Alicia One more thought. I wonder if part of the problem is that you're attempting to use ISA to do something it was not designed to deal with. Sometimes that can work and result in a clever new approach, but in this case, I don't see how it can work. As a recall, ISA is done by taking the product of relative abundance and relative frequency. Therefore, as mentioned by Jari, it can only be used with positive values of abundance and frequency. Yes, it is true that you can transform the residual abundances to make them all positive. That seems perfectly reasonable if you then intend to use the residual abundances (and the residual abundances only) to examine associations with environmental groups. What is puzzling me is how one calculates a relative frequency using residual abundances or how one calculates a "residual frequency." Is it your intention to calculate residual frequencies? It's not clear to me how that could be done. I suppose you could leave out the relative frequency when doing the ISA, but that leads me to the following question: Can you tell us why indicator species analysis is a better approach for determining indication of particular environmental conditions than are species scores generated from a capscale ordination? Given the flexibility of capscale to deal with categorical predictors, it has never been clear to me what advantages ISA has over species scores in interpreting environmental associations (other than generating a Monte Carlo-derived significance value). If there is not much difference, then I would do a capscale analysis using region as a Condition factor (as suggested by Arne) and then examine the species scores.
Steve J. Stephen Brewer Professor Department of Biology PO Box 1848 University of Mississippi University, Mississippi 38677-1848 Brewer web page - http://home.olemiss.edu/~jbrewer/ FAX - 662-915-5144 Phone - 662-915-1077 On 3/18/14 11:48 AM, "Alicia Valdés" <aliciavaldes1...@gmail.com> wrote: >Thanks for so many thoughts! > > >Arne: I could use Condition() for that but something like >capscale(communitydistances ~ environmentalvar+Condition(region)) >would not attempt what I need, because I need to fit a model only with >"region", extract residuals, and then do another analysis (ISA) with these >residuals. >However, if this kind of formulation is correct, it could also be useful >to >see the effects of environmental factors while controlling for the effect >of region > >Indicator species analysis could be done for each region separately. - >yes, >but I would like to try both approaches, ISA for all regions (but removing >the region effect) and ISA for each region separately, and compare the >results > > >Jari: OK I guess that even if they could be calculated, residuals from the >adonis() point of view are not suitable for me, as they are >dissimilarities. Sorry if I was confusing, what I need are residuals of >raw >data, as you say. So I guess the residuals() method of rda() should work. >Yes, I know that ISA needs non-negative values, I was attending to >transform these residuals to make them all positive. > >Pierre: I checked manyglm() function in the mvabund package and I think it >could be useful too, you can indeed get residuals from a mutivariate GLM >with family=binomial. > > >Ivailo: I think you maybe misunderstood my approach, I want to perform an >ISA based on differences in species composition, but I want to focus on >the >part of these differences which is not caused by the region studied. I >want >to identify indicator species for different environmental conditions and >caracteristics of forest patches (like landscape management type, forest >age and others), but I am not interested in finding the characteristic >species for the different regions. To make it clearer, I have data from >different regions, into each of the regions there are a series of forest >patches which vary in management type, age and other factors. So the >cluster I am using in multipatt() is a classification into management >types, age groups, etc. As I said before, I am not interested in use >"regions" for site classification. I hope this makes the analysis easier >to >understand now! > >So my main doubts now are: for this kind of purpose, 1) what is your >opinion on using a community data table in the ISA which contains not the >actual presence/absence, but residuals form a previous model with region >effect?; and 2) Which kind of residuals do you find more appropiate to use >here (rda residuals of multivariate GLM residuals)? > >Cheers, > >Alicia > > > >2014-03-18 16:23 GMT+01:00 Ivailo <ubuntero.9...@gmail.com>: > >> On Tue, Mar 18, 2014 at 5:02 PM, Alicia Valdés >> <aliciavaldes1...@gmail.com> wrote: >> ... >> > However, what I attempt to do is to perform an indicator species >>analysis >> > (ISA) with these residuals. I want to see if I can find species which >>are >> > indicators for different environmental conditions, but first I would >>like >> > to remove the differences in species composition due to the study >>region >> ... >> >> Isn't an indicator species analysis *based* on differences in species >> composition? How could one "remove the difference in species >> composition due to region" and then still hope to identify indicator >> species for the regions that are "equalized"? >> >> Cheers, >> Ivailo >> -- >> "The cure for boredom is curiosity. There is no cure for curiosity." -- >> Dorothy Parker >> > > [[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
