I am trying to run an ensemble species distribution, however the the results I am getting for MAXENT.Phillips model are not reasonable projections compared to the Random Forest and Boosted Regression models that I am using.
The projection for MAXENT deviates heavily from the other projections. I attached a picture demonstrating first the Random Forest projection, then the Boosted Regression, and on the right is the MAXENT projection, which appears to not be strict at all with it's predictions.
Using the same environmental variables, presence and background points, I ran a projection in the 'Dismo' package and obtained results more reasonable and in agreement to the Random Forest and Boosted models in BIOMOD2.
This leads me to believe that I have not adjusted a default setting in BIOMOD, but I am at a loss for what I need to do.
I included my code below. Why am I getting such different projections in MAXENT?
For the environmental variables, I used the getdata function.
ext.1<-extent(95,115,25,45) bio.15 <- getData('worldclim',var='bio',res=2.5) bio.15<-crop(bio.15,ext.1) bio.15<-stack(bio.15)
And for the projection environmental variables:
bio.70<-getData('CMIP5',var='bio',res=2.5,rcp=85,model='AC',year=70) bio.70<-crop(bio.70,ext.1) bio.70<-stack(bio.70) names(bio.70)<-names(bio.15)
Beyond that, I kept everything relatively similar to the BIOMOD2 vignette. The link to the csv is here csv_link:
file<-paste([csv_link]) data.short<-read.table(file,header=TRUE,sep=",") resp.name<-'GiantPanda' resp.test<-as.numeric(panda.data.short[,1]) resp.xy.test<-data.frame(panda.data.short[,c("POINT_X","POINT_Y")]) ensemble.test<-BIOMOD_FormatingData(resp.var=resp.test, expl.var=bio.15, resp.xy=resp.xy.test, resp.name=resp.name)
The .jar file is in the working directory, and there are no spaces in the working directory
ensembleoption<-BIOMOD_ModelingOptions() ensemble.out.test<-BIOMOD_Modeling( ensemble.test, models=c('RF','GBM','MAXENT.Phillips'), models.options=ensembleoption, NbRunEval=1, DataSplit=70, Prevalence=NULL, VarImport=1, models.eval.meth=c('TSS','ROC'), SaveObj=TRUE, rescal.all.models=FALSE, do.full.models=FALSE, modeling.id="3_28")
Then the projection is:
proj.future<-BIOMOD_Projection( modeling.output=ensemble.out.test, new.env=bio.70, proj.name='projection_future_3_28', selected.models='all', binary.meth=NULL, compress='xz', build.clamping.mask=T, output.format='.grd')