The metacommunity concept: a framework for multi-scale community ecology

Leibold et al. (2004) Ecology Letters 7: 601-612. DOI:10.1111/j.1461-0248.2004.00608.x. The metacommunity concept: a framework for multi-scale community ecology

metacommunity_types

Naughtily, this is a diagram of (roughly) the same concepts as discussed in this paper but from Logue et al. (2011). NM: Neutral Model, PD: Patch Dynamics, ME: Mass Effect, and SS: Species-Sorting.


Will Pearse

Will Pearse

What surprised me most about this paper was how much of it I feel I have absorbed, and yet I can’t consciously recall reading it. It’s a classic in the field, and I think either influenced or consolidated a lot of what people thought about metacommunity structure. It’s a great paper, and if you can’t recall reading it I suggest you go ahead and do so.

I don’t want to dig up old ground, but I was pleased to read the authors making explicit claims about how different processes would be picked up depending on the evolutionary history of the system. It’s great to see an attempt at integrating fields (when was the last time you heard someone call Neutral Theory a metacommunity model?) that doesn’t just stop at the line of ecology. Last time we discussed whether species truly neutrally dispersed, and how dispersal traits can interact with traits that we consider in a classic ‘here’s my quadrat what’s growing in it’ ecology. Metacommunity dynamics open up a whole range of additional processes and evolutionary interactions that can be simulated and estimated using empirical data – although whether we actually do that is a different question.

The authors claiming not to have covered spatially explicit models got me thinking. When we say ‘spatially explicit’, we typically mean ‘each individual has an x,y(,z) co-ordinate which we model’, and these models can be very difficult to fit. I think the authors are right that we don’t always have to use such models to capture interesting dynamics – three levels of hierarchical spatial nesting are often enough for me! However, if we were to fit a spatially explicit model over a large enough area, with different habitat types and dispersal across the entire space (perhaps separating between long-distance and short-distance dispersal), we should essentially be able to replicate metacommunity dynamics. I don’t think I’m alone in saying that, while there is a metacommunity, there’s no real such thing a community – it’s just what individuals happen to be in the unit that we’ve defined at that point in time to be useful for us to study something of interest (here’s some Vellend). It’s communities all the way down, each capturing a different scale of interactions or species, and perhaps we would have a better chance of capturing such dynamics if we examined whether we can get meta-community-like behaviour emerging natural from spatially explicit models. In passing, for every person who emails/comments screaming about how communities are real, I will donate $1 to the ‘I made a sweeping statement sorry everyone’ fund.


Lynsey McInnes

Lynsey McInnes

Contrary to Will, I found this paper tough-going. Not because it was bad, uninteresting or poorly written, probably just because it was extremely dense. And my mind constantly kept wandering and wondering – was this really published 10 years ago? How have we moved on from here?

I’ve always had a soft spot for meta- type models while never knowing many of the details. But from my ill-informed sideline position, I don’t really feel like we have moved on much from this landmark paper. Have we? Correct me if I’m wrong.

So, that nagging feeling led me to wonder why we might not have moved on much? Is it a data availability thing? A model availability thing? A every collection of ‘communities’ is different thing? Or what? Ja, ja, it’s probably just a combination of all three and more.

So, where would I like to see things go? Well, unlike me, I think we need to spend more time working out what makes a metacommunity ‘real’ before we can really tackle how it fluctuates through space and time. Maybe a good place to focus would be working out what populations within a ‘community’ interact, how stable or transient these interactions are and then add in links to neighbouring communities and quantify how strongly connected they are. I say – use genetics! Use the genome. Let the populations tell you how they are related to each other. Fit admixture models. Fit migration models. See how congruent models are among populations. Sure, this perspective is limited to a distinct time band, it won’t work for really transient metacommunities, but it will work for established ones and could help identify which populations are stable within (meta)communities and which fluctuate in importance and could lead to more informed models for faster-turnover metacommunities. If we use genetics to let populations speak for themselves, we also won’t go wrong if we add another layer of complexity and incorporate trait variation. We might be considering six communities, each with an overlapping set of species, but spatially-distributed populations of the same species will not have the same trait complement. Recognise this! Quantify it! Find out how it happens and why it matters!

No doubt these models are already been fitted, but how much crosstalk is there between pure ecologists, metacommunity ecologists and population biologists on the one hand and geneticists on the other hand. Let’s integrate!

My big dream is for us to one day understand how diversity gets organised from the scale of individual interactions through community dynamics to shifting ranges and ultimately species’ turnover. We will not get there without more communication from the people best placed to understand the processes occurring at each scale. The metacommunity concept is a great place to start as it links individuals, populations, trophic interactions and communities. We just need to use the best data to make inferences about all of these.
*Apologies for the rushed, overly exclamation-marked rant… Metacommunities are a great concept, let’s see how far we can push them. (And apologies if all this integration has happened and just passed me by…).

