A new dynamic null model for phylogenetic community structure

Pigot & Eitenne 2015 A new dynamic null model for phylogenetic community structure. Ecology Letters 18(2) 153-163

Figure 2 from Pigot & Etienne. Plots of likelihood of community membership under low (top) and high (bottom) rates of local extinction. Or, if you prefer, a series of variously coloured and not-coloured phylogenies.

Figure 2 from Pigot & Etienne. Plots of likelihood of community membership under low (top) and high (bottom) rates of local extinction.

Will Pearse

As if by magic, the ‘new’ approaches I hoped would appear last post have done so. It’s almost as if I know the posting schedule ahead of time!… Alex Pigot and Rampal Etienne have produced an analytical framework within which we can distingush between speciation, extinction, and colonisation in structuring an assemblage’s phylogenetic structure. Beyond that, they have developed a method that uses phylogeny to its true potential: not a proxy but unique data that helps us estimate evolutionary (speciation and extinction) and ecological (migration) processes of interest.

This is an important contribution because the problem of dispersal is one that has vexed many for some time, yet (with notable exceptions) received relatively little attention. Dispersal from a wider source pool provides an important link between ecological and evolutionary time-scales that we need to model. A species ‘appearing’ can either have an evolutionary (speciation) or ecological (dispersal) origin, and that their DAMOCLES model can at least start getting at that distinction is important. It is of little surprise to anyone who has followed these sorts of studies that phylogenetically overdispersed communities can result from something other than competition, but linking its origin to evolutionary and dispersal events outside a community is interesting.

There are, of course, additional complexities that could be built on top of this model, and I’m not going to bore you by rambling on about traits because I think you all know where we want that to be going. However, I think it’s important to explicitly consider the meta-community (or source pool, if you prefer) from which these species are being drawn. Focusing on one assemblage is useful, but the reality is that speciation and extinction dynamics are happening at biogeographic scales, and we desperately need to link community-scale models such as these with those. Considering multiple assemblages undergoing these kinds of dynamics could be a good place to start. I wonder if the limiting factor may be finding something analytically tractable; while simulating individual communities linked within a wider system is reasonably feasible, doing so analytically (as the authors seem to have started doing) is more difficult.

Lynsey McInnes

Lynsey Bunnefeld

Alex Pigot has a way with null models. He’s already shown that the arc of species’ range size over the course of a species’ lifetime is not necessarily the result of deterministic processes and now he (and Rampal Etienne) have shown that common patterns of community assembly need not be the result of negative biotic interactions if an appropriate null model is used. Wow.

This paper is a great example of a couple of key points that I am most definitely guilty of ignoring. 1. taking time to think whether your null model is biologically as well as statistically null is important. 2. important insights can be made even before your model includes every last contributing factor (see Will’s post above). 3. data examples are important to illustrate your method. Nice.

I’m now going to largely disregard all those things I just said were important and wonder how you might extend this model and wonder what pesky real world effects might topple the null expectation.

I wonder how biotic interactions with non-clade members affect community assembly, i.e., competitors, predators, prey, hosts, etc. I wonder what a null model for this might look like? Should hosts/parasites (for example) evolve in tight coevolution, or not? I wonder what repeat processes of community assembly look like? Always the same, or not (I’m sure this has been treated in microcosms and by Gould). How would phylogenies help with these questions? In the same vein, I wonder how what happens to the new sister species that does not enter the community of his ancestor? I guess I am pondering the effects of space. I have no doubt the authors have too, and would not be surprised if they already have the answers up their sleeve. The authors deal elegantly with variation in a quantitative trait (or traits) meant, I think, the characterise niche. I wonder what happens when you throw in variation in other traits, probably dispersal ability (haha, with all the trauma that goes with defining and measuring that!).


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?

Convergent structure of multitrophic communities over three continents

Segar et al. Ecology Letters 16(12): 1436-1445. Convergent structure of multitrophic communities over three continents

Figs and fig wasps. Taken from the excellent figweb site (c) Simon van Noort (Iziko Museums)

Figs and fig wasps. Taken from the figweb site. (c) Simon van Noort (Iziko Museums)

Will Pearse

Will Pearse

Put simply, this paper is excellent. The authors have amassed an impressive dataset, performed a thoughtful and sophisticated analysis, and then explained the whole thing so clearly that it almost sounds easy. I look forward to trying to play around with some of these ideas in other systems!

