Literature DB >> 17550880

The key mimetic features of hoverflies through avian eyes.

Roderick S Bain1, Arash Rashed, Verity J Cowper, Francis S Gilbert, Thomas N Sherratt.   

Abstract

Batesian mimicry occurs when a palatable species (the mimic) gains protection from predators by resembling an unpalatable or otherwise protected species (the model). While some mimetic species resemble their models closely, other species ('imperfect mimics') are thought to bear only a crude likeness. In an earlier study, pigeons (Columba livia) were trained to recognize wasp images in one experiment and non-mimetic (NM) fly images in another by rewarding the pigeons for pecking on the respective image types. These pigeons were subsequently presented with different images, including seemingly wasp-like hoverfly species, and the recorded peck rates on these images were used as a measure of the pigeons' perception of the hoverflies' mimetic similarity. To identify a candidate set of morphological features that the pigeons used when assessing this mimetic similarity, we first extracted a range of biometrical measurements from images originally presented to the pigeons. We then repeatedly optimized an empirical model in an attempt to match the recorded pigeon peck rates while using as few biometrical features as input as possible. Our models were able to fit the pigeon peck rates with considerable accuracy even while excluding many input features. Antennal length, a feature commonly used to discriminate between flies and wasps, was regularly retained as an input variable, but overall a different set of biometrical features was important for predicting the peck rates of pigeons rewarded for identifying wasps compared to those rewarded for identifying NM flies. In highlighting the importance of specific biometrical features in promoting mimicry and the irrelevance of others, our optimized models provide an explanation as to why certain species that appear to be poor mimics to humans are judged to be good mimics by birds.

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Year:  2007        PMID: 17550880      PMCID: PMC2275351          DOI: 10.1098/rspb.2007.0458

Source DB:  PubMed          Journal:  Proc Biol Sci        ISSN: 0962-8452            Impact factor:   5.349


  9 in total

1.  Artificial neural networks for species identification by taxonomists.

Authors:  Jonathan Y Clark
Journal:  Biosystems       Date:  2003-11       Impact factor: 1.973

2.  The evolution of inaccurate mimics.

Authors:  Rufus A Johnstone
Journal:  Nature       Date:  2002-08-01       Impact factor: 49.962

3.  The evolution of mimicry under constraints.

Authors:  Øistein Haugsten Holen; Rufus A Johnstone
Journal:  Am Nat       Date:  2004-09-29       Impact factor: 3.926

4.  Taking the best for learning.

Authors:  Sara J Shettleworth
Journal:  Behav Processes       Date:  2005-05-31       Impact factor: 1.777

5.  The confusion effect in predatory neural networks.

Authors:  Colin R Tosh; Andrew L Jackson; Graeme D Ruxton
Journal:  Am Nat       Date:  2005-01-12       Impact factor: 3.926

6.  Introduction. The use of artificial neural networks to study perception in animals.

Authors:  Colin R Tosh; Graeme D Ruxton
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2007-03-29       Impact factor: 6.237

7.  Constrained camouflage facilitates the evolution of conspicuous warning coloration.

Authors:  Sami Merilaita; Birgitta S Tullberg
Journal:  Evolution       Date:  2005-01       Impact factor: 3.694

8.  Predator discrimination error and the benefits of Müllerian mimicry.

Authors: 
Journal:  Anim Behav       Date:  1998-05       Impact factor: 2.844

9.  Conditioning pigeons to discriminate naturally lit insect specimens.

Authors:  P R Green; L Gentle; T M Peake; R E Scudamore; P K McGregor; F Gilbert; W H Dittrich
Journal:  Behav Processes       Date:  1999-05-03       Impact factor: 1.777

  9 in total
  14 in total

1.  A comparative analysis of the evolution of imperfect mimicry.

Authors:  Heather D Penney; Christopher Hassall; Jeffrey H Skevington; Kevin R Abbott; Thomas N Sherratt
Journal:  Nature       Date:  2012-03-21       Impact factor: 49.962

2.  Multi-trait mimicry and the relative salience of individual traits.

Authors:  Baharan Kazemi; Gabriella Gamberale-Stille; Olof Leimar
Journal:  Proc Biol Sci       Date:  2015-11-07       Impact factor: 5.349

3.  The evolution of imperfect floral mimicry.

Authors:  Nicolas J Vereecken; Florian P Schiestl
Journal:  Proc Natl Acad Sci U S A       Date:  2008-05-27       Impact factor: 11.205

4.  Climate-induced phenological shifts in a Batesian mimicry complex.

Authors:  Christopher Hassall; Jac Billington; Thomas N Sherratt
Journal:  Proc Natl Acad Sci U S A       Date:  2018-12-03       Impact factor: 11.205

Review 5.  The perfection of mimicry: an information approach.

Authors:  Thomas N Sherratt; Casey A Peet-Paré
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2017-07-05       Impact factor: 6.237

6.  Motion dazzle and camouflage as distinct anti-predator defenses.

Authors:  Martin Stevens; W Tom L Searle; Jenny E Seymour; Kate L A Marshall; Graeme D Ruxton
Journal:  BMC Biol       Date:  2011-11-25       Impact factor: 7.431

7.  Müllerian mimicry as a result of codivergence between velvet ants and spider wasps.

Authors:  Juanita Rodriguez; James P Pitts; Carol D von Dohlen; Joseph S Wilson
Journal:  PLoS One       Date:  2014-11-14       Impact factor: 3.240

8.  Walking like an ant: a quantitative and experimental approach to understanding locomotor mimicry in the jumping spider Myrmarachne formicaria.

Authors:  Paul S Shamble; Ron R Hoy; Itai Cohen; Tsevi Beatus
Journal:  Proc Biol Sci       Date:  2017-07-12       Impact factor: 5.349

9.  Ecological and evolutionary processes drive the origin and maintenance of imperfect mimicry.

Authors:  Joseph S Wilson; Joshua P Jahner; Kevin A Williams; Matthew L Forister
Journal:  PLoS One       Date:  2013-04-12       Impact factor: 3.240

10.  Cognitive dimensions of predator responses to imperfect mimicry.

Authors:  Lars Chittka; Daniel Osorio
Journal:  PLoS Biol       Date:  2007-12       Impact factor: 8.029

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