TALLER SDM
Yeimy Carolina Tirado
2022-11-03
library(sp)
library(raster)
library(dismo)
library(ENMTools)
library(readr)
library(leaflet)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:raster':
##
## intersect, select, union## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, uniontrabajaré profe con una base de datos que tengo de la roya naranja en caña de azucar, son unos datos a nivel nacional y quisiera ver como me sale el modelo de districión de este hongo.
## IMPORTAR DATOS A TRABAJAR
rn_cana <- read.csv("C:/Users/CAROLINA/OneDrive - FEDERACION NACIONAL DE ARROCEROS FEDEARROZ/Documentos/Maestría 2022/Análisis espacial/Taller distribución de especies/data.csv")
rn_cana## ESPECIE LATITUD LONGITUD
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## 3715 SC 5.910700 -75.96291## mapa de las localidades de ocurrencia de roya naranja en colombia
library(maptools)## Checking rgeos availability: TRUE
## Please note that 'maptools' will be retired during 2023,
## plan transition at your earliest convenience;
## some functionality will be moved to 'sp'.data(wrld_simpl)
plot(wrld_simpl, xlim=c(-80,-72), ylim=c(-7,13), axes=TRUE, col="light yellow")
# restore the box around the map
box()
# add the points
points(rn_cana$LONGITUD, rn_cana$LATITUD, col='orange', pch=20, cex=0.75)
# plot points again to add a border, for better visibility
points(rn_cana$LONGITUD, rn_cana$LATITUD, col='red', cex=0.75)
###Reducción espacial
###spThin, emplea aleatorización
#Reduce el set de datos según una distancia en Km. Retiene la información más útil, evitando a su vez el sesgo de muestreo.
library(spThin)## Loading required package: spam## Spam version 2.9-1 (2022-08-07) is loaded.
## Type 'help( Spam)' or 'demo( spam)' for a short introduction
## and overview of this package.
## Help for individual functions is also obtained by adding the
## suffix '.spam' to the function name, e.g. 'help( chol.spam)'.##
## Attaching package: 'spam'## The following objects are masked from 'package:base':
##
## backsolve, forwardsolve## Loading required package: grid## Loading required package: fields## Loading required package: viridis## Loading required package: viridisLite##
## Try help(fields) to get started.##
## Attaching package: 'fields'## The following object is masked from 'package:leaflet':
##
## addLegend## Loading required package: knitrclean = thin(
loc.data = rn_cana,
lat.col = "LATITUD",
long.col = "LONGITUD",
spec.col = "ESPECIE",
thin.par = 5,
reps = 1,
write.files = T,
out.dir = './',
out.base = "",
locs.thinned.list.return = T,
verbose = TRUE,
write.log.file = F
)## **********************************************
## Beginning Spatial Thinning.
## Script Started at: Mon Nov 28 11:25:58 2022
## lat.long.thin.count
## 65
## 1
## [1] "Maximum number of records after thinning: 65"
## [1] "Number of data.frames with max records: 1"
## [1] "Writing new *.csv files"## Warning in thin(loc.data = rn_cana, lat.col = "LATITUD", long.col =
## "LONGITUD", : Writing new *.csv files to output directory: ./## Warning in thin(loc.data = rn_cana, lat.col = "LATITUD", long.col = "LONGITUD", : ./_thin1_new.csv ' already exists. Renaming file
## to avoid overwriting.## Warning in thin(loc.data = rn_cana, lat.col = "LATITUD", long.col = "LONGITUD", : ./_thin1_new_new.csv ' already exists. Renaming file
## to avoid overwriting.## [1] "Writing file: ./_thin1_new_new.csv"db = clean[[1]]## extraer todas la variables climáticas
dir.create(path = "data")## Warning in dir.create(path = "data"): 'data' already existsdir.create(path = "output")## Warning in dir.create(path = "output"): 'output' already existsbioclim_data <- getData(name = "worldclim",
var = "bio",
res = 2.5,
path = "data/")## Warning in getData(name = "worldclim", var = "bio", res = 2.5, path = "data/"): getData will be removed in a future version of raster