It seems like there really has been convergence here: distantly related species are doing the same thing as each other in different places. So how the hell did this happen? While many evolutionary biologists I speak to seem to have a pretty good idea what they think convergence is, I think we’re still lacking a formal mechanistic model that can be tested. Yes, we can isolate parts of a phylogeny that looks convergent, but I don’t think we have a model of trait evolution we can use to model this and I’m not sure what it would even look like (what is the opposite of a Brownian walk?). Perhaps convergence happens when there’s insufficient dispersal for pre-adapted species to move in and occupy a particular niche. Perhaps convergence can only happen when there’s sufficient flexibility in a particular trait, thus labile behavioural traits should show more convergence and things like the Baldwin Effect will become important. Maybe there’s something special about fig wasps, and their emergence and mating on the surface of figs (they do that, right?) that makes them more susceptible to all this. Maybe it’s none of these things.

Perhaps the most important limiting factor would be the evolution of the figs themselves; I wonder if the most important methodological advance would be simultaneous evolution of fig and wasp traits, and simultaneous diversification/extinction of both taxa. Obviously work has been done on this already, but I’m talking about a more explicit derivation where, instead of individuals in a population interacting, there are individuals from two separate populations (figs and wasps) interacting according to some fixed set of rules. Thus a particular trait shift in one population would have to be matched by a complimentary shift in the other. I sense the maths would get quite intractable quite quickly (well, it would for me…), but simulation shouldn’t be impossible.

Lynsey McInnes

Lynsey McInnes

To maintain full disclosure, I am about to start collaborating with senior author, James Cook, so it is in my interests here to be constructive and probably err on the side of positivity. That said, I enjoyed this paper a lot! The fig wasp system is inherently cool and I thought the analyses here were exceedingly ambitious.The authors set out to test the relationships among fig wasp communities across three continents. According to measures of phylogenetic and ecological distance, do they follow the ‘inheritance’ (long term co-diversification, same ecological and phylogenetic diversities), ‘convergence’ (same ecological diversities got through different phylogenetic routes) or ‘constraint’ (ecological roles divergent because of constraints on colonisation and/or niche shifts by resident species (meaning phylogenetic diversity also different among communities) hypotheses. They find most support for fig wasp communities being similarly structured through ecological convergence.There are two sides of the fence on which one could sit with regard to this paper. On the one hand, the authors have built up a perhaps overly-complicated methodology in order to demonstrate ecological convergence when one has the feeling they already knew this result would emerge. These are fig wasp experts after all. For instance, the authors could have put the wasps into their guilds without any analyses at all. Similarly, I still don’t fully understand the ins and outs of the PVR and how that setup is able to decompose the variance into ecological, phylogenetic and joint components. I also worry about the low sample sizes and the power of a 35 species family spanning a ton of other wasp species (these qualms might be unfounded, I imagine Will would know).

BUT…my interest lies in rolling out such a methodology more broadly, perhaps to sets of communities with which one has little expertise. Then, for example, an objective way to delimit guilds is vital. And a step by step framework for analysis (the authors’ figure one) is a great tool. My mind is already ticking over to the time when one could stack various cross-continental analyses of community structure across groups into a big metaanalysis. Is convergence the norm? My feeling is such a meta-analysis is a long way off though.

One can imagine also developing the methodology within the fig wasp system (imagine having the data to do this for each of the 750 fig/fig wasp systems) or, as the authors suggest, looking at the structure in different parts of a single fig tree species’ range. I wonder if there are environmental correlates of the different signals?

I also liked a lot that the authors quantified both richness and relative abundance. I liked a lot that they had explanations for the reasons behind the signal of convergence (weird fig traits, niche shifts). I also liked that the authors distinguish constraint vs. convergence and wonder whether convergence ever follows constraint (and whether you could tell?).

I wonder if you could ever roll out these studies in some kind of experimental mesocosm? It would be cool to see the genetic underpinning of the various routes to similarity in community structure and how many replicates would get stuck in some setup due to constraint vs. reach the same ‘end’ due to convergence? You could add in the effect of various historical events (climate change! meteorites!), the possibilities are endless.

