Drawing ecological inferences from coincident patterns of population- and community-level biodiversity

Vellend et al. Molecular Ecology (early view) DOI:10.1111/mec.12756. Drawing ecological inferences from coincident patterns of population- and community-level biodiversity

What’s your favourite intraspecific (blue) and interspecific/community-level (red) trait-environment relationship? Taken from Vellend et al.

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Will Pearse

I picked this paper and broke our 6-month run of old-school classics (sorry!) because the topic looked interesting, and Mark Vellend seems to write so many good reviews I couldn’t help myself. Like Lynsey, I thought the paper was going to take a few different directions, but I enjoyed it nonetheless.

Figure 1 (which I’ve put at the top of this article) is the sort of graph that lots of people seem to grappling with, and I’m glad to see it in print. It’s an extension of one from another excellent paper, and the essential point is that an inter-specific relationship could be the opposite of the intra-specific relationship among the same species. The similarities with Felsenstein’s classic graph showing why we need to control for phylogenetic history are striking – different results are found at different (phylo)genetic scales. If you view individuals as existing in a hierarchy from clade–>region(–>community?)–>species–>individual, then I think it’s amazing that we find these reversals at all scales. Why is it that ecological/evolutionary units seems to have discrete, real boundaries among them? I sense it’s related to the balance between character displacement (species spread out into discrete groups) and niche filling (within those groups there is variation), but I sense we need some more simulation analyses to truly understand why this keeps happening. I may try something over on my R blog next week (sorry for the plug!).

I found the sudden appearance of a meta-analysis later in the paper surprising but kind of cool. The general idea is that discrete fragments of habitat should show a greater potential for genetic drift than same-sized sections of continuous habitat, and changes in the correlation between genetic and species diversity reveal this. The results seem to support their conclusions, and (like all such provocative graphs) leave me wondering how further we could tease this all apart. Biogeographic history, generation time, age of habitat patch, etc., should all affect these sorts of correlations, and on second thought it somewhat shocks me that we don’t have better data on all this (“correlations were calculated from data not collected for this purpose“). If we are to stand a chance of understanding some of the hierarchical patterns that I described above, we need slightly more joined-up analyses of different levels of diversity. Indeed, there’s enough meta-data on GenBank that maybe someone (not me!) could have a go at this.

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Lynsey McInnes

I was looking forward to this paper as I thought it would help clarify my own pet interest in integrating population and species level patterns of biodiversity and would give me more ammunition in my plot to have all above species level analyses incorporate intraspecific variation. The paper kind of managed to do this. It was slightly disconcerting how many times the authors stated what the paper was not going to talk about, but it did eventually end up focusing on congruent diversity patterns at the above (species numbers) and below (genetic diversity) species level and on congruent patterns of trait variation along environment gradients when considering multiple species or multiple populations of the same species. Both concepts sound fairly dull, let’s be honest, but have not been assessed vigourously in the literature so it was really nice to see a summary of these results. Conclusion: sometimes intra- and interspecies patterns are similar, sometimes not.

I guess where I felt the paper fell short (and this is probably due to me wanting the paper to be something it wasn’t trying to be) was that it did not really focus on how insights at one level of diversity could inform insights on another level. For instance, what are the characteristics (trait variation, genetic diversity) of populations that make up large range species vs. small range species? I.e. how do some species obtain large ranges, while others break up into multiple smaller ones? Are all large range species en route to becoming multiple small range ones or are there certain ‘types’ of population that can maintain a large range species over evolutionary time?

A ton of work has been done about edge and centre populations and adaption/movement in response to environmental change, I would have liked to have read more about how population characteristics can suggest how a species might respond (maladapted gene flow, good gene flow, rapid evolution, local extinction). Species’ ranges are dynamic because populations come and go. How do we integrate this knowledge with our desire to study emergent patterns above the species level (diversity fluxes at the regional scale, for instance)?

I did appreciate the authors’ discussion of trait vs. ‘diversity’ variation approaches to looking st intra/interspecific congruence. Molecular diversity measures are relatively easy to collect and more or less comparable across sampling units (a can of worms we won’t open here). Traits are harder to collect, it’s harder to choose relevant traits to collect in the first place and inference might be bamboozled if you are looking at a deficient trait or one collected poorly. I am guessing that a little way in the future, such studies will use the genetic variation behind the trait of interest (when genomes shower down on us like rain). Until then, a mix of both approaches is probably most informative.

Onwards and upwards.

Resolving the paradox of stasis: models with stabilizing selection explain evolutionary divergence on all timescales

 

Stasis: A population of mollusks is experiencing stasis, living, dying, and getting fossilized every few hundred thousand years. Little observable evolution seems to be occurring judging from these fossils. From Evolution 101.

