The long-term fitness consequences of early environmental conditions

 

(15) bighorns


adam-hayward

Adam Hayward

The conditions which an individual organism experiences during early development have a profound effect on their success in life. For example, poor maternal nutrition may lead to underdeveloped or small offspring, which are likely to have reduced fitness in later life. A host of studies in the lab and field have shown that the quality of environmental conditions (nutrition, population density, climate, predation, infection) during early life are strongly associated with body mass, survival, ageing rates, reproductive success, disease resistance and lifespan.

There are two (non-mutually exclusive) explanations for why early-life conditions influence later performance. First, a non-adaptive explanation: if conditions are good, development is good and the individual is well set-up for a successful life; if conditions are bad, development is sub-optimal in some way and the individual struggles. This is known as the ‘silver spoon’ hypothesis (although The Who would call it the ‘plastic spoon’ hypothesis), and under this scenario, a bad start in life always leads to poor performance in adulthood. Second, an adaptive explanation: the individual senses its environment during development, assumes that it reflects the environment it will encounter later on, and develops in such a way as to maximise its performance under those conditions. Under this scenario, if conditions match during early and later life, no matter how bad those conditions are, individuals will have higher fitness. This is the ‘predictive adaptive response’ hypothesis, and it has been a popular (but controversial) explanation for the origins of metabolic disease in humans.

Few studies have tested for predictive adaptive responses in long-lived wild animal populations, because it’s difficult: such a test requires measurement of environmental conditions in early life, plus measures of both environmental conditions and performance in later life. Gabriel Pigeon and colleagues, from the University of Sherbrooke in Canada, used more than 40 years of data on a bighorn sheep population to test for predictive adaptive responses in a recent paper. They concentrated on female sheep, and asked whether (1) probability of weaning a lamb and (2) probability of survival in a given year were dependent on early-life environmental conditions, current conditions, and an interaction between the two. They tested 12 different environment variables, including population density and a large number of climatic variables (although only density was important, with the hypothesis being that higher density = more competition = poorer nutrition).

They ran a large number of models for both response variables, including linear and non-linear effects of early-life variables and crucially, interactions between early-life and current conditions. They also attempted to separate out within- and between-cohort and –individual effects, which was rather cool, using a really nice approach developed a few years ago. In short, it was pretty thorough.

Population density at birth explained 32% of variation in weaning success: females born in high density years were less able to wean lambs. There was also an interaction with current population density: in high-density years, females were less likely to wean lambs, but this was only true of females who experienced high density around birth themselves. In other words, experiencing poor conditions in early life made individuals less able to deal with poor conditions in early life (Figure a below). However, population density at birth was very weakly (and non-significantly) associated with survival (Figure b below).(15) Pigeon Fig 2

There were some interesting (if rather mind-bending) results concerning how the current population density influenced weaning success, illustrated below (in the SI, where I went digging, so you/other PEGE members don’t have to!). In (a), each line represents the change in weaning success with increasing current density in a given cohort, and the redder the line, the higher the early density was in that cohort. There are between-cohort effects, because the cohorts are responding differently to current density; however, there is no average within-cohort effect, because the average cohort would show a line with a slope of approximately zero. In (b) each line represents an individual. There are between-individual effects, because individuals who experienced higher current density had lower fitness, but there are no within-individual effects, because all individuals responded in a similar manner to increases in density. This suggests the absence of individual plasticity, and that density affects all members of a cohort in a similar way.

(15) Pigeon S1

The main conclusion of the paper is that analyses did not support a predictive adaptive response. This is perhaps not surprising, given similar conclusions in a recent(ish) meta-analysis of experimental studies in plants and (short-lived) animals and even some not-especially-convincing (OK, it’s mine) stuff on humans using data on climate and famines. Predictive adaptive responses are an incredibly intuitive and lovely hypothesis at first glance. I’ve found this when teaching undergraduates: given the question ‘how can low nutrition during gestation lead to diabetes in later life?’, one or two will always come up with the idea of predicting the future environment. Evidence in support of such responses are rare though. An interesting question to ask is ‘how predictive is predictive?’ Do the fitness benefits of developmental plasticity need to arise when you’re halfway through life? Reaching sexual maturity? Surviving to weaning/fledging? Surviving the pre-natal period? All have been suggested, but the best examples of predictive adaptive responses (for me) are very short-term responses: one in voles, and one in humans. Are they predictive or even adaptive? It’s up for debate.