One final idea, it would also be interesting to look at multitrophic communities much closer together in space and see how movement across communities affects the patterns observed. Although the authors do suggest that their setup would work best for bounded communities. Hm.

So, yes, a very cool project. Thanks James and co 😉

Phylogenetic Diversity Theory Sheds Light on the Structure of Microbial Communities

O’Dwyer, Kembel & Green. PLoS Computational Biology 8(12): e1002832. doi:10.1371/journal.pcbi.1002832. Phylogenetic Diversity Theory Sheds Light on the Structure of Microbial Communities


This is the prettiest way of showing how communities can be assembled from a wider meta-community I’ve ever seen (from O’Dwyer et al.)

Jenna Morgan Lang

Jenna Morgan Lang

It’s become sooo cliché to say this, but I just can’t help myself: It’s a very exciting time to be a microbial ecologist! You lovers of life writ large enough to be viewed with the naked eye have had all the fun so far. Spilling gut contents and watching predators eat prey to fashion food webs, hunkering down to observe the social behaviors among and within species in a tropical rainforest, catching, marking, and releasing things to understand how they move through space and time, counting, collecting, cataloging. Now it’s our turn!

But wait, when it comes to piecing together the interactions of microbial communities, we have no guts to spill, no behaviors to observe, and while I suppose that in theory one could capture, mark, and release a microbe, I would certainly never hope to recapture it. We have been doing a ton of the collecting, counting, and cataloging in recent years, thanks to cheap and easy 16S rDNA sequencing from diverse environmental samples. We have learned that there is a stupid amount of phylogenetic diversity almost everywhere we “look,” and we can infer, from the functional potential encoded in the genes of the few genomes we’ve sequenced so far, that they are interacting with each other and their environments in really interesting ways.

However, this paper argues, we are not yet very good at using our high-throughput sequencing data to answer questions about the fundamental ecological processes that drive microbial community assembly. Now, everything I know about ecology I learned from reading David Quammen’s Song of the Dodo in 1996. I honestly don’t even remember why – maybe it was the simplicity and applicability of the models, maybe it was Quammen’s excellent storytelling, but I fell in love with Island Biogeography. Not “devote the rest of my professional life to it” kind of love, but more like “I once went on the most awesome road trip, and every time I think back to it, I yearn for it again” kind of love. So when roughly 10 years later, the field of microbial community ecology went bananas, and I found myself smack dab in the middle of it, my thoughts immediately and frequently turned to Island Biogeography. How cool would it be to take these models that have been tested and tinkered with for decades and adapt them for microbial communities? Unfortunately, I was not equipped or inclined to actually do this sort of work. But, there are people out there like James O’Dwyer, Steven Kembel, and Jessica Green, who are.

Having said that, I’m hoping that the real ecologists will chime in about the nuts and bolts of the model described in this paper, because I just want to provide a context for it. The authors propose that their framework will allow us to address two issues.

The first, more pragmatic, issue is related to how we census microbial communities. We cannot simply stake off grid and sit down for a few hours identifying and counting species. We have to scoop up the entire grid, put it in a blender, and extract DNA from it. Typically, even after millions of observations (sequences), you will still encounter new species. Think rainforest canopy fogging, like times 100 million. So, a Species Abundance Distribution (SAD,) with # of species on the y-axis and abundance on the x-axis, will have a very long tail. No big deal, except that obtaining millions of sequences for every sample can still be tricky. For example, I recently sequenced 15 samples on an Illumina MiSeq. I obtained ~20 million high-quality 16S rDNA sequences. Ideally, this would be more than 1 million per sample, unfortunately (and this is common), for reasons we don’t yet understand, the number of sequences per sample ranged from ~98,000-~2million. Most methods (e.g., UniFrac) used to compare phylogenetic diversity (PD) between samples involve subsampling all to the smallest sample size. In this case, I’d be ignoring 18,323,722 sequences! That’s 92.5% of my data. And, forget about it if I want to compare my data to something collected 10 years ago, or to the samples of the future with their bajillion sequences per sample!

