## ----initialize, include=TRUE, eval=FALSE, echo=TRUE---------------------
#
# setwd("~")
# setwd("PATH/TO/FOLDER WITH THIS SCRIPT")
#
# ## Run first time to install R package
## ----plots, include=TRUE, eval=FALSE, echo=TRUE--------------------------
# # install.packages("devtools")
# # devtools::install_github("FranzKrah/rMyCoPortal")
## ----libs, include=TRUE, eval=FALSE, echo=TRUE---------------------------
# ## Load libraries
# library("rMyCoPortal")
# library("biomod2") # make sure maxent.jar is in the same folder if you want
# # MaxEnt can be downloaded here https://biodiversityinformatics.amnh.org/open_source/maxent/
# library("sf")
# library("raster")
## ----download, include=TRUE, eval=FALSE, echo=TRUE-----------------------
# ## Let's download some data for the famous fly agaric
# am.rec <- mycoportal(taxon = "Amanita muscaria") # please run again if server doesn't respond immediatelly
# am.rec
## ----plot1, include=TRUE, eval=FALSE, echo=TRUE--------------------------
# # plot_distmap(x = x, mapdatabase = "world") # interactive version
# p.dist <- plot_distmap(x = am.rec, mapdatabase = "state", interactive = FALSE) # the default is interactive
# p.dist
## ----plot2, include=TRUE, eval=FALSE, echo=TRUE--------------------------
# p.heat <- plot_datamap(x = am.rec, mapdatabase = "state")
## ----clim, include=TRUE, eval=FALSE, echo=TRUE---------------------------
# rec <- am.rec@records
# rec <- rec[!(is.na(rec$lat) | is.na(rec$lon)), ]
#
# rec <- st_as_sf(x = rec,
# coords = c("lon", "lat"),
# crs = "+proj=longlat +datum=WGS84")
#
# ## crop to USA
# area = list(min_long = -130, max_long = -60, min_lat = 25, max_lat = 52)
# rec <- st_crop(rec,
# xmin = area$min_long,
# ymin = area$min_lat,
# xmax = area$max_long,
# ymax = area$max_lat
# )
#
# rec <- SpatialPointsDataFrame(coords = st_coordinates(rec),
# data = as.data.frame(rec))
# rec <- as.data.frame(rec)
#
# ## Retrieve WorldClim data for current climatic data
# clim <- raster::getData(name = "worldclim", res = "2.5", var = "bio")
# clim <- crop(clim, extent(area$min_long, area$max_long, area$min_lat, area$max_lat))
# clim <- stack(clim)
#
# # the name of studied species
# myRespName <- 'Amanita_muscaria'
#
# # the XY coordinates of species data
# myRespXY <- rec[,c("X","Y")]
# myRespXY[] <- apply(myRespXY, 2, function(x) as.numeric(as.character(x)))
#
# clim.coord <- coordinates(clim)
# colnames(clim.coord) <- colnames(myRespXY)
#
# # some pseudo absence data
# samp <- sample(nrow(clim.coord), 1000)
# myRespXY <- rbind(data.frame(myRespXY), clim.coord[samp,])
#
# # the presence/absences data for our species
# myResp <- c(rep(1, nrow(rec)), rep(0, length(samp)))
#
# d <- duplicated(paste(myRespXY$X, myRespXY$Y))
# myRespXY <- myRespXY[!d,]
# myResp <- myResp[!d]
## ----biomod, include=TRUE, eval=FALSE, echo=TRUE-------------------------
# myBiomodData <- BIOMOD_FormatingData(resp.var = myResp,
# expl.var = clim,
# resp.xy = as.matrix(myRespXY),
# resp.name = myRespName,
# na.rm = TRUE)
#
# ## Defining Models Options using default options
# myBiomodOption <- BIOMOD_ModelingOptions()
#
# ## Computing the models
# myBiomodModelOut <- BIOMOD_Modeling(
# myBiomodData,
# models = c("MAXENT.Phillips"),
# models.options = myBiomodOption, NbRunEval=1,
# DataSplit=80,
# Prevalence=0.5,
# VarImport=3,
# models.eval.meth = c('ROC', "TSS"),
# SaveObj = TRUE,
# rescal.all.models = TRUE,
# do.full.models = FALSE,
# modeling.id = paste(myRespName,"FirstModeling",sep=""))
#
#
# # get all models evaluation
# myBiomodModelEval <- get_evaluations(myBiomodModelOut)
#
# # let's print the ROC scores of all selected models
# myBiomodModelEval["ROC","Testing.data",,,]
#
# # print variable importances
# barplot(get_variables_importance(myBiomodModelOut)[,,,], beside = TRUE, las = 2)
# ## bio7: Temperature Annual Range
## ----projection, include=TRUE, eval=FALSE, echo=TRUE---------------------
# ## Projection on current environemental conditions
# myBiomodProj <- BIOMOD_Projection(
# modeling.output = myBiomodModelOut,
# new.env = clim,
# proj.name = 'current',
# selected.models = 'all',
# binary.meth = 'TSS',
# compress = 'xz',
# clamping.mask = F,
# output.format = '.grd')
#
# plot(myBiomodProj)
## ----future, include=TRUE, eval=FALSE, echo=TRUE-------------------------
# cc85 <- raster::getData('CMIP5', var='bio', res=2.5, rcp=85, model='CC', year=70)
# cc85 <- crop(cc85, extent(area$min_long, area$max_long, area$min_lat, area$max_lat))
# cc85 <- stack(cc85)
#
# names(cc85) <- names(clim)
#
# myBiomodProjectionFuture <- BIOMOD_Projection(
# modeling.output = myBiomodModelOut,
# new.env = cc85,
# proj.name = 'future',
# selected.models = 'all',
# binary.meth = 'TSS',
# compress = 'xz',
# clamping.mask = F,
# output.format = '.grd')
#
# plot(myBiomodProjectionFuture)
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