Functional Diversity Profile

Compute functional Hill numbers (Leinster & Cobbold 2012) across a continuous range of diversity orders (q), producing a functional diversity profile based on trait similarity.

Usage

diversityProfileFunc(
  x,
  traits,
  q = seq(0, 3, by = 0.1),
  type = c("both", "per_site", "regional"),
  dist_method = c("euclidean", "gower"),
  normalize = TRUE,
  coords = NULL
)

Arguments

x: A site-by-species matrix (abundance data). Column names must match row names in traits.
traits: A data.frame of species traits. Row names must match column names in x.
q: Numeric vector. Orders of diversity. Default seq(0, 3, by = 0.1).
type: Character. What to compute: "per_site", "regional", or "both" (default).
dist_method: Character. Distance method for trait matrix: "euclidean" (default) or "gower".
normalize: Logical. Normalize distances to [0, 1]? Default TRUE.
coords: Optional data.frame with x and y for spatial mapping.

Value

An object of class spacc_profile with $profile_type = "functional".

Details

Functional Hill numbers (Leinster & Cobbold 2012) incorporate trait similarity via a similarity matrix Z = 1 - D. When all species are maximally dissimilar (Z = identity), this reduces to standard Hill numbers.

References

Leinster, T. & Cobbold, C.A. (2012). Measuring diversity: the importance of species similarity. Ecology, 93, 477-489.

Examples

# \donttest{
species <- matrix(rpois(20 * 10, 2), nrow = 20,
                  dimnames = list(NULL, paste0("sp", 1:10)))
traits <- data.frame(
  body_size = rnorm(10), diet = rnorm(10),
  row.names = paste0("sp", 1:10)
)
prof <- diversityProfileFunc(species, traits)
print(prof)
# }