#Determine the number of less-important predictor variables that
#can be dropped.

myGbmModelOut.simplify.variables.abi.bal <- gbm.simplify(myGbmModelOut.abi.bal)
myGbmModelOut.simplify.variables.ace.rub <- gbm.simplify(myGbmModelOut.ace.rub)
myGbmModelOut.simplify.variables.ace.sac <- gbm.simplify(myGbmModelOut.ace.sac)
myGbmModelOut.simplify.variables.aln.inc <- gbm.simplify(myGbmModelOut.aln.inc)
myGbmModelOut.simplify.variables.bet.all <- gbm.simplify(myGbmModelOut.bet.all)
myGbmModelOut.simplify.variables.car.spp <- gbm.simplify(myGbmModelOut.car.spp)
myGbmModelOut.simplify.variables.cas.den <- gbm.simplify(myGbmModelOut.cas.den)
myGbmModelOut.simplify.variables.fag.gra <- gbm.simplify(myGbmModelOut.fag.gra)
myGbmModelOut.simplify.variables.fra.ame <- gbm.simplify(myGbmModelOut.fra.ame)
myGbmModelOut.simplify.variables.fra.nig <- gbm.simplify(myGbmModelOut.fra.nig)
myGbmModelOut.simplify.variables.jug.cin <- gbm.simplify(myGbmModelOut.jug.cin)
myGbmModelOut.simplify.variables.jug.nig <- gbm.simplify(myGbmModelOut.jug.nig)
myGbmModelOut.simplify.variables.lir.tul <- gbm.simplify(myGbmModelOut.lir.tul)
myGbmModelOut.simplify.variables.mag.acu <- gbm.simplify(myGbmModelOut.mag.acu)
myGbmModelOut.simplify.variables.ost.vir <- gbm.simplify(myGbmModelOut.ost.vir)
myGbmModelOut.simplify.variables.pin.str <- gbm.simplify(myGbmModelOut.pin.str)
myGbmModelOut.simplify.variables.pla.occ <- gbm.simplify(myGbmModelOut.pla.occ)
myGbmModelOut.simplify.variables.pru.ser <- gbm.simplify(myGbmModelOut.pru.ser)
myGbmModelOut.simplify.variables.que.alb <- gbm.simplify(myGbmModelOut.que.alb)
myGbmModelOut.simplify.variables.que.vel <- gbm.simplify(myGbmModelOut.que.vel)
myGbmModelOut.simplify.variables.til.ame <- gbm.simplify(myGbmModelOut.til.ame)
myGbmModelOut.simplify.variables.tsu.can <- gbm.simplify(myGbmModelOut.tsu.can)
myGbmModelOut.simplify.variables.ulm.ame <- gbm.simplify(myGbmModelOut.ulm.ame)



#Save workspace.

save.image(file="sdm.objects.with.Native.variables.biomod2.step.2.RData")