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pipeline_Niels_Raes.R
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rm(list = ls(all=T))
setwd("D:/Papers.Projects/Climate.Smart.Agriculture/R/")
# save(list=ls(all=TRUE), file="D:/Papers.Projects/Climate.Smart.Agriculture/R/CSA.v2.RData") # save RDATA for later use
load("D:/Papers.Projects/Climate.Smart.Agriculture/R/CSA.v2.RData")
library(raster)
library(rgdal)
library(dismo)
library(maptools)
library(XML)
library(SDMTools)
library(foreign)
library(rJava); # Sys.setenv(JAVA_HOME='C:\\Program Files\\Java\\jre7') # for 64-bit version
library(xlsx)
library(rgbif)
library(gdata)
library(taxize)
library(ade4)
library(rgeos)
library(fmsb)
source("D:/R/Scripts/VIF.R")
source("D:/R/Scripts/null_model_function.R")
#library(pheno)
#daylength(as.integer(180),52)
countries <- readShapeLines("D:/GIS/Administrative/world/33578/world_country_admin_boundary_shapefile_with_fips_codes")
P4S.latlon <- CRS("+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0")
proj4string(countries) <- P4S.latlon
plot(countries)
### 1. Download GBIF data Cucumis ####
#### Genera to be merged with Cucumis according Schaefer & Renner 2008
#### Cucumella, Dicoelospermum, Mukia, Myrmecosicyos, Oreosyce
cucumella.download <- gbif("cucumella", "*", geo=F) # 220 records
dicaelospermum.download <- gbif("dicaelospermum", "*", geo=F) # 1 records
dicoelospermum.download <- gbif("dicoelospermum", "*", geo=F) # 1 records
mukia.download <- gbif("mukia", "*", geo=F) # 1304 records
head(mukia.download); # table(mukia.download$species)
myrmecosicyos.download <- gbif("Myrmecosic*", "*", geo=F) # 5 records
oreosyce.download <- gbif("oreosyce", "*", geo=F) # 206 records
CDMMO.download <- rbind(cucumella.download, dicaelospermum.download, dicoelospermum.download, mukia.download, myrmecosicyos.download, oreosyce.download)
head(CDMMO.download)
### Additional synonyms from Schaefer 2007
bryonia.download <- gbif("bryonia", "leio*", geo=F) # 1 records
hymenosicyos.download <- gbif("hymenosicyos", "*", geo=F) # 0 records
karivia.download <- gbif("karivia", "*", geo=F) # 3 records
kedrostis.cinerea.download <- gbif("kedrostis", "ciner*", geo=F) # 1 records
kedrostis.engleri.download <- gbif("kedrostis", "engler*", geo=F) # 0 records
melothria.javanica.download <- gbif("melothria", "javan*", geo=F) # 8 records
melothria.leiosperma.download <- gbif("melothria", "leios*", geo=F) # 4 records
melothria.maderaspatana.download <- gbif("melothria", "maderas*", geo=F) # 128 records
melothria.ritchiei.download <- gbif("melothria", "ritch*", geo=F) # 0 records
melothria.rumphiana.download <- gbif("melothria", "rumph*", geo=F) # 0 records
additional.Schaefer.2007 <- rbind(bryonia.download,hymenosicyos.download, karivia.download, kedrostis.cinerea.download, kedrostis.engleri.download, melothria.javanica.download, melothria.leiosperma.download, melothria.maderaspatana.download, melothria.ritchiei.download, melothria.rumphiana.download)
head(additional.Schaefer.2007)
additional.cucumis <- rbind(CDMMO.download, additional.Schaefer.2007)
head(additional.cucumis)
dim(additional.cucumis) # 1882 25
### GBIF Cucumis
cucumis.gbif <- gbif("cucumis", "*", geo=F) # 27696 records
dim(cucumis.gbif) # 27696 25
head(cucumis.gbif)
### Cucumis all
cucumis <- rbind(cucumis.gbif, additional.cucumis)
dim(cucumis) # 29578 25
colnames(cucumis)
cucumis.all <- write.csv(cucumis, '../data/Cucumis/Cucumis.all.csv', row.names=F)
cucumis.all <- read.csv('../data/Cucumis/Cucumis.all.csv')
colnames(cucumis.all)
head(cucumis.all); dim(cucumis.all) # 29578 25
plot(countries); points(cucumis.all$lon, cucumis.all$lat, pch=19, col=cucumis.all$species)
table(cucumis.all$species)
table(as.character(cucumis.all$basisOfRecord))
cucumis.fossil <- cucumis.all[which(cucumis.all$basisOfRecord =="fossil"),]
cucumis.all <- cucumis.all[which(cucumis.all$basisOfRecord !="fossil"),] # remove fossil records
cucumis.living <- cucumis.all[which(cucumis.all$basisOfRecord =="living"),] # living collections
table(as.character(cucumis.living$species))
head(cucumis.living)
plot(countries); points(cucumis.living$lon, cucumis.living$lat, pch=19, col=cucumis.living$species)
cucumis.all <- cucumis.all[which(cucumis.all$basisOfRecord !="living"),] # remove living records, likely greenhouse grown plants and not wild species, or CWR
head(cucumis.all); dim(cucumis.all) #26656 25
table(as.character(cucumis.all$basisOfRecord))
cucumis.all[1:100,]
str(cucumis.all)
