MSA: Integrating biodiversity observations with airborne and satellite data to predict shifts in assemblage diversity and composition under global change

由于气候和人类活动的变化，生物多样性--我们这个星球的生存结构--正在迅速变化。这些快速变化正在对生态系统向人类提供商品和服务的能力产生负面影响。开发利用先进的遥感技术来描述和监测生物多样性的新方法，对于制定有效的管理战略和保护行动以应对全球变化至关重要。该项目将评估航空和卫星遥感数据的能力，以产生对毗邻的美国各地植物和鸟类多样性的可靠预测。该项目的更广泛影响包括几个组成部分。首先，该项目将从一个代表性不足的群体中培养一名早期科学家，进行科学和专业发展。其次，该项目将有助于培训下一代STEM研究人员，以调查人类世的生物多样性。第三，项目科学家将通过将项目成果整合到明尼苏达大学生物多样性科学的本科生和研究生课程中，让公众参与生物多样性教育。第四，该项目将有助于推动评估和监测全球生物多样性的国际努力。最后，研究人员将通过在线资源库将开源软件分发给世界各地的研究社区，促进重复性。该项目结合了来自不同学科的理论和新方法-包括宏观生态学、群落生态学和成像光谱学-以了解遥感数据在预测和监测生物多样性方面的潜力。具体地说，研究人员将测试这一假设，即从遥感得出的多样性衡量标准可以用作其他生物多样性衡量标准的替代品，从而在大范围的空间范围内监测生物多样性。该项目解决了我们预测物种分布和组合组成的能力方面的重要差距。将获得新的见解，以了解从遥感衍生的措施--如光谱阿尔法和贝塔多样性--预测物种分布和组合组成的潜力。通过这样做，该项目将有助于开发跨越时间和空间的生物多样性监测的新方法。总体而言，该项目提供了一个灵活的框架，允许集成多种预测和检测物种的手段，并推动监测生物多样性变化的努力。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Biodiversity—the living fabric of our planet—is rapidly changing due to alterations in the climate and human activities. These rapid changes are negatively impacting the capacity of ecosystems to provide goods and services to humanity. The development of new ways to characterize and monitor biodiversity that take advantage of advancing remote sensing technologies is critical for developing effective management strategies and conservation actions to address global change. The project will evaluate the capacity of airborne and satellite remote sensing data to produce reliable predictions of plant and bird diversities across the conterminous United States. The broader impacts of this project include several components. First, the project will provide scientific and professional development of an early-stage scientist from an underrepresented group. Second, the project will contribute to the training the next generation of STEM researchers to investigate biodiversity in the Anthropocene. Third, the project scientists will engage the public in biodiversity education through the integration of project results into an undergraduate and graduate course in Biodiversity Science at the University of Minnesota. Fourth, the project will contribute to advancing international efforts to assess and monitor biodiversity globally. Finally, researchers will distribute open-source software to the world-wide research community through online repositories, fostering reproducibility.The project combines theory and novel methods from different disciplines—including macroecology, community ecology and imaging spectroscopy—to understand the potential of remote sensing data in predicting and monitoring biodiversity. Specifically, researchers will test the hypothesis that diversity measures derived from remote sensing can be used as surrogates of other measures of biodiversity and hence for monitoring biodiversity at large spatial extents. The project addresses important gaps in our ability to predict species distributions and assemblage composition. New insights will be gained for understanding the potential of measures derived from remote sensing—such as spectral alpha and beta diversity—for the prediction of species distributions and assemblage composition. In doing so, the project will contribute to the development of novel approaches for biodiversity monitoring across time and space. Overall, the project provides a flexible framework that allows the integration of multiple means of predicting and detecting species and advances efforts to monitor changes in biodiversity.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.