Part_6.Rmd

# **Habitat Suitability and Distribution Models**
### with Applications in R
\
**by A. Guisan (1), W. Thuiller (2), N.E. Zimmermann (3) **,\
\
with contribution by V. Di Cola, D. Georges and A. Psomas\
\
_(1) University of Lausanne, Switzerland_\
_(2) CNRS, Université Grenoble Alpes, France_\
_(3) Swiss Federal Research Institute WSL, Switzerland_\


#### Cambridge University Press

http://www.cambridge.org/gb/academic/subjects/life-sciences/quantitative-biology-biostatistics-and-mathematical-modellin/habitat-suitability-and-distribution-models-applications-r

*Citation:* 
@book{
  title={Habitat Suitability and Distribution Models: With Applications in R},
  author={Guisan, A. and Thuiller, W. and Zimmermann, N.E.},
  isbn={9780521758369},
  series={Ecology, Biodiversity and Conservation},
  year={2017},
  publisher={Cambridge University Press}
}

*If you use any of these figures and code examples in a presentation or lecture, somewhere in your set of slides we would really appreciate if you please add the paragraph: "Some of the figures in this presentation are taken from "Habitat Suitability and Distribution Models: with applications in R"  (CUP, 2017) with permission from the authors: A. Guisan, W. Thuiller and N.E. Zimmerman " 
If you wish to use any of these figures in a publication, you must get permission from CUP, and each figure must be accompanied by a similar acknowledgement.*



# Part VI "Developed case studies"
# Chapter 19: The Biomod2 Modeling Package Examples
## Section 19.1 - Example 1: Habitat Suitability modeling of *Protea laurifolia* in South-Africa


Loading required packages
*biomod2*, *ggplot2*, *gridExtra* and *rgbif*
```{r load packages}
setwd("~/data")
library(rgbif)
library(biomod2)
library(ggplot2)
library(gridExtra)
library(raster)
```


#### Getting species data from GBIF using *rgbif* package
```{r get_data}
spp_Protea <- name_suggest(q='Protea laurifolia', rank='species',limit = 10000)
(spp_Protea <- spp_Protea[ grepl("^Protea laurifolia$", spp_Protea$canonicalName),])
```


Get species occurrences
```{r}
data <- occ_search(taxonKey = spp_Protea$key, 
                         country='ZA', 
                         fields = c('name','key','country','decimalLatitude','decimalLongitude'),  
                         hasCoordinate=T , 
                         limit=1000, 
                         return = 'data')

```
print the summary of the extracted object
```{r}
data

data <- data[['5637308']] 
```

```{r}
#remove blank spaces from species names.
data$name <- sub(" ", ".", data$name)
(spp_to_model <- unique(data$name))
```

```{r}

## Total number of occurrences: 
sort(table(data$name), decreasing = T)
```

#### Getting the environmental data

Get some worldclim environmental variables
```{r}
dir.create("WorldClim_data", showWarnings = F)
## curent bioclim
download.file(url = "http://biogeo.ucdavis.edu/data/climate/worldclim/1_4/grid/cur/bio_10m_esri.zip", 
              destfile = "WorldClim_data/current_bioclim_10min.zip", 
              method = "auto")

## GCM -> BCC-CSM1-1, year -> 2050, RCP -> 4.5
download.file(url = "http://biogeo.ucdavis.edu/data/climate/cmip5/10m/bc45bi50.zip", 
              destfile = "WorldClim_data/2050_BC_45_bioclim_10min.zip", 
              method = "auto")

## GCM -> BCC-CSM1-1, year -> 2080, RCP -> 4.5
download.file(url = "http://biogeo.ucdavis.edu/data/climate/cmip5/10m/bc45bi70.zip", 
              destfile = "WorldClim_data/2070_BC_45_bioclim_10min.zip", 
              method = "auto")

```


extract files
```{r}
unzip( zipfile = "WorldClim_data/current_bioclim_10min.zip", 
       exdir = "WorldClim_data/current", 
       overwrite = T)
list.files("WorldClim_data/current/bio/")

unzip( zipfile = "WorldClim_data/2050_BC_45_bioclim_10min.zip", 
       exdir = "WorldClim_data/2050/BC_45",
       overwrite = T)
list.files("WorldClim_data/2050/BC_45/")

unzip( zipfile = "WorldClim_data/2070_BC_45_bioclim_10min.zip",
       exdir = "WorldClim_data/2070/BC_45",
       overwrite = T)
list.files("WorldClim_data/2070/BC_45/")