### Cleaning - remove collections from greenhouses and markets, etc.
pattern <- "horti|greenhous|bot. gar|botanical garden|bot gar|wageningen|veredel|moscow|market|experi"
x <- grep(pattern, cucumis.all$cloc, ignore.case = T)
cucumis.all[x,'cloc']
dim(cucumis.all[x,]) # 318
cucumis.all <- cucumis.all[-x,]
dim(cucumis.all) #26338 25
head(cucumis.all)
table(cucumis.all$species, exclude=NULL)
pattern <- "Netherlands Antilles" # Gives error in geocode - see below
x <- grep(pattern, cucumis.all$cloc, ignore.case = T)
cucumis.all[x,'cloc']
dim(cucumis.all[x,]) # 318
cucumis.all <- cucumis.all[-x,]
dim(cucumis.all) #26313 25
head(cucumis.all)
table(cucumis.all$species, exclude=NULL)
### Cucumis hystrix
cucumis.hystrix.search <- grep('cucumis hystrix', cucumis.all$species, ignore.case=T, value=T)
cucumis.hystrix <- cucumis.all[which(cucumis.all$species %in% cucumis.hystrix.search),]
cucumis.hystrix # 19 records
cucumis.hystrix[,c('lat', 'lon', 'locality', 'cloc')]
### Unique species names
cucumis.all$species <- as.factor(as.character(cucumis.all$species))
species.names <- data.frame(table(cucumis.all$species))
species.names # 628
write.csv(species.names, '../data/Cucumis/cucumis.species.csv')
### 2. Georeference records lacking coordinates but with locality description ####
# Records with missing coordinates
head(cucumis.all); dim(cucumis.all) # 26313 25
cond <- ((is.na(cucumis.all$lat) & is.na(cucumis.all$lon)) | cucumis.all$lat ==0 & cucumis.all$lon ==0)
summary(cond)
# cucumis.coords <- subset(cucumis.all, ((!is.na(lat) & !is.na(lon)) | (cucumis.all$lat != 0 & cucumis.all$lon != 0)))
cucumis.coords <- cucumis.all[!cond,]
head(cucumis.coords); dim(cucumis.coords) # 9860 25 records with coordinates
data.frame(table(as.factor(as.character(cucumis.coords$species)), exclude=NULL))
str(cucumis.all)
cucumis.all$locality <- as.character(cucumis.all$locality)
cucumis.all$cloc <- as.character(cucumis.all$cloc)
# georef.1 <- subset(cucumis.all, (is.na(lat) & is.na(lon) & !is.na(cloc))) # lat & lon & cloc missing = NA
georef.1 <- cucumis.all[cond,]
head(georef.1); dim(georef.1) # 16453 25
georef.2 <- subset(georef.1, (!georef.1$cloc == "" & !georef.1$cloc == "Unknown" & !georef.1$cloc == "ND" & !georef.1$cloc == "Not available")) # remove empty, unknown and ND
head(georef.2); dim(georef.2) # 16187 25; localities lacking coordinates but with locality description
cucumis.hystrix.georef.2.search <- grep('cucumis hystrix', georef.2$species, ignore.case=T, value=T) # 19 records
cucumis.hystrix.georef.2.search
cucumis.hystrix.georef.2 <- georef.2[which(georef.2$species %in% cucumis.hystrix.georef.2.search),]
cucumis.hystrix.georef.2
georef.2[1:100, c("cloc")]
georef.3 <- subset(cucumis.all, (cucumis.all$lat == 0 & cucumis.all$lon == 0 & !is.na(cloc))) # lat & lon are 0
head(georef.3); dim(georef.3) # 0 25
georef <- rbind(georef.2, georef.3) # All records with cloc but without coordinates
head(georef); dim(georef) # 16187 25
colnames(georef)
str(georef)
duplicates.cloc <- duplicated(georef[, c('cloc')])
table(duplicates.cloc) # 2539 13648
unique.cloc <- georef[!duplicates.cloc,]
head(unique.cloc); dim(unique.cloc) # 2539 25; unique localities lacking coordinates but with locality description
write.csv(unique.cloc, '../data/Cucumis/unique.cloc.csv', row.names=F)
unique.cloc <- read.csv('../data/Cucumis/unique.cloc.csv', h=T)
#unique.cloc <- unique.cloc[,2:26]
head(unique.cloc); dim(unique.cloc) # 2539 25
str(unique.cloc)
unique.cloc$cloc <- as.character(unique.cloc$cloc)
str(unique.cloc)
#georef.Netherlands.Antilles <- unique.cloc[which(unique.cloc$cloc =="Netherlands Antilles"),] # gives weird error - cannot figure out what going wrong?
#georef <- georef[which(georef$cloc !="Netherlands Antilles"),]
### 3. Automated georeferencing using GOOGLE API ####
### Has a 2500 maximum daily number of requests - 1st run till 1098
### error: You have exceeded your daily request quota for this API
unique.cloc.2539 <- unique.cloc
try(geocode('Caimancito, JUJUY, Ledesma, Argentina, SOUTH AMERICA'))
try(geocode('Caimancito, Argentina'))
try(geocode('Ban-chiou-chian, Che-li Hsien, Yunnan, Jinghong, China, Asia'))
geocode('Che-li Hsien, Yunnan, Jinghong, China, Asia')
geocode("Jardin d'exp?rience de Collioure, de l'int?rieure de l'Afrique, Namibia")
geocode('Jardin d exp?rience de Collioure, de l int?rieure de l Afrique, Namibia')
unique.cloc$cloc.clean <- gsub("'", " ",unique.cloc$cloc) # remove ' from cloc names
### Replace coordinates with Google coordinates if accuracy is within uncertainty
uncertainty <- 10000 # in meters = 10km
#j=10
unique.cloc[1226,]
#for(j in 1:nrow(unique.cloc)){
for(j in 1463:nrow(unique.cloc)){
Sys.sleep(0.5) # wait 0.5 seconds
b <- geocode(unique.cloc$cloc.clean[j])
print(j)
#b
#str(b)
b2 <- subset(b, b$uncertainty == min(b$uncertainty)) # select record with least uncertainty
#b2
#dim(b2)
if(dim(b2)[1] == 0){
unique.cloc[j,'lat'] <- unique.cloc[j,'lat'] #; print(1)
unique.cloc[j,'lon'] <- unique.cloc[j,'lon'] #; print('a')
} else {
b2 <- b2[1,] # sometimes 2 same minimum values i.e. geocode('Austria,Niederoesterreich,Weinviertel,Katzelsdorf')
if(b2[,'uncertainty'] < uncertainty) {
unique.cloc[j,'lat'] <- b2[,'latitude']
unique.cloc[j,'lon'] <- b2[,'longitude']
unique.cloc[j,'coordUncertaintyM'] <- b2[,'uncertainty']
} else {
unique.cloc[j,'lat'] <- unique.cloc[j,'lat']
unique.cloc[j,'lon'] <- unique.cloc[j,'lon']
}
}
}
head(unique.cloc)
write.csv(unique.cloc[1463:2539,], '../data/Cucumis/unique.cloc.geocode.1463.2539.csv', row.names=F)
write.csv(unique.cloc, '../data/Cucumis/unique.cloc.geocode.x.csv', row.names=F)
# Pasted in excel
unique.cloc.geocode <- read.csv('../data/Cucumis/unique.cloc.geocode.1.2539.csv')
head(unique.cloc.geocode); dim(unique.cloc.geocode) # 2539 26
unique.cloc.geocode[1:100, c("lat", "lon")]
unique.cloc.geocode <- subset(unique.cloc.geocode, (!is.na(lat) & !is.na(lon)))
head(unique.cloc.geocode); dim(unique.cloc.geocode) # 837 26 localities georeferenced with Google
names(unique.cloc.geocode)
unique.cloc.geocode <- unique.cloc.geocode[,c('cloc', 'lat', 'lon', 'coordUncertaintyM')]
unique.cloc.geocode <- unique.cloc.geocode[order(unique.cloc.geocode[,'cloc']), ]
head(unique.cloc.geocode)
### 4. merge unique.cloc.geocode with georef to link coordinates to collection localities ####
head(georef)
names(georef)
dim(georef) # 16187 25
str(georef)
georef <- georef[order(georef[,'cloc']), ]
str(unique.cloc.geocode)
unique.cloc.geocode$cloc <- as.character(unique.cloc.geocode$cloc)
head(unique.cloc.geocode)
unique.cloc.geocode <- unique.cloc.geocode[order(unique.cloc.geocode[,'cloc']), ]
georef.coords <- merge(georef, unique.cloc.geocode, by='cloc', all.x=T, incomparables = NA)
dim(georef.coords) # 16187 28
head(georef.coords)
georef.coords[1:50,]
names(georef.coords)
georef.coords[1:50,c('lat.x', 'lat.y', 'lat.x', 'lat.y')]
dim(georef.coords)
head(georef.coords)
colnames(georef); colnames(georef.coords)
georef$lat <- georef.coords$lat.y
georef$lon <- georef.coords$lon.y # replace original NA's with merged data
head(georef)
georef[1:50, c("lat", "lon")]
georef.coords <- subset(georef, !is.na(lat) & !is.na(lon))
head(georef.coords); dim(georef.coords) # 1399 25
georef.coords[1:100,c("lat", "lon", "locality")]
### Cucumis hystrix
cucumis.hystrix.georef <- grep('cucumis hystrix', georef.coords$species, ignore.case=T, value=T)
cucumis.hystrix <- georef.coords[which(georef.coords$species %in% cucumis.hystrix.search),]
cucumis.hystrix # Only 6 received coordinates through Google below, which is correct!
