[R-sig-eco] Relating abundance and cover data

Carsten Dormann carsten.dormann at ufz.de
Fri Oct 29 08:25:32 CEST 2010

Dear Karen,

seconding the comments of Phil and Etienne: One key question is whether 
you can assume no error on the values of your predictors (i.e. run a 
model 1-regression). If you can, Ben Bolker's comments point in the 
right way; if you cannot, my heart goes out for the "simplistic" 
approach of Etienne and try to pad your results with a bit of 
"robustness testing".
(E.g. perturb/jitter your values and see if it makes a difference to 
your regression. This may not be "official" stats, but should show clear 
differences when the pattern is not robust. For example, the many 0s in 
your data may be caused by detection problems (rather than true 
absences) and hence giving them a random low cover/abundance (e.g. 1/2 
of the respective minimum value) should NOT change your results. If it 
does, I would interpret this as the data not supporting a clear 
correlation between abundance and cover.)



On 26.10.10 11:27, Karen Kotschy wrote:
> Dear list
> This seems like something I really should know by now, but I'm getting so
> confused, I'd really appreciate a little help!
> I am trying to model the relationship between relative abundance (%) and
> relative cover (%) data for plant species. I want to know to
> what extent the 2 measures correlate, and to compare the extent of this
> correlation at different sites. Obviously, both sets of data are
> zero-inflated and highly skewed.
> The "traditional" thing to do would be to log-transform both of them and
> use lm(). However, a recent paper (O'Hara&  Kotze, 2010) argues that a
> much better approach is to use glm() and to specify Poisson or negative
> binomial models, rather than using transformations. This does make a lot
> of sense, I think!
> I have tried using "quasipoisson" and "quasibinomial" families in glm(),
> but I am left with a number of questions:
> 1) Should relative abundance and relative cover be treated as "count"
> data, given that the values are not actually integers but rather
> percentages?
> 2) Which parts of the output of glm(...family=quasipoisson(link=log)) do I
> use to evaluate the fit? Just residual deviance and the p value?
> 3) How do I plot the data so as to graphically represent the model? If I
> am using a log link should I use log axes for x and y?
> Thanks so much for any help!
> Karen
> ---
> Karen Kotschy
> Centre for Water in the Environment
> University of the Witwatersrand, Johannesburg
> Tel: +2711 717-6425

Dr. Carsten F. Dormann
Department of Computational Landscape Ecology
Helmholtz Centre for Environmental Research-UFZ	
(Department Landschaftsökologie)
(Helmholtz Zentrum für Umweltforschung - UFZ)
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