```


#### Environmental variables selection
```{r}
#First we want to extract species occurrences from our data table.
ProLau_occ <- data[ data$name ==  "Protea.laurifolia", ]

library(raster)
#stack bio-climatic variables. 
bioclim_world <- stack(list.files("WorldClim_data/current/bio", pattern ="bio_", full.names=T), RAT = FALSE)
```


```{r}
#shape-file of Southern Africa in the `biomod2` package.

download.file(url = "https://sourceforge.net/projects/biomod2/files/data_for_example/south_of_africa.zip", destfile = "south_of_africa.zip")
unzip( zipfile = "south_of_africa.zip", 
       exdir = ".", 
       overwrite = T)
list.files("south_of_africa", recursive = T)

mask_south_of_africa <- shapefile("south_of_africa/South_Africa.shp")
bioclim_ZA <- mask(bioclim_world, 
                   mask_south_of_africa[ mask_south_of_africa$CNTRY_NAME == "South Africa", ])
bioclim_ZA <- crop(bioclim_ZA, mask_south_of_africa)
```


ids of cells where Protea laurifolia occurs.
```{r}
points_laurifolia<-data.frame(ProLau_occ[1:290,c("decimalLongitude", "decimalLatitude")])

ProLau_cell_id <- cellFromXY(subset(bioclim_ZA,1), points_laurifolia)
```