plot(countries); points(cucumis.hystrix$lon, cucumis.hystrix$lat, pch=19, col='red')
### 5. Combine datasets ####
cucumis.all.coords <- rbind(cucumis.coords, georef.coords)
colnames(cucumis.all.coords)
plot(countries); points(cucumis.all.coords$lon, cucumis.all.coords$lat, pch=19, col=cucumis.all.coords$species)
duplicates <- duplicated(cucumis.all.coords[,c("species", "lat", "lon")])
table(duplicates, exclude=NULL)
cucumis.all.coords <- cucumis.all.coords[!duplicates,]
dim(cucumis.all.coords) # 8308
str(cucumis.all.coords)
cucumis.all.coords$species <- as.factor(as.character(cucumis.all.coords$species))
plot(countries); points(cucumis.all.coords$lon, cucumis.all.coords$lat, pch=19, col=cucumis.all.coords$species)
### Cucumis hystrix
cucumis.hystrix.georef <- grep('cucumis hystrix', cucumis.all.coords$species, ignore.case=T, value=T)
cucumis.hystrix <- cucumis.all.coords[which(cucumis.all.coords$species %in% cucumis.hystrix.search),]
cucumis.hystrix # Only 5 unique with coordinates through Google below, which is correct!
plot(countries); points(cucumis.hystrix$lon, cucumis.hystrix$lat, pch=19, col='red')
### 6. resolve synonymy ####
species.628.ncbi <- read.table('../data/Cucumis/species.ncbi.txt', header=T, sep = '\t') # Hannes Hettling
head(species.628.ncbi); dim(species.628.ncbi)
cucumis.species <- read.csv('../data/Cucumis/cucumis.species.csv')
head(cucumis.species); dim(cucumis.species)
table(species.628.ncbi$ncbi_name, exclude=NULL)
cucumis.species <- as.data.frame(cucumis.species$Var1)
colnames(cucumis.species)[1] <- c("name")
cucumis.species <- cbind(cucumis.species, species.628.ncbi[,2:18])
head(cucumis.species)
### prepare data
head(cucumis.species); dim(cucumis.species) # 628 18
splist <- as.character(cucumis.species[,1])
write.table(splist, '../data/Cucumis/splist.csv', sep=",", row.names=F, col.names=F)
length(splist) # 628 unique names in data file
# tnrs function produces duplicate entries - reason not known??? -> solution loop per species
# Create empty results.matrix for results - results.matrix ###
tnrs1 <- tnrs(query=splist[1], getpost="POST", source="iPlant_TNRS"); tnrs1
seq1 <- seq(1:7) # tnrs returns 7 columns
results.matrix <- matrix(seq1, 1) # data, nrows, ncols, etc.
colnames(results.matrix) <- colnames(tnrs1)
results.matrix
results.matrix <- results.matrix[-1,] # remove dummy first row
dim(results.matrix)
rm(seq1, tnrs1) # remove seq1
#t=splist[4]; t=3
#t='Cucumis agrestis'
results <- results.matrix
{
for(t in 1:length(splist)){
splist_tnrs <- tnrs(query=splist[t], getpost="POST", source="iPlant_TNRS")
#str(splist_tnrs)
if(dim(splist_tnrs)[1] == 0) {
splist.names <- names(splist_tnrs)
splist_tnrs <- data.frame(matrix(rep(NA, 7), 1))
names(splist_tnrs) <- splist.names
} else {
splist_tnrs = splist_tnrs
}
results <- rbind(results, splist_tnrs)
cat("-") # print dash
if (t%%50 == 0) # %% is modulus
cat(" ", t, "\n")
# flush.console()
}
if (t%%50 != 0) {
cat(" ", t, "\n")
}
else {
cat("\n")
flush.console()
}
}
results <- cbind(data.frame(splist), results)
head(results); dim(results); names(results)
results <- cbind(results, species.628.ncbi[,2:18])
write.csv(results, '../data/Cucumis/species.628.ncbi.tnrs.csv', row.names=F)
##################################
### Manually check results !!! ###
##################################
cucumis.names <- read.csv('../data/Cucumis/species.628.ncbi.tnrs_corr.csv', h=T)
str(cucumis.all.coords); dim(cucumis.all.coords) # 8308 25
head(cucumis.all.coords)
names(cucumis.all.coords)
cucumis.all.coords <- cucumis.all.coords[order(cucumis.all.coords[,'species']), ]
names(cucumis.names)
head(cucumis.names) # splist.v2 -> cleaned blank spaces in splist
cucumis.names <- cucumis.names[order(cucumis.names[,'splist']), ]
### 7. Merge records ####
# Note splist from original GBIF names. These include double blank spaces which were removed in splist.v2
cucumis.corr <- merge(cucumis.all.coords, cucumis.names[,c('splist', 'acceptedname')], by.x="species", by.y="splist", all.x=TRUE) # right outer join
names(cucumis.corr)
head(cucumis.corr)
dim(cucumis.corr) # 8308 26
cucumis.corr <- cucumis.corr[which(cucumis.corr$acceptedname != "NA"), ]
cucumis.corr$acceptedname <- as.factor(as.character(cucumis.corr$acceptedname))
head(cucumis.corr)
dim(cucumis.corr) # 8160 26
str(cucumis.corr)
sort(unique(cucumis.corr$acceptedname))
as.data.frame(table(cucumis.corr$acceptedname))
plot(countries);points(cucumis.corr$lon, cucumis.corr$lat, pch=19, col=cucumis.corr$acceptedname)
data.frame(table(cucumis.corr$acceptedname, exclude=NULL))
dim(data.frame(table(cucumis.corr$acceptedname, exclude=NULL))) # 63 species with georeferenced records
### Cucumis hystrix
cucumis.hystrix.search2 <- grep('cucumis hystrix', cucumis.corr$acceptedname, ignore.case=T, value=T)
cucumis.hystrix.coords <- cucumis.corr[which(cucumis.corr$acceptedname %in% cucumis.hystrix.search2),]
cucumis.hystrix.coords # 5 records
plot(countries);points(cucumis.hystrix.coords$lon, cucumis.hystrix.coords$lat, pch=19, col='red')
sp.accept <- data.frame(c(sort(trim(unique(as.character(cucumis.corr$acceptedname))))))
sp.accept
coordinates(cucumis.corr) <- ~lon+lat
cucumis.corr@proj4string <- P4S.latlon
head(cucumis.corr)
plot(countries);plot(cucumis.corr, pch=19, col=cucumis.corr$acceptedname, add=T)
