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.

森林生态系统覆盖了美国土地地区的三分之一，是一种重要的经济和文化资源。 为未来管理森林取决于对其历史动态的了解。 例如，在上一个冰河时代之后，许多物种（包括树木）通常随着环境条件变得更加有利而向北移动，从而导致人口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）尽管种子分散能力有限，植物如何在大距离内建立在新的位置，（ii）物种在哪些程度上同步地作为社区或单独旅行，（iii）哪种物种移动了最快，为什么以及（iv）在最后一个冰时代居住在最后的冰上？传统上，科学家使用了三种类型的数据之一来解决以下问题：博物馆和草药中的标本与当代环境数据相匹配，DNA序列，这些序列具有近期和过去变化的烙印，以及物种在化石记录中的存在，包括沉积在湖沉积物中的古代花粉。但是，基于单个数据类型的研究无法完全解决上述问题，这主要是由于缺乏集成计算方法和基础架构。这项研究将开发方法和软件，这些方法和软件首次连贯地结合了三种主要数据类型和现有理论，以提供对物种的生物地理历史的更全面的理解。 每种类型的数据都有不同的优势和劣势。 利用每个优势将充分利用有关物种范围变化的总信息。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，长期存在的问题上实现科学进步，这些问题将为各种科学社区提供服务。 这项工作还可以为国家利益提供服务，通过实现对自然环境的未来研究，这是重要的文化和经济资源，从而促进了繁荣和福利。 通过使用这些新方法获得的知识可以帮助告知管理自然资源（即森林和草原）和生态系统的知识，并确定对环境压力有弹性的地理区域，或者可能包含独特的遗传资源来帮助物种适应。这些新的计算方法将以开源，在线，记录和透明的代码开发系统以及任何人都可以进行交互的方式以及两个将强调参与者多样性的交互式研讨会。该项目还将通过在多个教育层面上的更广泛的影响来推进科学教育，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研究经验，iii）在两个植物园中制作教育视频和展览，该植物园共同培训和培训，以培训和IV培训，以提供早期职业科学家和学生的培训。尽管数据可靠性和准确性持续提高，但有关第四纪物种范围变化的问题仍然引起了人们的争议。这场辩论的一部分是由已知的主要局限性和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物，当前和后广播物种分布模型以及当前和古代基因组数据）的主要数据类型的偏见。过去关于范围变化速度和从不同方法推断出的避难所位置的研究矛盾的结果，几十年来，古生态学的进展减慢了。该项目将开发全面，统计上强大的信息工具，以连贯整合不同和迄今为止断开的数据类型的信息内容以及推断物种的遗传，人口统计和生物地理历史的模型。这项研究的目的是建立信息基础架构，这些基础架构将帮助科学家利用跨越空间和时间的多个来源的信息到（a）更好地估计关键的人口统计参数，（b）在 - 固定后生成物种分布的地图，并且（c）从每种数据类型中解释了不确定性。该框架植根于近似贝叶斯计算，但具有其他模块，这些模块将基于生物地理推断的最先进。信息改进将在增加新颖性和数据集成的四个阶段，每个阶段都有特定的输出。通过分析模拟数据和现有的基础树种，绿灰，宾夕法尼亚州Fraxinus pennsylvanica的现有经验数据集，将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在基因组学，环境建模和古数据库中持续的文艺复兴时期所获得的最大收益，以帮助更好地理解过去物种的动态（人口统计学增长率，长距离分散，生物速度等）以前是无法实现的。科学界将通过开源，社区开发和GitHub上的编码以及两个动手研讨会参与模型和软件设计。该项目的结果可以在https://github.com/orgs/timberhub..tim.tim.tim.tim.com上在线找到，该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响评估审查标准，被认为值得通过评估。

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.