Impact of Decreased Flower Attractiveness on Pollinator Visitation Rates and Pollinator Community Composition
Abstract
Plant-pollinator interactions are extremely familiar to many different ecosystems all over the world. Many floral species and pollinators have developed intricate and essential adaptations to benefit one another. Aspects of plant reproduction require pollinator visits and many pollinators rely on floral species for food sources. Several studies have focused on how they serve one another and form a part of complex ecological networks. However, there is a need in community-level ecology to understand how disturbances within such networks can create ripple effects throughout the entire system. Therefore, our experimental design aims to provide information on pollinator adaptation to small-scale disturbances within an already-established plant community. To test this, we manipulated floral attractiveness in three different subalpine meadows of the Gunnison National Forest. At each transect, a floral species was sprayed with quinine and actively sampled pollinators who landed on all open flowers. We identified the type of pollinator caught and which floral species it landed on. This data was used to determine how pollinator total visits and pollinator community composition differed in the control and the manipulation throughout the summer season. Due to the application of quinine creating a disturbance to the pollinators’ food source, we saw a decrease in overall pollinator visits and a slight change in our insect ID groups. A decrease in the overall pollinator visits to the area could be attributed to certain species leaving for other floral communities. A change within insect group IDs can also be attributed to an increased need for competition for the flowers not containing quinine and their inability to adapt to the disturbance. This research question does not aim to explain all the ripple effects of a disturbance within a system. However, it is essential to continue testing the strength of these networks to understand how other more complex biological disturbances can potentially impact them.
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References (15)
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