Phylogenetic relatedness and the determinants of competitive outcomes

Godoy et al. 2014 Phylogenetic relatedness and the determinants of competitive outcomes. Ecology Letters 17 (7): 836-844

Figure 3 from Godoy et al. How fitness, demographic, and competitive differences vary with phylogenetic distance.

Figure 3 from Godoy et al. How fitness, demographic, and competitive differences vary with phylogenetic distance.

Will Pearse

In a fantastic follow-up to the many criticisms of the community phylogenetic approach, Godoy et al. fit a form of the Chesson framework to ecological data, and find that while fitness differences are greater among distant relatives, competitive differences are not. Being phylogenetically dissimilar did not mean that species were more likely to co-exist.

This is an excellent demonstration of a point that many have suspected for some time, but few (none?) have been able to conclusively show in a field experiment. This probably has something to do with the work involved in doing it…! Of course, that it’s been found once does not mean it’s a general pattern, but along with other work from the same authors decomposing traits into niche and fitness components, it seems empirical ecology is now matching its theoretical counterpart. Some are going to take papers such as these as the first nails in the coffin of community phylogenetics: personally, I think they open the door to a whole world of new approaches that we’ve been wanting to explore for some time.

Generating hypotheses about the kinds of traits that map onto different kinds of evolutionary processes means we can ask more sophisticated questions about evolutionary ecology. We don’t need to just stop at declaring that a trait shows ‘phylogenetic signal’, we can ask what model of evolution generated these traits, and (more importantly) how the evolution of those traits interacts with how they play out in species’ modern ecology. Indeed, that’s what many community phylogeneticists have been trying to do since the very beginning.

Now we can start asking more nuanced questions about the kinds of evolutionary models we are fitting. Measuring the traits that enable co-existence in one area is fantastic, but it’s unlikely that only the eighteen species in this study evolved in isolation. How did the surrounding flora (and interactions in other environments) affect the evolution of these interaction components? If (as the authors rightly argue) Brownian motion gives us very little predictive power for deeper phylogenetic structure, are there alternative models that might? Is it ever truly possible for competitive interactions and hierarchy to be strongly conserved, if diffuse competition among many competitors is frequent? If competitive hierarchies change over time, does it make sense to ask if a particular snapshot of them, in particular environmental conditions, is evolutionarily stable? Personally, I think it’s a good time to be a community phylogeneticist…

Lynsey McInnes

Lynsey Bunnefeld

Unlike Will, I’m not a community phylogeneticist (still not sure I buy into communities) and haven’t been following the recent developments in community phylogenetics that seem to be making it a much more robust field (see Will’s post above). Instead, I just jumped into this paper without previously ever having thought of the way you could split up species’ differences into stabilising niche- and average fitness- differences. What a good idea and what a shame that distinction wasn’t recognised long ago.

The authors then go on to see if they can untangle how these two features relate to phylogenetic distance using some nifty field experiments with 18 plant species. Again, I got overwhelmed by the fanciness of the experimental design and the work involved in it. And am happy to believe their findings that only average fitness differences show phylogenetic structure (more distant relatives have bigger differences) and that increased variance over longer phylogenetic distances mean that communities as a whole don’t show phylogenetic structure.

Being the macro person I am, I wonder how these results generalise to other communities and how you might go about finding out without having to conduct an epic field experiment every time you want to try. I think these authors have already published theory for these ideas so it is definitely time to get out of the computer and into the community (haha) but just how might you do it? Early community phylogeneticists went to town fitting models to species presence/absence in areas and giant phylogenies, clearly we need to be more nuanced than that. Could we go a roundabout way and find the traits that underlie the average fitness and the stabilising niche differences and use these in a similar framework to Godoy et al. advocate here? Has this been done already?

The authors find that variance increases with increasing phylogenetic distance, does this mean that clear patterns will not be found as we zoom out from narrowly defined communities? Is this OK?

