This document provides an overview of using phylogenies in comparative analysis. It discusses why phylogenies are important in comparative analysis to account for shared evolutionary history between taxa. It summarizes how re-analyzing Salisbury's data on stomatal density using independent contrasts and incorporating a phylogeny changed the conclusions. The document outlines how to obtain or generate a phylogeny and load it with trait data into R. It demonstrates using the CAPER package to conduct phylogenetic generalized least squares (pgls) to analyze traits while accounting for phylogeny, including for continuous traits and factors. It discusses visualizing and exploring phylogenies in R and other programs.

1. Comparative analysis including
phylogeny in R
AE Zanne et al. Nature 000, 1-4 (2013) doi:10.1038/nature12872
Time-calibrated maximum-likelihood estimate of the molecular phylogeny for 31,749 species of seed plants.
Bianca A. Santini
@myoldowlisdead
b.santini@sheffield.ac.uk

2. What is a phylogeny?
• Hypothesis that explains the evolutionary relationship among taxa*
*taxa: species, or higher taxonomic levels.
Also genes, or sequences
node
terminals/tips/leaves
root
A B C
D
Internal
branch
branch
External branch
ModifiedfromNatureScitable:
http://www.nature.com/scitable/topicpage/reading-a-phylogenetic-
tree-the-meaning-of-41956#

3. Why use phylogenies…
…in comparative analysis?
• Comparative analyses are used to assess the ecological significance of a
particular trait
• However, because there is a shared history…
a) they are not statistically independent data
b) the feature under study might exist
because of shared ancestry

4. One example of data analyzed without a
phylogeny
• Salisbury’s data (1927, yes 88yrs ago!)
• Observations
– Differences in stomata density (SD)
between sun(>) and shade (<) leaves
• Measured stomatal density and related
them to life-form, habitat type
• Conclusion: SD increases with exposure
SD
Trees Shrubs Herbs Woody Herbs
plants
SD
Marginal
herbs
Understory
herbs
Photo by A. Vazquez-Lobo

5. Re-analysis of Salisbury’s data
• Independent contrasts (Felsenstein,
1985)
– Introduces a phylogeny
– The trait changes along the branches of the
tree, should be associated to changes in the
explanatory variable
– no. of times the traits changes in concert
with the environmental variable
(agreements)
vs.
no. of times they do not (disagreements)
– Sign test
SD
Trees Shrubs Herbs
Woody Herbs
plants
SD
Marginal
herbs
Understory
herbs
Kelly and Beerling, 1995 (68 yrs after)

6. How to get your own phylogeny ?
phylomatic (uses a megatree)
http://phylodiversity.net/phylomatic/
Use and trim and already published
phylogeny:
- Dryad: http://datadryad.org/
- Ecological Archives (from the esa)
phyloGenerator (uses gen bank sequences)
http://willpearse.github.io/phyloGenerator/
This is if you don’t have the sequences, or are
not planning to get them.

7. Package CAPER : pgls()
Similar approach as in Independent Contrasts, but uses a matrix
of variances and covariances (tree)
N.B. If interested in phylogenies and evolution
analyses: geiger, adephylo, picante, phylolm, ape…

8. pgls: phylogenetic generalized least squares
what do you need?
1. Phylogeny
2. Data
– Make sure the rows in your data frame are the same as the tips of your tree
i.e. your data: Juncus bufonius
tree: Juncus_bufonius
> my.data$underscore.name=gsub(" ","_",my.data$underscore.name)
– Make sure you have one observation per species per trait:
3. Put them into a comparative.data()
Species names Leaf area Seed mass
Juncus_bufonius 120.2 0.24
Setaria_pumila 91.2 6.91

9. #1)PHYLOGENY
> tree<-read.tree ("Vascular_Plants_rooted.dated.tre”) ##or read.nexus()
> tree <- congeneric.merge(tree,my.data$underscore.name) ##pez package
Number of species in tree before: 401
Number of species in tree now: 550
> tree
Phylogenetic tree with 550 tips and 393 internal nodes.
Tip labels:
Gladiolus_italicus, Juncus_squarrosus, Juncus_bufonius, Bolboschoenus_maritimus, Isolepis_setacea,
Cyperus_fuscus, ...
Node labels:
, , , , , , …
Rooted; includes branch lengths.
pgls()
#You can always check for synonyms and replace (taxize)
> my.data$underscore.name<-recode(my.data$underscore.name, "'Aegilops_geniculata' =
'Aegilops_ovata'")
> plot.phylo(tree, cex=0.45, type="radial", edge.color=c("red", "orange", "blue"))

