CAREER: Advancing Macroecology Using Informatics and Entropy Maximization

职业：利用信息学和熵最大化推进宏观生态学

• 批准号: 0953694
• 负责人: Ethan White
• 金额: $ 65万
• 依托单位: Utah State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-03-15 至 2016-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0953694&HistoricalAwards=false
• 关键词: CAREER; Advancing; Macroecology; Using; Informatics

Climate change, invasive species and other important factors impacting ecological systems operate at continental to global scales. At these scales, conducting experiments can be difficult, if not impossible. Therefore, ecologists increasingly rely on analyses of large scale observational data to predict how these systems will respond to increasing changes in climate and habitat. Progress in this area of research has been slowed by the large number of patterns used to characterize ecological structure, and by the fact that most research focuses on a single group of species thus limiting the generality of the results. This project will increase the speed at which knowledge of ecological systems is acquired by characterizing the relationships among ecological patterns and focusing research on the small number of key patterns that need to be studied to understand the behavior of ecological systems. This will be accomplished using advanced methods from physics that characterize the most likely form of an ecological pattern given a small number of constraints on the system. This research will test the performance of this approach using data on wide variety of species. This method will then be combined with established ecological models to predict a suite of major ecological patterns using only a small number of environmental variables.This project will improve how ecologists test and establish the generality of theories by educating ecologists in advanced computational methods through online and university courses, by providing web-based resources on the collections of data that are available and how to utilize them, and by automating complicated database tasks using computer programs that download, configure, and install optimized versions of ecological databases, thus allowing the rapid incorporation of available data into research projects. This combined research and education program has the potential to substantially improve the rate at which the field of ecology advances by focusing the research effort on a smaller number of patterns and processes, and by allowing ecologists to rapidly determine if a given pattern or hypothesis is general and if not how it varies among ecological systems.

气候变化、入侵物种和其他影响生态系统的重要因素在大陆到全球范围内发挥作用。在这样的规模下，进行实验即使不是不可能，也是很困难的。因此，生态学家越来越依赖于对大规模观测数据的分析来预测这些系统将如何应对日益增加的气候和栖息地变化。这一研究领域的进展已经被用来描述生态结构的大量模式所减缓，而且大多数研究都集中在单一物种群上，从而限制了结果的普遍性。该项目将通过描述生态模式之间的关系，并将研究重点放在需要研究的少数关键模式上，以了解生态系统的行为，从而加快获得生态系统知识的速度。这将使用来自物理学的先进方法来完成，这些方法表征了给定系统中少量约束条件的最可能的生态模式形式。这项研究将使用各种物种的数据来测试这种方法的性能。然后，该方法将与已建立的生态模型相结合，仅使用少量环境变量就能预测一套主要的生态模式。该项目将通过在线和大学课程向生态学家传授先进的计算方法，提供基于网络的可用数据集资源以及如何利用这些资源，以及使用下载、配置和安装优化版本的生态数据库的计算机程序自动执行复杂的数据库任务，从而改进生态学家测试和建立理论的普遍性的方式。这样就可以快速地将现有数据纳入研究项目。这一研究与教育结合的项目有可能大幅提高生态学领域的发展速度，因为它将研究工作集中在少数模式和过程上，并使生态学家能够迅速确定一个给定的模式或假设是否普遍，如果不是，它在生态系统中是如何变化的。