Climate Change Data Portal
| RII Track-2 FEC: Harnessing Spatiotemporal Data Science to Predict Responses of Biodiversity and Rural Communities under Climate Change | |
| 项目编号 | 2019470 |
| Brian McGill (Principal Investigator) | |
| 项目主持机构 | University of Maine |
| 开始日期 | 2020-09-01 |
| 结束日期 | 2024-08-31 |
| 英文摘要 | 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. |
| 学科分类 | 13 - 管理科学;1303 - 宏观管理与政策 |
| 资助机构 | US-NSF |
| 项目经费 | 1998290 |
| 项目类型 | Cooperative Agreement |
| 国家 | US |
| 语种 | 英语 |
| 文献类型 | 项目 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/191011 |
| 推荐引用方式 GB/T 7714 | Brian McGill .RII Track-2 FEC: Harnessing Spatiotemporal Data Science to Predict Responses of Biodiversity and Rural Communities under Climate Change.2020. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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