Saturday, August 1, 2015

How to be a Postdoc.

I just participated in a career mentoring session at a scientific conference: Stickleback 2015 organized by Mike Bell. Many of the questions were related to the transition between graduate school and a career – essentially encompassing the postdoc period. I found myself saying a lot of things that I had intended to put in my next “How To …” post on postdocs (earlier posts are listed at the end). So, right after the session ended, I went off to a coffee shop to bang out a first draft.

An important point at the outset – as in my previous “How To …” posts – is that the suggestions I give aren’t universal truths. The reality is that postdoctoral positions and the gains derived from them will vary among countries, universities, disciplines, PIs, and the postdocs themselves. I will try to note some of these distinctions but I am sure I will forget some – please let me know what I have missed. Also, the postdoc you choose and the way in which you implement it should depend on your career goals and thus the types of skills, expertise, and experience that you need. For instance, such decisions depend on whether you want to pursue a career in government, the private sector, or various types of universities (primary undergraduate, research intensive, etc.). And, of course, your career path might not benefit much from a postdoc anyway.
Many career routes are possible: From

Postdoctoral positions are often the most rewarding, creative, and productive time of your career. You don’t have any of the limitations and constraints of a graduate student: you are already experienced and knowledgeable in research and you don’t have the same annoying and time-consuming non-research requirements (qualifying exams, classes, etc.). At the same time, you don’t have any of the non-research responsibilities (committees, committees, committees) of a faculty member. Now is the time when you can fully (or mostly) dedicate yourself to research and let your creativity and originality have (almost) free reign. Thus, a first important rule is POSTDOC AS LONG AS POSSIBLE. Never again can you be so free, so creative, and so inspired. Of course, there are exceptions when the postdoctoral position or project is very restrictive or just not very fun or you are very stressed about the future (but you needn’t be – as I will explain in a later post on “How to Get a Faculty Position”). Moreover, at some point, it might look bad to have been a postdoc for too long, perhaps somewhere around 6 years - depending on the discipline. Yet, I think that most faculty look back on their postdocs as a truly formative and fun time of their career. So let’s get to it.

Long postdoc periods are common: From

How to get a postdoc

The first necessity is usually money – one can rarely do a postdoc without decent funding for salary and for research. Funding options fall into several categories: competitive external fellowships, institutional/programmatic postdocs, and targeted project-based postdocs. (In writing these options out, I realize that I did one of each of them.) External postdoctoral fellowships (mine was from the Natural Sciences and Engineering Research Council of Canada – NSERC) are typically the most flexible, giving you the maximum range of options because it is less likely to tie you in advance to a specific university, lab, or project. Moreover, the PI should allow you more flexibility because they are not paying your salary out of their grant – and for the same reason, they might have more money to contribute to the research. So, it is a great idea to apply for these fellowships – frequently and widely. Institutional/programmatic postdoctoral positions are less common but often quite rewarding – mine was the “Darwin Fellowship” at UMASS Amherst – because you are often expected to take on a leadership role (sometimes with a bit of teaching) that begins to prepare you to be a faculty member. They can be great for building a range of collaborations and projects and for establishing a large network of colleagues. Project-based postdocs (mine was on Darwin’s finches with Jeff Podos at UMASS Amherst) are probably the most common option, especially in the U.S. These are usually advertised by the PI with whom you interview. The PI then funds your salary to do a particular project set out in advance. One advantage here is that the project is usually associated with a brand-new grant for which they have lots of money to spend. A limitation is the lack of flexibility and dependence on the PI funding you.


How to choose a postdoc

People often choose graduate schools largely for personal reasons – proximity to family, good weather, good skiing, good music, good surfing, etc. Although these considerations can also play into picking a postdoc position, they are more likely to be subsumed by decisions made with an eye to career advancement. That is, people typically want their postdoc to help them take their research to the next level and – in essence – get them the best possible job. So how does one make the choice of postdoctoral position beyond the first concern of making sure that some money is available? Several strategies are possible. All of them can work but they present different opportunities and risks.

Work with a famous PI. Famous PIs tend to be famous for a good reason – they do good work and the people in their labs are usually successful. If you can get into one of these labs, then you are likely to have good funds, good projects, and good career prospects owing to your good work, your “success by association,” and the contacts you will get. Of course, these positions can be hard to obtain because famous PIs usually have a lot of applicants and can afford to be picky. In addition, not all famous PIs are good supervisors or good for the careers of their postdocs – so you need to do your homework on how well the former postdocs in the lab have done. But, in the case of “good” famous PIs, this is probably the best route to career advancement. You will have to make sure you can demonstrate that your success is not simply a function of the famous PI, however – you have to be a good independent scientist (more on this point below).