In walks the central result of this paper: an analytical method to obtain the expected phylogenetic diversity of a local sample from a larger community. This they term the Edge-length Abundance Distribution (EAD,) and it is an analogue to the SAD. But, instead of counting species and plotting them against their abundance, we are now plotting the total amount of branch length leading to a given number of tips against that number of tips. Or something like that… Anyway, this EAD displays approximately power law behavior, which apparently means that we can use it to do ecology!

One thing we can do with it is use it to normalize the UniFrac distance between differently sized samples, so that’s nice because it makes the first issue go away. The other thing that it can be used for is to start testing hypotheses about the ecological processes that contribute to microbial community structure, and they provide some proof-of-principle examples of its use with human microbiome data. For example, they asked whether the microbiome of someone’s hand has more or less PD than expected if the microbiome were derived from a random sampling of all microbiomes. If the PD is lower than expected, we might hypothesize that some environmental filtering is taking place (ahem, hand sanitizer). I don’t know that any particularly mind-blowing ecological questions were answered in their proof-of-principle application, but now that we microbial ecologists have this phylogenetic framework, we can extend it, and most importantly, start designing experiments with these interesting ecological questions in mind.

Will Pearse

Will Pearse

I really, really enjoyed this paper. Since I’m a methods nerd, I’m going to talk first about the method, and then about why the biology is exciting here.

I’ve wasted hours of my life shuffling species around to make null distributions, so a method like this that allows us to exactly and quickly compute a null expectation is amazing!  The derivation is extremely neat, but I found it initially confusing because I was stuck thinking about PD (phylogenetic distance). They don’t use the distance between species, rather the ‘opposite’ of phylogenetic distance between species: the distance between the crown of a clade and the root of a phylogeny. I’m very much at the limit of my maths here, but this does make me wonder one thing. If all of these expectations are based on branch lengths for complete clades in the metacommunity phylogeny (i.e., it counts the tips descending from a node), how appropriate is that for situations where a community doesn’t contain all members of a clade? In such cases, is the expected variance in PD meaningful, or would it under-estimate what we see in practice, because not all species within a clade are going to be present in a particular community? I find it hard to imagine that I’ve hit upon a central problem with the paper, so I’d be grateful if someone could comment and clear up my confusion!

Moving on to the biology. Community phylogeneticists spend a lot of time looking at the importance of how a source pool is defined spatially (look at this lovely paper someone wrote), and that we can find similar patterns in the human microbiome is wonderful. The classical explanation for clustering at wider scales (communities vs. other humans and other habitats) is that there’s habitat filtering, and that overdispersion should be found in tighter definitions of source pool (community vs. other humans in the same habitat) suggests competition within habitat type. I think it would be cool to have some more functional data on what these microbes are doing; this overdispersion might actually reflect facilitation, whereby different microbes are performing different ecosystem services and together they’re making a more stable community. That might sound a bit group-selection-y, but I’m certain it would be an interesting avenue to explore.

Lynsey McInnes

Lynsey McInnes

My heart kinda sank when I saw the paper for this week. One, I knew Will would have smarter things to say than me and two, I just don’t enjoy community phylogenetics papers…I ummed and ahhed over what to write for my post, to sit in the fence and mumble on about interesting facets of the paper or to jump right in and have a poorly thought out and largely uninformed rant about community phylogenetics. I’m going to have a go at the second, but try to not fall flat on my face. Ho ho ho.

My biggest gripe with CP is what is a community? How can it be circumscribed? And the same goes for the metacommunity/regional pool. Of course, you can find out cool and interesting things  about delimited communities (whole humans vs. noses, continents vs. ecoregions, whatever), but it all just feels so forced. The authors here appear well aware of this and devote admirably long tracts of the paper in highlighting that methods such as theirs are still quite dissociated from actual ecological mechanisms and processes that drive species dynamics.

While the authors’ advances here, doing away with tedious null distributions and endless simluations is definitely great, I just feel like there is still a gap to cross before these kinds of metrics really help us…either to understand some fundamentals of assembly processes or have more practical ends like guiding conservation decisions or informing public health policy. Yes, I’m being vague and I’m not even sure what I ultimately want or feel is possible to get out of similar analyses, but as it is, methods are getting more and more swanky but with no real advance. Yes, metacommunity size matters, no shit! Its always about scale, scale, scale, scale. I’m guessing because communities and metacommunities are bordering on arbritrary concepts, any metric will always depend on scale? No?