 

Fundamental Theorems of Evolution

Queller 2017 Fundamental Theorems of Evolution. The American Naturalist. 189: 345-353.

Figure 1 from Queller (2017) illustrating the relationship between the Price equation and four other fundamental equations of evolutionary biology. An arrow from one equation to another indicates that the latter can be derived form the former. Variables and subscripts are explained in the main text.


 

  Brad Duthie

Conceptual unification of disparate phenomena is a major goal of theory in the natural sciences, and many of the most revolutionary scientific theories are those that have shown how seemingly disparate ideas and observations follow logically from a single unifying framework. The most momentous of these theories include Newton’s unification of gravity and the laws of planetary motion, Darwin’s explanations of adaptation and biodiversity as following from natural selection and descent with modification, respectively, and Einstein’s general relativity unifying gravity, space, and time. In all of these examples, theory has changed how scientists understand the world by revealing a fundamental concept, the consequences of which encompass an entire field of study.

Perhaps to this list of discoveries we should include the unifying equation of George Price, which, in a recent paper in the American Naturalist, David Queller argues to be the most fundamental theorem of evolution. The Price equation as a unifying framework has been a subject of recent interest both within evolutionary biology and across disciplines from mechanics to music. At its core, the Price equation is a unifying framework for understanding any correlated change between any two entities. Queller proposes it to be fundamental because it encompasses all evolutionary forces acting on a population, and because it can be used to derive other less general equations in population and quantitative genetics, all of which require stricter assumptions about the evolutionary forces and environmental conditions affecting entities in the population. The Price equation includes two terms to describe the change in any trait Δz.

The first term isolates how a trait (z) covaries with fitness (w) for entities (i), and encompasses the evolutionary processes of natural selection and drift. The second term encompasses everything else that affects trait change (often called the ‘transmission bias’), such as mutation or background changes in environment. Intepreting the Price equation can be a bit daunting at first, perhaps in part because of how abstract the entities (i) are — representing anything from alleles, to unmeasured genotypes, to organisms, to even groups of organisms as the situation requires. Likewise, traits (z) can be any aspect of phenotype associated with such entities, including fitness (w) itself!

It’s here where Queller’s synthesis really shines, as he carefully walks the reader through how Price’s abstract equation can be used to derive multiple other less fundamental equations in which variables represent something concrete and measureable in empirical populations. These equations include Fischer’s average excess equation describing allele frequency change in population genetics, the Robertson and breeder’s equations of trait change in quantitative genetics, and Fischer’s fundamental theorem of evolution. In all cases, Queller notes the additional assumptions that are required to use these equations, particularly that the second term of the Price equation equals zero meaning that no transmission bias exists.

In our discussion, we reviewed the Price equation and its importance in evolutionary biology. We noted the interesting timeline of the discoveries of the equations; although the Price equation is fundamental in the sense that all of the other equations that Queller cites can be derived from it, it is also the most recent equation to be published. All of the other equations, which serve fundamental roles in population or quantitative genetics, were published decades before Price’s equation and have been used regularly by specialists in these sub-fields. This led us to talk a bit about what we value from theorems in evolutionary biology, and whether all of these theorems are better taught as independent solutions to particular problems in evolutionary biology, or as sub-components of a more fundamental framework grounded in the Price equation. Finally, we discussed the second term of the Price equation, noting that all of the equations that can be derived from the Price equation assume that this term equals zero. This effectively isolates natural selection, or some partition of natural selection, but ignores processes that are known to be important for trait change, particularly changing environment.

An extended discussion of Queller’s paper can be found on Brad’s own website.

Evolution of dispersal strategies and dispersal syndromes in fragmented landscapes

seed_dispersal_infocomic_by_sarinasunbeam-da1dyce-png

Cote et al. Evolution of dispersal strategies and dispersal syndromes in fragmented landscapes. Ecography, in press. (Image from http://sarinasunbeam.deviantart.com/art/Seed-Dispersal-Infocomic-606992414)


Lynsey McInnes

Lynsey Bunnefeld

PEGE journal club has morphed into a hybrid in-person/online journal club hosted by the University of Stirling. One half of the PEGE admin has moved to Stirling as a lecturer (me) and is hoping to harness the insights of the department as a whole when discussing matters in the PEGE realm.

