Re: [R-sig-eco] RE : CCA vs NMDS and ordisurf
I would say that it *is* important, in general. However, you don't say if you retried running `monoMDS` on the Hellinger transformed data (without the Bray-Curtis metric - you should use Euclidean with Hellinger transformation)? If you didn't try rerunning with out Bray-Curtis and see if it converges. Otherwise, try many more iterations and get vegan to start monoMDS from the best solution from the first set of runs. See `?metaMDS for details. G On Mon, 2013-04-22 at 08:26 +, Aurélie Boissezon wrote: Hello everybody! I didn't imagine that my questions will lead to such a debate among researchers :) . It helps me to get ready for future reviewers' comments. ;) Just a question still opened about NMDS (Gavin?): Is it important to reach a convergent solution? since the best solution ordinate species always in similar way? Because as I said even with stricter criteria the analysis don't reach a convergent solution. Best regards, Aurélie --- 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.boisse...@unige.ch http://leba.unige.ch/team/aboissezon.html De : fgill...@gmail.com [fgill...@gmail.com] de la part de François Gillet [francois.gil...@univ-fcomte.fr] Date d'envoi : samedi 20 avril 2013 10:59 À : Gavin Simpson Cc: Aurélie Boissezon; r-sig-ecology@r-project.org Objet : Re: [R-sig-eco] RE : CCA vs NMDS and ordisurf 2013/4/19 Gavin Simpson gavin.simp...@ucl.ac.ukmailto:gavin.simp...@ucl.ac.uk I really don't see why this has to be an either/or situation. I fully agree: direct and indirect gradient analyses are complementary! Sorry for not having stressed that in my short answers... François -- %~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~% Dr. Gavin Simpson [t] +44 (0)20 7679 0522 ECRC, UCL Geography, [f] +44 (0)20 7679 0565 Pearson Building, [e] gavin.simpsonATNOSPAMucl.ac.uk Gower Street, London [w] http://www.ucl.ac.uk/~ucfagls/ UK. WC1E 6BT. [w] http://www.freshwaters.org.uk %~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~% ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] RE : CCA vs NMDS and ordisurf
I also suggest (like I have suggested before) that you run metaMDS with argument plot = TRUE. The convergence criteria in metaMDS are pretty stringent, but with plot argument you can see how different the solutions are. Two most typical non-convergence cases are that (1) most points are stable, but there are a some outliers that don't find their place in this universe, and (2) your data need more dimensions and you should increase 'k'. Then you should also check the stressplot( ). If the fit line shoots right up at the maximum observed dissimilarity, you may need to turn on 'noshare' argument in metaMDS to trigger step across dissimilarities. We claim that this rarely necessary with the monoMDS engine we use currently, but sometimes it is needed. Without hands on your data it is difficult to guess more. Cheers, Jari Oksanen Sent from my iPad On 22.4.2013, at 22.31, Gavin Simpson gavin.simp...@ucl.ac.uk wrote: I would say that it *is* important, in general. However, you don't say if you retried running `monoMDS` on the Hellinger transformed data (without the Bray-Curtis metric - you should use Euclidean with Hellinger transformation)? If you didn't try rerunning with out Bray-Curtis and see if it converges. Otherwise, try many more iterations and get vegan to start monoMDS from the best solution from the first set of runs. See `?metaMDS for details. G On Mon, 2013-04-22 at 08:26 +, Aurélie Boissezon wrote: Hello everybody! I didn't imagine that my questions will lead to such a debate among researchers :) . It helps me to get ready for future reviewers' comments. ;) Just a question still opened about NMDS (Gavin?): Is it important to reach a convergent solution? since the best solution ordinate species always in similar way? Because as I said even with stricter criteria the analysis don't reach a convergent solution. Best regards, Aurélie --- 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.boisse...@unige.ch http://leba.unige.ch/team/aboissezon.html De : fgill...@gmail.com [fgill...@gmail.com] de la part de François Gillet [francois.gil...@univ-fcomte.fr] Date d'envoi : samedi 20 avril 2013 10:59 À : Gavin Simpson Cc: Aurélie Boissezon; r-sig-ecology@r-project.org Objet : Re: [R-sig-eco] RE : CCA vs NMDS and ordisurf 2013/4/19 Gavin Simpson gavin.simp...@ucl.ac.ukmailto:gavin.simp...@ucl.ac.uk I really don't see why this has to be an either/or situation. I fully agree: direct and indirect gradient analyses are complementary! Sorry for not having stressed that in my short answers... François -- %~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~% Dr. Gavin Simpson [t] +44 (0)20 7679 0522 ECRC, UCL Geography, [f] +44 (0)20 7679 0565 Pearson Building, [e] gavin.simpsonATNOSPAMucl.ac.uk Gower Street, London [w] http://www.ucl.ac.uk/~ucfagls/ UK. WC1E 6BT. [w] http://www.freshwaters.org.uk %~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~% ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] RE : CCA vs NMDS and ordisurf
