ecospat (version 2.0)

ecospat.ESM.EnsembleProjection: Ensamble of Small Models: Projects Calibrated ESMs Into New Space Or Time.

Description

This function projects calibrated ESMs into new space or time.

Usage

ecospat.ESM.EnsembleProjection( ESM.prediction.output,  ESM.EnsembleModeling.output)

Arguments

ESM.prediction.output

a "BIOMOD.models.out" returned by ecospat.ESM.Projection

ESM.EnsembleModeling.output

a "BIOMOD.EnsembleModeling.out" object returned by ecospat.ESM.EnsembleModeling

Value

Returns the projections of ESMs for the selected single models and their ensemble. 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.

Details

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 further details please refer to BIOMOD_EnsembleForecasting.

References

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.

See Also

ecospat.ESM.Modeling, ecospat.ESM.Projection, ecospat.ESM.EnsembleModeling

BIOMOD_Modeling, BIOMOD_Projection, BIOMOD_EnsembleForecasting, BIOMOD_EnsembleModeling

Examples

   ## Not run: 
# # Loading test data for the niche dynamics analysis in the invaded range
# 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 form the package biomod2
# setwd(path.wd)
# t1 <- Sys.time()
# sp <- 1
# myBiomodData <- BIOMOD_FormatingData( resp.var = as.numeric(sp_occ[,sp]),
#                                       expl.var = current,
#                                       resp.xy = xy,
#                                       resp.name = colnames(sp_occ)[sp])
# 
# myBiomodOption <- Print_Default_ModelingOptions()
# 
# 
# ### Calibration of simple bivariate models
# my.ESM <- ecospat.ESM.Modeling( data=myBiomodData,
#                                 models=c('GLM','RF'),
#                                 models.options=myBiomodOption,
#                                 NbRunEval=2,
#                                 DataSplit=70,
#                                 weighting.score=c("AUC"),
#                                 parallel=F)  
# 
# 
# ### 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)
# 
# ## End(Not run)