Functional Beta Diversity Accumulation

Compute spatial accumulation of functional beta diversity, partitioned into turnover and nestedness components. Measures how functional trait space composition changes as sites are accumulated spatially.

Usage

spaccBetaFunc(
  x,
  coords,
  traits,
  n_seeds = 50L,
  method = "knn",
  index = c("sorensen", "jaccard"),
  distance = c("euclidean", "haversine"),
  parallel = TRUE,
  n_cores = NULL,
  progress = TRUE,
  seed = NULL
)

Arguments

x: A site-by-species matrix (presence/absence or abundance).
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.
n_seeds: Integer. Number of random starting points. Default 50.
method: Character. Accumulation method. Default "knn".
index: Character. Dissimilarity index: "sorensen" (default) or "jaccard".
distance: Character. Distance method. Default "euclidean".
parallel: Logical. Use parallel processing? Default TRUE.
n_cores: Integer. Number of cores.
progress: Logical. Show progress? Default TRUE.
seed: Integer. Random seed.

Value

An object of class spacc_beta with additional attribute beta_type = "functional".

Details

Functional beta diversity quantifies the turnover of functional traits across space. At each accumulation step, beta is computed based on the overlap of trait ranges (functional space) between the accumulated pool and the newly added site.

References

Baselga, A. (2012). The relationship between species replacement, dissimilarity derived from nestedness, and nestedness. Global Ecology and Biogeography, 21, 1223-1232.

Cardoso, P., Rigal, F. & Carvalho, J.C. (2015). BAT – Biodiversity Assessment Tools. Methods in Ecology and Evolution, 6, 232-236.

Examples

# \donttest{
coords <- data.frame(x = runif(50), y = runif(50))
species <- matrix(rbinom(50 * 20, 1, 0.3), nrow = 50)
traits <- matrix(rnorm(20 * 3), nrow = 20)
rownames(traits) <- colnames(species) <- paste0("sp", 1:20)

beta_func <- spaccBetaFunc(species, coords, traits)
plot(beta_func)
# }