Version:	1.6
Date:	2025-05-01
Title:	Individual Diversity-Area Relationships
Depends:	FD, picante, spatstat (≥ 2.0-0)
Imports:	ape, spatstat.geom, spatstat.explore, spatstat.random
Suggests:	ecespa, vegan
Description:	Computes and tests individual (species, phylogenetic and functional) diversity-area relationships, i.e., how species-, phylogenetic- and functional-diversity varies with spatial scale around the individuals of some species in a community. See applications of these methods in Wiegand et al. (2007) <doi:10.1073/pnas.0705621104> or Chacon-Labella et al. (2016) <doi:10.1007/s00442-016-3547-z>.
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation:	yes
Packaged:	2025-05-01 12:58:17 UTC; marcelino
Author:	Marcelino de la Cruz [aut, cre]
Maintainer:	Marcelino de la Cruz <marcelino.delacruz@urjc.es>
Repository:	CRAN
Date/Publication:	2025-05-01 13:30:05 UTC

Loosmore and Ford Goodness of Fit Test

Description

Performs the Loosmore and Ford (2006) test or the Maximum Absolute Deviation test for a spatial point pattern.

Usage

  LF.gof(X, rmin=NULL, rmax=NULL, na.rm=TRUE)

Arguments

Details

These function perform a tests for goodness-of-fit of a point pattern dataset to a point process model, based on Monte Carlo simulation from the model. The simulations should have been previously computed with the function envelope, applied with the argument savefuns=TRUE in order to save all the simulated functions, required for the computation of the test.

The test, popularized in the ecological field by Loosmore and Ford (2006) is also described in Diggle (2003, page 14), and according to Baddeley and Turner (2005) also in Diggle (1986) and Cressie (1991, page 667, equation (8.5.42)). If the arguments rmin and rmax are set to NULL, the integral of the GoF statistics will be computed over the complete range of r values.

Value

A list with the following components:

u

The GoF statistic, i.e., the value of the integral over the range of r's

p

The p-value of the test

na.count.by.r

Number of NA values for each r. It helps to evaluate the reliability of the computed u's, specially for small r's

Author(s)

Marcelino de la Cruz marcelino.delacruz@urjc.es

References

Cressie, N.A.C. (1991) Statistics for spatial data. John Wiley and Sons, 1991.

Diggle, P. J. (1986). Displaced amacrine cells in the retina of a rabbit : analysis of a bivariate spatial point pattern. J. Neuroscience Methods 18, 115-125.

Diggle, P.J. (2003) Statistical analysis of spatial point patterns, Second edition. Arnold.

Loosmore, N.B. and Ford, E.D. (2006) Statistical inference using the G or K point pattern spatial statistics. Ecology 87, 1925-1931.

See Also

dclf.test for an alternative implementation of the test in spatstat.

Examples

# some envelopesfor some idar function
# The argument "savefuns" must be set to "TRUE"
# BEWARE: in real tests nsim shpuld be 199 or higher
data(lansing)
maple.lambda<- density(unmark(lansing[lansing$marks=="maple"]))
multi.maple.sim <- multifocalsimulator(lansing, "maple", nsim=10,
                               simulate=expression(rpoispp(maple.lambda)))

isar.maple.env<- envelope(lansing, fun=isar, mimark="maple", nsim=10,
                                     simulate=multi.maple.sim, r=seq(0.01, 0.25, le=100),
                                      savefuns=TRUE)
# Estimate GoF test
LF.gof(isar.maple.env)

San Francisco forest plot.

Description

Point pattern describing the locations of 822 trees (belonging to 113 species) in the San Francisco forest (southern Ecuador). Accompanied by a phylogenetic tree and a file with wood density of some of the species.

Usage

data(SF)
data(SFphylotree)
data(SFtraits)

Format

SF is an object of class ppp of spatstat representing the point pattern of trees locations, with a data.frame of marks. See ppp.object for details of the format.The dataset has 822 points with the following marks:

species

Species to which each tree belongs

dbh

Diameter at breast height of each tree

SFphylotree is a phylogenetic tree of the class phylo of ape, showing the phylogenetic relationshios among 296 tree species.

SFtraits is a data.frame with just one column, giving the estimated wood density for some of the 296 tree species.

Details

This dataset represents the locations of trees with dbh >= 5 cm in a polygonal plot of approx. 100 x 70 m in the montane forest near to Reserva Biológica San Francisco (Zamora-Chinchipe, southern Ecuador). These are part of the data collected by Vicuña (2016) and other Ecuadorian botanists and have been analyzed several times ( e.g., Chacón et al. 2014). The coordinates of trees are given in meters and the dbh in cm. The phylogenetic tree has been extracted from Phylomatic and calibrated with the BLADJ algorithm of Phylocom. The data about wood density have been compiled from several sources and is expressed in g/cm^3.

Source

Vicuña, R. (2016) Estructura espacial y dinámica del bosque montano del Sur del Ecuador. Interacciones bióticas y limitaciones abióticas. Tesis Doctoral. Universidad Politécnica de Madrid.

References

Chacón-Labella et al. (2014) Negative density dependence and environmental heterogeneity effects on tree ferns across succession in a tropical montane forest. Perspectives in Plant Ecology, Evolution and Systematics 16(2): 52-63.

Check Trait and Phylo Data

Description

Checks consistency of names of species in spatial, phylogenetic and traits data.

Usage

  checktree(tree,  mippp, idar, correct.phylo)
  checktraits(traits, mippp, idar, correct.trait.na, correct.trait)
  

Arguments

Details

This functions check for the coincidence of species in the point pattern and in the phylogenetic or trait data and for the existence of missing data. If correct.phylo="mean", species which are absent in the phylogenetic tree or covariance matrix will be included and assigned mean phylogenetic covariance. If correct.phylo="exclude", missing species in the tree will be excluded from the analysis (i.e., will not be considered in the computation of the local phylogenetic indices). If correct.trait.na="TRUE", NA values for traits in the data.frame of traits will be assigned the mean trait value. If correct.trait="mean", missing species in the data.frame of traits will be included and assigned mean trait values. If correct.trait="exclude", missing species in the data.frame of traits will be excluded from the analysis (i.e., will not be considered in the computation of the local functional indices).

