Combining mechanism and maximum entropy in a dynamic hybrid theory of disturbance macroecology

扰动宏观生态学动态混合理论中的机制与最大熵的结合

• 批准号: 1751380
• 负责人: John Harte
• 金额: $ 46万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-04-15 至 2021-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1751380&HistoricalAwards=false
• 关键词: Combining; mechanism; maximum; entropy; dynamic

This research will advance the capacity of science to forecast the behavior of ecosystems undergoing change as a consequence of either natural or human disturbance. With the development of quantitative predictive theory and models, ecologists have already made considerable progress understanding the structure and properties of undisturbed ecosystems. But numerous observations indicate that this understanding fails when ecosystems are changing rapidly. The project will build upon ideas that have proven successful in statistical physics and the relatively new field of information theory. The expected output is a quantitative, predictive and thus testable, theory of ecosystem structure that will be applicable to both undisturbed ecosystems and to those that are changing rapidly in response to disturbance. Because our livelihoods are entangled with the health of our ecosystems in numerous ways, both insofar as our activities can alter nature and nature can enrich our lives, this research will contribute to the betterment of humankind as well as the advancement of basic science. This project will support a postdoctoral research fellow and provide research training experiences for graduate students. The objective of this research is to advance and test a dynamic theory that can predict patterns in the abundance, distribution, and energetics of individuals and species in ecosystems. The theory should apply to ecosystems that are either static or changing rapidly in response to disturbance. To achieve that goal, insights from statistical physics and information theory will be combined with explicit mechanisms governing organismal and population growth, as well as migration, diversification, and extinction. Stochastic mechanistic theory will predict the time-dependent probability distributions of a small set of governing state variables that play a role similar to that of pressure, volume, and temperature in thermodynamics. These state variables will then provide the constraints used in maximizing information entropy to yield predicted probability distributions for the detailed patterns displayed by individuals and species. The expected outcome is a general theory of both static and dynamic ecology. Ecological census data from the tropics to the tundra, from plants to animals, across a wide range of spatial scales, and under a variety of disturbances, will be used to test the predictions of the new theory.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这项研究将提高科学预测由于自然或人为干扰而发生变化的生态系统行为的能力。随着定量预测理论和模型的发展，生态学家对未扰动生态系统的结构和性质的认识已经取得了相当大的进展。 但大量的观察表明，当生态系统迅速变化时，这种理解就失败了。 该项目将建立在已被证明在统计物理学和信息理论的相对较新的领域成功的想法。 预期的产出是一个定量的、可预测的、因而是可检验的生态系统结构理论，它既适用于未受干扰的生态系统，也适用于那些因干扰而迅速变化的生态系统。 由于我们的生计在许多方面与我们生态系统的健康密切相关，无论是我们的活动可以改变自然，还是自然可以丰富我们的生活，这项研究都将有助于改善人类生活，促进基础科学的发展。该项目将支持一名博士后研究员，并为研究生提供研究培训经验。 这项研究的目的是推进和测试一个动态的理论，可以预测模式的丰度，分布和生态系统中的个人和物种的能量。 这一理论应该适用于那些要么是静态的，要么是对干扰做出快速反应而发生变化的生态系统。 为了实现这一目标，统计物理学和信息论的见解将与管理生物和人口增长以及迁移，多样化和灭绝的明确机制相结合。 随机力学理论将预测一小部分控制状态变量随时间变化的概率分布，这些状态变量的作用类似于热力学中的压力、体积和温度。 然后，这些状态变量将提供用于最大化信息熵的约束，以产生个体和物种所显示的详细模式的预测概率分布。预期的结果是静态和动态生态学的一般理论。 从热带到苔原，从植物到动物，在广泛的空间尺度上，在各种干扰下，生态普查数据将被用于测试新理论的预测。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

项目成果

Relating the strength of density dependence and the spatial distribution of individuals

• DOI: 10.3389/fevo.2021.691792
• 发表时间: 2019-11
• 期刊: bioRxiv
• 作者: Micah Brush;J. Harte

Shifting macroecological patterns and static theory failure in a stressed alpine plant community

• DOI: 10.1002/ecs2.3548
• 发表时间: 2021-06-01
• 期刊: ECOSPHERE
• 影响因子: 2.7
• 作者: Franzman，Juliette;Brush，Micah;Harte，John
• 通讯作者: Harte，John