Package overview

Data

First, we load the package phylosignal and the dataset carnivora from adephylo.

library(phylosignal)
library(adephylo)
library(ape)
library(phylobase)
data(carni19)

Here is a phylogenetic tree of 19 carnivora species.

tre <- read.tree(text=carni19$tre)

And we create a dataframe of 3 traits for the 19 carnivora species.

• Body mass
• Random values
• Simulated values under a Brownian Motion model along the tree

dat <- list()
dat$mass <- carni19$bm
dat$random <- rnorm(19, sd = 10)
dat$bm <- rTraitCont(tre)
dat <- as.data.frame(dat)

We can combine phylogeny and traits into a phylo4d object.

p4d <- phylo4d(tre, dat)

Visualizing the data

barplot.phylo4d(p4d, tree.type = "phylo", tree.ladderize = TRUE)

Measuring and testing the signal for each trait and different methods

phyloSignal(p4d = p4d, method = "all")

## $stat
##            Cmean          I         K    K.star       Lambda
## mass   0.5493887 0.39210678 0.7127747 0.7154914 9.640762e-01
## random 0.1186030 0.03765706 0.1409412 0.1411693 4.622919e-05
## bm     0.4281898 0.26558939 0.8723149 0.8842063 1.011576e+00
## 
## $pvalue
##        Cmean     I     K K.star Lambda
## mass   0.001 0.001 0.001  0.001  0.001
## random 0.150 0.187 0.384  0.430  1.000
## bm     0.002 0.005 0.001  0.001  0.001

Assessing the behavior of these methods with this phylogeny along a Brownian-Motion influence gradient

phylosim <- phyloSim(tree = tre, method = "all", nsim = 100, reps = 99)

plot(phylosim, stacked.methods = FALSE, quantiles = c(0.05, 0.95))

plot.phylosim(phylosim, what = "pval", stacked.methods = TRUE)

Assessing the signal depth with correlograms

mass.crlg <- phyloCorrelogram(p4d, trait = "mass")
random.crlg <- phyloCorrelogram(p4d, trait = "random")
bm.crlg <- phyloCorrelogram(p4d, trait = "bm")

plot(mass.crlg)

plot(random.crlg)

plot(bm.crlg)

Locating the signal with LIPA

carni.lipa <- lipaMoran(p4d)
carni.lipa.p4d <- lipaMoran(p4d, as.p4d = TRUE)

barplot.phylo4d(p4d, bar.col=(carni.lipa$p.value < 0.05) + 1, center = FALSE , scale = FALSE)

barplot.phylo4d(carni.lipa.p4d, bar.col = (carni.lipa$p.value < 0.05) + 1, center = FALSE, scale = FALSE)