Value

checktree returns a covariance matrix with the appropriate species corrections. checktraits returns a data.frame of traits with the appropriate corrections or a Gower distance matrix among the species from the corrected data.frame of traits if idar="ifdar".

Warning

The transcription of species names in the multivariate mippp, in the row names of the data.frame of traits (or in the names or dimnames of the distance matrices) should be identical. The same applies to the tiplabels of the phylogenetic tree.

Author(s)

Marcelino de la Cruz marcelino.delacruz@urjc.es

Compute Simulation Envelopes for IDAR(r) Functions

Description

Compute simulation envelopes for IDAR(r) functions.

Usage

  envelope4idar(mippp, mippp.sp.sim, mippp.sp, mimark=NULL,
    namesmark=NULL, r, idar="isar", buffer="adapt", bfw=NULL,
    nsim=NULL, nrank=1, tree = NULL, traits = NULL, 
    cross.idar=FALSE, savefuns=TRUE, correct.phylo="exclude",
    correct.trait.na=FALSE, correct.trait="mean" )
idar2(mippp.sp, mippp, mimark, idar="isar", buffer,bfw, r, 
    cross.idar=FALSE,  tree = NULL, traits = NULL)
    raoDmod(comm, phy = NULL) 
  

Arguments

Details

In 2007, Wiegand et al. developed the concept of Individual Species-Area Relationship. Basically, this consist in computing species accumulation curves by samping areas with varying radius r around the individual trees of a focal species. Here we provide a version of ISAR(r) (idar="isar"), but we also extend this concept to other diversity-area relationships and provide functions to compute individual phylogenetic diversity-area and individual functional diversity-area relationships. The individual phylogenetic functions are based in Helmus et al. (2007) measures, i.e., phylogenetic species variability (idar="ipsvar"), phylogenetic species richness (idar="ipsrar"), phylogenetic species evenness (idar="ipsear"), and phylogenetic species clustering (idar="ipscar"). Also, an individual version of the mean nearest taxon distance of Webb et al. (2002) (idar="imntd"). The individual functional-diversity function (idar="ifdar") is based in the functional dispersion measure (FDis) of Laliberté and Legendre (2010). Other available functions are based in a weighted community mean of traits (idar="icwmar"), on a verison of Rao quadratic entropy (idar="iraodar"), and on the same functions but computed in "rings" defined by the neighbour r values (idar="icwmar.O" and idar="iraodar.O"), which would eliminate the "accumulative" or "memory" effect on ICWMAR(r) and IRAODAR(r).

Although recent litterature (e.g., Wiegand and Moloney 2014) suggest that buffer correction is not necessary for this type of statistics, and by default all functions are estimated without buffer (e.g., buffer=0), several edge correction coould be employed. For example, an adaptative buffer correction could be used (buffer="adapt"), i.e., for each radius r, only individuals of the focal species that are placed at a distance >=r from the border of the plot are considered in the computation of the different meassures. It is also possible to set a fixed buffer width (e.g., buffer=30), which will accelerate te computations but will discard many useful trees. It is also possible to provide also a fixed window (in the argument bfw) to indicate the limits of the buffer area. This could be useful to computing the IDAR(r) functions in different subsets of the original plot (e.g., in different "habitats").

In general, computing envelopes with envelope4idar is a little faster than using envelope and the individual functions (e.g., ipsvar, ifdar, etc). In addition, envelope4idar has the possibility of computing "crossed" individual functions, i.e., using a focal species that is not part of the community whose diversities are being measured. This allows evaluating the diversity of e.g., young trees around older trees, etc. This is accomplished by seting the argument cross.idar=TRUE.

While envelope4idar manages data and results, idar2 actually computes the individual functions (both observed and simulated) . In general, idar2 would not be called directly by the user.

raoDmod is a modification of the function raoD of picante to accept distance matrices instead of phylogenetoc trees. It would not be called directly by the user.

Value

An object of class "fv", see fv.object, and envelope,which can be plotted directly using plot.fv. Essentially a data frame containing columns

r

the vector of values of the argument r at which the idar(r) function has been estimated

obs

values of the summary function for the data point pattern

lo

lower envelope of simulations

hi

upper envelope of simulations

nmean

estimated theoretical value of the summary function under the considered null model, computed by averaging simulated values

Warning

The transcription of species names in the multivariate mippp, in the row names of the data.frame of traits (or in the names or dimnames of the distance matrices) should be identical. The same applies to the tiplabels of the phylogenetic tree.

Author(s)

Marcelino de la Cruz marcelino.delacruz@urjc.es

References

Helmus M.R., Bland T.J., Williams C.K. and Ives A.R. (2007) Phylogenetic measures of biodiversity. American Naturalist, 169, E68-E83.

Laliberté, E. and Legendre, P. (2010) A distance-based framework for measuring functional diversity from multiple traits. Ecology 91, 299-305.

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.

Webb, C., Ackerly, D., McPeek, M., and Donoghue M. 2002. Phylogenies and community ecology. Annual Review of Ecology and Systematics 33:475-505

See Also

psd for a description of the phylogenetic measures of Helmus et al. (2007).

fdisp for a description of the functional dispersion measure (FDis) of Laliberté and Legendre (2010).

