Get Convex Hull Polygons for Species from GBIF Data — get_range_convex

Retrieves occurrence data for species from GBIF, clusters spatial points using DBSCAN, and generates convex hull polygons for each cluster.

Usage

get_range_convex_hulls(
  data_frame,
  species_col = "species_name",
  species_name = NULL,
  num_cores = 1,
  min_points = 5,
  min_distance = 1,
  gbif_limit = 2000
)

Arguments

data_frame: A dataframe containing scientific names of species. Must include a column named species_name.
species_col: Character string specifying the column in data_frame that contains species names. Default is "species_name".
species_name: An optional character vector of species names to process. Default is NULL. If NULL, all species in the dataframe are processed.
num_cores: The number of cores to use for parallel processing. Default is 1 (no parallel processing).
min_points: The minimum number of points required to form a range cluster. Default is 5.
min_distance: The minimum distance between points in a range cluster. Default is 1 epsilon (eps).
gbif_limit: The maximum number of GBIF records to retrieve per species. Default is 2000.

Value

A named list where each element corresponds to a species and contains a list of sf polygon objects representing convex hulls.

Details

The function:

Retrieves occurrence records from GBIF using rgbif::occ_search().
Filters and cleans records (removing invalid coordinates, duplicates, and certain basis of record types).
Clusters spatial points using dbscan::dbscan() with eps = min_distance and minPts = min_points.
Computes convex hull polygons for each cluster using sf::st_convex_hull().

Parallelization is handled via parallel::mclapply() when num_cores > 1.

Examples

# Generate a test dataframe with species Abies cephalonica
if (FALSE) { # \dontrun{
test_data <- data.frame(species_name = c("Abies_cephalonica"))
# Retrieve the unclipped range polygons 
unclipped_hulls <- get_range_convex_hulls(test_data)
print(unclipped_hulls)
} # }