ecospat (version 2.0)

ecospat.ESM.Projection: Ensamble of Small Models: Projects Simple Bivariate Models Into New Space Or Time

Description

This function projects simple bivariate models on new.env

Usage

ecospat.ESM.Projection( ESM.modeling.output, new.env, parallel=F)

Arguments

ESM.modeling.output
BIOMOD.formated.data object returned by BIOMOD_FormatingData
new.env
A set of explanatory variables onto which models will be projected. It could be a data.frame, a matrix, or a rasterStack object. Make sure the column names (data.frame or matrix) or layer Names (rasterStack) perfectly match with the names of variables used to build the models in the previous steps.
parallel
Logical. If TRUE, the parallel computing is enabled

Value

Returns the projections for all selected models (same as in biomod2) See "BIOMOD.projection.out" for details.

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) accoring to Breiner et al (2015). They provide full functionality of the approach described in Breiner et al. (2015).

The name of new.env must be a regular expression (see ?regex)

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.EnsembleModeling, ecospat.ESM.Modeling, ecospat.ESM.EnsembleProjection

BIOMOD_FormatingData, BIOMOD_ModelingOptions, BIOMOD_Modeling,BIOMOD_Projection

Examples

Run this code
   ## 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)

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