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Generate presence/pseudo-absence dataset stratified in environmental space

Source: R/h3sdm_pa.R
h3sdm_pa.Rd

Combines presence hexagons with pseudo-absences sampled in environmental space. Pseudo-absences are selected by clustering the environmental conditions of hexagons without presence records using k-means, then choosing the hexagon closest to each cluster centroid. This ensures pseudo-absences cover the full range of environmental conditions available in the AOI, reducing bias from spatially clustered occurrence records.

Usage

h3sdm_pa(pres_sf, predictors_sf, n_pseudoabs = 500, buffer_k = 1L)

Arguments

pres_sf

sf Presence hexagons returned by h3sdm_pres().

predictors_sf

sf Full hexagonal grid with extracted environmental variables, returned by h3sdm_predictors().

n_pseudoabs

integer Number of pseudo-absence hexagons to sample. If larger than the number of available hexagons without presence, all available hexagons are used. Default is 500.

buffer_k

integer Number of H3 grid rings to exclude around each presence hexagon when building the pseudo-absence candidate pool. Hexagons within buffer_k rings of any presence are removed before sampling, preventing pseudo-absences from being placed in areas likely occupied but not yet recorded. Default is 1. Set to 0 to disable.

Value

sf object with columns:

  • h3_address: H3 index of the hexagon.

  • presence: factor with levels "0" (pseudo-absence) and "1" (presence).

  • geometry: MULTIPOLYGON of each hexagon.

Details

The function scales all numeric predictor columns before clustering. Non-numeric columns and columns with zero variance are excluded from clustering. Pseudo-absences are selected as the hexagon nearest to each k-means centroid in scaled environmental space (Euclidean distance).

This function is designed to be used after h3sdm_pres() and h3sdm_predictors() in the following workflow:


pres        <- h3sdm_pres("Species name", aoi_sf, res = 7)
num_vars    <- h3sdm_extract_num(raster_stack, grid)
predictors  <- h3sdm_predictors(num_vars)
pa          <- h3sdm_pa(pres, predictors, n_pseudoabs = 500)

Examples

if (FALSE) { # \dontrun{
data(cr_outline_c, package = "h3sdm")
pres       <- h3sdm_pres("Agalychnis callidryas", cr_outline_c, res = 7)
grid       <- h3sdm_get_grid(cr_outline_c, res = 7)
num_vars   <- h3sdm_extract_num(bio, grid)
predictors <- h3sdm_predictors(num_vars)
pa         <- h3sdm_pa(pres, predictors, n_pseudoabs = 300)
} # }