ecospat: ecospat.CCV.communityEvaluation.prob – R documentation

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ecospat.CCV.communityEvaluation.prob

Evaluates community predictions directly on the probabilities (i.e., threshold independent)

Description

This function generates a number of community evaluation metrics directly based on the probability returned by the individual models. Instead of thresholding the predictions (ecospat.CCV.communityEvaluation.bin this function directly uses the probability and compares its outcome to null models or average expectations.)

Usage

ecospat.CCV.communityEvaluation.prob(ccv.modeling.data,
    community.metrics=c('SR.deviation','community.AUC','Max.Sorensen',
                         'Max.Jaccard','probabilistic.Sorensen',
                         'probabilistic.Jaccard'),
    parallel = FALSE, 
    cpus = 4)

Arguments

`ccv.modeling.data`	a `'ccv.modeling.data'` object returned by `ecospat.CCV.modeling`
`community.metrics`	a selection of community metrics to calculate (`'SR.deviation','community.AUC','Max.Sorensen','Max.Jaccard', 'probabilistic.Sorensen','probabilistic.Jaccard')`)
`parallel`	should parallel computing be allowed (`TRUE/FALSE`)
`cpus`	number of cpus to use in parallel computing

Value

`DataSplitTable`	a matrix with `TRUE/FALSE` for each model run (`TRUE`=Calibration point, `FALSE`=Evaluation point)
`CommunityEvaluationMetrics.CalibrationSites`	a 3-dimensional array containing the community evaluation metrics for the calibartion sites of each run (`NA` means that the site was used for evaluation)
`CommunityEvaluationMetrics.EvaluationSites`	a 3-dimensional array containing the community evaluation metrics for the evaluation sites of each run (`NA` means that the site was used for calibaration)

Note

If the community evaluation metric 'SR.deviation' is selected the returned tables will have the following columns:

SR.obs = observed species richness,
SR.mean = the predicted species richness (based on the probabilities assuming poission binomial distribution),
SR.dev = the deviation of observed and predicted species richness,
SR.sd = the standard deviation of the predicted species richness (based on the probabilities assuming poission binomial distribution),
SR.prob = the probability that the observed species richness falls within the predicted species richness (based on the probabilities assuming poission binomial distribution),
SR.imp.05 = improvement of species richness prediction over null-model 0.5,
SR.imp.average.SR = improvement of species richness prediction over null-model average.SR and
SR.imp.prevalence = improvement of species richness prediction over null-model prevalence.

If the community evalation metric community.AUC is selected the returned tables will have the following colums:

Community.AUC = The AUC of ROC of a given site (in this case the ROC plot is community sensitiviy [percentage species predicted corretly present] vs 1 - community specificity [percentage of species predicted correctly absent])

If the community evaluation metrics ('Max.Sorensen', 'Max.Jaccard') is selected the returned tables will have the follwing colums:

Max.Jaccard = The maximum possible Jaccard similarity based on an optimal site specific threshold.
Max.Sorensen = The maximum possible Sorensen similarity based on an optimal site specific threshold.

If the community evaluation metrics ('probabilistic.Sorensen', 'probabilistic.Jaccard') is selected the returned tables will have the follwing colums:

probabilistic.Jaccard = The probabilistic Jaccard similarity index based on Scherrer et al. 2019, Methods in Ecology and Evolution
probabilistic.Sorensen = The probabilistic Sorensen similarity index based on Scherrer et al. 2019, Methods in Ecology and Evolution
composition.imp.05 = improvement of species compostion prediction over the null-model 0.5.
composition.imp.average.SR = improvement of the species composition prediction over the null-model average.SR.
composition.imp.prevalence = improvement of the species composition prediction over the null-model prevalence.

For detailed descriptions of the null models see Scherrer et al. 2019

Author(s)

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

Examples

#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 = 2,
                                     minNbPredictors = 10,
                                     VarImport = 2)
                                     
  #Calculating the probabilistic community metrics
  metrics = c('SR.deviation','community.AUC','probabilistic.Sorensen','Max.Sorensen')
  myCCV.Eval.prob <- ecospat.CCV.communityEvaluation.prob(
          ccv.modeling.data = myCCV.Models, 
          community.metrics = metrics)

ecospat

Spatial Ecology Miscellaneous Methods

v3.2

GPL

Authors

Olivier Broennimann [cre, aut, ctb], Valeria Di Cola [aut, ctb], Blaise Petitpierre [ctb], Frank Breiner [ctb], Daniel Scherrer [ctb], Manuela D`Amen [ctb], Christophe Randin [ctb], Robin Engler [ctb], Wim Hordijk [ctb], Heidi Mod [ctb], Julien Pottier [ctb], Mirko Di Febbraro [ctb], Loic Pellissier [ctb], Dorothea Pio [ctb], Ruben Garcia Mateo [ctb], Anne Dubuis [ctb], Luigi Maiorano [ctb], Achilleas Psomas [ctb], Charlotte Ndiribe [ctb], Nicolas Salamin [ctb], Niklaus Zimmermann [ctb], Antoine Guisan [aut]

Initial release

2021-02-17