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

[Marek Šlenker]

Hello, please, exclude me from this mailing list.

than you very much!

Marek Šlenker

2015-05-18 11:42 GMT+02:00 Tim Richter-Heitmann <trichter at uni-bremen.de>:

> 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)
> FB02 - Biologie/Chemie
> Leobener Straße (NW2 A2130)
> D-28359 Bremen
> Tel.: 0049(0)421 218-63062
> Fax: 0049(0)421 218-63069
>
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