We are still straightening out details and may migrate to a new website soon, but in the meantime, a rotating series of bloggers from the Biological & Environmental Sciences department at the University of Stirling will write up a short blog summarising a paper and our discussion every two weeks. As before, we’d be really happy to hear your thoughts on the paper and our interpretation in the comments below. In case you are wondering, Will Pearse is now an assistant prof at Utah State University and we’re even still friends! 

This week, I (Lynsey) chose the paper and committed to writing up our discussion. What follows is my own interpretation of events, apologies if I have misrepresented anything we discussed.

 

I chose a paper by Cote and colleagues from a recent special issue on fragmentation published in Ecography. I was excited about this paper as it promised to integrate three areas of interest of mine: space (fragmentation), intraspecific trait variation (evolving strategies) and species categorisation (dispersal syndromes). However, these grand promises proved problematic. To summarise our discussion: we came out sceptical of the framework proposed by the authors to integrate these three angles; we deemed it infeasible at best and foolish at worse.

Dispersal is a fiendishly difficult phenomenon to get your head around. Do we mean dispersal capacity or propensity? Is a mean or a kernel adequate to categorise the dispersal ‘ability’ of all members of a species? How much intraspecific variation in dispersal ability exists? Is this variance constant? How does it evolve? The authors acknowledge all of these issues and propose to address them head on. They put forward the idea of dispersal syndromes with covarying traits that either enable, enhance or match – the authors thus do not consider dispersal ability as a trait, but rather an emergent feature that comes about as a result of a bunch of possible traits. So far, so interesting.

Where the paper crumbles (for me) is that they go on to overlay the complexity of categorising dispersal syndromes on top of a fragmented landscape. I’m no expert on the process of fragmentation, but I do know it’s a fiendishly complicated topic too. The authors list four ways in which fragmentation modifies a landscape: it reduces habitat quality, increases number of habitat patches, reduces patch size and increases isolation among patches. Each of these four effects are likely to interact with dispersal capacity AND propensity in non-linear ways. And that’s without even considering these effects as selective pressures promoting the evolution of increased or reduced dispersal.

And so we got stuck. We didn’t feel that we have a good grasp (even for a single snapshot of time) of how to adequately characterise dispersal (although we all agreed it was an interesting problem) and so we were hesitant as to the utility of a framework of predicting how dispersal ability (or the traits that covary with it) are likely to interact with or evolve in response to a fragmenting landscape. A pragmatic solution we came up with was to think about holding some variables constant and looking at the evolution of dispersal strategies in those contexts (for example, varying only one of fragmentation’s four effects, not all four).

To conclude, the authors’ aims were admirable, but we were unsure whether we are really in a position to populate their proposed framework at the moment and, even if we were, we were unsure what generalities could emerge: because dispersal ability is a complex phenomenon we were not convinced a framework could be developed that robustly predicts how it might respond and evolve in species found on fragmented landscapes. Are there not too many unknowns and idiosyncracies of species * landscape? Saying that, we would be happy to be proven wrong!

Next week, John Wilson has chosen a recent paper from Ecology by Menzel et al for us to discuss: Mycorrhizal status helps explain invasion success of alien plant species. Join us!

 

Rapid diversification associated with ecological specialization in Neotropical Adelpha butterflies

Ebel et al. 2015 Rapid diversification associated with ecological specialization in Neotropical Adelpha butterflies Molecular Ecology 20: 2392-2405.

adelpha

Figure 1 from Ebel et al. “Adelpha wing pattern and species diversity. (a) Examples of the nine Adelpha mimicry types. The number above each image indicates the number of species and subspecies with the pattern. From top left: A. iphiclus iphiclus, A. naxia naxia, A. thesprotia, A. cocala cocala, A. salmoneus colada, A. boreas boreas, A justina justina, A. zina zina, A. levona, A. rothschildi, A. epione agilla, A. lycorias wallisii, A. ethelda ethelda, A. leuceria juanna, A. gelania gelania, A. seriphia barcanti, A. mesentina mesentina, A. melona deborah. (b) Five species have a unique wing pattern. From left: A. seriphia egregia, A. demialba demialba, A. justina inesae, A. zina pyrczi, A. lycorias lara. (c) Adelpha species richness across the Neotropical region (modified with permission from Mullen et al. 2011).”