Examples

# compute envelope for isar around sp_44
data(SF)
data(SFtraits)
data(SFphylotree)

  
# 1) Create a list with simulations of the focal species point pattern  with simulador2()
#     or by hand. # Beware that each simulated ppp should be marked (with the mark
#      of the focal species)

#  Example for simulations of an inomogenous PP
# Adjust an IPP:
    
    sp_44.ipp<- density.ppp(unmark(SF[SF$marks$species=="sp_44"]))
   
# simulate 19 realizations of the adjusted IPP
# (BEWARE: in real tests you should use 199 or higher)
    sp_44.ipp.sim <- vector(mode="list", le=19)
    sp_44.ipp.sim <- lapply(sp_44.ipp.sim, function(x) x=rpoispp(sp_44.ipp))
   # mark each simulated pattern
    sp_44.ipp.sim.m <- lapply(sp_44.ipp.sim, function(x) 
                                       {marks(x) = factor(rep("sp_44", x$n)); return(x)})

# 2) compute.envelopes
     # ISAR
  isar.sp_44.ipp.env<- envelope4idar(mippp=SF, mippp.sp.sim= sp_44.ipp.sim.m,
                mimark="sp_44", namesmark="species",r=1:30, buffer=0) 
## Not run: 
     # IFDAR
 ifdar.sp_44.ipp.env<- envelope4idar(mippp=SF, mippp.sp.sim= sp_44.ipp.sim.m,
                mimark="sp_44",  namesmark="species", r=1:30, idar="ifdar", buffer=0, 
		 traits=SFtraits, correct.trait.na=TRUE)

    #IPSVAR
   ipsvar.sp_44.ipp.env<- envelope4idar(mippp=SF, mippp.sp.sim= sp_44.ipp.sim.m,
                 mimark="sp_44", namesmark="species", r=1:30, idar="ipsvar", buffer=0, 
		  tree=SFphylotree)

####################################
# Computing CROSS_IDAR
#--------------------------------------------------

# You need a focal pattern that its not part of the multivariate pattern which is "measured"
# to estimate diversity.
# For example, let's measure diversity of small trees  around large trees of the focal species.

# First, obtain the pattern of large and small trees
# the pattern of small trees will be the "measured" one, i.e., the argument "mippp"

SFlarge<- SF[SF$marks$dbh>=10]
SFsmall <- SF[SF$marks$dbh<10]

# pattern of the focal species (this will be the argument "mippp.sp")
sp_44.large<- SFlarge[SFlarge$marks$species=="sp_44"]

# list of simulated patterns of the focal species (e.g., from an IPP)
sp_44.large.ipp<- density.ppp(sp_44.large)
sp_44.large.ipp.sim <- vector(mode="list", le=99)
sp_44.large.ipp.sim <- lapply(sp_44.large.ipp.sim, function(x) x=rpoispp(sp_44.large.ipp))

# COMPUTE envelopes for cross-ISAR(r)
 isar.sp_44.large.cross.ipp.env<- envelope4idar(mippp=SFsmall, r=1:30, buffer=0,
			mippp.sp.sim= sp_44.large.ipp.sim, mippp.sp=sp_44.large,
                        namesmark="species", cross.idar =TRUE) 

# COMPUTE envelopes for cross-IFDAR(r)
ifdar.sp_44.large.cross.ipp.env<- envelope4idar(mippp=SFsmall, r=1:30, idar="ifdar", 
                       buffer=0, mippp.sp.sim= sp_44.large.ipp.sim,  mippp.sp=sp_44.large,
                       namesmark="species", traits=SFtraits, correct.trait.na=TRUE,
                       cross.idar =TRUE) 

# COMPUTE envelopes for cross-IPSVAR(r)
ipsvar.sp_44.large.cross.ipp.env<- envelope4idar(mippp=SFsmall, r=1:30, idar="ipsvar", 
                      buffer=0, mippp.sp.sim= sp_44.large.ipp.sim,  mippp.sp=sp_44.large,
                      namesmark="species",  tree=SFphylotree, cross.idar =TRUE) 


####################################
# Comparing the performance of envelope() and envelope4idar()
# 
#--------------------------------------------------
require(ecespa) # for the ipc.estK() function
data(SF)
SFsp<- unmark(SF)
marks(SFsp)<- SF$marks$species
sp_44.ppp<-unmark(SF[SF$marks$species=="sp_44"])
sp_44.pc<- ipc.estK(sp_44.ppp)


# use multifocalsimulator() to use the same simulations with both functions.
sp_44.pc.sim.mf0<-multifocalsimulator(pp=SFsp, mimark="sp_44",
                           simulate=expression(rIPCP(sp_44.pc)), nsim=99, nmin=sp_44.ppp$n)

# envelopes with function envelope()
gc()
t0<- Sys.time()
  ifdar.sp_44.pc.env.e2<- envelope(SFsp, fun=ifdar, mimark="sp_44", traits=SFtraits,
                            correct.trait.na=TRUE, nsim=99, simulate=sp_44.pc.sim.mf0, r=1:30,
                            savefuns=TRUE, buffer=0)
Sys.time()-t0

# envelopes with function envelope4idar()

# Here you should input simulated patterns only for the focal species so, first,
# extract it fom the list of simulated multivariate ppp

sp_44.pc.sim.mf00<- lapply(sp_44.pc.sim.mf0, function(x) {x=x[x$marks=="sp_44"];return(x)})

gc()
t0<- Sys.time()
ifdar.sp_44.pc.env2<- envelope4idar(mippp=SF, mippp.sp.sim= sp_44.pc.sim.mf00, mimark="sp_44",
				namesmark="species", r=1:30, idar="ifdar", buffer=0, 
                                nsim=99, traits=SFtraits, correct.trait.na=TRUE)
Sys.time()-t0



## End(Not run)

Average Functional Dispersion

Description

Computes average Functional Dispersion for several communities.

Usage

  fdis(x, traits)

Arguments

Details

This function is a wrap to fdisp in package FD. It manages some of the possible problems that could appear when computing automatically functional dispersion for local communities in ifdar (mainly "empty" communities, common when computing ifdar(r) for very small r's). It is a kind of internal function that wouldn't be usually called by the user.

Value

Numeric. The average functional dispersion of the communities in matrix x

Author(s)

Marcelino de la Cruz marcelino.delacruz@urjc.es

References

Laliberté, E. and Legendre, P. (2010) A distance-based framework for measuring functional diversity from multiple traits. Ecology 91, 299-305.

See Also

fdisp for a description of the functional dispersion measure (FDis) of Laliberté and Legendre (2010).

Simulate Multivariate Point Patterns

Description

These functions simulate multivariate point patterns from a variety of null models, in the way required to test IDAR(r) functions.

