Thursday, June 22, 2017

At the Speed of Life

Twenty years ago, Mike Kinnison and I started thinking seriously about evolutionary rates. Over the years since, we – with many collaborators – have assembled a database of evolutionary rates that has been used by ourselves and many others for various purposes. We are currently in the midst a big new push to add additional studies to the database and we would like YOUR help. In short, if you have studied rates of phenotypic change in contemporary populations, we think it would be cool to have your study in the database. Information on how to participate is at the end of this blog post that traces the history of the database.

The whole thing accelerated for us in 1997, when Mike Kinnison and I were graduate students sitting in adjacent desks at the School of Fisheries in the University of Washington. At the suggestion of our prescient supervisor, Tom Quinn, we were both studying salmon introduced to new environments with an eye to how rapidly they might be evolving. Mike was working on chinook salmon introduced into New Zealand and I was studying sockeye salmon introduced into Lake Washington. And we were finding plenty of evidence that, in both cases, substantial evolutionary change had occurred since the introductions.

Mike Kinnison checking on his chinook salmon in the mid 1990s, New Zealand.
Me with a sockeye salmon the mid 1990s - here in Alaska. 
In the midst of our work in 1997, two high profile papers were published that documented similarly rapid phenotypic changes in populations experimentally introduced into new environments – guppies in Trinidad (Reznick et al. – Science) and Anolis lizards in the Caribbean (Losos et al. – Nature). (These came to our attention via a commentary in TREE written by Erik Svensson.) A few papers showing pretty much the same thing had come out previously, but these two new papers were different in one important respect – both formally quantified evolutionary rates for their studies and compared them to evolutionary rates reported in the fossil record. For both guppies and lizards, evolution in contemporary time was several orders of magnitude more rapid than evolution typically observed in the fossil record, which brought a lot of scientific and media attention to how rapid ongoing evolution seemed to be. In short, instead of the “we see nothing of these slow changes in progress until the hand of time has marked the long lapse of ages” wisdom received from Darwin, a more correct conclusion might be “we can often see rapid changes in progress even though the hand of time has barely moved.”

“Wow, that’s pretty cool”, Mike and I thought, “let’s calculate rates of evolution in our own study systems and see how fast they are – which was when the whole thing began to complexify. As we delved into the details of how evolutionary rates were calculated and interpreted, it became clear that many ambiguities, uncertainties, caveats, and subtleties were involved. This caution seemed to us important enough – and certainly relevant enough to our own work – to warrant writing something about it, which we duly published in 1998 as a short note (Taking Time with Microevolution) in TREE.

My first publication with Mike. When I asked him what his middle initial stood for, he insisted it was "Michael The Kinnison."

After publishing this note, Mike and I continued to work on the problem and it became abundantly clear that it warranted a full-length treatment about how best to estimate evolutionary rates and that also calculated and reported those rates for a bunch of studies beyond the only two that (at that time) had done so for contemporary populations. The problem, of course, was finding the time to write the paper while we were both in the throes of finishing our PhDs and starting our postdocs, which were on quite different topics.

Fortunately, right about this time, I was invited to work on a project in southern France where data collection took place mainly by camera recordings. Thus, once the experiment was set up, I had some extra time to perhaps write something else up. Although I certainly had tons of things to write up, the different venue provided mental freedom enough to say, “why not try to blast out this evolutionary rate paper.” So I very rapidly wrote up a first draft that Mike and I then edited and – without much expectation – submitted it to Evolution as a Perspective. This submission was a bit of a stretch, or at least leap of faith (or hope), for Mike and I because – at this point – neither of us had published anything in an evolutionary journal (instead only in fish journals), nor had we really had much feedback from real evolutionary biologists (although we did get good comments from the Huey/Kingsolver lab meetings).

Lo and behold, and somewhat to our surprise, Evolution was happy to publish the paper: The Pace of Modern Life: Measure Rates of Contemporary Microevolution. In the immediate aftermath, or really lack thereof, not much happened – nor had we expected it to. In fact, we kind of viewed the paper as our own little curiosity project that helped us to think more deeply about our own empirical work. We guessed it might be a bit interesting to some few other researchers but probably of little practical use or importance to most. So, once published, we continued our progress toward other topics.

Database version 1.0 - from Hendry and Kinnison (1999).
Then – out of the blue – came an invitation from a journal (Genetica), we didn’t know much – if anything – about, to edit a special issue on contemporary evolution. Being quite green postdocs who had never received such an invitation, we immediately agreed. (Nowadays, of course, the many predatory journals are constantly inviting even inexperienced, as we were at the time, people to edit special issues.) Importantly, we decided to invite every famous evolutionary biologist that we knew of (but none of which we really knew personally) who might contribute moderately relevant papers: Grant, Reznick, Losos, Sinervo, Lande, Arnold, Riechert, Wade, Kingsolver, Gingerich, Magurran, Bell, Merila, Raymond, Smith, etc. Remarkably, nearly all of them agreed, something that still amazes me today – and, in fact, which I doubt would occur today.

