procella {ade4}R Documentation

Phylogeny and quantitative traits of birds

Description

This data set describes the phylogeny of 19 birds as reported by Bried et al. (2002). It also gives 6 traits corresponding to these 19 species.

Usage

data(procella)

Format

procella is a list containing the 2 following objects:

tre
is a character string giving the phylogenetic tree in Newick format.
traits
is a data frame with 19 species and 6 traits

Details

Variables of procella$traits are the following ones:
site.fid: a numeric vector that describes the percentage of site fidelity
mate.fid: a numeric vector that describes the percentage of mate fidelity
mass: an integer vector that describes the adult body weight (g)
ALE: a numeric vector that describes the adult life expectancy (years)
BF: a numeric vector that describes the breeding frequencies
col.size: an integer vector that describes the colony size (no nests monitored)

References

Bried, J., Pontier, D. and Jouventin, P. (2002) Mate fidelity in monogamus birds: a re-examination of the Procellariiformes. Animal Behaviour, 65, 235–246.

See a data description at http://pbil.univ-lyon1.fr/R/pps/pps037.pdf (in French).

Examples

data(procella)
pro.phy <- newick2phylog(procella$tre)
plot(pro.phy,clabel.n = 1, clabel.l = 1)
wt <- procella$traits
wt$site.fid[is.na(wt$site.fid)] <- mean(wt$site.fid[!is.na(wt$site.fid)])
wt$site.fid <- asin(sqrt(wt$site.fid/100))
wt$ALE[is.na(wt$ALE)] <- mean(wt$ALE[!is.na(wt$ALE)])
wt$ALE <- sqrt(wt$ALE)
wt$BF[is.na(wt$BF)] <- mean(wt$BF[!is.na(wt$BF)])
wt$mass <- log(wt$mass)
wt <- wt[, -6]
table.phylog(scalewt(wt), pro.phy, csi = 2)
gearymoran(pro.phy$Amat,wt,9999)

Worked out examples


> library(ade4)
> ### Name: procella
> ### Title: Phylogeny and quantitative traits of birds
> ### Aliases: procella
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(procella)
> pro.phy <- newick2phylog(procella$tre)
> plot(pro.phy,clabel.n = 1, clabel.l = 1)
> wt <- procella$traits
> wt$site.fid[is.na(wt$site.fid)] <- mean(wt$site.fid[!is.na(wt$site.fid)])
> wt$site.fid <- asin(sqrt(wt$site.fid/100))
> wt$ALE[is.na(wt$ALE)] <- mean(wt$ALE[!is.na(wt$ALE)])
> wt$ALE <- sqrt(wt$ALE)
> wt$BF[is.na(wt$BF)] <- mean(wt$BF[!is.na(wt$BF)])
> wt$mass <- log(wt$mass)
> wt <- wt[, -6]
> table.phylog(scalewt(wt), pro.phy, csi = 2)
> gearymoran(pro.phy$Amat,wt,9999)
class: krandtest 
Monte-Carlo tests
Call: as.krandtest(sim = matrix(res$result, ncol = nvar, byr = TRUE), 
    obs = res$obs, alter = alter, names = test.names)

Test number:   5 
Permutation number:   9999 
      Test          Obs    Std.Obs   Alter Pvalue
1 site.fid 0.0999203829 0.72186835 greater 0.2225
2 mate.fid 0.1775615101 1.27792725 greater 0.1120
3     mass 0.5446640572 3.85359789 greater 0.0004
4      ALE 0.0009167618 0.01570213 greater 0.4620
5       BF 0.1972100051 1.38215770 greater 0.0909

other elements: NULL
> 
> 
> 
> 

[Package ade4 Index]