Work with a cool system. Many graduate students work on study systems that lack key resources – such as annotated genomes, experimental manipulability, or good ecological background knowledge. These students are often so annoyed by such limitations that they choose to do their postdoc work with a better-developed “model system” – threespine stickleback! This can be a great way to conduct more sophisticated and advanced research, but it can also be difficult to establish an identity for yourself in a crowded research area with many researcher that are already well-established. One can also run afoul of the problem of having to stay on the cutting edge of research methodologies, which are expensive and often difficult to develop. In essence: you might be a big fish, but in a pond full of much bigger fish you will still look small.

Work with a fun lab. Some labs have all the fun. They are exciting (cool projects even if they aren’t publishing a lot in those weekly periodicals), dynamic (numerous energetic people, invigorating weekly lab meetings), and fun (they party hard at conferences, have heated but respectful debates, have fun retreats, and the like). These labs are always great to be a part of but sometimes aren’t the best way to career advancement (although they can be).

Follow your muse. Sometimes you just have your own ideas and you really want to pursue them: a novel study system, a novel method, a bizarre question. In this case, you need to pitch your
 idea to as many people as possible to find one who will let you forge your own way in their lab. This is generally a high-risk but potentially high-reward route. That is, you are less likely to publish a lot in fancy journals, but you also have the potential to do something creative and new that is totally yours and that can really change the way we think about the world or do science. That is, you can end up being by far the biggest fish in a very cool pond that is newly discovered. And, even if that doesn’t happen, at least you know you went your own way and on your own merits. And, of course, if you succeed, then you are clearly independent, successful, motivated, passionate, and creative (see comments below).

Importantly, these are not mutually exclusive options. In fact, a famous PI working on a model system can have a fun lab with a lot of money in which they will let you follow your own muse. And those people are ………………………..

Use your postdoc to “finish” your PhD

As noted earlier, many people expect their postdoc to be what “puts them over the top” or “takes them to the next level.” This might well be the case but it is a delayed payoff. Instead, research during your current postdoc rarely will be what gets you your next position. The reason is that most postdoctoral positions are too short for you to have any publications from the work by the time you are applying for your next position. Thus, people who do only a single 2-year postdoc are going to be chosen for an interview based on the publication record from their PhD, not their postdoc. Certainly the promise of your postdoctoral work (how good the project looks on paper, who the supervisor is, some preliminary data) will help, especially during an interview, but you won’t have many (or, more commonly, any) publications from your postdoc by the time you are applying for your next position. Hence, it is critical to use your postdoctoral time to finish up work you had been doing previously: publish all those PhD chapters, continue those side projects you started, write that review paper you had been thinking about. (Although it might seem that your postdoc work will be so much better you’re your PhD work that it isn’t worth finishing up the earlier stuff, you need to resist the grass-is-greener syndrome.) These will be the things that get you your next position. Your current postdoc will be what gets you tenure!!!!!! (Note also that this can be harder in a project-based postdoc.)

Establish your identity

In the panel discussion that prompted me to finally write this post, almost all of the panelists forged their current career trajectories during their postdocs. In short, postdocs are when you really establish the sort of work you want to do, the questions you want to ask, the collaborations you want to develop, and – more generally – the type of scientist you want to be. In addition to this maturation of yourself as a scientist, establishing an identity is also important from a practical career-advancement perspective. For instance, job search committees often spend time debating whether or not an applicant’s publications really reflect their own abilities or whether they instead reflect the abilities of the supervisor. Thus, it is great if you can generate some first-authored papers that do not have a “silverback” author on them – and the same is true during your PhD. These publications help to confirm that you can drive a research agenda and do good work independently of established mentors. In addition, search committees will want to see that you have a long-term plan in mind. Thus, you want to establish a research plan that is integrated, comprehensive, creative, exciting, and cohesive (completely different projects are OK as long as you have a body of work – with or without side projects – that builds to a greater whole). Your postdoc is the time to do this - indeed it is also the time you have to write those “research plans” that search committees want to see, and the time you have to construct compelling and exciting hour-long seminars that show not only what you have done but what you plan to do and how it all (or at least a bunch of it) fits together into a reasonably cohesive research agenda.

Related to this, it is generally a good idea to switch labs – and ideally universities and even countries – between your PhD and your postdoc. Doing so helps with all of the above points – and, of course, it broads your perspectives and knowledge and helps you to see a given problem from multiple angles. Also, this switch is sometimes required by particular search committees, departments, universities, or even countries. However, it isn’t absolutely essential in all cases. For instance, sometimes you have started something amazing with your PhD that you can really take to the next level only by continuing on in the same lab where you already know what you are doing, you have the resources and support, and you can most easily take the next step. Staying in the same lab, or at least the same institution, is sometimes also important for personal reasons, such as family.

