BACKGROUND AND AIMS: Flower shapes are important visual cues for pollinators. However, the ability of pollinators to discriminate between flower shapes under natural conditions is poorly understood. This study focused on the diversity of flower shape in Primula sieboldii and investigated the ability of bumblebees to discriminate between flowers by combining computer graphics with a traditional behavioural experiment. METHODS: Elliptic Fourier descriptors described shapes by transforming coordinate information for the contours into coefficients, and principal components analysis summarized these coefficients. Using these methods, artificial flowers were created based on the natural diversity of petal shape in P. sieboldii. Dual-choice tests were then performed to investigate the ability of the bumblebees to detect differences in the aspect ratio of petals and the depth of their head notch. KEY RESULTS: The insects showed no significant ability to detect differences in the aspect ratio of the petals under natural conditions unless the morphological distance increased to an unrealistic level. These results suggest the existence of a perception threshold for distances in this parameter. The bumblebees showed a significant preference for narrow petals even after training using flowers with wide petals. The bumblebees showed a significant ability to discriminate based on the depth of the petal head notch after training using artificial flowers with a deep head notch. However, they showed no discrimination in tests with training using extreme distances between flowers in this parameter. CONCLUSIONS: A new type of behavioural experiment was demonstrated using real variation in flower corolla shape in P. sieboldii. If the range in aspect ratios of petals expands much further, bumblebees may learn to exhibit selective behaviour. However, because discrimination by bumblebees under natural conditions was low, there may be no strong selective behaviour based on innate or learned preferences under natural conditions.
BACKGROUND AND AIMS: Flower shapes are important visual cues for pollinators. However, the ability of pollinators to discriminate between flower shapes under natural conditions is poorly understood. This study focused on the diversity of flower shape in Primula sieboldii and investigated the ability of bumblebees to discriminate between flowers by combining computer graphics with a traditional behavioural experiment. METHODS: Elliptic Fourier descriptors described shapes by transforming coordinate information for the contours into coefficients, and principal components analysis summarized these coefficients. Using these methods, artificial flowers were created based on the natural diversity of petal shape in P. sieboldii. Dual-choice tests were then performed to investigate the ability of the bumblebees to detect differences in the aspect ratio of petals and the depth of their head notch. KEY RESULTS: The insects showed no significant ability to detect differences in the aspect ratio of the petals under natural conditions unless the morphological distance increased to an unrealistic level. These results suggest the existence of a perception threshold for distances in this parameter. The bumblebees showed a significant preference for narrow petals even after training using flowers with wide petals. The bumblebees showed a significant ability to discriminate based on the depth of the petal head notch after training using artificial flowers with a deep head notch. However, they showed no discrimination in tests with training using extreme distances between flowers in this parameter. CONCLUSIONS: A new type of behavioural experiment was demonstrated using real variation in flower corolla shape in P. sieboldii. If the range in aspect ratios of petals expands much further, bumblebees may learn to exhibit selective behaviour. However, because discrimination by bumblebees under natural conditions was low, there may be no strong selective behaviour based on innate or learned preferences under natural conditions.
Authors: Nicolas B Langlade; Xianzhong Feng; Tracy Dransfield; Lucy Copsey; Andrew I Hanna; Christophe Thébaud; Andrew Bangham; Andrew Hudson; Enrico Coen Journal: Proc Natl Acad Sci U S A Date: 2005-07-11 Impact factor: 11.205
Authors: José M Gómez; Jordi Bosch; Francisco Perfectti; J D Fernández; Mohamed Abdelaziz; J P M Camacho Journal: Proc Biol Sci Date: 2008-10-07 Impact factor: 5.349
Authors: Alexander S T Papadopulos; Martyn P Powell; Franco Pupulin; Jorge Warner; Julie A Hawkins; Nicolas Salamin; Lars Chittka; Norris H Williams; W Mark Whitten; Deniz Loader; Luis M Valente; Mark W Chase; Vincent Savolainen Journal: Proc Biol Sci Date: 2013-06-26 Impact factor: 5.349
Authors: Hamady Dieng; Tomomitsu Satho; Nor Hafisa Syafina Binti Mohd Radzi; Fatimah Abang; Nur Faeza A Kassim; Wan Fatma Zuharah; Nur Aida Hashim; Ronald E Morales Vargas; Noppawan P Morales Journal: Insects Date: 2021-12-08 Impact factor: 2.769