metadat (version 1.4-0)

dat.white2020: Studies on the Relationship between Sexual Signal Expression and Individual Quality

Description

Results from 41 studies examining the relationship between measures of individual quality and the expression of structurally coloured sexual signals.

Usage

dat.white2020

Format

The object is a data frame which contains the following columns:

study_id	character	study-level ID
obs	character	observation-level ID
exp_obs	character	whether the study is observational or experimental
control	numeric	whether the study did (1) or did not (0) include a non-sexual control trait
class	character	class of the study organisms
genus	character	class of the study organisms
species	character	species of the study organisms
sex	character	sex of the study organisms
iridescent	numeric	whether the colour signals were iridescent (1) or not (0)
col_var	character	the colour variable quantified
col_component	character	whether the colour variable is chromatic or achromatic
quality_measure	character	the measure of individual quality used
region	character	the body region from which colour was sampled
n	numeric	study sample size
r	numeric	Pearson's correlation coefficient

Author

Thomas E. White,

Concepts

ecology, evolution, correlation coefficients

Details

The 186 rows in this dataset come from 41 experimental and observational studies reporting on the correlation between measures of individual quality (age, body condition, immune function, parasite resistance) and the expression of structurally coloured sexual signals across 28 species. The purpose of this meta-analysis was to test whether structural colour signals show heightened condition-dependent expression, as predicted by evolutionary models of 'honest' signalling.

Examples

### copy data into 'dat' and examine data
dat <- dat.white2020
head(dat, 10)

if (FALSE) {
### load metafor package
library(metafor)

### calculate r-to-z transformed correlations and corresponding sampling variances
dat <- escalc(measure="ZCOR", ri=r, ni=n, data=dat)

### fit multilevel meta-analytic model
res <- rma.mv(yi, vi, random = list(~ 1 | study_id, ~ 1 | obs), data=dat)
res
}