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geomorph (version 4.0.8)

compare.evol.rates: Comparing net rates of shape evolution on phylogenies

Description

Function calculates net rates of shape evolution for two or more groups of species on a phylogeny from a set of Procrustes-aligned specimens

Usage

compare.evol.rates(
  A,
  phy,
  gp,
  iter = 999,
  seed = NULL,
  method = c("permutation", "simulation"),
  print.progress = TRUE
)

Value

An object of class "evolrate" returns a list with the following components:

sigma.d.ratio

The ratio of maximum to minimum net evolutionary rates.

P.value

The significance level of the observed ratio.

Effect.Size

The multivariate effect size associated with sigma.d.ratio.

sigma.d.gp

The phylogenetic net evolutionary rate for each group of species on the phylogeny.

random.sigma

The sigma values found in random permutations of the resampling procedure.

permutations

The number of random permutations used.

Arguments

A

A 3D array (p x k x n) containing GPA-aligned coordinates for all specimens, or a matrix (n x variables)

phy

A phylogenetic tree of class = "phylo" - see read.tree in library ape

gp

A factor array designating group membership for individuals

iter

Number of iterations for significance testing

seed

An optional argument for setting the seed for random permutations of the resampling procedure. If left NULL (the default), the exact same P-values will be found for repeated runs of the analysis (with the same number of iterations). If seed = "random", a random seed will be used, and P-values will vary. One can also specify an integer for specific seed values, which might be of interest for advanced users.

method

One of "simulation" or "permutation", to choose which approach should be used to assess significance.

print.progress

A logical value to indicate whether a progress bar should be printed to the screen. This is helpful for long-running analyses.

Author

Dean Adams & Emma Sherratt

Details

The function compares net rates of morphological evolution for two or more groups of species on a phylogeny, under a Brownian motion model of evolution. It is assumed that the landmarks have previously been aligned using Generalized Procrustes Analysis (GPA) [e.g., with gpagen]. The approach is based on the outer-product matrix of between species differences in morphospace after phylogenetic transformation (Adams 2014). From the data the net rate of shape evolution for each group in the multi-dimensional space is calculated, and a ratio of rates is obtained. If three or more groups of species are used, the ratio of the maximum to minimum rate is used as a test statistic (see Adams 2014). The function can be used with univariate data (i.e. centroid size) if imported as matrix with rownames giving the taxa names.

The generic functions, print, summary, and plot all work with compare.evol.rates. The generic function, plot, produces a histogram of random rate-ratios associated with the resampling procedure.

Notes for geomorph 3.0.4 and subsequent versions

Significance testing is now accomplished in one of two ways. First, phylogenetic simulation may be used, in which tips data are obtained under Brownian motion using a common evolutionary rate pattern for all species on the phylogeny. Specifically, the common evolutionary rate matrix for all species is used, with the multi-dimensional rate used along the diagonal elements (see Denton and Adams 2015). This procedure is more general than the original simulation procedure, and retains the desirable statistical properties of earlier methods, and under a wider array of data types. Second, significance may be accomplished via permutation, where data values at the tips are permuted relative to the (see Adams and Collyer 2018). This procedure is shown to retain all appropriate statistical properties, including rotation-invariance of significance levels (see results of Adams and Collyer 2018). In addition, a multivariate effect size describing the strength of the effect is estimated from the empirically-generated sampling distribution (see details in Adams and Collyer 2019). Values from these distributions are log-transformed prior to effect size estimation, to assure normally distributed data.

References

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.

Adams, D.C. and M.L. Collyer. 2018. Multivariate comparative methods: evaluations, comparisons, and recommendations. Systematic Biology. 67:14-31.

Adams, D.C. and M.L. Collyer. 2019. Comparing the strength of modular signal, and evaluating alternative modular hypotheses, using covariance ratio effect sizes with morphometric data. Evolution. 73:2352-2367.

Examples

Run this code
if (FALSE) {

data(plethspecies) 
Y.gpa <- gpagen(plethspecies$land)    #GPA-alignment    
 gp.end <- factor(c(0,0,1,0,0,1,1,0,0))  #endangered species vs. rest
 names(gp.end) <- plethspecies$phy$tip

ER<-compare.evol.rates(A = Y.gpa$coords, phy = plethspecies$phy,
  method = "simulation", gp = gp.end)
summary(ER)
plot(ER)
}

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