Hello Peter,

I haven't transposed the data to fit it to adonis. I have used the same data
as I fed it to ANOSIM/mrpp functions.

You are exactly right, I'm interested if communities (of polychaetes) at
different sites (1-10 with 5 samples from each site) differ between and
within each other.

Cheers,
Roman



On Thu, Dec 17, 2009 at 5:38 PM, Peter Solymos <solymos@ualberta.ca> wrote:

> Hi Roman,
> it looks to me that you are using the transpose of the data in adonis,
> since covariates are species.
> Maybe I can't get you question, but is it what you want? I suspect you
> are interested in environmental variables vs community and not in
> species.
> Cheers,
> Peter
>
> Péter Sólymos
> Alberta Biodiversity Monitoring Institute
> Department of Biological Sciences
> CW 405, Biological Sciences Bldg
> University of Alberta
> Edmonton, Alberta, T6G 2E9, Canada
>
>
> On Thu, Dec 17, 2009 at 9:29 AM, Roman Luštrik <roman.lustrik@gmail.com>
> wrote:
> > Dear List,
> >
> > I'm trying to see if samples within and between locations differ (in case
> > they don't, I will have an argument to join subsamples into one). I have
> > tried ANOSIM and mrpp.
> >
> > anosim.result <- with(environmental.sub, anosim(community.sub.dist,
> > station)) #station is used as "strata"
> > mrpp.result <- with(environmental.sub, mrpp(community.sub.dist,
> station))#ditto
> >
> > ANOSIM produces a barplot with "skewed" notches (Crawley (2007) notes
> that
> > this is due to small sample size and high within-sample variance) and
> mrpp
> > Pvalue is 0.001.
> >
> > My question would be, in what way can I see between and within
> differences
> > in mrpp (can I plot this?) and how would a similar method be done in
> > adonis() (both ANOSIM and mrpp help files link to adonis as a more
> > appropriate method)? I have tried as displayed below, but clearly this is
> > not the solution (or am I missing something?). Can anyone advise on how
> to
> > improve it to match what I'm trying to achieve with ANOSIM/mrpp?
> >
> > 1> adonis.result <- adonis(community.sub.dist ~., data=environmental.sub,
> > strata=environmental.sub$station, permutations=999)
> > 1> adonis.result
> >
> > Call:
> > adonis(formula = community.sub.dist ~ ., data =
> > environmental.sub[1:41],      permutations = 999, strata =
> > environmental.sub$station)
> >
> >                    Df SumsOfSqs  MeanSqs  F.Model     R2 Pr(>F)
> > Acet_acet      1.00000   0.33879  0.33879  1.48305 0.0212  0.481
> > Cor_off        1.00000   0.71387  0.71387  3.12492 0.0446  0.082 .
> > Cyst_barb      1.00000   0.71871  0.71871  3.14610 0.0449  0.369
> > Cyst_comp      1.00000   0.32404  0.32404  1.41845 0.0203  0.606
> > Cyst_comp_ros  1.00000   0.37623  0.37623  1.64695 0.0235  0.027 *
> > Cyst_sp        1.00000   0.24372  0.24372  1.06686 0.0152  0.451
> > Flab_pet       1.00000   0.30608  0.30608  1.33986 0.0191  0.557
> > Halim_tun      1.00000   0.21332  0.21332  0.93381 0.0133  0.589
> > Hali_virg      1.00000   0.51746  0.51746  2.26517 0.0323  0.206
> > Halo_inc       1.00000   0.35300  0.35300  1.54523 0.0221  0.531
> > Hyd_spp        1.00000   0.28498  0.28498  1.24748 0.0178  0.429
> > Jani_sp        1.00000   0.18746  0.18746  0.82058 0.0117  0.657
> > Lith_spp       1.00000   0.43930  0.43930  1.92300 0.0275  0.338
> > Pad_pav        1.00000   0.40596  0.40596  1.77706 0.0254  0.667
> > Pey_poly       1.00000   0.48543  0.48543  2.12495 0.0303  0.292
> > Pey_squa       1.00000   0.42382  0.42382  1.85525 0.0265  0.336
> > Phy_sp         1.00000   0.62091  0.62091  2.71802 0.0388  0.062 .
> > Zan_prot       1.00000   0.37720  0.37720  1.65119 0.0236  0.194
> > Cer_spp        1.00000   0.32339  0.32339  1.41564 0.0202  0.051 .
> > Chae_sp        1.00000   0.30489  0.30489  1.33465 0.0191  0.443
> > Cham_parv      1.00000   0.25843  0.25843  1.13127 0.0162  0.246
> > Clad_spp       1.00000   0.29642  0.29642  1.29755 0.0185  0.458
> > Clad_spong     1.00000   0.24449  0.24449  1.07024 0.0153  0.294
> > Dict_dich      1.00000   0.47976  0.47976  2.10012 0.0300  0.316
> > Dict_lin       1.00000   0.35908  0.35908  1.57184 0.0224  0.881
> > Dict_poly      1.00000   0.35593  0.35593  1.55807 0.0222  0.504
> > Entero_sp      1.00000   0.31754  0.31754  1.38999 0.0198  0.479
> > Gel_lla_sp     1.00000   0.12456  0.12456  0.54526 0.0078  0.757
> > Gel_sp         1.00000   0.29574  0.29574  1.29459 0.0185  0.310
> > Gig_aci        1.00000   0.31233  0.31233  1.36722 0.0195  0.431
> > Grac_burs      1.00000   0.26052  0.26052  1.14041 0.0163  0.704
> > Halo_scop      1.00000   0.35658  0.35658  1.56091 0.0223  0.282
> > Laur_spp       1.00000   0.58577  0.58577  2.56420 0.0366  0.074 .
> > Poly_sp        1.00000   0.17517  0.17517  0.76681 0.0109  0.558
> > Pter_cap       1.00000   0.21960  0.21960  0.96127 0.0137  0.292
> > Spha_spp       1.00000   0.24061  0.24061  1.05326 0.0150  0.506
> > Ulva_sp        1.00000   0.46203  0.46203  2.02252 0.0289  0.091 .
> > Valo_utri      1.00000   0.14743  0.14743  0.64537 0.0092  0.837
> > Wrang_peni     1.00000   0.26278  0.26278  1.15029 0.0164  0.417
> > Residuals     10.00000   2.28443  0.22844          0.1428
> > Total         49.00000  15.99776                   1.0000
> > ---
> > Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> >
> >
> > Cheers,
> > Roman
> >
> >        [[alternative HTML version deleted]]
> >
> >
> > _______________________________________________
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> > R-sig-ecology@r-project.org
> > https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
> >
> >
>

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