SimEvo: Simulation of Shape Variation
In Evomorph: Evolutionary Morphometric Simulation

Description Usage Arguments Details Value Author(s) References Examples

SimEvo performs a simulation of shape variation using a modification of Lande's evolutionary model Polly 2004

1	SimEvo(vari, consensusvec, resids, ngen, fsamp)

`vari`	Variation coefficient
`consensusvec`	Consensus shape (vectorized form)
`resids`	GPA residuals matrix
`ngen`	Number of generations of simulations (Default: 1000000 steps).
`fsamp`	Frequency of samples (Default: 1000000 steps).

Lande's evolutionary model defines mean morphological variation over generations Δ Z as:

Delta Z = beta*G

where G is the additive genetic covariance matrix, and β are selection coefficients applied to the morphological structure. Polly 2004 proposes a modification of this equation in order to use it with morphological data instead of genetic data:

Delta Z = beta*H*P

where P is the phenotypic covariance matrix, and H is an heritability matrix (See Polly 2004 for more information). resids will be used as the phenotypic covariance matrix, and vari will be used to simulate β H term. After ngen simulations steps the new shape will be reconstructed from the starting shape consensusvec . The number of plots representing the new shapes can be modify using fsamp .

It returns a list of ngen/fsamp shapes (landmarks coordinates)

Cabrera Juan Manuel

Polly, P. D. (2004). On the simulation of the evolution of morphological shape: multivariate shape under selection and drift. Palaeontologia Electronica, 7(2), 1-28.

 
data("aegla_landmarks")

#Use GpaResiduals function to obtain GPA residual matrix and consensus
# coordinates from landmark configuration

a_data=GpaResiduals(aegla_landmarks)

#Simulate morphological evolution with a variation rate "vari"
# trough "ngen" generations and retrieve the last generation shape coordinates

simshape = SimEvo(vari = 2, consensusvec = a_data$cvectorized, 
resids = a_data$resid, ngen = 10000, fsamp = 10000)

#Plot consensus shape and the simulated shape

par(mfrow=c(1, 2))
plot(a_data$consens,type = "p",main = "Reference", xlab = "", ylab = "")
plot(simshape[[1]],type = "p",col = "red",main = "Target", xlab = "", ylab = "")

#Or you can use PlotVariations to see the difference more clearly

PlotVariations(simshape,a_data$consens)