Usage

  ipsim(pp, mimark,sigma=0, lambda=NULL, namesmark=NULL)
  ipsimlist(pp, mimark, listsim)
  simulador2(mimark, milambda, nsim=99)
  multifocalsimulator(pp, mimark, simulate,nsim=99,nmin=NULL)

Arguments

Details

This functions produce simulated point patterns appropriate to to compute envelopes of IDAR(r) functions. The usual tests of IDAR(r) functions require that the multivariate (i.e. multispecies) pattern remains fixed, except for the focal species, that is simulated acording to, e.g., an (inhomogeneous) Poisson process.

ipsim returns the multivariate pp pattern with all species in the same locations except the "focal" one (i.e., the one indicated by the argument mimark) that is simulated using rpoispp. If an intensity surface (argument lambda) is provided, the focal species will be simulated from this surface. If no lambda is provided but the argument sigma is >0, an intensity surface will be estimated with a Gaussian kernel with the sigma provided (using density.ppp) and the simulation will be made form this surface. If no lambda is provided and sigma=0, a homogeneos Poisson process will be simulated.

simulador2 generates a list (length = nsim) of marked (with mark = mimark) univariate point patterns from an intensity surface using rpoispp.

ipsimlist uses the results of simulador2 and the multivariate pp pattern to generate a list of multivariate point patterns with all species in the same locations except the "focal" one (i.e., the one indicated by the argument mimark) that has the locations simulated with simulador2.

multifocalsimulator is more flexible and allows the simulation of whichever null model of the focal species that could be described by an R expression.

Value

ipsim produces a multivariate point pattern (with the ppp format of spatstat); ipsimlist and multifocalsimulator produce a list of multivariate point patterns; simulador2 produces a list of univariate marked patterns.

Author(s)

Marcelino de la Cruz marcelino.delacruz@urjc.es

Examples

# Build a multivariate point pattern where maple is simulated according to a Poisson process
# and where all the other species are keep fixed in their original coordinates.
# (The warnings are because in the original lansing point pattern there is a dupplicated
#  point)

data(lansing) 
ipsim(pp=lansing, mimark="maple")

# Build a multivariate point pattern where maple is simulated according to an Inhomogeneous
# Poisson process from an intensity surface estimated "on the fly" with a Gaussian kernel with
# sd = "sigma", and where all the other species are keep fixed in their original coordinates.

ipsim(pp=lansing, mimark="maple", sigma=0.1)

# Build a multivariate point pattern where maple is simulated according to an Inhomogeneous
# Poisson process from a predefined  intensity surface "lambda" and where all the other
# species are keep fixed in their original coordinates. "Lambda" is an im object resulting
# from density.ppp(), from predict.ppm() or converted from any other rasterized image. 

maple.lambda<- density.ppp(lansing[lansing$marks=="maple"])
ipsim(pp=lansing, mimark="maple", lambda=maple.lambda)

# Build a list of 19 multivariate point pattern where maple is simulated according to an
# Inhomogeneous Poisson process from a predefined  intensity surface "lambda" and where all
# the other species are keep fixed in their original coordinates. "Lambda" is an im object
# resulting  from density.ppp(), # from predict.ppm() or converted from any other rasterized
# image. 

  # Estimate the intensity of maple
   maple.lambda<- density(unmark(lansing[lansing$marks=="maple"]))

  # first simulate the individual maple patterns
   maple.sim<- simulador2(mimark="maple", milambda=maple.lambda, nsim=19)

  # Then, mix the simulated maple patterns with the rest of the multivariate pattern
  # (which remains "fixed")
   multi.maple.sim<- ipsimlist(pp=lansing, mimark="maple", listsim=maple.sim)

## Not run: 
## Use of multifocalsimulator() ##

# The same but in a single step with multifocalsimulator(): Build a list of 99 multivariate
# point pattern where maple is simulated according to an Inhomogeneous Poisson process
# from a predefined  intensity surface "lambda" and where all the other species are keep 
# fixed in their original coordinates. "Lambda" is an im object resulting from density.ppp(),
# from predict.ppm() or converted from any other rasterized image. 

   # Estimate the intensity of maple
   maple.lambda<- density(unmark(lansing[lansing$marks=="maple"]))

   # get 99 simulated multivariate point patterns where only maple varies,
   # according to an inhomogeneous Poisson process
   multi.maple.sim <- multifocalsimulator(lansing, "maple", nsim=99,
                                         simulate=expression(rpoispp(maple.lambda)))


   # Use the simulated multivariate patterns to compute  envelopes for the ISAR against
   # a null model of IPP for maple 
   isar.maple.env<- envelope(lansing, fun=isar, mimark="maple", nsim=99, savefuns=TRUE,
                                         r=seq(0.01, 0.25, le=100), simulate=multi.maple.sim)
   plot( isar.maple.env)



# Use multifocalsimulator()  to compute  envelopes for the ISAR against a null model of
# Poisson cluster for maple.

   # First, adjust a Poisson Cluster process to maple
   require(ecespa)
   maple.pc<- ipc.estK(unmark(lansing[lansing$marks=="maple"]))

  # generate list of simulated multivariate partterns (all other species fixed and maple
  # simulated according to the adjusted PC process):
   maple.pc.sim<-multifocalsimulator(pp=lansing, mimark="maple", nsim=99,nmin=NULL,
                                                 simulate=expression(rIPCP(maple.pc)))

  # compute envelopes
   isar.maple.pc.env<- envelope(lansing, fun=isar, mimark="maple", nsim=99, savefuns=TRUE, 
                                            simulate=maple.pc.sim, r=seq(0.01, 0.25, le=100))
   plot( isar.maple.pc.env)


# Compute  envelopes for the IFDAR against a null model of Poisson cluster for sp_44 in
# San Francisco forest.
   data(SF)
   data(SFtraits)

   # first, get the original point pattern but with marks only for the species (i.e., dsicard 
   # the data.frame of marks and keep only the vector of species names)
   SFsp<- unmark(SF)
   marks(SFsp)<- SF$marks$species

