Abstract Lygus lineolaris populations have become increasingly problematic in Virginia cotton since 2013. Although production practices have changed, changes in landscape composition and climatic conditions are also anecdotally associated with increasing populations. To better understand the recent increase in L. lineolaris infestations in Virginia cotton, 56 commercial cotton fields were sampled weekly in 2017 and 2018 for a nine-week period during the growing season. Insect density was measured using sweep net and black drop cloth sampling, targeting adult and nymphal populations, respectively. Geospatial crop production and forest disturbance data were used to measure the composition and management history of landscapes adjacent to sample fields. We also used spatial climate data sets to document the relationship between increasing L. lineolaris and temperature variation. We investigated the significance of landscape composition and climatic effects increasing L. lineolaris infestation intensity using a regression modeling approach. We found the best overall predictor of L. lineolaris abundance was higher annual minimum temperatures. Several crop components in the landscape were significant, specifically corn and double-crop winter wheat and soybeans, but these habitat patches did not explain high levels of variability. Results of this study suggest that increasing minimum temperatures will favor L. lineolaris and may translate into elevated infestation intensity and increasing dependence on insecticide inputs to manage this problematic pest of cotton. Results of this study will be important to describe short- and long-term factors driving L. lineolaris infestation risk in cotton agroecosystems.

中文翻译：

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景观和气候效应驱动棉花农业生态系统中 Lygus lineolaris（半翅目： Miridae）的时空丰度

摘要 自 2013 年以来，弗吉尼亚棉花中的线虫种群问题日益严重。虽然生产实践发生了变化，但景观组成和气候条件的变化也与种群的增加有关。为了更好地了解最近弗吉尼亚棉花中 L. lineolaris 感染的增加，2017 年和 2018 年每周对 56 个商业棉田进行采样，为期九周的生长季节。昆虫密度是使用扫网和黑色滴布采样测量的，分别针对成虫和若虫种群。地理空间作物生产和森林干扰数据被用来衡量样本田附近景观的组成和管理历史。我们还使用空间气候数据集来记录增加 L 之间的关系。线虫和温度变化。我们使用回归建模方法研究了景观组成和气候影响增加 L. lineolaris 侵染强度的重要性。我们发现 L. lineolaris 丰度的最佳整体预测因子是较高的年度最低温度。景观中的几种作物成分很重要，特别是玉米和双季冬小麦和大豆，但这些栖息地斑块并不能解释高度的变异性。这项研究的结果表明，提高最低温度将有利于 L. lineolaris，并可能转化为更高的侵染强度和增加对杀虫剂投入的依赖，以管理这种有问题的棉花害虫。这项研究的结果对于描述驱动 L. 的短期和长期因素非常重要。