Similarity-based diversity of a community

Calculates the diversity of order \(q\) of a probability vector according to a similarity matrix.

Usage

Dqz(NorP, q = 1, Z = diag(length(NorP)), ...)
bcDqz(Ns, q = 1, Z = diag(length(Ns)), Correction = "Best", CheckArguments = TRUE)
# S3 method for class 'ProbaVector'
Dqz(NorP, q = 1, Z = diag(length(NorP)), ..., 
  CheckArguments = TRUE, Ps = NULL)
# S3 method for class 'AbdVector'
Dqz(NorP, q = 1, Z = diag(length(NorP)), Correction = "Best", ..., 
  CheckArguments = TRUE, Ns = NULL)
# S3 method for class 'integer'
Dqz(NorP, q = 1, Z = diag(length(NorP)), Correction = "Best", ..., 
  CheckArguments = TRUE, Ns = NULL)
# S3 method for class 'numeric'
Dqz(NorP, q = 1, Z = diag(length(NorP)), Correction = "Best", ..., 
  CheckArguments = TRUE, Ps = NULL, Ns = NULL)

Arguments

Ps: A probability vector, summing to 1.
Ns: A numeric vector containing species abundances.
NorP: A numeric vector, an integer vector, an abundance vector (AbdVector) or a probability vector (ProbaVector). Contains either abundances or probabilities.
q: A number: the order of diversity. Default is 1.
Z: A relatedness matrix, i.e. a square matrix whose terms are all positive, strictly positive on the diagonal. Generally, the matrix is a similarity matrix, i.e. the diagonal terms equal 1 and other terms are between 0 and 1. Default is the identity matrix to calculate neutral diversity.
Correction: A string containing one of the possible corrections: "None" (no correction), "HorvitzThomson", "MarconZhang" or "Best", the default value. The "MarconZhang" correction assumes a similarity matrix.
...: Additional arguments. Unused.
CheckArguments: Logical; if TRUE, the function arguments are verified. Should be set to FALSE to save time when the arguments have been checked elsewhere.

Details

Diversity is calculated following Leinster and Cobbold (2012): it is the reciprocal of the (generalized) average (of order q) of the community species ordinariness.

A similarity matrix is used (as for Dqz), not a distance matrix as in Ricotta and Szeidl (2006). See the example.

Bias correction requires the number of individuals. Use bcHqz and choose the Correction. Correction techniques are from Marcon et al. (2014).

Currently, the "Best" correction is the max value of "HorvitzThomson" and "MarconZhang".

The functions are designed to be used as simply as possible. Dqz is a generic method. If its first argument is an abundance vector, an integer vector or a numeric vector which does not sum to 1, the bias corrected function bcDqz is called. Explicit calls to bcDqz (with bias correction) or to Dqz.ProbaVector (without correction) are possible to avoid ambiguity. The .integer and .numeric methods accept Ps or Ns arguments instead of NorP for backward compatibility.

Value

A named number equal to the calculated diversity. The name is that of the bias correction used.

References

Leinster, T. and Cobbold, C. (2012). Measuring diversity: the importance of species similarity. Ecology 93(3): 477-489.

Marcon, E., Zhang, Z. and Herault, B. (2014). The decomposition of similarity-based diversity and its bias correction. HAL hal-00989454(version 3).

Examples

# Load Paracou data (number of trees per species in two 1-ha plot of a tropical forest)
data(Paracou618)
# Prepare the similarity matrix
DistanceMatrix <- as.matrix(Paracou618.dist)
# Similarity can be 1 minus normalized distances between species
Z <- 1 - DistanceMatrix/max(DistanceMatrix)
# Calculate diversity of order 2
Dqz(Paracou618.MC$Ns, 2, Z)
#>    Best 
#> 1.48295