预测美国南部的Helicoverpa zea (Lepidoptera: Noctuidae) 种群的情况
Eric Yanchenko1, Brian J Reich2, George G Kennedy3
1Global Connectivity Program, Akita International University, Akita, Japan.
Environmental entomology
|December 9, 2025
概括
使用机器学习模型预测玉米耳 (Helicoverpa zea) 种群可以改善预测. 陷位置和年份是关键预测因素,允许减少陷密度而不会失去准确性.
科学领域:
- 农业昆虫学 农业昆虫学
- 虫害管理 虫害管理 虫害管理
- 生态预测 生态预测
背景情况:
- 诸如玉米耳虫 (Helicoverpa zea) 这样的昆虫害虫造成了大量的作物损失.
- 由于它们的分散和宿主范围,预测害虫种群具有挑战性.
- 有效的预测有助于减少杀虫剂的使用和改善作物管理.
研究的目的:
- 开发一个预测框架,用于区域农作物害虫Helicoverpa zea.
- 评估不同统计和机器学习模型的预测性能.
- 确定H. zea丰富度的关键预测因素,并优化监测策略.
主要方法:
- 利用了来自美国南部12个州的H. zea陷历史数据.
- 我们比较了四种模型:线性回归 (固定效应),贝叶斯空间截面 (有/没有固定效应) 和随机森林.
- 评估预测因素的重要性,包括陷位置,年份和当地宿主作物丰富度.
- 评估了陷密度对北卡罗来纳州预测准确性的影响.
主要成果:
- 随机森林模型证明了对H. zea种群动态最有效的预测.
- 陷的位置和年份是H. zea丰富度的最重要的预测因素.
- 与位置和年份相比,当地的宿主作物丰度是一个影响力较小的预测因素.
- 在北卡罗来纳州,陷密度可以减少约25%,同时保持预测准确度.
结论:
- 机器学习,特别是随机森林,为预测H. zea.提供了一个强大的方法.
- 优化陷放置和减少监测密度可以提高预测效率.
- 改善H. zea的年度预测在美国棉花带是可行的.
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