## ----load_library-------------------------------------------------------------
library(ecospat)
citation("ecospat")
## -----------------------------------------------------------------------------
data(ecospat.testData)
names(ecospat.testData)
## -----------------------------------------------------------------------------
data(ecospat.testNiche.inv)
names(ecospat.testNiche.inv)
## -----------------------------------------------------------------------------
data(ecospat.testNiche.nat)
names(ecospat.testNiche.nat)
## -----------------------------------------------------------------------------
if(requireNamespace("ape")){
fpath <- system.file("extdata", "ecospat.testTree.tre", package="ecospat")
tree<-ape::read.tree(fpath)
tree$tip.label
plot(tree, cex=0.6)
}
## ----mantel_cor---------------------------------------------------------------
ecospat.mantel.correlogram(dfvar=ecospat.testData[c(2:16)],colxy=1:2, n=100,
colvar=3:7, max=1000, nclass=10, nperm=100)
## -----------------------------------------------------------------------------
colvar <- ecospat.testData[c(4:8)]
x <- cor(colvar, method="pearson")
ecospat.npred (x, th=0.75)
## -----------------------------------------------------------------------------
x <- cor(colvar, method="spearman")
ecospat.npred (x, th=0.75)
## -----------------------------------------------------------------------------
x <- ecospat.testData[c(4:8)]
p<- x[1:90,] #A projection dataset.
ref<- x[91:300,] # A reference dataset
## -----------------------------------------------------------------------------
ecospat.climan(ref,p)
## -----------------------------------------------------------------------------
x <- ecospat.testData[c(2,3,4:8)]
proj<- x[1:90,] #A projection dataset.
cal<- x[91:300,] #A calibration dataset
mess.object<-ecospat.mess (proj, cal, w="default")
ecospat.plot.mess (mess.object, cex=1, pch=15)
## -----------------------------------------------------------------------------
if(requireNamespace("ape")){
fpath <- system.file("extdata", "ecospat.testTree.tre", package="ecospat")
tree <- ape::read.tree(fpath)
data <- ecospat.testData[9:52]
pd<- ecospat.calculate.pd(tree, data, method = "spanning", type = "species", root = TRUE, average = FALSE, verbose = FALSE )
plot(pd)
}
## -----------------------------------------------------------------------------
library(ade4)
inv <- ecospat.testNiche.inv
nat <- ecospat.testNiche.nat
pca.env <- ade4::dudi.pca(rbind(nat,inv)[,3:10],scannf=F,nf=2)
ecospat.plot.contrib(contrib=pca.env$co, eigen=pca.env$eig)
## -----------------------------------------------------------------------------
# PCA scores for the whole study area
scores.globclim <- pca.env$li
# PCA scores for the species native distribution
scores.sp.nat <- ade4::suprow(pca.env,nat[which(nat[,11]==1),3:10])$li
# PCA scores for the species invasive distribution
scores.sp.inv <- ade4::suprow(pca.env,inv[which(inv[,11]==1),3:10])$li
# PCA scores for the whole native study area
scores.clim.nat <- ade4::suprow(pca.env,nat[,3:10])$li
# PCA scores for the whole invaded study area
scores.clim.inv <- ade4::suprow(pca.env,inv[,3:10])$li
## -----------------------------------------------------------------------------
# gridding the native niche
grid.clim.nat <- ecospat.grid.clim.dyn(glob=scores.globclim,
glob1=scores.clim.nat,
sp=scores.sp.nat, R=100,
th.sp=0)
## -----------------------------------------------------------------------------
# gridding the invasive niche
grid.clim.inv <- ecospat.grid.clim.dyn(glob=scores.globclim,
glob1=scores.clim.inv,
sp=scores.sp.inv, R=100,
th.sp=0)
## -----------------------------------------------------------------------------
# Compute Schoener's D, index of niche overlap
D.overlap <- ecospat.niche.overlap (grid.clim.nat, grid.clim.inv, cor = TRUE)$D
D.overlap
## -----------------------------------------------------------------------------
eq.test <- ecospat.niche.equivalency.test(grid.clim.nat, grid.clim.inv,rep=10,
intersection = 0.1,
overlap.alternative = "higher",
expansion.alternative = "lower",
stability.alternative = "higher",
unfilling.alternative = "lower")
## -----------------------------------------------------------------------------
ecospat.plot.overlap.test(eq.test, "D", "Equivalency")
## -----------------------------------------------------------------------------
sim.test <- ecospat.niche.similarity.test(grid.clim.nat, grid.clim.inv,rep=10,
overlap.alternative = "higher",
expansion.alternative = "lower",
stability.alternative = "higher",
unfilling.alternative = "lower",
intersection = 0.1,
rand.type=1)
## -----------------------------------------------------------------------------
ecospat.plot.overlap.test(sim.test, "D", "Similarity")
## ----niche.dyn----------------------------------------------------------------
niche.dyn <- ecospat.niche.dyn.index (grid.clim.nat, grid.clim.inv)
## -----------------------------------------------------------------------------
ecospat.plot.niche.dyn(grid.clim.nat, grid.clim.inv, quant=0.25, interest=2,
title= "Niche Overlap", name.axis1="PC1",
name.axis2="PC2")
ecospat.shift.centroids(scores.sp.nat, scores.sp.inv, scores.clim.nat, scores.clim.inv)
