This function reads the models generated either by do_any or do_many (i.e. one model per partition per algorithm) and summarizes them into a final model for each species-algorithm combination. All the final models are created from the mean of the raw continuous models (raw_mean). From these means, several outputs can be created, see which_models for details about the final outputs available. The uncertainty between partitions, taken as ranges (maximum - minimum values) between partitions may also be calculated. Just as there are no silver bullets in correlative ecological niche modeling, no method for presenting this models is always better and these are only a subset of the possibilities.

final_model(species_name, algorithms = NULL, scale_models = TRUE,
consensus_level = 0.5, models_dir = "./models",
final_dir = "final_models", proj_dir = "present",
which_models = c("raw_mean"), mean_th_par = c("spec_sens"),
uncertainty = FALSE, png_final = TRUE, sensitivity = 0.9, ...)

Arguments

species_name

A character string with the species name. Because species name will be used as a directory name, avoid non-ASCII characters, spaces and punctuation marks. Recommendation is to adopt "Genus_species" format. See names in example_occs as an example

algorithms

Character vector specifying which algorithms will be processed. Note that it can have length > 1, ex. c("bioclim", "rf"). Defaults to NULL: if no name is given it will process all algorithms present in the evaluation files

scale_models

Logical. Whether input models should be scaled between 0 and 1

consensus_level

Which proportion of binary models will be kept when creating bin_consensus

models_dir

Character. Folder path where the input files are located

final_dir

Character. Name of the folder to save the output files. A subfolder will be created, defaults to "final_model"

proj_dir

Character. The name of the subfolder with the projection. Defaults to "present" but can be set according to the other projections (i.e. to execute the function in projected models)

which_models

Which final_model will be used? Currently it can be:

raw_mean

Continuous model generated by the mean of the raw models (scale from 0 to 1)

raw_mean_th

Cuts the raw_mean by the mean of the threshold selected in mean_th_par to make a binary model

raw_mean_cut

Recovers raw_mean values above the threshold selected in mean_th_par. Generates a continuous model above this threshold while keeping null values below it

bin_mean

The mean of the binary models, created by cutting the raw mean models by the threshold selected in mean_th_par. Generates a model in a discrete scale (0 to 1 in 1/n intervals where n is the number of partitions)

bin_consensus

The binary consensus from bin_mean. Parameter consensus_level must be defined, 0.5 means a majority consensus

mean_th_par

A threshold that will be used to cut the raw mean models if which_models is set to "raw_mean_th" or to transform the raw mean models into binary models if which_models is set to "bin_consensus". Defaults to "spec_sens" but any dismo threshold can be used (see function threshold): "kappa", "no_omission", "prevalence", "equal_spec_sens", "sensitivity"

uncertainty

Whether an uncertainty map, measured as range (max-min) should be calculated

png_final

Logical. If TRUE, writes png files of the final models

sensitivity

The sensitivity value for threshold "sensitivity". Defaults to 0.9

...

Other parameters from writeRaster, especially overwrite = TRUE, when needed

Value

Returns a data frame with final statistics of the partitions included in the final model

Writes on disk a data frame with mean statistics of the partitions included in the final model

Writes on disk a set of ecological niche models (.tif files) in the final_dir subfolder

If png_final = TRUE writes .png figures in the final_dir subfolder

References

There are no references for Rd macro \insertAllCites on this help page.

threshold in dismo package

writeRaster in raster package

Examples

if (FALSE) {
# run setup_sdmdata
sp <- names(example_occs)[1]
sp_coord <- example_occs[[1]]
sp_setup <- setup_sdmdata(species_name = sp,
occurrences = sp_coord,
predictors = example_vars,
clean_uni = TRUE)

# run do_any
sp_bioclim <- do_any(species_name = sp,
predictors = example_vars,
algorithm = "bioclim")

# run final_model
sp_final <- final_model(species_name = sp,
algorithms = "bioclim",
which_models = c("bin_consensus"),
consensus_level = 0.5,
overwrite = TRUE)
}