QEIB: Stochastic Spatial Spread: Models and Experiments

QEIB：随机空间分布：模型和实验

• 批准号: 0516150
• 负责人: Alan Hastings
• 金额: $ 29.19万
• 依托单位: University of California-Davis
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-01 至 2009-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0516150&HistoricalAwards=false
• 关键词: QEIB; Stochastic; Spatial; Spread; Models

How fast a species expands its range depends on characteristics of the species and on random variation in time and space. This is a difficult problem to study in nature because of the large time and space scales that may be involved, and the lack of repeatability. To address these limitation, this project examines the problem in the laboratory using the flour beetle, Tribolium castaneum, as a model system. The study will include many replicates of artificial landscapes and varying demographic parameters under controlled conditions in an incubator. The experimental work will be coordinated with the development of mathematical models that incorporate variability. The models will allow extension to other systems and the development of general principles.The problem of spatial spread is of both great scientific and practical importance. The spread of invasive species is one of the most important and costly environmental issues facing the United States. Yet, there have been essentially no detailed, repeated experiments on how predictable spread is, and on how demographic parameters affect spread. As the current problem illustrates, progress in many ecological problems depends on the joint application of experimental and mathematical methods, so the training received by undergraduates during the course of this work will have very significant long term impacts.

物种扩大其范围的速度取决于物种的特征以及时间和空间的随机变化。 这是一个很难研究的问题，因为可能涉及的时间和空间尺度很大，并且缺乏可重复性。 为了解决这些限制，该项目使用面粉甲虫，tribolium castaneum作为模型系统来研究实验室中的问题。该研究将包括许多人造景观的重复和在孵化器中受控条件下的不同人口参数。 实验工作将与结合变异性的数学模型的发展进行协调。 这些模型将允许扩展到其他系统和一般原则的发展。空间传播的问题既具有巨大的科学和实际重要性。 入侵物种的传播是美国面临的最重要，最昂贵的环境问题之一。 然而，基本上没有关于可预测的传播以及人口参数如何影响传播的详细，重复的实验。 如当前的问题所示，许多生态问题的进展取决于实验和数学方法的共同应用，因此本工作期间，本科生接受的培训将产生非常重大的长期影响。