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Coverage-Based Spatial Rarefaction

Source: R/coverage.R
spaccCoverage.Rd

Compute spatial accumulation curves with sample coverage tracking. Allows standardization by completeness (coverage) rather than sample size, following Chao & Jost (2012) or the sample-based estimator of Chiu (2023).

Usage

spaccCoverage(
  x,
  coords,
  n_seeds = 50L,
  method = "knn",
  distance = c("euclidean", "haversine"),
  coverage = c("chao", "chiu"),
  parallel = TRUE,
  n_cores = NULL,
  progress = TRUE,
  seed = NULL,
  map = FALSE
)

Arguments

x

A site-by-species matrix with abundance data.

coords

A data.frame with columns x and y, or a spacc_dist object.

n_seeds

Integer. Number of random starting points. Default 50.

method

Character. Accumulation method. Default "knn".

distance

Character. Distance method: "euclidean" or "haversine".

coverage

Character. Coverage estimator to use: "chao" (default) for the individual-based Chao & Jost (2012) estimator using singletons/doubletons, or "chiu" for the sample-based Chiu (2023) estimator using incidence frequency counts (Q1/Q2) and unique-species abundance (G1). The Chiu estimator is recommended for spatially aggregated data where sampling units are plots/sites rather than independent individuals.

parallel

Logical. Use parallel processing? Default TRUE.

n_cores

Integer. Number of cores.

progress

Logical. Show progress? Default TRUE.

seed

Integer. Random seed.

map

Logical. If TRUE, run accumulation from every site as seed and store per-site final coverage and richness for spatial mapping. Enables as_sf() and plot(type = "map"). Default FALSE.

Value

An object of class spacc_coverage containing:

richness

Matrix of species richness (n_seeds x n_sites)

individuals

Matrix of individual counts

coverage

Matrix of coverage estimates

coverage_type

Coverage estimator used ("chao" or "chiu")

coords, n_seeds, n_sites, method

Parameters used

Details

Sample coverage estimates the proportion of the total community abundance represented by observed species. It provides a measure of sampling completeness that is independent of sample size.

The Chao-Jost (2012) estimator counts singletons (f1) and doubletons (f2) in the cumulative abundance vector. It assumes individuals are sampled independently, which may not hold for plot-based spatial data.

The Chiu (2023) estimator uses incidence frequency counts instead: Q1 (species in exactly 1 site), Q2 (species in exactly 2 sites), and G1 (total abundance of Q1 species). It gives near-unbiased coverage estimates when organisms are spatially aggregated across sampling units.

Coverage-based rarefaction allows fair comparison of diversity across communities with different abundances by standardizing to equal completeness rather than equal sample size.

References

Chao, A. & Jost, L. (2012). Coverage-based rarefaction and extrapolation: standardizing samples by completeness rather than size. Ecology, 93, 2533-2547.

Chiu, C.-H. (2023). A sample-based estimator for sample coverage. Ecology, 104, e4099.

See also

iNEXT::iNEXT() for coverage-based rarefaction without spatial structure

Examples

# \donttest{
coords <- data.frame(x = runif(50), y = runif(50))
species <- matrix(rpois(50 * 30, 2), nrow = 50)

cov <- spaccCoverage(species, coords)
plot(cov)

# Sample-based coverage (recommended for spatial data)
cov_chiu <- spaccCoverage(species, coords, coverage = "chiu")

# Interpolate richness at 90% and 95% coverage
interp <- interpolateCoverage(cov, target = c(0.90, 0.95))
# }