RII Track-2 FEC: Harnessing Spatiotemporal Data Science to Predict Responses of Biodiversity and Rural Communities under Climate Change

RII Track-2 FEC：利用时空数据科学预测气候变化下生物多样性和农村社区的反应

• 批准号: 2019470
• 负责人: Brian McGill
• 金额: $ 399.54万
• 依托单位: University of Maine
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2019470&HistoricalAwards=false
• 关键词: RII; Track; FEC; Harnessing; Spatiotemporal

Policy efforts are increasingly focusing on climate adaptation rather than mitigation. We seek to understand how communities of plants and animals, including forest plants and wildlife, diseases and their vectors, and agricultural crops, will respond to climate change. We will do this by building some of the first mechanistic models of shifts in species ranges in response to climate change. We further seek to model how farmers and rural human societies in the U.S. that depend on these organisms will adapt in response. To do this, we will develop novel approaches and software tools for modeling, visualizing, and forecasting spatial and temporal data. We seek to provide our model results to farmers to improve their ability to adapt to climate change, to better understand what kinds of data farmers need, and how scientists can better communicate complex spatiotemporal data with farmers. We will use our research framework to provide curriculum and training sessions at the high school, undergraduate, graduate, and faculty levels in data science, a rapidly expanding job market in New England. We will also increase research capacity in these fields at the University of Maine, University of Vermont, University of Maine at Augusta, and Champlain College.Significant climate change over the next century cannot be fully avoided, and is now inevitable. We know that one of the main responses of plants and animals to climate change is that populations of species will move to new locations where the climate is more hospitable. Where will these species end up, and how will that affect farmers and rural communities in the U.S.? We aim to produce detailed predictions of where wildlife, forest plants, disease and their carriers, and agricultural crops in New England will shift to live over the next 100 years. We will also analyze how farmers will respond to these shifts in which crop plants are potentially viable on their land. To do this, we will first develop new software tools for scientists working with data on patterns changing in time and space simultaneously. We will work closely with farmers to understand what data they need to adapt to climate change, to communicate our results, and to improve how scientists communicate complex data. Finally, we will provide significant training in data science, a rapidly growing job market, at multiple age levels. We will also increase research capacity in these fields at the University of Maine, University of Vermont, University of Maine at Augusta, and Champlain College.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.

政策努力越来越关注气候适应而不是减缓。我们寻求了解植物和动物群落，包括森林植物和野生动物、疾病及其媒介以及农作物，将如何应对气候变化。我们将通过建立一些第一个物种范围因气候变化而变化的机械模型来实现这一目标。我们进一步寻求模拟依赖这些生物体的美国农民和农村人类社会将如何适应。为此，我们将开发新的方法和软件工具来建模、可视化和预测空间和时间数据。我们寻求向农民提供我们的模型结果，以提高他们适应气候变化的能力，更好地了解农民需要什么样的数据，以及科学家如何更好地与农民沟通复杂的时空数据。我们将利用我们的研究框架为高中、本科生、研究生和教师级别的数据科学提供课程和培训课程，数据科学是新英格兰快速扩张的就业市场。我们还将提高缅因大学、佛蒙特大学、缅因大学奥古斯塔分校和尚普兰学院在这些领域的研究能力。下个世纪的重大气候变化无法完全避免，现在已经不可避免。我们知道，动植物对气候变化的主要反应之一是物种种群将迁移到气候更适宜的新地点。这些物种最终会去哪里？这将如何影响美国的农民和农村社区？我们的目标是对未来 100 年新英格兰的野生动物、森林植物、疾病及其携带者以及农作物将转移到哪里生活进行详细预测。我们还将分析农民将如何应对这些作物在其土地上可能可行的转变。为此，我们将首先为科学家开发新的软件工具，以处理时间和空间同时变化的模式数据。我们将与农民密切合作，了解他们需要哪些数据来适应气候变化，传达我们的结果，并改进科学家传达复杂数据的方式。最后，我们将在多个年龄段提供数据科学方面的重要培训，这是一个快速增长的就业市场。我们还将提高缅因大学、佛蒙特大学、缅因大学奥古斯塔分校和尚普兰学院在这些领域的研究能力。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和技术进行评估，认为值得支持。更广泛的影响审查标准。

项目成果

Simple null model analysis subsumes a new species co‐occurrence index: A comment on Mainali et al. (2022)

简单的零模型分析包含一个新物种共现指数：对 Mainali 等人的评论。

• DOI: 10.1111/jbi.14486
• 发表时间: 2022
• 期刊: Journal of Biogeography
• 影响因子: 3.9
• 作者: Ulrich, Werner;Sfenthourakis, Spyros;Strona, Giovanni;Gotelli, Nicholas J.
• 通讯作者: Gotelli, Nicholas J.

Deriving topological relations from topologically augmented direction relation matrices

• DOI: 10.5311/josis.2021.23.155
• 发表时间: 2021-12
• 期刊: Journal of Spatial Information Science
• 影响因子: 1.4
• 作者: Matthew P. Dube

Estimating species relative abundances from museum records

• DOI: 10.1111/2041-210x.13705
• 发表时间: 2021-09-07
• 期刊: METHODS IN ECOLOGY AND EVOLUTION
• 影响因子: 6.6
• 作者: Gotelli, Nicholas J.;Booher, Douglas B.;Primack, Richard B.
• 通讯作者: Primack, Richard B.

A review of the heterogeneous landscape of biodiversity databases: Opportunities and challenges for a synthesized biodiversity knowledge base

• DOI: 10.1111/geb.13497
• 发表时间: 2022-04-18
• 期刊: GLOBAL ECOLOGY AND BIOGEOGRAPHY
• 影响因子: 6.4
• 作者: Feng, Xiao;Enquist, Brian J.;Lopez-Hoffman, Laura
• 通讯作者: Lopez-Hoffman, Laura