EAGER: Bioforecasting: understanding and predicting species persistence in ecological communities under changing environments

EAGER：生物预测：了解和预测不断变化的环境下生态群落中物种的持久性

• 批准号: 2024349
• 负责人: Serguei Saavedra
• 金额: $ 19.93万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-15 至 2023-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2024349&HistoricalAwards=false
• 关键词: EAGER; Bioforecasting; understanding; predicting; species

Biodiversity is defined as the presence of all species of plants, animals and other living things at a given place and time. These species share their environment and interact in complex ways. Scientists know that biodiversity is critical for providing many ecological services necessary for the planet’s health. Examples of these services include nutrient cycling, water purification, and soil formation. Thus, understanding and predicting changes in biodiversity (called "bioforecasting") is important for human wellbeing. However, because of larger and faster environmental changes, knowing whether a species in a given place will persist has become one of the biggest ecological challenges that scientists face. The main problem is that it’s virtually impossible to know how, where, when and which environmental conditions will change. The changes are obvious only in hindsight. This project will create a new way to solve the problem. The goal is to estimate the chance that a species will persist in a given place and how its presence (or absence) can affect the chance of other species persisting there, too. This project will also help launch the careers of a scientist and the students he will mentor as they together craft a new theory.The main difficulty in understanding and predicting species persistence resides in knowing the exact equations governing the dynamics of ecological communities and the high uncertainty regarding initial conditions, parameter values, intrinsic randomness, and more importantly, how the changing external conditions will affect all of these dynamics. While bioforecasting is already a well-established endeavor in ecological research, current bioforecasting approaches demand extensive amounts of data and their generalization often lacks experimental validation. Thus, it has been emphasized that radically new frameworks are needed. Development of such frameworks involve large risks and many challenges. This project will break new ground by integrating concepts from the ensemble theory of statistical mechanics with the mathematical concepts of structural stability, applied to population dynamics to tackle current limitations in bioforecasting. Specifically, instead of aiming to study the future behavior of a system by inferring the main conditions acting upon it, this project will provide a testable methodology to estimate and interpret the probability of a future behavior based on the fraction of possible conditions compatible with the observability of such behavior. The investigator will develop model-driven and data-driven approaches to estimate such probabilities and validate them with empirical data already compiled in the literature.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.

生物多样性的定义是在特定的地点和时间存在所有种类的植物，动物和其他生物。这些物种共享它们的环境，并以复杂的方式相互作用。科学家们知道，生物多样性对于提供地球健康所需的许多生态服务至关重要。这些服务的例子包括养分循环、水净化和土壤形成。因此，了解和预测生物多样性的变化（称为“生物预测”）对人类福祉至关重要。然而，由于环境变化更大，更快，了解一个物种在一个给定的地方是否会继续存在已成为科学家面临的最大生态挑战之一。主要的问题是，几乎不可能知道环境条件将如何、在何处、何时以及何种情况下发生变化。这些变化只有在事后才是显而易见的。这个项目将为解决这个问题开辟一条新路。其目标是估计一个物种在给定地方持续存在的机会，以及它的存在（或不存在）如何影响其他物种在那里持续存在的机会。该项目还将帮助启动一个科学家和他将指导的学生的职业生涯，因为他们一起制定一个新的理论。理解和预测物种持久性的主要困难在于知道控制生态群落动态的精确方程和初始条件的高度不确定性，参数值，内在随机性，更重要的是，不断变化的外部条件将如何影响所有这些动态。虽然生物预测在生态学研究中已经是一项成熟的奋进，但目前的生物预测方法需要大量的数据，而且它们的推广往往缺乏实验验证。因此，有人强调，需要有全新的框架。制定这种框架涉及很大的风险和许多挑战。该项目将通过将统计力学的集合理论的概念与结构稳定性的数学概念相结合来开辟新天地，应用于人口动态，以解决生物预测的当前局限性。具体来说，该项目的目的不是通过推断作用于系统的主要条件来研究系统的未来行为，而是提供一种可测试的方法，根据与这种行为的可观察性兼容的可能条件的比例来估计和解释未来行为的概率。研究人员将开发模型驱动和数据驱动的方法来估计这种概率，并与文献中已经汇编的经验数据进行验证。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

Observed Ecological Communities Are Formed by Species Combinations That Are among the Most Likely to Persist under Changing Environments

观察到的生态群落是由最有可能在不断变化的环境下持续存在的物种组合形成的

• DOI: 10.1086/711663
• 发表时间: 2021
• 期刊: The American Naturalist
• 作者: Medeiros, Lucas P.;Boege, Karina;del-Val, Ek;Zaldívar-Riverón, Alejandro;Saavedra, Serguei
• 通讯作者: Saavedra, Serguei

Untangling the complexity of priority effects in multispecies communities

理清多物种群落中优先效应的复杂性

• DOI: 10.1111/ele.13870
• 发表时间: 2021
• 期刊: Ecology Letters
• 影响因子: 8.8
• 作者: Song, Chuliang;Fukami, Tadashi;Saavedra, Serguei;Wootton, ed., Tim
• 通讯作者: Wootton, ed., Tim

Structural forecasting of species persistence under changing environments

变化环境下物种持久性的结构预测

• DOI: 10.1111/ele.13582
• 发表时间: 2020
• 期刊: Ecology Letters
• 影响因子: 8.8
• 作者: Saavedra, Serguei;Medeiros, Lucas P.;AlAdwani, Mohammad;Boettiger, ed., Carl
• 通讯作者: Boettiger, ed., Carl

Impact of colonization history on the composition of ecological systems.

• DOI: 10.1103/physreve.103.052403
• 发表时间: 2021-05
• 期刊: Physical review. E
• 作者: Zhao N;Saavedra S;Liu YY
• 通讯作者: Liu YY

Experimental evidence of the importance of multitrophic structure for species persistence

• DOI: 10.1073/pnas.2023872118
• 发表时间: 2021-03-23
• 期刊: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• 影响因子: 11.1
• 作者: Bartomeus, Ignasi;Saavedra, Serguei;Godoy, Oscar
• 通讯作者: Godoy, Oscar