Suzanne Estes and Steven J Arnold. The American Naturalist 169: 227-244. Resolving the paradox of stasis: models with stabilizing selection explain evolutionary divergence on all timescales

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Lynsey McInnes

 

The last time I read this paper was when I was complaining that all the macroevolutionary analyses I was attempting to conduct were kind of crap and far-fetched. Someone recommended this paper to me as a great example of an elegant, meaningful analysis of a heterogeneous dataset with a surprisingly simple outcome. I liked it then, but it made me despair even more about the state of my exclusively macroevolutionary analyses even more.

Now that I’ve jumped ship and am trying to find my way within the field of population genetics (with a lot of exposure to quantitative genetics), I like this paper even more. But enough angst from me. What about the paper itself? The authors quickly assume that stabilising selection is the general explanation for the extensive amounts of stasis observed in temporal datasets of a variety of phenotypes and set about attempting to find what kinds of models of phenotypic evolution can generate observed datasets.

This paper is a beautiful example of an attempt to cut to the chase of a bunch of models floating around in the literature using a set-up that makes just the right amount of simplifying assumptions for a tractable answer to emerge. Estes & Arnold find that the best model of the evolution of phenotypic means (where ‘stasis’ appears to be the norm) is one of tracking a fitness optimum that can move within fixed limits. They do this by seeing what quantitative genetics model fits best to a dataset of phenotypic mean changes across one to over a million generations (so, anagenetic rather than cladogenetic/splitting evolution). As an aside, I love that their analysis could be distilled as – does our elegant QG model generate points that fit within an ellipse around our data, or not. Genius!

Their set-up allows them to dismiss the common Brownian motion model (see Will’s post below) as well as the punctuational peak shift model in favour of a model that fits nicely with Simpson’s model of adaptive zones. Phew. This is a pleasing outcome for me as it sits comfortably with a lot of macro-scale analyses (using totally different data) that often find reasonably-sized clades filling up niche space to a certain point and then not really increasing in disparity or diversity until they jump over to new empty niche space (of course, there are counter examples left, right and centre). The matching results are convincing and underline further how naïve models of trait evolution are really quite unhelpful.

The data here consists of phenotypic means through time rather than across lineages at one time point (the typical format for macroevolutionary trait evolution datasets). I wonder how you could conduct a similar meta-analysis on such data? (Related tests have been done on individual traits like body size using the Ornstein-Uhlenbeck model of bounded evolution). I wonder if the signal Este & Arnold obtain is because they include phenotypic change across time-scales (from a single to millions of generations). Their best fitting model fits the amount of change observable at these vastly different time scales (i.e., massive change on a short-time scale that irons out into ‘stasis’ at macroevolutionary time-scales). Is it possible and/or interesting to attempt this kind of analysis across lineages? What do I even mean by this?

Taking a possibly more useful track – how can this result influence how we set up and test our cross lineage trait evolution studies? Can it be used to create more useful null models?

Most of my interest in thinking of stasis in phenotypic evolution comes from thinking about and observing phylogenetic niche conservatism (really just the narrow-sense niche encompassing abiotic environmental variables). The literature is replete with purported examples of strong evidence of PNC, but pretty bare on the process of keeping a niche axis conserved. I like this paper as it demonstrates to us how stabilising selection can generate the right amount of evolution observed at different time-scales. My favoured next step would be to add in some ecology to find out the mechanisms that prevent a lineage’s niche (or elements/axes within) from wandering amok?

Apologies for the rambling nature of this point. I’d be very keen to hear what others thought of it and how this result could be used to inform future analyses, particularly at the macro scale.

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Will Pearse

Too few papers draw links between models of evolution among and within species (phylogenetics vs. quantitative genetics to my mind). Lynsey is doing just that, so I’m not surprised she picked this paper this week! I liked it, if only (but not just) for its excellent summary of a lot of quantitative genetic ground.

The authors make reference to how, under a Brownian motion model, noise increases through time. This is a good point that’s often missed – I’ve brought this up to comparative biologists in the past, and they often retort that the signal of Brownian motion is never lost. This is very true, but if the noise  is so large that is swamps the signal (look at figure 1 in this), then what’s the point? Drawing broad generalisations, I think this reflects how most biologists are taught statistics; we’re taught that bias is always a bad thing (beware a biased predictor! bad!) whereas machine learning people are fine absorbing a little bit of bias if the precision is sufficiently increased (intro chapter of this excellent book). Yes, under Brownian motion the central tendency doesn’t change, but the precision of your prediction is tiny because so much error is introduced given sufficient time. Thus we can still make inferences about the deep past, but sometimes we might do better asking a different question.

