ensemble.bioclim: Suitability mapping based on the BIOCLIM algorithm
In BiodiversityR: Package for Community Ecology and Suitability Analysis
ensemble.bioclim R Documentation
Suitability mapping based on the BIOCLIM algorithm
Description
Implementation of the BIOCLIM algorithm more similar to the original BIOCLIM algorithm and software than the implementation through bioclim. Function ensemble.bioclim creates the suitability map. Function ensemble.bioclim.object provides the reference values used by the prediction function used by predict .
Usage
ensemble.bioclim(x = NULL, bioclim.object = NULL,
RASTER.object.name = bioclim.object$species.name, RASTER.stack.name = x@title,
RASTER.format = "GTiff",
CATCH.OFF = FALSE)
ensemble.bioclim.object(x = NULL, p = NULL, fraction = 0.9,
quantiles = TRUE,
species.name = "Species001",
factors = NULL)
Arguments
x
RasterStack object (stack) containing all environmental layers for which suitability should be calculated, or alternatively a data.frame containing the bioclimatic variables.
bioclim.object
Object listing optimal and absolute minima and maxima for bioclimatic variables, used by the prediction function that is used internally by predict. This object is created with ensemble.bioclim.object.
RASTER.object.name
First part of the names of the raster file that will be generated, expected to identify the species or crop 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.
CATCH.OFF
Disable calls to function tryCatch.
p
presence points used for calibrating the suitability models, typically available in 2-column (lon, lat) dataframe; see also prepareData and extract.
fraction
Fraction of range representing the optimal limits, default value of 0.9 as in the original BIOCLIM software.
quantiles
If TRUE then optimal limits are calculated as quantiles corresponding to 0.5-fraction/2 and 0.5+fraction/2 percentiles. If FALSE then optimal limits are calculated from the normal distribution with mean - cutoff*sd and mean + cutoff*sd with cutoff calculated as qnorm(0.5+fraction/2).
species.name
Name by which the model results will be saved.
factors
vector that indicates which variables are factors; these variables will be ignored by the BIOCLIM algorithm
Details
Function ensemble.bioclim maps suitability for a species based on optimal (percentiles, typically 5 and 95 percent) and absolute (minimum to maximum) limits for bioclimatic variables. If all values at a given location are within the optimal limits, suitability values are mapped as 1 (suitable). If not all values are within the optimal limits, but all values are within the absolute limits, suitability values are mapped as 0.5 (marginal). If not all values are within the absolute limits, suitability values are mapped as 0 (unsuitable).
Function ensemble.bioclim.object calculates the optimal and absolute limits. Optimal limits are calculated based on the parameter fraction, resulting in optimal limits that correspond to 0.5-fraction/2 and 0.5+fraction/2 (the default value of 0.9 therefore gives a lower limit of 0.05 and a upper limit of 0.95). Two methods are implemented to obtain optimal limits for the lower and upper limits. One method (quantiles = FALSE) uses mean, standard deviation and a cutoff parameter calculated with qnorm. The other method (quantiles = TRUE) calculates optimal limits via the quantile function. To handle possible asymmetrical distributions better, the second method is used as default.
When x is a RasterStack and point locations are provided, then optimal and absolute limits correspond to the bioclimatic values observed for the locations. When x is RasterStack and point locations are not provided, then optimal and absolute limits correspond to the bioclimatic values of the RasterStack.
Applying to algorithm without providing point locations will provide results that are similar to the ensemble.novel function, whereby areas plotted as not suitable will be the same areas that are novel.
Value
Function ensemble.bioclim.object returns a list with following objects:
lower.limits
vector with lower limits for each bioclimatic variable
upper.limits
vector with upper limits for each bioclimatic variable
minima
vector with minima for each bioclimatic variable
maxima
vector with maxima for each bioclimatic variable
means
vector with mean values for each bioclimatic variable
medians
vector with median values for each bioclimatic variable
sds
vector with standard deviation values for each bioclimatic variable
cutoff
cutoff value for the normal distribution
fraction
fraction of values within the optimal limits
species.name
name for the species
Author(s)
Roeland Kindt (World Agroforestry Centre) with inputs from Trevor Booth (CSIRO)
References
Nix HA. 1986. A biogeographic analysis of Australian elapid snakes. In: Atlas of Elapid Snakes of Australia. (Ed.) R. Longmore, pp. 4-15. Australian Flora and Fauna Series Number 7. Australian Government Publishing Service: Canberra.
