Resolving the paradox of warning signal diversity with predator learning

Datasets and R codes associated with the manuscript titled "Predator learning can resolve the paradox of local warning signal diversity"
Below is a list of files

1. attack_prob_curated.csv: dataset used for the meta-analysis on the variation in final attack probability

author: first author of the publication
journal: the journal in which the study was published
volume: the volume of the journal
species: study species
prey_type: type of prey (almond, mealworm, chicken feed, etc)
prey_presentation: the manner in which prey were presented to the predators (sequential or simultaneous)
cs: type of conditioned stimulus, or the ways in which profitable and control prey consistently differed
cs2: same as cs, coded as either color, pattern, color+pattern, or color+other (size or odor)
cs_property: additional comments or notes for cs
us: cause of unprofitability for artificial prey, recorded as either the type of bitter chemicals (when applicable) or prey defense us2: same as us, but coded as either bitterness (when prey unprofitability was from the use of bitter chemicals) or toxin (when prey extracts were used)
us_concentration: the original concentration of bitter chemicals used (expressed as weight percentage)
us_concentration_cal: calibrated bitterness, accounting for the fact that denatonium benzoate was 20 times more bitter than quinine derivatives
attack_prob: final attack probabilities of predators against unprofitable prey
way_of_learning: how predators learn, recorded as either direct or indirect (i.e., through social learning or generalization)
prey_presentation2: same information as prey_presentation, but coded as either 1 or 2 for statistical purposes
species2: same as species, but coded as 1-4 for statistical purposes
cs3: same as cs2, but coded as 1-4 for statistical purposes
us3: same as us2, but coded as either 1 or 2 for statistical purposes

2. P&F.csv: Final attack probabilities and forgetting rates from selected predators

species: name of the predator species
taxa: taxomomic group of the predator species
P: final attack probabilities
F: forgetting rates
source: the study from which I obtained the data
note: miscellaneous notes

3. learning_IMB.R: R code for performing individual-based simulations of predator-prey interactions

4. learning_ODE.R: R code for performing population-level simulations of predator-prey interactions

5. learning_functions.R: containing custom functions necessary for performing simulations

6. meta-analysis.R: R code for statistical meta-analysis