ecospat (version 2.0)

ecospat.grid.clim.dyn: Dynamic Occurrence Densities Grid

Description

Create a grid with occurrence densities along one or two gridded environmental gradients.

Usage

ecospat.grid.clim.dyn (glob, glob1, sp, R, th.sp, th.env)

Arguments

glob

A two-column dataframe (or a vector) of the environmental values (in column) for background pixels of the whole study area (in row).

glob1

A two-column dataframe (or a vector) of the environmental values (in column) for the background pixels of the species (in row).

sp

A two-column dataframe (or a vector) of the environmental values (in column) for the occurrences of the species (in row).

R

The resolution of the grid.

th.sp

The quantile of species densities at species occurences used as a threshold to exclude low species density values.

th.env

The quantile of environmental densities at all study sites used as a threshold to exclude low environmental density values.

Value

Details

Using the scores of an ordination (or SDM prediction), create a grid z of RxR pixels (or a vector of R pixels when using scores of dimension 1 or SDM predictions) with occurrence densities. Only scores of one, or two dimensions can be used.

References

Broennimann, O., M.C. Fitzpatrick, P.B. Pearman, B. Petitpierre, L. Pellissier, N.G. Yoccoz, W. Thuiller, M.J. Fortin, C. Randin, N.E. Zimmermann, C.H. Graham and A. Guisan. 2012. Measuring ecological niche overlap from occurrence and spatial environmental data. Global Ecology and Biogeography, 21:481-497.

Petitpierre, B., C. Kueffer, O. Broennimann, C. Randin, C. Daehler and A. Guisan. 2012. Climatic niche shifts are rare among terrestrial plant invaders. Science, 335:1344-1348.

See Also

ecospat.plot.niche.dyn

Examples

## Not run: 
# spp <- ecospat.testNiche
# clim <- ecospat.testData[2:8]
# 
# occ.sp_test <- na.exclude(ecospat.sample.envar(dfsp=spp,colspxy=2:3,colspkept=1:3,dfvar=clim,
# colvarxy=1:2,colvar="all",resolution=25))
# 
# occ.sp<-cbind(occ.sp_test,spp[,4]) #add species names
# 
# # list of species
# sp.list<-levels(occ.sp[,1])
# sp.nbocc<-c()
# 
# for (i in 1:length(sp.list)){sp.nbocc<-c(sp.nbocc,length(which(occ.sp[,1] == sp.list[i])))} 
# #calculate the nb of occurences per species
# 
# sp.list <- sp.list[sp.nbocc>4] # remove species with less than 5 occurences
# nb.sp <- length(sp.list) #nb of species
# ls()
# # selection of variables to include in the analyses 
# # try with all and then try only worldclim Variables
# Xvar <- c(3:7)
# nvar <- length(Xvar)
# 
# #number of interation for the tests of equivalency and similarity
# iterations <- 100
# #resolution of the gridding of the climate space
# R <- 100
# #################################### PCA-ENVIRONMENT ##################################
# data<-rbind(occ.sp[,Xvar+1],clim[,Xvar]) 
# w <- c(rep(0,nrow(occ.sp)),rep(1,nrow(clim)))
# pca.cal <- dudi.pca(data, row.w = w, center = TRUE, scale = TRUE, scannf = FALSE, nf = 2)
# 
# ####### selection of species ######
# sp.list
# sp.combn <- combn(1:2,2)
# 
# for(i in 1:ncol(sp.combn)) {
#   row.sp1 <- which(occ.sp[,1] == sp.list[sp.combn[1,i]]) # rows in data corresponding to sp1
#   row.sp2 <- which(occ.sp[,1] == sp.list[sp.combn[2,i]]) # rows in data corresponding to sp2
#   name.sp1 <- sp.list[sp.combn[1,i]]
#   name.sp2 <- sp.list[sp.combn[2,i]]
#   # predict the scores on the axes
#   scores.clim <- pca.cal$li[(nrow(occ.sp)+1):nrow(data),]  #scores for global climate
#   scores.sp1 <- pca.cal$li[row.sp1,]					#scores for sp1
#   scores.sp2 <- pca.cal$li[row.sp2,]					#scores for sp2
# }
# # calculation of occurence density and test of niche equivalency and similarity 
# z1 <- ecospat.grid.clim.dyn(scores.clim, scores.clim, scores.sp1,R=100)
# z2 <- ecospat.grid.clim.dyn(scores.clim, scores.clim, scores.sp2,R=100)
# ## End(Not run)