Build collaborations – but don’t get carried away

Continuing the above themes, one way to build an identity, show creativity and independence, explore new directions, and generally have a good time is to build collaborations. This statement holds true during graduate school but even more so during your postdoc: now is the time to seek links with labs employing sophisticated methodologies (various -omics!), with people having important skills (bioinformatics, theory, stats) or good ideas, with complementary systems (stickleback, guppies, and finches!), and so on. But you have to be careful. First, you will want to work with people you like personally – it can be miserable to be stuck in a project with someone you can’t stand (or, much more likely in my case, someone who can’t stand you). Second, you would ideally have some collaborative projects that will generate first-authored papers for you. First-authored papers are vastly more important for getting a job than are co-authored papers. It will not benefit your career if you accumulate a bunch of co-authored papers at the expense of first-authored papers. And collaborations take time – so don’t start them just because you think you should be collaborating more. Third – and related to the above points – you need to be a GOOD collaborator. For instance, you shouldn’t be the one who holds up the project; that builds bad blood and annoyance, and if you get a reputation as a bad collaborator that is very bad for your job prospects (word does get around!). Fourth, some of the best collaborations emerge organically or by chance, such as during late-night conversations over beer. Of course, you will likely also want to seek out collaborators with particular skills and contact them specifically about collaborations. Both approaches can work.

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With so many papers out there, you need to promote your work if it is to be noticed. From

Network, network, network

Collaborations are increasingly common: From
In the old days, maybe 50 years or so ago, so few journals existed in a given discipline that everyone in the field would read (or at least see) all of the same papers. This made further promotion of your work somewhat unnecessary. Now, however, so many journals are publishing so many papers that each scientist in a given discipline reads (or even sees) only a small fraction of the papers in that discipline. In this high-volume era, it is essential to further promote your work – especially as a postdoc hoping to get a permanent position. You need to get your work and yourself in front of as many people as possible and do your best to explain the importance and excitement of your work while acknowledging the importance of related work (especially that conducted by the person you are talking to). A key route to this networking is to attend scientific meetings in your field and give talks. Posters can work too, but at the postdoc stage you should be presenting orally as much as possible. Job interviews often hinge primarily on the research presentation, so you need to get practice. You might also “pre-impress” potential colleagues and employers who might be in the audience (word gets around here too). Also, try to attend social events at meetings and actively seek out and talk energetically to as many people as possible in your field. These sorts of interactions can make a difference in cases where search committees are debating among various candidates to interview.

Networking is hard for shy people who just won’t be comfortable becoming a social butterfly. However, it is great to try to interact as much as you can – it will almost always be rewarding. (And note that sometimes the bigwig you are talking too can be just as shy.) In addition, shy people can sometimes promote their work and careers remotely, such as through email and social media. More generally, social media is an effective means of promoting your work and yourself regardless of where you fall on the shy–bold continuum. Blogs (as long as they are good and regular) can raise your profile, and Twitter (or equivalent outlets) can get your name and papers and ideas to a wide audience within your specific field and within science more generally. Yet, in a day with limited minutes, doing your science can trade off with promoting your science. So it is worth asking yourself: just how many new Twitter followers would it take to make up for not publishing a first-authored paper. Many thousands certainly. In addition, a big social media profile is really only good for a career in academia if you can back it up with good science. (See the quirky paper on the Kardashian Index for scientists with a social media presence out of proportion to the influence of their actual research.)  

A strong social media presence should be backed up with a strong research record. From


A postdoc is a stepping-stone, and in life generally you don't step onto a stepping-stone without some picture of what the *next* stepping-stone will be, and perhaps the next one after that, and where the path overall is leading.  In other words, use your postdoc strategically; it should be crafted to connect you to the skills, the systems, the people, the institutions, etc., that will facilitate your future path.  The coolest, most fun, most interesting postdoc is not very useful if it is a stepping-stone in a direction that is not ultimately the direction in which you want to go.  Of course it's hard to know and plan at this level when you've just finished your PhD, but try.

*Written by Ben Haller who felt the post needed a conclusion. His suggested text was so good, I just put it in verbatim. Thanks also to the other panelists at the career mentoring session at Stickleback 2015: Katie Peichel, Matt Wund, Ionna Katsiadaki, Juha Merila, and Windsor Aguirre.

Previous "How to" posts

Links to other blog posts with advice for postdocs

Dynamic Ecology


The Professor Is In

The Trophic Link

I will add more as people suggest them

Wednesday, July 22, 2015

Habitat preference and speciation

Back in 2006, during my field work on Vancouver Island for my postdoc project with Andrew Hendry on threespine stickleback in contiguous lake and stream habitats, I was deeply impressed by the phenotypic differentiation these populations exhibit across extremely steep ecological transitions – often without any obvious physical dispersal barrier. For example, the left picture below shows the abrupt transition from Robert’s Lake (background) into its outlet stream (foreground). Morphological divergence between these populations was immediately obvious in the field (right picture: adult stream male on top, adult lake male below), and analyzing microsatellite data later showed that phenotypic divergence was paralleled by equally striking genetic shifts (for details see Berner et al. 2009, Evolution). This made me really think about what reproductive barrier(s) could maintain such steep divergence in the face of dispersal opportunities, and my intuition was that perhaps lake and stream fish simply wanted to stay in their native habitats, rather than disperse.

