Calculates similarity-sensitive normalised subcommunity beta diversity (an
estimate of the effective number of distinct subcommunities). This
measure may be calculated for a series of orders, represented as a vector
of qs.
norm_sub_beta(meta, qs)object of class metacommunity
vector of mode numeric containing q values
norm_sub_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 subcommunity beta diversity
norm_sub_beta(meta, 0:2)
#> measure q type_level type_name partition_level partition_name
#> 1 normalised beta 0 types subcommunity a
#> 2 normalised beta 0 types subcommunity b
#> 3 normalised beta 1 types subcommunity a
#> 4 normalised beta 1 types subcommunity b
#> 5 normalised beta 2 types subcommunity a
#> 6 normalised beta 2 types subcommunity b
#> diversity dat_id transformation normalised k max_d
#> 1 1.000000 naive NA NA NA NA
#> 2 1.000000 naive NA NA NA NA
#> 3 1.016552 naive NA NA NA NA
#> 4 1.060660 naive NA NA NA NA
#> 5 1.031250 naive NA NA NA NA
#> 6 1.125000 naive NA NA NA NA