Booth TH, Nix HA, Busby JR and Hutchinson MF. 2014. BIOCLIM: the first species distribution modelling package, its early applications and relevance to most current MAXENT studies. Diversity and Distributions 20: 1-9
See Also
bioclim, ensemble.bioclim.graph and ensemble.novel
Examples
## 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)
# subset based on Variance Inflation Factors
predictors <- subset(predictors, subset=c("bio5", "bio6",
"bio16", "bio17", "biome"))
predictors
predictors@title <- "base"
# presence points
presence_file <- paste(system.file(package="dismo"), '/ex/bradypus.csv', sep='')
pres <- read.table(presence_file, header=TRUE, sep=',')[,-1]
background <- dismo::randomPoints(predictors, n=100)
colnames(background)=c('lon', 'lat')
pres.dataset <- data.frame(extract(predictors, y=pres))
names(pres.dataset) <- names(predictors)
pres.dataset$biome <- as.factor(pres.dataset$biome)
Bradypus.bioclim <- ensemble.bioclim.object(predictors, quantiles=T,
p=pres, factors="biome", species.name="Bradypus")
Bradypus.bioclim
# obtain the same results with a data.frame
Bradypus.bioclim2 <- ensemble.bioclim.object(pres.dataset, quantiles=T,
species.name="Bradypus")
Bradypus.bioclim2
# obtain results for entire rasterStack
Bradypus.bioclim3 <- ensemble.bioclim.object(predictors, p=NULL, quantiles=T,
factors="biome", species.name="America")
Bradypus.bioclim3
ensemble.bioclim(x=predictors, bioclim.object=Bradypus.bioclim)
ensemble.bioclim(x=predictors, bioclim.object=Bradypus.bioclim3)
par.old <- graphics::par(no.readonly=T)
graphics::par(mfrow=c(1,2))
rasterfull1 <- paste("ensembles//Bradypus_base_BIOCLIM_orig.tif", sep="")
raster::plot(raster(rasterfull1), breaks=c(-0.1, 0, 0.5, 1),
col=c("grey", "blue", "green"), main="original method")
rasterfull2 <- paste("ensembles//America_base_BIOCLIM_orig.tif", sep="")
raster::plot(raster(rasterfull2), breaks=c(-0.1, 0, 0.5, 1),
col=c("grey", "blue", "green"), main="America")
graphics::par(par.old)
# compare with implementation bioclim in dismo
bioclim.dismo <- bioclim(predictors, p=pres)
rasterfull2 <- paste("ensembles//Bradypus_base_BIOCLIM_dismo.tif", sep="")
raster::predict(object=predictors, model=bioclim.dismo, na.rm=TRUE,
filename=rasterfull2, progress='text', overwrite=TRUE)
par.old <- graphics::par(no.readonly=T)
graphics::par(mfrow=c(1,2))
raster::plot(raster(rasterfull1), breaks=c(-0.1, 0, 0.5, 1),
col=c("grey", "blue", "green"), main="original method")
raster::plot(raster(rasterfull2), main="dismo method")
graphics::par(par.old)
# use dummy variables to deal with factors
predictors <- stack(predictor.files)
biome.layer <- predictors[["biome"]]
biome.layer
ensemble.dummy.variables(xcat=biome.layer, most.frequent=0, freq.min=1,
overwrite=TRUE)
predictor.files <- list.files(path=paste(system.file(package="dismo"), '/ex', sep=''),
pattern='grd', full.names=TRUE)
predictors <- stack(predictor.files)
predictors.dummy <- subset(predictors, subset=c("biome_1", "biome_2", "biome_3",
"biome_4", "biome_5", "biome_7", "biome_8", "biome_9", "biome_10",
"biome_12", "biome_13", "biome_14"))
predictors.dummy
predictors.dummy@title <- "base_dummy"
Bradypus.dummy <- ensemble.bioclim.object(predictors.dummy, quantiles=T,
p=pres, species.name="Bradypus")
Bradypus.dummy
ensemble.bioclim(x=predictors.dummy, bioclim.object=Bradypus.dummy)
par.old <- graphics::par(no.readonly=T)
graphics::par(mfrow=c(1,2))
rasterfull3 <- paste("ensembles//Bradypus_base_dummy_BIOCLIM_orig.tif", sep="")
raster::plot(raster(rasterfull1), breaks=c(-0.1, 0, 0.5, 1), col=c("grey", "blue", "green"),
main="numeric predictors")
raster::plot(raster(rasterfull3), breaks=c(-0.1, 0, 0.5, 1), col=c("grey", "blue", "green"),
main="dummy predictors")
graphics::par(par.old)
## End(Not run)
BiodiversityR documentation built on Oct. 22, 2023, 5:06 p.m.
