Hi again,

Thnak you very much for you help, may I show you more data and continue
brainstorming?

I tried to see if "a priori" grouping was possible with ANOSIM test and
that is:

> dist1 <- vegdist(community.sq)
> attach(env.table)
> anosim1 <- anosim(dist1, location)
> summary(anosim1)

Call:
anosim(dat = dist1, grouping = location)
Dissimilarity: bray

ANOSIM statistic R: 0.5617
      Significance: 0.001

Based on  999  permutations

Empirical upper confidence limits of R:
   90%    95%  97.5%    99%
0.0267 0.0359 0.0454 0.0513

Dissimilarity ranks between and within classes:
        0%     25%    50%     75% 100%    N
Between 10 2275.00 2275.0 2275.00 2275 3024
BA       9  222.25  481.0 2275.00 2275   66
BM       2  162.25  343.0  658.50 2275   66
BRE     11  156.75  431.0  563.75 2275   66
SIG     37  606.50 2275.0 2275.00 2275   66
SIMa    12  383.25  919.5 2275.00 2275   66
SIMb     1   61.50  171.0  319.75 2275   66
SZV     43  279.00  672.0 1024.75 2275   66

The behavior of the SIG site is a little bit strange, what do you think?
 Moreover I tried to fit factors and vectors into my NMDS and I got:

> envfit(NMDS.sqrt, env.table, permu=1000) ->NMDS.sqef
> save.image("C:\\R\\grafici_new\\new.RData")
> NMDS.sqef

***VECTORS

                   NMDS1     NMDS2     r2   Pr(>r)
organic.matter -0.494181 -0.869359 0.0573 0.108891
total.lime     -0.879461  0.475972 0.2824 0.000999 ***
clay           -0.913022 -0.407910 0.4476 0.000999 ***
silt           -0.999660 -0.026088 0.5315 0.000999 ***
bulk.density    0.952095  0.305802 0.1484 0.001998 **
pH             -0.834558  0.550920 0.2407 0.000999 ***

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
P values based on 1000 permutations.

***FACTORS:

Centroids:
               NMDS1   NMDS2
locationBA    1.1273  1.0380
locationBM   -0.0640 -0.3905
locationBRE  -0.1975  0.0435
locationSIG  -1.4468  0.2258
locationSIMa  0.1984 -1.0945
locationSIMb  0.7214 -0.2354
locationSZV  -0.3388  0.4131

Goodness of fit:
             r2   Pr(>r)
location 0.7144 0.000999 ***

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
P values based on 1000 permutations.

The R2 is a little bit high, but it seems significant...

The I tried to use the betadisper( ) function R gave me an error...

> groups <-
factor(c(rep(1,12),rep(13,24),rep(25,36),rep(37,48),rep(49,60),rep(61,72),rep(73,84),+
labels=c("SIG","BA","SIMa","SIMb","SZV","BM","BRE")))

> betadiv <- betadisper(dist1, groups)

Error: (subscript) logical subscript too long


How to solve it?

Thank you very much for your help and sorry for the length of the email...

Gian







2011/11/17 gabriel singer <gabriel.singer@univie.ac.at>

> ... dangerous wording, there could in fact be a location effect of
> 'location' and/or a dispersion effect of 'location'.
>
> Gian, I suggest you add a test of a dispersion effect using the function
> betadisper(), then you know a bit more about the type of effect.
>
> gabriel
>
>
> On 11/16/11 11:02 PM, Gavin Simpson wrote:
>
>> On Wed, 2011-11-16 at 03:43 +0100, Gian Maria Niccolò Benucci wrote:
>>
>>> Hi all,
>>>
>>> I had 84 samples collected in 7 different sites.
>>> In each sample were individuated the different fungal species and
>>> recorded.
>>> I would test if exist a real difference between the sites and if exist a
>>> sort of site effect that structure the fungal communities...
>>> Then, I did adonis test
>>>
>>>  adonis(community.sq ~ location, data=env.table, permutations=999)
>>>>
>>> Call:
>>> adonis(formula = community.sq ~ location, data = env.table, permutations
>>> =
>>> 999)
>>>
>>>           Df SumsOfSqs MeanSqs F.Model      R2 Pr(>F)
>>> location   6    12.593 2.09886  6.8867 0.34922  0.001 ***
>>> Residuals 77    23.467 0.30477         0.65078
>>> Total     83    36.060                 1.00000
>>> ---
>>> Signif. codes:  0 *** 0.001 ** 0.01 * 0.05 . 0.1   1
>>>
>>>
>>>
>>> The significance is  R2=0.349 at P=0.001
>>> Can I assure that exist a strong site effect in structuring the
>>> communities
>>> in each site?
>>>
>> Depends. The test is one of no effect of `location`. You have found
>> evidence against this hypothesis and thus could reject this hypothesis,
>> instead accepting the alternative hypothesis that there is an effect of
>> `location`. As to the strength of this effect? ~35% of the sums of
>> squares can be explained by `location`. Substantially more of the
>> variance remains unexplained. As I know nothing about your subject area,
>> I am unable to comment further on the strength of the relationship.
>>
>> Seeing as many ecologists whose work I read would say an effect is
>> significant if the p-value was>= 0.05. Not that I subscribe to this way
>> or working, but by that criterion, you have identified a significant
>> `location` effect.
>>
>> HTH
>>
>> G
>>
>>  Thanks for helping,
>>>
>>> G.
>>>
>>>        [[alternative HTML version deleted]]
>>>
>>> ______________________________**_________________
>>> R-sig-ecology mailing list
>>> R-sig-ecology@r-project.org
>>> https://stat.ethz.ch/mailman/**listinfo/r-sig-ecology<https://stat.ethz.ch/mailman/listinfo/r-sig-ecology>
>>>
>>
> --
> Dr. Gabriel Singer
> Department of Limnology - University of Vienna
> and Wassercluster Lunz Biologische Station GmbH
> +43-(0)664-1266747
> gabriel.singer@univie.ac.at
>
>
**




-- 
Gian Maria Niccolò Benucci
Ph.D. Candidate

University of Perugia
Department of Applied Biology

Borgo XX Giugno, 74
I-06121 - Perugia, ITALY
Tel: +39.0755856433
Fax: +39.0755856069
Email: gian.benucci@gmail.com



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