ecospat.ESM.Projection {ecospat} R Documentation

## Ensemble 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,
name.env,
parallel,
cleanup)


### Arguments

 ESM.modeling.output list object returned by ecospat.ESM.Modeling 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. name.env A name for the new.env object. If not specified (default) the name of the new.env object will be used. It is necessary to specify a unique name when projecting various new.env objects in a loop. parallel Logical. If TRUE, the parallel computing is enabled cleanup Numeric. Calls removeTmpFiles() to delete all files from rasterOptions()\$tmpdir which are older than the given time (in hours). This might be necessary to prevent running over quota. No cleanup is used by default

### 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)

### Value

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

### Author(s)

Frank Breiner frank.breiner@wsl.ch

with contributions of Olivier Broennimann olivier.broennimann@unil.ch

### 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.

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

ecospat.ESM.Modeling