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| ensemble.bioclim | R Documentation |
Suitability mapping based on the BIOCLIM algorithm
Description
Implementation of the BIOCLIM algorithm more similar to the original BIOCLIM algorithm and software than the implementation through bioclim. Function ensemble.bioclim creates the suitability map. Function ensemble.bioclim.object provides the reference values used by the prediction function used by predict .
Usage
ensemble.bioclim(x = NULL, bioclim.object = NULL,
RASTER.object.name = bioclim.object$species.name, RASTER.stack.name = x@title,
RASTER.format = "GTiff",
CATCH.OFF = FALSE)
ensemble.bioclim.object(x = NULL, p = NULL, fraction = 0.9,
quantiles = TRUE,
species.name = "Species001",
factors = NULL)
Arguments
x |
RasterStack object ( |
bioclim.object |
Object listing optimal and absolute minima and maxima for bioclimatic variables, used by the prediction function that is used internally by |
RASTER.object.name |
First part of the names of the raster file that will be generated, expected to identify the species or crop 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 |
CATCH.OFF |
Disable calls to function |
p |
presence points used for calibrating the suitability models, typically available in 2-column (lon, lat) dataframe; see also |
fraction |
Fraction of range representing the optimal limits, default value of 0.9 as in the original BIOCLIM software. |
quantiles |
If |
species.name |
Name by which the model results will be saved. |
factors |
vector that indicates which variables are factors; these variables will be ignored by the BIOCLIM algorithm |
Details
Function ensemble.bioclim maps suitability for a species based on optimal (percentiles, typically 5 and 95 percent) and absolute (minimum to maximum) limits for bioclimatic variables. If all values at a given location are within the optimal limits, suitability values are mapped as 1 (suitable). If not all values are within the optimal limits, but all values are within the absolute limits, suitability values are mapped as 0.5 (marginal). If not all values are within the absolute limits, suitability values are mapped as 0 (unsuitable).
Function ensemble.bioclim.object calculates the optimal and absolute limits. Optimal limits are calculated based on the parameter fraction, resulting in optimal limits that correspond to 0.5-fraction/2 and 0.5+fraction/2 (the default value of 0.9 therefore gives a lower limit of 0.05 and a upper limit of 0.95). Two methods are implemented to obtain optimal limits for the lower and upper limits. One method (quantiles = FALSE) uses mean, standard deviation and a cutoff parameter calculated with qnorm. The other method (quantiles = TRUE) calculates optimal limits via the quantile function. To handle possible asymmetrical distributions better, the second method is used as default.
When x is a RasterStack and point locations are provided, then optimal and absolute limits correspond to the bioclimatic values observed for the locations. When x is RasterStack and point locations are not provided, then optimal and absolute limits correspond to the bioclimatic values of the RasterStack.
Applying to algorithm without providing point locations will provide results that are similar to the ensemble.novel function, whereby areas plotted as not suitable will be the same areas that are novel.
Value
Function ensemble.bioclim.object returns a list with following objects:
lower.limits |
vector with lower limits for each bioclimatic variable |
upper.limits |
vector with upper limits for each bioclimatic variable |
minima |
vector with minima for each bioclimatic variable |
maxima |
vector with maxima for each bioclimatic variable |
means |
vector with mean values for each bioclimatic variable |
medians |
vector with median values for each bioclimatic variable |
sds |
vector with standard deviation values for each bioclimatic variable |
cutoff |
cutoff value for the normal distribution |
fraction |
fraction of values within the optimal limits |
species.name |
name for the species |
Author(s)
Roeland Kindt (World Agroforestry Centre) with inputs from Trevor Booth (CSIRO)
References
Nix HA. 1986. A biogeographic analysis of Australian elapid snakes. In: Atlas of Elapid Snakes of Australia. (Ed.) R. Longmore, pp. 4-15. Australian Flora and Fauna Series Number 7. Australian Government Publishing Service: Canberra.
