[R-sig-eco] Choice of constrained ordination method on complex data

[Tim Richter-Heitmann]

Sorry, i messed up the first link:

Here is the actual image with summaries obtained by R:

http://s22.postimg.org/7vlhuhrap/Picture1.png

I am really sorry.

On 18.05.2015 11:42, Tim Richter-Heitmann wrote:
> Good Morning,
>
> i need to once more ask you for advice.
> My dataset includes 360 samples, 60 samples per date, which are 
> randomly distributed among a square grid. I measured bacterial 
> abundance as my outcome.
> My final aim is to calculate the variance partitioning for a number of 
> classes of constraints, including spatial (via PCNM or cubic 
> polynomes) and temporal components.
>
> Here is what i tried so far in order to visualize the data, for this 
> example without taking into account any spatial or temporal variance 
> (for the sake of the example). They key point of this dataset is the 
> presence of few dramatically different plots. CCA does a good job in 
> showing those, but fail to separate any other samples (due to to 
> presence of basically two types of plots with many "0" introduced in 
> the respective other type). RDA and capscale do not find these 
> differences, but find better gradients in the general dataset.
>
> The question is if there are better options to ordinate my data, 
> especially with my main aim in mind (variance partitioning).
>
> Here is what i have done:
>
> 1. CCA on non-transformed (but relative) species and environmental data
> 2. RDA on hellinger-transformed total species counts and standardized 
> environmental data
> 3. capscale on hellinger-transformed total species counts and 
> standardized environmental data
>
> Capscale was done on the gower metric, as rankindex from vegan showed 
> its far superiority to bray-curtis and a slight one compared to 
> euclidian (R=0.15 > 0.12 >>> 0.02).
>
> Here are the results from the R-output:
> http://s15.postimg.org/r1bq7863v/Untitled.png
> Here are the procrustes rotation between all three ordinations:
> http://s13.postimg.org/ainxqsljr/Rplot.png
>
> These are just basic ordinations without forward selection or variance 
> inflation.
>
> The presence of a few dramatically shifted sites is shown in CCA, but 
> not in RDA or CAP, but the latter two explain more of the general 
> variance in the dataset. The big question is if i am doing it wrong on 
> any level, or if there are better ways to visualize the data. NMDS 
> with envfit is similar to CCA, by the way.
>
> Here are some more questions:
> 1) Can i apply vegan's toolbox for RDA also to objects created by 
> capscale (including goodness.cca)?
> 2) Can i apply forward selection of "packfor" to vegan's 
> capscale-derived objects?
> 3) Are spatial coordinates (PCNM or cubic polynomes) suitable for 
> capscale, or shall i go back to RDA?
> 4) Connected to that, vegan's Varpart function only works with RDA, 
> not with capscale?
>
> 5) What could be an explanation why bray-curtis distances of my 
> species data show no correlation (0.02) at all with the predictor 
> variables, but a somewhat stronger correlation with euclidian (0.12) 
> or gower (0.15) distances, as calculated with rankindex()? I am 
> surprised by it, as i have fairly typical species data, for which 
> bray-curtis was designed.
>
> Thank you very much. I am not so sure what directions i should take at 
> this point.
>
> Cheers,
>
>


-- 
Tim Richter-Heitmann (M.Sc.)
PhD Candidate



International Max-Planck Research School for Marine Microbiology
University of Bremen
Microbial Ecophysiology Group (AG Friedrich)
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Tel.: 0049(0)421 218-63062
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