Interpreting Data from Trapping of Stochastic Movers

解释随机动量陷阱的数据

• 批准号: 1411411
• 负责人: Jeffrey Schenker
• 金额: $ 30万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-15 至 2019-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1411411&HistoricalAwards=false
• 关键词: Interpreting; Data; Trapping; Stochastic; Movers

This collaboration between mathematical physicist Prof. Jeff Schenker and entomologist Prof. James Miller aims at a fundamental understanding of the science of trapping of insects, particularly agricultural pests. Capture of individual insects in traps baited with a lure, often a pheromone, has long been a critical measurement technique in field biology. The number of individuals caught in a trap can reveal which insects are present and when they active. However, the difficult problem of converting the number captured into an accurate estimate of pest density (the number of pests to be found in a given region) is at present poorly understood. This project aims to solve that problem through a combination of field experiments, computer simulations, and theoretical investigations into random movements that approximate insect flight. Better understanding of trapping will be of use to agricultural pest managers, in the detection and management of invasive species, and in monitoring and controlling the spread of insect disease vectors. In particular, the aimed-for improvements in population estimation will be a boon for farmers, allowing pest managers to recommend interventions like insecticide sprays only when there is clear scientific evidence that the cost of application is offset by the potential loss to the grower. The work will follow an integrated program of mathematical investigations on competitive trapping of stochastic movers and field experiments of marked release/capture of insects. The aim is to answer two questions: (1) Given an array of several traps what information about the dispersal, mobility and population density may be inferred from the various catches? (2) Given several traps how may we best arrange them to accurately infer estimates on population density from catch data? In the past, a missing link in regards to both problems has been an accurate representation of the probability of catch as it varies with the distances animals originated from the trap. The investigators have made considerable progress on modeling catch using computer simulated random walkers. By bringing mathematical analysis and numerical study of diffusion and random walks to bear on the problem, the research team anticipates being able to extract important scientific measurements from data that can be gathered easily and inexpensively in the field.

数学物理学家杰夫·申克教授和昆虫学家詹姆斯·米勒教授之间的合作旨在从根本上理解诱捕昆虫的科学，特别是农业害虫。在诱饵(通常是信息素)诱捕的陷阱中捕捉单个昆虫，长期以来一直是野外生物学中的一项关键测量技术。陷阱中捕获的个体数量可以揭示哪些昆虫存在以及它们何时活跃。然而，将捕获的数量转换为害虫密度(在特定地区发现的害虫数量)的准确估计的困难问题目前鲜为人知。这个项目旨在通过野外实验、计算机模拟和对昆虫飞行的随机运动的理论研究相结合的方式来解决这个问题。更好地了解诱捕法将有助于农业害虫管理人员检测和管理入侵物种，以及监测和控制病媒昆虫的传播。特别是，旨在改善人口估计的措施将对农民有利，只有在有明确的科学证据表明使用杀虫剂的成本被种植者的潜在损失抵消时，害虫管理人员才能建议喷洒杀虫剂等干预措施。这项工作将遵循一项关于随机移动竞争诱捕的数学调查和标记释放/捕获昆虫的田间实验的综合计划。其目的是回答两个问题：(1)给定几个陷阱的阵列，可以从不同的捕获物中推断出关于扩散、流动性和种群密度的什么信息？(2)给定几个捕获物，我们如何最好地安排它们，以便从捕获量数据中准确地推断出种群密度的估计？在过去，在这两个问题上缺少的一个环节是准确地表示捕获的概率，因为它随着动物从陷阱出发的距离而变化。研究人员在用计算机模拟随机游走者模拟捕获物方面取得了相当大的进展。通过对扩散和随机游动进行数学分析和数值研究，研究小组预计能够从现场可以轻松和廉价收集的数据中提取重要的科学测量数据。