## . Please use the geodata package insteadpredictors <- stack(bioclim_data)
predictors## class : RasterStack
## dimensions : 3600, 8640, 31104000, 19 (nrow, ncol, ncell, nlayers)
## resolution : 0.04166667, 0.04166667 (x, y)
## extent : -180, 180, -60, 90 (xmin, xmax, ymin, ymax)
## crs : +proj=longlat +datum=WGS84 +no_defs
## names : bio1, bio2, bio3, bio4, bio5, bio6, bio7, bio8, bio9, bio10, bio11, bio12, bio13, bio14, bio15, ...
## min values : -278, 9, 8, 64, -86, -559, 53, -278, -501, -127, -506, 0, 0, 0, 0, ...
## max values : 319, 213, 96, 22704, 489, 258, 725, 376, 365, 382, 289, 10577, 2437, 697, 265, ...names(predictors)## [1] "bio1" "bio2" "bio3" "bio4" "bio5" "bio6" "bio7" "bio8" "bio9"
## [10] "bio10" "bio11" "bio12" "bio13" "bio14" "bio15" "bio16" "bio17" "bio18"
## [19] "bio19"plot(predictors)
## observación de variables correlacionadas la bio 14 y bio 17 están correlacionadas, así como la bio 12 y bio 16. las demás tienen cierto grado de correlación. Para este caso dejaré la bio 14 que es la precipitación del mes más seco y la bio 12 la precipitación anual
install.extras()#Funciones adicionales de ENMtools## Warning: packages 'leaflet', 'fields', 'viridis' are in use and will not be
## installed## Installing packages into 'C:/Users/CAROLINA/AppData/Local/R/win-library/4.2'
## (as 'lib' is unspecified)##
## There is a binary version available but the source version is later:
## binary source needs_compilation
## hypervolume 3.0.4 3.1.0 TRUE
##
## package 'mgcv' successfully unpacked and MD5 sums checked
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## package 'randomForest' successfully unpacked and MD5 sums checked## Warning: cannot remove prior installation of package 'randomForest'## Warning in file.copy(savedcopy, lib, recursive = TRUE):
## problema al copiar C:\Users\CAROLINA\AppData\Local\R\win-
## library\4.2\00LOCK\randomForest\libs\x64\randomForest.dll
## a C:\Users\CAROLINA\AppData\Local\R\win-
## library\4.2\randomForest\libs\x64\randomForest.dll: Permission denied## Warning: restored 'randomForest'## package 'ape' successfully unpacked and MD5 sums checked
## package 'ranger' successfully unpacked and MD5 sums checked## Warning: cannot remove prior installation of package 'ranger'## Warning in file.copy(savedcopy, lib, recursive = TRUE):
## problema al copiar C:\Users\CAROLINA\AppData\Local\R\win-
## library\4.2\00LOCK\ranger\libs\x64\ranger.dll a C:
## \Users\CAROLINA\AppData\Local\R\win-library\4.2\ranger\libs\x64\ranger.dll:
## Permission denied## Warning: restored 'ranger'## package 'CalibratR' successfully unpacked and MD5 sums checked
## package 'caret' successfully unpacked and MD5 sums checked## Warning: cannot remove prior installation of package 'caret'## Warning in file.copy(savedcopy, lib, recursive = TRUE): problema al copiar C:
## \Users\CAROLINA\AppData\Local\R\win-library\4.2\00LOCK\caret\libs\x64\caret.dll
## a