Meeting Abstract
Bumblebees are critical pollinators whose populations have been declining over the past several decades. Successful foraging improves colony fitness, thus understanding how anthropogenic influences modulate foraging behavior may aid conservation efforts. Odor pollution can have negative impacts on bumble- and honey-bees foraging behavior. However, given the vast array of potential scent contaminants, individually testing pollutants is an ineffective approach. The ability to quantitatively measure how much scent-pollution of a floral-odor bumblebees can tolerate would represent a paradigm shift in odor-pollution studies. Current statistical methods derive the dimensions of an ‘odor-space’ from the odorants within a dataset; therefore, when the dataset is modified the odor-space itself is reconstructed. In this way statistical methods such as principle components analysis (PCA) or non-metric multidimensional scaling (NMDS) have excellent descriptive power, but are less effective at prediction This study presents an alternative method of analyzing complex odor blends based on the encoding properties of insect olfactory systems. This “Compounds Without Borders” (CWB) method represents odors as a vector in a multidimensional space representing the functional group and carbon characteristics of their component odorants. The dimensions of this space are independent of the data described within it. These vectors allow the angular distance between any two odors to be calculated – including a learned odor and its polluted counterpart. Data presented here indicate that CWB-angles are capable of both describing and predicting bumblebee odor-discrimination behavior: odor pairs with angular distances in the 20-29° range are generalized, while odor pairs over 30 degrees are differentiated.