In the same period and in the same study region, Dan Bolnick and his crew performed a very cool field experiment: they sampled stickleback from a lake and its inlet stream, marked the sampled fish according to the habitat in which they had been found, released them in the habitat transition, and checked where they then dispersed by re-capture. Put simply, this experiment made clear that each population preferred to return into its original habitat (Bolnick et al. 2009, Evolution).

Inspired by these observations, I felt that theory was needed to explore how habitat preference can promote adaptive divergence and speciation. Of course, a great deal of theory was already available demonstrating that many forms of non-random dispersal can promote divergence, but this evidence seemed rather scattered and often disconnected from biologically realistic contexts. Hence I started a large-scale simulation project with Xavier Thibert-Plante, a theoretician and friend I got to know in Montreal during my postdoc with Andrew. This work was initially tailored to parapatric lake-stream stickleback, but then we abandoned that specific context in favor of a broader study. Our simulation study considers the scenario in which two populations diverge in the face of gene flow, allowing for the simultaneous evolution of ecological adaptation and dispersal modification. This dispersal modification is modeled to arise from several different habitat preference mechanisms, including habitat imprinting during the juvenile stage, phenotype-dependent habitat preference, or a genetically hard-wired preference for a certain habitat type. We felt that this diversity in the mechanisms underlying habitat preference was crucial, because in natural systems where habitat preference has been inferred, the proximate cause of habitat preference is often not clear.

We find that habitat preference will often evolve so as to reduce gene flow between the habitats. This works best and promotes adaptive divergence most effectively when gene flow between the populations is initially substantial – a domain where migration–selection balance under random dispersal permits weak divergence only. This makes habitat preference a particularly relevant factor in the early stages of speciation when other reproductive barriers are still weak or absent. Comparing the different habitat preference mechanisms further reveals that speciation is strongly facilitated by habitat imprinting, whereas mechanisms like direct genetic preference or pure density-dependence are ineffective contributors to divergence. Moreover, we find that divergence with habitat preference is influenced by asymmetry in the size of the diverging populations, and by variation in the number of genetic factors encoding the simulated traits. Overall, our study indicates that habitat preference deserves much wider recognition when studying the divergence and reproductive isolation of populations. If interested, see Berner & Thibert-Plante 2015, J. Evol. Biol. in press.


The figure above is taken from the paper and shows the evolution of habitat preference via an imprinting trait (upper row), and the facilitation of adaptive divergence by this mechanism relative to random dispersal (bottom row), across combinations of the strength of divergent selection and initial dispersal.  (Click image to see at larger size.)

Saturday, July 11, 2015

Phenotype-genotype-fitness maps and genetic bridges between populations

[ This post is by Aaron Comeault; I am just putting it up.  –B. ]

Since Darwin, we have come a long way in refining our knowledge of selection as a driver of evolutionary change. To give just a few examples, we now know that selection exists as different ‘types’ (e.g. divergent, disruptive, or negative frequency-dependent), and can change in its form and strength through space and time. It is also becoming apparent that selection is constantly acting on the genome and therefore influences patterns of molecular variation. These examples help illustrate how our understanding of selection has developed over the last 150 years. However, there is still a great deal to learn.

Recently, access to genetic data has become easier due to new sequencing technologies and methods of genome manipulation. As such, determining the genetic basis of traits is now more feasible, for a wider range of organisms, than ever before. This is a significant advance for evolutionary biologists because knowledge of the genetic basis of traits allow us to forge explicit links between evolutionary processes such as natural selection and the evolutionary response to selection (Gratten et al. 2008; Johnston et al. 2013). An important goal in modern evolutionary biology is therefore to quantify how phenotype, genotype, and fitness interact to influence evolutionary trajectories (i.e. to quantify ‘phenotype-genotype-fitness [PGF] maps’) (Barrett and Hoekstra 2011; Bierne et al. 2011). The Californian stick insect Timema cristinae provides an opportunity to quantify a PGF map because their evolutionary ecology is fairly well known and genomic resources for them are emerging.

