Modelling competition and dispersal in a statistical phylogeographic framework

Ranjard et al. 2014 Modelling Competition and Dispersal in a Statistical Phylogeographic Framework. Systematic Biology. 63: 743-752.

phylo_comp_dispersal

Figure 1 from Ranjard et al. The three scenarios are based on the landscape and colored locations displayed at the bottom left.’ Can you guess which says which?


Will Pearse

The authors have put together an impressive method that detects whether competition and (consequentially?) biased dispersal can be detected using data on species’ present-day distributions and phylogeny. Does the method work? Well, that depends on whether you agree (1) with the model, and (2) that present-day distributions map in a 1:1 fashion onto past processes/distributions.

I’m pretty sold one (1), but then again I have neither the time nor the capacity to go through the maths in much detail! The introduction was interesting to me, since it smelt a little bit like it invokes the community phylogenetics sin everyone loves to hate; I would get mercilessly slaughtered for invoking competition or co-existence on the basis of niche overlap. Inferring about past processes at this scale, however, is a different beast – we’re data and method limited, so I think it’s completely acceptable to start putting testable models out there. Moreover, unlike those sinful community phylogenetics papers, this paper has a testable, verifiable model.

My only real problem comes with (2), and I somehow doubt the authors would disagree with me. The island case-study they use is a great one, and I think the model works perfectly for data like that where we’re dealing with very discrete, very tractable movements between patches of land. However, I think some care would have to be taken when dealing with continental distributions where variation in habitat type will mask the dispersal events the authors are looking at. Again, I think the authors are more than aware of this, and I don’t think they intend this method to be fit to GBIF data or something, but I’m interested to see if this could be tried with some modifications. Combined with this paper (which we’re covering soon), it’s a good time to be a phylogeny nerd – the methods are coming in, so now we need to just use them 😀


Lynsey McInnes

Lynsey Bunnefeld

I enjoyed this paper, even though it made my brain explode. It is a funny mix of idealistic and hopeful. The authors set up a framework to detect the effects of dispersal and competition on genealogies. They argue that population expansion and establishment (eventually speciation) are processes affected by dispersal ability (here characterised by an overall rate of dispersal away from a source population and a dispersal kernel shape parameter), but also by competition (here characterised by how much an already occupied location is refractory to further occupation). The authors argue that researchers have focussed on the effect of dispersal (generally characterised as distance among sites rather than intrinsic organismic traits) while ignoring the effects of competition (despite a large body of ecological literature on the effects of competitive exclusiveness). This is probably true and is likely due to the fact, as the authors note, that it is pretty damn hard to capture the effects of competition adequately.

The authors make a start here by setting up a relatively simple model to find out whether genealogical shapes and geographic patterns of occupancy suggest evidence for biased dispersal among sites (more to closer sites) and competitive exclusion (longer branches to the present as fewer sites are available for colonisation/establishment). They find evidence for both in a bush cricket genus endemic to the Hawaiian archipelago and their sensitivity analyses generally suggest they are able to detect competition when it has had an effect.

So far, so good. I don’t really feel qualified to destroy their methodology, so I’m not quite going to. I imagine there are better way to characterise these processes and that there are probably alternative explanations for the signals they recover, but we won’t dwell on them here.

My biggest problem with this paper and I am not at all sure whether it is not just me being dim is that I’m not really sure at what time-scales this method is expected to perform well. The authors jump back and forth between species and populations. Considering populations, I doubt you would be able to reconstruct bifurcating phylogenies with the resolution needed to detect longer/shorter terminal branches. Indeed, bifurcating trees are not the expectation and only populations geographically very far away from each other are expected to be isolated enough that gene flow from neighbouring populations would not homogenise gene pools. I.e. if a lot of individuals from the source population are dropping on to a second population, they are unlikely to be excluded unless they are so locally adapted to some place else that the environment kicks them out rather than conspecific competitors? Perhaps this is what the authors are trying to characterise anyway, and I am just misunderstanding their definition of competitor/competition.

I think my concern is that any successful model of genetic variation in space should probably take into account dispersal ability, competitors, but also environmental variation (this might be abiotic factors, but also biotic factors such as interspecific competition or intraspecific competition (if populations of conspecifics vary according to other aspects of the environment).

Again, I might be confused, as my head has yet to settle on whether it thinks according to bifurcating trees in macroevolutionary time or networks in population genetics time. My hunch is the authors have tried to operate across both time-scales, and I am not sure it works (or at least I can’t quite grasp it).

I’ll leave it there. Any help, much appreciated!

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