Mimicry in motion and morphology: do information limitation, trade-offs or compensation relax selection for mimetic accuracy?
Cite this dataset
McLean, Donald; Herberstein, Marie (2021). Mimicry in motion and morphology: do information limitation, trade-offs or compensation relax selection for mimetic accuracy? [Dataset]. Dryad. https://doi.org/10.5061/dryad.15dv41nwj
Many animals mimic dangerous or undesirable prey as a defence from predators. We would expect predators to reliably and exclusively avoid animals that closely resemble dangerous prey, yet imperfect mimics are common. There have been many hypotheses suggested to explain imperfect mimicry, but comparative tests across multiple mimicry systems are needed to determine which are applicable, and which—if any—represent general principles of imperfect mimicry. We tested four hypotheses on Australian ant mimics and found support for only one of them: the information limitation hypothesis. A predator with incomplete information will be unable to discriminate some poor mimics from their models. We also show that since predators must make decisions while they are learning, they are likely to never sample broadly enough to gain and utilise the full information needed to discriminate poor mimics from their models. We found no evidence that one accurate mimetic trait can compensate for another, or that rapid movement reduces selection pressure for good mimicry. Based on our results, we argue that information limitation is likely to be a general principle behind imperfect mimicry of any complex traits, while interactions between components of mimicry may apply to some mimicry systems but not others.
This data set is a zip file containing both data and code (R scripts). Data include videos of walking arthropods, from which trajectories are extracted, and outlines derived from still photographs, which are used to analyse body shape. The R scripts are used to process and analyse the data. The data set contains README.md and README.txt files that describe the structure of the data set in more detail.
The data set contains README.md and README.txt files that describe the data set.
Australian Research Council, Award: DP170101617