   # second, adjust a PCP to sp_44
   sp_44.pc<- ipc.estK(unmark(SFsp[SFsp$marks=="sp_44"]))

  # third, generate multivariate simulated patterns with only sp_44 varying according
  # to the adjusted PCP
   sp_44.pc.sim<-multifocalsimulator(pp=SFsp, mimark="sp_44",nsim=99,nmin=NULL,
                                                 simulate=expression(rIPCP(sp_44.pc)))

   # finally, compute envelopes
   ifdar.sp_44.pc.env<- envelope(SFsp, fun=ifdar, mimark="sp_44", traits=SFtraits, r=1:30,
                        correct.trait.na=TRUE, nsim=99, simulate=sp_44.pc.sim,  savefuns=TRUE)

   plot( ifdar.sp_44.pc.env)


## End(Not run)

Individual Diversity Area Relationships

Description

Estimate different Individual Diversity-Area Relationships from a multivariate point pattern.

Usage

  isar(mippp, mippp.sp=NULL, mimark=NULL,  namesmark=NULL, r=NULL,
   buffer=0, bfw=NULL)
  ipscar(mippp, mippp.sp=NULL, mimark=NULL,  namesmark=NULL,
   tree=NULL, r=NULL, buffer=0, bfw=NULL, correct.phylo="mean")
  ipsear(mippp, mippp.sp=NULL, mimark=NULL,  namesmark=NULL,
   tree=NULL, r=NULL, buffer=0, bfw=NULL, correct.phylo="mean")
  ipsvar(mippp, mippp.sp=NULL, mimark=NULL,  namesmark=NULL,
   tree=NULL, r=NULL, buffer=0, bfw=NULL, correct.phylo="mean")
  ipsrar(mippp, mippp.sp=NULL, mimark=NULL,  namesmark=NULL,
   tree=NULL, r=NULL, buffer=0, bfw=NULL, correct.phylo="mean")
  ifdar(mippp, mippp.sp=NULL, mimark=NULL,  namesmark=NULL,
   traits=NULL, r=NULL, buffer=0, bfw=NULL, correct.trait.na=FALSE,
     correct.trait="mean")

Arguments

Details

In 2007, Wiegand et al. developed the concept of Individual Species-Area Relationship. Basically, this consist in computing species accumulation curves by samping areas with varying radius r around the individual trees of a focal species. Here we extend this concept to other diversity-area relationships and provide functions to compute individual phylogenetic diversity-area and individual functional diversity-area relationships. The individual phylogenetic functions are based in Helmus et al. (2007) measures, i.e., phylogenetic species variability (ipsvar), phylogenetic species richness (ipsrar), phylogenetic species evenness (ipsear), and phylogenetic species clustering (ipscar). The individual functional-diversity function (ifdar) is based in the functional dispersion measure (FDis) of Laliberté and Legendre (2010).

Although recent litterature (e.g., Wiegand and Moloney 2014) suggest that buffer correction is not necessary for this type of statistics, and by default all functionsare estimated without buffer (e.g., buffer=0), several edge correction coould be employed. For example, an adaptative buffer correction could be used (buffer="adapt"), i.e., for each radius r, only individuals of the focal species that are placed at a distance >=r from the border of the plot are considered in the computation of the different meassures. It is also possible to set a fixed buffer width (e.g., buffer=30), which will accelerate te computations but will discard many useful trees. It is also possible to provide also a fixed window (in the argument bfw) to indicate the limits of the buffer area. This could be useful to computing the IDAR(r) functions in different subsets of the original plot (e.g., in different "habitats").

Value

An object of class "fv", see fv.object, which can be plotted directly using plot.fv.

Essentially a data frame containing a column named r with the vector of values of the argument r at which the IDAR(r) function has been estimated and aonther column, named "isar", "ipsvar", "ipsrar", "ipsear", "ipscar" or "ifdar", according to the selected idar argment. This column contains an estimate of the selected IDAR(R) function.

Warning

The transcription of species names in the multivariate mippp, in the row names of the data.frame of traits (or in the names or dimnames of the distance matrices) should be identical. The same applies to the tiplabels of the phylogenetic tree.

Simulation envelopes

To compute simulation envelopes for the IDAR(r) functions, use envelope. See the examples in this help page and in ipsim to know how to compute simulation envelopes from appropriate null models.

To compute envelopes for "crossed" IDAR(r) functions or to accelerate the computation of "single" IDAR(r) functions, use envelope4idar.

Author(s)

Marcelino de la Cruz marcelino.delacruz@urjc.es

References

Helmus M.R., Bland T.J., Williams C.K. and Ives A.R. (2007) Phylogenetic measures of biodiversity. American Naturalist, 169, E68-E83.

Laliberté, E. and Legendre, P. (2010) A distance-based framework for measuring functional diversity from multiple traits. Ecology 91, 299-305.

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.

See Also

psd for a description of the phylogenetic measures of Helmus et al. (2007).

fdisp for a description of the functional dispersion measure (FDis) of Laliberté and Legendre (2010).

Examples

# ISAR
   # Point pattern with a data.frame of marks
   data(SF)
   isar.sp_44 <- isar(mippp = SF, mimark="sp_44", namesmark="species", r=1:40) 
   plot(isar.sp_44)
 
   # Point pattern with just a vector of marks
    data(lansing)
    isar.blackoak <- isar(mippp = lansing, mimark="blackoak",  r=seq(0.01, 0.25, le=100))
    plot(isar.blackoak)

   # Examples of the use of different buffers
     # No  buffer at all (by deffault, buffer = 0)
     isar.sp_44.0 <- isar(mippp = SF, mimark="sp_44", namesmark="species", r=1:18)    

     # Adaptive buffer (for each r, use only points within a r distance form the border)
     isar.sp_44.a <- isar(mippp = SF, mimark="sp_44", namesmark="species", r=1:18,
                                buffer="adapt") 