These flightless insects can be found on two common plants in the chaparral of coastal California near Santa Barbara: Ceanothus spinosus and Adenostoma fasciculatum. In populations found on C. spinosus the majority of individuals have solid green colouration, but when found on A. fasciculatum, individuals are usually green with a conspicuous white stripe down their back (see image for examples). This dorsal ‘stripe’ is under strong divergent selection between host plants (Nosil and Crespi 2006). Despite the evidence for strong divergent selection acting on the green and green-striped phenotypes, maladaptive (i.e. mismatched with respect to host plant) phenotypes can be found at varying frequencies within any given population (i.e., green individuals on A. fasciculatum and green-striped individuals on C. spinosus). In part, this maladaptive variation is maintained by ongoing gene flow between populations (Sandoval 1994b; Nosil 2009; also see this EcoEvoEvoEco post by Tim Farkas regarding the ecological effects of maladaptive gene flow in T. cristinae). This begs the question: in T. cristinae, what processes maintain gene flow among populations, and maladaptive phenotypic variation within populations, despite strong divergent selection acting on color pattern? Enter the “dark morph” of the T. cristinae system…

Pattern and colour phenotypes of Timema cristinae (click to see at larger size)

In addition to the green phenotypes mentioned above, a “dark morph” that has previously been referred to as “red” or “grey” (Sandoval 1994a) is found at modest frequencies (~10 %) in most populations of T. cristinae. We here refer to this phenotype as “melanistic” (see image above). At that moment during everyone’s PhD studies when they say to themselves, “what the heck is my thesis going to be about?”, Patrik and I discussed focusing on understanding the evolutionary forces working to maintain this enigmatic melanistic phenotype of T. cristinae. At that time we knew little about the ecological / selective factors maintaining the melanistic phenotype; fleshing that out, particularly when added to our growing knowledge of the genetic basis of the colour and colour-pattern phenotypes, could lead to a better understanding of ecological speciation in the T. cristinae system. In a recent paper (Comeault et al. 2015) we present the work that emerged from this. The paper shows how selection acting within populations of T. cristinae can maintain the melanistic phenotype; it also suggests that the melanistic phenotype maintains gene flow among populations, thus acting as a ‘genetic bridge’ that constrains adaptive differentiation and speciation. Below we summarize the diverse data we collected.

A graphical abstract of Comeault et al. 2015 (click to see at larger size)

We first used ecological measurements to provide evidence that melanistic T. cristinae are maintained by factors including resistance to fungal infections, a mating advantage, and increased crypsis on the stems of both host species. We also found evidence that melanistic T. cristinae may disperse between host plants more than green T. cristinae. These results indicated to us that selection acting on the melanistic phenotype is different than that acting on the green and green-striped phenotypes: a multifarious balance of selective agents maintains the melanistic phenotype within both host plant species, whereas green and green-striped individuals are under divergent selection between the two hosts. We now had an idea of the ‘phenotype’ and ‘fitness’ components of the PGF map for colour and colour-patterning in T. cristinae. We next set off to quantify the genetic basis of these traits. This can be an important step in understanding the evolutionary consequences of phenotypic variation because the response to selection is influenced by aspects of the genetic architecture of traits, such as dominance relationships among alleles, epistasis, and the number of loci underlying phenotypic variation.

Using a combination of classical genetic crosses and multilocus genome-wide association mapping, we identified aspects of the genetic architecture of colour patterning and colour in T. cristinae. Results from these analyses revealed the following: (1) different loci control pattern versus color, (2) both traits are under ‘simple’ genetic control with a single locus for each trait controlling the vast majority of phenotypic variation, (3) green colour alleles are dominant to melanistic alleles, and within green individuals the green allele is dominant to the green-striped allele, and (4) both traits map to the same linkage group (i.e., the loci controlling pattern and color are physically linked).

With all three components of the PGF map now in hand, we developed the hypothesis that melanistic T. cristinae act as a ‘genetic bridge’ among populations. Our logic was as follows: if melanistic individuals are not under divergent selection between host plants, they may be free to move between plants and thus facilitate gene flow between them. In addition to simply moving between environments, they would be capable of shuttling locally maladaptive alleles between host plants – because, crucially, green patterning alleles (striped or unstriped) are not expressed in melanistic individuals. To test this hypothesis, our modeling wizard Sam Flaxman helped us develop a tailor-fit individual-based simulation program.

Simulations enabled us to test the relative importance of individual aspects of the melanistic phenotype (such as a mating advantage for melanistic individuals) in facilitating gene flow between host plants. Indeed, the simulations found that the presence of melanistic T. cristinae reduced adaptive differentiation between populations on different hosts and increased rates of inter-host mating (see figure below). These results show how the melanistic phenotype acts as an ‘anti-speciation’ phenotype, mitigating the effects of divergent selection acting on the stripe phenotype. Moreover, we found that these results were robust to removing individual factors attributed to the melanistic phenotype, such as their mating advantage or their propensity to disperse. The one factor that, when removed from the model, did reduce the ‘anti-speciation’ effect of melanistic individuals was the genetic architecture of the colour and pattern traits: when we removed dominance relationships among alleles at the colour and colour-pattern loci, we found that melanistic individuals did not promote maladaptive gene flow or increase levels of inter-host mating. These simulations therefore used the PGF map to empirically quantify an example of an ‘anti-speciation’ phenotype. Our results also highlight how a combined understanding of both the evolutionary ecology and the genetic architecture of phenotypic variation can help us better understand the evolutionary process. We hope that future work in other taxa will continue to illuminate how phenotypes, their underlying genetic architecture, and fitness relationships interact – not only to drive adaptive evolution, but also to constrain it.