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Molecular evolutionary signatures reveal the role of host ecological dynamics in viral disease emergence and spread

Dule-Sylvester et al., 2013. Philosophical Transactions of the Royal Society B 368 – 1614. DOI:10.1098/rstb.2012.0194. Molecular evolutionary signatures reveal the role of host ecological dynamics in viral disease emergence and spread

Below, we give our first impressions of this article. Please comment below, or tweet Will or Lynsey (maybe use #pegejc). Think of this as a journal club discussion group!


Will Pearse

Will Pearse

We covered this paper in a (real, live, in-person) journal club here at the University of Minnesota, so the views below are probably not just mine. So, if you like what’s written below, it’s from the community genetics reading group; if you don’t like what’s written below, it’s all from me.

This study links an epidemiological model of how rabies spreads among raccoons to the structure of the genealogy that the rabies would have given present-day sequence data. I really like the modeling framework this paper presents. Inferring ecological patterns directly from a genealogy is a brave thing to try and do, and while others have done similar work this is probably the most explicit model I’ve seen someone trying to fit. This is also the paper’s greatest weakness: I’m not sure that these models could ever be fit successfully to real data. Taking figure 3 as an example, the authors state that they can detect the influence of long-distance dispersal because the exponential growth model fits their data better; I don’t think we would ever get such neat graphs with real data, and the predictions of their linear and exponential models look too similar (to me) to distinguish between in the presence of experimental noise. Indeed, while the authors use parameters derived from real data, they don’t actually attempt to fit their models to real genetic data; I wonder if they would be able to do so.

Moving past those rather snarky comments, this paper interested me because they’re attempting to model the ecological processes that might produce a particular genealogical (phylogenetic) structure. By looking for what kinds of signals long-distance dispersal leaves in the genome of rabies, they’re able to make useful predictions about what the rabies is doing right now – that’s presumably a lot of help if you’re trying to control an epidemic. I’d never really thought about how perfect a system diseases are for eco-phylogeneticists – they jump from host-to-host, making lineages nice and separate, and they evolve really quickly. Let’s just ignore multiple infections and DNA saturation for a moment, and think about the opportunities for fitting these kinds of complex models. Maybe we can all start linking phylogenetic (whoops – genealogical) structure to explicit models of evolution that incorporate ecology, and in the process help better-understand disease dynamics. As an eco-phylogeneticist, that kind of excites me!


Lynsey McInnes

Lynsey McInnes

First, apologies for the delay to this week’s post – I got caught up in Easter Monday laziness and what follows is largely random thoughts that popped into my mind as I read this paper on the train into work this morning.

I really enjoyed the idea behind this paper. I haven’t read much of the literature around the eco-evolutionary dynamics of virus evolution, but it sounds like crazy fun. I have read A LOT of the literature around models of spatially-explicit diversification and this paper definitely made me want to see more cross-talk between these two research areas (neatly incorporating my new field of statistical phylogeography/population genetics).

(I think) just like Will, I was excited by the possibilities that the authors outline, impressed by their modelling framework, but dubious about some of their outcomes and the likelihood that such a detailed model could often be used for predictive inference. I’d be happy to proven wrong however, and have very little feeling for how much data is really need/exists for such models to be powerful for, e.g. public health decision making. I’m also not convinced by figure 2 – is there not a ton of pseudoreplication going on in there – should there not be only five data points (as in figure 1b). Dare I say it – how about a mixed effects model?

Although the authors did perform sensitivity analyses and spend time discussing the effects of landscape heterogeneity and demographic stochasticity on their ability to infer process, I would have liked to see have seen more exploration of the effect of missing or biased data (for example how noisy can the data be before signal becomes distorted/lost?). I concede I have not checked the supplementary information and this information might be in there…

As a side project, I’ve been thinking about the effects of dispersal on macroevolutionary diversification and it was refreshing in this paper to see local and long distance dispersal so simply made distinct. I think this clarity of distinction is lacking from macroevolutionary analyses (so that when people look for the effects of dispersal on diversification they get conflicting results depending on whether they are looking inside a restricted area or beyond it (to cut a long story short)). Here, the authors have clear hypotheses on the differences expected whether or not the host moves beyond its immediate neighbourhood. One imagines that there isn’t really two distinct categories, but there is certainly more than one. So, hooray.

This comment might have come across as overly negative. I did not mean it to. I really enjoyed reading this paper, it was extremely well-written and thought-provoking (such that someone with no real background in disease dynamics could understand both the rationale and methods). I am going to check out the other articles in the special issue of Phil Trans that this article came from and look forward both to seeing how these types of models develop and hopefully to pilfering some of these ideas across into macroevolutionary diversification (that is similarly affected by processing acting on ecological time-scales (always good to end on a blatant note of self-citation).

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