str(cucumis.corr)
### 8. Create PET ####
files.present <- list.files('D:/GIS/Worldclim/Present/5arcmin/bio/', pattern="[.]bil$", full.names=T) # alternatives for pattern (c|C)(e|E)(l|L)$
# files <- list.files('Z:/World/Climate/Worldclim/05arcmin/Present/bio/', pattern="[.]bil$", full.names=T) # alternatives for pattern (c|C)(e|E)(l|L)$
files.present
present.stack <- stack(files.present)
present.df <- as.data.frame(present.stack, xy=T)
head(present.df)
present.df <- na.omit(present.df)
dim(present.df) # 2287025 23
present.df$bio01a <- present.df$bio01/10 # calculate PET (Loiselle 2008; http://onlinelibrary.wiley.com/doi/10.1111/j.1365-2699.2007.01779.x/abstract)
present.df$bio01a[present.df$bio01a < 0] <- 0
present.df$bio01a[present.df$bio01a > 30] <- 0
present.df$PET <- ((present.df$bio01a/present.df$bio12)*58.93)
present.df$PET[present.df$PET > 100] <- 100 # or NA
summary(present.df$PET)
coordinates(present.df) <- ~x+y
gridded(present.df) = T
str(present.df)
present.df@proj4string <- P4S.latlon
str(present.df)
head(present.df)
dim(present.df) # 2287025 21
summary(present.df$PET)
drops <- c("bio01a")
present.df <- present.df[,!(names(present.df) %in% drops)]
r <- raster(present.df, 'PET')
plot(r); plot(cucumis.corr, add=T)
alt <- raster(files.present[1])
plot(alt)
extent(alt)
r <- extend(r, extent(alt))
writeRaster(r,
filename = "D:/GIS/Worldclim/Present/5arcmin/PET/PET.present.asc",
format = 'ascii',
NAflag = -9999,
overwrite = T)
PET.present <- raster('D:/Papers.Projects/Climate.Smart.Agriculture/GIS/present/PET.present.asc')
plot(PET.present)
### 9. Compile climate data ####
files.present <- list.files('D:/GIS/Worldclim/Present/5arcmin/bio/', pattern="[.]bil$", full.names=T) # alternatives for pattern (c|C)(e|E)(l|L)$
# files <- list.files('Z:/World/Climate/Worldclim/05arcmin/Present/bio/', pattern="[.]bil$", full.names=T) # alternatives for pattern (c|C)(e|E)(l|L)$
files.present
PET.file <- "D:/GIS/Worldclim/Present/5arcmin/PET/PET.present.asc"
DSL.file <- "D:/GIS/Worldclim/Present/5arcmin/DSL/dsl.100.present.asc"
GDD.10.file <- "D:/GIS/Worldclim/Present/5arcmin/GDD/gdd.base.10.asc"
files.present <- c(files.present, PET.file, DSL.file, GDD.10.file)
present.stack <- stack(files.present[2:23])
head(present.stack)
present.df <- as.data.frame(present.stack, xy=T)
coordinates(present.df) <- ~x+y
gridded(present.df) <- T
present.df@proj4string <- P4S.latlon
present.df$grid.index <- [email protected] # Add grid.index value
head(present.df)
image(present.df, 'gdd.base.10')
### 10. Get abiotic data and remove duplicates - cucumis.unique ####
str(cucumis.corr)
str(present.df)
cucumis.abiotic <- over(cucumis.corr, present.df) # Get climate variables + grid.index for Cucumis collections
str(cucumis.abiotic)
head(cucumis.abiotic)
dim(cucumis.abiotic); dim(cucumis.corr)
head(cucumis.corr); str(cucumis.corr)
cucumis.corr <- cbind(cucumis.corr, cucumis.abiotic) # Link collections and climate data
head(cucumis.corr)
duplicates <- duplicated(cucumis.corr[,c("acceptedname", "grid.index")]) # Duplicates on grid.index
table(duplicates) # 6695 F 1465 T
cucumis.unique <- cucumis.corr[!duplicates,] # remove duplicates
str(cucumis.unique)
head(cucumis.unique)
dim(cucumis.unique) # 6695 49
summary(cucumis.unique)
cucumis.unique <- cucumis.unique[which(cucumis.unique$PET !="NA"),]
dim(cucumis.unique) # 6615 49
str(cucumis.unique)
head(cucumis.unique)
data.frame(table(cucumis.unique$acceptedname)) # 63 species
plot(raster(present.df, 'gdd.base.10')); points(cucumis.unique$lon, cucumis.unique$lat, col=cucumis.unique$acceptedname)
### 11. Cross check georeference with country polygons
### and remove records from non-native countries
countries.poly <- readShapePoly("D:/GIS/Administrative/world/33578/world_country_admin_boundary_shapefile_with_fips_codes")
proj4string(countries.poly) <- "+proj=longlat +datum=WGS84"
plot(countries.poly)
class(countries.poly)
names(countries.poly)
str(countries.poly)
head(countries.poly$CNTRY_NAME)
names(cucumis.unique)
coordinates(cucumis.unique) <- ~lon+lat
proj4string(cucumis.unique) <- "+proj=longlat +datum=WGS84"
ov <- over(cucumis.unique, countries.poly) # Get country values from shape
head(ov)
ov$CNTRY_NAME <- as.character(ov$CNTRY_NAME)
cucumis.unique$country <- as.character(cucumis.unique$country)
j <- which(ov$CNTRY_NAME != cucumis.unique$country) # Identify records with different country at records level than from polygon
j
cbind(j, cbind(cucumis.unique$country, ov$CNTRY_NAME)[j,])
j2 <- c(349, 355, 369, 404, 545, 827, 951, 1148, 1195, 1389, 1446, 1862, 1905, 2074, 2080, 2677, 2689, 3093, 3437, 3482, 3709, 3722, 4032, 4059, 4100, 4125, 4136, 5133, 5183, 5264, 5321, 5589, 5623, 5686, 5929, 6150, 6198, 6514, 6515, 6572)
cucumis.unique.corr <- cucumis.unique[-j2,] # Remove wrong records
ov2 <- over(cucumis.unique.corr, countries.poly)
head(ov2)
ov2$CNTRY_NAME <- as.character(ov2$CNTRY_NAME)
cucumis.unique.corr$country <- as.character(cucumis.unique.corr$country)
j3 <- which(ov2$CNTRY_NAME != cucumis.unique.corr$country)
j3
cbind(j3, cbind(cucumis.unique.corr$country, ov2$CNTRY_NAME)[j3,])
head(cucumis.unique.corr)
dim(cucumis.unique.corr) # 6575 47
data.frame(table(cucumis.unique.corr$acceptedname))