C:\Users\CAROLINA\AppData\Local\R\win-library\4.2\caret\libs\x64\caret.dll:
## Permission denied## Warning: restored 'caret'## package 'ResourceSelection' successfully unpacked and MD5 sums checked
## package 'rJava' successfully unpacked and MD5 sums checked## Warning: cannot remove prior installation of package 'rJava'## Warning in file.copy(savedcopy, lib, recursive = TRUE): problema al copiar C:
## \Users\CAROLINA\AppData\Local\R\win-library\4.2\00LOCK\rJava\libs\x64\rJava.dll
## a C:\Users\CAROLINA\AppData\Local\R\win-library\4.2\rJava\libs\x64\rJava.dll:
## Permission denied## Warning: restored 'rJava'## package 'vip' successfully unpacked and MD5 sums checked
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## package 'reshape2' successfully unpacked and MD5 sums checked## Warning: cannot remove prior installation of package 'reshape2'## Warning in file.copy(savedcopy, lib, recursive = TRUE):
## problema al copiar C:\Users\CAROLINA\AppData\Local\R\win-
## library\4.2\00LOCK\reshape2\libs\x64\reshape2.dll a C:
## \Users\CAROLINA\AppData\Local\R\win-library\4.2\reshape2\libs\x64\reshape2.dll:
## Permission denied## Warning: restored 'reshape2'## package 'progress' successfully unpacked and MD5 sums checked
##
## The downloaded binary packages are in
## C:\Users\CAROLINA\AppData\Local\Temp\RtmpS2jSNc\downloaded_packages## installing the source package 'hypervolume'raster.cor.plot(predictors, method = "pearson")## $cor.mds.plot
##
## $cor.heatmap
##gráfico de una sola capa en un RasterStack y trazar algunos datos adicionales encima.
library(maptools)
data(wrld_simpl)
plot(predictors, 1)
plot(wrld_simpl, add=TRUE)
points(db, col='blue')
###Backgroundpoints 500
presvals <- extract(predictors, db)
set.seed(0)
backgr <- randomPoints(predictors, 500)## Warning in randomPoints(predictors, 500): generated random points = 0.87 times
## requested numberabsvals <- extract(predictors, backgr)
pb <- c(rep(1, nrow(presvals)), rep(0, nrow(absvals)))
sdmdata <- data.frame(cbind(pb, rbind(presvals, absvals)))
head(sdmdata)## pb bio1 bio2 bio3 bio4 bio5 bio6 bio7 bio8 bio9 bio10 bio11 bio12 bio13 bio14
## 1 1 172 96 90 290 224 118 106 170 170 175 168 2089 260 69
## 2 1 163 78 90 265 207 121 86 160 161 165 159 2560 318 111
## 3 1 147 90 90 255 197 98 99 143 148 149 143 2168 295 98
## 4 1 172 82 91 281 219 129 90 168 171 174 168 2727 337 131
## 5 1 175 94 89 348 228 123 105 170 176 179 170 2579 352 98
## 6 1 186 92 91 251 238 137 101 187 188 188 182 2167 259 114
## bio15 bio16 bio17 bio18 bio19
## 1 36 706 250 583 551
## 2 31 869 400 724 763
## 3 34 736 344 658 736
## 4 29 891 457 500 798
## 5 35 919 368 748 919
## 6 27 673 382 510 628tail(sdmdata)## pb bio1 bio2 bio3 bio4 bio5 bio6 bio7 bio8 bio9 bio10 bio11 bio12 bio13
## 495 0 280 53 91 134 310 252 58 278 280 281 278 2001 248
## 496 0 258 103 68 1302 340 190 150 244 262 275 240 1264 208
## 497 0 251 105 64 1850 330 166 164 266 247 268 224 1321 215
## 