Measurement and Partitioning of Similarity-Sensitive Biodiversity

Provides a framework for the measurement and partitioning of the (similarity-sensitive) biodiversity of a metacommunity and its constituent subcommunities. Richard Reeve, et al. (2016) .

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rdiversity is a package for R based around a framework for measuring biodiversity using similarity-sensitive diversity measures. It provides functionality for measuring alpha, beta and gamma diversity of metacommunities (e.g. ecosystems) and their constituent subcommunities, where similarity may be defined as taxonomic, phenotypic, genetic, phylogenetic, functional, and so on. It uses the diversity framework described in the arXiv paper arXiv:1404.6520 (q-bio.QM), How to partition diversity.

This package has now reached a stable release and is cross-validated against our Julia package Diversity.jl, which is developed independently. Please raise an issue if you find any problems.


To install rdiversity from CRAN, simply run the following from an R console:


Getting started

Before calculating diversity a metacommunity object must be created. This object contains all the information needed to calculate diversity.

# Load the package into R
# Example population
pop <- data.frame(a=c(1,1,0),b=c(2,0,0),c=c(3,1,0))
# Create metacommunity object
meta <- metacommunity(pop)

The metacommunity() function takes two arguments, partition and similarity, and creates an object containing:

  • @type_abundance : the abundance of types within a population,
  • @similarity : the pair-wise similarity of types within a population,
  • @ordinariness : the ordinariness of types within a population,
  • @subcommunity_weights : the relative weights of subcommunities within a population,
  • @type_weights : the relative weights of types within a population, and

A metcommunity originating from a phylogeny will contain three additional slots:

  • @raw_abundance : the relative abundance of present-day species (where types are then considered to be historical species),
  • @raw_structure : the length of evolutionary history of each historical species
  • @parameters : parameters associated with historical species

Calculating diversity

First we need to calculate the low-level diversity component seperately, by passing a metacommunity object to the appropriate function; raw_alpha(), norm_alpha(), raw_beta(), norm_beta(), raw_rho(), norm_rho(), or raw_gamma().

# First, calculate the normalised subcommunity alpha component
component <- norm_alpha(meta)

Afterwhich, subdiv() or metadiv() are used to calculate subcommunity or metacommunity diversity, respectively (since both subcommunity and metacommunity diversity measures are transformations of the same low-level components, this is computationally more efficient).

# Then, calculate species richness
subdiv(component, 0)
# or the average species richness across the whole population
metadiv(component, 0)
# We can also generate a diversity profile (for a single diversity measure, or multiple measures of the same level) by calculating multiple q-values simultaneously
df <- subdiv(component, 0:30)

In some instances, it may be useful to calculate all subcommunity (or metacommunity) measures. In which case, a metacommunity object may be passed directly to subdiv() or metadiv():

# To calculate all subcommunity diversity measures
subdiv(meta, 0:2)
# To calculate all metacommunity diversity measures
metadiv(meta, 0:2)

Alternatively, if computational efficiency is not an issue, a single measure of diversity may be calculated directly by calling a wrapper function:

norm_sub_alpha(meta, 0:2)

A complete list of these functions is shown below:

  • raw_sub_alpha() : estimate of naive-community metacommunity diversity
  • norm_sub_alpha() : similarity-sensitive diversity of subcommunity j in isolation
  • raw_sub_rho() : redundancy of subcommunity j
  • norm_sub_rho() : representativeness of subcommunity j
  • raw_sub_beta() : distinctiveness of subcommunity j
  • norm_sub_beta() : estimate of effective number of distinct subcommunities
  • raw_sub_gamma() : contribution per individual toward metacommunity diversity
  • raw_meta_alpha() : naive-community metacommunity diversity
  • norm_meta_alpha() : average similarity-sensitive diversity of subcommunities
  • raw_meta_rho() : average redundancy of subcommunities
  • norm_meta_rho() : average representativeness of subcommunities
  • raw_meta_beta() : average distinctiveness of subcommunities
  • norm_meta_beta() : effective number of distinct subcommunities
  • meta_gamma() : metacommunity similarity-sensitive diversity

General tools

  • qD_single() : the Hill number / naive-type diversity of order q of a single population
  • qD() : the Hill number / naive-type diversity of a series of independent populations for a series of orders
  • qDZ_single() : the similarity-sensitive diversity of order q of a single population
  • qDZ() : the similarity-sensitive diversity of a series of independent populations for a series of orders
  • similarity_shimatani()
  • similarity_phylo()


rdiversity v1.0 (Release date: 2016-06-14)


  • Initial submission to CRAN

rdiversity v1.1 (Release date: 2018-05-08)


  • Removed redundant argument chainsaw(depth); Renamed chainsaw(interval) to chainsaw(depth); Minor edits to documentation.

rdiversity v1.2 (Release date: 2018-06-12)


  • Allow partition types to be input in a random order, or with missing or additional types; Tests added for new functionality.

rdiversity v1.2.1 (Release date: 2018-06-19)


  • Fix problems that occur when user doesn't have row names in the partition object
  • Update citation

Reference manual

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2.0 by Sonia Mitchell, 2 years ago

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Browse source code at

Authors: Sonia Mitchell [cre, aut] , Richard Reeve [aut, ths] , Tom White [ctb]

Documentation:   PDF Manual  

GPL-3 license

Imports binaryLogic, methods, reshape2, stats, utils

Suggests ape, testthat, knitr, rmarkdown, covr, vcfR

Imported by econetwork.

See at CRAN