Testing difference between diversity indices with vegan::oecosimu

Hello all,

I'd like to test if total diversity differs between two communities. For
each community several samples were taken and abundances collapsed over
groups to compute total diversity for each group. I tried to use
vegan::oecosimu to test non-randomness of my statisitc (difference in
Simpson-Diversity indices of collapsed abundances) - however, I am not
quite sure if I oversee posssible pitfalls:

library(vegan)
data(dune)

# a grouping variable:
gr <- gl(2, nrow(dune)/2)

divdiff <- function(x) abs(diversity(colSums(x[gr == "1", ]), "simp") -
? ? ? ? ? ? ? ? ? ? ? ? ? diversity(colSums(x[gr == "2", ]), "simp"))
# testing function:
divdiff(dune)

oecosimu(dune, divdiff, "r2dtable", nsimul = 1999)
# oecosimu with 1999 simulations
# simulation method r2dtable
# alternative hypothesis: true mean is not equal to the statistic
# ? ? ? ? ? statistic ? ? ? ?z ? ? 2.5% ? ? ?50% 97.5% Pr(sim.)
# statistic ? 0.00275 -0.20996 ?0.00013 ?0.00280 ?0.01 ? ? 0.98

Dear Kay,

I am not sure about any possible pitfalls with your approach, but I
have tested the same data using the randomisation functions of the
"rich" library, and found that neither the Simpson diversity nor the
simple species richness differ significantly among the defined groups.

Here are the results following your example:

library(rich)

# prepare data
one <- as.data.frame(dune[gr == "1", ])
two <- as.data.frame(dune[gr == "2", ])

data <- list(one, two)

# compare cumulative species richness
c2cv(com1=data[[1]],com2=data[[2]],nrandom=1999)
#$res
#
#cv1                  27.0000
#cv2                  28.0000
#cv1-cv2              -1.0000
#p                     0.4220 # N.S.
#quantile 0.025       -4.0000
#quantile 0.975        4.0000
#randomized cv1-cv2    0.0225
#nrandom            1999.0000

# compare the Simpson diversity
simp.one <- diversity(dune[gr == "1", ], "simp")
simp.two <- diversity(dune[gr == "2", ], "simp")
c2m(pop1=simp.one,pop2=simp.two,nrandom=1999,verbose=FALSE)
#done.
#$res
#
#mv1                 8.630e-01
#mv2                 8.773e-01
#mv1-mv2            -1.439e-02
#p                   2.440e-01 # N.S.
#quantile 0.025     -3.456e-02
#quantile 0.975      3.351e-02
#randomized mv1-mv2  3.899e-04
#nrandom             1.999e+03
#########################

The possible pitfalls might be hidden under the different results ;-)

Cheers,
Ivailo

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