Evolutionary effects of the melanistic phenotype. The presence of melanistic individuals within simulated populations reduced adaptive differentiation (Fst) at the locus controlling pattern phenotypes (A), had a slight effect on levels of neutral genomic differentiation (B), and increased levels of inter-host mating (IHM) (C).  (Click to see at larger size).

Works cited

Barrett, R. D. H., and H. E. Hoekstra. 2011. Molecular spandrels: tests of adaptation at the genetic level. Nat. Rev. Genet. 12:767–780. Nature Publishing Group.

Bierne, N., J. Welch, E. Loire, F. Bonhomme, and P. David. 2011. The coupling hypothesis: why genome scans may fail to map local adaptation genes. Mol. Ecol. 20:2044–72.

Comeault, A. A., S. M. Flaxman, R. Riesch, C. Emma, V. Soria-Carrasco, Z. Gompert, T. E. Farkas, M. Muschick, T. L. Parchman, J. Slate, and P. Nosil. 2015. Selection on a genetic polymorphism counteracts ecological speciation in a stick insect. Curr. Biol. (in press).

Gratten, J., A. J. Wilson, A. F. McRae, D. Beraldi, P. M. Visscher, J. M. Pemberton, and J. Slate. 2008. A localized negative genetic correlation constrains microevolution of coat color in wild sheep. Science 319:318–320.

Johnston, S. E., J. Gratten, C. Berenos, J. G. Pilkington, T. H. Clutton-Brock, J. M. Pemberton, and J. Slate. 2013. Life history trade-offs at a single locus maintain sexually selected genetic variation. Nature 502:93–96.

Nosil, P. 2009. Adaptive population divergence in cryptic color-pattern following a reduction in gene flow. Evolution 63:1902–1912.

Nosil, P., and B. J. Crespi. 2006. Experimental evidence that predation promotes divergence in adaptive radiation. Proc. Natl. Acad. Sci. U. S. A. 103:9090–9095.

Sandoval, C. P. 1994a. Differential visual predation on morphs of Timema cristinae (Phasmatodeae:Timemidae) and its consequences for host range. Biol. J. Linn. Soc. 52:341–356.

Sandoval, C. P. 1994b. The effects of the relative geographic scales of gene flow and selection on morph frequencies in the walking-stick Timema cristinae. Evolution 48:1866–1879.

Sunday, June 28, 2015

Speciation, genomes, and pancakes

A decade ago, I began my PhD at Vanderbilt University in Nashville, Tennessee, where I was interested in studying the evolutionary process of speciation (or how new biological species evolve). I was very lucky during my PhD to be surrounded by great people. Case in point, I shared an office for part of the year with a visiting collaborator, Patrik Nosil, who studied speciation in a group of stick insects called Timema. Second, my PhD advisor encouraged me to invite great thinkers on speciation to be part of my dissertation committee – enter Jeff Feder from the University of Notre Dame, who studied speciation in a group of fruit-feeding flies called Rhagoletis and served as my external committee member. These connections made during the beginning of my PhD last to this day.

Figure 1. The Pancake Pantry in Nashville, TN, USA.
During a fateful visit to a common grad student hangout (circa 2007), the Pancake Pantry (Fig. 1), Patrik Nosil and I and a group of graduate students started discussing the age-old debate about the number of genes involved in adaptation (and speciation): few versus many? And whether the traits responsible for adaptation and speciation were polygenic traits or traits with a simple genetic basis? One way we thought to test this was to use as many molecular markers as you could survey, distributed across the genome, and ask the question: how many of these gene regions exhibit significant population differentiation, but are restricted to populations adapting to different environments? We came up with ideas of how to test it, and what type of tools we would need, right over our plates of pancakes! I think we even had a budget by the time we walked back in our calorie coma from lunch.  My major takeaway from this lunch was that I now considered the genome as an active player, not a passive mediator, in the speciation process and I would never think about speciation in the same way again!