## -----------------------------------------------------------------------------
ecospat.plot.overlap.test(sim.test, "expansion", "Similarity")
ecospat.plot.overlap.test(sim.test, "stability", "Similarity")
ecospat.plot.overlap.test(sim.test, "unfilling", "Similarity")
## -----------------------------------------------------------------------------
# gridding the native niche
grid.clim.t.nat <- ecospat.grid.clim.dyn(glob=as.data.frame(rbind(nat,inv)[,10]),
glob1=as.data.frame(nat[,10]),
sp=as.data.frame(nat[which(nat[,11]==1),10]),
R=1000, th.sp=0)
# gridding the invaded niche
grid.clim.t.inv <- ecospat.grid.clim.dyn(glob=as.data.frame(rbind(nat,inv)[,10]),
glob1=as.data.frame(inv[,10]),
sp=as.data.frame(inv[which(inv[,11]==1),10]),
R=1000, th.sp=0)
t.dyn<-ecospat.niche.dyn.index (grid.clim.t.nat, grid.clim.t.inv)
ecospat.plot.niche.dyn(grid.clim.t.nat, grid.clim.t.inv, quant=0,
interest=2, title= "Niche Overlap",
name.axis1="Average temperature")
## ----co_occ-------------------------------------------------------------------
data <- ecospat.testData[c(9:16,54:57)]
## -----------------------------------------------------------------------------
ecospat.co_occurrences (data)
## ----Cscore-------------------------------------------------------------------
data<- ecospat.testData[c(53,62,58,70,61,66,65,71,69,43,63,56,68,57,55,60,54,67,59,64)]
nperm <- 100
outpath <- getwd()
ecospat.Cscore(data, nperm, outpath)
## ----cor-plot-----------------------------------------------------------------
data <- ecospat.testData[,4:8]
ecospat.cor.plot(data)
## -----------------------------------------------------------------------------
data <- ecospat.testData
caleval <- ecospat.caleval (data = ecospat.testData[53], xy = data[2:3],
row.num = 1:nrow(data), nrep = 2, ratio = 0.7,
disaggregate = 0.2, pseudoabs = 100, npres = 10,
replace = FALSE)
head(caleval)
## -----------------------------------------------------------------------------
fit <- ecospat.testData$glm_Saxifraga_oppositifolia
## -----------------------------------------------------------------------------
obs<-ecospat.testData$glm_Saxifraga_oppositifolia[which(ecospat.testData$Saxifraga_oppositifolia==1)]
## ----boyce--------------------------------------------------------------------
ecospat.boyce (fit, obs, nclass = 0, window.w = "default", res = 100,
PEplot = TRUE)$cor
## -----------------------------------------------------------------------------
eval<-ecospat.testData[c(53,62,58,70,61,66,65,71,69,43,63,56,68,57,55,60,54,67,59,64)]
pred<-ecospat.testData[c(73:92)]
## -----------------------------------------------------------------------------
CommunityEval<-ecospat.CommunityEval (eval, pred, proba = TRUE, ntir=5,verbose = T)
## -----------------------------------------------------------------------------
library(biomod2)
## -----------------------------------------------------------------------------
# species
# occurrences
xy <- inv[,1:2]
head(xy)
sp_occ <- inv[11]
# env
current <- inv[3:7]
head(current)
## BIOMOD
t1 <- Sys.time()
sp<-1
## -----------------------------------------------------------------------------
### Formating the data with the BIOMOD_FormatingData() function form the package biomod2
myBiomodData <- biomod2::BIOMOD_FormatingData( resp.var = as.numeric(sp_occ[,sp]),
expl.var = current,
resp.xy = xy,
resp.name = colnames(sp_occ)[sp])
## ----ESM.Modeling-------------------------------------------------------------
### Calibration of simple bivariate models
# remove insivible(capture.output)) to print output in the console
# this is just to keep the vignette short
invisible(capture.output(my.ESM <- ecospat.ESM.Modeling( data=myBiomodData,
models=c('GLM'),
NbRunEval=2,
DataSplit=70,
weighting.score=c("AUC"),
parallel=F)
)
)
## ----ESM.EnsembleModeling-----------------------------------------------------
### Evaluation and average of simple bivariate models to ESMs
my.ESM_EF <- ecospat.ESM.EnsembleModeling(my.ESM,weighting.score=c("SomersD"),threshold=0)
## ----ESM.Projection-----------------------------------------------------------
### Projection of simple bivariate models into new space
my.ESM_proj_current <- ecospat.ESM.Projection(ESM.modeling.output=my.ESM,
new.env=current)
## ----ESM.EnsembleProjection---------------------------------------------------
### Projection of calibrated ESMs into new space
my.ESM_EFproj_current <- ecospat.ESM.EnsembleProjection(ESM.prediction.output=my.ESM_proj_current,
ESM.EnsembleModeling.output=my.ESM_EF)
## -----------------------------------------------------------------------------
proba <- ecospat.testData[,73:92]
## -----------------------------------------------------------------------------
sr <- as.data.frame(rowSums(proba))
## ----SESAM--------------------------------------------------------------------
prr<-ecospat.SESAM.prr(proba, sr)
head(prr)[,1:4]
## -----------------------------------------------------------------------------
presence<-ecospat.testData[c(53,62,58,70,61,66,65,71,69,43,63,56,68,57,55,60,54,67,59,64)]
pred<-ecospat.testData[c(73:92)]
## -----------------------------------------------------------------------------
nbpermut <- 100
outpath <- getwd()
ecospat.cons_Cscore(presence, pred, nbpermut, outpath)