Batesian mimicry or general food deception? An evolutionary game between plants for pollinator services

Hongchun Qu, Aviad Heifetz, Merav Seifan

Research output: Contribution to journalArticlepeer-review

Abstract

Floral food deception is a well-known phenomenon which is not thoroughly understood. Particularly, it is unclear what drives a plant towards Batesian mimicry or towards generalized food deception. We analysed the evolutionary game between a Model species with nectar-secreting flowers and a Deceiver species that provides no nectar who share pollinators for reproduction. We focused our analysis on the effect of similarity of floral signals between participating plants and on costs of nectar production. We defined payoffs in the game between Models and Deceivers as the stationary visitation frequencies to participating species with different signal similarities and nectar costs. Therefore, fitness payoff of each strategy was a product of complex interactions between plant species composing the community and the pollinators visiting them. Our model provides a unified framework in which consequences of Model species interaction with different deception modes can be compared. Our findings suggest that plant-pollinator systems, like other mutualistic systems, are prone to exploitation, and that exploitation may persist at a large range of conditions. We showed that floral similarity, and thus, pollinators’ ability to discriminate between Model and deceptive species, governs the stability of Batesian mimicry, while pollinator switching and sampling behaviour enables the persistence of general food deception.

Original languageEnglish
Article number111609
JournalJournal of Theoretical Biology
Volume575
DOIs
StatePublished - 7 Nov 2023

Keywords

  • ESS
  • Floral traits
  • Game theory
  • Markov process
  • Nectar
  • Pollinator preferences

ASJC Scopus subject areas

  • General Agricultural and Biological Sciences
  • Applied Mathematics
  • General Biochemistry, Genetics and Molecular Biology
  • General Immunology and Microbiology
  • Statistics and Probability
  • Modeling and Simulation

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