Spatial Functional Diversity Accumulation

Compute spatial accumulation of functional diversity metrics based on traits.

Usage

spaccFunc(
  x,
  coords,
  traits,
  metric = c("fdis", "fric"),
  n_seeds = 50L,
  method = "knn",
  distance = c("euclidean", "haversine"),
  parallel = TRUE,
  n_cores = NULL,
  progress = TRUE,
  seed = NULL,
  map = FALSE
)

Arguments

x

A site-by-species matrix (abundance data recommended).

coords

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

traits

A species-by-traits matrix. Row names should match species (columns of x).

metric

Character vector. Metrics to compute:

"fdis": Functional Dispersion (mean distance to centroid)
"fric": Functional Richness (convex hull volume approximation)

n_seeds

Integer. Number of random starting points. Default 50.

method

Character. Accumulation method. Default "knn".

distance

Character. Site distance method: "euclidean" or "haversine".

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 values for spatial mapping. Enables as_sf() and plot(type = "map"). Default FALSE.

Value

An object of class spacc_func containing:

curves: Named list of matrices, one per metric (n_seeds x n_sites)
metric: Metrics computed
coords, n_seeds, n_sites, method: Parameters used

Details

Functional diversity metrics quantify trait space occupation:

FDis (Functional Dispersion): Abundance-weighted mean distance from the community centroid in trait space. Captures functional divergence.
FRic (Functional Richness): Volume of trait space occupied (convex hull). Requires more species than traits to compute.

References

Laliberté, E. & Legendre, P. (2010). A distance-based framework for measuring functional diversity from multiple traits. Ecology, 91, 299-305.

Examples

# \donttest{
coords <- data.frame(x = runif(50), y = runif(50))
species <- matrix(rpois(50 * 20, 2), nrow = 50)

# Trait matrix (species x traits)
traits <- matrix(rnorm(20 * 3), nrow = 20)
rownames(traits) <- paste0("sp", 1:20)
colnames(species) <- rownames(traits)

func <- spaccFunc(species, coords, traits, metric = c("fdis", "fric"))
plot(func)
# }