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

政策努力越来越侧重于适应气候变化，而不是减缓气候变化。我们寻求了解植物和动物，包括森林植物和野生动物，疾病及其载体，以及农作物的社区将如何应对气候变化。我们将通过建立一些第一个应对气候变化的物种范围变化的机械模型来做到这一点。我们还试图模拟依赖这些生物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，用于建模，可视化和预测空间和时间数据。我们试图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交流复杂的时空数据。我们将利用我们的研究框架，在高中，本科，研究生和教师水平的数据科学，在新英格兰迅速扩大的就业市场提供课程和培训课程。我们还将加强缅因州大学、佛蒙特大学、缅因州奥古斯塔大学和尚普兰学院在这些领域的研究能力。下一个世纪重大的气候变化不能完全避免，现在是不可避免的。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。这些物种最终会在哪里，这将如何影响美国的农民和农村社区？我们的目标是对新英格兰的野生动物、森林植物、疾病及其携带者和农作物在未来100年将转移到哪里进行详细的预测。我们还将分析农民将如何应对这些变化，在这些变化中，农作物可能在他们的土地上存活。为了做到这一点，我们将首先为科学家开发新的软件工具，这些工具将同时处理时间和空间变化模式的数据。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，并改善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。我们还将提高缅因州大学、佛蒙特大学、缅因州奥古斯塔大学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知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.