基于生态位模型预测新疆准噶尔盆地大沙鼠适生区分布及风险评估

目的 探讨采用空间信息技术及生态位理论预测新疆准噶尔盆地大沙鼠的适生区分布,提高鼠疫监测效率.方法 通过现场调查获得准噶尔盆地大沙鼠分布的全球定位系统经纬度信息,通过遥感获得环境变量图层,利用Maxent软件建模,结合地理信息系统获得大沙鼠适生区分布图,对模型评价并划分风险分级图,叠加人口数据标示重点关注地区.结果 模型预测精度较高,曲线下面积值为0.968,灵敏度为91.4％,特异度为63.3％,准确度为73.8％,阳性预测值为59.7％,阴性预测值为92.6％,Kappa系数为0.495.大沙鼠适生区分布在准噶尔盆地及其周边大部分区县,高风险地区总面积约37 304 km2,约占总面积的6.2％,主要分布在昌吉回族自治州、乌鲁木齐市米东区及克拉玛依市,其中米东区、克拉玛依市辖区及乌尔禾区的分布较广;区域内人口约12万,分布于261 km2区域内.重点监测地区包括乌鲁木齐市、五家渠市、克拉玛依市、博乐市、精河县、奎屯市、阜康市、吉木萨尔县及木垒哈萨克自治县.结论 生态位理论和遥感环境数据可预测新疆准噶尔盆地大沙鼠潜在适生区,并显著缩小了重点监测的靶地区.

Study on the spatial distribution and related risks of Rhombomys opimus,based on the ecological niche modeling in Junggar Basin,Xinjiang

Objective In order to understand the distribution of the host animals in Junggar Basin,this study intended to map the spatial distribution and identifying the risk of Rhombomys opimus in the framework of ecological niche theory based on the "3S" technology. Methods Data on Rhombomys opimus was obtained through a series of field surveys. Environmental variables were achieved through data from Remote Sensing. Maxent modeling was built to map the potential distribution of Rhombomys opimus,with its risks identified. Results The prediction model showed ideal accuracy,with the AUC value as 0.968. Probability of Maximum Youden Index was defined as the threshold being used. The sensitivity and specificity showed as 91.4% and 63.3%,respectively. The accuracy was 73.8%,and the Kappa coefficient was 0.495. The positive predictive value was 59.7%. The negative predictive value was 92.6%. The predicted high risk area was 37 304 km²,with 6.2% in the whole area,distributed in 18 counties,including Changji Hui Autonomous Prefecture,Urumqi,Karamay and so on. The number of people under high risk would come about 120 000,scattering in the areas of 261 square kilometers. Conclusion It was feasible to predict the potential distribution of Rhombomys opimus based on the ecological niche theory as well as environmental variables derived from data through remote sensing. More specific high-risk areas could be identified under this technique so as to guide the monitoring programs.