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

dat.white2020R Documentation

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

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.

Concepts

ecology, evolution, correlation coefficients

Author(s)

Thomas E. White, thomas.white@sydney.edu.au

Source

White, T. E. (2020). Structural colours reflect individual quality: A meta-analysis. Biology Letters, 16(4), 20200001. https://doi.org/10.1098/rsbl.2020.0001

Examples

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

## Not run: 

### 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


## End(Not run)

metadat documentation built on April 6, 2022, 5:08 p.m.