A multifunctionality index rooted in Hill numbers. getMF_eff get multifunctionality index defined by function and effective number of functions

getMF_eff(
  data,
  vars,
  q = 1,
  standardized = FALSE,
  standardize_function = standardizeUnitScale,
  D = NULL,
  tau = NULL
)

Arguments

data

A data frame with functions in columns and rows as replicates as well as other information.

vars

Name of function variables

q

Order of the diversity measure. Defaults to the Shannon case where q = 1. For Simpson, q=2.

standardized

Use standardized number of functions (scaled by total number of functions, so between 0-1), or just raw effective number of functions for calculation. Defaults to FALSE.

standardize_function

A function to standardize each individual function to the same scale, such as standardizeUnitScale or standardizeZScore

D

A distance matrix describing dissimilarity between functions. Defaults to NULL, and the index is calculated assuming all functions are different. If it is not null, it must be a symmetric matrix with dimensions matching the number of functions listed in vars.

tau

A cutoff for degree of dissimilarity under which functions are considered to be different. If tau is the minimum non-zero value of D, all functions are different. if tau is the maximum value of D are greater, all functions are considered the same.

Value

Returns a vector of effective or standardized effective multifunctionality.

Details

Takes a data frame, variable names, a standardizing function, whether we want an index standardized by number of functions or not, an order of Hill number for our effective number of functions as well as a dissimilarity matrix (if desired) and value for a dissimilarity cutoff (defaults to the average dissimilarity). It then calculates both the average standardized function in each plot and the effective number of functions and returns their product as a measure of effective multifunctionality.

References

Chao, A., Chiu, C.-H., Villéger, S., Sun, I.-F., Thorn, S., Lin, Y.-C., Chiang, J.-M. and Sherwin, W. B. 2019. An attribute-diversity approach to functional diversity, functional beta diversity, and related (dis)similarity measures. Ecological Monographs. 89: e01343.

Jost, L. 2006. Entropy and diversity. Oikos 113(2): 363-375.

Hill, M. 1973. Diversity and evenness: A unifying notation and its consequences. Ecology 54: 427-432.

Author

Jarrett Byrnes.