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Collaborative Research: ABI Innovation: RUI: Quantifying biogeographic history: a novel model-based approach to integrating data from genes, fossils, specimens, and environments

合作研究：ABI 创新：RUI：量化生物地理历史：一种基于模型的新颖方法来整合来自基因、化石、标本和环境的数据

基本信息

批准号：
1759797
负责人：
Allan Strand
金额：
$ 12.39万
依托单位：
College of Charleston
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-07-15 至 2023-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1759797&HistoricalAwards=false
关键词：
Collaborative Research ABI Innovation RUI

项目摘要

Forest ecosystems cover a third of the land area of the United States and are a significant economic and cultural resource. Managing forests for the future depends on knowledge of their historical dynamics. For example, after the last Ice Age many species, including trees, generally moved north as environmental conditions became more favorable, leading to large changes in population size and geographic range. Fundamental biological questions about these shifts include: (i) how do plants establish in new locations across great distances in spite of having limited seed dispersal abilities, (ii) to what degree do species travel synchronously as communities or individually, (iii) which species moved the fastest and why, and (iv) where did species reside during the last Ice Age? Traditionally scientists have used one of three types of data to address these questions: specimens from museums and herbaria matched with contemporary environmental data, DNA sequences that hold imprints of recent and past changes, and species' presence in the fossil record including ancient pollen deposited in lake sediments. However, studies based on single data types have not been able to fully resolve the aforementioned questions, largely due to lack of integrative computational methods and infrastructure. This research will develop methods and software that, for the first time, coherently combine the three main data types and existing theory to provide a more comprehensive understanding of species' biogeographic history. Each type of data has different strengths and weaknesses; utilizing the strengths of each will make best use of the total information on species' range shifts. The methods developed will provide the infrastructure needed to leverage "big data" and enable scientific progress on significant, long-standing questions about species historical dynamics, which will serve a variety of scientific communities. This work also serves the national interest, advancing prosperity and welfare by enabling future studies of natural environments which are an important cultural and economic resource. Knowledge gained by using these new methods can help inform management of natural resources (i.e. forests and grasslands) and functioning ecosystems, and identify geographic regions that are resilient to environmental stress or may contain unique genetic resources to help species adapt. These new computational methods will be produced in an open-source, online, documented, and transparent code development system with which anyone can interact, as well as through two interactive workshops that will emphasize participant diversity. This project will also advance science education through broader impacts at multiple educational levels by i) partnering with an established K-12 educational program to teach ecological concepts, ii) designing a course-based undergraduate research experience, iii) producing educational videos and exhibits at two botanical gardens that collectively reach two million visitors, and iv) providing training and mentoring to early career scientists and students. Despite continued improvements in data reliability and accuracy, questions about Quaternary species range shifts remain hotly debated. This debate is fueled in part by known, substantial limitations and biases of the primary data types used to reconstruct biogeographic history (i.e., fossils and inferred paleo-vegetation, current and hindcast species distribution models, and current and ancient genomic data). Conflicting results from past studies regarding the speed of range shifts and location of refugia inferred from different approaches have slowed progress in paleoecology for decades. This project will develop comprehensive, statistically robust informatic tools to coherently integrate the information content of disparate and heretofore disconnected data types and models for inferring species' genetic, demographic, and biogeographic history. The objective of this research is to build informatic infrastructure that will help scientists leverage information from multiple sources spanning space and time to (a) better estimate key demographic parameters, (b) generate maps of species distributions post-glaciation, and (c) account for uncertainty from each data type. The framework is rooted in Approximate Bayesian Computation but with additional modules that will build on the state-of-the-art in biogeographic inference. The informatic improvements will occur in four stages of increasing novelty and data integration, with specific outputs at each stage. The informatic advances will be evaluated for computational efficiency and effectiveness through analyses of both simulated data and an existing empirical dataset for a foundational tree species, green ash, Fraxinus pennsylvanica. This research will help scientists from many fields make the most benefit from the ongoing renaissance in methods and databases in genomics, environmental modeling, and paleo-data to help achieve better understanding of past species' dynamics (demographic growth rates, long distance dispersal, biotic velocities, etc.) at a spatial and temporal resolution that was previously unachievable. The scientific community will be involved in model and software design via open source, community development and coding on GitHub, and two hands-on workshops. Results from this project can be found online at https://github.com/orgs/TIMBERhub.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.

