ecospat.ESM.threshold: Thresholds for Ensamble of Small Models

Description

This function evaluates the full model which is used for projections and provides thresholds to produce binary maps.

Usage

ecospat.ESM.threshold( ESM.EnsembleModeling.output, 
                           PEplot = FALSE)

Arguments

ESM.EnsembleModeling.output

a list object returned by ecospat.ESM.EnsembleModeling

PEplot

logical. Should the predicted to expected ratio along the suitability class from the boyce index be plotted. Default FALSE (see ecospat.boyce

Value

A data.frame with evluation scores and thresholds.

Details

This function provides evaluation scores of the full model (no split sampling) and thresholds which can be used to convert suitability maps into binary maps. Various thresholds are provided: TSS (where sensitivity and specificity are maximised), MPA 1.0 (where all presences are prdicted positive), MPA 0.95 (where 95% of all presences are predicted positive), MPA 0.90 (where 90% of all presences are predicted positive), Boyce.th.min (the lowest suitability value where the predicted/expected ratio is >1) and Boyce.th.max (the highest suitability value where the predicted/expected ratio is =1).

References

Hirzel, Alexandre H., et al. Evaluating the ability of habitat suitability models to predict species presences. Ecological modelling, 199.2 (2006): 142-152.

Engler, Robin, Antoine Guisan, and Luca Rechsteiner. An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudo-absence data. Journal of applied ecology, 41.2 (2004): 263-274.

Fielding, Alan H., and John F. Bell. A review of methods for the assessment of prediction errors in conservation presence/absence models." Environmental conservation, 24.1 (1997): 38-49.

Examples

Run this code

# NOT RUN {
   
# }
# NOT RUN {
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','CTA'),
                                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)


output.TH <- ecospat.ESM.threshold(my.ESM_EF,PEplot = TRUE)

# }

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