[R-sig-eco] Partitioning spatial effects using trend surface analysis or PCNM

Wed May 6 19:27:40 CEST 2015

Hi,

thank you very much for your advise as well.
The point was, that i already saw the community shift by simple  
clustering/heatmapping the abundance tables. In fact, less than 20%  
OTUs are shared between the 10 samples featuring that dramatic  
community shift and the vast majority of the rest of the samples. So,  
indeed the dominating OTUs in these 10 samples represent rare species  
in the entire dataset, but they are very dominant in those 10.
I started with the rule of thumb to check for the axis length of the  
DCA; it was long enough to decide for CCA for the further downstream  
analysis. I have to admit that i never touched RDA until i realized  
that my variance partitioning approach (vegan's varpart) was actually  
using RDA rather than CCA; i thus calculated partial RDAs as well, but  
i realized that RDA couldnt separate my "outlying samples" from the  
rest. However, the separation is real in terms of OTU abundances, so i  
figured that RDA is not the best tool to visualize my data.
I now have learned that CCA was just very sensible to these highly  
different samples (in fact if you look at the ordination plot, cca1 is  
only separating the 10 samples from the rest, but is not able to  
resolve the remaining samples - these are separated by cca2).
I will now do a varpart with both RDA on hellinger and chi2  
transformed OTU tables and see if the results are dramatically  
different.

Thank you again!

Tim

Zitat von François Gillet <francois.gillet at univ-fcomte.fr>:

