ensemble.ecocrop {BiodiversityR}

Suitability mapping via absolute and optimal precipitation and temperature limits as in the ECOCROP model.

Description

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

Usage

ensemble.ecocrop(x = NULL, ecocrop.object = NULL, 
    RASTER.object.name = ecocrop.object$name, 
    RASTER.stack.name = "xTitle",
    RASTER.format = "GTiff", RASTER.datatype = "INT2S", RASTER.NAflag = -32767,
    CATCH.OFF = FALSE)

ensemble.ecocrop.object(temp.thresholds, rain.thresholds, name = "crop01", 
    temp.multiply = 1, annual.temps = TRUE, transform = 1)

Arguments

x

RasterStack object (stack) containing all environmental layers for which suitability should be calculated.

ecocrop.object

Object listing optimal and absolute minima and maxima for the rainfall and temperature values, used by the prediction function that is used internally by predict. This object is created with ensemble.ecocrop.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.

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.

CATCH.OFF

Disable calls to function tryCatch.

temp.thresholds

Optimal and absolute thresholds for temperatures. These will be sorted as: absolute minimum temperature, optimal minimum temperature, optimal maximum temperature and absolute maximum temperature.

rain.thresholds

Optimal and absolute thresholds for annual rainfall. These will be sorted as: absolute minimum rainfall, optimal minimum rainfall, optimal maximum rainfall and absolute maximum rainfall.

name

Name of the object, expect to expected to identify the species or crop

temp.multiply

Multiplier for temperature values. The value of 10 is to be used with raster layers where temperature was multiplied by 10 such as Worldclim version 1 or AFRICLIM.

annual.temps

If TRUE then temperature limits are assumed to apply to mean annual temperature (bioclimatic variable bio1). If FALSE then minimum temperature limits are assumed to apply to the temperature of the coldest month (bioclimatic variable bio6) and maximum temperature limits are assumed to apply to the temperature of the hottest month (bioclimatic variable bio5). See also biovars.

transform

Exponent used to transform probability values obtained from interpolating between optimal and absolute limits. Exponent of 2 results in squaring probabilities, for example input probabilities of 0.5 transformed to 0.5^2 = 0.25.

Details

Function ensemble.ecocrop maps suitability for a species or crop based on optimal and absolute temperature and rainfall limits. Where both temperature and rainfall are within the optimal limits, suitability of 1000 is calculated. Where both temperature and rainfall are outside the absolute limits, suitability of 0 is calculated. In situations where temperature or rainfall is in between the optimal and absolute limits, then suitability is interpolated between 0 and 1000, and the lowest suitability from temperature and rainfall is calculated. Setting very wide rainfall limits will simulate the effect of irrigation, i.e. where suitability only depends on temperature limits.

For a large range of crop and plant species, optimal and absolute limits are available from the FAO ecocrop database (https://gaez.fao.org/pages/ecocrop-search), hence the name of the function. A different implementation of suitability mapping based on ecocrop limits is available from ecocrop. Ecocrop thresholds for several species are available from: getCrop

Value

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

name

name for the crop or species

temp.thresholds

optimal and absolute minimum and maximum temperature limits

rain.thresholds

optimal and absolute minimum and maximum annual rainfall limits

annual.temps

logical indicating whether temperature limits apply to annual temperatures

transform

exponent to transform suitability values

Author(s)

Roeland Kindt (World Agroforestry Centre)

See Also

biovars

Examples

## Not run: 
# test with Brazil nut (limits from FAO ecocrop)
# temperature: (12) 20-36 (40)
# annnual rainfall: (1400) 2400-2800 (3500)

# 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", "bio12"))
predictors
predictors@title <- "base"

# As the raster data correspond to WorldClim version 1,
# the temperatures need to be multiplied by 10
Brazil.ecocrop <- ensemble.ecocrop.object(temp.thresholds=c(20, 36, 12, 40), 
    rain.thresholds=c(2400, 2800, 1400, 3500),
    temp.multiply=10,
    annual.temps=FALSE, name="Bertholletia_excelsa")
Brazil.ecocrop
ensemble.ecocrop(predictors, 
                 ecocrop.object=Brazil.ecocrop,
                 RASTER.stack.name="base")

dev.new()
par.old <- graphics::par(no.readonly=T)
graphics::par(mfrow=c(1,2))

rasterfull1 <- paste("ensembles//ecocrop//Bertholletia_excelsa_base.tif", sep="")
rasterfull1 <- raster(rasterfull1)
# raster file saved probabilities as integer values between 0 and 1000
rasterfull1 <- rasterfull1/1000
raster::plot(rasterfull1, main="Ecocrop suitability")

GBIFloc <- gbif(genus="Bertholletia", species="excelsa", geo=TRUE)
GBIFpres <- GBIFloc[, c("lon", "lat")]
GBIFpres <- GBIFpres[complete.cases(GBIFpres), ]
GBIFpres <- GBIFpres[duplicated(GBIFpres) == FALSE, ]
point.suitability <- extract(rasterfull1, y=GBIFpres)
point.suitability[is.na(point.suitability)] <- -1

GBIFpres.optimal <- GBIFpres[point.suitability == 1, ]
GBIFpres.suboptimal <- GBIFpres[point.suitability < 1 & point.suitability > 0, ]
GBIFpres.not <- GBIFpres[point.suitability == 0, ]

raster::plot(rasterfull1, main="GBIF locations", 
    sub="blue: optimal, cyan: suboptimal, red: not suitable")
bg.legend <- c("blue", "cyan", "red")

points(GBIFpres.suboptimal, pch=21, cex=1.2, bg=bg.legend[2])
points(GBIFpres.optimal, pch=21, cex=1.2, bg=bg.legend[1])
points(GBIFpres.not, pch=21, cex=1.2, bg=bg.legend[3])

graphics::par(par.old)

## End(Not run)