Linking theory with data in spatial ecology and epidemiology

将空间生态学和流行病学的理论与数据联系起来

• 批准号: 386590-2010
• 负责人: Bolker, Benjamin
• 金额: $ 1.89万
• 依托单位: McMaster University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2013
• 资助国家: 加拿大
• 起止时间: 2013-01-01 至 2014-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=528956
• 关键词: Linking; theory; data; spatial; ecology

Understanding the spatial patterns occurring in ecological communities and during outbreaks of disease can tell us a great deal about the underlying processes involved. Understanding these mechanisms can in turn help us to predict and manage these biological systems. For example, knowing which environmental variables affect the spatial spread of disease vectors or invasive species, or the distribution of threatened species, as well as their ability to move across the landscape, will help us understand how best to eradicate, control, or preserve them. Mathematicians have built many abstract models that describe the spatial dynamics of organisms, but these models can be hard to connect with data. Statisticians develop tools for quantifying spatial patterns, but they can be hard to connect with the biological mechanisms.

了解生态群落和疾病爆发期间发生的空间模式可以告诉我们很多关于所涉及的潜在过程的信息。了解这些机制可以反过来帮助我们预测和管理这些生物系统。 例如，了解哪些环境变量会影响病媒或入侵物种的空间传播，或受威胁物种的分布，以及它们在景观中移动的能力，将有助于我们了解如何最好地根除，控制或保护它们。数学家已经建立了许多描述生物体空间动态的抽象模型，但这些模型很难与数据联系起来。统计学家开发了量化空间模式的工具，但它们很难与生物机制联系起来。