Obtain Pseudoabsences

This function obtains pseudoabsences given a set of predictors.

Usage

pseudoabsences(occ,
               pred = NULL,
               method = "random",
               n_set = 10,
               n_pa = NULL,
               variables_selected = NULL,
               th = 0,
               size = 1,
               crs = 4326)

n_pseudoabsences(i)

pseudoabsence_method(i)

pseudoabsence_data(i)

Arguments

occ: A occurrences_sdm or input_sdm object.
pred: A sdm_area object. If NULL and occ is a input_sdm, pred will be retrieved from occ.
method: Method to create pseudoabsences. One of: "random", "bioclim", "mahal.dist" or "buffer_sdm".
n_set: numeric. Number of datasets of pseudoabsence to create.
n_pa: numeric. Number of pseudoabsences to be generated in each dataset created. If NULL then the function prevents imbalance by using the same number of presence records (n_records(occ)). If you want to address different sizes to each species, you must provide a named vector (as in n_records(occ)).
variables_selected: A vector with variables names to be used while building pseudoabsences. Only used when method is not "random".
th: numeric Threshold to be applied in bioclim/mahal.dist projections. See details.
size: numeric The distance between the record and the margin of the buffer (i.e. buffer radius).
crs: numeric Indicates which EPSG it the size in.
i: A input_sdm object.

Value

A occurrences_sdm or input_sdm object with pseudoabsence data.

Details

pseudoabsences is used in the SDM workflow to obtain pseudoabsences, a step necessary for most of the algorithms to run. We implemented three methods so far: "random", which is self-explanatory, "bioclim", "mahal.dist" and "buffer_sdm". The two last are built with the idea that pseudoabsences should be environmentally different from presences. Thus, we implemented two presence-only methods to infer the distribution of the species. "bioclim" uses an envelope approach (bioclimatic envelope), while "mahal.dist" uses a distance approach (mahalanobis distance). th parameter enters here as a threshold to binarize those results. Pseudoabsences are retrieved outside the projected distribution of the species.

n_pseudoabsences returns the number of pseudoabsences obtained per species.

pseudoabsence_method returns the method used to obtain pseudoabsences.

pseudoabsence_data returns a list of species names. Each species name will have a lists with pseudoabsences data from class sf.

Author

Luíz Fernando Esser (luizesser@gmail.com) https://luizfesser.wordpress.com

Examples

# Create sdm_area object:
sa <- sdm_area(parana, cell_size = 25000, crs = 6933)
#> ! Making grid over study area is an expensive task. Please, be patient!
#> ℹ Using GDAL to make the grid and resample the variables.

# Include predictors:
sa <- add_predictors(sa, bioc) |> select_predictors(c("bio1", "bio4", "bio12"))
#> ! Making grid over the study area is an expensive task. Please, be patient!
#> ℹ Using GDAL to make the grid and resample the variables.

# Include scenarios:
sa <- add_scenarios(sa)

# Create occurrences:
oc <- occurrences_sdm(occ, crs = 6933) |> join_area(sa)
#> Warning: Some records from `occ` do not fall in `pred`.
#> ℹ 2 elements from `occ` were excluded.
#> ℹ If this seems too much, check how `occ` and `pred` intersect.

# Create input_sdm:
i <- input_sdm(oc, sa)

# Pseudoabsence generation:
i <- pseudoabsences(i, method="random")