On less vague and ranty notes….some other thoughts that struck me. How did the authors generate phylogenies of the microbiome? What breadth of microbial diversity is found in humans and how well characterised is it? How robust are these methods to these kinds of mega phylogenies? This kind of thing probably interests me more than applying some crazy metrics?

Another thing that I wonder about CP and I’m fairly sure that some work on this kind of thing already exists is, what happens when you think about CP across trophic levels? Does bringing in trophic interactions help explain, or bring consistency to, the patterns observed? Because, of course, species don’t just interact with other co-occurring species in their clade.

I think I will stop here and leave the rant at that. Apologies to the authors, this was a well-written, balanced and indeed innovative paper…whose subject I just don’t happen like. Probably my loss more than anybody elses…

Spatial scaling of functional structure in bird and mammal assemblages

Belmaker et al. The American Naturalist 181(4): 464-478. DOI:10.1086/669906. Spatial scaling of functional structure in bird and mammal assemblages.

This is a guest post with Chris Trisos. 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!

Chris Trisos

Chris Trisos

Belmaker and Jetz tackle two major problems in community ecology. First, community assembly processes such as environmental filtering and competitive exclusion can leave either the same pattern or can act in opposing directions to leave a random pattern in community trait structure, neither of which can be interpreted as clear evidence for either assembly process. Second, how the relative importance of community assembly processes changes across spatial scales is still unclear.

The first problem is dealt with by delimiting an ecological species pool. This consists of the subset of species within the regional species pool that also fall within the trait volume of the local community, and thus can survive under the local environmental conditions. Now, the functional difference between the regional pool and the ecological pool can be used to test for environmental filtering and, because the signal of environmental filtering has been factored out, it should be easier to detect the influence of species interactions on community assembly using the functional difference between the ecological pool and the local community.

Belmaker and Jetz apply this approach to regions and local communities over a range of nested spatial scales from 400 to 3 million km2. The general expectation is that environmental filtering dominates at relatively large spatial scales and biotic interactions at smaller spatial scales. Interestingly, their results, at least for mammals, suggest that both environmental filtering and competitive exclusion operate across the entire range of spatial scales.

Now for some picking at the details. This paper is not the first to delimit ecological species pools, but it is novel in quantifying the functional difference between regional, ecological, and local species pools, as opposed to just the trait structure within each pool. The functional difference used here is the average nearest-neighbour distance in trait space between the species absent from the ecological or local species pool and those present in it (fig. 1). This is a pretty cool approach and certainly forces you to consider more explicitly what is different about the species that are absent when compared with focusing only within communities.

However, I am not as confident as the authors that high and low functional differences between species pools map as neatly as they suggest onto clustering and overdispersion in trait space within species pools. This is especially true for the link between low functional difference, trait overdispersion, and competitive exclusion in community assembly. One can easily imagine a case where the functional difference measure is very similar for two separate ecological pool to local community pairings, but where the pattern of trait overdispersion within the two local communities is very different. Fig. 2c and d gives an example of this. Because of this I would like to have seen a comparison of functional difference with a measure of trait overdispersion or regular spacing within local communities (e.g. standard deviation in neighbour distances). This is an important link to clarify given that a lot of theory on species interactions structuring local communities makes predictions about the overdispersion or clustering of trait structures within local communities as opposed to between communities and species pools. It would also be neat (if the dataset is available – maybe for plants?) to link functional difference measures to measures of species interaction strength. It might be that, for competitive exclusion, a given functional difference is associated with a higher strength or more asymmetrical interaction between pairings of present and absent species than between pairings where both species are present in the local community.

I’m also unsure about whether the standardized effect size of functional differences can be used to infer the importance of a given community assembly process, as is done in the paper to test for the importance of biotic interactions in the tropics (any thoughts?). A process that results in weak effect sizes could still have a very important role in structuring a community. For example, the potential for environmental change aside, a species that is only just unable to survive at a site due to local environmental conditions is perhaps no more or less environmentally filtered than a species with trait values far from the survival set for the local environmental conditions.