### Remove unlikely countries, i.e. Netherlands
# Native range of Cucumis is restricted to Africa/Madagascar - Asia - Australia
# Not to blur the niche properties with agricultural treatments we removed all records from non-native range
names(cucumis.unique.corr)
unique.countries <- sort(unique(cucumis.unique.corr$country))
unique.countries
str(unique.countries)
non.native.countries <- c("Albania", "Argentina", "Austria", "Belgium", "Belize", "Bolivia", "Bosnia and Herzegovina", "Brazil", "Bulgaria", "Canada", "Chile", "Croatia", "Colombia", "Costa Rica", "Cuba", "Cyprus", "Czech Republic", "Dominican Republic", "Ecuador", "El Salvador", "Finland", "France", "Germany", "Greece", "Guatemala", "Guyana", "Honduras", "Hungary", "Italy", "Japan", "Liechtenstein", "Macedonia", "Mexico", "Moldova", "Netherlands", "New Caledonia", "Nicaragua", "Norway", "Panama", "Paraguay", "Peru", "Poland", "Portugal", "Puerto Rico", "Romania", "Russia", "Serbia", "Slovakia", "South Korea", "Spain", "Sweden", "Switzerland", "Ukraine", "United Kingdom", "United States", "Venezuela")
cucumis.unique.corr <- cbind(cucumis.unique.corr@coords, cucumis.unique.corr@data)
str(cucumis.unique.corr)
head(cucumis.unique.corr)
x <- cucumis.unique.corr$country %in% non.native.countries
table(x)
cucumis.unique.corr$non.native <- x
head(cucumis.unique.corr)
cucumis.unique.corr <- cucumis.unique.corr[cucumis.unique.corr$non.native == FALSE,]
dim(cucumis.unique.corr) # 4374 50
cucumis.unique.corr <- cucumis.unique.corr[(cucumis.unique.corr$lon > -30 & cucumis.unique.corr$lat < 47),]
dim(cucumis.unique.corr) # 4371 50
plot(countries); points(cucumis.unique.corr$lon, cucumis.unique.corr$lat, col=cucumis.unique.5$acceptedname, pch=19)
### 10. Select records for species wit more than 5 records ####
head(cucumis.unique.corr)
dim(cucumis.unique.corr) # 4371 50
str(cucumis.unique.corr)
cucumis.unique.corr$acceptedname <- as.factor(as.character(cucumis.unique.corr$acceptedname))
data.frame(table(cucumis.unique.corr$acceptedname, exclude=NULL))
cucumis.5 <- data.frame(table(cucumis.unique.corr$acceptedname))
cucumis.5
str(cucumis.5)
cucumis.5 <- cucumis.5[which(cucumis.5$Freq >= 5),]
cucumis.5 <- c(as.character(cucumis.5$Var1))
cucumis.unique.5 <- cucumis.unique.corr[cucumis.unique.corr$acceptedname %in% cucumis.5, ]
dim(cucumis.unique.5) # 4335 50
length(unique(cucumis.unique$acceptedname)) # 63
length(unique(cucumis.unique.5$acceptedname)) # 41 species with >=5 records
str(cucumis.unique.5)
head(cucumis.unique.5)
write.csv(cucumis.5, "D:/Papers.Projects/Climate.Smart.Agriculture/data/Cucumis/cucumis.5.csv", row.names=F)
write.csv(cucumis.unique.5, "D:/Papers.Projects/Climate.Smart.Agriculture/data/Cucumis/cucumis.unique.5.csv", row.names=F)
plot(countries); points(cucumis.unique.5$lon, cucumis.unique.5$lat, col=cucumis.unique.5$acceptedname, pch=19)
cucumis.unique.5 <- read.csv("D:/Papers.Projects/Climate.Smart.Agriculture/data/Cucumis/cucumis.unique.5.csv")
head(cucumis.unique.5); dim(cucumis.unique.5) # 4335 50
cucumis.5 <- read.csv("D:/Papers.Projects/Climate.Smart.Agriculture/data/Cucumis/cucumis.5.csv")
head(cucumis.5)
str(cucumis.5)
cucumis.5 <- as.vector(cucumis.5)
### 11. PCA Cucumis.unique.5 ####
head(cucumis.unique.5)
str(cucumis.unique.5)
names(cucumis.unique.5)
summary(cucumis.unique.5[,27:48])
pc <- dudi.pca(cucumis.unique.5[,27:48], center=T, scale=T, scannf=F) # PCA
str(pc)
pc$tab$acceptedname <- cucumis.unique.5$acceptedname
plot(pc$li$Axis1, pc$li$Axis2, asp=1, col=pc$tab$acceptedname)
scatter(pc)
pc$eig
barplot(pc$eig)
var1 <- (pc$eig[1]/sum(pc$eig))*100
var1 # 38.93919
var2 <- (pc$eig[2]/sum(pc$eig))*100
var2 # 26.31047
var3 <- (pc$eig[3]/sum(pc$eig))*100
var3 # 9.544308
var <- var1 + var2 + var3
var # 74.79396
### 12. Run Maxent models in 1000 km buffered area around presences to balance prevalence - from null.model.buffer ####
### Visualize data ###
r <- raster("Z:/World/Climate/Worldclim/05arcmin/Present/bio/bio01.bil")
r <- raster("D:/GIS/Worldclim/Present/5arcmin/bio/bio01.bil")
plot(r)
plot(countries, add=T)
str(r)
dim(r)
### Write ascii layers and create dataframe for present
files.present
for(i in files.present[2:23]) {
raster <- raster(i)
#raster <- crop(raster, ext.sunda)
writeRaster(raster,
filename = paste("D:/Papers.Projects/Climate.Smart.Agriculture/GIS/present/", basename(i), sep = ""),
format = 'ascii',
NAflag = -9999,
overwrite = T)
}
present.stack <- stack(files.present[2:23])
present.df <- as.data.frame(present.stack, xy=T)
head(present.df); dim(present.df) # 7776000 24
### Create empty mask layer
mask <- raster(files.present[1])
plot(mask)
# set.seed(1963)
# bg <- randomPoints(mask, 100)
# head(bg)
plot(!is.na(mask)); points(bg, pch=19, cex=0.5) # 100 random points from mask
mask <- !is.na(mask)
mask[mask == 0] <- NA
plot(mask)
summary(mask)
writeRaster(mask, filename = "D:/Papers.Projects/Climate.Smart.Agriculture/GIS/present_project/mask.asc", format = 'ascii', NAflag = -9999, overwrite = T)
mask <- raster('D:/Papers.Projects/Climate.Smart.Agriculture/GIS/present_project/mask.asc')
plot(mask, col='red')
# add mask to present.df
# present.df$mask <- as.data.frame(stack('D:/Papers.Projects/Climate.Smart.Agriculture/GIS/present_project/mask.asc'))
# head(present.df); dim(present.df) # 7776000 25
### Loop all SDMs
i=22
i=3
for(i in 3:length(cucumis.5)){ # First 2 species not Cucumis!!!
