dat.white2020.RdResults from 41 studies examining the relationship between measures of individual quality and the expression of structurally coloured sexual signals.
dat.white2020The 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 |
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.
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
ecology, evolution, correlation coefficients
### copy data into 'dat' and examine data
dat <- dat.white2020
head(dat, 10)
#> study_id obs exp_obs control class genus species sex iridescent
#> 1 p105 e001 exp 0 insecta pseudomantis Pseudomantis_albofimbriata female 0
#> 2 p11 e002 obs 0 aves Cyanocitta Cyanocitta_stelleri male 0
#> 3 p11 e003 obs 0 aves Cyanocitta Cyanocitta_stelleri male 0
#> 4 p11 e004 obs 0 aves Cyanocitta Cyanocitta_stelleri male 0
#> 5 p11 e005 obs 0 aves Cyanocitta Cyanocitta_stelleri male 0
#> 6 p11 e006 obs 0 aves Cyanocitta Cyanocitta_stelleri male 0
#> 7 p11 e007 obs 0 aves Cyanocitta Cyanocitta_stelleri male 0
#> 8 p11 e008 exp 0 aves Cyanocitta Cyanocitta_stelleri male 0
#> 9 p11 e009 exp 0 aves Cyanocitta Cyanocitta_stelleri male 0
#> 10 p11 e010 exp 0 aves Cyanocitta Cyanocitta_stelleri male 0
#> col_var col_component quality_measure region n r
#> 1 brightness achromatic condition abdomen 50 0.437
#> 2 hue chromatic condition wing 22 0.410
#> 3 chroma chromatic condition wing 22 -0.330
#> 4 brightness achromatic condition wing 22 -0.130
#> 5 hue chromatic parasite wing 59 -0.080
#> 6 chroma chromatic parasite wing 59 -0.310
#> 7 brightness achromatic parasite wing 59 -0.120
#> 8 hue chromatic parasite wing 9 0.150
#> 9 chroma chromatic parasite wing 9 -0.090
#> 10 brightness achromatic parasite wing 18 0.270
### 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
#>
#> Multivariate Meta-Analysis Model (k = 186; method: REML)
#>
#> Variance Components:
#>
#> estim sqrt nlvls fixed factor
#> sigma^2.1 0.0153 0.1237 41 no study_id
#> sigma^2.2 0.0649 0.2548 186 no obs
#>
#> Test for Heterogeneity:
#> Q(df = 185) = 759.2939, p-val < .0001
#>
#> Model Results:
#>
#> estimate se zval pval ci.lb ci.ub
#> 0.1573 0.0329 4.7742 <.0001 0.0927 0.2218 ***
#>
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>