Which brings me to the different models that were tested, many of which are ~two decades old, which is awesome in every sense of the word. A lot of people are scrambling to build ever-more complicated models that incorporate more and more detail, and yet more are turning to methods like Approximate Bayesian Computation as the only way to fit such complex models. This paper shows that might not be needed: they/Lande simplify by taking polynomial approximations of difficult equations, and then work with those. I’m a huge fan of non-linear interactions, but even these can (under certain conditions) be linearised and approximated to draw inferences about biology. The authors go to some pains to talk about whether some of these models could be fitted to phylogenetic data (some already are); were we to make such simplifications I really can’t see why these, and even more complex models, couldn’t be.

In passing, it’s interesting that they view models of DNA evolution in phylogenetics as successfully integrated and all fine and dandy. I really don’t – I think we need to start taking into account geography, and I occasionally see someone talk about ways to integrate directional selection into phylogenetics which sounds fantastic. I shudder when I consider how many phylogenies are built using loci under incredibly strong directional selection, like rbcL and matK (I do it!), and in so doing violate so many of the assumptions phylogenetics is based on.

Plus ça change — a model for stasis and evolution in different environments

Peter Sheldon. Palaeogeography, Palaeoclimatology, Palaeoecology 127: 209-227. Plus ça change — a model for stasis and evolution in different environments

Storm of the Bastille – plus ça change? From Wikimedia (unknown artist)

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Lynsey McInnes

Continuing our choosing-classics strand of PEGE, I chose this paper after reading it years ago and remembering it now as impressively daring. I’ve got a soft spot for discursive papers, where the authors are not scared to be a bit radical and talk their way through an argument, throwing caution and data to the winds.

Rereading the paper this week, I knew I was on to a good thing as Sheldon starts with a quote from Levin about scale:  ‘the problem of relating phenomena across scales
is the central problem in biology.’ And a consideration of scale is one of the issues that has popped up in many PEGE posts this year. Since this paper, there has been tons of literature produced for and against punctuated equilibrium, see the great piece by Pennell et al just published in TREE sorting the whole jumble out, but Sheldon, here, provides, to my mind, a very even handed treatment of what you can, and cannot, hope to ascertain from the extremely patchy fossil record stretching from biases in perception, the links between micro- and macroevolution to emergent macroecological patterns (and much inbetween).

Temporal scale…stating the obvious, we might think patterns are a mix of punctuated bursts and stasis from our contemporary view, but they are actually pretty damn gradual.

Spatial scale…let’s think about the environments lineages are persisting through when deciding whether there is stasis, gradual or bursts of evolution.

I’m realising more and more that I am most grounded in macroecology – however much I ‘want’ to be an evolutionary biologist or a population geneticist. So, I really appreciated Sheldon cutting to the chase on the processes that might generate high tropical diversity (specialist species, easier to speciate, gain some ecological distance and persist as a ‘good’ species, rather than generalists populating (on land) temperate areas, where the generalist ancestral phenotype works best, swallowing up precocious young species trying to match themselves to every last environmental fluctuation (excuse the gross anthropomorphisms). He just states as obvious the expected broad-scale effects of abiotic factors and briefly mentions higher expected impacts of biotic interactions among specialist species and other factors that have been discussed to death in the ensuing two decades of macroecological research. He touches on my pet topic intraspecific variation, although he goes on to suggest (I think) that locally adapted populations responding to broad-scale environmental change could lead to punctuated bursts of evolution (or at least the signal of such), something I’m not too sure about.

I also wonder how his thoughts on the effect of contemporary climate change and evoluationary responses to it were taken at the time of publication. We are so used to these ideas now, but were they radical then? Not sure. I loved that he matter of factedly states that predicting species’ responses is going to be exceedingly difficult.

I’ve written this post in a rush and I’ve realised it’s pretty thin on the ground in terms of actual commentary – my lasting impression of this paper is being awed by Sheldon’s ability to cut to the chase across a range of fields from biases in the fossil record to drivers of species’ diversity. If I had more time, I’d like to go through his conjectures with a fine-toothed comb to see which have stood the test of time and the ravages of ‘proper’ analysis. My hunch is quite a few. Not least the idea that geological timescales are just really long versions of ecological timescales, this can be interpreted in various ways – at the most basic – generalists do better – across timescales – in fluctuating environments.

In short, this paper is well worth a read, if for no other reason that the multitude of brilliant metaphors…pullovers, human rebellion, loud sneezes.

