Hi everybody,

I have some questions about ordination analysis and interpretation of ordisurf() output. So huge thanks to people who will help me to clean up my confused brain.
So I am working on cover data of aquatic plants (%). I made 7 quadrat sampling between 2009 and 2012 in a semi permanent shallow pond (n=1200 approximately without empty quadrat). Due to fluctuating water regime and small topographic variations, my sampling units are distributed along a gradient of inundation conditions from permanently wet to frequently dry. Clearly the vegetation responded to water level condition occurring the previous year. Community following several years of high levels was very different from the one occuring the year after a severe drought of the waterbody (a lot of charophytes, pionneer species). I quantified this "drought disturbance gradient" by calculating when (which season?), and for how many days each quadrat dried before each field sampling.
My purpose is to explore the relationship between the composition of the community and those "drought indexes". And in particular to highlight the succession of species along the gradients.
My first reflex was to implement a CCA but someone tell me to explore unconstrained approach and in particular NMDS.
The CCA ordination shows a strong arch effect but is highly significant and perfectly ecologically interpretable and congruent with my field observations. To summarize submerged species are separated from helophytes species by duration of drought during growing season (submerged species need water from winter to summer). And submerged species succeeded each other along a gradient of duration of drought at the end of the growth season, in autumn.
But to see if I had similar results when looking at the whole variation of the community data set and when using a more suitable distance measure, I run a NMDS on Hellinger-transformed data based on Bray-Curtis distances.
With NMDS I didn't reach a "convergent solution" even after setting stricter criteria maxit and sratmax. Nevertheless the stress is acceptable (8 with k=3 ) and the species are ordinated similarly to the CCA. I implement the same analysis on a simplified version of my data set by averaging the cover of species by date, by depth clusters (10 centiles) and by area of the lake leading to 131 observations instead of 1200 quadrats initially (which is very large). Here the nmds reached quickly a convergent solution (after 20 or 50 runs) and gave always a similar ordination of species.
So is it important not to reach a convergent solution with NMDS in my case?

I tried to overlay environmental informations on NMDS ordination using envfit function and then ordisurf which allows the environmental parameter to vary non linearly in the ordination space (on the contrary to CCA). I am really satisfied with graphical outputs  which are ecologically meaningfull but I am afraid to misinterprete them.
In ecological studies we are used to explain the distribution of species with environmental/ explanatory variables. Here is it the same? If I understand well, ordisurf implement a 2d surface gam of the explanatory/environmnetal variable with the scores of sites ordinated in the n dimensions of the nmds..... that means that the explanatory variable become the response variable.
Thus can I interprete the position of species in the ordination space with GAM surface resulting from ordisurf???? Like species X is present in sites never dried during spring, but between 10 and 20 days during autumn...etc....
I think yes since relevés were ordinated on the basis of the structure of the macrophytes community...but I am not so sure!

Thanks a lot for your help!
Best regards,

Aurélie

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Aurélie Rey-Boissezon
Ph-D Student
University of Geneva
Section of Earth and Environmental Sciences - Institute F.-A. Forel
Aquatic Ecology Group
Uni Rondeau
Site de Battelle - Bâtiment D
7, route de Drize - 1227 Carouge
Geneva
Switzerland
Tel. 0041 (0) 22379 04 88

Aurelie.Boissezon@unige.ch
http://leba.unige.ch/team/aboissezon.html

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