The importance of insect herbivore density to induced metabolite blends in tea plants (Camellia sinensis) and implications for tea quality.


🏆 First place in section. Plant metabolic responses to herbivory are generally elucidated using experiments that compare the metabolite concentrations in attacked plants to un-attacked plants. In these experiments, herbivore density is often arbitrary, and may only elicit one possible set of responses by plants. In nature, however, plants experience varying degrees of herbivory. Studies that explicitly manipulate herbivore density often find substantial variation in the responses of individual metabolites. Some metabolites have dose-dependent responses, while others may be induced in an “all-or-nothing” manner. This results in the potential for metabolite blends to change as herbivore density increases and may have important ecological and experimental implications. For example, the ratio of volatile compounds in blends can carry information for parasitoids, and may change tritrophic interactions as herbivore density increases. Experimentally exposing plants to only one density of herbivores may result in an incomplete understanding of downstream effects. Additionally, the quality of many crop plants, such as tea (Camellia sinensis), depends greatly on metabolite blends that are impacted by herbivory. For example, leafhopper damage has been reported to have a positive influence on tea quality. Using high-throughput plant volatile sampling combined with multivariate statistical techniques, we show that the effect of leafhopper (Empoasca onukii) herbivory on the volatile profile of tea is dependent on leafhopper density. Furthermore, tea plant genotype may influence the effects of leafhopper herbivory on volatile metabolite blends, leading to different optimal densities for improving tea quality.

Aug 12, 2018 12:00 AM ET
Vancouver Convention Centre, Meeting Room 204

🏆 First place in section.

Scientific Programmer & Educator

Scientific Programmer & Educator at the University of Arizona in the CCT Data Science group.

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