2013/4/19 Gavin Simpson gavin.simp...@ucl.ac.uk I really don't see why this has to be an either/or situation. I fully agree: direct and indirect gradient analyses are complementary! Sorry for not having stressed that in my short answers... François [[alternative HTML version deleted]] ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
[R-sig-eco] RE : CCA vs NMDS and ordisurf
Dear all, Thanks for your help. It took me some time to replace all informations together in my little bit less confused brain. Maybe I should give some explanations about the context of my study and the purpose to go further with this discussion. Theory: The objective of my phD thesis is to improve scientific knowledge about the ecology of a very particular family of aquatic plants : the charophytes. I choose to study closely the response of species (cover and life cycle) to fine-scale gradients. The study site is a hotspot for aquatic plants (Rey-Boissezon and Auderset Joye, 2012. Arch. Sciences. in press) and in particular for charophytes species -- that's why I made this longitudinal research on this waterbody. The main purpose is to understand how disturbance gradient affect the composition of the macrophyte community, in particular the distribution of Charophytes (V3 mission in Anderson et al 2011). Practical: I want to ignore double zero because there is no reason to consider that double zeros indicate similarity.-- avoid euclidean-distance based method such as PCA and RDA The succession of a high number of species generated numerous zero in my species dataset (long environmental gradient). -- one more argument against RDA Finally vegetation was well sampled so rarest species were truly rare in the water body. Nevertheless I am not particularly interest by those rare species so I deleted them before multivariate analysis. For all these reasons, I firstly I tried CCA ordination. But I did not tried dbRDA. Should I on the basis of my practical limits? Would it be really best than CCA ? I guess I have to try following Pierre's method. The main positive point for dbRDA is that I can use any dissimilarity matrix (if I understand well), hellinger or bray curtis for example. Why not explore unconstrained ordination methods and went further with NMDS (V2 mission in Anderson et al 2011)? I understood that I was wrong when using Bray-Curtis distance on hellinger transformed data before NMDS, I have to choose. But that I am right when superimposing vector or gam surface on NMDS ordinations. But could someone explained briefly how to interpret outputs? in particular the position of each species on surface, the r2-adjusted and deviance explained by gam... At last but not least, I am not sure that the longitudinal nature of my dataset is really a problem. Do you mean autocorrelation problems might happened ? Cheers, Aurélie --- 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.boisse...@unige.ch http://leba.unige.ch/team/aboissezon.html De : r-sig-ecology-boun...@r-project.org [r-sig-ecology-boun...@r-project.org] de la part de Pierre THIRIET [pierre.d.thir...@gmail.com] Date d'envoi : jeudi 18 avril 2013 14:52 À : r-sig-ecology@r-project.org Objet : Re: [R-sig-eco] CCA vs NMDS and ordisurf Dear Aurélie, About the dissimilarity measures and data you used: Bray-curtis is usually the most appropriate, on raw abundance/biomass/cover data, or square root/log transformed. So why do you Hellinger transform before? This transformation is dedicated to be used with euclidean distance, and resulted ordinations (PCA or RDA) have a distinct meaning than PCoA or CAP/db-RDA (with bray-curtis) because joint abscence are included in first cases and excluded in the latter. See picture below from Anderson et al 2011 Navigating the multiple meanings of b diversity: a roadmap for the practicing ecologist So, if you want do constrained ordinations (constrained by drought disturbance gradient, I guess), I would suggest dbRDA (vegan::capscale) with bray curtis, or RDA on Hellinger transformed data, depending on what you want to emphasis. For unconstrained ordinations, this will be respectively PCoA and PCA. Pay attention in using NMDS. As you said, it is rank-based, this is why fitting environmental vectors to NMDS biplot is not so appropriate, despite widely done. I don't see the problem about ordisurf and PCoA or CAP: Ordisurf enables you to fit environnemental variables that have non-linear relationships with PC of distance based ordinations. If you use bray-curtis, I would suggest to use distance among group centroids instead of computing averages over groups followed by bray-curtis About hypotheses testing (in capscale or adonis for instance), pay attention to the longitudinal nature of your data. Some questions about repeated measure and adonis are already in R-SIG-ECO archives, have a alook. I guess you are interested in identifying the species which are the most responsible of community change over drought disturbance gradien?! If yes, I think
Re: [R-sig-eco] RE : CCA vs NMDS and ordisurf
