ensemble.novel: Mapping of novel environmental conditions (areas where some of the environmental conditions are outside the range of environmental conditions of a reference area).

Description

Function ensemble.novel creates the map with novel conditions. Function ensemble.novel.object provides the reference values used by the prediction function used by predict .

Usage

ensemble.novel(x = NULL, novel.object = NULL, RASTER.object.name = novel.object$name, RASTER.stack.name = x@title, RASTER.format = "raster", RASTER.datatype = "INT1S", RASTER.NAflag = -127,  KML.out = FALSE, KML.maxpixels = 100000, KML.blur = 10)
ensemble.novel.object(x = NULL, name = "reference1", mask.raster = NULL, quantiles = FALSE, probs = c(0.025, 0.975))

Arguments

RasterStack object (stack) containing all environmental layers for which novel conditions should be calculated. With ensemble.novel.object, x can also be a data.frame.

novel.object

Object listing minima and maxima for the environmental layers, used by the prediction function that is used internally by predict. This object is created with ensemble.novel.object.

RASTER.object.name

First part of the names of the raster file that will be generated, expected to identify the area and time period for which ranges were calculated

RASTER.stack.name

Last part of the names of the raster file that will be generated, expected to identify the predictor stack used

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.

KML.out

If TRUE, then kml files will be saved in a subfolder 'kml/zones'.

KML.maxpixels

Maximum number of pixels for the PNG image that will be displayed in Google Earth. See also KML.

KML.blur

Integer that results in increasing the size of the PNG image by KML.blur^2, which may help avoid blurring of isolated pixels. See also KML.

name

Name of the object, expect to expected to identify the area and time period for which ranges were calculated and where no novel conditions will be detected

mask.raster

RasterLayer object (raster) that can be used to select the area for which reference values are obtained (see mask)

quantiles

If TRUE, then replace minima and maxima with quantile values. See also quantile)

probs

Numeric vector of probabilities [0,1] as used by quantile)

Value

Function ensemble.novel.object returns a list with following objects: returns a list with following objects:

Details

Function ensemble.novel maps zones (coded '1') that are novel (outside the minimum-maximum range) relative to the range provided by function ensemble.novel.object. Values that are not novel (inside the range of minimum-maximum values) are coded '0'. In theory, the maps show the same areas that have negative Multivariate Environmental Similarity Surface (MESS) values ((mess))

Examples

Run this code


## Not run: 
# # 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)
# predictors <- subset(predictors, subset=c("bio1", "bio5", "bio6", "bio7", "bio8", 
#     "bio12", "bio16", "bio17"))
# predictors
# predictors@title <- "base"
# 
# # reference area to calculate environmental ranges
# ext <- extent(-70, -50, -10, 10)
# extent.values2 <- c(-70, -50, -10, 10)
# predictors.current <- crop(predictors, y=ext)
# predictors.current <- stack(predictors.current)
# 
# novel.test <- ensemble.novel.object(predictors.current, name="noveltest")
# novel.test
# novel.raster <- ensemble.novel(x=predictors, novel.object=novel.test, KML.out=T)
# novel.raster
# 
# plot(novel.raster)
# # no novel conditions within reference area
# rect(extent.values2[1], extent.values2[3], extent.values2[2], extent.values2[4])
# 
# # use novel conditions as a simple species suitability mapping method
# # presence points
# presence_file <- paste(system.file(package="dismo"), '/ex/bradypus.csv', sep='')
# pres <- read.table(presence_file, header=TRUE, sep=',')[,-1]
# pres.data <- data.frame(extract(predictors, y=pres))
# 
# # ranges and maps
# Bradypus.ranges1 <- ensemble.novel.object(pres.data, name="Bradypus", quantiles=F)
# Bradypus.ranges1
# Bradypus.novel1 <- ensemble.novel(x=predictors, novel.object=Bradypus.ranges1, KML.out=T)
# Bradypus.novel1
# 
# par(mfrow=c(1,2))
# 
# plot(Bradypus.novel1)
# points(pres[, 2] ~ pres[, 1], pch=1, col="red", cex=0.8)
# 
# # use 95
# Bradypus.ranges2 <- ensemble.novel.object(pres.data, name="BradypusQuantiles", quantiles=T)
# Bradypus.ranges2
# Bradypus.novel2 <- ensemble.novel(x=predictors, novel.object=Bradypus.ranges2, KML.out=T)
# Bradypus.novel2
# plot(Bradypus.novel2)
# points(pres[, 2] ~ pres[, 1], pch=1, col="red", cex=0.8)
# 
# par(mfrow=c(1,1))
# 
# ## End(Not run)