498 0 -109 111 16 20064 214 -442 656 133 -324 133 -376 371 70
## 499 0 164 100 39 5425 306 52 254 113 231 238 99 832 142
## 500 0 -128 98 18 16165 158 -372 530 58 -147 82 -325 276 48
## bio14 bio15 bio16 bio17 bio18 bio19
## 495 105 25 655 344 452 655
## 496 10 64 511 46 222 469
## 497 35 58 540 110 349 164
## 498 8 71 192 27 192 31
## 499 4 67 399 32 48 388
## 500 9 53 125 29 124 50summary(sdmdata)## pb bio1 bio2 bio3
## Min. :0.00 Min. :-233.00 Min. : 38.0 Min. :11.00
## 1st Qu.:0.00 1st Qu.: 49.75 1st Qu.: 91.0 1st Qu.:27.00
## Median :0.00 Median : 175.00 Median :112.0 Median :48.00
## Mean :0.13 Mean : 132.82 Mean :114.9 Mean :49.62
## 3rd Qu.:0.00 3rd Qu.: 243.00 3rd Qu.:140.2 3rd Qu.:68.00
## Max. :1.00 Max. : 294.00 Max. :184.0 Max. :93.00
## bio4 bio5 bio6 bio7
## Min. : 134 Min. :-62.0 Min. :-517.00 Min. : 58.0
## 1st Qu.: 1342 1st Qu.:222.0 1st Qu.:-144.00 1st Qu.:165.0
## Median : 5144 Median :287.5 Median : 53.00 Median :283.5
## Mean : 6041 Mean :277.7 Mean : -12.14 Mean :289.9
## 3rd Qu.: 9282 3rd Qu.:342.0 3rd Qu.: 132.25 3rd Qu.:384.2
## Max. :22474 Max. :478.0 Max. : 252.00 Max. :716.0
## bio8 bio9 bio10 bio11
## Min. :-276.0 Min. :-411.00 Min. :-107.0 Min. :-461.00
## 1st Qu.: 121.0 1st Qu.: -47.75 1st Qu.: 157.8 1st Qu.: -77.00
## Median : 179.0 Median : 174.00 Median : 217.0 Median : 134.00
## Mean : 176.2 Mean : 93.53 Mean : 207.4 Mean : 53.59
## 3rd Qu.: 250.2 3rd Qu.: 232.00 3rd Qu.: 272.2 3rd Qu.: 198.25
## Max. : 347.0 Max. : 340.00 Max. : 363.0 Max. : 278.00
## bio12 bio13 bio14 bio15
## Min. : 0.0 Min. : 0.00 Min. : 0.00 Min. : 0.00
## 1st Qu.: 281.0 1st Qu.: 55.75 1st Qu.: 2.00 1st Qu.: 34.00
## Median : 613.0 Median : 107.00 Median : 11.00 Median : 56.00
## Mean : 928.3 Mean : 148.47 Mean : 27.46 Mean : 62.86
## 3rd Qu.:1459.5 3rd Qu.: 245.50 3rd Qu.: 37.00 3rd Qu.: 86.00
## Max. :4162.0 Max. :1109.00 Max. :208.00 Max. :196.00
## bio16 bio17 bio18 bio19
## Min. : 0.0 Min. : 0.00 Min. : 0.00 Min. : 0.00
## 1st Qu.: 141.0 1st Qu.: 9.00 1st Qu.: 90.75 1st Qu.: 22.75
## Median : 276.0 Median : 41.50 Median : 202.00 Median : 67.00
## Mean : 390.1 Mean : 98.54 Mean : 259.86 Mean : 202.77
## 3rd Qu.: 632.2 3rd Qu.:128.50 3rd Qu.: 392.00 3rd Qu.: 233.25
## Max. :2983.0 Max. :662.00 Max. :1275.00 Max. :1117.00## guardo las dos fuentes de datos
saveRDS(sdmdata, "sdm.Rds")
saveRDS(presvals, "pvals.Rds")#Model fitting, prediction, and evaluation
library(dismo)
sdmdata <- readRDS("sdm.Rds")
presvals <- readRDS("pvals.Rds")## en los predictores quitaré bio17 y bio 16 de acuerdo al grafico de la correlacion de variables
pred_nf <- dropLayer(predictors, 'bio17','bio16')## grupo de train and test
set.seed(0)
group <- kfold(db, 5)
pres_train <- db[group != 1, ]
pres_test <- db[group == 1, ]ext <- extent(-80, -72, -7, 13)set.seed(10)
backg <- randomPoints(pred_nf, n=1000, ext=ext, extf = 1.25)
colnames(backg) = c('lon', 'lat')
group <- kfold(backg, 5)
