[R-sig-eco] predict community by community and environment

[mailbox Franz]

Thanks Leonard and Erick,

I found ecodist function MRM to fulfill my purpose!
Seems to be a „hard test“ of several independent distance matrixes (community and env data) and a response distance matrix. 

I am new to this whole community explains community thing and find it quit confusing. 
I had a sig. correlation using mantel test, no sig. with the procrustes test; then I had a sig. predicting relation using coccorrespondance analysis. 
Then I ran the MRM and had again no sig. effect. 
I thought the cocorrespondance (R package cocorresp) is already a hard test weather a community can predict another community. 
Thus I am confused why the MRM says no sig… I mean internally they work quit different, however, what to trust?

Maybe someone can help?

Best, Franz

> Am 25.10.2016 um 21:01 schrieb Leonardo Ré Jorge <leonardorejorge at gmail.com>:
> 
> Dear Franz,
> 
> An approach that should fit quite well to your intention is Generalized Dissimilarity Modellling (GDMs - Ferrier 2007: http://doi.wiley.com/10.1111/j.1472-4642.2007.00341.x <http://doi.wiley.com/10.1111/j.1472-4642.2007.00341.x>). You can model the compositional dissimilarities among your communities relative to both environmental differences and differences in composition of another taxonomic group. This has also the advantage of allowing you to include the spatial distance among your communities as a predictor, accounting for spatial autocorrelation. Your final output should give you a measure of relative effects of each predictor. GDMs are implemented in the R package gdm.
> 
> Best regards,
> Leonardo
> 
> On Tue, Oct 18, 2016 at 12:54 PM, LeBrun, Erick <Erick_LeBrun at baylor.edu <mailto:Erick_LeBrun at baylor.edu>> wrote:
> Hello Franz,
> 
> One possibility would be to do ordinations (preferably NMDS) and then do a Procrustes analysis. It is touted to be potentially more sensitive than a mantel test and using the protest command, you can get a correlation statistic and significance associated with the two datasets. The simplified explanation is that it scales and rotates the ordinations to the best fit possible. You can also view error plots that show you “movement” of the points in ordination space that can show trends or differences.
> 
> Best regards,
> Erick
> 
> Erick LeBrun
> PhD Student, Department of Biology
> Kang Microbial Ecology Laboratory
> Erick_LeBrun at baylor.edu <mailto:Erick_LeBrun at baylor.edu>
> 
> On 10/18/16, 8:35 AM, "R-sig-ecology on behalf of mailbox Franz" <r-sig-ecology-bounces at r-project.org <mailto:r-sig-ecology-bounces at r-project.org> on behalf of f.krah at mailbox.org <mailto:f.krah at mailbox.org>> wrote:
> 
>     Hi all,
> 
>     I am searching for a method to test weather a community is better predicted by an other community or by the environment in a mantel-test like framework?
>     Any help would be appreciated. Platform preferably R.
> 
>     All the best,
>     Franz
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