Kudos to U-M EEB graduate student Sasha Bishop on receiving the 2023 Most Outstanding Student Paper Award for her first-authored paper, published in Evolution Letters.

“Not just flowering time: a resurrection approach shows floral attraction traits are changing over time” was co-authored by Bishop, Shu-Mei Chang (University of Georgia), and Professor Regina S. Baucom, Bishop’s advisor. She was surprised to receive an award. 

It is well known that springtime flowering in plants is shifting earlier in the year. Work like Bishop’s examines just how important changes from the shifting climate play in the evolution of plants such as wild morning glories, the main plant focused on in this study. Her coauthors and herself studied the size and changes of these flowers for clues to how they interact with local pollinators. They deduced that the overall growth in flower size from 2003 to 2012 increased to play a role in attracting pollinators. Additionally, these plants also shifted to an earlier flowering time. 

Baucom gushed about Bishop's recent work.

 “I am psyched that Sasha has gotten this recognition for her work! She combined careful analyses of large datasets with both a broad and deep read of the evolutionary ecology literature, positioning her to show that floral traits beyond flowering phenology are responding to our changing planet,” said Baucom. “I am excited to see her produce a well-received paper on a topic -- global climate change -- that she cares deeply about.”

Bishop feels grateful to those who spent time with her work. 

“I’m honored that they felt the work contributes significantly to our field,” said Bishop. “This paper marked the beginning of a significant change in the focus of my research toward contemporary evolution and anthropogenic-driven changes.”

Bishop’s current research focuses on investigating floral trait changes, a focus of this paper, mainly how these traits manifest in more complex environments. Her current work is partially supported by a grant from the American Genetic Association to characterize the genomic architecture of regions under selection.

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