Müllerian mimicry between oil-producing orchids and Malpighiaceae? An old hypothesis finally tested

2021 ◽  
Vol 109 (1) ◽  
Author(s):  
Jonas B. Castro ◽  
Glauco Machado ◽  
Rodrigo B. Singer
Evolution ◽  
1977 ◽  
Vol 31 (2) ◽  
pp. 452-453
Author(s):  
P. M. Sheppard ◽  
J. R. G. Turner

2017 ◽  
Vol 284 (1849) ◽  
pp. 20162080 ◽  
Author(s):  
Gabriel A. Jamie

‘Mimicry’ is used in the evolutionary and ecological literature to describe diverse phenomena. Many are textbook examples of natural selection's power to produce stunning adaptations. However, there remains a lack of clarity over how mimetic resemblances are conceptually related to each other. The result is that categories denoting the traditional subdivisions of mimicry are applied inconsistently across studies, hindering attempts at conceptual unification. This review critically examines the logic by which mimicry can be conceptually organized and analysed. It highlights the following three evolutionarily relevant distinctions. (i) Are the model's traits being mimicked signals or cues? (ii) Does the mimic signal a fitness benefit or fitness cost in order to manipulate the receiver's behaviour? (iii) Is the mimic's signal deceptive? The first distinction divides mimicry into two broad categories: ‘signal mimicry’ and ‘cue mimicry’. ‘Signal mimicry’ occurs when mimic and model share the same receiver, and ‘cue mimicry’ when mimic and model have different receivers or when there is no receiver for the model's trait. ‘Masquerade’ fits conceptually within cue mimicry. The second and third distinctions divide both signal and cue mimicry into four types each. These are the three traditional mimicry categories (aggressive, Batesian and Müllerian) and a fourth, often overlooked category for which the term ‘rewarding mimicry’ is suggested. Rewarding mimicry occurs when the mimic's signal is non-deceptive (as in Müllerian mimicry) but where the mimic signals a fitness benefit to the receiver (as in aggressive mimicry). The existence of rewarding mimicry is a logical extension of the criteria used to differentiate the three well-recognized forms of mimicry. These four forms of mimicry are not discrete, immutable types, but rather help to define important axes along which mimicry can vary.


2008 ◽  
Vol 95 (8) ◽  
pp. 681-695 ◽  
Author(s):  
Thomas N. Sherratt

2012 ◽  
Vol 279 (1736) ◽  
pp. 2099-2105 ◽  
Author(s):  
Eira Ihalainen ◽  
Hannah M. Rowland ◽  
Michael P. Speed ◽  
Graeme D. Ruxton ◽  
Johanna Mappes

Müllerian mimicry describes the close resemblance between aposematic prey species; it is thought to be beneficial because sharing a warning signal decreases the mortality caused by sampling by inexperienced predators learning to avoid the signal. It has been hypothesized that selection for mimicry is strongest in multi-species prey communities where predators are more prone to misidentify the prey than in simple communities. In this study, wild great tits ( Parus major ) foraged from either simple (few prey appearances) or complex (several prey appearances) artificial prey communities where a specific model prey was always present. Owing to slower learning, the model did suffer higher mortality in complex communities when the birds were inexperienced. However, in a subsequent generalization test to potential mimics of the model prey (a continuum of signal accuracy), only birds that had foraged from simple communities selected against inaccurate mimics. Therefore, accurate mimicry is more likely to evolve in simple communities even though predator avoidance learning is slower in complex communities. For mimicry to evolve, prey species must have a common predator; the effective community consists of the predator's diet. In diverse environments, the limited diets of specialist predators could create ‘simple community pockets’ where accurate mimicry is selected for.


2013 ◽  
Vol 337 ◽  
pp. 101-110 ◽  
Author(s):  
V. Llaurens ◽  
S. Billiard ◽  
M. Joron

2008 ◽  
Vol 19 (2) ◽  
pp. 362-368 ◽  
Author(s):  
Eira Ihalainen ◽  
Leena Lindström ◽  
Johanna Mappes ◽  
Sari Puolakkainen

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