Since the mid-19th century, grasshoppers have posed a substantial threat to North American rangelands as well as adjacent croplands and have the potential to cost the economy millions of dollars in annual damages. The United States Department of Agriculture (USDA) Animal and Plant Health Inspection Service (APHIS) have gone to great lengths to ensure that rangeland grasshopper populations remain below an economic impact threshold across the western United States. However, current grasshopper forecasting efforts by the USDA are based solely on the previous year’s grasshopper density and do not take region-specific environmental factors (e.g., climate and topography) into account. To better understand the effects of climate and landscape heterogeneity on rangeland grasshopper populations, we assessed the relationship between grasshopper density survey data from across 56 sites between 2007 and 2017 for four counties in north central Wyoming with 72 biologically relevant geographic information system (GIS)-based environmental variables. A regression model was developed to predict mean adult grasshopper density from 2012 to 2016, which was then used to forecast grasshopper density in 2017. The best-fit predictive model selected using Akaike’s Information Criterion (AICc) explained 34.5% of the variation in mean grasshopper density from 2012 to 2016. October precipitation and past mean grasshopper density from 2007 to 2011 were among the best predictors of mean grasshopper density in 2012–2016. Our results also suggest that rangelands in central Sheridan County, southwest Johnson County, and southeast Washakie County are more prone to grasshopper outbreaks. Most importantly, this study demonstrated that both biotic and abiotic environmental variables influence grasshopper density and should be considered in future forecasting efforts.

中文翻译：

---

使用景观级预测制图方法模拟牧场蚱蜢（直翅目：蝗科）种群密度

自 19 世纪中叶以来，蚱蜢对北美牧场以及邻近的农田构成了重大威胁，并有可能给经济造成每年数百万美元的损失。美国农业部 (USDA) 动植物卫生检验局 (APHIS) 竭尽全力确保美国西部的牧场蚱蜢种群保持在经济影响阈值以下。然而，美国农业部当前的蚱蜢预测工作仅基于前一年的蚱蜢密度，并未考虑特定地区的环境因素（例如气候和地形）。为了更好地了解气候和景观异质性对牧场蚱蜢种群的影响，我们评估了 2007 年至 2017 年间怀俄明州中北部四个县的 56 个地点的蚱蜢密度调查数据与 72 个基于生物相关地理信息系统 (GIS) 的环境变量之间的关系。开发了一个回归模型来预测 2012 年至 2016 年的平均成年蚱蜢密度，然后用于预测 2017 年的蚱蜢密度。使用 Akaike 信息准则 (AICc) 选择的最佳拟合预测模型解释了平均蚱蜢中 34.5% 的变化2012 年至 2016 年的密度。2007 年至 2011 年 10 月的降水量和过去的平均蝗虫密度是 2012-2016 年平均蝗虫密度的最佳预测指标之一。我们的研究结果还表明，谢里登县中部、约翰逊县西南部的牧场、和东南部的沃什基县更容易爆发蝗虫。最重要的是，这项研究表明，生物和非生物环境变量都会影响蚱蜢密度，应在未来的预测工作中加以考虑。