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Will Pearse

There are a number of really cool ideas in here that really spoke to me, and it’s been quite interesting to imagine the impact this paper had on a younger Lynsey! I’m afraid I’m not going to focus on the main thrust of the paper, not because I don’t like it, but because I got wildly over-excited about one aspect of the paper.

A racemose phylogeny (look here if you’re not a plant person) is a  phylogeny with lots of bristly, transient off-shoots that die out quite quickly (it’s attributed to Williams), and it immediately brings to my mind that first phylogeny Darwin drew. People get very excited at the idea that particular sub-populations of a species can act so differently; if we all talked about raceme phylogenies and how our definition of species is somewhat arbitrary a little more explicitly (and not just when we’re leading that Biology 101 class), I think we wouldn’t be so surprised. Species are collections of populations, always budding off one-another and then re-joining the main body. This got me thinking: what would our expectations of trait evolution look like if we accepted a raceme where species are constantly being born and die, and each separate raceme spike has a slightly different trait? Remember that these tiny, off-shoot branches are probably never truly lost, and maybe they just act as repositories of genetic diversity that get pulled back into the main population.

I have never been sure what an evolutionary response over geological time looks like. I think of evolution as the outcome of lots of ecology over lots of time, and as such I have always found it hard to imagine the outcome of evolution as anything more than the emergent property of ecology. But when coordinated with the raceme ideas above, I think I finally see it. Geological time is like the mother of all ecological storage effects – perhaps species and traits that are (maybe only slightly) mal-adaptive now can survive over longer periods of time (perhaps in the tips of these racemes…) until they are useful later, and then those traits come to dominate. Thus the species survives through these stored pools of variation, in a constant state of flux, and yet somehow appearing the same. Plus ça change.

Functional extinction of birds drives rapid evolutionary changes in seed size

Galetti et al. Science 340(6136): 1086-1090. DOI:0.1126/science.1233774. Functional extinction of birds drives rapid evolutionary changes in seed size

Birds only disperse what they can carry! From Galetti et al.

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Will Pearse

Wam-bam, this is a paper I would have loved to put in my undergrad essays. Plants need birds to disperse their seeds, and so when large birds go locally-extinct, plants evolve smaller seeds that smaller birds can carry. This happens really, really fast (within the last 75/100 years!) and so is a great example of rapid evolution.

A nastier man than I would point out that this is somewhat inferred; with no data on what seed size was like 100 years ago there’s a fair bit of supposition going on here. However, their variance decomposition (34% due to birds in forest, 0.1% differences among sites) is really quite striking, so I’m quite happy to go along with this. There’s such a clear link between seed size and probability of being dispersed (figure 2b) that I’m quite happy to accept the smoking gun of a huge selective pressure and observable differences.

Which leaves me with a slight problem, because I always assume that we can ignore both intraspecific variation and rapid evolution when doing ecosystem service work. If trait can evolve this rapidly, treating species’ ecosystem services and traits as fixed is no longer acceptable. Indeed, the situation is doubly problematic because there are going to be a lot of downstream effects of changing seed size, not just on the plant species itself (it’s now shifted on the simplified on the r vs. k selection spectrum), but also other species that interact with that plant. There is a huge literature on how phenology shifts are worse in tri-trophic interaction networks because not every component of the system can keep up with change – I see no reason for this not to be a concern here.

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Lynsey McInnes

This is to all intents and purposes a very neat demonstration of purported rapid evolutionary change in the face of a new selective pressure brough about by human-mediated loss of large-gaped frugivores from forest fragments. One could quibble on whether the frugivore loss is driving the contraction in seed size variation, or whether fragmentation caused the frugivore loss, and so on, but the authors do a thorough job of dismissing other possible correlates….environmental differences among sites, checking the time needed for such a response, and I’m pretty convinced the relationship holds.

This is bad news! It suggests many of those stacks of papers predicting responses to climate change or habitat fragmentation that brush evolutionary responses under the carpet are probably missing key elements of the response game, Similarly, how does this two trophic level result cascade to additional trophic levels. Without big palm seeds and thus big healthy palms, what grows in their place? What effect do these newly dominant plant species have on other pieces of the forest ecosystem.  Ah, its frightening.

What is the next step? Can we rejoin the forest fragments and get the large-gaped frugivores back? Is there enough genetic variation left to get back the large seeds?

This must have been a time-consuming study and its just not feasible to initiate tons of new studies at similar scales to ascertain how pervasive such rapid evolutionary responses are. I would naively guess that it might be better to continue with this system and see if we can work out this change’s effect on additional chunks of the forest ecosystem. Perhaps the authors are already working in that.