> Hi Tim,
>
> As CA, CCA is known to be very sensitive to rare species and this is maybe
> partly the reason of the "shift" you observe in your communities, due to
> some OTUs. RDA is less sensitive to rare species and there is no need to
> remove them.
> To benefit from the advantages of RDA mentioned by Gavin, you should
> pre-transform your community data frame with Hellinger (site profiles) or
> chi-squared (double profiles, close to what is achieved in CCA) and check
> with a PCA if you still observe a major shift along the first axis.
> The choice of a method must not be guided by the pre-supposed better
> results you want to get ;-)
>
> All the best,
>
> François
>
>
>
> -------------------------------------------------------------------------------
> Prof. *François Gillet*
> Université de Franche-Comté - CNRS
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>
> 2015-05-06 11:47 GMT+02:00 trichter <trichter at uni-bremen.de>:
>
>> Thank you very much for adressing my problem.
>>
>> Maybe i can re-formulate it in a different way - moving away from
>> specific partial CCA/RDAs to the core of my task.
>>
>> If said task is the approximate quantification of partial effects on my
>> bacterial counts (for example edaphic soil properties, above-ground
>> plant diversity, and spatial autocorrelation) via the varpart function
>> in vegan, is the classical way of having orthogonal polynomes of the x,y
>> axis as CCA/RDA constraints still considered valid?
>>
>> If i understand you correctly, i would alternatively:
>> - generate PCNMs from my x,y coordinate matrices
>> - extract those with a Moran I >0
>> - perform a RDA/CCA with forward selection
>> - use the PCNM found to be significant in the varpart function?
>>
>> I think the only forward selection for CCA would be ordistep function in
>> vegan. What would be an acceptable treshold for entering into my final
>> set of accepted significant PCNMs?
>>
>> The other problem is that on the one hand, RDA is not able to separate
>> my community shifts as well as CCA, on the other hand varpart is based
>> on RDA. I wonder if i can justify using varpart when my ordination of
>> choice is based on CCA. But i have never seen a dedicated variance
>> partition function for CCA. I just read an old answer of yours:
>>
>> http://r.789695.n4.nabble.com/partitioning-variation-using-the-Vegan-CCA-routine-td823966.html
>>
>> So, i can basically transform my raw data to chi2 and use them in an RDA
>> to have a CCA proxy?
>>
>>
>> Thank you very much! as you can see, i am not really trained in statistics.
>>
>> Tim
>>
>>
>>
>> On 06.05.2015 02:45, Gavin Simpson-2 [via r-sig-ecology] wrote:
>> > Hi "trichter"
>> >
>> > On 5 May 2015 at 13:34, trichter <[hidden email]
>> > </user/SendEmail.jtp?type=node&node=7579428&i=0>> wrote:
>> > <snip />
>> >
>> > > Here is what i do:
>> > >
>> > > spat <- as.data.frame(poly(as.matrix(spatxy), degree=3))
>> > >
>> > > cca1_s <- cca(OTU~., data=spat)
>> > > #significances
>> > > anova(cca1_s)
>> > > anova(cca1_s, by="term", perm=999)
>> > >
>> >
>> > Don't think last analysis makes much sense; if you have a cubic
>> > polynomials
>> > plus interactions you should only consider the interactions first for
>> > removal, then decide if quadratic rather than cubic are needed
>> >
>> >
>> > >
>> > > #forward selection for most parsimonious model
>> > > cca1_s.f <- ordistep(cca(OTI~1, data=spat), scope=formula(cca1_s),
>> > > direction="forward", pstep=1000)
>> > > sig1_s.f <- anova(cca1_s.f, by="term", perm=999)
>> > >
>> >
>> > Again, as above, you have to be very careful with this. Just because you
>> > made a matrix with 9 "covariates" it doesn't mean it makes sense to
>> > cherry
>> > pick from these terms.
>> >
>> >
>> > > The result is a significant CCA object. Spat is usuable in VarPart and
>> > > yields a low but significant value for overall autocorrelation.
>> > >
>> > > For PCNM i do
>> > >
>> > > rs <- rowSums(OTU)/sum(OTU)
>> > > pcnmw <- pcnm(dist(spatxy), w = rs)
>> > > cca1_pcnm <- cca(acido1 ~ scores(pcnmw))
>> > >
>> > > pcnmw consists of 250 vectors, and the result is a non-significant CCA
>> > > object, where i expected a "finer" spatial decomposition.
>> > >
>> >
>> > You are supposed to choose from among the set of PCNMs which explain the
>> > species data best, not use them all in the model. The problem appears
>> > to be
>> > that you have a model that is far too complex with lots of redundant axes
>> > (or more likely too few constraints).
>> >
>> > One suggestion is to use only those PCNMs that have positive spatial
>> > correlation. Compute that using Moran's I of which there are a few
>> > implementations around in various R packages. You can do CCA analysis
>> > with
>> > the positive spatial correlation PCNMs separately from the negatively
>> > correlated PCNMs if you wish.
>> >
>> > You will probably need to do some type of forward selection but the
>> > preferred method seems to be limited to RDA (because the adjusted R2
>> > measure used in the global significance test isn't worked out for
>> > CCA). If
>> > you skip the global test, you could just do forward selection on the
>> > positive PCNMs, but you probably want to try to control for accepting too
>> > many PCNMs by having low entry threshold for significance.
>> >
>> > HTH
>> >
>> > G
>> >
>> >
>> >
>> > > The same is true if i am using total count data (hellinger
>> > transformed or
>> > > not).
>> > >
>> > > I am sure i am doing it wrong, so if you have advise to properly do the
>> > > calculation, please let me know. Thank you for the help.
>> > >
>> > >
>> > >
>> > >
>> > >
>> > > --
>> > > View this message in context:
>> > >
>> >
>> http://r-sig-ecology.471788.n2.nabble.com/Partitioning-spatial-effects-using-trend-surface-analysis-or-PCNM-tp7579427.html
>> > > Sent from the r-sig-ecology mailing list archive at Nabble.com.
>> > >
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>> >
>> >
>> > --
>> > Gavin Simpson, PhD
>> >
>> >         [[alternative HTML version deleted]]
>> >
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>> --
>> Tim Richter-Heitmann (M.Sc.)
>> PhD Candidate
>>
>>
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