#for(i in 1:2){
species <- cucumis.5[i]
species
# head(cucumis.unique.5)
cucumis.unique.species <- cucumis.unique.5[cucumis.unique.5$acceptedname %in% species, ] # retrieve species records
dim(cucumis.unique.species) # 16 50
head(cucumis.unique.species)
cucumis.unique.species <- cucumis.unique.species[, c("acceptedname", "lon", "lat")]
names(cucumis.unique.species) <- c("species", "lon", "lat")
head(cucumis.unique.species)
plot(countries); points(cucumis.unique.species$lon, cucumis.unique.species$lat, pch=19, col='red')
cucumis.unique.species <- cucumis.unique.species[which(cucumis.unique.species$lon < 170),] # otherwise the polygons cannot be drawn, extent over 180 degrees
write.csv(cucumis.unique.species, gsub(' ', '_', (paste('D:/Papers.Projects/Climate.Smart.Agriculture/data/Cucumis/maxentSpecies/', species, '.csv', sep=""))), row.names=F) # write species points file
# Convert to spatial Points Data Frame and create 1000km buffer
coordinates(cucumis.unique.species) <- ~lon+lat
proj4string(cucumis.unique.species) <- P4S.latlon
plot(countries); points(cucumis.unique.species, pch=19, cex=0.5, col='red')
x <- circles(cucumis.unique.species, d=1000000, lonlat=TRUE) # 1000 km
pol <- gUnaryUnion(x@polygons) # dissolve polygons
# extent(pol)
plot(pol, col='blue', add=T); points(cucumis.unique.species, pch=19, cex=0.5, col='red')
# extract cell numbers for the circles
v <- extract(mask, x@polygons, cellnumbers=T)
# str(v)
# use rbind to combine the elements in list v
v <- do.call(rbind, v)
# head(v); dim(v)
# remove ocean cells
v <- unique(na.omit(v))
head(v); dim(v)
# to display the results
m <- mask
m[] <- NA
m[as.vector(v[,1])] <- 1
plot(m, col='red')
extent(m)
str(m); summary(m)
plot(m, ext=extent(x@polygons)+1, col='blue', add=T) # xlim = c((x@polygons@bbox[1,1]-5),(x@polygons@bbox[1,2]+5)), ylim = c((x@polygons@bbox[2,1]-5),(x@polygons@bbox[2,2]+5)),
plot(x@polygons, add=T)
points(cucumis.unique.species, pch=19, cex=0.5, col='red')
plot(countries, add=T)
# str(m)
# Write mask for buffered areas
writeRaster(m, filename = "D:/Papers.Projects/Climate.Smart.Agriculture/GIS/present/mask.asc", format = 'ascii', NAflag = -9999, overwrite = T)
# mask.buffer <- raster("D:/Papers.Projects/Climate.Smart.Agriculture/GIS/present/mask.asc")
# plot(mask.buffer)
### Add mask.buffer to present.df
head(present.df); dim(present.df) # 7776000 24
# present.df <- subset(present.df, select = -c(mask)) # remove mask column
present.species.df <- present.df # copy present.df
mask.buffer.df <- as.data.frame(stack('D:/Papers.Projects/Climate.Smart.Agriculture/GIS/present/mask.asc'), xy=T) # read mask layer
head(mask.buffer.df); dim(mask.buffer.df); colSums(mask.buffer.df, na.rm=T, dims=1)
present.species.df$mask <- mask.buffer.df[,'mask']
head(present.species.df); dim(present.species.df)
present.species.df <- na.omit(present.species.df)
head(present.species.df); dim(present.species.df)
### Select uncorrelated variables using VIF
### Variance Inflation Factor within buffered area ####
# A VIF for a single explanatory variable is obtained using the r-squared value of the regression of that variable against all other explanatory variables (http://www.r-bloggers.com/collinearity-and-stepwise-vif-selection/)
x <- sample(1:(dim(present.species.df)[1]), 10000, replace=F) # sample 10k background points for the VIF
sample.df <- present.species.df[x,]
head(sample.df); dim(sample.df)
plot(countries); points(sample.df$x, sample.df$y, col='green'); plot(countries, add=T)
sample.matrix <- as.matrix(sample.df[,3:25])
head(sample.matrix); dim(sample.matrix) # 10000 23
### VIF ###
#keep.dat <- vif_func(in_frame = sample.matrix[,1:22], thresh=5, trace=T) # thresh=5
#keep.dat
keep.dat <- colnames(sample.matrix[,1:22]) # To use all variables
###########
keep.dat <- c(keep.dat, 'mask')
str(keep.dat)
sample.matrix.keep <- sample.matrix[, (colnames(sample.matrix) %in% keep.dat)]
head(sample.matrix.keep); dim(sample.matrix.keep) # 10000 10 - BACKGROUND SAMPLE
summary(sample.matrix.keep)
sample.df.keep <- data.frame(sample.matrix.keep)
dim(sample.df.keep) # BACKGROUND SAMPLE DATAFRAME
# Species dataframe for keep.dat
cucumis.unique.species.df <- cucumis.unique.5[cucumis.unique.5$acceptedname %in% species, (colnames(cucumis.unique.5) %in% keep.dat)] # retrieve species records
head(cucumis.unique.species.df); dim(cucumis.unique.species.df) # 35 9
cucumis.unique.species.df$mask <- 1 # Add mask column
names(cucumis.unique.species.df)
### Create directory
species
mainDirMaxent <- "D:/Papers.Projects/Climate.Smart.Agriculture/data/Cucumis/maxentOutput"