#### Principal Component Analysis.
```{r}
library(ade4)
bioclim_ZA_df <- na.omit(as.data.frame(bioclim_ZA))
head(bioclim_ZA_df)

pca_ZA <- dudi.pca(bioclim_ZA_df,scannf = F, nf = 2)

## PCA scores on first two axes
plot(pca_ZA$li[,1:2])

## tail of distributions
sort(pca_ZA$li[,1])[1:10]
## ids of points to remove
(to_remove <- which(pca_ZA$li[,1] < -10))

## remove points and recompute PCA
if(length(to_remove)){ ## remove outliers
  bioclim_ZA_df <- bioclim_ZA_df[ - to_remove,]
  pca_ZA <- dudi.pca(bioclim_ZA_df,scannf = F, nf = 2)  
}

par(mfrow=c(1,2))

## Discriminate Protea laurifolia presences from the entire South African environmental space. 
s.class(pca_ZA$li[,1:2],
        fac= factor(rownames(bioclim_ZA_df) %in% ProLau_cell_id, 
                    levels = c("FALSE", "TRUE" ),
                    labels = c("backgroud", "ProLau")), 
        col=c("red","blue"), 
        csta = 0,
        cellipse = 2,
        cpoint = .3,
        pch = 16)

s.corcircle(pca_ZA$co, clab = 1)

# Sub-selection of the variables.
bioclim_ZA_sub <- stack(subset(bioclim_ZA, c("bio_5", "bio_7", "bio_11", "bio_19")))


library(biomod2)
ProLau_data <- BIOMOD_FormatingData(resp.var = rep(1, nrow( ProLau_occ ) ),
                                    expl.var = bioclim_ZA_sub,
                                    resp.xy = ProLau_occ[,c('decimalLongitude', 'decimalLatitude')],
                                    resp.name = "Protea.laurifolia",
                                    PA.nb.rep = 3,
                                    PA.nb.absences = 500,
                                    PA.strategy = 'random')

## formatted object summary
ProLau_data
## plot of selected pseudo-absences
plot(ProLau_data)


ProLau_opt <- BIOMOD_ModelingOptions(GLM = list( type = 'quadratic',
                                                 interaction.level = 1 ),
                                     GBM = list( n.trees = 1000 ),
                                     GAM = list( algo = 'GAM_mgcv' ) )

ProLau_models <- BIOMOD_Modeling( data = ProLau_data,
                                  models = c("GLM", "GBM", "RF", "GAM"),
                                  models.options = ProLau_opt,
                                  NbRunEval = 4,
                                  DataSplit = 80,
                                  VarImport = 3,
                                  do.full.models = F,
                                  modeling.id = "ex2" )

## get models evaluation scores
ProLau_models_scores <- get_evaluations(ProLau_models)

## ProLau_models_scores is a 5 dimension array containing the scores of the models
dim(ProLau_models_scores)
dimnames(ProLau_models_scores)

models_scores_graph(ProLau_models, by = "models" , metrics = c("ROC","TSS"), 
                    xlim = c(0.5,1), ylim = c(0.5,1))
models_scores_graph(ProLau_models, by = "cv_run" , metrics = c("ROC","TSS"), 
                    xlim = c(0.5,1), ylim = c(0.5,1))
models_scores_graph(ProLau_models, by = "data_set" , metrics = c("ROC","TSS"), 
                    xlim = c(0.5,1), ylim = c(0.5,1))

(ProLau_models_var_import <- get_variables_importance(ProLau_models))

## make the mean of variable importance by algorithm
apply(ProLau_models_var_import, c(1,2), mean)
ProLau_glm <- BIOMOD_LoadModels(ProLau_models, models='GLM')
ProLau_gbm <- BIOMOD_LoadModels(ProLau_models, models='GBM')
ProLau_rf <- BIOMOD_LoadModels(ProLau_models, models='RF')
ProLau_gam <- BIOMOD_LoadModels(ProLau_models, models='GAM')

#+ 6, cache=TRUE ,opts.label = "half_page_figure"
glm_eval_strip <- biomod2::response.plot2(
    models  = ProLau_glm,
    Data = get_formal_data(ProLau_models,'expl.var'), 
    show.variables= get_formal_data(ProLau_models,'expl.var.names'),
    do.bivariate = FALSE,
    fixed.var.metric = 'median',
    legend = FALSE,
    display_title = FALSE,
    data_species = get_formal_data(ProLau_models,'resp.var'))

gbm_eval_strip <- biomod2::response.plot2(
    models  = ProLau_gbm,
    Data = get_formal_data(ProLau_models,'expl.var'), 
    show.variables= get_formal_data(ProLau_models,'expl.var.names'),
    do.bivariate = FALSE,
    fixed.var.metric = 'median',
    legend = FALSE,
    display_title = FALSE,
    data_species = get_formal_data(ProLau_models,'resp.var'))

rf_eval_strip <- biomod2::response.plot2(
      models  = ProLau_rf,
      Data = get_formal_data(ProLau_models,'expl.var'), 
      show.variables= get_formal_data(ProLau_models,'expl.var.names'),
      do.bivariate = FALSE,
      fixed.var.metric = 'median',
      legend = FALSE,
      display_title = FALSE,
      data_species = get_formal_data(ProLau_models,'resp.var'))

