Skip to contents

Retrieve species occurrence records within an Area of Interest

Source: R/get_records.R
get_records.Rd

Retrieves species occurrence records from specified data providers within a given Area of Interest (AOI). The results are returned as an `sf` object containing point geometries. Duplicates can be removed based on geometry. This is a wrapper of the `occ` function from the spocc package.

Usage

get_records(species, aoi_sf, providers = NULL,
  limit = 500, remove_duplicates = FALSE, date = NULL)

Arguments

species

(`character`) Vector of species names to query.

aoi_sf

(`sf`) An `sf` object representing the Area of Interest. Must have a valid CRS.

providers

(`character`) Data providers to query (e.g., `"gbif"`, `"inat"`). Default is `NULL`, which queries all available providers.

limit

(`integer`) Maximum number of records to retrieve per provider. Default is 500.

remove_duplicates

(`logical`) Whether to remove duplicate geometries. Default is `FALSE`.

date

(`character`) Vector of length two specifying the date range (e.g., `c("YYYY-MM-DD", "YYYY-MM-DD")`). Records outside this range will be excluded. Default is `NULL` (no date filtering).

Value

(`sf`) An `sf` object containing species occurrence records within the specified AOI that match the query criteria. Returns `NULL` if no records are found or if input parameters are invalid.

Details

This function simplifies retrieving occurrence records by wrapping the functionality of the `spocc::occ` function. It handles AOI spatial filtering and optional removal of duplicates.

Examples

# \donttest{
library(sf)
nc <- sf::st_read(system.file("shape/nc.shp", package="sf"))
#> Reading layer `nc' from data source 
#>   `/home/runner/work/_temp/Library/sf/shape/nc.shp' using driver `ESRI Shapefile'
#> Simple feature collection with 100 features and 14 fields
#> Geometry type: MULTIPOLYGON
#> Dimension:     XY
#> Bounding box:  xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965
#> Geodetic CRS:  NAD27
records <- get_records(
  species = "Lynx rufus",
  aoi_sf = nc,
  providers = c("gbif", "inat"),
  limit = 200
)
#> Warning: attribute variables are assumed to be spatially constant throughout all geometries
head(records)
#> Simple feature collection with 6 features and 18 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: -81.58028 ymin: 36.29134 xmax: -81.44359 ymax: 36.35344
#> Geodetic CRS:  WGS 84
#> # A tibble: 6 × 19
#>   name   prov  date       key    AREA PERIMETER CNTY_ CNTY_ID NAME  FIPS  FIPSNO
#>   <chr>  <chr> <date>     <chr> <dbl>     <dbl> <dbl>   <dbl> <chr> <chr>  <dbl>
#> 1 Lynx … gbif  2023-01-02 4516… 0.114      1.44  1825    1825 Ashe  37009  37009
#> 2 Lynx … gbif  2023-03-29 4171… 0.114      1.44  1825    1825 Ashe  37009  37009
#> 3 Lynx … gbif  2023-03-10 4171… 0.114      1.44  1825    1825 Ashe  37009  37009
#> 4 Lynx … gbif  2023-03-06 4172… 0.114      1.44  1825    1825 Ashe  37009  37009
#> 5 Lynx … gbif  2023-04-28 4171… 0.114      1.44  1825    1825 Ashe  37009  37009
#> 6 Lynx … gbif  2023-04-03 4172… 0.114      1.44  1825    1825 Ashe  37009  37009
#> # ℹ 8 more variables: CRESS_ID <int>, BIR74 <dbl>, SID74 <dbl>, NWBIR74 <dbl>,
#> #   BIR79 <dbl>, SID79 <dbl>, NWBIR79 <dbl>, geometry <POINT [°]>
# }