PAICE-package {PAICE}

Phylogeographic Analysis of Island Colonization Events

Description

A package for inferring inter-island colonization events in island-like systems.

Details

Estimation of the number of infer inter-island colonization events in an island-like system by analyzing the geographic distribution of uniparentally inherited haplotypes and their genealogical relationships. Furthermore, by building rarefaction curves based on both genetic sampling (variable positions) and field sampling (populations/individuals), an estimation of the number of colonization events corrected by sampling effort could be done. The method used in the PAICE package is described in Coello et al. (2022).

PAICE functions

colonization to infer the minimun number of colonization events

geneticResampling to simplify the genealogy by deleting a variable position

maxCol to calculate asymptotic estimators considering genetic and field sampling

plot.maxCol to plot curves generated by maxCol

plot.rarecol to plot rarefaction curves

rarecol to generate rarefaction curves of colonization events

read.rarecol to read previously saved rarefaction curve files

PAICE datasets

CmonsData haplotype distribution of Cistus monspeliensis in the Canary Islands

CmonsNetwork genealogy of Cistus monspeliensis

CmonsRare example data of rarefaction curves for Cistus monspeliensis

Author(s)

Alberto J. Coello, Mario Fernandez-Mazuecos, Ruben H. Heleno and Pablo Vargas

Maintainer: Alberto J. Coello <[e-mail]>

References

Coello, A.J., Fernandez-Mazuecos, M., Heleno, R.H., Vargas, P. (2022). PAICE: A new R package to estimate the number of inter-island colonizations considering haplotype data and sample size. Journal of Biogeography, 49(4), 577-589.DOI: 10.1111/jbi.14341

Examples

# Inference of minimum number of inter-island colonization events
data(CmonsData)
data(CmonsNetwork)
col <- colonization(data = CmonsData, network = CmonsNetwork)
col
summary(col)

# Asumptotic estimators of colonization events
# 25 replicates used in each sampling variable
set.seed(31)
CmonsRare <- rarecol(data = CmonsData, network = CmonsNetwork,
    replicates_field = 25, replicates_genetic = 25, monitor = TRUE,
    mode = c(1, 2))
maxcol <- maxCol(data = CmonsRare)
maxcol
summary(maxcol)

# Plotting results
old.par <- par(no.readonly = TRUE) # To restore previous options
par(mfrow = c(2, 2))
plot(CmonsRare)
par(fig = c(0, 1, 0, 0.5), new = TRUE)
plot(maxcol)
par(old.par)