A lot of questions, some responses below... 2013/4/19 Aurélie Boissezon aurelie.boisse...@unige.ch The main purpose is to understand how disturbance gradient affect the composition of the macrophyte community, in particular the distribution of Charophytes (V3 mission in Anderson et al 2011). Basically, to address this kind of question, you need constrained ordination. I want to ignore double zero because there is no reason to consider that double zeros indicate similarity.-- avoid euclidean-distance based method such as PCA and RDA Again: with appropriate transformations, such as Hellinger, double zeros are not taken into account in RDA! The succession of a high number of species generated numerous zero in my species dataset (long environmental gradient). -- one more argument against RDA Finally vegetation was well sampled so rarest species were truly rare in the water body. Nevertheless I am not particularly interest by those rare species so I deleted them before multivariate analysis. Bad idea. RDA on Hellinger-transformed cover data is not that much sensitive to rare (unfrequent) species, contrary to CCA. My advice is to keep all species in your dataset. For all these reasons, I firstly I tried CCA ordination. But I did not tried dbRDA. Should I on the basis of my practical limits? Would it be really best than CCA ? I guess I have to try following Pierre's method. The main positive point for dbRDA is that I can use any dissimilarity matrix (if I understand well), hellinger or bray curtis for example. dbRDA on Bray-Curtis dissimilarity matrix is an acceptable alternative to RDA on Hellinger-transformed data. CCA is based on a double standardization of sites and species and is known to give high weight to rare species: if you are not primarily interested by the indication of these species, forget CCA. Why not explore unconstrained ordination methods and went further with NMDS (V2 mission in Anderson et al 2011)? Just because your purpose is to explain community structure by environmental variables (a regression-oriented question). Direct gradient analysis (especially with RDA and adjusted R-square) is in this case more powerful than indirect gradient analysis (from NMDS or any other unconstrained ordination). I understood that I was wrong when using Bray-Curtis distance on hellinger transformed data before NMDS, I have to choose. But that I am right when superimposing vector or gam surface on NMDS ordinations. That's right, but you can fit a GAM model on RDA results as well! Cheers, François --- Prof. *François Gillet* Université de Franche-Comté - CNRS UMR 6249 Chrono-environnement UFR Sciences et Techniques 16, Route de Gray F-25030 Besançon cedex France http://chrono-environnement.univ-fcomte.fr/ http://chrono-environnement.univ-fcomte.fr/spip.php?article530 Phone: +33 (0)3 81 66 62 81 iPhone: +33 (0)7 88 37 07 76 Location: La Bouloie, Bât. Propédeutique, *-114L* --- Editor of* Plant Ecology and Evolution* http://www.plecevo.eu --- * *** [[alternative HTML version deleted]] ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] RE : CCA vs NMDS and ordisurf
A contrary view in-lined below: On Fri, 2013-04-19 at 15:19 +0200, François Gillet wrote: A lot of questions, some responses below... snip / Why not explore unconstrained ordination methods and went further with NMDS (V2 mission in Anderson et al 2011)? Just because your purpose is to explain community structure by environmental variables (a regression-oriented question). Direct gradient analysis (especially with RDA and adjusted R-square) is in this case more powerful than indirect gradient analysis (from NMDS or any other unconstrained ordination). I think you need to justify the more powerful there! :-) I see uses for both the constrained and unconstrained methods here. A comparison, especially if your do PCA vs RDA (with Hellinger or similar transformation) or PCoA vs capscale (with any distance measure) allows you to investigate the degree to which your constraints relate to the major patterns in the species responses. These are complementary approaches and one would do well to use them both. I understood that I was wrong when using Bray-Curtis distance on hellinger transformed data before NMDS, I have to choose. But that I am right when superimposing vector or gam surface on NMDS ordinations. That's right, but you can fit a GAM model on RDA results as well! You can, but the axes are still formed through linear functions of the constraints. The constrained methods don't fit non-linear functions (well you can introduce quadratic terms...) in the constraints. I really don't see why this has to be an either/or situation. G Cheers, Franois --- Prof. *Franois Gillet* Universit de Franche-Comt - CNRS UMR 6249 Chrono-environnement UFR Sciences et Techniques 16, Route de Gray F-25030 Besanon cedex France http://chrono-environnement.univ-fcomte.fr/ http://chrono-environnement.univ-fcomte.fr/spip.php?article530 Phone: +33 (0)3 81 66 62 81 iPhone: +33 (0)7 88 37 07 76 Location: La Bouloie, Bt. Propdeutique, *-114L* --- Editor of* Plant Ecology and Evolution* http://www.plecevo.eu --- * *** [[alternative HTML version deleted]] ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
