NSF Postdoctoral Fellowship in Biology FY 2017: Leveraging digital herbaria and crowd-sourced photos to understand climate-driven disruption of community flowering phenology

2017 财年 NSF 生物学博士后奖学金：利用数字植物标本室和众包照片来了解气候驱动的群落开花物候学破坏

• 批准号: 1711936
• 负责人: Ian Breckheimer
• 金额: $ 13.8万
• 依托单位: Breckheimer Ian K
• 依托单位国家: 美国
• 项目类别: Fellowship Award
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-11-01 至 2019-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1711936&HistoricalAwards=false
• 关键词: NSF; Postdoctoral; Fellowship; Biology; FY

This is an NSF Postdoctoral Research Fellowship in Biology, under the program Research Using Biological Collections. The fellow, Ian Breckheimer, is conducting research and receiving training that utilizes biological collections in innovative ways, and is being mentored by Andrew Richardson at Harvard University. Specifically, the fellow will use digital records from museum collections and crowd-sourced photos uploaded by the public to measure where and when plants in different environments are exposed to risky climatic events like drought and growing season freezing during vulnerable periods in their seasonal cycle. Climate plays an important role in determining which organisms occur in which habitats, but exactly how this happens is still unclear, and understanding the mechanisms will allow us to better forecast the economic and ecological impacts of changes to the environment. Recent work suggests that, for non-woody plants like wildflowers, extreme events such as freezing or drought during vulnerable periods such as flowering strongly influence which plants can survive in which environments. The fellow will test this hypothesis for a large group of plants that inhabit mountain meadows in the Western USA. These are economically important ecosystems that attract millions of visitors each summer, and also experience extreme variations in climate. This project will advance our understanding of how climate affects the distribution of organisms, identify environments and species at risk of climatic disruption, and provide an important proof-of-principle for using crowd-sourced images to track spatial and temporal patterns in ecosystems.At approximately a dozen heavily-visited natural areas in the western USA, the fellow will use species occurrence information from digital herbarium collections along with image classifications from volunteer citizen-scientists and cutting-edge computer vision algorithms to identify common subalpine and alpine wildflowers in public geo-located photos hosted on social media platforms. By accounting for variation in the observation process, new statistical tools will allow the fellow to use these unstructured and imperfect observations to reliably measure the seasonal timing of flowering and fruiting. The fellow will then combine these observations with environmental information from satellites and weather stations to understand how that reproductive timing is affected by climate, and how geographic distributions and species responses affect the risk of disruptive climatic events during reproduction at each site. In addition to developing new knowledge about climatic disruption of ecosystems, this fellowship will support training in cutting-edge modeling and machine learning techniques, and advance new methods for combining citizen observations and museum collections. Results from these studies will be published in peer-reviewed journals and presented at scientific meetings.

这是美国国家科学基金会生物学博士后研究奖学金，隶属于生物馆藏研究项目。这位名叫伊恩·布雷克海默（Ian brekheimer）的研究员正在进行研究，并接受以创新方式利用生物标本的培训，他的导师是哈佛大学的安德鲁·理查森（Andrew Richardson）。具体来说，他将使用博物馆收藏的数字记录和公众上传的众包照片来测量不同环境中的植物在何时何地暴露于危险的气候事件，如干旱和生长季节在其季节性周期的脆弱时期冻结。气候在决定哪些生物出现在哪些栖息地方面起着重要作用，但具体是如何发生的尚不清楚，了解其机制将使我们能够更好地预测环境变化对经济和生态的影响。最近的研究表明，对于像野花这样的非木本植物来说，在开花等脆弱时期发生的冰冻或干旱等极端事件严重影响了哪些植物能在哪些环境中生存。该研究员将在美国西部山区草地上的一大群植物上验证这一假设。这些生态系统在经济上很重要，每年夏天都会吸引数百万游客，同时也会经历极端的气候变化。该项目将促进我们对气候如何影响生物分布的理解，识别面临气候破坏风险的环境和物种，并为使用众包图像跟踪生态系统的时空模式提供重要的原理证明。在美国西部大约十几个游客密集的自然区域，该研究员将使用数字植物标本馆收集的物种发生信息，以及志愿者公民科学家的图像分类和尖端的计算机视觉算法，在社交媒体平台上托管的公共地理定位照片中识别常见的亚高山和高山野花。通过考虑观察过程中的变化，新的统计工具将允许研究员使用这些非结构化和不完善的观察来可靠地测量开花和结果的季节时间。然后，他将把这些观测结果与卫星和气象站的环境信息结合起来，了解气候如何影响繁殖时间，以及地理分布和物种反应如何影响每个地点繁殖过程中破坏性气候事件的风险。除了开发有关气候破坏生态系统的新知识外，该奖学金还将支持尖端建模和机器学习技术的培训，并推进将公民观察和博物馆收藏相结合的新方法。这些研究的结果将发表在同行评议的期刊上，并在科学会议上发表。