floral mimicry
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2021 ◽  
Vol 9 ◽  
Author(s):  
Avery L. Russell ◽  
Stephanie R. Sanders ◽  
Liam A. Wilson ◽  
Daniel R. Papaj

Mutualisms involve cooperation, but also frequently involve conflict. Plant-pollinator mutualisms are no exception. To facilitate animal pollination, flowering plants often offer pollen (their male gametes) as a food reward. Since plants benefit by maximizing pollen export to conspecific flowers, we might expect plants to cheat on pollen rewards. In intersexual floral mimicry, rewarding pollen-bearing male flowers (models) are mimicked by rewardless female flowers (mimics) on the same plant. Pollinators should therefore learn to avoid the unrewarding mimics. Plants might impede such learning by producing phenotypically variable flowers that cause bees to generalize among models and mimics during learning. In this laboratory study, we used partially artificial flowers (artificial petals, live reproductive parts) modeled after Begonia odorata to test whether variation in the size of rewarding male flowers (models) and unrewarding female flowers (mimics) affected how quickly bees learned both to recognize models and to reject mimics. Live unrewarding female flowers have 33% longer petals and have 31% greater surface area than live rewarding male flowers, which bees should easily discriminate. Yet while bees rapidly learned to reduce foraging effort on mimics, learning was not significantly affected by the degree to which flower size varied. Additionally, we found scant evidence that this was a result of bees altering response speed to maintain decision accuracy. Our study failed to provide evidence that flower size variation in intersexual floral mimicry systems exploits pollinator cognition, though we cannot rule out that other floral traits that are variable may be important. Furthermore, we propose that contrary to expectation, phenotypic variability in a Batesian mimicry system may not necessarily have significant effects on whether receivers effectively learn to discriminate models and mimics.


2021 ◽  
Author(s):  
Callan Cohen ◽  
William R. Liltved ◽  
Jonathan F. Colville ◽  
Adam Shuttleworth ◽  
Jerrit Weissflog ◽  
...  

2020 ◽  
Vol 144 ◽  
pp. 103466
Author(s):  
Imane Laraba ◽  
Susan P. McCormick ◽  
Martha M. Vaughan ◽  
Robert H. Proctor ◽  
Mark Busman ◽  
...  
Keyword(s):  

2020 ◽  
Vol 375 (1802) ◽  
pp. 20190469 ◽  
Author(s):  
Avery L. Russell ◽  
David W. Kikuchi ◽  
Noah W. Giebink ◽  
Daniel R. Papaj

Mimicry is common in interspecies interactions, yet conditions maintaining Batesian mimicry have been primarily tested in predator–prey interactions. In pollination mutualisms, floral mimetic signals thought to dupe animals into pollinating unrewarding flowers are widespread (greater than 32 plant families). Yet whether animals learn to both correctly identify floral models and reject floral mimics and whether these responses are frequency-dependent is not well understood. We tested how learning affected the effectiveness and frequency-dependence of imperfect Batesian mimicry among flowers using the generalist bumblebee, Bombus impatiens , visiting Begonia odorata , a plant species exhibiting intersexual floral mimicry. Unrewarding female flowers are mimics of pollen-rewarding male flowers (models), though mimicry to the human eye is imperfect. Flower-naive bees exhibited a perceptual bias for mimics over models, but rapidly learned to avoid mimics. Surprisingly, altering the frequency of models and mimics only marginally shaped responses by naive bees and by bees experienced with the distribution and frequency of models and mimics. Our results provide evidence both of exploitation by the plant of signal detection trade-offs in bees and of resistance by the bees, via learning, to this exploitation. Critically, we provide experimental evidence that imperfect Batesian mimicry can be adaptive and, in contrast with expectations of signal detection theory, functions largely independently of the model and mimic frequency. This article is part of the theme issue ‘Signal detection theory in recognition systems: from evolving models to experimental tests’.


2018 ◽  
Vol 30 (1) ◽  
pp. 213-222 ◽  
Author(s):  
Michael R Whitehead ◽  
Anne C Gaskett ◽  
Steven D Johnson

Author(s):  
Katherine R Goodrich
Keyword(s):  

2017 ◽  
Vol 217 (1) ◽  
pp. 74-81 ◽  
Author(s):  
Katherine R. Goodrich ◽  
Andreas Jürgens

Evolution ◽  
2017 ◽  
Vol 71 (9) ◽  
pp. 2275-2276
Author(s):  
Susanne S. Renner

Author(s):  
Steven D. Johnson ◽  
Florian P. Schiestl
Keyword(s):  

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