Diversity.jl

A package for measuring and partitioning diversity

The main Diversity package provides basic numbers-equivalent diversity measures (described in Hill, 1973), similarity-sensitive diversity measures (generalised from Hill, and described in Leinster and Cobbold, 2012), and related alpha, beta and gamma diversity measures at the level of the metacommunity and its component subcommunities (generalised in turn from Leinster and Cobbold, and described in Reeve et al, 2014). The diversity functions exist both with unicode names (e.g. ᾱ()), which are not automatically exported (as we feel they are too short) and with matching longer ASCII names (e.g. NormalisedAlpha()), which are. We also provide functions to calculate appropriate subdiv() and metadiv() values for each measure, a general diversity() function for extract any diversity measure at a series of scales.

Accessing the main functionality in the package is simple:

julia> # Load the package into R
       using Diversity

julia> # Example population
       pop = [1 1 0; 2 0 0; 3 1 4];

julia> pop = pop ./ sum(pop);

julia> # Create Metacommunity object
       meta = Metacommunity(pop);

julia> diversities = norm_meta_alpha(meta, [0, 1, 2, Inf])
4×8 DataFrame
 Row │ div_type  measure          q        type_level  type_name  partition_level  partition_name  diversity 
     │ String    String           Float64  String      String     String           String          Float64   
─────┼───────────────────────────────────────────────────────────────────────────────────────────────────────
   1 │ Unique    NormalisedAlpha      0.0  types                  metacommunity                      2.16667
   2 │ Unique    NormalisedAlpha      1.0  types                  metacommunity                      1.86121
   3 │ Unique    NormalisedAlpha      2.0  types                  metacommunity                      1.63636
   4 │ Unique    NormalisedAlpha    Inf    types                  metacommunity                      1.0

julia> Z = [1.0 0 0; 0 1 1; 1 1 1];

julia> meta_z = Metacommunity(pop, Z);

julia> rho = RawRho(meta_z);

julia> redundancies = subdiv(rho, 2)
3×8 DataFrame
 Row │ div_type     measure  q      type_level  type_name  partition_level  partition_name  diversity 
     │ String       String   Int64  String      String     String           String          Float64   
─────┼────────────────────────────────────────────────────────────────────────────────────────────────
   1 │ Arbitrary Z  RawRho       2  types                  subcommunity     1                     2.0
   2 │ Arbitrary Z  RawRho       2  types                  subcommunity     2                     3.0
   3 │ Arbitrary Z  RawRho       2  types                  subcommunity     3                     3.0

Diversity.Diversity — Module

Diversity package

The main Diversity package provides basic numbers-equivalent diversity measures (described in Hill, 1973), similarity-sensitive diversity measures (generalised from Hill, and described in Leinster and Cobbold, 2012), and related alpha, beta and gamma diversity measures at the level of the metacommunity and its component subcommunities (generalised in turn from Leinster and Cobbold, and described in Reeve et al, 2014). The diversity functions exist both with unicode names (e.g. ᾱ()), which are not automatically exported (as we feel they are too short) and with matching longer ASCII names (e.g. NormalisedAlpha()), which are. We also provide functions to calculate appropriate subcommunityDiversity() and metacommunityDiversity() values for each measure, a general diversity() function for extract any diversity measure at a series of scales.