# subDirMaxent <- gsub(" ", "_", species)
#if (file.exists(subDirMaxent)){
# print('directory exists')
# } else {
# dir.create(file.path(mainDirMaxent, subDirMaxent))
#}
##############
### MAXENT ###
##############
### CHECK FOLDER NAMES !!!
### Logistic
swd <- rbind(cucumis.unique.species.df, sample.df.keep); dim(swd) # swd dataframe
pa <- c(rep(1, nrow(cucumis.unique.species.df)), rep(0, nrow(sample.df.keep))); length(pa) # presence/absence vector
me <- maxent(swd, pa, args = c("noproduct", "nothreshold", "nohinge", "noextrapolate", "outputformat=logistic", "jackknife", "applyThresholdRule=10 percentile training presence", "projectionlayers=D:/Papers.Projects/Climate.Smart.Agriculture/GIS/present", "redoifexists"), path=file.path(mainDirMaxent)) # path=file.path(mainDirMaxent, subDirMaxent), "projectionlayers=D:/Papers.Projects/Climate.Smart.Agriculture/GIS/present,D:/Papers.Projects/Climate.Smart.Agriculture/GIS/present_project" 10 percentile training presence
### Raw format
#me <- maxent(swd, pa, args = c("noproduct", "nothreshold", "nohinge", "noextrapolate", "outputformat=raw", "jackknife", "projectionlayers=D:/Papers.Projects/Climate.Smart.Agriculture/GIS/present,D:/Papers.Projects/Climate.Smart.Agriculture/GIS/present_project", "redoifexists"), path=file.path(mainDirMaxent)) # path=file.path(mainDirMaxent, subDirMaxent) , "applyThresholdRule=Maximum training sensitivity plus specificity"
# present - with species specific mask
# present_project - global projection: BE AWARE i.e. Temp. seasonality can be similar in tropical and temperate regions, therefore global projections are not reliable!!!
me
# str(me)
# me@lambdas
# plot(me)
response(me)
eval <- evaluate(me, p=cucumis.unique.species.df, a=sample.df.keep)
eval
str(eval)
AUC <- eval@auc
AUC
threshold(eval)
plot(eval, 'ROC')
# Replace file names with species names
filez <- list.files(path=file.path(mainDirMaxent), pattern="species", full.names=T)
filez
sapply(filez, FUN = function(eachPath){
file.rename(from = eachPath, to = sub(pattern ="species", replacement = gsub(" ", "_", species), eachPath))
})
### Some files are locked by r.sessions - file.copy solution
filez <- list.files(path=file.path(mainDirMaxent), pattern="species", full.names=T)
filez
file.info(filez)
for(i in 1:length(filez)){
file.copy(filez[i], gsub('species', gsub(" ", "_", species), filez[i]))
}
# maxentResults
file.rename(from = paste(file.path(mainDirMaxent), '/maxentResults.csv', sep=""), to = paste(file.path(mainDirMaxent), "/", gsub(" ", "_", species), '_maxentResults.csv', sep=""))
# plots folder
filez2 <- list.files(paste(path=file.path(mainDirMaxent), '/plots', sep=""), pattern="species", full.names=T)
filez2
sapply(filez2, FUN = function(eachPath){
file.rename(from = eachPath, to = sub(pattern ="species", replacement = gsub(" ", "_", species), eachPath))
})
### Species map ###
# par(mfrow=c(1,2))
# maxent.present.raster <- raster(paste(path=file.path(mainDirMaxent), '/', gsub(" ", "_", species), '_present.asc', sep=""))
# maxent.present.raster.crop <- crop(maxent.present.raster, extent(pol)+10)
# countries.crop <- crop(countries, extent(pol)+10)
# plot(countries.crop, xlim = c((extent(pol)@xmin-5),(extent(pol)@xmax+5)), ylim = c((extent(pol)@ymin-5),(extent(pol)@ymax+5)))
# plot(maxent.present.raster.crop, add=T)
# plot(pol, add=T); plot(countries.crop, add=T); points(cucumis.unique.species, pch=19, cex=0.5, col='red')
# box()
# extent(maxent.present.raster)
# dev.off()
### Null-model ###
maxentResults <- read.csv(paste(file.path(mainDirMaxent), '/', gsub(" ", "_", species), '_maxentResults.csv', sep=""))
# maxentResults
vector <- maxentResults$X.Training.samples
# str(vector)
vector <- as.vector(sort(vector))
vector
head(present.species.df); dim(present.species.df)
x <- present.species.df[, (colnames(present.species.df) %in% keep.dat)]
head(x)
## Run null-model from source
nm <- nullModel(x, n = vector, rep = 100)
nm # shows the evaluations of the 'rep' null models created
auc <- sapply(nm, function(x){slot(x,'auc')})# get just the auc values of the null models
auc <- auc[order(auc, decreasing = TRUE)]
hist(auc) #make a histogram
write.csv(auc, paste(file.path(mainDirMaxent), '/', gsub(" ", "_", species), '_nm_auc.csv', sep=""))
maxentResults$nm <- auc[5] # Add null-model value to maxentResults
write.csv(maxentResults, file=paste(file.path(mainDirMaxent), '/', gsub(" ", "_", species), '_maxentResults.csv', sep=""))
}
############################
#### END ###################
############################
# present.me <- predict(me, present.stack, progress='text') # takes a long time
# plot(present.me)
# density.Project Present
system(command=paste('java -cp D:/Programs/Maxent.3.3.3k/maxent.jar density.Project ', file.path(mainDirMaxent, subDirMaxent), '/species.lambdas D:/Papers.Projects/Climate.Smart.Agriculture/GIS/present ', file.path(mainDirMaxent, subDirMaxent), '/', subDirMaxent, '_present noproduct nothreshold nohinge noextrapolate "outputformat=logistic" jackknife "applyThresholdRule=Maximum training sensitivity plus specificity" redoifexists', sep=""))
