Calculates similarity-sensitive normalised metacommunity beta diversity (the effective number of distinct subcommunities. This measure may be calculated for a series of orders, represented as a vector of qs.

norm_meta_beta(meta, qs)



object of class metacommunity


vector of mode numeric containing q values


norm_meta_beta returns a standard output of class rdiv


R. Reeve, T. Leinster, C. Cobbold, J. Thompson, N. Brummitt, S. Mitchell, and L. Matthews. 2016. How to partition diversity. arXiv 1404.6520v3:1–9.


pop <- data.frame(a = c(1,3), b = c(1,1))
row.names(pop) <- paste0("sp", 1:2)
pop <- pop/sum(pop)
meta <- metacommunity(pop)

# Calculate normalised metacommunity beta diversity
norm_meta_beta(meta, 0:2)
#>           measure q type_level type_name partition_level partition_name
#> 1 normalised beta 0      types             metacommunity               
#> 2 normalised beta 1      types             metacommunity               
#> 3 normalised beta 2      types             metacommunity               
#>   diversity dat_id transformation normalised  k max_d
#> 1  1.000000  naive             NA         NA NA    NA
#> 2  1.031047  naive             NA         NA NA    NA
#> 3  1.060714  naive             NA         NA NA    NA