Brewer Michael J
Department of Entomology, Texas A&M AgriLife Research, Corpus Christi, TX, United States.
Front Insect Sci. 2024 Nov 25;4:1496184. doi: 10.3389/finsc.2024.1496184. eCollection 2024.
This study considers concepts and tools of landscape ecology and geographic information systems (GIS) to prioritize insect monitoring in large-scale crops, using the cotton agroecosystem of the Texas Gulf Coast and two plant bug species ( Distant and (Reuter) [Hemiptera: Miridae]) as a case study. The two species differed in host plants and time span as cotton pests.
and abundance in early growth of cotton were regressed on landscape metrics. Comparisons of three approaches to select landscape variables in stepwise multiple regressions were made across spatial scales and two weeks of insect data extracted from monitoring of 21 cotton fields, years 2010 through 2013.
The spatial variation of plant bug abundance and the landscape features were substantial, aiding the regression approach. For full stepwise regression models using 18 landscape variables, regression model fit using data was modestly better in week one of sampling when adults and young nymphs were detected ( range of 0.56 to 0.82), as compared with model fit at week two ( range of 0.49 to 0.77). The smallest scale (2.5 km radius) models had the greatest number of variables selected and highest , while two broader scales (5 and 10 km) and truncating the models to three variables produced a narrower range of s (0.49 to 0.62) and more consistent entry of variables. Wetland composition had a consistent positive association with abundance, supporting its association with seepweeds which are common in coastal wetlands. When selected, the composition of cotton and grassland/shrubland/pasture also had a positive association with abundance. Aggregation metrics were also relevant, but composition metrics in the models were arguably more easily utilized in prioritizing insect monitoring. In contrast, there were few significant regressions using data, possibly due to the widespread distribution of its weedy host plants and lower abundance. Overall, selected landscape features served as indicators of infestation potential in cotton particularly grown near coastal wetlands, but landscape features were not useful for infestation potential in cotton.
本研究运用景观生态学和地理信息系统（GIS）的概念与工具，以得克萨斯湾沿岸的棉花农业生态系统以及两种植食性蝽象（Distant和（路透社）[半翅目：盲蝽科]）作为案例研究，对大规模农作物中的昆虫监测进行优先级排序。这两种蝽象作为棉花害虫，在寄主植物和时间跨度上存在差异。
将棉花早期生长阶段的[蝽象名称未给出]和[蝽象名称未给出]丰度与景观指标进行回归分析。在2010年至2013年期间，从21个棉田的监测数据中提取了两周的昆虫数据，跨越空间尺度对逐步多元回归中选择景观变量的三种方法进行了比较。
植食性蝽象丰度的空间变异和景观特征显著，有助于回归分析方法。对于使用18个景观变量的全逐步回归模型，在采样的第一周检测到[蝽象名称未给出]成虫和若虫时，使用[蝽象名称未给出]数据的回归模型拟合效果略好（调整R²范围为0.56至0.82），相比之下，第二周的模型拟合效果（调整R²范围为0.49至0.77）。最小尺度（半径2.5公里）的模型选择的变量数量最多，R²最高，而两个较大尺度（5公里和10公里）以及将模型截断为三个变量时，R²范围较窄（0.49至0.62），变量进入模型的情况更一致。湿地组成与[蝽象名称未给出]丰度始终呈正相关，支持了其与滨海湿地常见的海草的关联。当被选中时，棉花与草地/灌木地/牧场的组成也与[蝽象名称未给出]丰度呈正相关。聚集指标也具有相关性，但模型中的组成指标在确定昆虫监测优先级方面可能更容易应用。相比之下，使用[蝽象名称未给出]数据的显著回归较少，可能是由于其杂草寄主植物分布广泛且丰度较低。总体而言，所选景观特征可作为棉花特别是靠近滨海湿地种植的棉花中[蝽象名称未给出]侵染潜力的指标，但景观特征对棉花中[蝽象名称未给出]的侵染潜力并无用处。
