Relating abundance and cover data
Dear Karen, I was recently confronted with a similar problem, see paper: http://www.elaliberte.info/Laliberte_et_al_2010_RangEcolManag.pdf?attredirects=0 We ended up using major axis regression on transformed data, among other things. Then we simply plotted the relative abundance vs relative cover of different species and compared against the 1:1 line. I do realize that this is simplistic, a bit ad hoc and not very pretty (in part because normality is assumed with MA regression). That said, I thought it did allow us to quickly see which sampling method over/under-estimates different species, which was the main goal. But I'd be interested in knowing what approach you end up using. If anything, you could cite that rather unexciting paper as a good example of what the "bad approach" is -- it may end up being the only time it ever gets cited! :) Cheers Etienne
On Tue, 2010-10-26 at 11:27 +0200, 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
Etienne Lalibert? ================================ School of Plant Biology, M090 The University of Western Australia 35 Stirling Highway Crawley, Perth Western Australia 6009 Phone: +61 8 6488 2214 www.elaliberte.info