Results from studies examining the association between maternal size, offspring size, and number of offsprings.

dat.lim2014

Format

The object is a list containing data frames m_o_size, m_o_fecundity, o_o_unadj, and o_o_adj that contain the following columns and the corresponding phylogenetic trees called m_o_size_tree, m_o_fecundity_tree, o_o_unadj_tree, and o_o_adj_tree:

articlenumericarticle id
authorcharacterstudy author(s)
yearnumericpublication year
speciescharacterspecies
amniotescharacterwhether the species was amniotic
environmentcharacterwhether the species were wild or captive
reprounitcharacterwhether the data were based on lifetime reproductive output or a single reproductive event (only in m_o_size and m_o_fecundity)
rinumericcorrelation coefficient
ninumericsample size

Details

The object dat.lim2014 includes 4 datasets:

m_o_sizeon the correlation between maternal size and offspring size
m_o_fecundityon the correlation between maternal size and number of offsprings
o_o_unadjon the correlation between offspring size and number of offsprings
o_o_adjon the correlation between offspring size and number of offsprings adjusted for maternal size

Objects m_o_size_tree, m_o_fecundity_tree, o_o_unadj_tree, and o_o_adj_tree are the corresponding phylogenetic trees for the species included in each of these datasets.

Source

Lim, J. N., Senior, A. M., & Nakagawa, S. (2014). Heterogeneity in individual quality and reproductive trade-offs within species. Evolution, 68(8), 2306--2318. https://doi.org/10.1111/evo.12446

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.lim2014$o_o_unadj
dat[1:14, -c(2:3)]
#>    article                  species amniotes environment     ri  ni
#> 1        1      Sceloporus_virgatus      yes        wild  0.100  21
#> 2        1      Sceloporus_virgatus      yes        wild -0.170  14
#> 3        1      Sceloporus_virgatus      yes        wild -0.070  21
#> 4        1      Sceloporus_virgatus      yes        wild -0.140  14
#> 5        2          Marmota_marmota      yes        wild -0.540  74
#> 6        3           Vipera_ursinii      yes        wild  0.487 105
#> 7        4   Pantherophis_obsoletus      yes        wild -0.290 104
#> 8        5       Anas_platyrhynchos      yes     captive  0.395  49
#> 9        6     Tropidonophis_mairii      yes        wild -0.130 318
#> 10      10 Urocitellus_richardsonii      yes        wild -0.670  51
#> 11      10 Urocitellus_richardsonii      yes        wild -0.400  38
#> 12      11 Urocitellus_richardsonii      yes        wild -0.530 134
#> 13      11 Urocitellus_richardsonii      yes        wild -0.500  43
#> 14      15            Daphnia_magna       no     captive  0.030 215

# \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.lim2014$o_o_unadj_tree

### compute branch lengths
tree <- compute.brlen(tree)

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

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

### create effect size id variable
dat$esid <- 1:nrow(dat)

### fit multilevel phylogenetic meta-analytic model
res <- rma.mv(yi, vi,
   random = list(~ 1 | article, ~ 1 | esid, ~ 1 | species, ~ 1 | species.phy),
   R=list(species.phy=A), data=dat)
res
#> 
#> Multivariate Meta-Analysis Model (k = 170; method: REML)
#> 
#> Variance Components:
#> 
#>             estim    sqrt  nlvls  fixed       factor    R 
#> sigma^2.1  0.1387  0.3725    125     no      article   no 
#> sigma^2.2  0.0093  0.0962    170     no         esid   no 
#> sigma^2.3  0.0000  0.0000    120     no      species   no 
#> sigma^2.4  0.0572  0.2392    120     no  species.phy  yes 
#> 
#> Test for Heterogeneity:
#> Q(df = 169) = 1823.4239, p-val < .0001
#> 
#> Model Results:
#> 
#> estimate      se     zval    pval    ci.lb   ci.ub   ​ 
#>  -0.1564  0.1277  -1.2242  0.2209  -0.4067  0.0940    
#> 
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 

# }