Results from 457 studies on assortative mating in various species.

dat.moura2021

Format

The object is a list containing a data frame called dat that contains the following columns and a phylogenetic tree called tree:

study.idcharacterstudy id
effect.size.idnumericeffect size id
speciescharacterspecies
species.idcharacterspecies id (as in the Open Tree of Life reference taxonomy)
subphylumcharacterthe subphyla of the species
phylumcharacterthe phyla of the species
assortment.traitcharacterthe measure of body size
trait.dimensionscharacterdimensionality of the measure
field.collectioncharacterwhether data were collected in the field
publication.yearnumericpublication year of the study
pooled.datacharacterwhether data were pooled either spatially and/or temporally
spatially.pooled.datacharacterwhether data were pooled spatially
temporally.pooled.datacharacterwhether data were pooled temporally
rinumericcorrelation coefficient
ninumericsample size

Details

The 457 studies included in this dataset provide 1828 correlation coefficients describing the similarity in some measure of body size in mating couples in 341 different species.

Source

Rios Moura, R., Oliveira Gonzaga, M., Silva Pinto, N., Vasconcellos-Neto, J., & Requena, G. S. (2021). Assortative mating in space and time: Patterns and biases. Ecology Letters, 24(5), 1089--1102. https://doi.org/10.1111/ele.13690

References

Cinar, O., Nakagawa, S., & Viechtbauer, W. (in press). Phylogenetic multilevel meta-analysis: A simulation study on the importance of modelling the phylogeny. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210X.13760

Hadfield, J. D., & Nakagawa, S. (2010). General quantitative genetic methods for comparative biology: Phylogenies, taxonomies and multi-trait models for continuous and categorical characters. Journal of Evolutionary Biology, 23(3), 494--508. https://doi.org/10.1111/j.1420-9101.2009.01915.x

Nakagawa, S., & Santos, E. S. A. (2012). Methodological issues and advances in biological meta-analysis. Evolutionary Ecology, 26(5), 1253--1274. https://doi.org/10.1007/s10682-012-9555-5

Examples

### copy data into 'dat' and examine data
dat <- dat.moura2021$dat
head(dat)
#>                   study.id effect.size.id             species                         species.id
#> 1 Adams and Greenwood 1983              1      Gammarus pulex Gammarus_pulex_gallicus_ott1024016
#> 2       Adams et al. 1985a              2 Trapezia ferruginea       Trapezia_bidentata_ott787242
#> 3       Adams et al. 1985a              3 Trapezia ferruginea       Trapezia_bidentata_ott787242
#> 4       Adams et al. 1985a              4 Trapezia ferruginea       Trapezia_bidentata_ott787242
#> 5       Adams et al. 1985b              5   Asellus aquaticus        Asellus_aquaticus_ott335971
#> 6       Adams et al. 1985b              6   Asellus aquaticus        Asellus_aquaticus_ott335971
#>   subphylum     phylum assortment.trait trait.dimensions field.collection publication.year
#> 1 Crustacea Arthropoda      body length              uni              yes             1983
#> 2 Crustacea Arthropoda  carapace length              uni              yes             1985
#> 3 Crustacea Arthropoda   carapace width              uni              yes             1985
#> 4 Crustacea Arthropoda  cheliped length              uni              yes             1985
#> 5 Crustacea Arthropoda           weight              uni              yes             1985
#> 6 Crustacea Arthropoda           weight              uni              yes             1985
#>   pooled.data spatially.pooled.data temporally.pooled.data   ri ni
#> 1          no                    no                     no 0.23 53
#> 2         yes                   yes                     no 0.57 23
#> 3         yes                   yes                     no 0.54 23
#> 4         yes                   yes                     no 0.53 23
#> 5          no                    no                     no 0.85 12
#> 6          no                    no                     no 0.82 15

