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 Stra?e (NW2 A2130)
D-28359 Bremen
Tel.: 0049(0)421 218-63062
Fax: 0049(0)421 218-63069