gam_eval_strip <- biomod2::response.plot2(
      models  = ProLau_gam,
      Data = get_formal_data(ProLau_models,'expl.var'), 
      show.variables= get_formal_data(ProLau_models,'expl.var.names'),
      do.bivariate = FALSE,
      fixed.var.metric = 'median',
      legend = FALSE,
      display_title = FALSE,
      data_species = get_formal_data(ProLau_models,'resp.var'))


ProLau_ensemble_models <- BIOMOD_EnsembleModeling( modeling.output = ProLau_models,
                                                   em.by = 'all',
                                                   eval.metric = 'TSS',
                                                   eval.metric.quality.threshold = 0.8,
                                                   models.eval.meth = c('KAPPA','TSS','ROC'),
                                                   prob.mean = FALSE,
                                                   prob.cv = TRUE, 
                                                   committee.averaging = TRUE,
                                                   prob.mean.weight = TRUE,
                                                   VarImport = 0 )

(ProLau_ensemble_models_scores <- get_evaluations(ProLau_ensemble_models))

### Current projections ###
ProLau_models_proj_current <- BIOMOD_Projection( modeling.output = ProLau_models,
                                                 new.env = bioclim_ZA_sub,
                                                 proj.name = "current",
                                                 binary.meth = "TSS",
                                                 output.format = ".img",
                                                 do.stack = FALSE )

ProLau_ensemble_models_proj_current <- 
  BIOMOD_EnsembleForecasting( EM.output = ProLau_ensemble_models,
                              projection.output = ProLau_models_proj_current,
                              binary.meth = "TSS",
                              output.format = ".img",
                              do.stack = FALSE )

### Future projections ###

## load 2050 bioclim variables
bioclim_world_2050_BC45 <- 
  stack( c( bio_5 = "WorldClim_data/2050/BC_45/bc45bi505.tif",
            bio_7 = "WorldClim_data/2050/BC_45/bc45bi507.tif",
            bio_11 = "WorldClim_data/2050/BC_45/bc45bi5011.tif",
            bio_19 = "WorldClim_data/2050/BC_45/bc45bi5019.tif"), RAT = FALSE )

## crop on our area
bioclim_ZA_2050_BC45 <- crop( bioclim_world_2050_BC45, mask_south_of_africa)
bioclim_ZA_2050_BC45 <- mask( bioclim_ZA_2050_BC45, 
                              mask_south_of_africa[ mask_south_of_africa$CNTRY_NAME == "South Africa", ] )
bioclim_ZA_2050_BC45 <- stack( bioclim_ZA_2050_BC45 )

## Save this rasterstack on the hard drive if needed. 

ProLau_models_proj_2050_BC45 <- BIOMOD_Projection( modeling.output = ProLau_models,
                                                   new.env = bioclim_ZA_2050_BC45,
                                                   proj.name = "2050_BC45",
                                                   binary.meth = "TSS",
                                                   output.format = ".img",
                                                   do.stack = FALSE )

ProLau_ensemble_models_proj_2050_BC45 <- 
  BIOMOD_EnsembleForecasting( EM.output = ProLau_ensemble_models,
                              projection.output = ProLau_models_proj_2050_BC45,
                              binary.meth = "TSS",
                              output.format = ".img",
                              do.stack = FALSE )

## load 2070 bioclim variables
bioclim_world_2070_BC45 <- stack( c( bio_5 = "WorldClim_data/2070/BC_45/bc45bi705.tif",
                                     bio_7 = "WorldClim_data/2070/BC_45/bc45bi707.tif",
                                     bio_11 = "WorldClim_data/2070/BC_45/bc45bi7011.tif",
                                     bio_19 = "WorldClim_data/2070/BC_45/bc45bi7019.tif"), RAT = FALSE )
## crop on our area
bioclim_ZA_2070_BC45 <- crop( bioclim_world_2070_BC45, mask_south_of_africa )
bioclim_ZA_2070_BC45 <- mask( bioclim_ZA_2070_BC45, 
                              mask_south_of_africa[ mask_south_of_africa$CNTRY_NAME == "South Africa", ])
bioclim_ZA_2070_BC45 <- stack( bioclim_ZA_2070_BC45 )

## You may save these rasters on the hard drive.

ProLau_models_proj_2070_BC45 <- BIOMOD_Projection( modeling.output = ProLau_models,
                                                   new.env = bioclim_ZA_2070_BC45,
                                                   proj.name = "2070_BC45",
                                                   binary.meth = "TSS",
