Estimate of number of subpopulations (circular-buffer method)

Estimates the number of subpopulations by the "circular buffer" method: a circle of radius resol_km is drawn around every occurrence, the circles are dissolved (unioned), and the number of resulting disjoint polygons is the estimated number of subpopulations. Occurrences whose buffers overlap are treated as belonging to the same subpopulation.

Usage

calc_subpop(points, resol_km = NULL)

Arguments

points

An sf of POINT geometries (one species). Use read_occurrences to produce it.

resol_km

Circle radius in kilometres. NULL (default) uses one tenth of the maximum distance between two occurrences.

Value

A list with n_subpop (integer estimate), resol_km (the radius actually used), n_unique (distinct coordinates), subpop (the dissolved subpopulation polygons as an sfc in WGS84, or NULL) and crs_laea (the equal-area proj string).

Details

This is the approach popularised by the ConR package (Dauby et al. 2017) and originally proposed by Rivers et al. (2010). The buffering is done on a data-centred Lambert Azimuthal Equal-Area projection (see laea_crs), consistent with the EOO/AOO computations elsewhere in mappingAS.

The buffer radius follows the widely used default of one tenth of the greatest distance separating two occurrences (Rivers et al. 2010): when resol_km is NULL it is computed from the data. Because the most distant pair of points always lies on the convex hull, the maximum distance is measured over the hull vertices only (great-circle distance on the WGS84 ellipsoid), which keeps the computation fast for large datasets.

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.

See also

calc_locations, calc_aoo

Examples

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