Lynsey McInnes

Lynsey Bunnefeld

This was a funny choice from Will as it seems much more up my street than his. Indeed, my colleague James Nicholls and co. are developing similar phylogenomic methodologies to look at rapid diversification within Inga. James uses a targeted sequence approach that seems to also have worked pretty well. But I am too lazy to make this a post about the pros and cons of different genomic techniques.

In fact I’m not sure what to make this post about. I’m not sure what I think about this paper. On the one hand, it clearly represents an amazing amount of work – processing the samples, doing the bioinformatics and the bazillion versions of phylogenetic reconstruction and the assessment of missing data effects. I could not find any details in the main text, but they also appear to have dated the tree (and apparently better than previous attempts). And then they find neat relationships between toxicity of a common host plant family and convergent mimicry patterns across a variety of species. It’s a really nice story.

On the other hand, I am still not 100% convinced by the robustness of the various available methods of character state reconstruction (not discussed in the text) or of diversification rate shift detection (discussed a little bit). No doubt about it, a better phylogenetic hypothesis helps make these tests more meaningful, but they still rely heavily on accurate dating (at least relatively) and on some degree of rate conservatism across the tree(s) – or else you might infer different rates on every branch.

Grumble grumble. I am consistently amazed that these methods, that seem so dodgy, often recover relationships that make ecological sense (as here). So I should probably stop complaining and concede that they might be recovering real patterns (at least now that the phylogenetic hypotheses are more robust and the effects of missing data or rubbish dating priors are better characterized).

So where to next? Should the focus be on improving these methods so it is easier to detect real patterns, should it be on collecting more data for interesting clades to fill in missing data (species, traits) or should it be on collating multiple such datasets to look for concordant patterns across clades? For instance, here, what are the plants doing? To answer that we need to consider the interaction of multiple plant families with a single butterfly genus. How do we do that?

And what are the limits of these macro-methods? This butterfly clade appears to be a recent and rapid radiation. How do we look at character evolution and predictors of rate shifts when species might still be hybridizing? Is there real scope to link population and phylogenomics? How quickly will technology and bioinformatics pipelines progress in order to use complete genomes (and tons of them) to answer such questions. My gut feeling is actually not that fast and that the next ten years or so are going to see a flurry of methods to continue asking these questions with dodgy, patchy data and then, in 10 years, we will have to start over when we are confronted with a whole different kind of dataset requiring a whole different set of techniques.

As ever, exciting times.


Will Pearse

Sorry this post is so late; entirely my fault, not Lynsey’s. This feels like an excellent paper to get back on the horse with, because (as all phylogenetics papers do these days), it makes me feel very old. I feel as if I just popped out for a packet of cigarettes and suddenly the whole world changed – pyRAD? Is that like… PAUP*? What year is it?

And yet, somehow, the problems are the same. We have thousands of loci, but we have to concatenate them. We have a wonderfully resolved tree, but we still have to use the same old ancestral state reconstructions. I’m not criticising this paper, which is excellent, and I’m not even sure I’m criticising the methods, which are the best we can do at present. Yet, somehow, I still feel a bit worried whenever I see any  sort of reconstruction – even (especially?) when I’m doing it myself.

Which is why it’s so nice to see methods being applied with care, and in such great diversity, to a system with strong a priori hypotheses. Above Lynsey asks whether we need new models or more data. It’s easy to look at the grey branches (“we don’t know”) on these phylogenies and think that we need more data, but in reality I think some of that would be gap-filling. The data we have has already cost a lot of blood and sweat, and is beyond a phylogeneticist’s wildest dreams a decade ago – is that not enough? What we need are explicit models that tell us what an adaptive radiation looks like. That does mean a lot more sweat, and it could mean even more data collection than these authors have already done, but without it, we’ll have no broad framework within which to place data-rich case studies such as these.

*in this case, the asterisk stands for “absolutely not”.

Evolutionary responses to environmental change: trophic interactions affect adaptation and persistence

Mellard et al. 2015 Evolutionary responses to environmental change: trophic interactions affect adaptation and persistence. Proc Roy Soc B 282: 20141351.

Effect of niche width on herbivore (solid red line), plant with herbivore (dotted green line), and plant alone (dashed blue line); under smaller (top) and greater (lower) temperature change. Moral of the story: bifurcations matter, people.

