BiodiversityR (version 2.11-3)

ensemble.red: Area of Occupancy (AOO) and Extent of Occurrence (EOO) via the red library.

Description

Function ensemble.red is a wrapper function for estimation of AOO and EOO computed for redlisting of species based on IUCN criteria (https://www.iucnredlist.org/about/regional). Function ensemble.chull.create creates a mask layer based on a convex hull around known presence locations, inspired by mcp argument of the map.sdm function.

Usage

ensemble.red(x)
ensemble.chull.create(x.pres = NULL, p = NULL, buffer.width = 0.2,
    RASTER.format = "raster", RASTER.datatype = "INT1U", RASTER.NAflag = 255,
    overwrite = TRUE, ...)
ensemble.chull.apply(x.spec = NULL, mask.layer=NULL, keep.old=T,
    RASTER.format="raster", RASTER.datatype="INT1U", RASTER.NAflag=255,
    overwrite=TRUE, ...)

Arguments

x

RasterLayer object (raster), representing 'count' suitability layers (available from the 'count' and 'consensuscount' subdirectories of the 'ensembles' directory)

x.pres

RasterLayer object (raster), representing 'presence' suitability layers (available from the 'presence' and 'consensuspresence' subdirectories of the 'ensembles' directory)

p

known presence locations, available in 2-column (lon, lat) dataframe; see also prepareData and extract

buffer.width

multiplier to create buffer (via gBuffer) by multiplying the maximum distance among the presence locations (calculated via pointDistance)

RASTER.format

Format of the raster files that will be generated. See writeFormats and writeRaster.

RASTER.datatype

Format of the raster files that will be generated. See dataType and writeRaster.

RASTER.NAflag

Value that is used to store missing data. See writeRaster.

overwrite

Overwrite existing raster files. See writeRaster.

...

Additional arguments for writeRaster.

x.spec

RasterLayer object (raster), representing any suitability layer for the species under investigation)

mask.layer

RasterLayer object (raster), representing the mask based on the convex hull around known presence locations. The function will replace all values in x.spec to zero where corresponding values in the mask.layer are zero.

keep.old

keep a copy of the RasterLayer before the mask is applied.

Value

Function ensemble.red returns an array with AOO and EOO Function ensemble.chull.create creates a mask layer based on a convex hull around known presence locations.

Details

Function ensemble.red calculates AOO (aoo) and EOO (aoo) statistics calculated for areas with different consensus levels on species presence (1 model predicting presence, 2 models predicting presence, ...). In case that these statistics are within IUCN criteria for Critically Endangered (CR), Endangered (EN) or Vulnerable (VU), then this information is added in columns documenting the types of AOO and EOO.

Function ensemble.chull.create first creates a convex hull around known presence locations. Next, a buffer is created around the convex hull where the width of this buffer is calculated as the maximum distance among presence locations (pointDistance) multiplied by argument buffer.width. Finally, the mask is created by including all polygons of predicted species presence that are partially covered by the convex hull and its buffer.

References

Cardoso P. 2017. red - an R package to facilitate species red list assessments according to the IUCN criteria. Biodiversity Data Journal 5:e20530. https://doi.org/10.3897/BDJ.5.e20530

Kindt R. 2018. Ensemble species distribution modelling with transformed suitability values. Environmental Modelling & Software 100: 136-145. https://doi.org/10.1016/j.envsoft.2017.11.009

See Also

ensemble.batch

Examples

# NOT RUN {
# }
# NOT RUN {
## Not run: 
# based on examples in the dismo package

# get predictor variables
library(dismo)
predictor.files <- list.files(path=paste(system.file(package="dismo"), '/ex', sep=''),
    pattern='grd', full.names=TRUE)
predictors <- stack(predictor.files)
# subset based on Variance Inflation Factors
predictors <- subset(predictors, subset=c("bio5", "bio6", 
    "bio16", "bio17"))
predictors
predictors@title <- "red"

# presence points
presence_file <- paste(system.file(package="dismo"), '/ex/bradypus.csv', sep='')
pres <- read.table(presence_file, header=TRUE, sep=',')

# fit 5 ensemble models (could take some time!)
# (examples for the red package use 100 models)
ensembles <- ensemble.batch(x=predictors, 
    xn=c(predictors),
    species.presence=pres, 
    thin.km=100,
    k.splits=4, k.test=0, 
    n.ensembles=5, 
    SINK=TRUE, 
    ENSEMBLE.best=10, ENSEMBLE.exponent=c(1, 2, 3), 
    ENSEMBLE.min=0.6,
    MAXENT=0, MAXNET=1, MAXLIKE=1, GBM=1, GBMSTEP=0, RF=1, CF=1,
    GLM=1, GLMSTEP=1, GAM=1, GAMSTEP=1, MGCV=1, MGCVFIX=1, 
    EARTH=1, RPART=1, NNET=1, FDA=1, SVM=1, SVME=1, 
    BIOCLIM.O=1, BIOCLIM=1, DOMAIN=1, MAHAL=0, MAHAL01=1,
    PROBIT=TRUE,
    Yweights="BIOMOD",
    formulae.defaults=TRUE)

# first application of ensemble.red before applying the convex hull mask
# AOO and EOO are determined for each count level
library(red)
count.file <- paste(getwd(), "/ensembles/consensuscount/Bradypus variegatus_red.grd", sep="")
count.raster <- raster(count.file)
ensemble.red(count.raster)

# do not predict presence in polygons completely outside convex hull
# of known presence locations
pres.file <- paste(getwd(), "/ensembles/consensuspresence/Bradypus variegatus_red.grd", sep="")
pres.raster <- raster(pres.file)
pres1 <- pres[, -1]
chull.created <- ensemble.chull.create(x.pres=pres.raster, p=pres1)

mask.raster <- chull.created$mask.layer
mask.poly <- chull.created$convex.hull
par.old <- graphics::par(no.readonly=T)
par(mfrow=c(1,2))
plot(pres.raster, breaks=c(-1, 0, 1), col=c("grey", "green"),
    main="before convex hull")
points(pres1, col="blue")

pres.chull <- ensemble.chull.apply(pres.raster, mask=mask.raster, keep.old=T)
# load new
pres.file <- paste(getwd(), "/ensembles/consensuspresence/Bradypus variegatus_red.grd", sep="")
pres.raster <- raster(pres.file)
plot(pres.raster, breaks=c(-1, 0, 1), col=c("grey", "green"),
    main="after convex hull")
plot(mask.poly, add=T, border="blue")

# new application of ensemble.red
dev.new()
plot(count.raster, main="before convex hull")
ensemble.red(count.raster)
# all cells where species is predicted not to be present according to the mask layer
# will be modified to a count of zero
count.chull <- ensemble.chull.apply(count.raster, mask=mask.raster, keep.old=T)
# load new
count.file <- paste(getwd(), "/ensembles/consensuscount/Bradypus variegatus_red.grd", sep="")
count.raster <- raster(count.file)
ensemble.red(count.raster)
dev.new()
plot(count.raster, main="after convex hull")
par.old <- graphics::par(no.readonly=T)

# }