Booth TH, Nix HA, Busby JR and Hutchinson MF. 2014. BIOCLIM: the first species distribution modelling package, its early applications and relevance to most current MAXENT studies. Diversity and Distributions 20: 1-9
See Also
bioclim, ensemble.bioclim.graph and ensemble.novel
Examples
## 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)
# subset based on Variance Inflation Factors
predictors <- subset(predictors, subset=c("bio5", "bio6",
"bio16", "bio17", "biome"))
predictors
predictors@title <- "base"
# presence points
presence_file <- paste(system.file(package="dismo"), '/ex/bradypus.csv', sep='')
pres <- read.table(presence_file, header=TRUE, sep=',')[,-1]
background <- dismo::randomPoints(predictors, n=100)
colnames(background)=c('lon', 'lat')
pres.dataset <- data.frame(extract(predictors, y=pres))
names(pres.dataset) <- names(predictors)
pres.dataset$biome <- as.factor(pres.dataset$biome)
Bradypus.bioclim <- ensemble.bioclim.object(predictors, quantiles=T,
p=pres, factors="biome", species.name="Bradypus")
Bradypus.bioclim
# obtain the same results with a data.frame
Bradypus.bioclim2 <- ensemble.bioclim.object(pres.dataset, quantiles=T,
species.name="Bradypus")
Bradypus.bioclim2
# obtain results for entire rasterStack
Bradypus.bioclim3 <- ensemble.bioclim.object(predictors, p=NULL, quantiles=T,
factors="biome", species.name="America")
Bradypus.bioclim3
ensemble.bioclim(x=predictors, bioclim.object=Bradypus.bioclim)
ensemble.bioclim(x=predictors, bioclim.object=Bradypus.bioclim3)
par.old <- graphics::par(no.readonly=T)
graphics::par(mfrow=c(1,2))
rasterfull1 <- paste("ensembles//Bradypus_base_BIOCLIM_orig.tif", sep="")
raster::plot(raster(rasterfull1), breaks=c(-0.1, 0, 0.5, 1),
col=c("grey", "blue", "green"), main="original method")
rasterfull2 <- paste("ensembles//America_base_BIOCLIM_orig.tif", sep="")
raster::plot(raster(rasterfull2), breaks=c(-0.1, 0, 0.5, 1),
col=c("grey", "blue", "green"), main="America")
graphics::par(par.old)
# compare with implementation bioclim in dismo
bioclim.dismo <- bioclim(predictors, p=pres)
rasterfull2 <- paste("ensembles//Bradypus_base_BIOCLIM_dismo.tif", sep="")
raster::predict(object=predictors, model=bioclim.dismo, na.rm=TRUE,
filename=rasterfull2, progress='text', overwrite=TRUE)
par.old <- graphics::par(no.readonly=T)
graphics::par(mfrow=c(1,2))
raster::plot(raster(rasterfull1), breaks=c(-0.1, 0, 0.5, 1),
col=c("grey", "blue", "green"), main="original method")
raster::plot(raster(rasterfull2), main="dismo method")
graphics::par(par.old)
# use dummy variables to deal with factors
predictors <- stack(predictor.files)
biome.layer <- predictors[["biome"]]
biome.layer
ensemble.dummy.variables(xcat=biome.layer, most.frequent=0, freq.min=1,
overwrite=TRUE)
predictor.files <- list.files(path=paste(system.file(package="dismo"), '/ex', sep=''),
pattern='grd', full.names=TRUE)
predictors <- stack(predictor.files)
predictors.dummy <- subset(predictors, subset=c("biome_1", "biome_2", "biome_3",
"biome_4", "biome_5", "biome_7", "biome_8", "biome_9", "biome_10",
"biome_12", "biome_13", "biome_14"))
predictors.dummy
predictors.dummy@title <- "base_dummy"
Bradypus.dummy <- ensemble.bioclim.object(predictors.dummy, quantiles=T,
p=pres, species.name="Bradypus")
Bradypus.dummy
ensemble.bioclim(x=predictors.dummy, bioclim.object=Bradypus.dummy)
par.old <- graphics::par(no.readonly=T)
graphics::par(mfrow=c(1,2))
rasterfull3 <- paste("ensembles//Bradypus_base_dummy_BIOCLIM_orig.tif", sep="")
raster::plot(raster(rasterfull1), breaks=c(-0.1, 0, 0.5, 1), col=c("grey", "blue", "green"),
main="numeric predictors")
raster::plot(raster(rasterfull3), breaks=c(-0.1, 0, 0.5, 1), col=c("grey", "blue", "green"),
main="dummy predictors")
graphics::par(par.old)
## End(Not run)
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