backg_train <- backg[group != 1, ]
backg_test <- backg[group == 1, ]r <- raster(pred_nf, 1)
plot(!is.na(r), col=c('white', 'light grey'), legend=FALSE)
plot(ext, add=TRUE, col='red', lwd=2)
points(backg_train, pch='-', cex=0.5, col='yellow')
points(backg_test, pch='-', cex=0.5, col='black')
points(pres_train, pch= '+', col='green')
points(pres_test, pch='+', col='blue')
## Profile methods bioclim
bc <- bioclim(pred_nf, pres_train)
plot(bc, a=1, b=2, p=0.85)
## evaluacion del modelo
e_1 <- evaluate(pres_test, backg_test, bc, pred_nf)
e_1## class : ModelEvaluation
## n presences : 13
## n absences : 200
## AUC : 0.9923077
## cor : 0.6714381
## max TPR+TNR at : 0.01913077tr <- threshold(e_1, 'spec_sens')
tr## [1] 0.01913077pb <- predict(pred_nf, bc, ext=ext, progress='')
pb## class : RasterLayer
## dimensions : 480, 192, 92160 (nrow, ncol, ncell)
## resolution : 0.04166667, 0.04166667 (x, y)
## extent : -80, -72, -7, 13 (xmin, xmax, ymin, ymax)
## crs : +proj=longlat +datum=WGS84 +no_defs
## source : memory
## names : layer
## values : 0, 0.5769231 (min, max)par(mfrow=c(1,2))
plot(pb, main='Bioclim, raw values')
plot(wrld_simpl, add=TRUE, border='dark grey')
plot(pb > tr, main='presence/absence')
plot(wrld_simpl, add=TRUE, border='dark grey')
points(pres_train, pch='+')
##Domain
dm <- domain(pred_nf, pres_train)
e_dm <- evaluate(pres_test, backg_test, dm, pred_nf)
e_dm## class : ModelEvaluation
## n presences : 13
## n absences : 200
## AUC : 0.9811538
## cor : 0.6478093
## max TPR+TNR at : 0.3460538pd = predict(pred_nf, dm, ext=ext, progress='')
par(mfrow=c(1,2))
plot(pd, main='Domain, raw values')
plot(wrld_simpl, add=TRUE, border='dark grey')
tr <- threshold(e_dm, 'spec_sens')
plot(pd > tr, main='presence/absence')
plot(wrld_simpl, add=TRUE, border='dark grey')
points(pres_train, pch='+')
## según el modelo de maxent las variables predictoras de mayor contribución son la bio3 y la bio 111 las cuales tienen que ver con temperatura.
maxent()## Loading required namespace: rJava## This is MaxEnt version 3.3.3kxm <- maxent(predictors, pres_train)
plot(xm)
e_mx <- evaluate(pres_test, backg_test, xm, predictors)
e_mx## class : ModelEvaluation
## n presences : 13
## n absences : 200
## AUC : 0.985
## cor : 0.7515213
## max TPR+TNR at : 0.2394593px <- predict(predictors, xm, ext=ext, progress='')
par(mfrow=c(1,2))
plot(px, main='Maxent, raw values')
plot(wrld_simpl, add=TRUE, border='dark grey')
tr <- threshold(e_mx, 'spec_sens')
plot(px > tr, main='presence/absence')
plot(wrld_simpl, add=TRUE, border='dark grey')
points(pres_train, pch='+')
## comparación de los modelos
### modelo bioclim
plot(e_1, 'ROC')
## comparación de los modelos
### modelo domain
plot(e_dm, 'ROC')
## comparación de los modelos
### modelo domain
plot(e_mx, 'ROC')
###para este hongo el mejor modelo que me dió fue domain según el AUC, según estuve revisando se puede deber al hecho de que hay muchos datos sesgados en la evaluación.