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: 1927319
• 负责人: Andrew Rominger
• 金额: $ 39.81万
• 依托单位: Santa Fe Institute
• 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 2021-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1927319&HistoricalAwards=false
• 关键词: IIBR; RoL; Collaborative; Research; Rules; 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.

由于越来越多的数据越来越多的数据，这是一个前所未有的机会，可以提高对管理生命多样性和动态的生物学规则的理解。 在人类系统破坏这些非常动态的关键时刻，理解生物多样性动态的目标是实现的。但是，从数据中得出理解所需的科学和计算工具仍然缺失。 大量的用户社区需要访问此类工具，从而促进参与和创新。 该项目将（1）建立一个计算模型，以针对生物多样性物种的多个方面，遗传，功能和系统发育； （2）通过测试三个示例系统中生物多样性的生成和维持生物多样性的主要假设来使用和完善该模型； （3）通过构建一个开源平台来准备各种数据源并运行模型，使科学界可以访问该模型； （4）创建教学上有效的课程和研讨会，以使来自许多背景的学生，研究人员和利益相关者能够了解生物多样性理论以及使用数据测试这些理论所需的数据科学工具。生命规则（角色）模型将是一种机械性的，基于模拟的基于模拟的假设检验和数据综合研究框架，以实现多种多样的数据，以实现多态性数据，以实现多态度的研究框架。生物多样性模式。该榜样将在机器学习中应用新技术，以将模型拟合到高维，跨尺度数据。该模型将模拟来自基于个体的生态和遗传中性模型的生态进化社区组装建筑，并增加了非中性，基于性状的竞争和环境过滤。新物种和特征将通过在当地社区的元社区和快速发展的长时间演变中产生。当地社区中的种群遗传学和物种丰度将通过出生，死亡，移民和突变进行建模。项目研究团队将通过在三个生物地理系统中测试四个假设的生活规则来完善和说明榜样的使用，现在可以为其提供多尺度的生物多样性数据。假设解决了社区组装中移民与物种形成的相对作用，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/如何/系统如何达到平衡。项目负责人建立了一个由14个合作者组成的网络，其中包括国家生态天文台网络，他们将在其多样化的系统中使用榜样并传播更广泛的采用。为了进一步减少使用障碍，将作为开源软件提供榜样模型框架，包括带有标准化元数据输出的R语言闪亮应用程序接口，以促进可重复性和共享。从角色模型中获得的见解与保护直接相关，例如，社区是否主要是通过物种形成或移民来组装的，还是强烈决定了它们对人为压力和最佳保护管理的反应。为了鼓励参与定量生物多样性研究，项目负责人将使用榜样作为互动教学工具，通过圣塔菲学院的复杂探索者计划开发大规模开放的在线课程。与数据木工和软件木工一起，研究团队还将提供面对面的数据科学培训研讨会。该角色项目的结果可以在https：//role-model.github.io.io中找到，该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响审查标准，认为值得通过评估来获得支持。