BIOMOD_PresenceOnly: Evaluate models with presence-only metrics
In biomod2: Ensemble Platform for Species Distribution Modeling

BIOMOD_PresenceOnly

R Documentation

Evaluate models with presence-only metrics

Description

This function computes presence-only evaluation metrics (Boyce index and Minimal Predicted Area) for BIOMOD.models.out or BIOMOD.ensemble.models.out objects that can be obtained with the BIOMOD_Modeling or BIOMOD_EnsembleModeling functions.

Usage

BIOMOD_PresenceOnly(bm.mod = NULL, bm.em = NULL, bg.env = NULL, perc = 0.9)

Arguments

`bm.mod`	a `BIOMOD.models.out` object returned by the `BIOMOD_Modeling` function
`bm.em`	a `BIOMOD.ensemble.models.out` object returned by the `BIOMOD_EnsembleModeling` function
`bg.env`	(optional, default `NULL`) A `matrix`, `data.frame`, `SpatVector` or `SpatRaster` object containing values of environmental variables (in columns or layers) extracted from the background (if presences are to be compared to background instead of absences or pseudo-absences selected for modeling) Note that old format from raster and sp are still supported such as `RasterStack` and `SpatialPointsDataFrame` objects.
`perc`	a `numeric` between `0` and `1` corresponding to the percentage of correctly classified presences for Minimal Predicted Area (see `ecospat.mpa()` in ecospat)

Details

em.by parameter of BIOMOD_EnsembleModeling must have been set to PA+run in order to have an ensemble for each RUN of the NbRunEval parameter of the BIOMOD_Modeling function for evaluation.

The Boyce index returns NA values for SRE models because it can not be calculated with binary predictions.
This is also the reason why some NA values might appear for GLM models if they do not converge.

Value

A data.frame containing evaluation scores both for the evaluation metrics used in the BIOMOD_Modeling function and additional Boyce index and Minimal Predicted Area.

Note

In order to break dependency loop between packages biomod2 and ecospat, code of ecospat.boyce() and ecospat.mpa() in ecospat) functions have been copied within this file from version 3.2.2 (august 2022).

Author(s)

Frank Breiner, Maya Gueguen

References

Engler, R., Guisan, A., and Rechsteiner L. 2004. An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudo-absence data. Journal of Applied Ecology, 41(2), 263-274.
Hirzel, A. H., Le Lay, G., Helfer, V., Randin, C., and Guisan, A. 2006. Evaluating the ability of habitat suitability models to predict species presences. Ecological Modelling, 199(2), 142-152.

Examples

library(terra)

# Load species occurrences (6 species available)
data(DataSpecies)
head(DataSpecies)

# Select the name of the studied species
myRespName <- 'GuloGulo'

# Get corresponding presence/absence data
myResp <- as.numeric(DataSpecies[, myRespName])

# Get corresponding XY coordinates
myRespXY <- DataSpecies[, c('X_WGS84', 'Y_WGS84')]

# Load environmental variables extracted from BIOCLIM (bio_3, bio_4, bio_7, bio_11 & bio_12)
data(bioclim_current)
myExpl <- terra::rast(bioclim_current)



# --------------------------------------------------------------- #
file.out <- paste0(myRespName, "/", myRespName, ".AllModels.models.out")
if (file.exists(file.out)) {
  myBiomodModelOut <- get(load(file.out))
} else {

  # Format Data with true absences
  myBiomodData <- BIOMOD_FormatingData(resp.var = myResp,
                                       expl.var = myExpl,
                                       resp.xy = myRespXY,
                                       resp.name = myRespName)

  # Create default modeling options
  myBiomodOptions <- BIOMOD_ModelingOptions()

  # Model single models
  myBiomodModelOut <- BIOMOD_Modeling(bm.format = myBiomodData,
                                      modeling.id = 'AllModels',
                                      models = c('RF', 'GLM'),
                                      bm.options = myBiomodOptions,
                                      CV.strategy = 'random',
                                      CV.nb.rep = 2,
                                      CV.perc = 0.8,
                                      metric.eval = c('TSS','ROC'),
                                      var.import = 3,
                                      seed.val = 42)
}


file.EM <- paste0(myRespName, "/", myRespName, ".AllModels.ensemble.models.out")
if (file.exists(file.EM)) {
  myBiomodEM <- get(load(file.EM))
} else {

  # Model ensemble models
  myBiomodEM <- BIOMOD_EnsembleModeling(bm.mod = myBiomodModelOut,
                                        models.chosen = 'all',
                                        em.by = 'all',
                                        em.algo = c('EMmean', 'EMca'),
                                        metric.select = c('TSS'),
                                        metric.select.thresh = c(0.7),
                                        metric.eval = c('TSS', 'ROC'),
                                        var.import = 3,
                                        seed.val = 42)
}


# --------------------------------------------------------------- #
# # Evaluate models with Boyce index and MPA
# myBiomodPO <- BIOMOD_PresenceOnly(bm.mod = myBiomodModelOut,
#                                   bm.em = myBiomodEM)
# myBiomodPO
# 
# # Evaluate models with Boyce index and MPA (using background data)
# myBiomodPO <- BIOMOD_PresenceOnly(bm.mod = myBiomodModelOut,
#                                   bm.em = myBiomodEM, 
#                                   bg.env = myExpl)
# myBiomodPO

biomod2 documentation built on July 9, 2023, 6:05 p.m.