森林生态系统占美国陆地面积的三分之一，是一种重要的经济和文化资源。未来对森林的管理取决于对其历史动态的了解。例如，在上一个冰河时代之后，随着环境条件变得更加有利，包括树木在内的许多物种通常向北迁徙，导致种群规模和地理范围的巨大变化。关于这些变化的基本生物学问题包括：(I)尽管种子传播能力有限，植物如何在遥远的新地点建立；(Ii)物种作为群落或个体在多大程度上同步移动；(Iii)哪些物种移动最快，为什么；以及(Iv)在上一个冰河时代，物种居住在哪里？传统上，科学家使用三种类型的数据之一来解决这些问题：来自博物馆和草药的标本与当代环境数据相匹配，保存着最近和过去变化印记的DNA序列，以及化石记录中物种的存在，包括沉积在湖泊沉积物中的古代花粉。然而，基于单一数据类型的研究未能完全解决上述问题，这主要是由于缺乏综合的计算方法和基础设施。这项研究将开发方法和软件，首次将三种主要数据类型和现有理论连贯地结合在一起，以提供对物种生物地理历史的更全面的了解。每种类型的数据都有不同的长处和短处；利用每种数据的长处将最大限度地利用物种范围变化的全部信息。所开发的方法将提供所需的基础设施，以利用“大数据”，并使关于物种历史动态的重大、长期存在的问题能够取得科学进展，这将为各种科学界服务。这项工作也符合国家利益，通过使未来能够研究自然环境这一重要的文化和经济资源来促进繁荣和福利。通过使用这些新方法获得的知识可以帮助管理自然资源(即森林和草原)和正常运作的生态系统，并确定对环境压力具有弹性或可能包含独特遗传资源的地理区域，以帮助物种适应。这些新的计算方法将在一个任何人都可以与之互动的开源、在线、有文档和透明的代码开发系统中产生，并将通过两个强调参与者多样性的互动研讨会来产生。该项目还将通过以下方式在多个教育层面通过更广泛的影响推动科学教育：i)与已建立的教授生态概念的K-12教育项目合作，ii)设计基于课程的本科生研究体验，iii)制作教育视频和在两个植物园展出，总共有200万游客，以及iv)为早期职业科学家和学生提供培训和指导。尽管数据的可靠性和准确性不断提高，但关于第四纪物种范围转移的问题仍然存在激烈的辩论。用于重建生物地理历史的主要数据类型(即化石和推断的古植被、现代和后备物种分布模型以及现代和古代基因组数据)的已知实质性限制和偏见在一定程度上加剧了这场辩论。过去关于不同方法得出的范围转移速度和避难所位置的研究结果相互矛盾，几十年来减缓了古生态学的进展。该项目将开发全面的、统计上可靠的信息工具，以连贯地整合不同和迄今互不相关的数据类型和模型的信息内容，以推断物种的遗传、人口统计和生物地理历史。这项研究的目标是建立信息基础设施，帮助科学家利用跨越空间和时间的多个来源的信息来(A)更好地估计关键的人口统计参数，(B)生成冰川后物种分布图，以及(C)考虑每种数据类型的不确定性。该框架植根于近似贝叶斯计算，但具有其他模块，这些模块将建立在生物地理推理的最新技术之上。信息学的改进将分四个阶段进行，即增加新颖性和数据整合，每个阶段都有具体的产出。将通过分析模拟数据和现有的基础树种白蜡树的经验数据集来评估信息进步的计算效率和有效性。这项研究将帮助来自许多领域的科学家最大限度地受益于基因组学、环境模拟和古数据方面正在进行的方法和数据库的复兴，以帮助更好地理解过去物种的动态(人口增长率、远距离扩散、生物速度等)。以以前无法达到的空间和时间分辨率。科学界将通过开放源码、GitHub上的社区开发和编码以及两个动手讲习班参与模型和软件设计。该项目的结果可以在https://github.com/orgs/TIMBERhub.This网站上找到，该奖项反映了国家科学基金会的法定使命，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。