The traditional pollination syndrome groups floral traits into syndromes based on the functional group (i.e. pollinator) that the traits are associated with. The concept, while well supported, is a poor predictor of pollinator identity in generalist systems, such as those common to the family Asteraceae. One potential avenue for future refinement of the concept is the combination of large floral trait datasets, quantitative pollinator data, and phylogenetic comparative methods. Helianthus is a well studied genus of North American aster whose species include the agriculturally significant H. annuus, which represents the third largest oilseed crop globally. The genus is primarily bee pollinated and, while much is known about traits that are correlated with bee attraction at short ranges common to agricultural and horticultural settings, there has been little research on long range visual and chemical attraction traits within the genus. The proposed research aims to combine pollinator data, floral trait data, and phylogenetic comparative methods to identify traits that are responsible for attracting pollinators of Helianthus species at long range. I will use data on display side and shape, petal color and pigmentation, floral volatile emission, and floret depth, collected from Helianthus species grown in a common garden, to create phylogenetic generalized least squares models predicting visitation of pollinators as a function of floral traits. This work will further our understanding of pollination within Asteraceae and will inform artificial selection of traits that increase crop yields via increased pollinator services.
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