URoL:EN: Emergence of function and dynamics from ecological interaction networks

URoL:EN：生态相互作用网络中功能和动态的出现

• 批准号: 2222478
• 负责人: Michael Gil
• 金额: $ 300万
• 依托单位: University of Colorado at Boulder
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2027-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2222478&HistoricalAwards=false
• 关键词: URoL; EN; Emergence; function; dynamics

Biological systems at virtually all levels of organization are defined by diversity – diversity not only of their constituent units, but also of the interactions among those units. Understanding how the core functions of these systems emerge from such diverse interactions is a fundamental challenge that cuts across fields ranging from physiology and medicine to ecology and environmental engineering. Scientists have long sought to simplify biological interactions by classifying them as either negative (e.g., competitors vying for resources) or positive (e.g., cells cooperating within tissue). Yet, upon close inspection, biological interactions regularly defy this simple dichotomy, and are instead “multivalent”: they involve many different types of interaction occurring simultaneously. Examples include bacteria that exchange genes conferring antibiotic resistance while competing for limiting nutrients, or trees that share carbon through mycorrhizal networks even as they compete for light and water. Multivalent interactions such as these appear to be ubiquitous in biology, but they are not easily incorporated into the conventional network models that are widely used to study system-scale dynamics. This project aims to reveal the Rule of Life that governs how function and dynamics emerge in systems of multivalent interactors. The researchers will decipher this Rule using a model ecosystem: coral reefs. In coral reefs, consumption of algae by fish drives dynamics of diverse fish populations and promotes a coral-dominated ecosystem state. The project team will rigorously measure how multivalent interactions among fish species scale up to influence ecosystem dynamics. In doing so, the team will develop data collection technologies and mathematical tools that will provide a generalizable methodology for measuring and understanding one of the most elusive, yet fundamental aspects of complex biological systems: biological interactions themselves. The project’s findings will inform management of coral reef ecosystems vital to over 500 million people worldwide. Moreover, the project will create a technical undergraduate internship program for students from historically underrepresented backgrounds and an international reef monitoring program that empowers local citizen scientists.Through three aims, the project team will develop methods that discover hidden structure in complex networks of ecological interactions and exploit that structure to understand ecosystem-level function and responses to environmental change. The first phase of the project will use field experiments, new camera technologies, and computer vision to directly measure ecological interactions among species, along with a novel algorithmic modeling framework to describe interaction behaviors based on quantitative behavioral traits. The second phase will use manifold learning methods to search behavioral trait data for “functional clusters” of species and exploit this structure to derive coarse-grained dynamical models of the system. These models will be used to evaluate how the structure of interactions drives emergent ecosystem function. In the third phase, the team will use data-driven dynamical models to project how system function and state will respond to future environmental change. Coarse-grained ecosystem models will be developed to understand long-term, system-scale responses to environmental perturbations. These models will incorporate empirically derived evolutionary rates to evaluate the capacity for evolutionary rescue to influence how ecosystems will respond to change. The high-throughput data-collection technologies, dimension reduction tools, and dynamical modeling methods developed in this research will help provide a novel toolkit for data-driven discovery of the dynamical structure of interactions and their consequences in complex biological systems.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.

几乎所有组织的生物系统都由多样性定义 - 多样性不仅是其宪法单位的多样性，而且是这些单位之间的相互作用。了解这些系统的核心功能是如何从这种多样性相互作用中出现的，这是一个基本挑战，它跨越了从生理学和医学到生态学和环境工程的领域。长期以来，科学家具有将生物相互作用归类为负面的（例如，争夺资源的竞争者）或正（例如，在组织中合作的细胞）来简化生物学相互作用。然而，经过仔细检查，生物学相互作用经常违背这种简单的二分法，而是“多价”：它们涉及许多同时发生的不同类型的相互作用。例子包括细菌在竞争限制营养的同时赋予抗生素耐药性的细菌，或者通过菌根网络共享碳的树木，即使它们争夺光和水。诸如此类的多价相互作用似乎在生物学上是无处不在的，但是它们不容易被整合到广泛用于研究系统规模动力学的传统网络模型中。该项目旨在揭示生活在多价交互者系统中的功能和动态如何出现的生活规则。研究人员将使用模型生态系统：珊瑚礁破译该规则。在珊瑚礁中，鱼类食用藻类驱动潜水员鱼类种群的动态，并促进了珊瑚为主的生态系统状态。项目团队将严格衡量鱼类物种之间的多价相互作用如何扩大影响生态系统动态。在这样做的过程中，团队将开发数据收集技术和数学工具，这些技术将提供一种可普遍的方法，用于衡量和理解复杂生物学系统的最难以捉摸，最基本的方面之一：生物学相互作用本身。该项目的发现将为管理珊瑚礁生态系统的管理提供信息，对于全球超过5亿人至关重要。此外，该项目将为来自历史上代表性不足的背景和国际珊瑚礁监测计划的学生创建技术本科课程，以赋予当地公民科学家的能力。通过三个目标，项目团队将开发出在生态互动的复杂网络中发现隐藏结构的方法，并利用该结构，以理解生态系统层面功能和对环境的响应。该项目的第一阶段将使用现场实验，新的相机技术和计算机视觉直接测量物种之间的生态相互作用，以及一种新颖的算法建模框架来描述基于定量行为性状的相互作用行为。第二阶段将使用多种学习方法来搜索物种“功能簇”的行为特征数据，并探索这种结构以得出系统的粗粒动力学模型。这些模型将用于评估相互作用的结构如何驱动新生态系统功能。在第三阶段，团队将使用数据驱动的动态模型来投影系统功能和状态如何应对未来的环境变化。将开发粗粒细粒的生态系统模型，以了解对环境扰动的长期，系统规模的响应。这些模型将纳入经验得出的进化速率，以评估进化救援的能力，以影响生态系统如何应对变化。 The high-throughput data-collection technologies, dimension reduction tools, and dynamic modeling methods developed in this research will help provide a novel toolkit for data-driven discovery of the dynamic structure of interactions and their consequences in complex biological systems.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.

项目成果

Demystifying image-based machine learning: a practical guide to automated analysis of field imagery using modern machine learning tools

揭秘基于图像的机器学习：使用现代机器学习工具自动分析现场图像的实用指南

• DOI: 10.3389/fmars.2023.1157370
• 发表时间: 2023
• 期刊: Frontiers in Marine Science
• 影响因子: 3.7
• 作者: Belcher, Byron T.;Bower, Eliana H.;Burford, Benjamin;Celis, Maria Rosa;Fahimipour, Ashkaan K.;Guevara, Isabela L.;Katija, Kakani;Khokhar, Zulekha;Manjunath, Anjana;Nelson, Samuel
• 通讯作者: Nelson, Samuel