What emerged initially from this pursuit were two comparative AFLP genome scans of two different study systems, each undergoing speciation driven by divergent ecology, that were published in the journal Evolution (Nosil et al. 2008; Egan et al. 2008).  These studies were very informative in highlighting the proportion of gene regions (AFLPs) in the genome exhibiting strong differentiation between divergent populations, and possibly addressed the repeatability of gene regions associated with adaptation to two environments (in our case, host plants).  But we were also left with many more questions than answers. How were these divergent loci distributed and arrayed across the genome? And were the loci exhibiting strong differentiation driven by selection or other evolutionary phenomena?

Fast-forward to 2010 – I finished my PhD and I was awarded a Faculty Fellowship at the University of Notre Dame, which came with some seed money for research and the chance to work more closely with my external committee member, Jeff Feder. Almost immediately upon arriving in South Bend, IN, Patrik (now in Sheffield, UK), Jeff, and I had a set of conference calls and email exchanges that started the project that would result in the Ecology Letters MS I will summarize below. (Jeff and Patrik had just finished a sabbatical in Berlin the year before where they spent much of their time ruminating on the genome-level phenomena influencing the speciation process.) We recruited other evolutionary biologists well trained in Rhagoletis biology (Tom Powell, Glen Hood, and Greg Ragland), as well as two computer scientists (Scott Emrich and his PhD student Lauren Assour) with the ability to process the large amount of data we would gather.

Our interests were to better understand the role the genome might play in the evolution of new species. We were inspired by a paper published over 30 years ago by Joe Felsenstein (1981), where he described the difficulty of building up many-locus differences between populations if gene flow was ongoing and recombination was breaking up associations. This conflict between selection and gene flow would form the basis for our project. How is it that populations can diverge in the face of ongoing gene flow? What are the properties or characteristics of species that are suspected of speciation-with-gene-flow which facilitated their divergence?

Figure 2. Rhagoletis pomonella exploring the fruit of the hawthorn tree (Crataegus mollis). Photo credit: Hannes Schuler
Rhagoletis pomonella offered a great study system to test these ideas, as it is a well-documented case of speciation-with-gene-flow (Fig. 2). Rhagoletis pomonella is a member of a sibling species complex containing numerous geographically overlapping taxa proposed to have radiated in sympatry by adapting to many new host plants from several different plant families. Rhagoletis flies infest the fruits of their host plants, where host fruits are typically available for a discrete window of time over the growing season and each fly species completes one generation per year. Adult flies meet exclusively on or near the host fruits to mate; females oviposit into the host fruit; larvae consume the fruit, then burrow into the soil to pupate, entering a pupal diapause that lasts until the following year. Thus, phenological matching of fly to host-plant fruiting is critical to fly fitness.

The most recent example of a host shift driving speciation is the shift of R. pomonella from its native host hawthorn to introduced, domesticated apple, which occurred in the mid-1800’s in the eastern United States. Genetic and field studies have shown that apple and hawthorn flies represent partially reproductively isolated host races and that gene flow has been continuous between the fly races since their origin. One key trait that differs between the races is the timing of diapause termination, which varies between the races to match the 3–4 week earlier fruiting time of apple versus hawthorn trees (Fig. 3). Rhagoletis emerge from their fruits as late-instar larvae and overwinter in the soil in a facultative pupal diapause. The earlier fruiting time of apples therefore results in apple flies having to withstand warmer temperatures for longer periods prior to winter. As a result, natural selection favors increased diapause intensity, or greater recalcitrance to cues that trigger premature diapause termination in apple flies.
Figure 3. Fruit on apple trees ripens 3-4 weeks earlier than hawthorn fruit (dashed lines). Apple flies eclose earlier as adults (solid lines) and are exposed to warmer temperatures as pupae in the soil for a longer period of time before winter.
Jeff had the perfect experiment frozen in his freezer from 20 years ago. Previously, his lab had reared the ancestral haw race of Rhagoletis under the phenological conditions of both host plants it attacks in nature. He had previously looked at changes in a set of allozymes and microsatellites, but did not have the ability at the time to look across the genome at tens of thousands of SNPs.  Specifically, he exposed ancestral hawthorn fly pupae to warm temperatures for a short 7-day (‘hawthorn-like’ control) vs. long 32-day (‘apple-like’ experimental) period prior to winter (Fig. 4).

Figure 4. In the selection experiment, hawthorn flies were exposed to a short (7-day) versus long (32-day) prewinter period to emulate the time difference experienced by hawthorn versus apple-fly pupae in nature. 
We also had a specific hypothesis we wanted to test that integrated Jeff’s selection experiment with sampling from natural populations. We tested whether the changes across the genome induced by the lab experiment on divergent host-plant phenology would predict the genome-wide differences observed at these same loci between natural sympatric populations. In this experiment, we stressed that we were quantifying the total genome-wide impact of selection, which involves both direct effects, where natural selection favors the causal variants underlying selected traits, and indirect effects, where additional loci respond because they are correlated due to linkage disequilibrium with these causal variants. Thus, the ‘total’ impact of divergent selection (i.e. direct + indirect effects) that we quantify here can involve changes at many loci (Gompert et al. 2014; Soria-Carrasco et al. 2014).