Effect of niche width on herbivore (solid red line), plant with herbivore (dotted green line), and plant alone (dashed blue line); under smaller (top) and greater (lower) temperature change. Moral of the story: bifurcations matter, people.


Lynsey McInnes

Lynsey Bunnefeld

‘We have an urgent need to understand and predict the response of individual species as well as whole communities and ecosystems to global change. However, the most commonly employed methods for predicting the response of species to climate change do not explicitly incorporate all fundamental ecological and evolutionary processes that may be major determinants of species responses to climate change.’

Haven’t we all read (and written) statements like that before?! Seems like we are missing a lot: dispersal ability, intraspecific variation, adaptive capacity, species interactions. These are all important issues to incorporate into predicting species’ responses to climate change. Different people take on different aspects and you get the crazies that try to model everything in one go and there is not a single study that does not have to resort to a suite of simplifying assumptions. And each study inevitably finds that their chosen issue IS important. What to do? It all sometimes feels completely overwhelming, as if the only worthwhile reaction is to shrug our shoulders and just wait and see what elements will be important and what not.

Meh. In the spirit of curiosity though and taking a step back from all the grimness, studies such as this one – a modelling study considering the different ways interactions between a plant and a herbivore could impact their responses’ to a changing environment – are, quite simply, interesting problems to tackle.

No doubt the authors could have set things up differently, included different assumptions or different parameters, but, from my limited knowledge of such studies, they appear to cover a broad parameter space and uncover a bunch of interesting responses. E.g., if plant and herbivore traits are correlated, their impacts on each other depend on their relative niche widths and initial conditions of the environment. To a big degree. This is cool stuff. Sobering, but cool. I love the line where they let slip that adding in a carnivore could change things further still!

Do you know what I would like to see though? I would like these authors, or other modellers, to make me a program or a package where I can play around with my own parameter combinations, perhaps from my chosen species or set of species, and make my own plots and response curves. Because I am not an expert in modelling like these guys are, I find myself having to believe what they say and the setup that they have chosen. I have faith that its probably fine, but it would be fun and informative to play around and recreate these plots and make my own. I think the more people that are comfortable (biologists and non biologists alike) with these kinds of analyses, the easier it will be to convince people that tipping points exist, that interactions matter, that responses are going to be quirky, but might be predictable sometimes. I think we need to get our hands much dirtier still. Do such tools exist?


Will Pearse

I always say a modelling exercise is only worthwhile if it tells you something you couldn’t have predicted at the outset. I find it hard to believe I could have guessed that the presence of a herbivore could so profoundly alter the evolution of a plant species, and so, while I would be lying if I said I followed all of it, I enjoyed this paper.

We say it very casually all the time, but predicting changes in species under climate change is all the more terrifying because we have no idea how those species will interact with each other in the future. Studies like this are very sobering to me, since things can get incredibly complex with only two actors. By complex, I mean switch-points that lead to different optima, because anything non-linear with some sort of tipping-point scares the goodness out of me. I have no idea how we could hope to empirically (and analytically) determine what would happen if there were another trophic level or additional herbivore/plant in this system. Perhaps arguments can be made for motifs (regularly repeating units of interactions) making things easier to model, but to be honest I’m sceptical. Grouping 300 interacting species into 10-30 different groups and then modelling those might be easier, but I’m not sure it would be easy. I’m happy to be corrected!

The authors discuss how pushing the plant towards a colder niche would badly-prepare it for climate change. I wonder if the number species we find doing ‘the wrong thing’ (leaving aside model slips!), which we sometimes attribute to species interactions, could be due to shifts like this. If a population is pushed into a particular region of parameter space, then I suppose the only thing to do is make the best of it. What if the ‘wrong move’ is only sub-optimal when we view things solely through the lens of climate? Maybe climate’s only part of the picture…

Phylogenies support out-of-equilibrium models of biodiversity

Manceau et al. 2015 Phylogenies support out-of-equilibrium models of biodiversity. Ecology Letters 18 (4): 347-356

Overview of speciation underManceau et al.'s model.

Overview of the new Speciation by Genetic Differentation model of Manceau et al.


Will Pearse

It’s been a good few months for Neutral Theory in Ecology Letters (…and so, by extension, PEGE!). In this paper, Manceau et al. put forward an extension of Neutral Theory, including a new model of speciation (Speciation by Genetic Differentation), and relax the dependence upon a static metacommunity. Both are exciting extensions to the theory.

