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

生物多样性的定义是在给定的地点和时间出现所有种类的植物、动物和其他生物。这些物种共享他们的环境，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”)对人类福祉非常重要。然而，由于环境变化更大、更快，知道某个地方的某个物种是否会持续存在，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、在哪里、何时以及哪些情况会发生变化。这些变化只有在事后才能明显看到。这个项目将创造一种新的方法来解决这个问题。目标是估计一个物种在给定地方生存的可能性，以及它的存在(或消失)如何影响其他物种也在那里生存的机会。这个项目还将帮助一位科学家和他将指导的学生开始他们的职业生涯，因为他们共同创造了一个新的理论。理解和预测物种持久性的主要困难在于了解控制生态群落动态的准确方程，以及关于初始条件、参数值、内在随机性的高度不确定性，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久负盛名的努力，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，需要全新的框架。开发这类框架涉及巨大的风险和许多挑战。这个项目将通过将统计力学集合理论的概念与结构稳定性的数学概念结合起来，应用于种群动力学，以解决目前生物预测的局限性，从而开辟新的天地。具体地说，这个项目的目标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来估计和解释未来行为的概率，该方法基于与这种行为的可观察性兼容的可能条件的分数。研究人员将开发模型驱动和数据驱动的方法来估计这些概率，并用文献中已经汇编的经验数据来验证它们。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.