                                                   output.format = ".img",
                                                   do.stack = FALSE )

ProLau_ensemble_models_proj_2070_BC45 <- 
  BIOMOD_EnsembleForecasting( EM.output = ProLau_ensemble_models,
                              projection.output = ProLau_models_proj_2070_BC45,
                              binary.meth = "TSS",
                              output.format = ".img",
                              do.stack = FALSE )


plot(ProLau_ensemble_models_proj_2070_BC45, 
     str.grep = "EMca|EMwmean")

## load binary projections
ProLau_bin_proj_current <- stack( 
  c( ca = "Protea.laurifolia/proj_current/individual_projections/Protea.laurifolia_EMcaByTSS_mergedAlgo_mergedRun_mergedData_TSSbin.img",
     wm = "Protea.laurifolia/proj_current/individual_projections/Protea.laurifolia_EMwmeanByTSS_mergedAlgo_mergedRun_mergedData_TSSbin.img") )

ProLau_bin_proj_2050_BC45 <- stack( 
  c( ca = "Protea.laurifolia/proj_2050_BC45/individual_projections/Protea.laurifolia_EMcaByTSS_mergedAlgo_mergedRun_mergedData_TSSbin.img",
     wm = "Protea.laurifolia/proj_2050_BC45/individual_projections/Protea.laurifolia_EMwmeanByTSS_mergedAlgo_mergedRun_mergedData_TSSbin.img") )

ProLau_bin_proj_2070_BC45 <- stack( 
  c( ca = "Protea.laurifolia/proj_2070_BC45/individual_projections/Protea.laurifolia_EMcaByTSS_mergedAlgo_mergedRun_mergedData_TSSbin.img",
     wm = "Protea.laurifolia/proj_2070_BC45/individual_projections/Protea.laurifolia_EMwmeanByTSS_mergedAlgo_mergedRun_mergedData_TSSbin.img") )


## SRC current -> 2050
SRC_current_2050_BC45 <- BIOMOD_RangeSize( ProLau_bin_proj_current,
                                           ProLau_bin_proj_2050_BC45 )

SRC_current_2050_BC45$Compt.By.Models

## SRC current -> 2070
SRC_current_2070_BC45 <- BIOMOD_RangeSize( ProLau_bin_proj_current,
                                           ProLau_bin_proj_2070_BC45 )
SRC_current_2070_BC45$Compt.By.Models

ProLau_src_map <- stack(SRC_current_2050_BC45$Diff.By.Pixel, SRC_current_2070_BC45$Diff.By.Pixel)
names(ProLau_src_map) <- c("ca cur-2050", "wm cur-2050", "ca cur-2070", "wm cur-2070")
## mask by environmental area
ProLau_src_map <- mask(ProLau_src_map,
                       mask_south_of_africa[ mask_south_of_africa$CNTRY_NAME == "South Africa", ])

library(rasterVis)
my.at <- seq(-2.5,1.5,1)
myColorkey <- list(at=my.at, ## where the colors change
                   labels=list(
                     labels=c("lost", "pres", "abs","gain"), ## labels
                     at=my.at[-1]-0.5 ## where to print labels
                   ))
rasterVis::levelplot( ProLau_src_map, 
                      main = "Protea laurifolia range change",
                      colorkey = myColorkey,
                      layout = c(2,2) )

ref <- subset(ProLau_bin_proj_current, "ca")

## define the facets we want to study
mods <- c("GLM", "GBM", "RF", "GAM", "caByTSS", "wmeanByTSS")
data_set <- c("PA1", "PA2", "PA3", "mergedData")
cv_run <- c("RUN1", "RUN2", "RUN3", "RUN4", "mergedRun")

## construct combination of all facets
groups <- as.matrix( expand.grid( models = mods, 
                                  data_set = data_set, 
                                  cv_run = cv_run,
                                  stringsAsFactors = FALSE) )

## load all projections we have produced
all_bin_proj_files <- list.files( path = "Protea.laurifolia",  
                                  pattern = "_TSSbin.img$",
                                  full.names = TRUE, 
                                  recursive = TRUE)

## current versus 2070 (removed the projections by 2050)
current_and_2070_proj_files <-grep(all_bin_proj_files, pattern="2070", value=T)

## keep only projections that match with our selected facets groups
selected_bin_proj_files <- apply(
  groups, 1, 
  function(x){
    proj_file <- NA
    match_tab <- sapply(x, grepl, current_and_2070_proj_files)
    match_id <- which( apply(match_tab,1,all) )
    if(length(match_id)) proj_file <- current_and_2070_proj_files[match_id]
    return(proj_file)
  })

## remove no-matching groups
to_remove <- which(is.na(selected_bin_proj_files))
if(length(to_remove)){
  groups <- groups[-to_remove,]
  selected_bin_proj_files <- selected_bin_proj_files[-to_remove]
}

## build stack of selected projections
proj_groups <- stack(selected_bin_proj_files)


ProbDensFunc( initial = ref,
              projections = proj_groups,
              groups = t(groups),
              plothist = FALSE,
              cvsn = FALSE,
              filename = paste("Protea.laurifolia/ProbDensFuncPlot.png"))

