idar (version 1.5)

proportion.idar: Determine the Proportion of Accumulator and Repeller Species

Description

This function determines the proportion of species in a community that "accumulate" or "repell" diversity, in the sense of Wiegand et al. (2007).

Usage

proportion.idar(envlist, alfa = 0.05)
     # S3 method for pidar
plot(x,cols=c(1,2,3),type=c("l","o","o"), pch=c(NA,19,19),
    lty=c(1,1,1),  legend=TRUE, p.legend="topleft",...)

Value

proportion.idar produces an object of class "pidar", basically a list with components

percentage

A data.frame with 3 columns ("p.accumulators", "p.repellers", "p.neutrals") indicating the percentage in each category for each r (rows)

nsp

The total number of species for which the percentage has been computed.

nsim

The number of simulations

alfa

The alpha value employed to calculate the deviations from null model and the assignation to each of the categories

r

The vector of r values at which the idar(r) functions have been estimated

behaviour

A data.frame with the behaviour ("A"= accumulator, "R" = repeller) of each species at each radius r

plot.pidar plots the result.

Arguments

envlist

A list whose elements are the result of applying envelope to a set of point patterns.

alfa

alpha value to calculate deviations from null model

x

The result of proportion.idar

cols

A vector (length =3) with the color names or codes for the curves of each of the 3 categories: "neutral", "accumulator", "repeller".

type

A vector (length =3), with the type of plot for each category. See plot.default.

pch

A vector (length =3), with the point type for each category. See points

lty

A vector (length =3), with the line type for each category. See par

legend

Logical. Should a legend be added to the plot?

p.legend

Position of the legend. It can be a vector wit the xycoordinates of the upperleft corner of the legend box or a keyword accepted by legend

...

Additional graphical parameters passed both to funtions plot and lines

Author

Marcelino de la Cruz

Details

This function determines the proportion of accumulator, repeller and neutral species at each scale r, following the approach of Wiegand et al (2007). A species is classified as an accumulator at scale r if there are less than \((nsim+1) *alpha/2\) simulated values greater than the observed idar(r). On the contrary, a species is classified as repeller at scale if there are less than \((nsim+1) *alpha/2\) simulated values smaller than the observed idar(r). The percentage is computed over the total of species in envlist. It is necessary that the objects in envlist (i.e. the envelope objects) had been computed with the argument "simfuns=TRUE".

References

Wiegand,T., Gunatilleke, C.V.S., Gunatilleke, I.A.U.N. and Huth, A. (2007) How individual species structure diversity in tropical forests. PNAS 104, 19029-19033.

Examples

# Compute percentage of accumulator, neutral an repeller species (for ISAR) in Lansing woods
# In this example, against a null model of IPP.
data(lansing)

# Compute rISAR and plot envelopes for an inhomogeneous Poisson model
# of each species in San Francisco plot
# Split sfsp point pattern ppp by species
lansing.sp<- split(lansing)

# Set parameters for the simulations, estimation of intensity surface for IPP, etc.
# BEWARE: THIS is for R-TESTING ALONE. REAL TESTS SHOULD EMPLOY 199 simulations or higher
nsim<-8
r<- seq(0.01,0.25, by=0.01)
# Create list to store the results
isar.lansing<- list()
# start computation

for( i in 1: length(lansing.sp)){
   print(i)
   # estimate intensity of the focal species
   lambda<- density(lansing.sp[[i]])
   # obtain simulated patterns were all species except the focal remain fixed
   # and the focal varies according to an inhomomgeneous Poiisson process
   simulados<- multifocalsimulator(lansing, mimark=levels(lansing$marks)[i],
                            simulate=expression(rpoispp(lambda)), nsim=nsim)
  # compute isar and envelopes for each species
  # It is COMPULSORY that the argument "savefuns" it is set to TRUE
   isar.lansing[[i]] <- envelope(lansing, isar, mimark=levels(lansing$marks)[i],  r=r,
         simulate=simulados,nsim=nsim, savefuns=TRUE, buffer=0)
}


prop.isar.lansing <- proportion.idar(isar.lansing)
head(prop.isar.lansing $percentage)
head(prop.isar.lansing $behaviour)
plot(prop.isar.lansing, p.legend=c(0.15,60))

if (FALSE) {

# Compute percentage of accumulator, neutral an repeller species (for ISAR) in San Francisco
# forest.  In this example, against a null model of IPP.
data(SF)

# Discard the size mark and keep the species mark in SF ppp:
sfsp<- ppp(SF$x, SF$y, window=SF$window, marks=SF$marks$species)

# Compute ISAR and plot envelopes for an inhomogeneous Poisson model
# of each species in San Francisco plot
# Split sfsp point pattern ppp by species
sfsp.sp<- split(sfsp)
# Select species with >= 10 individuals (to get some statistical power)
sfsp10 <- sapply(sfsp.sp, function(x) x$n>=10)
#Get names of those species
nombressf<- names(sfsp10[sfsp10])
# Set parameters for the simulations, estimation of intensity surface for IPP, etc.
nsim<-199
nmin<-10
sigma <- 8
r<- seq(1,15, by=0.5)
# Create list to store the results
isar.sf<- list()
# start computation
# BEWARE: THIS TAKES QUITE A FEW MINUTES!!!
for( sp in nombressf){
   print(sp)
   # estimate intensity of the focal species
   lambda<- density(unmark(sfsp[sfsp$marks==sp]), sigma=sigma, positive=TRUE)
   # obtain simulated patterns were all species except the focal remain fixed
   # and the focal varies according to an inhomomgeneous Poiisson process
   simulados<- multifocalsimulator(sfsp, mimark=sp,
                            simulate=expression(rpoispp(lambda)), nsim=nsim,nmin=nmin)
  # compute isar and envelopes for each species
  # It is COMPULSORY that the argument "savefuns" it is set to TRUE
   isar.sf[[sp]] <- envelope(sfsp, isar, mimark=sp,  r=r,
         simulate=simulados,nsim=nsim, savefuns=TRUE, buffer=0)
}


prop.isar.SF <- proportion.idar(isar.sf)
head(prop.isar.SF$percentage)
head(prop.isar.SF$behaviour)
plot(prop.isar.SF)

}