     # Predefined window, for example with a buffer of 7 m within plot limits
     mibfw<- erosion(SF$win, r=7)
      isar.sp_44.w <- isar(mippp = SF, mimark="sp_44", namesmark="species", r=1:18, bfw=mibfw) 


   ######################
   ### Phylogenetic functions ###
   ######################
   
data(SFphylotree)

# IPSCAR
    ipscar.sp_44 <- ipscar(mippp = SF, mimark="sp_44", namesmark="species", r=1:40,
                                     tree=SFphylotree)
    plot(ipscar.sp_44)
 
# IPSEAR
    ipsear.sp_44 <- ipsear(mippp = SF, mimark="sp_44", namesmark="species", r=1:40,
                                  tree=SFphylotree)
    plot(ipsear.sp_44)

# IPSVAR
    ipsvar.sp_44 <- ipsvar(mippp = SF, mimark="sp_44", namesmark="species", r=1:40,
                                   tree=SFphylotree)
    plot(ipsvar.sp_44)

# IPSRAR
    ipsrar.sp_44 <- ipsrar(mippp = SF, mimark="sp_44", namesmark="species", r=1:40,
                                  tree=SFphylotree)
    plot(ipsrar.sp_44)
 
  #####################
  ###  Functional functions ###
  #####################
 
data(SFtraits)

# IFDAR
   # this will cause an error becuse some species have NA's in the vector of trait values
## Not run: 
  # ifdar.sp_44 <- ifdar(mippp = SF, mimark="sp_44", namesmark="species", traits=SFtraits,
  #                           r=1:40, correct.trait="exclude")

## End(Not run)
   # "correct" NA's in trait values  by assigning tospecies without traits the average of the trait 
   # for  all the other species
   ifdar.sp_44 <- ifdar(mippp = SF, mimark="sp_44", namesmark="species",  traits=SFtraits,
			      r=1:40, correct.trait.na=TRUE)

    #"correct"  the existence of NA's in trait values  by excluding species without traits from the
    # analysis
    ifdar.sp_44 <- ifdar(mippp = SF, mimark="sp_44", namesmark="species", traits=SFtraits,
                               r=1:40, correct.trait.na=TRUE,correct.trait="exclude")

     plot(ifdar.sp_44)

# For examples of envelopes for these functions  see the help page of ipsim() or envelope4idar()

Map Local Diversity Area Relationships

Description

Estimates and maps local diversity-area relationships.

Usage

localdar(mippp, mippp.sp = NULL, nx = NULL, ny = NULL, mimark = NULL, idar = "isar", 
            buffer = 0, bfw = NULL, r, cross.idar = FALSE, tree = NULL, traits = NULL, 
            namesmark = NULL, correct.trait.na = TRUE, correct.trait = "mean",
            correct.phylo="mean")
fdismap(comm, traits)
raoDmap(comm, phy = NULL)

Arguments

Details

localdar estimates any of the individual diversity area indices (isar, ipsvar,ipsrar, ipsear, ipscar, icwmar, icwmar.O, iraodar or imntdar) at specific locations, such as the locations of trees of a "focal" point partern or at some grid of points (i.e., "mapping" local diversity area relationships). If no predefined focal point pattern is provided (by the arguments mippp.sp or mimark), localdar will, by default, estimate the selected idar function in a 30 x 30 point-grid and return a map (the size of the grid can be modified by the arguments nx and ny). fdismap and raoDmap are internal functions used by localdar to get the individual componentes of iraoD and FDis, instead of the averages obtained by envelope4idar.

Value

If a focal point patternn has been provided by the arguments mippp.sp or mimark, localdar will return a list of marked point patterns (as long as the vector r) with the marks showing the estimation of the selected diversity index for the local community defined by a circle of radius r around each of the points of the focal pattern. If no focal point pattern is provided, it will return a list of maps (as long as the vector r) each with the format im of spatstat, each pixel showing the estimation of the selected diversity index for the local community defined by a circle of radius r around the pixel center.

Author(s)

Marcelino de la Cruz marcelino.delacruz@urjc.es

See Also

envelope4idar

Examples

# Map of local species area-relationship [ISAR(r)] in San Francisco plot at r=5 and r= 6 m
  data(SF)
  data(SFphylotree)
  data(SFtraits)

  isarSF <- localdar(SF, r=5:6, namesmark="species")
  
  isarSF
  
  plot(isarSF[[1]])
  
  
# Map of local species area-relationship [ISAR(r)] in San Francisco plot at r=5 and r= 6 m,
# with an adaptive buffer

isarSF <- localdar(SF, r=5:6, namesmark="species",buffer="adapt")



# Map of local species area-relationship [ISAR(r)] in lansing woods at different radii,
# with an fixed  buffer (only for rectangular windows).

data(lansing)
lansing.bfw<- owin(c(0.2,0.8),c(0.2,0.8))
lansing.lsar.bf<-localdar(lansing, r=seq(0.05,0.2,by=0.05), bfw=lansing.bfw)


# Map of local species area-relationship [ISAR(r)] in San Francisco plot at r=5 and r= 6 m,
# with a buffer of 7 m within plot limits

  mibfw<- erosion(SF$win, r=7)

 isarSF <- localdar(SF, r=5:6, namesmark="species", bfw=mibfw)



# Estimate local species area-relationship [ISAR(r)] in the local communities 
#  in circles with radius  r=5 and r= 6 m around the individuals of sp_44

  sp_44_sar <- localdar(SF, r=5:6, namesmark="species", mimark="sp_44")
  sp_44_sar
  plot(sp_44_sar[[1]])
  sp_44_sar[[1]]$marks
  


# Estimate local species area-relationship [ISAR(r)] in the local communities 
#  in circles with radius  r=5 and r= 6 m around the individuals of sp_44
# EXCLUDING the focal species from species counts.

  sp_44_sar <- localdar(SF, r=5:6, namesmark="species", mimark="sp_44", cross.idar=TRUE)
  sp_44_sar
  plot(sp_44_sar[[1]])
  sp_44_sar[[1]]$marks
 
  
  


