Ensamble of Small Models: Projects Calibrated ESMs Into New Space Or Time.
This function projects calibrated ESMs into new space or time.
ecospat.ESM.EnsembleProjection( ESM.prediction.output,
ESM.EnsembleModeling.output,
chosen.models = 'all')ESM.prediction.output |
a list object returned by |
ESM.EnsembleModeling.output |
a list object returned by |
chosen.models |
a character vector (either 'all' or a sub-selection of model names, e.g. c(GLM, GBM)) to remove models from the ensemble. Default is 'all'. |
The basic idea of ensemble of small models (ESMs) is to model a species distribution based on small, simple models, for example all possible bivariate models (i.e. models that contain only two predictors at a time out of a larger set of predictors), and then combine all possible bivariate models into an ensemble (Lomba et al. 2010; Breiner et al. 2015).
The ESM set of functions could be used to build ESMs using simple bivariate models which are averaged using weights based on model performances (e.g. AUC) according to Breiner et al. (2015). They provide full functionality of the approach described in Breiner et al. (2015). For projections only the full models (100
For further details please refer to BIOMOD_EnsembleForecasting.
Returns the projections of ESMs for the selected single models and their ensemble (data frame or raster stack). ESM.projections ‘projection files’ are saved on the hard drive projection folder. This files are either an array or a RasterStack depending the original projections data type.
Load these created files to plot and work with them.
Frank Breiner frank.breiner@wsl.ch
Lomba, A., L. Pellissier, C.F. Randin, J. Vicente, F. Moreira, J. Honrado and A. Guisan. 2010. Overcoming the rare species modelling paradox: A novel hierarchical framework applied to an Iberian endemic plant. Biological Conservation, 143,2647-2657.
Breiner F.T., A. Guisan, A. Bergamini and M.P. Nobis. 2015. Overcoming limitations of modelling rare species by using ensembles of small models. Methods in Ecology and Evolution, 6,1210-1218.
Breiner F.T., Nobis M.P., Bergamini A., Guisan A. 2018. Optimizing ensembles of small models for predicting the distribution of species with few occurrences. Methods in Ecology and Evolution. doi: 10.1111/2041-210X.12957
library(biomod2)
# Loading test data
data(ecospat.testNiche.inv)
inv <- ecospat.testNiche.inv
# species occurrences
xy <- inv[,1:2]
sp_occ <- inv[11]
# env
current <- inv[3:10]
### Formating the data with the BIOMOD_FormatingData() function from the package biomod2
sp <- 1
myBiomodData <- BIOMOD_FormatingData( resp.var = as.numeric(sp_occ[,sp]),
expl.var = current,
resp.xy = xy,
resp.name = colnames(sp_occ)[sp])
### Calibration of simple bivariate models
my.ESM <- ecospat.ESM.Modeling( data=myBiomodData,
models=c('GLM','RF'),
NbRunEval=2,
DataSplit=70,
weighting.score=c("AUC"),
parallel=FALSE)
### Evaluation and average of simple bivariate models to ESMs
my.ESM_EF <- ecospat.ESM.EnsembleModeling(my.ESM,weighting.score=c("SomersD"),threshold=0)
### Projection of simple bivariate models into new space
my.ESM_proj_current<-ecospat.ESM.Projection(ESM.modeling.output=my.ESM,
new.env=current)
### Projection of calibrated ESMs into new space
my.ESM_EFproj_current <- ecospat.ESM.EnsembleProjection(ESM.prediction.output=my.ESM_proj_current,
ESM.EnsembleModeling.output=my.ESM_EF)
## get the model performance of ESMs
my.ESM_EF$ESM.evaluations
## get the weights of the single bivariate models used to build the ESMs
my.ESM_EF$weights
## get the variable contributions of ESMs
ecospat.ESM.VarContrib(my.ESM,my.ESM_EF)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.