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In situ modeling of multimodal floral cues attracting wild pollinators across environments. | LitMetric

AI Article Synopsis

Article Abstract

With more than 80% of flowering plant species specialized for animal pollination, understanding how wild pollinators utilize resources across environments can encourage efficient planting and maintenance strategies to maximize pollination and establish resilience in the face of environmental change. A fundamental question is how generalist pollinators recognize "flower objects" in vastly different ecologies and environments. On one hand, pollinators could employ a specific set of floral cues regardless of environment. Alternatively, wild pollinators could recognize an exclusive signature of cues unique to each environment or flower species. Hoverflies, which are found across the globe, are one of the most ecologically important alternative pollinators after bees and bumblebees. Here, we have exploited their cosmopolitan status to understand how wild pollinator preferences change across different continents. Without employing any a priori assumptions concerning the floral cues, we measured, predicted, and finally artificially recreated multimodal cues from individual flowers visited by hoverflies in three different environments (hemiboreal, alpine, and tropical) using a field-based methodology. We found that although "flower signatures" were unique for each environment, some multimodal lures were ubiquitously attractive, despite not carrying any reward, or resembling real flowers. While it was unexpected that cue combinations found in real flowers were not necessary, the robustness of our lures across insect species and ecologies could reflect a general strategy of resource identification for generalist pollinators. Our results provide insights into how cosmopolitan pollinators such as hoverflies identify flowers and offer specific ecologically based cues and strategies for attracting pollinators across diverse environments.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5740637PMC
http://dx.doi.org/10.1073/pnas.1714414114DOI Listing

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