# Map and estimation of local Phylogenetic Species Variety
local_psvar<-localdar(SF, r=5:6, idar="ipsvar", tree=SFphylotree, namesmark="species")
sp44_psvar <- localdar(SF, r=5:6, idar="ipsvar", tree=SFphylotree, namesmark="species",
                                    mimark="sp_44", buffer="adapt")

# Map and estimation of local Functional Dispersion
local_fdar <- localdar(SF, nx=50, ny=25, r=5:6,  idar="ifdar", traits=SFtraits,
                                 namesmark="species", correct.trait.na=TRUE)
sp44_fdar <- localdar(SF, nx=50, ny=25, r=5:6,  idar="ifdar", traits=SFtraits,
                                  namesmark="species", mimark="sp_44", correct.trait.na=TRUE)


# Map of a local community weighted mean of wood density
# first, put the wood desnsity data as a named vector
 wood.density.vec<-unlist(SFtraits[,  "wood.density", drop=FALSE])
    names(wood.density.vec)<- rownames(SFtraits)
    
local_cwd <- localdar(SF, r=5:6,  idar="icwmar", traits=wood.density.vec,
                                  namesmark="species", correct.trait.na=TRUE)
local_O.cwm <-localdar(SF, r=5:6, idar="icwmar.O", traits=wood.density.vec, 
                                     namesmark="species", correct.trait.na=TRUE)

# Map of Rao's phylogenetic diversity
local_rao<- localdar(SF,  r=5:6, idar="iraodar",  tree=SFphylotree, namesmark="species")
local_O.rao <- localdar(SF, r=5:6, idar="iraodar.O",  tree=SFphylotree, namesmark="species")

# Map of local mean nearest taxon distance 
local_mntd <- localdar(SF, r=5:6, idar="imntdar",  tree=SFphylotree, namesmark="species")

Customize the Individual Diversity-Area Relationship Function

Description

A wrapper to develop new Individual Diversity-Area Relationship functions on the fly.

Usage

midar(mippp, mippp.sp = NULL, mimark = NULL, namesmark = NULL, traits = NULL,
tree = NULL, r = NULL, buffer = 0, bfw = NULL, what = NULL)

Arguments

Details

midar allows computing new IDAR(r) functions. The basis of all idar functions are the local communities defined around each point (e.g., each tree) of a focal species (mimark) for a certain circular neighborhood of radius r. Some diversity measure is computed on each community an the average of all of them is returned as the idar value for this r, i.e., as IDAR(r). The function midar applies the R function or expression defined by the argument what to each of the "community data tables" (matrices) generated by mitable (one for each r interval defined by the argument r) and return the result as a spatial summary function like all the others in the idar package (e.g., isar or pisar). The R expression or function should accept a community matrix (sites x species) as input and return a unique numeric value.

Value

midar return an object of class fv, see fv.object, which can be plotted directly using plot.fv. Essentially, a data frame containing a column named r with the vector of values of the argument r at which the proposed function had been estimated and another column, named "midar" which contains an estimate of the selected function.

Simulation envelopes

To compute simulation envelopes for midar functions, use envelope. See the examples in this help page and in multifocalsimulator to know how to compute simulation envelopes from appropriate null models.

Author(s)

Marcelino de la Cruz marcelino.delacruz@urjc.es

See Also

isar or pisar in this package.

Examples

data(SF)
data(SFphylotree)
 # 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 "individual Simpsom diversity-area relationship" around sp_44
# using function diversity from package vegan
require(vegan)
simpsom_sp_44<- midar(sfsp, mimark="sp_44", what =function(x) mean(diversity(x, "simpson")),
                                  r=1:15)


# test "individual Simpsom diversity-area relationship" against an inhomogeneous Poisson
# null modellfor sp_44. 
  # estimate intensityh surface for sp_44
   lambda<- density.ppp(unmark(sfsp[sfsp$marks=="sp_44"]), positive=TRUE)
  
  # generate 19 realizations of the null model, keeping all the other species fixed in their
  #  original coordinates.
   simulados<- multifocalsimulator(sfsp, mimark="sp_44", simulate=expression(rpoispp(lambda)),
                      nsim=19, nmin=15)

  # for simplicity define the function that we want to apply to each "local community"
  #  around each tree of sp_44
  mean_simpsom <- function(x) mean(diversity(x, "simpson"))

  # compute envelopes and plot them.
  simpsom_sp_44.env<-envelope(sfsp, midar, mimark="sp_44", what =mean_simpsom ,r=1:15,
                      nsim=19, simulate=simulados)
  plot(simpsom_sp_44.env)


# compute IPSVAR(r) "by hand"
   # first, check tree as would check it ipsvar
   arbol <- checktree(SFphylotree,  SF, "ipsvar", correct.phylo="exclude")

    # define function to obtain the averge psv from the set of local communities
    #  at each neigborhood radii r
     mipsv <- expression(mean(psv(x, tree=tree, compute.var=FALSE)$PSVs, na.rm=TRUE))
     # compute ipsvar "by hand" 
     sp_44_psv<- midar(sfsp, mimark="sp_44", tree=arbol, what =mipsv ,r=1:15)
     plot(sp_44_psv)

# compare it with the result of the built-in function
     plot(ipsvar(sfsp, mimark="sp_44", tree=arbol, r=1:15), add=TRUE, col="blue")
    

Tabulate Marks in Neighbourhood of Every Point in a Point Pattern

Description

For a sequence of radii defining different neighbourhood sizes, visit each point in a focal point pattern, find the neighbouring points in a target point pattern, and compile a frequency table of the marks of these neighbour points for each radii.

Usage

  mitable(ppp1,ppp2,r)
  

Arguments

Details

Given both a "focal" and a "target" point patterns, mitable visits each point in the focal point pattern, finds the neighbouring points in the target point pattern, and compile a frequency table of the marks of these neighbour points, where the neibourhood is defined by circles orf radius r around the focal points. From an ecological point of view, it provides a relevé of the local community around each focal tree. It repeats the process for each provided r.

