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Estimate of number of locations (occupied-grid-cell method, optionally protected-area aware)

Source: R/subpop_locations.R
calc_locations.Rd

Estimates the number of "locations" (in the IUCN sense: geographically or ecologically distinct areas in which a single threatening event can rapidly affect all individuals) by overlaying a regular grid and counting the occupied cells, optionally decoupling occurrences that fall inside protected areas from those outside. This mirrors the two complementary approaches used by the ConR package (Dauby et al. 2017): the grid method, and the protected-area integration (method_protected).

Usage

calc_locations(
  points,
  grid_km = 10,
  n_rep = 100,
  protected = NULL,
  method_protected = c("no_more_than_one", "other"),
  cell_scale = NULL
)

Arguments

points

An sf of POINT geometries (one species).

grid_km

Grid cell side length in kilometres (default 10). Ignored when cell_scale is supplied.

n_rep

Integer; number of translated grids to test (default 100). The minimum occupied-cell count over the replicates is returned.

protected

Optional protected-area layer (an sf/sfc of polygons, e.g. from protected_areas). NULL (default) uses the plain grid method.

method_protected

How protected areas enter the count when protected is given: "no_more_than_one" (default) or "other". See Details.

cell_scale

Optional numeric fraction for the sliding-scale cell size: the cell side becomes cell_scale times the maximum distance between two occurrences. When supplied it overrides grid_km.

Value

A list with n_locations (integer estimate), grid_km (the cell size actually used), method ("grid", "no_more_than_one" or "other"), n_out / n_in (locations outside / inside protected areas; n_in is NA for the plain grid), n_cells, cells (gridded location cells as an sfc in WGS84), pa_locations (the protected areas counted as locations under "no_more_than_one", else NULL) and crs_laea.

Details

As for the AOO, the occupied-cell count depends on where the grid is placed, so several translated grids (a deterministic lattice) are tested and the smallest count is returned (the most conservative, i.e. most threatened, estimate). The grid is built on a data-centred Lambert Azimuthal Equal-Area projection and this function reuses mappingAS's own AOO grid machinery, so it differs from calc_aoo only in the (larger) default cell size.

Cell size. By default a fixed cell of grid_km (10 km) is used, representing the scale at which a single threat could affect the whole occupied cell. Alternatively a species-specific "sliding scale" (Rivers et al. 2010) is available: set cell_scale to a fraction (e.g. 0.05) and the cell side becomes that fraction of the maximum distance between two occurrences (cell_scale then overrides grid_km).

Protected areas. When protected is a protected-area layer (e.g. from protected_areas), occurrences are split into those inside and outside protected areas, because a subpopulation inside a protected area is not subject to the same threats as one outside:

  • method_protected = "no_more_than_one" (default): every protected area holding at least one occurrence counts as exactly one location (all occurrences within a protected area share a location); occurrences outside protected areas are gridded as usual. Overlapping or nested designations (common in WDPA, e.g. a national park inside an environmental protection area) are counted once: the smallest set of protected areas that holds every inside occurrence is used. The total is (occupied cells outside) + (size of that set).

  • method_protected = "other": occurrences inside protected areas are gridded separately from those outside, and the two counts are added. Two occurrences closer than the cell size are then still counted as separate locations when one is inside and the other outside a protected area.

With protected = NULL (the default) no protected-area information is used and the plain grid count is returned.

References

Dauby G. et al. (2017) ConR: An R package to assist large-scale multispecies preliminary conservation assessments using distribution data. Ecology and Evolution 7:11292-11303. doi:10.1002/ece3.3704

Rivers M.C. et al. (2010) How many herbarium specimens are needed to detect threatened species? Biological Conservation 143:2545-2560.

IUCN Standards and Petitions Committee. Guidelines for Using the IUCN Red List Categories and Criteria.

See also

calc_subpop, calc_aoo, protected_areas

Examples

f <- system.file("extdata", "example_occurrences.csv", package = "mappingAS")
occ <- read_occurrences(f)
sp1 <- occ[occ$species == occ$species[1], ]
calc_locations(sp1)$n_locations
#> [1] 4