ecospat.CCV.modeling: Runs indivudual species distribuion models with SDMs or ESMs

Description

Creates probabilistic prediction for all species based on SDMs or ESMs and returns their evaluation metrics and variable importances.

Usage

ecospat.CCV.modeling(sp.data, 
                     env.data, 
                     xy,
                     DataSplitTable=NULL,
                     DataSplit = 70, 
                     NbRunEval = 25,
                     minNbPredictors =5,
                     validation.method = "cross-validation",
                     models.sdm = c("GLM","RF"), 
                     models.esm = "CTA", 
                     modeling.options.sdm = NULL, 
                     modeling.options.esm = NULL, 
                     ensemble.metric = "AUC", 
                     ESM = "YES",
                     parallel = FALSE, 
                     cpus = 4,
                     VarImport = 10,
                     modeling.id)

Value

modelling.id: character, the ID (=name) of modeling procedure
output.files: vector with the names of the files written to the hard drive
speciesData.calibration: a 3-dimensional array of presence/absence data of all species for the calibration plots used for each run
speciesData.evaluation: a 3-dimensional array of presence/absence data of all species for the evaluation plots used for each run
speciesData.full: a data.frame of presence/absence data of all species (same as sp.data input)
DataSplitTable: a matrix with TRUE/FALSE for each model run (TRUE=Calibration point, FALSE=Evaluation point)
singleSpecies.ensembleEvaluationScore: a 3-dimensional array of single species evaluation metrics ('Max.KAPPA', 'Max.TSS', 'AUC of ROC')
singleSpecies.ensembleVariableImportance: a 3-dimensional array of single species variable importance for all predictors
singleSpecies.calibrationSites.ensemblePredictions: a 3-dimensional array of the predictions for each species and run at the calibration sites
singleSpecies.evaluationSites.ensemblePredictions: a 3-dimensional array of the predictions for each species and run at the evaluation sites
allSites.averagePredictions.cali: a matrix with the average predicted probabilities for each site across all the runs the sites were used for model calibration
allSites.averagePredictions.eval: a matrix with the average predicted probabilities for each site across all the runs the sites were used as independent evaluation sites

Arguments

sp.data: a data.frame where the rows are sites and the columns are species (values 1,0)
env.data: either a data.frame where rows are sites and colums are environmental variables or a raster stack of the envrionmental variables
xy: two column data.frame with X and Y coordinates of the sites (most be same coordinate system as env.data)
DataSplitTable: a table providing TRUE/FALSE to indicate what points are used for calibration and evaluation. As returned by ecospat.CCV.createDataSplitTable
DataSplit: percentage of dataset observations retained for the model training (only needed if no DataSplitTable provided)
NbRunEval: number of cross-validatio/split sample runs (only needed if no DataSplitTable provided)
minNbPredictors: minimum number of occurences [min(presences/Absences] per predicotors needed to calibrate the models
validation.method: either "cross-validation" or "split-sample" used to validate the communtiy predictions (only needed if no DataSplitTable provided)
models.sdm: modeling techniques used for the normal SDMs. Vector of models names choosen among 'GLM', 'GBM', 'GAM', 'CTA', 'ANN', 'SRE', 'FDA', 'MARS', 'RF', 'MAXENT.Phillips' and 'MAXENT.Tsuruoka'
models.esm: modeling techniques used for the ESMs. Vector of models names choosen among 'GLM', 'GBM', 'GAM', 'CTA', 'ANN', 'SRE', 'FDA', 'MARS', 'RF', 'MAXENT.Phillips' and 'MAXENT.Tsuruoka'
modeling.options.sdm: modeling options for the normal SDMs. "BIOMOD.models.options"" object returned by BIOMOD_ModelingOptions
modeling.options.esm: modeling options for the ESMs. "BIOMOD.models.options" object returned by BIOMOD_ModelingOptions
ensemble.metric: evaluation score used to weight single models to build ensembles: 'AUC', 'Kappa' or 'TSS'
ESM: either 'YES' (ESMs allowed), 'NO' (ESMs not allowed) or 'ALL' (ESMs used in any case)
parallel: should parallel computing be allowed (TRUE/FALSE)
cpus: number of cpus to use in parallel computing
VarImport: number of permutation runs to evaluate variable importance
modeling.id: character, the ID (=name) of modeling procedure. A random number by default

Author

Daniel Scherrer <daniel.j.a.scherrer@gmail.com>

Details

The basic idea of the community cross-validation (CCV) is to use the same data (sites) for the model calibration/evaluation of all species. This ensures that there is "independent" cross-validation/split-sample data available not only at the individual species level but also at the community level. This is key to allow an unbiased estimation of the ability to predict species assemblages (Scherrer et al. 2018). The output of the ecospat.CCV.modeling function can then be used to evaluate the species assemblage predictions with the ecospat.CCV.communityEvaluation.bin or ecospat.CCV.communityEvaluation.prob functions.

References

Scherrer, D., D'Amen, M., Mateo, M.R.G., Fernandes, R.F. & Guisan , A. (2018) How to best threshold and validate stacked species assemblages? Community optimisation might hold the answer. Methods in Ecology and Evolution, in review

Examples

Run this code

# \donttest{
#Loading species occurence data and remove empty communities
testData <- ecospat.testData[,c(24,34,43,45,48,53,55:58,60:63,65:66,68:71)]
sp.data <- testData[which(rowSums(testData)>0), sort(colnames(testData))]

#Loading environmental data
env.data <- ecospat.testData[which(rowSums(testData)>0),4:8]

#Coordinates for all sites
xy <- ecospat.testData[which(rowSums(testData)>0),2:3]

#Running all the models for all species
myCCV.Models <- ecospat.CCV.modeling(sp.data = sp.data,
                                     env.data = env.data,
                                     xy = xy,
                                     NbRunEval = 5,
                                     minNbPredictors = 10,
                                     VarImport = 3)
# }

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