10. pgls()
##trim your tree
> tree <- drop.tip(tree, setdiff(tree$tip.label, my.data$underscore.name))
#2) YOUR DATA
> dat<-data.frame(read.csv(“mydata.csv",header=T))
#3)PUT THEM together in comparative data, which will drop rows with NAs for you
and match the rows to the tips of the phylogeny :D
> cdat <- comparative.data(data = dat, phy = tree, names.col = ”underscore.name”,
scope=leaf.area~seed.mass, vcv=TRUE) #na.omit=FALSE #warn.dropped=TRUE
> cdat$dropped #to see what has been dropped.

11. #to get the phylogenetic signal : lambda=‘ML’
#0 is a star phylogeny (no phylo signal), and 1 is an structured phylogeny, or all is explained by the
phylogeny.
> fit= pgls(leaf.area~seed.mass, cdat, lambda='ML')
> summary(fit)
pgls()
> summary(fit)
Call:
pgls(formula = leaf.area ~ seed.mass, data = dat,
lambda = "ML")
Residuals:
Min 1Q Median 3Q Max
-0.176405 -0.046501 0.003632 0.047885 0.227434
Branch length transformations:
kappa [Fix] : 1.000
lambda [ ML] : 0.863
lower bound : 0.000, p = < 2.22e-16
upper bound : 1.000, p = < 2.22e-16
95.0% CI : (0.771, 0.919)
delta [Fix] : 1.000
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.730721 0.318898 8.563 4.441e-16 ***
seed.mass 0.442324 0.042318 10.452 < 2.2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.07308 on 373 degrees of freedom
Multiple R-squared: 0.2265, Adjusted R-squared: 0.2245
F-statistic: 109.2 on 1 and 373 DF, p-value: < 2.2e-16

12. But what if you want to analyze
factors?
#CAPER has some bugs…
> cdat <- comparative.data(data = dat, phy = tree, names.col = ”underscore.name”,scope =
leaf area ~nitro.class, vcv=TRUE)
> fit= pgls(leaf area ~nitro.class, cdat, lambda='ML')
> anova(fit)
Error in terms.formula(formula,data=data):
invalid model formula in ExtractVars

13. #solve it like (below)
> fit= pgls(leaf.area~nitro.class, cdat, lambda='ML')
> fit1= pgls(leaf.area~1, cdat, lambda='ML')
> anova(fit, fit1)
Error in anova.pglslist(object, ...) :
models were fitted with different branch length transformations.
##If you click on summary, you’ll see
Call:
pgls(formula = leaf.area~ nitro.class, data = dat,
lambda = "ML")
Residuals:
Min 1Q Median 3Q Max
-0.200441 -0.049287 -0.002017 0.051002 0.200019
Branch length transformations:
kappa [Fix] : 1.000
lambda [ ML] : 0.867
lower bound : 0.000, p = < 2.22e-16
upper bound : 1.000, p = < 2.22e-16
95.0% CI : (0.772, 0.926)
delta [Fix] : 1.000
Call:
pgls(formula = leaf.area ~ 1, data = dat, lambda = "ML")
Residuals:
Min 1Q Median 3Q Max
-0.274585 -0.061252 0.003683 0.053449 0.253368
Branch length transformations:
kappa [Fix] : 1.000
lambda [ ML] : 0.896
lower bound : 0.000, p = < 2.22e-16
upper bound : 1.000, p = < 2.22e-16
95.0% CI : (0.825, 0.940)
delta [Fix] : 1.000

14. #giving both models the same value
> fit= pgls(leaf.area~nitro.class, cdat, lambda=0.885)
> fit1= pgls(leaf.area~1, cdat, lambda=0.885)
> anova(fit, fit1)

15.  You can also use gls(), instead of lambda do method=‘ML’
 Visualize your tree, always exciting!
 In R
> plot(tree)
> help(plot.phylo) #install ape
#and: http://www.r-phylo.org/wiki/Main_Page
 Use FigTree (drop the file and it will do the phylogeny for you)
 phytools

16. >plot.phylo(tree, cex=0.3)

17. >plot.pylo(tree, cex=0.45, type="cladogram", show.Ep.label=FALSE

18. >plot.phylo(tree, cex=0.45, type="fan", edge.color=c("red", "orange", "green","blue"), edge.lty=5)

19. Thanks
Bianca A. Santini
@myoldowlisdead
b.santini@sheffield.ac.uk

20. From Freckleton et al. 2002.