IIBR RoL: Collaborative Research: A Rules Of Life Engine (RoLE) Model to Uncover Fundamental Processes Governing Biodiversity

IIBR RoL：协作研究：揭示生物多样性基本过程的生命规则引擎 (RoLE) 模型

• 批准号: 1927520
• 负责人: Michael Dawson
• 金额: $ 10.62万
• 依托单位: University of California - Merced
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1927520&HistoricalAwards=false
• 关键词: IIBR; RoL; Collaborative; Research; Rules

An unprecedented opportunity to advance understanding of the biological rules that govern the diversity and dynamics of life now exists thanks to the large quantity and variety of data that are becoming increasingly available. This goal of understanding biodiversity dynamics is enabled at a critical moment when human systems are disrupting those very dynamics. However, the scientific and computational tools needed to derive understanding from data are still missing. Such tools need to be accessible to a broad community of users, thereby catalyzing involvement and innovation. This project will (1) build a computational model for multiple aspects of biodiversity-species abundance, genetic, functional, and phylogenetic; (2) use and refine this model by testing major hypotheses about the generation and maintenance of biodiversity in three exemplar systems; (3) make the model accessible to the scientific community by building an open-source platform to prepare diverse data sources and run the model; and (4) create pedagogically effective courses and workshops to enable students, researchers, and stakeholders from many backgrounds to understand biodiversity theory and the data science tools needed to test those theories with data.The Rules of Life Engine (RoLE) model will be a mechanistic, simulation-based hypothesis-testing and data synthesis framework enabling scientists with multi-dimensional biodiversity data to generate and test hypotheses about the processes driving biodiversity patterns. The RoLE model will apply new techniques in machine learning to fit models to high dimensional, cross-scale data. The model will simulate eco-evolutionary community assembly building from individual-based ecological and genetic neutral models with added non-neutral, trait-based competition and environmental filtering. New species and traits will arise through long time scale evolution in the meta-community and rapid evolution in the local community. Population genetics and species abundances in the local community will be modeled through birth, death, immigration, and mutation. The project research team will refine and illustrate the use of the RoLE model by testing four hypothesized rules of life across three bio-geographic systems for which multi-scale biodiversity data are now available. The hypotheses address the relative roles of immigration versus speciation in community assembly, how species interactions influence diversity, how different assembly histories determine the strength of species interactions, and whether/how systems come to equilibrium. The project leaders have established a network of 14 collaborators, including the National Ecological Observatory Network, who will use the RoLE model in their diverse systems and propagate wider adoption. In order to further reduce barriers to use, the RoLE model framework will be made available as open source software, including an R language Shiny App interface with standardized metadata outputs to promote reproducibility and sharing. The insights gained from the RoLE model are of direct relevance to conservation, e.g., whether or not communities are assembled primarily by speciation or immigration strongly determines their response to anthropogenic pressures and optimal conservation management. To encourage participation in quantitative biodiversity research, the project leaders will develop a massively open online course through the Santa Fe Institute?s Complexity Explorer program using the RoLE model as an interactive teaching tool. In conjunction with Data Carpentry and Software Carpentry, the research team will also provide an in-person data science training workshop. Results from the RoLE project can be found at https://role-model.github.io.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.

由于数据的数量和种类越来越多，现在存在着一个前所未有的机会，可以促进对支配生命多样性和动态的生物学规则的理解。 理解生物多样性动态的目标是在人类系统破坏这些动态的关键时刻实现的。然而，从数据中获得理解所需的科学和计算工具仍然缺乏。 这类工具需要向广大用户提供，从而促进参与和创新。 该项目将（1）建立一个生物多样性多方面的计算模型--物种丰度、遗传、功能和系统发育;（2）通过在三个示范系统中测试关于生物多样性产生和维持的主要假设来使用和完善该模型;（3）通过建立一个开放源平台来准备各种数据源并运行该模型，使科学界可以使用该模型;以及（4）创建教学有效的课程和研讨会，使来自不同背景的学生，研究人员和利益相关者能够理解生物多样性理论以及用数据测试这些理论所需的数据科学工具。生命规则引擎（RoLE）模型将是一个机械的，基于模拟的假设测试和数据综合框架，使科学家能够进行多种多维生物多样性数据，以生成和测试有关驱动生物多样性模式的过程的假设。RoLE模型将应用机器学习中的新技术，将模型拟合到高维、跨尺度的数据中。该模型将从基于个体的生态和遗传中性模型中加入非中性、基于特征的竞争和环境过滤，模拟生态进化群落的构建。新的物种和性状将通过元社区的长时间尺度进化和当地社区的快速进化而产生。当地社区的种群遗传学和物种丰度将通过出生、死亡、移民和突变来建模。项目研究小组将通过测试三个生物地理系统中的四种假设的生命规则来完善和说明RoLE模型的使用，其中多尺度生物多样性数据现已可用。该假说解决移民与物种形成在社区组装的相对作用，物种相互作用如何影响多样性，不同的组装历史如何决定物种相互作用的强度，以及系统是否/如何达到平衡。项目负责人已经建立了一个由14个合作者组成的网络，包括国家生态观测站网络，他们将在其不同的系统中使用RoLE模型，并推广更广泛的采用。为了进一步减少使用障碍，RoLE模型框架将作为开源软件提供，包括一个R语言的Shiny App界面，具有标准化的元数据输出，以促进可重复性和共享。从RoLE模型中获得的见解与保护直接相关，例如，群落是否主要通过物种形成或移民聚集在一起，在很大程度上决定了它们对人为压力的反应和最佳的保护管理。为了鼓励参与定量生物多样性研究，项目负责人将通过圣达菲研究所开发一个大规模开放的在线课程。的复杂性探索程序使用角色模型作为交互式教学工具。与Data Carpentry和Software Carpentry一起，研究团队还将提供一个面对面的数据科学培训研讨会。RoLE项目的结果可以在www.example.com上找到https://role-model.github.io.This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。