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Per-Site Accumulation Metrics

Source: R/metrics.R
spaccMetrics.Rd

Compute spatial accumulation metrics for each site as a starting point. Useful for identifying sites with high or low accumulation rates, visualizing spatial patterns in diversity, and understanding edge effects.

Usage

spaccMetrics(
  x,
  coords,
  metrics = c("slope_10", "half_richness", "auc"),
  method = c("knn", "kncn", "random"),
  distance = c("euclidean", "haversine"),
  parallel = TRUE,
  n_cores = NULL,
  progress = TRUE
)

Arguments

x

A site-by-species matrix (rows = sites, cols = species).

coords

A data.frame with columns x and y containing site coordinates, or a spacc_dist object from distances().

metrics

Character vector. Metrics to compute. Options include: "slope_10" (initial slope, first 10% of sites), "slope_25" (initial slope, first 25% of sites), "half_richness" (sites to reach 50% of total species), "richness_50pct" (alias for half_richness), "richness_75pct" (sites to reach 75% of species), "richness_90pct" (sites to reach 90% of species), "auc" (area under accumulation curve), "final_richness" (total species starting from this site).

method

Character. Accumulation method: "knn", "kncn", "random". Default "knn".

distance

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

parallel

Logical. Use parallel processing? Default TRUE.

n_cores

Integer. Number of cores for parallel processing.

progress

Logical. Show progress bar? Default TRUE.

Value

An object of class spacc_metrics containing:

metrics

Data frame with one row per site and columns for each metric

coords

Original coordinates

metric_names

Names of computed metrics

n_sites

Number of sites

n_species

Total species count

Details

This function runs a spatial accumulation curve starting from each site individually, then extracts summary metrics from each curve. This allows you to identify:

  • Sites in species-rich areas (high initial slope)

  • Core vs edge sites (fast vs slow accumulation)

  • Spatial patterns in community structure

The metrics can be plotted as a heatmap using plot(result, type = "heatmap"), which requires the ggplot2 package. For more sophisticated spatial visualization with study area boundaries, see the areaOfEffect package.

References

Soberon, J.M. & Llorente, J.B. (1993). The use of species accumulation functions for the prediction of species richness. Conservation Biology, 7, 480-488.

See also

spacc() for standard accumulation curves

Examples

# \donttest{
coords <- data.frame(x = runif(50), y = runif(50))
species <- matrix(rbinom(50 * 30, 1, 0.3), nrow = 50)
metrics <- spaccMetrics(species, coords,
                        metrics = c("slope_10", "auc"))
metrics$metrics$slope_10
# }