The concept of the meta-community is something that’s always troubled me about Neutral Theory, because it seems a bit much to appeal to something outside the system to keep the system stable. Yet at the same time the only thing I can think of that’s less realistic than a meta-community is not having a meta-community, since clearly no community evolves in isolation. In this model the meta-community can change through time (i.e., it’s not at equilibrium); it’s no longer a deus ex machina, and is instead a part of the ecological theatre (see what I did there? :p).

Equally, the speciation mechanism the authors put forward is a useful development. I’m a fan of protracted speciation models, but my general problem comes from fitting them onto a specific evolutionary process. I don’t doubt they describe pattern and process well, but they don’t seem to be linked to one process in particular. Thus the genetic differentation model the authors suggest is, to me, extremely exciting. As with all these exciting new models, it’s almost a shame that the cleverest bit – the maths – is too complex to present in the body of the paper (I say almost a shame, because I’m certain I wouldn’t understand it!).

To me, the most important concept in this paper is that telling phrase ‘out of equilibrium’. Arguments about whether diversification is or isn’t density-dependent are never going to go away, but there are some who are calling for the debate to happen in the context of recent ecological theory about what carrying capacities in systems look like. Personally, I think that a discussion on density-dependence has to happen with an understanding of species’ abundances, and that means individual-based models. Work like this is an important step towards this.


Lynsey McInnes

Lynsey Bunnefeld

It has indeed been a good couple of months for neutral theory at PEGE. Here, we have another tweak to Hubbell’s original theory to bring phylogenetic tree topologies more in line with empirically observed trees. Specifically, the authors tweak the speciation mechanism from point or random fission speciation (i.e., instantaneous) into ‘speciation with genetic differentiation’ such that new species form only when they have accumulated enough mutations to be distinct genetic ‘types.’ Furthermore, the authors relax the assumption of constant metacommunity size instead allowing size to vary stochastically according to the growth (birth – death) rate of the clade.

I must admit I read this paper far quicker than it merited, so my thoughts are a bit hazy and any qualms might be unfounded. On that note, here goes…

I did appreciate the positivity bouncing around in this paper. The authors were resolutely positive about the capacity of phylogenies and macroevolution in general to inform us on diversity patterns. This was nice to see as many people, myself included, often despair on the ability of phylogenies to tell us anything.

Their two tweaks to neutral theory also sound, on the whole, sensible tweaks that make sense given what we know about species and given that we agree we want to retain the simplicity of the neutral theory while identifying the key assumptions that make it fall down.

First, speciation by genetic differentiation. Indeed, closely related species generally do differ genetically. Whether this difference is just an accumulation of neutral mutations or some kind of adaptive divergence (and which of the two kinds is more common) is another question. I felt like the authors could have discussed this issue more deeply because as a naive reader I was left wondering about the biological reality of such an abstract speciation mechanism. Sure, the model does not claim to be 100% realistic, but a discussion of the different signatures expected depending on the speciation mode would have been nice. The authors talk a lot about future directions and models they would like to compare theirs too (e.g., Pigot’s biogeographic model) and I look forward to hearing about these extensions. They somewhat cryptically refer to some lineages acting like speciation hubs that presumably shoot out new species willy-nilly. What kind of lineages would these be? Large ranged? Sexually selected? Weird mating system? Host shifter?

Second, growing/shrinking metacommunity. I agree with the authors that a constant metacommunity seems unreasonable. But I would have liked to hear more about how a growing/shrinking metacommunity might come about. Are we talking about finer partitioning of a finite area or colonisation of new habitats or competitive exclusion of crappy species, or what? Is a metacommunity the right term to use when we are thinking about the interactions of ENTIRE species. Could populations of the same species occupy different metacommunities? (Meta-metacommunities!! :$).

My hunch is the authors have also thought of all of the above and this is just a first pass attempt, albeit an impressive one that (again) shows that with just a few small tweaks the overall premise of the neutral theory is really useful in understanding general diversity patterns. I remain on the fence whether all this tweaking is destroying the original premise of the neutral theory (as I see it, to provide a conceptually simple null with which we can work out which non-neutral processes really do matter).

