EAGER: Quantifying Great Basin butterfly-pollen networks across spatial and temporal disturbance gradients.

从我们餐桌上的食物，到丰富我们生活的花朵，授粉者，包括各种各样的蜜蜂，蝴蝶，飞蛾和其他昆虫，是美国数千种植物物种生存所必需的。然而，开花植物和它们的传粉者之间的相互作用的网络还没有很好地解决。我们对植物-传粉者网络的大部分了解，即使是对一些常见的和经济上重要的植物，也仅仅是基于对昆虫访问花朵的观察。确定昆虫是某种植物的传粉者的一种方法是检查它们的身体以寻找该植物的花粉。博物馆中大量的昆虫收藏品为量化昆虫身上的花粉提供了完美的资源。利用内华达州大学自然历史博物馆（UMMNH）的昆虫收藏，研究人员将确定花粉-蝴蝶相互作用的大型网络，重点是内华达州的大盆地沙漠。这些网络还将允许推断植物-传粉者的相互作用如何受到上个世纪大盆地气候变化的影响。此外，这项研究还将深入了解传粉者如何受到不断变化的环境的威胁。该项目将包括在生态学和博物馆科学的培训学生，扩大从博物馆标本和蝴蝶在现场遇到的花粉样本收集的CIMNH收藏，并创建大型互动显示为公众，说明蝴蝶在大盆地授粉者的角色。 该研究将整合新技术，结合从博物馆标本，实地观察和人工智能（AI）收集的数据，以描述过去100年来的昆虫花粉网络。从综合方法的数据将允许量化的历史和当代花粉蝴蝶在大盆地的相互作用网络。这些信息将用于检验授粉网络因极端天气事件和其他普遍测量的全球变化参数而发生变化的假设。该项目将产生一个相关的花粉集合，一个在线花粉图像数据库，以及一个用于从图像中识别花粉种类的人工智能模型。这项工作的结果有可能改变气候变化将如何影响相互作用的植物和昆虫网络的已知知识。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。

From the food on our table, to the flowers that enrich our lives, pollinators, which include a diverse group of bees, butterflies, moths, and other insects, are required for the persistence of thousands of plant species in the United States. However, the webs of interactions between flowering plants and their pollinators are not very well resolved. Much of what we know about plant-pollinator networks, even for some common and economically important plants, is based only on observations of insects visiting flowers. One way to be sure that insects are acting as pollinators of a particular plant species is to examine their bodies for pollen of that plant. Large collections of insects in museums provide a perfect resource for quantifying pollen on insect bodies. Using the insect collections at the University of Nevada Museum of Natural History (UNRMNH), researchers will identify large webs of pollen-butterfly interactions, with a focus on the Great Basin Desert in Nevada. These networks will also allow inferences about how plant-pollinator interactions have been affected by changes in climate in the Great Basin over the last century. Additionally, the research will provide insight into how pollinators may be threatened by a changing environment. The project will include training students in ecology and museum science, expanding UNRMNH collections with pollen samples collected from museum specimens and butterflies encountered in the field, and creating large interactive displays for the public that illustrate the roles of butterflies as pollinators in the Great Basin. The research will integrate new techniques, combining data gleaned from museum specimens, field observations, and artificial intelligence (AI) to characterize insect-pollen networks over the last 100 years. Data from the combined approaches will allow quantification of historic and contemporary pollen-butterfly interaction networks in the Great Basin. This information will be used to test hypotheses about changes in pollination networks in response to extreme weather events and other commonly measured global change parameters. The project will produce an associated pollen collection, an online pollen image database, and an AI model for identifying pollen species from images. The results from this work have the potential to transform what is known about how climate change will affect networks of interacting plants and insects.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.