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

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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.