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A beginner's question to constrained ordinations with vegan

2 messages · Tim Richter-Heitmann, stephen sefick

Original
Tim Richter-Heitmann
# 
Hi there,

i have a typical ecological problem (modelling abiotic parameters to 
bacterial abundances - i have 9 of these explanatory variables (but also 
a variety of spatial and biotic parameters, who may serve as 
explanators), many bacterial species and hundreds of sites).

My species gradients seem to be very long in the DCA, so i began my 
analysis with CCA modelling all 9 abiotic parameters to the species 
matrix, and using the triplot as a final result.

However, i have two very distinct bacterial communities in the DCA with 
a huge gap on the x-axis between them (one community is defining 90% of 
all samples, and the smaller one is found in 10% of the samples), so i 
was fiddling around with performing rda's
(which i believe is recommended for small species gradients) on the two 
subsets.

Now, a colleague was actually recommending me to use unconstrained 
ordinations like PCA and use envfit to fit the explanatory variables later.

ord.OTU <- rda(OTU)
ef <- envfit(ord.OTU, Env, perm=999)

instead of

ord.OTU <- rda(OTU~., Env)

However, i fail to grasp the ideas and differences behind and between 
the two approaches - in my case, an envfitted PCA looked different than 
the "equivalent" RDA. As far as i have been taught, constrained 
ordination techniques like RDA or CCA search for the best explaining
variables in the direct gradients, so i would use those for problems 
like mine per default. So, what are the benefits in using the 
unconstrained techniques first?

Since i am new to the field, i lack the experience to evaluate this. Any 
advice would make me a very happy student.

Thank you very much, and my apologies if i have asked something that was 
asked many times before. In fact, i tried to find the answer online, but 
wasnt too successful.
stephen sefick
# 
Tim,

I would take a look at Numerical Ecology with R. This book may not 
address your particular question, but should be useful as a general 
reference for using R for quantitative ecology.

Some questions:
Are you interested in what is structuring the community along an 
environmental gradient? What is the rational for investigating the 
sub-gradients?

My own curiosity:
Is there a literature source with quantitative data demonstrating that 
particular ordinations "better" uncover true environmental/distance 
relationships?

A couple of comments (please correct my misunderstandings):
RDA and PCA followed by envfit will give different results because they 
are doing very different things. From my understanding, rda uses the 
predicted value matrix from a multivariate regression of Comm_Mat ~ 
Env_Mat and then preforms a PCA on the resulting matrix (mean value 
given the environmental predictors; constrained). A PCA on the 
(appropriately hellinger transformed?) Comm_Mat is unconstrained by the 
environmental variation and projects sites along the direction of 
maximum variance in Comm_Mat only. Therefore, these techniques will give 
very different results.

I hope that helps, and my explanation is not very far from the reality 
of the techniques.

kindest regards,

Stephen
On 10/09/2014 07:26 AM, Tim Richter-Heitmann wrote: