What metacommunity ecology can learn from population genetics (UPDATED)

This post is something new for me, and I’m pretty excited about it. I’m going to share some ideas that I’m hoping to publish in a peer-reviewed paper. In the past, I’ve had posts that eventually became papers, but they weren’t planned as “dry runs” for papers. This time, I have some ideas for a paper that’s been on the backburner for a while. The idea for the paper grew out of a sabbatical I spent in evolutionary biologist Rees Kassen’s lab a few years ago. Rees and I keep talking about writing the paper but we haven’t yet; this post is my way of making some sort of headway (and to be clear, any mistakes, omissions, etc. are my fault, not Rees’). I hope that by posting, I can get some feedback that will encourage me to write the paper, and help make the paper better. It’s a long-ish post, but that’s because the ideas are all intended to go into one paper. It’d be artificial to break them up into a bunch of posts.

The idea is for a perspectives-type piece on what metacommunity ecology (and community ecology more broadly) can learn from population genetics, and vice-versa. The analogy between spatial community ecology (“metacommunity ecology”) and spatial population genetics has been widely noted. Most famously, the neutral models of Steve Hubbell and Graham Bell are just standard asexual population genetics models, reinterpreted as describing the movement and competition of different species rather than different asexual genotypes. Less famously, the replicator equation, a standard mathematical framework for describing certain sorts of selection-driven evolution, is mathematically equivalent to the Lotka-Volterra competition model from community ecology. Vellend (2010) is a great review laying out the analogy between population genetics and community ecology.

Both population genetics and community ecology work as follows: Living organisms reproduce, die, and move (or send dispersing propagules) from place to place, and they pass on their traits to their offspring. Their rates of reproduction, mortality, and movement may vary over space and time, due to spatial and temporal variation in biotic and abiotic factors, and due to pure luck. As a consequence of these processes, organisms with certain traits may increase in frequency at the expense of organisms with different traits. And occasionally, a new type of organism suddenly appears in the system, which in population genetics is known as a mutation, but in community ecology can be thought of as a speciation event, or the arrival of an immigrant from some unspecified location outside the system. The analogy is closest between asexual populations and communities of competing species, with each asexual genotype being analogous to a competing species. In sexual populations there are important factors like recombination and epistasis that have no obvious community ecology analogue. And non-competitive interactions like predator-prey interactions and plant-pollinator interactions don’t really have any population genetic analogue. Predators and their prey, or plants and their pollinators, or etc., aren’t analogous to a single evolving population.

I want to take the next step, and go beyond merely noting the analogy or translating specific population genetics models into ecological models. I want to suggest concrete insights that metacommunity ecology can take from (asexual) population genetics in general. In a future post, I’ll also suggest that metacommunity ecology has some things to teach population genetics. Each field can learn a lot from the other because, with a few notable exceptions like neutral models, the two fields model the same basic biology in very different ways. The strengths of each modeling approach come with corresponding weaknesses, so that each field has blind spots that the other field doesn’t have.

Briefly, metacommunity theory has been built from the “bottom up”, based on models that explicitly describe the ecology of the system and then work out the implications for species’ realized demographic rates and population sizes. Leibold et al. (2004) review metacommunity theory and group existing models into four classes, based on similarities in their ecological assumptions (“species sorting”, “mass effects” (aka “source-sink”), “colonization-extinction”, and “neutral”). In contrast, spatial population genetics has been built from the “top down”. Population genetics models don’t specify the ecology, but instead specify as parameters the total population size N and the per-capita demographic rates (fitnesses) of the competing asexual genotypes. Depending on the model, those parameters might vary over space and/or time in some specified fashion. But even there the pattern of spatio-temporal variation in total abundance and demography is simply assumed, rather than derived from underlying ecological assumptions. In short, metacommunity models specify the ecology, while population genetics models summarize the consequences of unspecified underlying ecology. (UPDATE: via email, a correspondent suggests, quite fairly, that the distinction in modeling styles between ecology and population genetics isn’t as clear-cut as this paragraph suggests. There are some “top down” metacommunity models and some “bottom up” pop gen models. I’ve received several thoughtful comments via email, for which I’m grateful. I’ve updated the post to note a few comments that can be easily summarized and seem like they might be of broad interest to readers).

The advantage of the population genetics approach is that the parameter space is smaller and can be systematically explored. This means you can discover the full range of possible dynamics and outcomes. There’s an infinity of possible ecologies—but a much smaller range of possible dynamical behavior to which the underlying ecology might give rise. Modeling different ecologies one at a time, as has been done in metacommunity theory, runs a serious risk of missing some of the possibilities. Indeed, I’ll argue below that existing metacommunity theory has missed at least one important kind of metacommunity, which population genetics has identified. Simpler models also are more tractable mathematically, and so we can learn more about their behavior analytically. The disadvantage of the population genetics approach is more subtle: some possible dynamical behaviors can seem unlikely, because they can only be produced by making very specific assumptions about model parameters. Making such specific assumptions often feels highly artificial, like you’re rigging the model to produce unlikely behavior. But if you actually modeled the underlying ecology explicitly and derived your population genetic parameters from those ecological assumptions, you might well find that the resulting population genetics model is quite likely to exhibit that “unlikely” behavior. Conversely, dynamical behaviors that seem likely to a population geneticist actually can be quite unlikely, arising only from highly implausible assumptions about the underlying ecology.

Insights for metacommunity ecology from asexual population genetics

1. A new class of metacommunity dynamic: clonal interference. Leibold et al. (2004) identified four kinds of metacommunities. They never said those were the only four possibilities—they were merely reviewing and categorizing the possibilities that had been identified in the literature. Unfortunately, much recent work in metacommunity ecology implicitly or explicitly assumes that the four kinds of metacommunities identified by Leibold et al. (2004) are the only four kinds, or else that any other kinds are mixtures of the four kinds identified by Leibold et al. (2004). For instance, in a very influential paper, Cottenie (2005) suggested a way to use ordination methods to classify natural metacommunities into the four types identifed by Leibold et al. (2004). This approach now dominates the empirical literature on metacommunities.

There are also distinct dynamical regimes in asexual population genetics, some of which correspond to metacommunity types identified by Leibold et al. (2004). For instance, as noted above, neutral metacommunity models are just reinterpreted asexual population genetics models. What ecologists call mass effects or source-sink models are what population geneticists call migration-selection balance models. Etc. But asexual population genetics also identifies distinct dynamical regimes that metacommunity ecology has missed. In particular, metacommunity ecology has missed an important phenomenon known as “clonal interference” (Gerrish and Lenski 1998).

In asexual populations, when beneficial mutations are extremely rare, there is a separation of timescales between slow mutation and fast selection, such that each beneficial mutation either sweeps to fixation or is lost to drift before the next one occurs. But when the beneficial mutation rate is not extremely small, a new beneficial mutation can occur in the ancestral population before the previous beneficial mutation fixes. This slows the rate of increase of both beneficial mutations, since one of them is no longer the fittest allele in the population, while the other does not have as large a fitness advantage over the new mutant as it had over the ancestor. Depending on the rates of beneficial mutation and selection, several different beneficial mutations might be competing for fixation at any moment in time. Gerrish and Lenski (1988) termed such competition among beneficial mutations in an asexual population “clonal interference” (CI). Recent theoretical work builds on the results of Gerrish and Lenski (1998) in various ways (e.g., see Patwa and Wahl 2008 for a review of theoretical work on fixation probability). And laboratory experiments on evolving bacterial populations confirm that CI occurs in real populations and is not just a theoretical curiosity (e.g. de Visser and Rozen 2008).

CI is very different from species sorting. Species sorting models are basically equivalent to population genetics models in which selection operates on standing (i.e. pre-existing) variation and there’s no migration, mutation, or drift. In contrast, CI is all about the interplay of mutation and selection. In ecological terms, CI is all about the interplay of the input of new, initially-rare species into the system via speciation or immigration, and their subsequent “sorting” via competition and drift. CI highlights that the input process and the subsequent sorting process aren’t independent: how close a system is to neutrality (=no selection; see below) is not independent of the rate of input of new, initially-rare variants. In the CI regime, even the fittest species is at increased risk of being lost to drift, compared to a situation in which the rate of immigration or speciation was lower. The importance of drift for community and metacommunity dynamics therefore depends not just on the total community size (which mediates the strength of drift), but also on the rate at which new species colonize local sites from within and outside the system.

CI also is different than mass effects, since in CI mutations do not occur so fast that deleterious mutants can be added to the system faster than selection can remove them. Nor is CI a mixture of species sorting and mass effects. It’s a qualitatively distinct dynamical regime, not some kind of average or hybrid of the species sorting and mass effects regimes.

CI models predict complex relationships between rates of beneficial mutation and the temporal dynamics of the mean fitness of the population. In ecological terms, mean fitness can be interpreted as a measure of how well “adapted” or “matched” the local community is to the local environment. “Adaptedness” is high when the community is dominated by species with high fitness under local environmental conditions. Empirical studies testing for correlations between local environmental conditions and the phenotypic traits of the dominant species can be thought of as attempting to test community “adaptedness”, because trait-environment correlations are thought to arise when species with appropriate traits come to dominate local sites at the expense of others (this assumption is actually questionable [Fox 2012], but that’s another story…). Such studies would be on much sounder theoretical footing if they were based on predictions derived from models that allow for the possibility of CI.

CI highlights the importance of long-term transients. For instance, initially-rare invaders in CI models can become transiently common before subsequently declining to extinction. Such dynamics are a familiar feature of community assembly and metacommunity models of secondary succession, which often attribute such dynamics to competition-colonization trade-offs. But transient increases in abundance followed by subsequent declines will occur in any site in which the best competitor is not the first to colonize, for instance when arrival order is random with respect to invader competitive ability, and in which colonization is not so slow as to lead to a strict separation of timescales between colonization and local competition. Population genetics models provide theoretical tools to study such transients. For instance, the expected rate of substitution (the rate at which beneficial mutations fix) can be thought of as a measure of the rate of succession—the rate of ongoing temporal turnover in species composition as new, superior competitors arrive and eventually replace less-fit residents. Equivalent predictions are difficult or impossible to derive from most existing ecological models, which have many more parameters and which focus on equilibrium features of communities and metacommunities.

In summary, while it’s a little unfortunate that metacommunity ecology has missed at least one (and possibly more) important classes of dynamics, that’s fixable. Clonal interference models, and population genetics models more broadly, are a rich source of new, testable hypotheses for metacommunity ecology. I’ve identified some of those hypotheses above, but I think I’ve only scratched the surface.

In the meantime, I think metacommunity ecology ought to hit the “pause” button on trying to use ordination methods to classify metacommunities into known types. I don’t think we yet have nearly a good enough idea what all the types are. And until we’re pretty sure we have all the “boxes”, it’s obviously problematic to try to sort observed metacommunities into those “boxes”. And I think population genetics-type models are the best way to try to identify all the possible types of metacommunities one might see. If you want to exhaustively explore the possible dynamics in some parameter space, the parameter space needs to be fairly small. Of course, even if we go with population genetics-type models, it’s not going to be easy to identify all possible dynamical regimes. Standard population genetics models like the Wright-Fisher model and the Moran model were around for decades before Gerrish and Lenski discovered the CI regime.

2. Stochastic drift is not synonymous with neutrality, and matters even in a non-neutral world.

I have an old post on this, but it bears repeating. In population genetics, a neutral model is a model with no selection. If all individuals have the same expected fitness (whether or not they have identical phenotypes), then there is no selection. Stochastic drift arises when the realized fitnesses of individuals exhibit random deviations from their expected values, whether or not those expected values are all equal. “Neutrality” and “drift” thus are not synonyms. For instance, it is perfectly possible for a neutral system to exhibit zero or negligible drift, by virtue of having both no selection and an extremely large total population size (that such a system is unrealistic, and that it would have extremely simple and uninteresting dynamics, does not affect the conceptual point). Conversely, even a system with extremely strong selection can have strong drift, just by virtue of having a small total population size.

Unfortunately, existing metacommunity models mostly either include selection but omit drift (as in most species sorting, mass effects, and colonization-extinction models), or else omit selection but include drift (as in neutral models). Existing metacommunity theory is incomplete, because hardly any existing metacommunity models include both selection and drift.

Thus, the oft-repeated claim that existing neutral models comprise a “limiting case” or a “null model” for existing non-neutral metacommunity models is incorrect. Neutral models in metacommunity ecology differ from non-neutral ones not just by omitting a key process—selection—but also by including a key process—stochastic drift. Existing neutral models in ecology thus do not comprise a limiting case or null model for existing non-neutral models. Rather, existing neutral and non-neutral models in community ecology both are limiting cases: each comprises a different limit of models that include both selection and drift. No population geneticist would ever use a model which population size N is finite and the selection coefficient s is 0 (i.e. drift but no selection) as a null, with a model in which population size N is infinite and the selection coefficient s is non-zero (i.e. selection but no drift) as the alternative. Metacommunity ecologists shouldn’t do so either.

Don’t get me wrong: there are good reasons for metacommunity theory to focus on cases in which either selection or drift are absent. For instance, such cases are tractable. But if you want to take a null hypothesis testing approach, using a model with drift but no selection as the null (and I’m not arguing that you should or shouldn’t, I’m just assuming that’s what you want to do), then the alternative has to be a model with drift and selection. There are a few such metacommunity models out there (if memory serves, I think Michel Loreau has done some work on such models). We need more of them.

(UPDATE: via email, a correspondent suggests that the point I’m making in this section is increasingly widely recognized, as reflected, e.g., in the increasing interest in “stochastic niche” models in metacommunity theory.)

3. Entire communities or metacommunities are never dominated by a single process

As noted above, much recent empirical research in metacommunity ecology attempts to categorize metacommunities based on which of type of metacommunity model best approximates them. This is useful because the dynamics of different types of metacommunities are thought to be dominated by different processes or combinations of processes (e.g., drift vs. selection vs. migration). Also as noted above, for this approach to work you need to have identified all the possible types. You also need to be sure that your categorization method works (existing categorization methods work badly when applied to simulated data generated by known processes; Gilbert and Bennett 2010, Smith and Lundholm 2010. UPDATE: But see Tuomisto et al. 2012 for an argument that variance partitioning methods fail in these simulation studies for correctable statistical reasons. Thank you to commenter and friend Owen Petchey for pointing out Tuomisto et al. In response, I’d say that I still think there’s scope for a great deal of work validating variance partitioning methods on data generated by known processes, and I think variance partitioning methods should be applied with caution until such validation has been done. I’d say the same for any general-purpose method intended to infer process from pattern).

But population genetic theory reveals a more fundamental problem with any attempt to classify communities or metacommunities into classes based on the identity of the dominant process or processes. Except in unrealistic extreme cases in which some processes are entirely absent, it is arguably not meaningful to speak of a dominant process for the entire community or metacommunity. Rather, one has to talk about the processes that dominate the dynamics of particular species.

For instance, consider an asexual population with discrete generations in which both selection and drift are present (i.e. an asexual Wright-Fisher model, for you population genetics connoisseurs). Assume that population size N is large but finite, and that selection s is weak but not zero. The dynamics of each asexual allele (genotype) will be dominated by different processes depending on its relative abundance (frequency). Rare alleles, meaning those with frequencies <1/Ns, have dynamics dominated by drift; more common alleles have dynamics dominated by selection. Analogously, it’s not entire communities or metacommunities that have dynamics dominated by drift or not; it’s individual species within those communities or metacommunities.**

This is not a minor technicality. Species entering the system via speciation or immigration ordinarily will be very rare initially, and so their dynamics initially will be dominated by drift. Drift necessarily plays a key role in any realistic model of a system open to speciation or immigration–but its role is to determine which rare variants happen to get common enough to be “seen” by selection. And while one could try to rank-order entire communities or metacommunities based on the proportion of species with drift- or selection-dominated dynamics, that seems to me to be just a rather ad hoc and uninformative summary of the species-level information.

Don’t misunderstand: I’m all for trying to understand the interplay of drift, selection, and migration (plus immigration and speciation from outside the system) in driving metacommunity dynamics. Heck, that’s what a lot of my own work is about! And I’m sure that the rates or “strengths” of these processes, and their net outcomes, vary among metacommunities. I’m just a little skeptical that the best way to understand the interplay of these processes is to try to classify metacommunities based on which process or processes are “dominant” at the level of the entire metacommunity. I’m skeptical in part because most attempts to infer process from pattern fail. And I’m skeptical in part because population geneticists are faced with the analogous problem–but as far as I know, they’ve mostly tried to address it in different ways, theoretically and empirically. Granted, many of their empirical approaches probably can’t be translated over to ecology, though perhaps some can. But I do think their theoretical approaches translate pretty well. I think that large body of theoretical work is an incredible resource that metacommunity ecologists haven’t yet drawn on nearly as much as they could.

4. Other insights for metacommunity ecology from population genetics

I don’t actually know all that much about population genetics (the pop gen course I took in grad school was a looooooong time ago), so I’m betting there are other insights that I’m missing. For instance, Rees thought of something that hadn’t occurred to me: there’s an analogy between the distribution of “mutational effect sizes” (i.e. the change in expected fitness due to the mutation) and the distribution of “effect sizes” of introduced species. Most mutations are deleterious, and mutations that are more than slightly beneficial are extremely rare. Analogously, most species introductions fail completely, and only a very few introduced species ever become extremely abundant. Further, the distribution of effect sizes of mutations that get fixed, or rise to appreciable frequency, is skewed by the fact that most mutations are lost due to drift and/or selection. Analogously, the distribution of any observed property of introduced species is skewed by the fact that most introductions fail and so are never observed. If memory serves, population genetics actually provides a quite clever and general theoretical argument about the expected shape of the mutational effect size distribution, both before and after drift and selection have had a chance to act. I wonder if this argument could be translated to make testable predictions about the distribution of introduced species’ “effect sizes” (which of course begs the question of precisely what property of an introduced species is analogous to the effect size of a mutation on fitness…).

What do you think? Do these ideas seem interesting? Important? Promising? Idiosyncratic? Unclear? Utter rubbish, the reading of which wasted X minutes of your life that you’ll never get back? (in which case, sorry about that…) Let me know in the comments.

Next time: what population genetics can learn from metacommunity ecology!

*And perhaps it’s naïve of me, but I’m not really concerned about anyone stealing my ideas and preempting me. This post isn’t close to a complete paper, so it’d be a lot of work for someone to take this post and turn it into a publication. In my experience plagiarists are lazy. Heck, in the past I’ve posted ideas on this blog that I think are publishable but don’t plan to publish, in the explicit hope that someone else would pick them up and run with them (here, and here). No one ever has.

**Aside: Clatterbuck et al. 2013 go even further than I have here, arguing that it makes no sense to speak of selection “dominating” drift, or vice-versa, even when one is referring to the dynamics of a single allele or species rather than an entire population or community. As I understand it, theirs is a philosophical claim about what it means to compare the “strength” of two different processes, and that it is a poor choice of words to describe 1/Ns as a threshold between selection “dominance” and drift “dominance”. I appreciate the point, although I don’t entirely agree with it. Verbal shorthand necessarily is imprecise compared to math. As long as people understand the math, one’s choice of verbal shorthand shouldn’t do much harm. And if people don’t understand the math, no choice of verbal shorthand will fix that. In any case, their paper certainly reinforces my point that selection and drift are not alternatives, or even ends of a continuum. They’re two different things.

18 thoughts on “What metacommunity ecology can learn from population genetics (UPDATED)

  1. Hi Megan, a very interesting post. I think there are very many parallel tracks, but not always treaded in the same way. Note that the inventor of the ANOVA was a population geneticist, although it’s much more used in ecology nowadays.
    A few other examples: 1) A very recent paper by Legendre & De Cáceres (august issue of Ecology Letters) on diversity partitioning in community ecology seems to have picked up some ideas from population genetics (Excoffier et al. 1992). 2) The species-area relation has its parallel in population genetics as well, where the expected equilibrium genetic diversity is determined by the effective size of the population and the mutation rate (and migration rate in systems that aren’t closed). This has applications in biodiversity conservation, by showing we have a species extinction debt, but in genetics we can also use it to recognize genetic extinction debts and “predict” the extinction of particular species in particular communities. 3) In 2006 and 2007 Lou Jost first shook up the community ecology community by challenging the way we partition diversity (alpha + beta = gamma??) (2006: Oikos; 2007: Ecology), and in 2008 (Molecular Ecology) he did the same thing with population genetics, showing that Fst (the traditional measure of population differentiation) doesn’t actually measure genetic differentiation. The basis of that reasoning was identical for both papers. 4) Just like we can decompose species richness into diversity times (un)evenness (Jost 2010: Diversity), we can decompose the census size of a population Nc in its effective size Ne and the variance in reproductive success among individuals of that population. Etcetera.

    For your upcoming post: considering myself as a population geneticist, I think there a two major things we can learn from community ecology.
    First, I perceive generally lower standards in statistical etiquette in population genetics. Most of the time, population genetic studies start with data exploration, then some nice pattern pops up, which is then tested “a priori”. See previous posts on this blog. Or even worse, many studies conduct data exploration, which yield a hypothesis (e.g., clustering of individuals into populations using Structure). Instead of then testing the hypothesis with independent data, it is considered a fact and used as such. Critical assumptions of tests statistics are very rarely tested explicitly, and are sometimes not even mentioned in software manuals or papers describing the software. A quick check on the use of a particular software (no need to mention which) showed that out of 20 papers citing this software in 2013 just a single paper mentioned the assumption but didn’t test it, 18 out of 20 clearly violated the assumptions (which was obvious from the data), and came to wrong conclusions. So far, this software has been cited over 2500 times. I detect similar problems with other genetic test statistics. This suggests that there is a fundamental problem among population genetic authors, but equally among reviewers, which is even more disturbing.
    Second: despite these problems of statistical etiquette, statistical machismo is becoming the rule in population genetics. If you don’t use very fancy tests, even though they may be superfluous, it’s hard to get your paper published. Given that assumptions are often just waived, this makes such practices scientifically very questionable. The funny thing is that this is often combined with very simple but clearly outdated tests. An example is a Mantel test where you correlate pairwise genetic distances with pairwise geographic distances. Apart from the fact that there are much better ways to test for spatial patterns (borrowed from communitiy ecology, and which are actually more appropriate in population genetics than in community ecology, as we can assume neutrality with neutral markers), it starts by making a matrix of pairwise genetic distances using Fst. Fst is a measure of population genetic differentiation that assumes the island model of gene flow. This model assumes that there is no spatial structure among the populations. This is then used to try to find a spatial pattern in the data… And still referees are asking me to use this test, over and over again, and are at the same time questioning much better standard community ecology methods (but applied to popgen) to detect spatial patterns. (Yes a paper got rejected recently because of this… had to get this off my chest :-))

    So to summarize, there seems to be a huge gap between community ecology and population genetics in practice, whereas they both study the same processes but at a different level of biological organization. Yes, a paper that would help to close that gap seems very timely and useful.

    • Thanks very much for the thoughtful and informed comments. I had been thinking more of conceptual insights for pop gen from metacommunity ecology, hadn’t occurred to me that there might be methodological insights as well. I confess I’m unsure how well-placed I am to pursue that line of thought, not knowing much about pop gen (Rees isn’t really a population geneticist either, he’s more of an evolutionary geneticist).

      p.s. this post was by me, not Meg. 🙂

  2. Another process that is rarely recognized as such in the models of community assembly is that of priority effects (with an analogue in population genetics: persistent founder effects). Although it is sometimes treated as a special (temporal) case of mass effects, it is truly something very different. Priority effects hinge exactly on stochastic drift combined with pre-emption of niche space. A competitively superior species arriving later in a habitat than an inferior species, this latter having already occupied and consumed a critical part of the niche (for example trees pre-empting light of seedlings), may not establish because of the priority effect exerted by the inferior species. Due to stochastic drift, the occasionally immigrating individuals of the superior species disappear from the community over and over again. Suppose that a tree dies and leaves a gap in the canopy, this is more likely to be filled up rapidly by the continuous emergence of seedlings from the inferior species (there can be thousands of them …) than by an occasional immigrating individual of a superior species. The resulting pattern of community assembly may look neutral, as only a spatial pattern emerges. But in fact the underlying process is that of priority effects. I bet this can explain a lot of the neutral patterns of biodiversity across a wide range of spatial scales.

    • Thanks for the further comments Joachim.

      It’s true that metacommunity theory hasn’t taken much notice of the possibility of priority effects, though I don’t know that they’re often lumped in with mass effects (I think it’s widely recognized that the two are quite distinct, as you note). You’re certainly right that priority effects are a prime example of the interplay of stochasticity and determinism. Jon Chase has a number of prominent papers on this topic.

  3. Great post.

    Just throwing some bones to gnaw, I’d add a few other areas where metacommunity ecology and population genetics should collide a wee bit more:
    1. linkage desiquilibrium: in population genetics, it’s a notion of how alleles at different loci might be statistically associated. It seems quite intuitive to try to apply a similar framework to species from different compartments (e.g. different trophic levels) as both species might thrive or decline together in synchrony due to common selective rpessures;
    2. in the same vein, epistasis surely has its community ecology counterpart, i.e. contexts in which effects of alleles at different loci are more or less additive. Think e.g. of multiple infections in a single host (take parasites over all hosts as a metacommunity).
    3. Group selection (not kin selection), as e.g. here: http://onlinelibrary.wiley.com/doi/10.1111/j.1558-5646.2012.01835.x/abstract, could be mirrored in the metacommunity world too: associations of particular preys and predators (or more easily, mutualist species) could be selected together at the landscape scale. Increased or decreased tendency to break apart from a particularly good or bad interacting species might be a trait selected for in a species, etc.
    4. Relating models of soft vs. hard selection in population genetics to some equivalents in the metacommunity world. It is now known that this soft vs. hard categorization can be well explained by the life cycle chosen for a particular population genetics model (http://user.iiasa.ac.at/~dieckman/reprints/RavigneEtal2004.pdf). With that in mind, what would happen to classic metacommunity models, say Mouquet and Loreau 2002, if life cycle were differently tuned? Would the conclusions still hold?

    Finally, I must add one caveat related to too much colliding between the two disciplines. What defines a patch or a deme in population genetics or metacommunity ecology is intrinsically different – population genetics defines demes based on reproduction (i.e. it’s the spatial scale at which geneotypes mix up through mating), while metacommunity ecology is not clear at all about how it defines a patch (shameless self-promotion: I made this case here http://tinyurl.com/mhm7cum). A patch can be defined by the scale of competition among species or it can be defined by reproduction or it can even be defined by the scale at which perturbations occur (as in Hanski or Levins’ metapopulation models). This is really one piece of the puzzle that has been left out of the game for very long and that requires clarification, especially when species move around at different paces and scales and the environment is not clearly patchy (i.e. not ponds or mountain tops).

    • Great comments Francois, lots to chew on here. A few quick thoughts:

      Re: linkage disequilibrium and epistasis, I confess I’m still unclear how to draw a tight analogy between those phenomena and metacommunity ecology. I guess I can see loose analogies. Certainly, lots of people have looked for statistical associations in the distribution of different species among sites–that’s a whole cottage industry in community ecology. And in the biodiversity-ecosystem function literature, people have drawn what I think is a fairly precise analogy between epistasis and how different species may have non-additive effects on some ecosystem function, like primary productivity. But in the context of this post I wasn’t really thinking of ecosystem function stuff. Maybe I should? Broadening the ms beyond metacommunity ecology could have advantages, but could also get unwieldy. Will think more about it.

      Re: your caveat, good point. I think it can be addressed, at least in a theoretical context, because many metacommunity models assume that patches are well-mixed (so just like well-mixed demes in pop gen), and then assume something about rates of migration among patches. But you’re absolutely right, it’s a tricky issue, especially empirically because there often aren’t any naturally-defined patches and different species interact with one another but yet move at different paces and scales. Modeling interactions of species that operate at different scales (but not so different that you can do separation of scales) is an important and largely (not entirely) unexplored frontier for modeling work, I think.

  4. Quote: “You also need to be sure that your categorization method works (existing categorization methods work badly when applied to simulated data generated by known processes; Gilbert and Bennett 2010, Smith and Lundholm 2010).”

    Quote from Tuomisto et al (2012 Ecography): “One of the most evident implications of our study concerns the interpretation of simulation studies. Such studies have recently claimed that variance partitioning is not a reliable method to assess the relative importance of niche and neutral processes, because analysis results do not reflect the known process strengths (Gilbert and Bennett 2010, Smith and Lundholm 2010, Stegen and Hurlbert 2011). However, only one of the studies identified the cause of the problem (Stegen and Hurlbert 2011), which is a prerequisite for finding a solution.”

    (Below is a comment I previously made, with a small addition.)
    See Tuomisto, Ruokolainen, Ruokolainen (2012) in Ecography for a description of the causes of the apparently serious problems pointed out by Gilbert and Bennett, and for solutions. The proximate cause of the problem seems to be saturation of dissimilarity measures (so check for this first). I.e., Once communities share no species in common, the dissimilarity is zero, regardless of environmental or spatial distance. Saturated dissimilarities has two potential causes: long environmental gradients and insufficient sampling. There is statistical solution to the first cause, and an empirical solution to the second (sample sufficiently).

    • Hi O,

      yes, saw your previous comment, just hadn’t gotten around to replying (I’m working on our paper). Thanks for pointing out Tuomisto et al., will update the post to link to it.

      I remain skeptical of variance partitioning approaches, because the truth is that they haven’t been validated on simulated data from a wide range of process-based models. My default is to assume that general purpose methods for inferring process from pattern don’t work until they’re shown to work. And I’m unclear on how one could show such methods to work, besides testing them on data generated by known processes (which means either simulated data, or data from well-studied empirical systems in which process rates have been set via experimental manipulation, or independently determined via other methods).

    • O, I’d welcome any comments you have on the other concerns this post raises about variance partitioning approaches as currently practiced (that we haven’t identified all possible metacommunity “types”, and that entire communities or metacommunities can’t really be said to have dynamics dominated by a single process because different species have dynamics dominated by different processes). Those concerns are, to my knowledge, independent of the statistical and sampling issues addressed by Tuomisto et al.

  5. Hi Jeremy

    Again, apologies for the rather terse comment before. As I said, it was nothing more than wanting to get to bed as soon as possible 🙂

    Re. variance partitioning… know first that I’m so far just a spectator (not used it yet, but thinking of doing so). From where I sit, it seems that the statistical method is still being explored and verified. Some of the conditions necessary for a reliable result are now known, perhaps others are not. Use with caution and awareness, I would say.
    Lets say that we can reliably use variance partitioning… then I could tell you with great confidence that of all of the variance in composition among my local communities, 20% is associated with local environment, 30% with space, and 10% shared between the two. Would you find that useful? There has to be a bigger question, I guess. How useful is variance partitioning will depend largely (again I guess) on this broader context.

    Re. the four metacommunity types… I’ve never favoured categorical classifications of things that are obviously continuous. I briefly presented my thoughts on this related to metacommunities at Intecol this summer. I had a triangular space, with the three sides being the strength (rate actually) of different processes. I then tried put the four types into this space. I’m not sure it really worked, but it served the purpose. Cottenie (2005 Ecology Letters) (I tried to put links at the end of this comment) has a one dimensional metacommunity space, and though I’m not sure its enough, I really like the use of a continuum. Some of this is discussed in Winegardner et al (2012 TREE, The terminology of metacommunity ecology) and in Logue et al (2011, TREE, Empirical approaches to metacommunities: a review and comparison with theory). Pinning down the “space” that metacommunities exist in, by finding the dimensions of this space, is perhaps what you’re suggesting in your post (or part of what you suggest)? Once we know that space, we can explore it systematically. And, if we must, then make some categories.

    Cottenie 2005

    Winegardner et al 2012

    Logue et al 2011

  6. Thanks so much for this thought provoking post. I am a firm believer that many answers and insights can be obtained from our kin-disciplines.

    Would you be able to give examples for: “If memory serves, population genetics actually provides a quite clever and general theoretical argument about the expected shape of the mutational effect size distribution, both before and after drift and selection have had a chance to act.”

    I am curious if these arguments could aid future work in modeling Allee effects of invasive founder populations. Thanks so much.

  7. A recent paper uses a phylogenetic idea to understand the effect of landscape fragmentation on biodiversity patterns. http://onlinelibrary.wiley.com/doi/10.1111/ele.12160/abstract OK it’s not population genetic, but it nicely illustrates the outcrossing between genetic and ecological disciplines, and it takes a kind of stochastic drift into account with regards to community composition as a result of fragmentation and a reduction in “species” flow among communities.

  8. Pingback: Modelling cyclic populations: thoughts on the workshop | Dynamic Ecology

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  10. So… um… did Fox and Kassen 201X ever get going? Any subsequent thoughts that haven’t yet made it to posts or publication?

  11. Pingback: Book review: The Theory of Ecological Communities by Mark Vellend | Dynamic Ecology

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