Will Pearse

Will Pearse

This is a cool paper that examines some fundamental aspects of ecology: using functional trait data to examine shifts in community structure. I don’t think it’s immediately obvious on a first-reading just how massive the bird trait database they’ve collected is – this is an incredibly useful resource, and this alone probably makes the paper worth reading.

I’m particularly interested in definitions of source pool, and so I like the authors’ attempt to understand what an ecological source pool actually is. My only concern is that they define their ecological pool by drawing a convex hull drawn around observed communities’ trait distances (top of p. 470); I think it’s going to be harder to detect deviation from the ecological pool on the basis of functional traits if you’ve used those traits to construct the ecological pool. However, the authors have explicitly defined their ecological pools in an intuitive way that’s readily applicable to other study systems, and they do detect some pattern, so perhaps I’m being unduly harsh. In passing, we’ve discussed the methods the authors use in a previous post.

It’s beyond the scope of this paper, but here’s something I’m always interested in: can we use these functional measures to detect certain kinds of community? Are there some examples of communities in this dataset where there’s no overlap in species between communities, but their functional composition is the same? In other words, can we find communities that are functional analogues of one another? Can we use these convex hull methods to partition communities into different sub-components, either to examine the functional dispersion within  them, or to see if these functional groups exist in other communities? These are big questions (I’m missing obvious references about them, right?) but they’re exactly the kinds of questions that papers like this make me want to explore.

Lynsey McInnes

Lynsey McInnes

Congrats for making it down to the third comment! Thanks Chris for choosing a great paper and contributing loads of interesting discussion points. Now, as a non-community ecologist, I feel a bit out on a limb here, but let’s see if I can contribute something useful too!

First, I did really enjoy this paper although I sometimes struggled to keep the various scales and metrics straight in my head (probably a function of me not using the associated figures to their full potential). I appreciated that they set up the reasoning behind the paper well and I bought in to most of their arguments for why this is an interesting question and why their methods are good for answering it. I second Will in being impressed with their trait dataset too!

Although not an innovation of these authors, I appreciated the simple distinction of ecological vs. regional pools – quickly and effectively removing one element of the question – environmental filtering. Makes you wonder why people didn’t do this (or some permutation of it) straight from the birth of community phylogenetics? I wonder how much richness of the local community matters to what breadth of things get caught in the ecological pool and whether this causes any problems with extracting functional difference between the two scales. I guess repeating the analyses at a whole host of spatial scales helped address this potential issue and looking at the effect of latitude helped untangle whether things are different in packed out tropical areas as opposed to spacious temperate ones.

If I understand correctly, the authors find that functional difference is generally lower than random between the ecological pool and the local community indicating that the local community is a kind of matching subset of species able to cope with the local environmental conditions, suggesting some kind of assembly rules/competitive exclusion is going on.  I swing between thinking this a really cool result and wondering what all the fuss is about, but I recognize that such a reaction is simply due to not being well versed in the decades of controversy surrounding these ideas.

My, more real, concern is that the authors purport to have gotten at process/mechanism in their analyses and it feels to me more like they have carried out an impressively robust and thorough analysis to show a set of results that they already kind of knew. That sounds unduly harsh – what I really want to say is – now comes the fun part! There are still some differences among communities and regions, and it would be exciting to know what leads to these differences: for example when are simple physical problems like dispersal limitation the cause of certain community compositions, when is it competitive exclusion? Is it intrinsic traits, extrinsic environment or stochastic events that underline such differences? It strikes me that the next step might be to incorporate a temporal perspective on the arrival of species (or rather traits) into a community and the effects this has, to incorporate finer-scale characterization of the landscape and environment (paleo conditions too, why not?) and, as Chris hinted at, get plant/producer information in there as well. That all sounds massively difficult and like a step away from finding generalities to finding specifics; I would counter that the authors have done a great job at clearing up longstanding issues in community assembly and their setup stands them in the best position to take the next step. I’d be interested to see how this goes.

*As a sidenote, one day prior to reading this paper I read a recent paper by Alex Pigot & Jo Tobias in Ecology Letters – Species interactions constrain geographic range expansion over evolutionary time – the authors take a temporal perspective on this question and it strikes me that some modification of their setup could be helpful in tackling more head on the temporal element of community assembly. Just a thought.

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