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Code for: Threshold assessment, categorical perception, and the evolution of reliable signaling

Citation

Peniston, James; Green, Patrick; Zipple, Matthew; Nowicki, Stephen (2020), Code for: Threshold assessment, categorical perception, and the evolution of reliable signaling, Dryad, Dataset, https://doi.org/10.5061/dryad.rr4xgxd71

Abstract

Animals often use assessment signals to communicate information about their quality to a variety of receivers, including potential mates, competitors, and predators. But what maintains reliable signaling and prevents signalers from signaling a better quality than they actually have? Previous work has shown that reliable signaling can be maintained if signalers pay fitness costs for signaling at different intensities and these costs are greater for lower quality individuals than higher quality ones. Models supporting this idea typically assume that continuous variation in signal intensity is perceived as such by receivers. In many organisms, however, receivers have threshold responses to signals, in which they respond to a signal if it is above a threshold value and do not respond if the signal is below the threshold value. Here, we use both analytical and individual-based models to investigate how such threshold responses affect the reliability of assessment signals. We show that reliable signaling systems can break down when receivers have an invariant threshold response, but reliable signaling can be rescued if there is variation among receivers in the location of their threshold boundary. Our models provide an important step towards understanding signal evolution when receivers have threshold responses to continuous signal variation.

Methods

A full descripition of the methods can be found in the main text and the appendix of the assossicated publication.

Usage Notes

There are two .zip files: 1) the code and documentation for the individual-based simulation of the evolution of signals when receivers have threshold assessment ("threshold_assessment_IBMs.zip") and 2) the code, data, and metadata required for making the figures in the associated publication ("R_code_and_simulation_results.zip").

The "threshold_assessment_IBMs.zip" file contains the code and documentation for two individual-based models, one in which receiers' thresholds cannot coevolve and one in which receiers' thresholds can coevolve. Both models are written in C++. Documentation on how to run the code is given in the "README" file. We have also provided example input files which will allow the user to replicate data in the manuscript. Information on how to use these files is also provided in the "README" file.

The "R_code_and_simulation_results.zip" file contains R code for producing the figures in the manuscript (which includes code for analytical solutions) as well as the results from the individual-based simulations. Metadata for all results files is provided in the "README" file.

Funding

Duke University, Award: Office of the Provost

Human Frontier Science Program Fellowship, Award: LT000460/2019-L

National Science Foundation

Human Frontier Science Program Fellowship, Award: LT000460/2019-L