), 2020, 28(10): 1523-1532. DOI: 10.13930/j.cnki.cjea.200244
引用本文: 赵金鹏, 王闫利, 陆兴利, 沈沾红, 王明田, 李庆, 王茹琳. 软枣猕猴桃在中国的适生区分析及对未来气候变化的响应[J]. 中国生态农业学报(中英文), 2020, 28(10): 1523-1532. DOI: 10.13930/j.cnki.cjea.200244
ZHAO Jinpeng, WANG Yanli, LU Xingli, SHEN Zhanhong, WANG Mingtian, LI Qing, WANG Rulin. Climatic suitable area analysis and response to climate change of Actinidia arguta in China[J]. Chinese Journal of Eco-Agriculture, 2020, 28(10): 1523-1532. DOI: 10.13930/j.cnki.cjea.200244
Citation: ZHAO Jinpeng, WANG Yanli, LU Xingli, SHEN Zhanhong, WANG Mingtian, LI Qing, WANG Rulin. Climatic suitable area analysis and response to climate change of Actinidia arguta in China[J]. Chinese Journal of Eco-Agriculture, 2020, 28(10): 1523-1532. DOI: 10.13930/j.cnki.cjea.200244

软枣猕猴桃在中国的适生区分析及对未来气候变化的响应

Climatic suitable area analysis and response to climate change of Actinidia arguta in China

  • 摘要: 对软枣猕猴桃在中国的适生区进行分析,旨在为软枣猕猴桃资源调查、保护、开发利用和引种栽培提供参考依据。采用生态位建模软件MaxEnt(maximum entropy model),结合ArcGIS(geographic information system)软件,研究影响软枣猕猴桃分布的关键环境因子及取值范围,预测当前气候状态及未来不同气候变化背景下软枣猕猴桃在中国的适生区。研究结果表明:影响软枣猕猴桃分布的关键环境因子有6个,其重要程度依次为:7月降水量> 4月均温>温度季节性变化标准差> 3月均温>最暖季降水量>海拔。当前情景下,软枣猕猴桃在中国的高适生区总面积为9.287×105 km2,主要集中于东北东南部、华北东南部、华东北部和东南部、华中西部及西南东部;中适生区总面积为1.786×106 km2,主要分布在东北中部和南部、华北南部、华东北部和南部、华东北部、西南西部及华南北部。在只考虑气候因子和海拔高度的情况下,RCP2.6、RCP4.5和RCP8.5情景下,预测2070s软枣猕猴桃在中国的高适生区面积分别增加3.758×105 km2、1.725×105 km2和6.300×103 km2,而中适生区面积在RCP2.6减少1.902×105 km2,在RCP4.5、RCP8.5分别增加2.617×105 km2和9.760×104 km2。RCP2.6和RCP4.5情景下,高适生区和总适生区的质心到2070s将向东北移动;RCP8.5情景下,高适生区质心向东北移动,总适生区中心将向东南移动。研究分别对当前及未来3种不同情景下软枣猕猴桃在中国的适生区进行了10次预测,预测结果的AUC(Area Under Curve)平均值均高于0.98,表明此模型预测结果具有较高的可靠性。

     

    Abstract: Actinidia arguta is suitable for growing in a cool and humid environment. Compared with other species, A. arguta has higher nutritional and medicinal value and is also resistant to diseases, pests, drought, and cold. Determining a climactically suitable area for A. arguta may provide a reference for its investigation, protection, development, utilization, and cultivation in China. The MaxEnt (maximum entropy model) and ArcGIS (geographic information system) were used to study the key environmental factors and value range affecting the distribution of A. arguta. Predictions for suitable areas were performed under current and future climate scenarios. Environmental factors were tested for significance (via Jackknife), correlations were determined (via Pearson correlation coefficient), and six key environmental factors affecting the distribution of A. arguta were found (listed in order of significance): precipitation in July > mean temperature in April > temperature seasonality > mean temperature in March > precipitation during the warmest quarter > altitude. Presently, the total highly-suitable area is 9.287×105 km2 and is concentrated in the east of Southwest China, the west of Central China, the southeast of North China, the north and southeast of East China, and the southeast of Northeast China. The total moderately suitable area, distributed around the highly suitable area, is 1.786×106 km2. Representative concentration pathways (RCP) (i.e., future climate scenarios) predicted that the highly suitable areas will increase (RCP2.6=3.758×105 km2, RCP4.5=1.725×105 km2, and RCP8.5=6.300×103 km2), the moderately suitable areas will decrease in the RCP2.6 by 1.902×105 km2, while it will increase in the RCP4.5 and RCP8.7 (RCP4.5=2.617×105 km2, and RCP8.5=9.760×104 km2). In the RCP2.6 and RCP4.5 scenarios, the geometric center of the highly suitable areas and the total suitable areas will move to the northeast by the 2070s. In the RCP8.5 scenario, the geometric center of the highly suitable areas will move to the northeast, but the geometric center of the total suitable area will move to the southeast by the 2070s. The MaxEnt model was used to predict suitable cultivation areas for A. arguta in the present day and future climate scenarios. All of the 'area under the curve' (AUC) averages were higher than 0.98, indicating high reliability of the predicted model results.

     

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