Value

A list, with length = length(r). Within each element of the list, a matrix with dimensions np X nsp, where np is the number of points of the focal point pattern and nsp is the number of unique species (i.e., unique marks) in the target point pattern. Cell values in the matrix represent number of neighbours of the j species for individual i at the considered neibourhood size.

Author(s)

Marcelino de la Cruz marcelino.delacruz@urjc.es

See Also

marktable for a similar function.

Examples

# Compute number of trees of different species  within circles of several  radii r 
# around each individual maple tree in lansing woods

data(lansing)

maple<- unmark(lansing[lansing$marks=="maple"])
maple
r= c(0.05, 0.08, 0.1)

counts <- mitable(ppp1=maple, ppp2=lansing, r=r) 
dim(counts[[1]])
head(counts[[1]])

Phylogenetic Individual Species Area Relationship

Description

Compute Phylogenetic Individual Species Area Relationship function, i.e., PISAR(r) and its normalized version rISAR(r).

Usage

pisar(mippp, mippp.sp = NULL, mimark = NULL, namesmark = NULL, d = NULL, r = NULL,
         buffer = 0, bfw = NULL)
risar(mippp, mippp.sp = NULL, mimark = NULL, namesmark = NULL, d = NULL, d0 = NULL,
        r = NULL, buffer = 0, bfw = NULL)
controldis(d, m, mimark)

Arguments

Details

The original definition of ISAR(r) (Wiegand et al. 2007) was reformulated as:

ISAR_f (r) =\sum_{m=1}^{S} \delta_{fm} D_{fm} (r)

(Wiegand and Moloney 2014; Wang et al. 2016), where D_{fm}(r) describe the probabilities that the nearest species m neighbor of the typical individual of the focal species f is located within distance r, and \delta_{fm} yields a value of zero if f = m and a value of one otherwise (note that in their original proposal ISAR was formulated as if the value assigned to \delta_{fm} were 1 for all species pairs, including f = m). Based in this re-formulation, they defined the Phylogenetic Individual Species Area Relationship, i.e., PISAR(r), as:

PISAR_f (r) =\sum_{m=1}^{S} \delta_{fm}^{phy}D_{fm} (r)

where \delta_{fm}^{phy} is an index of phylogenetic (or functional) dissimilarity between species f and m. PISAR(r) quantifies the expected phylogenetic (or functional) diversity of species within the neighborhood with radius r around the typical individual of the focal species f.

They also defined rISAR(r) as a a function that is independent of local species richness within the neighborhood r; for this, they divided the PISAR function by the ISAR function:

rISAR_f (r) = \frac{\sum_{m=1}^{S} \delta_{fm}^{phy}D_{fm} (r)}{\sum_{m=1}^{S} \delta_{fm}D_{fm} (r)}

If the placement of the focal species f is unrelated with functional or phylogenetic relationships with their neighbors, the rISAR_f(r) will approximate the mean pairwise functional (or phylogenetic) dissimilarity \Delta_f^P = \sum_m \delta_{fm}^{phy}/(S-1) between an individual of the focal species f and all other species in the plot.

The function controldis controls that the order of species in the phylogenetic distance matrix matches the order of species amomng the levels of species marks in the point pattern, and extracts the vector of distances from all species to the focal one (mimark).

Value

pisar and risar return an object of class fv, see fv.object, which can be plotted directly using plot.fv.

Essentially a data frame containing a column named r with the vector of values of the argument r at which the PISAR(r) or rISAR(r) function had been estimated and another column, named "risar" or "pidar",which contains an estimate of the selected function.

controldis returns either the vector of distances between the focal and the rest of species or a vector of 1's if there is not phyloegenetic distance provided.

Simulation envelopes

To compute simulation envelopes for pisar or risar functions, use envelope. See the examples in this help page and in multifocalsimulator to know how to compute simulation envelopes from appropriate null models.

NOTE

When computing risar it is necessary to provide a phylogenetic or functional distance matrix to the argument d. By default, argument d0 will be set to a vector of 1's. It is however possible to provide a different matrix to d0 and compute instead, e.g., a ratio of phylogenetic to functional diversity.

Author(s)

Marcelino de la Cruz marcelino.delacruz@urjc.es

References

Wang, X., et al. (2016). Stochastic dilution effects weaken deterministic effects of niche-based processes in species rich forests. Ecology, in press

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.

Wiegand, T., and K.A. Moloney. (2014). A handbook of spatial point pattern analysis in ecology. Chapman and Hall/CRC press, Boca Raton, FL

See Also

See also isar for other individual diversity area functions.

Examples

data(SF)
data(SFphylotree)

# 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 phylogenetic distance among species
dphy <- cophenetic(SFphylotree)

# compute and plot PISAR function for sp_44
pisar_44 <- pisar(sfsp, mimark="sp_44", r=1:15,  d=dphy)
plot(pisar_44)

## Not run: 

# Compute rISAR and plot envelopes for an inhomogeneous Poisson model
#  of each species in San Francisco plot 
# BEWARE: THIS TAKES QUITE A FEW MINUTES !!!

# Split sfsp point pattern ppp by species
sfsp.sp<- split(sfsp)

#  Species with >= 10 individuals
sfsp10 <- sapply(sfsp.sp, function(x) x$n>=10)
#names of those species
nombressf<- names(sfsp10[sfsp10])

# parameters for the simulations, estimation of intensity, etc.
nsim<-199
nmin<-10
sigma <- 8
r<- seq(1,15, by=0.5)

# list to store results
risar.sf<- list()
# start computation
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 ecept 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 risar
   risar.sf[[sp]] <- envelope(sfsp, risar, mimark=sp, d=dphy, r=r,
        simulate=simulados,nsim=nsim, savefuns=T, buffer=0)
}

# plot the results
 dev.new(height=7, width=16)
 par(mfrow=c(3,9))
 for(i in 1:27) plot(risar.sf[[i]], legend=F, main=nombressf[i])


## End(Not run)

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 class '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",...)

Arguments

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".

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.

Author(s)

Marcelino de la Cruz marcelino.delacruz@urjc.es

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<-5
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))

## Not run: 

# 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)


## End(Not run)