```




## Section 19.2 - Example 2: Creating diversity maps for the *Laurus* species

```{r}
## Getting species data 

if(!require(rgbif)){
  install.packages("rgbif")
  require(rgbif)
}

## get the species list belonging to Larus genus
spp_larus <- name_lookup(query = 'Larus', rank="species", return = 'data',
                         status = 'ACCEPTED', limit = 1000 )
## clean up the species list
(spp_larus <- spp_larus[ grepl("^Larus ", spp_larus$scientificName),])

## get species occurrences
occ_larus <- occ_search(taxonKey = spp_larus$key, continent='europe', fields = c('name','key','country','decimalLatitude','decimalLongitude'),  hasCoordinate=T , limit=500, return = 'data')

## join all data in a single data.frame
data <- NULL
for(sp in names(occ_larus)){
  if( !is.null( dim( occ_larus[[sp]] ) ) ){
    data <- rbind(data, occ_larus[[sp]]) 
  }
}

## replace " " by "." in species names
data$name <- sub(" ", ".", data$name)

## keep only species having more than 20 occurrences
table(data$name)
(spp_to_model <- names(table(data$name))[ table(data$name)>20 ] )

{{length(spp_to_model)}}

## get some worldclim environmental variables
dir.create("WorldClim_data", showWarnings = F)
## curent bioclim
download.file(url = "http://biogeo.ucdavis.edu/data/climate/worldclim/1_4/grid/cur/bio_10m_esri.zip", 
              destfile = "WorldClim_data/current_bioclim_10min.zip", 
              method = "auto")

## GCM -> BCC-CSM1-1, year -> 2050, RCP -> 4.5
download.file(url = "http://biogeo.ucdavis.edu/data/climate/cmip5/10m/bc45bi50.zip",
              destfile = "WorldClim_data/2050_BC_45_bioclim_10min.zip", 
              method = "auto")

## GCM -> BCC-CSM1-1, year -> 2080, RCP -> 4.5
download.file(url = "http://biogeo.ucdavis.edu/data/climate/cmip5/10m/bc45bi70.zip", 
              destfile = "WorldClim_data/2070_BC_45_bioclim_10min.zip", 
              method = "auto")

## unzip climatic files
unzip( zipfile = "WorldClim_data/current_bioclim_10min.zip", 
       exdir = "WorldClim_data/current", 
       overwrite = T)
list.files("WorldClim_data/current/bio/")

unzip( zipfile = "WorldClim_data/2050_BC_45_bioclim_10min.zip", 
       exdir = "WorldClim_data/2050/BC_45", 
       overwrite = T)
list.files("WorldClim_data/2050/BC_45/")

unzip( zipfile = "WorldClim_data/2070_BC_45_bioclim_10min.zip", 
       exdir = "WorldClim_data/2070/BC_45", 
       overwrite = T)
list.files("WorldClim_data/2070/BC_45/")
```


```{r}
## load libraries
library(biomod2)
library(gridExtra)
library(rasterVis)
library(reshape2)

europe_ext <- extent(-11,41,35,72)

## load environmental variables within a 'RasterStack' object
stk_current <- stack( list.files(path = "WorldClim_data/current/bio/", 
                                 pattern = "bio_", 
                                 full.names = T), RAT=F )


## Clip the environmental variables to the European extent
stk_current <- crop(stk_current, europe_ext)

## convert our environmental Stack into data.frame
current_df <- as.data.frame(stk_current)
current_df <- na.omit(current_df)

## calculate Pearson correlations between pairs of variables 
cor_current <- cor(current_df)

## reformat correlation table for graphical analyse
cor_current[ upper.tri(cor_current, diag = T) ] <- NA
cor_current_resh <- na.omit( melt( cor_current ) )
colnames(cor_current_resh) <- c("var1", "var2", "correlation")

## only consider absolute value of correlations
cor_current_resh$correlation <- abs(cor_current_resh$correlation)

## make a correlation plot
gg_cor <- ggplot(cor_current_resh, aes(x = var1, y = var2 , fill = correlation) )
gg_cor <- gg_cor + geom_tile() + xlab("") + ylab("") + theme( axis.text.x  = element_text(angle=90, vjust=0.5))
print(gg_cor)

selected_vars <- c("bio_1", "bio_12", "bio_8")

## check correlations between selected variables
(cor_sel <- cor(current_df[,selected_vars]))

{{round(max(abs(cor_sel[upper.tri(cor_sel, diag = F)])), digits =2)}}
## keep only the selected variables
stk_current <- stack(subset(stk_current, selected_vars))

plot(stk_current)

## NOTE : respect layer names and order across stacks
stk_2050_BC_45 <- stack( c( bio_1 = "WorldClim_data/2050/BC_45/bc45bi501.tif",
                            bio_12 = "WorldClim_data/2050/BC_45/bc45bi5012.tif",
                            bio_8 = "WorldClim_data/2050/BC_45/bc45bi508.tif"),
                         RAT=F)
stk_2050_BC_45 <- stack(crop(stk_2050_BC_45, europe_ext))

stk_2070_BC_45 <- stack( c( bio_1 = "WorldClim_data/2070/BC_45/bc45bi701.tif",
                            bio_12 = "WorldClim_data/2070/BC_45/bc45bi7012.tif",
                            bio_8 = "WorldClim_data/2070/BC_45/bc45bi708.tif"),
                         RAT=F)
stk_2070_BC_45 <- stack(crop(stk_2070_BC_45, europe_ext))

