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

合作研究：ABI 创新：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

• 批准号: 1759729
• 负责人: John Robinson
• 金额: $ 23.22万
• 依托单位: Michigan State University
• 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=1759729&HistoricalAwards=false
• 关键词: Collaborative; Research; ABI; Innovation; Quantifying

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.

森林生态系统覆盖美国三分之一的土地面积，是重要的经济和文化资源。 未来的森林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移，导致种群规模和地理范围发生巨大变化。 关于这些转变的基本生物学问题包括：（i）尽管种子传播能力有限，植物如何远距离在新地点定居，（ii）物种作为群落或单独同步移动的程度如何，（iii）哪些物种移动速度最快以及原因，以及 (iv) 上一个冰河世纪期间，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：来自博物馆和植物标本室的标本与当代环境数据相匹配，保留最近和过去变化印记的DNA序列，以及化石记录中存在的物种，包括沉积在古代花粉中的物种。湖泊沉积物。然而，基于单一数据类型的研究未能完全解决上述问题，很大程度上是由于缺乏综合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起来，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优点和缺点； 利用每个物种的优势将充分利用有关物种分布范围变化的全部信息。开发的方法将提供利用“大数据”所需的基础设施，并在有关物种历史动态的重大、长期存在的问题上取得科学进展，这将为各个科学界服务。 这项工作还服务于国家利益，通过对作为重要文化和经济资源的自然环境的未来研究来促进繁荣和福利。 通过使用这些新方法获得的知识可以帮助为自然资源（即森林和草原）和功能生态系统的管理提供信息，并确定能够抵御环境压力或可能包含独特遗传资源以帮助物种适应的地理区域。这些新的计算方法将在开源、在线、记录和透明的代码开发系统中生成，任何人都可以与之交互，并通过两个强调参与者多样性的交互式研讨会来生成。该项目还将通过在多个教育层面产生更广泛的影响来推进科学教育，具体方式如下：i) 与已建立的 K-12 教育计划合作，教授生态概念；ii) 设计基于课程的本科生研究体验；iii) 在以下网站制作教育视频和展览：两个植物园，总共吸引了 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，但有关第四纪物种范围变化的问题仍然存在激烈争论。这场争论在一定程度上是由于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、当前和事后物种分布模型以及当前和古代基因组数据）的已知的实质性限制和偏差而加剧的。过去关于范围变化速度和通过不同方法推断的避难所位置的研究结果相互矛盾，导致古生态学的进展缓慢了数十年。该项目将开发全面的、统计上强大的信息工具，以连贯地整合不同且迄今为止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）更好地估计关键人口参数，（b）生成冰川后物种分布图，以及（c）解释每种数据类型的不确定性。该框架植根于近似贝叶斯计算，但具有基于最先进的生物地理推理的附加模块。信息改进将分四个阶段进行，即增加新颖性和数据集成，每个阶段都有具体的产出。通过对基础树种——绿白蜡树——宾夕法尼亚水曲柳的模拟数据和现有经验数据集的分析，将评估信息学进展的计算效率和有效性。这项研究将帮助许多领域的科学家从基因组学、环境建模和古数据的方法和数据库的不断复兴中获得最大利益，以帮助更好地了解过去物种的动态（人口增长率、长距离扩散、生物多样性）。速度等）以以前无法实现的空间和时间分辨率。科学界将通过开源、社区开发和 GitHub 上的编码以及两个实践研讨会参与模型和软件设计。该项目的结果可在网上找到：https://github.com/orgs/TIMBERhub。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Inference of biogeographic history by formally integrating distinct lines of evidence: genetic, environmental niche and fossil

通过正式整合不同的证据来推断生物地理历史：遗传、环境生态位和化石

• DOI: 10.1111/ecog.04327
• 发表时间: 2019
• 期刊: Ecography
• 影响因子: 5.9
• 作者: Hoban, Sean;Dawson, Andria;Robinson, John D.;Smith, Adam B.;Strand, Allan E.
• 通讯作者: Strand, Allan E.

Assessing the relative importance of nurse species on Mediterranean human‐altered areas

评估地中海人类改变地区保育物种的相对重要性

• DOI: 10.1111/rec.13402
• 发表时间: 2021
• 期刊: Restoration Ecology
• 影响因子: 3.2
• 作者: Garrote, Pedro J.;Castilla, Antonio R.;Fedriani, Jose M.
• 通讯作者: Fedriani, Jose M.

Spatiotemporal dynamics of genetic variation at the quantitative and molecular levels within a natural Arabidopsis thaliana population

自然拟南芥种群内定量和分子水平遗传变异的时空动态

• DOI: 10.1111/1365-2745.13981
• 发表时间: 2022
• 期刊: Journal of Ecology
• 影响因子: 5.5
• 作者: Méndez‐Vigo, Belén;Castilla, Antonio R.;Gómez, Rocío;Marcer, Arnald;Alonso‐Blanco, Carlos;Picó, F. Xavier
• 通讯作者: Picó, F. Xavier

Examining the spatiotemporal variation of genetic diversity and genetic rarity in the natural plant recolonization of human-altered areas

检查人类改变地区自然植物重新殖民中遗传多样性和遗传稀有性的时空变化

• DOI: 10.1007/s10592-023-01503-8
• 发表时间: 2023
• 期刊: Conservation Genetics
• 影响因子: 2.2
• 作者: Garrote, Pedro J.;Castilla, Antonio R.;Picó, F. Xavier;Fedriani, Jose M.
• 通讯作者: Fedriani, Jose M.