 

Spatially varying selection shapes life history clines among populations of Drosophila melanogaster from sub-Saharan Africa

Fabian et al. (2015). Journal of Evolutionary Biology. Spatially varying selection shapes life history clines among populations of Drosophila melanogaster from sub-Saharan Africa

Various kinds of Drosophila melanogaster mutants. I don't think any of these show clinal variation across Africa!

Various kinds of Drosophila melanogaster mutants. I don’t think any of these show clinal variation across Africa… Taken from D’Avila et al. (2008)


Lynsey McInnes

Lynsey Bunnefeld

I picked this paper on a whim as it looked like it dealt in genetics and ecology, but not in the phylogeographic sense that I am usually drawn to. I liked it a lot, mostly for the approach it outlined rather than any specific results.

In brief, the authors are looking to see if they find evidence for adaptive differentiation in life history traits among tropical populations of Drosophila melanogaster as a function of altitude or longitude, arguing that such clines are seen as a function of latitude, and longitude, and particularly altitude, could be considered parallel gradients in environmental conditions. Indeed, they purport to find evidence for this differentiation.

I had some doubts about aspects of their methodology, particularly the mismatch in the genetic resources they assign to each population (i.e., they do not come from the populations they sampled for life history variation), but I am also happy to believe that irrespective of some potentially dodgy leaps of faith, the results they uncover do reflect reality.

And so, I really enjoyed this paper. My brain is still wired in as a macroecologist and any paper I read that tried to tackle some of the many assumptions of macroecological analyses is an impressive one to me. Here, the authors have taken a small(ish) dataset from a band of tropical populations and measured a ton of stuff in order to test a specific hypothesis on differentiation expected as a function of a geographic cline. Sounds so simple, but is not really that common in macroecology despite its concern with spatial diversity patterns. Oops.

And then you come to the next macroecological qualm. OK, Drosophila melanogaster is a wide-ranging species, so intraspecific variation is to be expected, but so are many other species and it is not so often (although things are definitely improving) that macro scale studies consider this intraspecific variation in life history or ecological or behavioural traits. We need more of this!

One could argue that the scale that macroecology operates on means that this kind of variation is not important, that it gets swamped by interspecific variation, but I doubt it. Because, on the flip side, there IS a general consensus that processes acting at multiple scales matter to understand species diversity patterns, so finer (and conversely broader) scales than species-as-unit analyses are relevant.
Don’t worry, I don’t think I am the only person to think this way, I am just still in the beating myself up about how late these things have dawned on me phase.
So, what to do? We probably need more sampling, more genetic resources, more models and better formulation of testable hypotheses. But the hardest thing will be (at least for me) accepting that really interesting insights can be made using model systems or subsets of a taxonomic group, gone is the possibility to use ‘all mammals’, ‘all birds’, etc. (Yes, I am that kind of dirty charismatic vertebrate macro person).  Perhaps general patterns/rules of thumb will emerge quite quickly, along the lines of you need a range of this size to show X and a body size of this size to show Y, or you need to live in environment A to show Z. And then the really interesting part will be piecing together how well intraspecific diversity patterns might predict speciation or extinction probabilities.
Interesting times.

Will Pearse

It’s hard to argue with a paper that does exactly what it says on the tin. This is a nice demonstration of variation within a species across environmental gradients, and an excellent demonstration of how to set up a question and then just go right ahead and answer it.

I was struck by the lack of variation in viability despite the variation in what many people would call life history traits. It’s sobering to consider that there can be this much variation in how a species operates, and yet no general variation in something that’s quite an important component of fitness. Traits play out in their environmental context, and if there can be this much variation within a species we should all be a little more careful when interpreting the importance of very slight differences across species. Of course the authors get at this with their trade-off analysis, but for me (at any rate) it was a nice reminder. I liked that the authors linked all of this variation to climatic variables, but unless I missed something I didn’t see where they explicitly tested climatic factors vs. geographical summaries. I do buy their argument from parsimony that temperature (not altitude + latitude + longitude), and I imagine co-linearities made testing things difficult, but somehow I wanted to see it.

Speaking of variation, staring at the regressions and their oddly high r2 values (I’m becoming a pastiche of myself), I noticed that mixed effects models they used detect a lot of within-line variation. I’m not saying this as a criticism; rather, I think it’s incredible how they were able to partition this out so neatly. It really drives home the importance of variation within species (and populations!), and definitely got me thinking about how biased our measures of trait values are going to be if we can’t grow species in culture like the authors were able to do. I really do just take gene-environment interactions for granted, and completely ignore the micro-processes that Lynsey is now studying. Maybe we do need to start more explicitly linking micro-processes to the macro-ones that I tend to think about.