```

```{r}
## build a biomod2 modelling wrapper

biomod2_wrapper <- function(sp){
  cat("\n> species : ", sp)
  
  ## get occurrences points
  sp_dat <- data[ data$name == sp, ]
  
  ## formating the data
  sp_format <- BIOMOD_FormatingData(resp.var = rep( 1, nrow(sp_dat) ), 
                                    expl.var = stk_current,
                                    resp.xy = sp_dat[,c("decimalLongitude","decimalLatitude")],
                                    resp.name = sp,
                                    PA.strategy = "random", 
                                    PA.nb.rep = 3, 
                                    PA.nb.absences = 1000)
  ## print formatting summary
  sp_format
  
  ## save image of input data summary
  if(!exists(sp)) dir.create(sp)
  pdf(paste(sp, "/", sp ,"_data_formated.pdf", sep="" ))
  try(plot(sp_format))
  dev.off()
  
  ## define models options
  sp_opt <- BIOMOD_ModelingOptions()
  
  ## model species
  sp_model <- BIOMOD_Modeling( sp_format, 
                               models = c('GLM','FDA','RF'), 
                               models.options = sp_opt, 
                               NbRunEval = 3, 
                               DataSplit = 70, 
                               Yweights = NULL, 
                               VarImport = 3, 
                               models.eval.meth = c('TSS','ROC'),
                               SaveObj = TRUE,
                               rescal.all.models = FALSE,
                               do.full.models = FALSE,
                               modeling.id = "ex3")
  
  ## save some graphical outputs
  #### models scores
  pdf(paste(sp, "/", sp ,"_models_scores.pdf", sep="" ))
  try( gg1 <- models_scores_graph(sp_model, metrics = c("TSS","ROC"), by = 'models', plot=F) )
  try( gg2 <- models_scores_graph(sp_model, metrics = c("TSS","ROC"), by = 'data_set', plot=F) )
  try( gg3 <- models_scores_graph(sp_model, metrics = c("TSS","ROC"), by = 'cv_run', plot=F) )
  try(grid.arrange(gg1,gg2,gg3))
  dev.off()
  
  ## build ensemble models
  sp_ens_model <- BIOMOD_EnsembleModeling( modeling.output = sp_model,
                                           chosen.models = 'all',
                                           em.by = 'all',
                                           eval.metric = c('TSS'),
                                           eval.metric.quality.threshold = c(0.7),
                                           models.eval.meth = c('TSS','ROC'),
                                           prob.mean = TRUE,
                                           prob.cv = TRUE,
                                           prob.ci = FALSE,
                                           prob.ci.alpha = 0.05,
                                           prob.median = FALSE,
                                           committee.averaging = TRUE,
                                           prob.mean.weight = TRUE,
                                           prob.mean.weight.decay = 'proportional' )
  
  ## do projections
  proj_scen <- c("current", "2050_BC_45", "2070_BC_45")
  
  for(scen in proj_scen){
    cat("\n> projections of ", scen)
    
    ## single model projections
    sp_proj <- BIOMOD_Projection(  modeling.output = sp_model,
                                   new.env = get(paste("stk_", scen, sep = "")),
                                   proj.name = scen,
                                   selected.models = 'all',
                                   binary.meth = "TSS",
                                   filtered.meth = NULL,
                                   compress = TRUE,
                                   build.clamping.mask = TRUE,
