Abstract Boll weevil, Anthonomous grandis grandis Boheman first invaded U.S. cotton in Texas in the late 1800s, and spread throughout U.S. cotton-growing regions by the 1920s. Boll weevil eradication efforts initiated in the eastern United States in the 1980s resulted in its elimination except in the southernmost region of Texas and adjoining areas of Mexico. We focused on geographic information system (GIS)-based mapping and spatial analyses of boll weevil trap data to consider whether landscape features were associated with spatially variable detections of boll weevils. Seven years of trap data were overlaid with data layers of vector-based classifications of cropland, transportation, and hydrological features. New boll weevil detections in 2018 were 108 km north of cotton fields in the Lower Rio Grande Valley (LRGV) where more persistent detections occurred. Focusing on LRGV data, 14 of 24 correlations of number of boll weevil captures to nearest distances to selected landscape features were negative. In follow-up analyses, best data fit was seen using stepwise regression. In 2010, waterbodies and watermelon fields were influential linear terms (partial R2 = 0.14 and 0.064, respectively; model R2 = 0.32). In 2014, the Rio Grande River as a linear term was influential (partial R2 = 0.15; model R2 = 0.24). Boll weevil captures tended to increase in closer proximity to these landscape features. Results of 2010 were consistent with expectations of remnant populations in the LRGV spreading locally, while 2014 results may reflect remnant populations or re-introductions from boll weevil moving longer distances into the LRGV.

中文翻译：

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基于地理信息系统 (GIS) 的制图和空间分析应用于棉铃象鼻虫（鞘翅目：仙茅科）检测的风险评估和资源分配

摘要 棉铃象鼻虫 Anthonomous grandis grandis Boheman 于 1800 年代后期首次入侵德克萨斯州的美国棉花，并于 1920 年代传播到美国棉花种植区。1980 年代在美国东部发起的棉铃象鼻根除工作导致除德克萨斯州最南端地区和墨西哥相邻地区外的其他地区被消灭。我们专注于基于地理信息系统 (GIS) 的棉铃象鼻虫陷阱数据的制图和空间分析，以考虑景观特征是否与棉铃象鼻虫的空间变量检测相关。七年的圈闭数据与基于矢量的耕地、交通和水文特征分类数据层叠加。2018 年在下里奥格兰德河谷 (LRGV) 棉田以北 108 公里处检测到新的棉铃象鼻虫，在那里发生了更持久的检测。关注 LRGV 数据，棉铃象鼻虫捕获数量与所选景观特征最近距离的 24 个相关性中有 14 个为负。在后续分析中，使用逐步回归可以看到最佳数据拟合。2010 年，水体和西瓜田是有影响的线性项（部分 R2 分别为 0.14 和 0.064；模型 R2 = 0.32）。2014 年，里奥格兰德河作为线性项具有影响力（部分 R2 = 0.15；模型 R2 = 0.24）。在靠近这些景观特征的地方，棉铃象鼻虫的捕获量往往会增加。2010 年的结果与 LRGV 中的残余种群在当地传播的预期一致，