Quantifying the impact of selection genome-wide is important because, as populations diverge, the effects that individual genes have on reproductive isolation (RI) can become coupled, strengthening barriers to gene flow and promoting speciation (Barton 1983, Bierne et al. 2011). If predicated solely on new mutations, this transition could take a long time and populations could go extinct or conditions change without speciation, which may explain why sympatric speciation is difficult to observe and test. Thus, a prediction for systems with the potential for speciation-with-gene-flow is that they exhibit large stores of standing variation and consequently, show extensive, genome-wide responses to selection when challenged by divergent ecology.

In our selection experiment, about 6% of the SNPs showed significant frequency shifts between the short and long prewinter periods. However, because of extensive linkage disequilibrium (LD) in Rhagoletis, these SNPs did not provide an estimate of the independent number of gene regions influenced by selection. Thus, we assessed the pattern of LD between SNPs to delimit independent sets of loci.  We determined that the 6% of responding SNPs represented 162 different sets whose members were in LD with each other, but in equilibrium with all other SNPs. After accounting for the table-wide null expectation of 52 significant sets due to type I error, using a modeling approach we detail in our Supplemental material, a lower bound estimate of 110 gene regions responded to selection. To determine how physically widespread the response was across the genome, we constructed a recombination linkage map for Rhagoletis that contained 2,352 SNPs. About 13% of mapped SNPs showed significant frequency shifts in the selection experiment and were dispersed widely across the five major chromosomes of the R. pomonella genome (Fig. 5). Thus, numerous independent gene regions responded to selection and they were distributed throughout the genome.

Figure 5. Genome-wide comparison of allele frequency shifts in the selection experiment (red line; left axis) versus divergence between field-collected sympatric host races (blue line; right axis) along chromosomes 1-5. Circles above panels denote SNPs showing statistically significant response in the selection experiment (open red) or difference between the host races (solid blue). Correlation coefficient (r) is reported independently for each chromosome.
Now we tested our main hypothesis: does the genomic response in the selection experiment reflect nature?  The answer is yes. The direction and magnitude of allele frequency changes for all 32,455 SNPs in the selection experiment was highly predictive of genetic differences between the sympatric hawthorn and apple host races at the Grant, MI, site (r = 0.39, P < 10-6). Most strikingly, for the SNPs showing significant responses in both our selection experiment and host divergence in nature, the allele that increased in frequency in the hawthorn race after selection was the exact same allele in higher frequency in the apple race in nature (P = (½)154 = 4.4x10-47).

To what extent did the single bout of selection on hawthorn flies genetically create the derived apple race?  The answer is a good deal. For all 32,455 SNPs, the mean SNP frequency for hawthorn flies surviving the long prewinter treatment shifted 38.9% of the difference between the host races toward apple flies. For the 154 SNPs showing significant responses in the selection experiment and host divergence, the shift was 84.1%.

Why is the impact of divergent ecological adaptation so pronounced and pervasive in Rhagoletis?  One contributing factor is the extensive LD in the fly, some of which is due to inversions, requiring additional DNA sequence analysis to resolve. A second factor is the presence of substantial standing genetic variation in R. pomonella, which supports the hypothesis that such stores may define taxa having a greater capacity for speciation-with-gene-flow. Finally, when ecological adaptation involves traits like diapause that can be highly polygenic, selection may more often have genome-wide consequences. In this regard, microarray studies of R. pomonella have revealed hundreds of loci varying in expression during diapause breakage that are potential targets of selection (Ragland et al. 2011).
Figure 6. Rhagoletis pomonella fly exploring apple fruit. Photo credit: Andrew Forbes
Interestingly, this work shares some important similarities and differences with other recent studies combining selection experiments with surveys of genome-wide genetic variation in natural populations, including the Timema ecotypes that are the mainstay of the Nosil lab. In both a within-generation (Gompert et al. 2014; similar to the Rhagoletis study here) and a between-generation study of selection in the field (Soria-Carrasco et al. 2014), a genome-wide response involving many loci was observed. However, LD was much lower in the Timema ecotypes, and thus the association between genetic differences induced in those selection experiments did not match natural genetic variation as closely as in the Rhagoletis experiment.

In summary, divergent ecological selection can have genome-wide effects even at early stages of speciation. Large stores of standing variation in Rhagoletis flies may potentiate the evolution of genome-wide reproductive isolation and their adaptive radiation with gene flow. As the study of speciation genomics expands, it will be possible to test the degree to which other taxa prone to ecological sympatric speciation share similar characteristics as R. pomonella, and to assess the relationship between standing variation and clade richness.

That was one productive plate of pancakes!


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