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Alpha-Beta-Gamma Diversity Partitioning

Source: R/diversity.R
diversityPartition.Rd

Decompose regional (gamma) diversity into local (alpha) and turnover (beta) components using multiplicative partitioning of Hill numbers.

Usage

diversityPartition(x, q = c(0, 1, 2), weights = "equal", coords = NULL)

Arguments

x

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

q

Numeric vector. Orders of diversity. Default c(0, 1, 2).

weights

Character or numeric. Site weights for alpha calculation:

  • "equal": Equal weights (default)

  • "proportional": Weights proportional to site abundance

  • Numeric vector of custom weights

coords

Optional data.frame with columns x and y for spatial mapping. When provided, enables as_sf() and plot(type = "map").

Value

An object of class spacc_partition containing:

alpha

Mean alpha diversity (effective number of species per site)

beta

Beta diversity (effective number of communities)

gamma

Gamma diversity (regional species pool)

q

Orders of diversity

n_sites

Number of sites

n_species

Total species count

Details

This function implements Jost (2007) multiplicative partitioning:

$$\gamma = \alpha \times \beta$$

Where:

  • Alpha: Mean effective number of species per site

  • Beta: Effective number of distinct communities (1 = all identical, n_sites = all completely different)

  • Gamma: Total effective number of species in the region

Beta diversity is interpreted as the effective number of communities:

  • Beta = 1: All sites have identical composition

  • Beta = n_sites: Sites share no species

References

Jost, L. (2007). Partitioning diversity into independent alpha and beta components. Ecology, 88, 2427-2439.

Chao, A., Chiu, C.H. & Jost, L. (2014). Unifying species diversity, phylogenetic diversity, functional diversity, and related similarity and differentiation measures through Hill numbers. Annual Review of Ecology, Evolution, and Systematics, 45, 297-324.

See also

alphaDiversity(), gammaDiversity(), spaccBeta() for spatial beta diversity accumulation

Examples

# Simulated community data
species <- matrix(rpois(50 * 30, 2), nrow = 50)

# Partition diversity
part <- diversityPartition(species, q = c(0, 1, 2))
print(part)

# Beta near 1 = homogeneous region
# Beta near n_sites = heterogeneous region