NSF Postdoctoral Fellowship in Biology FY 2020: Integrating biological collections and observational data sources to estimate long-term butterfly population trends

这一行动资助了NSF 2020财年生物学博士后研究奖学金，使用生物收藏进行研究。该奖学金支持将以创新方式使用生物收藏的研究员的研究和培训。最近的研究表明，有可能正处于一场“昆虫末日”之中，但由于数据的限制，大多数昆虫物种的真实下降速度尚不清楚。对于许多昆虫物种来说，生物收集(包括数字标本)是关于昆虫数量和多样性的唯一可用长期数据来源。这个博士后研究项目旨在利用生物收集数据和其他观察数据来源来确定蝴蝶种群变化的当前趋势和原因。该项目还将提高公众对保护和博物馆藏品价值的认识，并促进更多以博物馆为基础的研究。为了扩大这项工作的影响，将设计一个博物馆展览和教育模块，并将在研究方面指导本科生。生物收藏的数据显示出时空和收藏者特有的偏见，这使得仅使用数字化的博物馆记录来估计种群趋势变得困难。这些偏见限制了博物馆记录的使用，以至于有大量未开发的数据可用于评估全球变化对昆虫的影响。该项目使用多种数据来源，包括数字化的博物馆记录、机会主义的公民科学观察，以及来自遵循标准化协议的监测方案的结构化计数数据，评估新开发的数据集成方法在估计长期人口趋势方面的效用。将使用广泛的模拟研究来评估将这三种数据类型整合在一起以跟踪长期人口趋势是有用和合理的条件。一个经验性的案例研究重点是估计中西部平原32种开放栖息地蝴蝶的长期趋势。这些研究目标的实现将导致一个可转移的建模框架，使研究人员能够使用生物收集数据来重建随着时间的推移的人口趋势。这项研究还将被北美网络专题收集网络鳞翅目昆虫研究所用来确定北美最适合纳入综合分析的地理区域，并应在今后有针对性的数字化工作中优先考虑这些区域。为了扩大这项工作的影响，本科生将在独立研究中接受指导，并将生成关于蝴蝶生态的互动博物馆展示和K-12教育模块。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。

This action funds an NSF Postdoctoral Research Fellowship in Biology for FY 2020, Research Using Biological Collections. The fellowship supports research and training of the Fellow that will use biological collections in innovative ways. Recent studies suggest the possibility of being in the middle of an “insect apocalypse,” but true rates of decline for most insect species are unknown because of data limitations. For many insect species, biological collections (including digital specimens) are the only available long-term source of data on insect numbers and diversity. This postdoctoral research project aims to use biological collection data and other observational data sources to identify current trends and reasons for butterfly population changes. The project will also raise public awareness about conservation and the value of museum collections and promote additional museum-based research. To broaden the impact of the work, a museum exhibit and educational module will be designed, and undergraduates will be mentored in research. Biological collection's data exhibit spatiotemporal and collector-specific biases that make it difficult to estimate population trends using only digitized museum records. These biases have limited the use of museum records such that there is an untapped wealth of data available to evaluate the impacts of global changes on insects. This project evaluates the utility of newly developed data integration methods for estimating long-term population trends using multiple data sources, including digitized museum records, opportunistic citizen science observations, and structured count data from monitoring schemes that follow standardized protocols. An extensive simulation study will be used to evaluate the conditions under which it is useful and reasonable to integrate these three data types to track long-term population trends. An empirical case study focuses on estimating long-term trends for 32 open-habitat butterfly species in the Midwest Plains. Achievement of these research objectives will result in a transferable modeling framework that allows researchers to use biological collections data to reconstruct population trends through time. This research will also be used by the Lepidoptera of North America Network Thematic Collection’s Network to identify geographic regions in North America that are most amenable for inclusion in an integrated analysis and should be prioritized in future targeted digitization efforts. To broaden the impact of the work, undergraduates will be mentored in independent research, and an interactive museum display and K-12 educational module on butterfly ecology will be generated.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.