A Neutral Theory for Interpreting Correlations between Species and Genetic Diversity in Communities

Laroche et al. The American Naturalist 185(1): 59-69. A Neutral Theory for Interpreting Correlations between Species and Genetic Diversity in Communities

Figure 4 from Laroche et al. The Species-Genetic Diversity Correlation plotted against mutation rate (m) and carrying capacity (K). Personally, I (Will) think it looks a bit like a scene from Interstellar if you squint a little. That's not a comment on the science; I just really enjoyed Interstellar.

Figure 4 from Laroche et al. The Species-Genetic Diversity Correlation plotted against mutation rate (m) and carrying capacity (K). Ignore the white splodges; they’re unimportant for our purposes. Hopefully we’ve just nerd-sniped you into reading the paper!


Lynsey McInnes

Lynsey Bunnefeld

Oh the dangers of picking a paper because you like the keywords and finding them cooked in a different way to you had imagined in your head. I have a slow-burning interest in how thinking about intraspecific variation can help explain interspecific patterns of diversity, turnover, etc, and this paper’s keywords fall right into that gap…

Here, the authors are interested in understanding why you often find, or expect to find, positive correlations between genetic diversity of a focal species and species diversity in the same area (i.e., not quite the same thing). They elegantly explain accepted thinking on the effects of local competition and connectivity and size of sites in a metacommunity as being the factors underlying these expected/often observed patterns.

The paper is concerned with adding the omitted factor of mutation ‘regime’ into the mix. If mutation occurs at the same rate as migration among sites, the expected correlation between genetic and species diversity could break down. I’m not going to lie, the way the authors get to this outcome remains somewhat opaque to me. My general understanding is that when mutation rate is high, the impact of migration among sites is less predictable as there will be a greater variance in what amount of diversity is transferred among sites and this leads to unpredictable knock-on effects on genetic diversity-species diversity patterns. How, you might ask, how indeed?

What I did like about this paper, probably because it harks back to what I liked about the keywords is the incorporation of more actual genetics into the model. Mutation regime is a necessary addition to thinking about genetic diversity and, as the authors rightly point out it is going to be easier (and at the same time much more complicated) to deal with as genomic data pours in. We appear to be on the cusp of understanding how these different levels of diversity impact each other and it’s mega exciting! Models such as this one are pretty awesome, and set the stage for the next step which would be incorporating mutation rate heterogeneity, including at selected loci. Population genetics has the machinery to deal with this variation, we just might need a bit more crosstalk with ecologists and theoretical biologists to get to more refined characterisations of patterns (if there are any) at the macro scale.


Will Pearse

Maybe this is off-topic, but I was dreading reading this paper because these sorts of analyses terrify me. I wasn’t familiar with the ‘ODD model‘ of describing biological models, but the authors use it to such excellent effect that my fears were completely unfounded. If you’re a theoretical person, please use this approach!

This is a paper about within-species diversity (community genetics, not community phylogenetics), and so almost by definition they cannot examine speciation processes. However, I was left wondering how speciation would interact with these dynamics; I assume it’s tricky to model because otherwise a ‘smart’ thing for a genotype to do would be to speciate and thus avoid competition with the genotypes it left behind. Perhaps you’d end up moving to a more coalsecent-esque model in which individuals’ competition strengths are a function of time since coalescence – species identity itself would be something a bit arbitrary. I’m interested because I think there are so many parallels with this model and the more Neutral Theory models (and some of the models of fitness we’ve discussed in the past). I wonder what the dynamics would look like if you just shunted some of these dynamics inside a classic Neutral model.

Presumably this sort of literature applies only to neutral alleles – if there is an allele that confers a selective advantage, then natural selection et al. kick in. Which is where I was wondering how competition steps into this framework – I think it’s at the step where new individuals are drawn (please correct me!), in which case I can see how migration and mutation rates would affect what we find. On another side-note, I particularly liked that the authors had worked sampling into their model – it made it a lot easier to draw this back to what would be expected empirically, and helped the authors make sense of how such empirical results seem to disagree with this model at first. More of this as well, please!

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?

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