摘要
农田恶性杂草相比普通杂草的传播更为迅速且难以有效防治,对农业生产危害严重.明确典型恶性杂草当前潜在分布面积及未来气候变化下对耕地的潜在入侵风险对农业生产管理具有重要意义.以广泛分布于青藏高原农田中的3种常见恶性杂草,即野燕麦(Avena fatua L.)、一年生早熟禾(Poa annua L.)和狗尾草[Setaria viridis(L.)P.Beauv.]为研究对象,利用广义增强模型(GBM)、广义线性模型(GLM)、人工神经网络(ANN)、最大熵(MaxEnt)、随机森林(RF)及多元自适应回归样条(MARS)算法集合预测上述3种杂草在青藏高原的潜在地理分布以及驱动其变化的关键因子,以评估其对耕地的入侵风险.未来气候场景采用最新的第六次国际耦合模式比较计划(CMIP6)框架下2050年的4种共享经济路线(SSP1-2.6、2-4.5、3-7.0、5-8.5).结果显示:野燕麦适宜分布区面积约为3.5912×10^(5) km^(2),主要分布于四川西南部及青海东部,零星分布于甘肃、西藏和新疆;一年生早熟禾和狗尾草的适宜分布区面积约为4.3046×10^(5) km^(2)和2.0036×10^(5) km^(2),均主要分布于四川西南部和西藏东南部,零星分布于青海东部和甘肃南部.年均温是3种杂草分布的最主要驱动因子.此外,人类足迹和土壤有效氮是影响野燕麦分布的相对重要因子;土壤酸碱度、最暖季降水量是影响一年生早熟禾分布的重要因子;温度季节性、最暖季降水量是影响狗尾草分布的重要因子.预计至2050年,3种杂草在4种情境下均会出现不同程度的扩张,狗尾草的扩张面积表现出随辐射强迫的增强呈先升高后趋于稳定的趋势,而另两种杂草则呈先升后降的趋势.预计3种杂草的潜在分布面积在耕地中的占比与扩张面积的变化趋势一致,且在主产区的占比高于非主产区.模拟结果表明,未来气候变化下,随着3种恶性杂草的适宜分布区面积的扩张,其对青藏高原耕地的入侵风险将增加,尤
Malignant weeds in agricultural lands spread more rapidly than normal weeds and are difficult to control effectively, thereby posing a severe threat to agricultural production. Identifying the current potential distribution areas of representative malignant weeds and their potential invasion of croplands under future climate changes are of great significance in the management of agricultural production. To predict the potential geographic distribution of the three malignant weeds, namely Avena fatua L., Poa annua L., and Setaria viridis(L.) P. Beauv., within the range of Qinghai-Tibet Plateau and to assess their invasive risk to cropland, we used an ensemble modeling approach, which comprised six algorithms: the generalized boosting model(GBM), artificial neural network(ANN),maximum entropy(MAXENT), random forest(RF), generalized linear models(GLM), and multivariate adaptive regression splines(MARS). The future climate change scenarios were described by four shared socioeconomic pathways(SSPs) for 2050a under the framework of CMIP6. Currently, the suitable area for A. fatua is 359 120 km^(2),mainly in southwestern Sichuan and eastern Qinghai, with sporadic distributions in Gansu, Tibet, and Xinjiang.P. annua and S. viridis were predicted to have suitable areas of 430 460 km^(2) and 200 360 km^(2), respectively,primarily in southwestern Sichuan and southeastern Tibet, with scattered distributions in eastern Qinghai and southern Gansu. Mean annual temperature had the largest effect on the distribution of A. fatua, P. annua, and S.viridis. Subsequently, the human footprint index and soil available nitrogen were the dominant factors affecting the distribution of A. fatua, whereas soil pH and precipitation in the warmest quarter were relatively important for P. annua. Temperature seasonality and precipitation in the warmest quarter were the two significant factors in the distribution of S. viridis. By 2050a, under the four SSP scenarios, all three weeds showed different degrees of simulated expansion. The expansion ar
作者
申源
廖梓延
林可欣
伍小刚
张凤英
张林
潘开文
SHEN Yuan;LIAO Ziyan;LIN Kexin;WU Xiaogang;ZHANG Fengying;ZHANG Lin;PAN Kaiwen(CAS Key Laboratory of Mountain Ecological Restoration and Bioresource Utilization&Ecological Restoration Biodiversity Conservation Key Laboratory of Sichuan Province,Chengdu Institute of Biology,Chinese Academy of Sciences,Chengdu 610041,China;University of Chinese Academy of Sciences,Beijing 100049,China;Swiss Federal Institute for Forest,Snow and Landscape Research WSL,Birmensdorf CH-8903,Switzerland;Sichuan Forestry and Grassland investigation and Planning Institute,Chengdu 610084,China)
出处
《应用与环境生物学报》
CAS
CSCD
北大核心
2022年第4期909-919,共11页
Chinese Journal of Applied and Environmental Biology
基金
第二次青藏高原综合科学考察研究项目(2019QZKK0303)资助。
关键词
恶性杂草
集合模型
潜在适生区
入侵风险
青藏高原
malignant weed
ensemble model
potential suitable area
invasion risk
Qinghai-Tibet Plateau