The macroecologist in me also ponders the feasibility and merits of expanding the scope of such studies. Perhaps to a mesoscale at least. I am reminded of Phillimore et al‘s very slick mesoscale studies on variation in phenological responses across space in British frogs. Here, the authors were looking to distinguish local adaptation vs. plasticity governing the spatial variation that they saw in order to predict how populations would cope in the face of climate change that will alter the timing of temperature cues. In short, the authors conclude climate change is expected to outpace the frogs’ ability to respond. However, they ignored the potential for microevolutionary change, as the timescales they were thinking of were so short. The challenge now seems to be to incorporate this possible response? Admittedly, easier said than done…

Convergence, adaptation, and constraint

Jonathan B. Losos. Evolution 65(7): 1827-1840. DOI:10.1111/j.1558-5646.2011.01289.x. Convergence, adaptation, and constraint

Are these Anolis dewdaps constrained? Maybe more than you’d think… (PLoS One; click for source)

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Will Pearse

We’ve covered too many data papers recently (that’s not a joke, but it does read like one), and so I picked this paper to help us step back a little and think. I’m pleased with the result: this is an excellent essay, that really made me think about what convergent evolution actually is. I’m particularly keen to hear what you all think of my comments about history!

Losos argues convergence is scale-dependent: there are many ways to evolve a long beak, and while there may be divergent evolution of the actual genes involved, the resulting phenotype (a long beak) is convergent. We’ve covered convergent evolution in bacteria, where the same genes (but different regions of those genes) mutated in parallel in separate lineages. I like this scale-dependency – it allows us to define convergence so that it’s amenable to study at all levels from phenotype to genetic mechanism.

I think we can push this framework further, and compare very different systems in meaningful ways. For instance, maybe examining constraints to evolution in responses to predation in Daphnia is easier when you consider what constrains their tolerance of the abiotic environment. Maybe seeing particular stressors and evolved responses as analogous to one another allows us to better compare evolution among clades, and view constraints to evolution in a more holistic way..

Apparently, there are some who take the view that evolutionary changes are incomparable historical events, and so the whole idea of convergence is a nonsense. I think this is rather peculiar; while there is a debate in history as to whether the field is a science (I think it is, but I’m not a historian!), every historian I know compares periods and events in history, with the precise aim of drawing parallels among periods. Thus I think the argument that evolution is the study of history, and therefore will not allow us to compare events, is not one even a historian would agree with!

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Lynsey McInnes

Commenting on a Losos paper is always going to be tricky, as this is a man who knows his evolutionary biology! You can tell this in two ways, first simply by the breadth of examples he draws on and second by his daring to question the be all and end all of phylogenetically-informed analyses, another recent examplesof his critique of such analyses can be found here.

Like Will, I appreciated having a week off from data bashing and am currently juggling all the different issues that Losos brings up on what is and is not convergence, parallelism, adaptation, exaptation, etc. The biggest take home message I got from the essay was that, as always, scale matters. Birds and bats both have wings that let them fly, are these convergent traits? Depends on your scale of comparison. It seems like identifying instances of convergent evolution would be simplified immeasurably if the researcher concerned just set out the scale across which he is looking and perhaps also mentions whether he is worried about the trait being the ‘same’ at the genetic, phenotypic, morphological and/or morphological level. Hey presto, confusion and agro could be gotten rid of.

I can’t help comparing the issues brought up here to the ones Losos, and plenty of others, have attempted to deal with concerning identifying instances of niche conservatism. Again, it all depends on scale. Cooper et al. provide an excellent roadmap for conducting analyses on nice conservatism, I’d like to see a companion piece to this essay detailing the practical approaches to sensible analyses of putative instances of convergent evolution.

I’ve recently shifted the scale of my own analyses to incorporate (currently to deal exclusively with) intraspecific variation. In practice, this has meant starting to think about different models of mutation (infinite site, infinite allele, shitty recombination raising its ugly head begging to be dealt with) so I find my scale of analysis shifting to the genetic level, wanting to see mutations in same genes, indeed at the same sites to qualify as parallel evolution. For this reason, I really appreciated this essay as it forced me to address my newfound genetics-only bias and realise that interesting, valid and evolutionarily important convergent changes at the functional (or even just phenotypic) level need not be produced from identical genetic changes.

The recent bacteria study that we discussed here at PEGE was a brilliant example of a standalone set-up for studying evolution across these different levels (genetic, phenotypic, etc.), the next step, as always, is to devise a set-up that facilitates similar inference in systems where access to all these levels might be patchy. Losos’ essay will undoubtedly be helpful in this regard.