Lestina Jordan, Cook Maxwell, Kumar Sunil, Morisette Jeffrey, Ode Paul J, Peairs Frank
Department of Forest and Rangeland Stewardship, Colorado State University, 1001 West Drive, Fort Collins, CO 80523 (
Natural Resource Ecology Laboratory, Colorado State University, 1231 East Drive, Fort Collins, CO 80523 (
Environ Entomol. 2016 Dec;45(6):1343-1351. doi: 10.1093/ee/nvw095. Epub 2016 Sep 22.
Wheat stem sawfly (Cephus cinctus Norton, Hymenoptera: Cephidae) has long been a significant insect pest of spring, and more recently, winter wheat in the northern Great Plains. Wheat stem sawfly was first observed infesting winter wheat in Colorado in 2010 and, subsequently, has spread rapidly throughout wheat production regions of the state. Here, we used maximum entropy modeling (MaxEnt) to generate habitat suitability maps in order to predict the risk of crop damage as this species spreads throughout the winter wheat-growing regions of Colorado. We identified environmental variables that influence the current distribution of wheat stem sawfly in the state and evaluated whether remotely sensed variables improved model performance. We used presence localities of C. cinctus and climatic, topographic, soils, and normalized difference vegetation index and enhanced vegetation index data derived from Moderate Resolution Imaging Spectroradiometer (MODIS) imagery as environmental variables. All models had high performance in that they were successful in predicting suitable habitat for C. cinctus in its current distribution in eastern Colorado. The enhanced vegetation index for the month of April improved model performance and was identified as a top contributor to MaxEnt model. Soil clay percent at 0-5 cm, temperature seasonality, and precipitation seasonality were also associated with C. cinctus distribution in Colorado. The improved model performance resulting from integrating vegetation indices in our study demonstrates the ability of remote sensing technologies to enhance species distribution modeling. These risk maps generated can assist managers in planning control measures for current infestations and assess the future risk of C. cinctus establishment in currently uninfested regions.
小麦茎蜂（Cephus cinctus Norton，膜翅目：茎蜂科）长期以来一直是春小麦的重要害虫，最近在大平原北部地区也成为冬小麦的害虫。2010年，在科罗拉多州首次观察到小麦茎蜂侵害冬小麦，随后它迅速蔓延至该州的小麦产区。在此，我们使用最大熵建模（MaxEnt）来生成栖息地适宜性地图，以便预测随着该物种在科罗拉多州冬小麦种植区的扩散，作物受损的风险。我们确定了影响该州小麦茎蜂当前分布的环境变量，并评估了遥感变量是否能提高模型性能。我们将小麦茎蜂的出现地点以及从中等分辨率成像光谱仪（MODIS）图像中获取的气候、地形、土壤、归一化植被指数和增强植被指数数据作为环境变量。所有模型都具有很高的性能，因为它们成功地预测了科罗拉多州东部当前分布区域内适合小麦茎蜂生存的栖息地。4月份的增强植被指数提高了模型性能，并被确定为MaxEnt模型的主要贡献因素。0 - 5厘米深度的土壤黏土百分比、温度季节性和降水季节性也与科罗拉多州小麦茎蜂的分布有关。我们的研究中通过整合植被指数提高了模型性能，这表明遥感技术能够增强物种分布建模。生成的这些风险地图可以帮助管理人员规划针对当前虫害的控制措施，并评估小麦茎蜂在当前未受侵害地区定殖的未来风险。
