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Data from: State-dependent decision-making by predators and its consequences for mimicry

Cite this dataset

Aubier, Thomas G.; Sherratt, Thomas N. (2020). Data from: State-dependent decision-making by predators and its consequences for mimicry [Dataset]. Dryad.


The mimicry of one species by another provides one of the most celebrated examples of evolution by natural selection. Edible Batesian mimics deceive predators into believing they may be defended, whereas defended Müllerian mimics have evolved a shared warning signal, more rapidly educating predators to avoid them. However, it may benefit hungry predators to attack defended prey, while the benefits of learning about unfamiliar prey depends on the future value of this information. Previous energetic state-dependent models of predator foraging behaviour have assumed complete knowledge, while informational state-dependent models have assumed fixed levels of hunger. Here, we identify the optimal decision rules of predators accounting for both energetic and informational states. We show that the nature of mimicry is qualitatively and quantitatively affected by both sources of state dependence. Associative learning weakens the extent of parasitic mimicry by edible prey because naive predators often attack defended models. More importantly, mimicry among equally highly defended prey may be parasitic or mutualistic depending on the ecological context. Finally, mimicry by prey with intermediate defences corresponds to Batesian or Müllerian mimicry depending on whether the mimic is profitable to attack by hungry predators, but it is not a special case of mimicry.


Datasets are simulated using the computer code provided in the Repository.

Usage notes

Simulations are run using Julia (tested on version 1.0.1). The R code is used to analyse the outputs of the simulations. Please see the README.txt file for further information.