NSF Postdoctoral Fellowship in Biology FY 2021: Integrating phenology and species interactions into future predictions of spring ephemeral distributions

2021 财年 NSF 生物学博士后奖学金：将物候学和物种相互作用纳入春季短暂分布的未来预测

• 批准号: 2109482
• 负责人: Chelsea Miller
• 金额: $ 13.8万
• 依托单位: Miller, Chelsea N
• 依托单位国家: 美国
• 项目类别: Fellowship Award
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2109482&HistoricalAwards=false
• 关键词: NSF; Postdoctoral; Fellowship; Biology; FY

This action funds an NSF Postdoctoral Research Fellowship in Biology for FY 2021, Integrative Research Investigating the Rules of Life Governing Interactions Between Genomes, Environment and Phenotypes. The fellowship supports research and training of the fellow that will contribute to the area of Rules of Life in innovative ways. This project will use living collections of spring ephemeral plants at botanical gardens across the eastern US to determine whether changes in the timing of interaction with different species can help the plants adapt to climate change. Understanding the factors that help determine how species are distributed can help us understand ecology in general, and can also help inform conservation efforts. Much of the data for this project will be collected by citizen scientists, and the Fellow will engage with students by participating in outreach events at the host institution, establishing novel partnerships with local youth organizations, and mentoring local high school and college students, focusing on underrepresented groups in STEM. The Holden Arboretum in Kirtland, Ohio, is the host institution. Four additional institutions in the Midwest and southeast comprise the network of common gardens. Institutional volunteers will be trained to collect data on the timing of leaf-out, flowering, fruiting, pollination, seed dispersal, and herbivory for three spring ephemeral species in 2023-2024. These data will be used to parameterize trait species distribution models (TraitSDMs), a new statistical method that estimates the capacity of species to adapt to climate change (CC) by incorporating trait plasticity into projections of spatial distributions. Climate data will be obtained from the source of plant accessions and common gardens to predict spatial-temporal distributions of phenology traits. Traditional SDMs—which use the statistical relationship between species occurrences and climate variables to predict distributions—will also be built. It is anticipated that CC-induced poleward range shifts will be reflected in both trait- and traditional SDMs. However, because phenotypic plasticity has been shown to mitigate the effects of CC, range shifts predicted by TraitSDMs are expected to be smaller. Student interns will be recruited to support this project via data collection; interns will be further advised to develop related independent projects. The Fellow will partner with the Cleveland Footpath Foundation to develop a spring wildflower hike in an urban park, which will teach field botany skills to underserved youth while conveying the effects of CC on local habitats.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.

该行动资助了2021财年的NSF生物学博士后研究奖学金，即调查基因组，环境和表型之间相互作用的生命规则的综合研究。该研究金支持研究员的研究和培训，以创新的方式为生活规则领域做出贡献。该项目将使用美国东部植物园的春季短命植物的活体收集，以确定与不同物种相互作用的时间变化是否有助于植物适应气候变化。了解有助于确定物种分布的因素可以帮助我们了解一般的生态学，也可以帮助为保护工作提供信息。该项目的大部分数据将由公民科学家收集，研究员将通过参加主办机构的外联活动，与当地青年组织建立新的伙伴关系，并指导当地高中和大学生，重点关注STEM中代表性不足的群体，与学生互动。位于俄亥俄州克特兰的霍尔顿植物园是主办机构。中西部和东南部的另外四个机构组成了公共花园网络。机构志愿者将接受培训，收集2023-2024年三种春季短命物种的出叶、开花、结果、授粉、种子传播和食草时间的数据。这些数据将用于参数化特征物种分布模型（TraitSDMs），这是一种新的统计方法，通过将特征可塑性纳入空间分布预测来估计物种适应气候变化的能力。气候数据将从植物种质资源和普通园林中获得，以预测物候特征的时空分布。传统的空间数据模型--利用物种出现和气候变量之间的统计关系来预测分布--也将建立起来。预计CC引起的极向距离偏移将反映在性状和传统的SDM中。然而，由于表型可塑性已被证明可以减轻CC的影响，TraitSDM预测的范围偏移预计会更小。将招募学生实习生通过数据收集支持本项目;实习生将进一步建议开发相关的独立项目。该研究员将与克利夫兰步道基金会合作，在一个城市公园开展春季野花徒步旅行，向服务不足的年轻人传授野外植物学技能，同时传达CC对当地栖息地的影响。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。