present.raster <- raster(paste(file.path(mainDirMaxent, subDirMaxent), '/', subDirMaxent, '_present.asc', sep=""))
plot(present.raster); plot(countries, add=T)
# density.Project present_project
system(command=paste('java -cp D:/Programs/Maxent.3.3.3k/maxent.jar density.Project ', file.path(mainDirMaxent, subDirMaxent), '/species.lambdas D:/Papers.Projects/Climate.Smart.Agriculture/GIS/present_project ', file.path(mainDirMaxent, subDirMaxent), '/', subDirMaxent, '_project noproduct nothreshold nohinge noextrapolate "outputformat=logistic" jackknife "applyThresholdRule=Maximum training sensitivity plus specificity" redoifexists', sep="")) # doclamp fadebyclamping
project.raster <- raster(paste(file.path(mainDirMaxent, subDirMaxent), '/', subDirMaxent, '_project.asc', sep=""))
plot(project.raster); plot(countries, add=T)
### GlobCover 2009 ####
glob.cover.2009 <- raster('D:/GIS/Globcover2009_V2.3_Global/GLOBCOVER_L4_200901_200912_V2.3.tif')
str(glob.cover.2009)
dim(glob.cover.2009)
legend <- read.xlsx('D:/GIS/Globcover2009_V2.3_Global/Globcover2009_Legend.xls', 1)
head(legend)
glob.cover.2009@crs <- P4S.latlon
glob.cover.2009.africa <- crop(glob.cover.2009, ext.africa)
writeRaster(glob.cover.2009.africa,
filename = "D:/Papers.Projects/Andel.Tinde.van/medicinal.plants/predictors/mask/glob.cover.2009.africa.asc",
format = 'ascii',
NAflag = -9999,
overwrite = T)
rm(glob.cover.2009)
plot(glob.cover.2009.africa)
summary(glob.cover.2009.africa)
glob.cover.2009.africa[glob.cover.2009.africa >= 210] <- NA # remove water & ice etc. see legend.xls
glob.cover.2009.africa[glob.cover.2009.africa >= 190] <- 0 # set urban & bare to 0
glob.cover.2009.africa.natural.vegetation <- glob.cover.2009.africa
glob.cover.2009.africa.natural.vegetation[glob.cover.2009.africa.natural.vegetation <= 30] <- 0 # set cropland to 0
glob.cover.2009.africa.natural.vegetation[glob.cover.2009.africa.natural.vegetation > 0] <- 1 # set natural forest cover to 1
writeRaster(glob.cover.2009.africa.natural.vegetation,
filename = "D:/Papers.Projects/Andel.Tinde.van/medicinal.plants/predictors/mask/gc2009nvhighres.asc",
format = 'ascii',
NAflag = -9999,
overwrite = T)
str(glob.cover.2009.africa.natural.vegetation)
plot(glob.cover.2009.africa.natural.vegetation)
glob.cover.2009.africa.natural.vegetation@ncols #18720
bio01.TA@ncols # 624
glob.cover.2009.africa.natural.vegetation@ncols/bio01.TA@ncols # 30
gc.2009.africa.nv.mean.5arcmin <- aggregate(glob.cover.2009.africa.natural.vegetation, fact = 30, fun=mean) # % natural vegetation at 5 arcmin
plot(gc.2009.africa.nv.mean.5arcmin)
str(gc.2009.africa.nv.mean.5arcmin)
gc.2009.africa.nv.05.5arcmin <- gc.2009.africa.nv.mean.5arcmin
gc.2009.africa.nv.05.5arcmin[gc.2009.africa.nv.05.5arcmin > 0.5] <- 1 # above 50% present
gc.2009.africa.nv.05.5arcmin[gc.2009.africa.nv.05.5arcmin <= 0.5] <- 0 # below 50% absent
gc.2009.africa.nv.05.5arcmin[gc.2009.africa.nv.05.5arcmin <= 0.5] <- NA # below 50% absent
plot(gc.2009.africa.nv.05.5arcmin, col=rainbow(2))
summary(gc.2009.africa.nv.05.5arcmin)
writeRaster(gc.2009.africa.nv.05.5arcmin,
filename = "D:/Papers.Projects/Andel.Tinde.van/medicinal.plants/predictors/mask/gc.2009.africa.nv.05.5arcmin.asc",
format = 'ascii',
NAflag = -9999,
overwrite = T)
gc.2009.africa.nv.min.5arcmin <- aggregate(glob.cover.2009.africa.natural.vegetation, fact = 30, fun=min) # % natural vegetation at 5 arcmin
plot(gc.2009.africa.nv.min.5arcmin)
writeRaster(gc.2009.africa.nv.mean.5arcmin,
filename = "D:/Papers.Projects/Andel.Tinde.van/medicinal.plants/predictors/mask/gc2009nvmean.asc",
format = 'ascii',
NAflag = -9999,
overwrite = T)
writeRaster(gc.2009.africa.nv.min.5arcmin,
filename = "D:/Papers.Projects/Andel.Tinde.van/medicinal.plants/predictors/mask/gc2009nvmin.asc",
format = 'ascii',
NAflag = -9999,
overwrite = T)
### Range measures ####
sp.dist <- list.files('D:/Papers.Projects/Andel.Tinde.van/medicinal.plants/maxent.output/', pattern="_present_thresholded[.]asc$", full.names=T)
gc.nv <- "D:/Papers.Projects/Andel.Tinde.van/medicinal.plants/predictors/mask/gc.2009.africa.nv.05.5arcmin.asc"
sp.dist <- c(sp.dist, gc.nv)
names.sp.dist <- gsub('_present_thresholded.asc', '', basename(sp.dist))
names.sp.dist <- gsub('gc.2009.africa.nv.05.5arcmin.asc', 'gc.nv', names.sp.dist)
sp.dist.asc2df <- asc2dataframe(sp.dist, varnames = names.sp.dist)
head(sp.dist.asc2df)
dim(sp.dist.asc2df)
colnames(sp.dist.asc2df)
table(sp.dist.asc2df[1:5,3:17])
x <- data.frame(colSums(sp.dist.asc2df[,3:16]))
y <- data.frame(colSums(sp.dist.asc2df[sp.dist.asc2df$gc.nv==1,3:16]))
x$nv <- y[,1]
x$nv.perc <- round((x[,2]/x[,1])*100,0)
write.csv(x, file='../ms/range.sizes.csv')
### From dismo ##########
ecocrop('potato', 5:16, 15:26, runif(12)*100)
getCrop('Acacia brachystachya Benth.')
crop <- getCrop('Hot pepper')
ecocrop(crop, 5:16, 15:26, rainfed=FALSE)