Will sees these developments as a kind of new dawn for community phylogenetics. I just wonder whether the new dawn is not just tearing the field apart in increasingly nuanced ways. I for one am not confident that we can use phylogeny to robustly predict how communities will respond to change or use snapshots of current communities to work out how they got put together. At least not without a lot of knowledge of the system in hand and then who needs these phylogenetic metrics anyway?


Predicting ecosystem stability from community composition and biodiversity

de Mazancourt et al. (2013). Ecology Letters 16(5): 617-625. DOI:10.1111/ele.12088. Predicting ecosystem stability from community composition and biodiversity

Decomposing variation in community structure is… exactly as difficult as you would expect (taken from de Mazancourt et al.)

Will Pearse

Will Pearse

I’ve lost track of how many papers have tried to put forward a new way to understand ecosystem stability. I was drawn to this paper because it develops a novel conceptual framework that requires no more data than we already have, yet has greater explanatory power than other methods. The math is better, and so the model is better.

de Mazancourt et al. use data on individual species to predict what might seem like an abstract component of ecosystems – the coefficient of variation of community biomass. They’re not just predicting biomass or community composition, rather the stability of that composition over time. You’ve probably noticed I’m always desperate to link species’ ecology with how those species evolved; I wonder what the evolution of synchrony of environmental responses looks like. Do species that have coexisted for millions of years tend to be more synchronised? Or do they respond differently, and by responding differently ensure stable coexistence because they are occupying different niches (which reminds me of last week’s diversification limits paper)?

A fair bit of space is taken up with mechanisms by which observational error (which is an important component of their model) could have a biological interpretation. I’m not sure I quite follow, but I would be interested to know what effect intraspecific variation (which might be viewed as ‘error’) could have on all this. Intraspecific variation is a real (if, in my opinion, small) source of variation, and we might expect it to play a greater role in species that are more prevalent within a community (there are more of them, and so more opportunity for variation).

Finally, it is almost unbelievable that they were able to explain more than 70% of variation in the Texas dataset. So, seeing as how they’ve done unbelievably well in some datasets, and just plain-old-fashioned-very-well in others, why is there this variation? What is it about Texas that is so amenable to this method, and what makes Jena so different? I have literally no idea, and would be very grateful for ideas!

Lynsey McInnes

Lynsey McInnes

I told Will the paper I had chosen was too hard (what do you think – check it out here) and he came back with this one! Much harder! Although, so neat. I think I more or less get it. The authors set out to develop new theory as to why we ecosystems are so often more stable when they are more species rich. They neatly set out the conundrum of increasing richness stabilizing total community biomass, but at the same time destabilizing individual species abundances. Why does stabilization win out in most ecosystems?

The author list comprises a distinguished group of researchers working in this field and they bring together theory with four amazing time-series datasets. They are all set to make progress. And they do!  Basically (I believe) they find that stability is obtained through three distinct, but of course interacting, mechanisms: and I paraphrase, in more diverse communities you get a nicely complementary set of species that response asynchronously to environmental fluctuations so there is less chance of community collapse; in more diverse communities demographic stochasticity is weakened so you don’t get crashes of individual species; and finally more diverse communities, in effect, homogenize the intrinsic heterogeneity of an area through the provision of a set of species that altogether occupy all available ‘niches.’

The neat thing about this paper is that all of these mechanisms have been floating around in the literature already and have now been brought together into a single model and that model is verified with four independent empirical studies. The authors provide visually satisfying path diagrams to show how one gets from species richness to observed coefficient of variation of community biomass through each of the above mechanisms and they show the strength and direction of each effect.

Although this paper was a bit of dive back in time to one or two of my undergraduate lectures on overyielding and the insurance hypothesis, I did appreciate this paper and was thankful that it was well-written and well-explained. So many people work on elements of this puzzle, particularly motivated by curbing biodiversity loss into the future, but I think it’s grand projects like these – that set the situation up in a coherent framework – that might be most helpful in really demonstrating why and how diversity is beneficial to ecosystem stability. I imagine the authors’ heads are already teeming with next steps: climate change, evolutionary responses, invasive species, etc. One might want to know both how perturbing a system affects stability, but also if the relationship between diversity and stability stays the same during and post perturbation.

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