#!/usr/bin/R
#####
#Demo CRACLE implementation
####
#for a single locality with the species:
data(climondbioclim) #packaged data from CliMond for the Eastern US. Should replace with climate grids of your choice
sp_list = c(
"Quercus rubra",
"Pinus strobus",
"Vaccinium angustifolium",
"Betula papyrifera",
"Rhododendron maximum",
"Toxicodendron radicans"
)
extr = getextr(
sp_list,
climondbioclim,
maxrec = 200,
#Should set higher in practice!
schema = 'flat',
factor = 2,
#adjust factor to spatially thin. Use a higher number (4 to 8) for spatial grids of 2.5 arcminutes or less.
nmin = 10,
parallel = FALSE
)
densall = dens_obj(extr,
climondbioclim,
manip = 'condi',
kern = 'gaussian',
bg.n = 40) #consider using parallel=TRUE if bg.n>500
and = and_fun(densall)
optim = get_optim(and)
print(optim$origk) #As implemented in Harbert and Nixon, 2015
print(optim$conintkde) #with 95% confidence intervals on the likelihood distribution.
n = 1
multiplot(densall, names(climondbioclim)[[n]])
addplot(and, names(climondbioclim)[[n]])
abline(v = median(optim$origk[, n]), col = 'green') #Midpoint will be very close to the optimum. Original method of Harbert and Nixon, 2015 returned a top 1% range to introduce some slop
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