View source: R/rarexpected_fun.R
rarexpected_fun | R Documentation |
The function calculates functional diversity rarefaction curves using the method proposed by Ricotta et al. (2010).
rarexpected_fun(comm,dist_f)
comm |
a community data frame or matrix with N plots as rows, S species as columns. Only presence/absence (1/0) of species in plots are allowed as entries. |
dist_f |
an object of class |
The expected occupancy frequencies for species at an increasingly larger cumulative number of plots N are used for the calculation of Rao's quadratic entropy to obtain an expected functional diversity between species in the N random plots. The expected occupancy frequency is the probability for every species in the community to find that species in the N random plots.
A vector containing the values of the expected functional diversity for every sampling dimension is returned.
Giovanni Bacaro gbacaro@units.it
Elisa Thouverai elisa.th95@gmail.com
Botta-Dukat, Z. (2005) Rao's quadratic entropy as a measure of functional diversity based on multiple traits. Journal of Vegetation Science, 16, 533–540.
Rao, C.R. (1982) Diversity and dissimilarity coefficients: a unified approach. Theoretical Population Biology, 21, 24–43.
Ricotta, C., Burrascano, S., Blasi, C. (2010) Incorporating functional dissimilarities into sample-based rarefaction curves: from taxon resampling to functional resampling. Journal of Vegetation Science, 21, 280–286.
ser_functional
require(ade4)
require(vegan)
data(duneFVG)
data(duneFVG.tr8)
tr8_N<-duneFVG.tr8$traits.tot[,c(1,3,4)]
tr8_D<-data.frame(duneFVG.tr8$traits.tot[,2])
tr8_Q<-duneFVG.tr8$traits.tot[,5:15]
tr8dist<-dist.ktab(ktab.list.df(list(tr8_N,tr8_D,tr8_Q)),type=c('N','D','Q'))
tr8dist<-cailliez(tr8dist)
d<-decostand(duneFVG$total, method='pa')
rare<-rarexpected_fun(d,tr8dist)
plot(rare, ylab="Rao QE", xlab="Number of plots", type="l")
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