Function calculates rates of shape evolution for two or more multi-dimensional traits on a phylogeny from a set of Procrustes-aligned specimens
compare.multi.evol.rates(A, gp, phy, Subset = TRUE, iter = 999,
print.progress = TRUE)A matrix (n x [p x k]) or 3D array (p x k x n) containing GPA-aligned coordinates for a set of specimens
A factor array designating group membership
A phylogenetic tree of class phylo - see read.tree in library ape
A logical value indicating whether or not the traits are subsets from a single landmark configuration (default is TRUE)
Number of iterations for significance testing
A logical value to indicate whether a progress bar should be printed to the screen. This is helpful for long-running analyses.
An object of class "evolrate" returns a list of the following:
The phylogenetic evolutionary rates for each trait.
The ratio of maximum to minimum evolutionary rates.
The significance level of the observed rate ratio.
Matrix of pairwise significance levels comparing each pair of rates.
The matched call.
The function compares rates of morphological evolution for two or more multi-dimensional traits
on a phylogeny, under a Brownian motion model of evolution following the procedure of Denton and
Adams (2015). It is assumed that the landmarks for all traits have previously been aligned using
Generalized Procrustes Analysis (GPA) [e.g., with gpagen]. The approach calculates
multivariate evolutionary rates found from the distances between species in morphospace after
phylogenetic transformation (sensu Adams 2014). From the data the rate of shape evolution for
each multi-dimensional trait is calculated, and a ratio of rates is obtained. If three or more
traits are used, the ratio of the maximum to minimum rate is used as a test statistic (see
Denton and Adams 2015). Significance testing is accomplished by phylogenetic simulation in
which tips data are obtained under Brownian motion using a an evolutionary rate matrix
for all traits, which contains a common rate for all trait dimensions (Denton and Adams 2015).
If three or more traits are used, pairwise p-values are
also returned.
The shape data may be input as either a 3D array (p x k x n) containing GPA-aligned coordinates for a set of species, or as a matrix (n x [p x k]) whose rows correspond to each species. In both cases, species names must be provided as rownames (for a matrix) or as the names of the third dimension of the array. Landmark groups for each trait are then specified by a factor array designating which landmark belongs to which trait. Additionally, if the method is to be used with other data (i.e., a set of length measurements), the input A should be a matrix of n rows of species and p columns of variables. In this case, the grouping factor should have each variable assigned to a trait group.
Comparisons of evolutionary rates between traits may be accomplished in one of two ways. First, if the traits are are part of a single shape that was subjected to a single Procrustes superimposition (i.e., they are subsets of landmarks in the configuration), then the procedure is performed without alteration as described above. However, if the shapes are derived from different structures (shapes) that were superimposed separately, then the estimates of the rates must take the difference in the number of trait dimensions into account (see discussion in Denton and Adams 2015). This option is identified by selecting Subset = FALSE.
The generic functions, print, summary, and plot all work with
compare.multi.evol.rates.
The generic function, plot, produces a histogram of random rate-ratios associated with
the resampling procedure.
Adams, D.C. 2014. Quantifying and comparing phylogenetic evolutionary rates for shape and other high-dimensional phenotypic data. Syst. Biol. 63:166-177.
Denton, J.S.S., and D.C. Adams. 2015. A new phylogenetic test for comparing multiple high-dimensional evolutionary rates suggests interplay of evolutionary rates and modularity in lanternfishes (Myctophiformes; Myctophidae). Evolution. 69:2425-2440.
data(plethspecies)
Y.gpa<-gpagen(plethspecies$land) #GPA-alignment
land.gp<-c("A","A","A","A","A","B","B","B","B","B","B") #mandible and cranium subsets
EMR<-compare.multi.evol.rates(A=Y.gpa$coords,gp=land.gp,
Subset=TRUE, phy= plethspecies$phy,iter=999)
summary(EMR)
plot(EMR)
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