10.5061/DRYAD.RR4XGXD71
Peniston, James
0000-0003-3577-1035
University of Florida
Green, Patrick
University of Exeter
Zipple, Matthew
Duke University
Nowicki, Stephen
Duke University
Code for: Threshold assessment, categorical perception, and the evolution
of reliable signaling
Dryad
dataset
2020
FOS: Biological sciences
Duke University
https://ror.org/00py81415
Office of the Provost
Human Frontier Science Program Fellowship*
LT000460/2019-L
National Science Foundation
https://ror.org/021nxhr62
Human Frontier Science Program Fellowship
LT000460/2019-L
2020-10-28T00:00:00Z
2020-10-28T00:00:00Z
en
https://doi.org/10.1101/2020.05.30.125518
119659 bytes
2
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
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.
A full descripition of the methods can be found in the main text and the
appendix of the assossicated publication.
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.