# \dontrun{

### load metafor package
library(metafor)

### load ape package
library(ape, warn.conflicts=FALSE)

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

### copy tree to 'tree'
tree <- dat.moura2021$tree

### turn tree into an ultrametric one
tree <- compute.brlen(tree)

### compute phylogenetic correlation matrix
A <- vcv(tree, corr=TRUE)

### make copy of the species.id variable
dat$species.id.phy <- dat$species.id

### fit multilevel phylogenetic meta-analytic model
res <- rma.mv(yi, vi,
   random = list(~ 1 | study.id, ~ 1 | effect.size.id, ~ 1 | species.id, ~ 1 | species.id.phy),
   R=list(species.id.phy=A), data=dat)
res
#> 
#> Multivariate Meta-Analysis Model (k = 1828; method: REML)
#> 
#> Variance Components:
#> 
#>             estim    sqrt  nlvls  fixed          factor    R 
#> sigma^2.1  0.0192  0.1384    457     no        study.id   no 
#> sigma^2.2  0.0145  0.1202   1828     no  effect.size.id   no 
#> sigma^2.3  0.0557  0.2359    341     no      species.id   no 
#> sigma^2.4  0.0512  0.2263    341     no  species.id.phy  yes 
#> 
#> Test for Heterogeneity:
#> Q(df = 1827) = 10743.8076, p-val < .0001
#> 
#> Model Results:
#> 
#> estimate      se    zval    pval   ci.lb   ci.ub    ​ 
#>   0.3682  0.1300  2.8311  0.0046  0.1133  0.6230  ** 
#> 
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 

### examine if spatial and/or temporal pooling of data tends to yield larger correlations
res <- rma.mv(yi, vi,
   mods = ~ spatially.pooled.data * temporally.pooled.data,
   random = list(~ 1 | study.id, ~ 1 | effect.size.id, ~ 1 | species.id, ~ 1 | species.id.phy),
   R=list(species.id.phy=A), data=dat)
res
#> 
#> Multivariate Meta-Analysis Model (k = 1828; method: REML)
#> 
#> Variance Components:
#> 
#>             estim    sqrt  nlvls  fixed          factor    R 
#> sigma^2.1  0.0198  0.1406    457     no        study.id   no 
#> sigma^2.2  0.0144  0.1202   1828     no  effect.size.id   no 
#> sigma^2.3  0.0525  0.2292    341     no      species.id   no 
#> sigma^2.4  0.0532  0.2307    341     no  species.id.phy  yes 
#> 
#> Test for Residual Heterogeneity:
#> QE(df = 1824) = 10618.9794, p-val < .0001
#> 
#> Test of Moderators (coefficients 2:4):
#> QM(df = 3) = 7.6097, p-val = 0.0548
#> 
#> Model Results:
#> 
#>                                                     estimate      se     zval    pval    ci.lb​ 
#> intrcpt                                               0.3457  0.1327   2.6047  0.0092   0.0856 
#> spatially.pooled.datayes                              0.0810  0.0390   2.0780  0.0377   0.0046 
#> temporally.pooled.datayes                             0.0599  0.0269   2.2272  0.0259   0.0072 
#> spatially.pooled.datayes:temporally.pooled.datayes   -0.0729  0.0452  -1.6107  0.1073  -0.1615 
#>                                                      ci.ub 
#> intrcpt                                             0.6059  ** 
#> spatially.pooled.datayes                            0.1574   * 
#> temporally.pooled.datayes                           0.1126   * 
#> spatially.pooled.datayes:temporally.pooled.datayes  0.0158     
#> 
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 

### estimated average correlation without pooling, when pooling spatially,
### when pooling temporally, and when pooling spatially and temporally
predict(res, newmods = rbind(c(0,0,0),c(1,0,0),c(0,1,0),c(1,1,1)), transf=transf.ztor, digits=2)
#> 
#>   pred ci.lb ci.ub pi.lb pi.ub 
#> 1 0.33  0.09  0.54 -0.41  0.81 
#> 2 0.40  0.16  0.60 -0.34  0.84 
#> 3 0.38  0.14  0.58 -0.36  0.83 
#> 4 0.39  0.15  0.59 -0.35  0.83 
#> 

# }