                                   do.stack = FALSE,
                                   output.format = ".img" )
    
    ## ensemble model projections
    sp_ens_proj <- BIOMOD_EnsembleForecasting(EM.output = sp_ens_model,
                                              projection.output = sp_proj,
                                              binary.meth = "TSS",
                                              compress = TRUE,
                                              do.stack = FALSE,
                                              output.format = ".img")
    
  }
  
  return(paste(sp," modelling completed !", sep=""))
  
}

if(require(snowfall)){ ## parallel computation
  ## start the cluster
  sfInit(parallel = TRUE, cpus = 2) ## here we only require 2 cpus
  sfExportAll()
  sfLibrary(biomod2)
  ## launch our wrapper in parallel
  sf_out <- sfLapply(spp_to_model, biomod2_wrapper)
  ## stop the cluster
  sfStop()
} else { ## sequencial computation
  for (sp in spp_to_model){
    biomod2_wrapper(sp)
  }
  ## or with a lapply function in sequential model
  ## all_species_bm <- lapply(spp_to_model, biomod2_wrapper)
}

## current conditons
### load binary projections
f_em_wmean_bin_current <- paste(spp_to_model,
                                "/proj_current/individual_projections/", 
                                spp_to_model,
                                "_EMwmeanByTSS_mergedAlgo_mergedRun_mergedData_TSSbin.img",
                                sep = "")

### sum all projections
if( length(f_em_wmean_bin_current) >= 2 ){
  ## initialisation
  taxo_alpha_div_current <- raster( f_em_wmean_bin_current[1] ) 
  for(f in f_em_wmean_bin_current){
    taxo_alpha_div_current <- taxo_alpha_div_current + raster(f)
  }
}

### mask by environmental mask (will be remove with next version of biomod2)
taxo_alpha_div_current <- mask(taxo_alpha_div_current, subset(stk_current,1))

## 2050 conditons
### load binaries projections
f_em_wmean_bin_2050 <- paste(spp_to_model,
                             "/proj_2050_BC_45/individual_projections/", 
                             spp_to_model,
                             "_EMwmeanByTSS_mergedAlgo_mergedRun_mergedData_TSSbin.img",
                             sep = "")

### sum all projections
if( length(f_em_wmean_bin_2050) >= 2 ){
  ## initialisation
  taxo_alpha_div_2050 <- raster( f_em_wmean_bin_2050[1] ) 
  for(f in f_em_wmean_bin_2050){
    taxo_alpha_div_2050 <- taxo_alpha_div_2050 + raster(f)
  }
}

### mask by environmental mask (will be remove with next version of biomod2)
taxo_alpha_div_2050 <- mask(taxo_alpha_div_2050, subset(stk_2050_BC_45,1))

## 2070 conditons
### load binaries projections
f_em_wmean_bin_2070 <- paste(spp_to_model,
                             "/proj_2070_BC_45//individual_projections/", 
                             spp_to_model,
                             "_EMwmeanByTSS_mergedAlgo_mergedRun_mergedData_TSSbin.img",
                             sep = "")

### sum all projections
if( length(f_em_wmean_bin_2070) >= 2 ){
  ## initialisation
  taxo_alpha_div_2070 <- raster( f_em_wmean_bin_2070[1] ) 
  for(f in f_em_wmean_bin_2070){
    taxo_alpha_div_2070 <- taxo_alpha_div_2070 + raster(f)
  }
}
```

```{r}
### mask by environmental mask (will be remove with next version of biomod2)
taxo_alpha_div_2070 <- mask(taxo_alpha_div_2070, subset(stk_2070_BC_45,1))

## plot the results
levelplot( stack( c(current = taxo_alpha_div_current, 
                    in_2050 = taxo_alpha_div_2050, 
                    in_2070 = taxo_alpha_div_2070 ) ),
           main = expression(paste("Larus ", alpha, "-diversity")),
           par.settings = BuRdTheme)
```