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

从我们餐桌上的食物，到丰富我们生活的鲜花，传粉者--包括各种蜜蜂、蝴蝶、蛾和其他昆虫--是美国成千上万种植物生存所必需的。然而，开花植物和它们的传粉者之间的相互作用网络并没有得到很好的解决。我们对植物传粉者网络的大部分了解，甚至对于一些常见的和经济上重要的植物来说，都只是基于对昆虫造访花朵的观察。确定昆虫作为特定植物物种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。博物馆收藏的大量昆虫为量化昆虫身上的花粉提供了完美的资源。利用内华达大学自然历史博物馆的昆虫收藏，研究人员将识别花粉和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上个世纪大盆地气候变化对植物-传粉者相互作用的影响。此外，这项研究将为传粉者如何受到环境变化的威胁提供洞察力。该项目将包括对学生进行生态学和博物馆科学方面的培训，利用从博物馆标本和野外遇到的蝴蝶中收集的花粉样本扩大联合国妇女署的收藏品，并为公众制作大型互动展示，向公众展示蝴蝶在大盆地作为传粉者的作用。这项研究将整合新的技术，结合从博物馆标本、野外观察和人工智能(AI)收集的数据，来表征过去100年的昆虫-花粉网络。来自综合方法的数据将有助于量化大盆地历史上和当代的花粉-蝴蝶相互作用网络。这些信息将被用来测试有关传粉网络变化的假设，以响应极端天气事件和其他通常测量的全球变化参数。该项目将产生一个相关的花粉收集，一个在线花粉图像数据库，以及一个从图像中识别花粉物种的人工智能模型。这项工作的结果有可能改变关于气候变化将如何影响相互作用的植物和昆虫网络的已知情况。这一奖项反映了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。

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.