Model-based approaches and eco-phylogenetics.

Ever since I was a bright eyed naive undergrad,  I’ve been indoctrinated into the distance metric coupled with randomization test community statistical paradigm of Legendre and Anderson (among others). That was all I knew, and I thought I could apply this plethora of techniques reasonably well. I knew model-based techniques to analyse community data sets existed, but largely ignored them. That was until I met Will Pearce ( and my  metric/randomization world has slowly been slipping since.

As David Warton suggests (see the link to mvabund),metric/randomization techniques were great short cuts, but these shortcuts are not necessary now due to increases in computing power. I think he slightly overstates the mean-variance problem as most people that deal with community abundance data apply some type transformation. Nonetheless, model-based approaches, such as mvabund and the eco-phylogenetic PGLMM have numerous advantages in terms of power and flexibility (see Wang et al 2012 and  Ives and Helmus 2011). Interestingly, considering the advantages offered, both packages (particularly PGLMM ) aren’t used commonly with 39 citations for PGLMM and just over 100 for mvabund compared to the thousands (I’m guessing) that have used metric/randomization approaches. I wonder when this will change? They require only marginally more effort to use and there are plenty of well written guides to ease the skeptical community ecologist into it (see Will’s below). Maybe, for most people, it takes some convincing to convert away from techniques you learnt during your PhD…

I’m not saying that metric/randomisation methods will become redundant by the way. In fact currently, if you are interested in what landscape/biotic factors shape phylogenetic patterns like I am,  I don’t know of any other set of methods that work better. If I’m missing something – let me know! Also, as Ives and Helmus suggest, metric/randomisation methods will also still be important, at minimum, for exploring complex community data sets prior to conducting PGLMM or other similar methods.



Wang et al, 2012:

Ives and Helmus 2011:

Will’s PGLMM guide:


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