Not sure if its relevant, but this paper and associated function
*beta.div.comp* {adespatial} may be of interest to disentangle between
richness and turnover in composition data
https://doi.org/10.1111/geb.12207
Tania Bird MSc
*"There is a sufficiency in the world for man's need but not for man's
greed" ~ Mahatma Gandhi*
https://www.linkedin.com/in/taniabird
On Thu, 4 Apr 2019 at 10:28, Torsten Hauffe <torsten.hau...@gmail.com>
wrote:
> Great point David!
>
> Since Tim was referring to microbial communities, the gjam package is
> similar to mvabund, boral etc. and the microbial example discussed in the
> following paper might be of interest.
>
> https://esajournals.onlinelibrary.wiley.com/doi/full/10.1002/ecm.1241
>
> With that being about R itself, I may go a bit off topic:
> In all those multivariate GLM approaches, is there a way to disentangle
> richness differences (or nestedness) and turnover like we can do with
> pairwise distances?
> (See the inspiring discussion between Carvalho et al. and Baselga et al.;
> summarized in
> http://onlinelibrary.wiley.com/doi/10.1111/geb.12207/abstract
> )
> Since different biological processes may cause these patterns, separating
> richness differences and species turnover is of interest. Maybe the the row
> effect in those multivariate GLMs could be estimated as response to
> environmental predictors?
>
> Cheers,
> Torsten
>
>
>
> On Thu, 4 Apr 2019 at 01:19, David Warton <david.war...@unsw.edu.au>
> wrote:
>
> > Hi Tim,
> > Yes you are right this is an issue, BC (and other distance metrics) are
> > sensitive to sampling intensity, which is often an artefact of the
> sampling
> > technique. Transformation is not a great solution to the problem - it
> > works imperfectly and will have different effects depending on the
> > properties of your data. There are lots of different types of datasets
> out
> > there, each with different properties, and different behaviours under
> > different transformation/standardisation strategies, so there is no
> > one-transformation-suits-all solution. An illustration of this (in the
> > case of row standardisation) is in the below paper:
> >
> > https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.12843
> >
> > The strategy I would advise here is to go a very different route and
> build
> > a statistical model for the data. You can then include row effects in
> the
> > model to handle variation in sampling intensity across rows of data
> (along
> > the lines of equation 2 of the above paper). Or if the magnitude of the
> > variation in sampling intensity is known (e.g. it is due to changes in
> > sizes of quadrats used for sampling, and quadrat size has been recorded),
> > then the standard approach to handle this is to add an offset to the
> > model. There is plenty of software out there that can fit suitable
> > statistical models with row effects (and offsets) for this sort of data,
> > including the mvabund, HMSC, boral, and gllvm packages on R.
> Importantly,
> > these packages come with diagnostic tools to check that the analysis
> > approach adequately captures key properties of your data - an essential
> > step in any analysis.
> >
> > All the best
> > David
> >
> >
> > Professor David Warton
> > School of Mathematics and Statistics, Evolution & Ecology Research
> Centre,
> > Centre for Ecosystem Science
> > UNSW Sydney
> > NSW 2052 AUSTRALIA
> > phone +61(2) 9385 7031
> > fax +61(2) 9385 7123
> >
> > http://www.eco-stats.unsw.edu.au
> >
> >
> >
> > ----------------------------------------------------------------------
> >
> > Date: Tue, 2 Apr 2019 17:15:45 +0200
> > From: Tim Richter-Heitmann <trich...@uni-bremen.de>
> > To: r-sig-ecology@r-project.org
> > Subject: [R-sig-eco] interpreting ecological distance approaches (Bray
> > Curtis after various data transformation)
> > Message-ID: <3834fea1-040a-12b5-c3a3-633e68dc6...@uni-bremen.de>
> > Content-Type: text/plain; charset="utf-8"; Format="flowed"
> >
> > Dear list,
> >
> > i am not an ecologist by training, so please bear with me.
> >
> > It is my understanding that Bray Curtis distances seem to be sensitive to
> > different community sizes. Thus, they seem to deliver inadequate results
> > when the different community sizes are the result of technical artifacts
> > rather than biology (see e.g. Weiss et al, 2017 on microbiome data).
> >
> > Therefore, i often see BC distances made on relative data (which seems to
> > be equivalent to the Manhattan distance) or on data which has been
> > subsampled to even sizes (e.g. rarefying). Sometimes i also see Bray
> Curtis
> > distances calculated on Hellinger-transformed data,
> >
> > which is the square root of relative data. This again makes sample sizes
> > unequal (but only to a small degree), so i wondered if this is a valid
> > approach, especially considering that the "natural" distance choice for
> > Hellinger transformed data is Euclidean (to obtain, well, the Hellinger
> > distance).
> >
> > Another question is what different sizes (i.e. the sums) of Hellinger
> > transformed communities represent? I tested some datasets, and couldnt
> > find a correlation between original sample sizes and their hellinger
> > transformed counterparts.
> >
> > Any advice is very much welcome. Thank you.
> >
> > --
> > Dr. Tim Richter-Heitmann
> >
> > University of Bremen
> > Microbial Ecophysiology Group (AG Friedrich)
> > FB02 - Biologie/Chemie
> > Leobener Straße (NW2 A2130)
> > D-28359 Bremen
> > Tel.: 0049(0)421 218-63062
> > Fax: 0049(0)421 218-63069
> >
> >
> >
> > _______________________________________________
> > R-sig-ecology mailing list
> > R-sig-ecology@r-project.org
> > https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
> >
>
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