In our invitations, we took a strategy which I now adopt in nearly every special issue that I edit – and there have been a lot of them: invite the best people, give them the general topic (in this case, Microevolution: rate, pattern, and process), and tell them they can write specifically about whatever they want to write about in that general area. This strategy really is the best way to make sure you get great people and great topics – although the cleverness of it was not something we knew in advance. The only specific additional request we gave was that, if they were reporting phenotypic changes over relatively short time frames (several hundred years or fewer), they should formally calculate and report evolutionary rates in accordance with our suggestions from the earlier Evolution paper.

The main paper that Mike and I contributed to the Genetica special issue was a follow up of our original Pace of Life paper, wherein we now calculated evolutionary rates for more previous studies and formally analyzed the resulting database to try to answer big questions about evolution. What is the distribution of contemporary evolutionary rates and how do they compare to selection intensities? Do different types of traits evolve at different rates? How do evolutionary rates scale with time interval? In the end, the special issue had 30 contributions, a number of which have been very heavily cited (7 more than 100 times on Web Of Science).

Database Version 2.0 from Kinnison and Hendry (2001).
In 2002 or thereabouts, both Mike and I started faculty positions, me at McGill and Mike at U Maine. (I had interviewed for the same position at Maine but they chose Mike. Of course, things turned out well for the both of us.) Earlier that year, I had given a talk on contemporary evolution in the ASN Young Investigator Prize symposium and the editor of TREE, who had seen the talk, asked if I wanted to write a paper on contemporary evolution for the journal. I proposed several options, one of which was the implications of contemporary evolution for conservation biology, which was the topic the editor chose. However, I knew little about conservation and both Mike and I felt a bit overwhelmed in our new jobs, so we contacted Craig Stockwell, who worked on both contemporary evolution and conservation. This was an excellent decision as Craig very cleverly tied the two fields together, and generated a paper (Contemporary Evolution Meets Conservation Biology) that was quite influential, with 870 citations on Google Scholar.

Database Version 2.1 (really, the same as 2.0) from Stockwell et al. (2003).
But the actual database of rates was getting quite out of date. Fortunately, I taught a graduate class at McGill where one of the student task was to write a review/meta-analysis. I pitched to several students the idea of expanding the evolutionary rate database, and Thomas Farrugia – and undergrad at the time – was interested. So we then spent several months adding papers to the database and reanalyzing it to follow up the Genetic paper. A great venue for publishing this work then presented itself as Tom Smith and Louis Bernatchez were organizing a symposium at UCLA on Evolutionary Change in Human Altered Environments and were going to edit a special issue in Molecular Ecology.

Me and Mike and the mid-2000s - in this case rockin the facial hair in Trinidad.
Thomas had moved on to other things by then, and so the task fell to me to finish up the paper, which I spun into a consideration relevant to the symposium/issue: whether or not human influences increased rates of phenotypic change. Turns out they did and the paper (Human Influences on Rates of Phenotypic Change in Wild Animal Populations) has been heavily cited in that regard. A reviewer also forced me to do a quick analysis, which I felt the database was not large enough for yet, that considered which types of human disturbance led to the greatest rate of change. This last topic proved to resonate with others into the future, as we will see below.

Database Version 3.0 from Hendry et al. (2008).
A first, reviewer-insisted, analysis of rates of change associated with different types of human disturbance - from Hendry et al. (2008).
A difference this time was that we also published the existing database online, which meant that anyone could grab it and re-analyze it (usually with additions) to answer their own questions. As result, papers have now come arguing that rates of change are greatest when humans act as predators (Darimont et al. 2009 - PNAS) but are not especially noteworthy in the case of invasive species (Westley et al. 2011 – Am Nat), that evolution should be possible in response to climate change (Skelly et al. 2007 - Cons Biol), and that trait evolution should have ecological consequences (Palkovacs et al. 2012). The database has also been used to examine the links between micro- and macro-evolution (Uyeda et al. 2011 - PNAS), the pace of cultural evolution (Perreault et al. 2012 - PLoS ONE), and a wide variety of other topics.

Through the years, we have expanded the database a bit – and fixed various errors and inconsistencies – to then re-analyze and publish papers showing that (for example) body size does not evolve faster than other traits (Gotanda et al. 2015 - Evolution) and that rates of change are especially high in some urban contexts (Alberti et al. 2017 - PNAS).

Database Version 4.0 from Alberti et al. (2017).
Yet all of these analyses are still working with a database that, while large and growing, is very incomplete. A large number of relevant studies of phenotypic in natural populations have been published in the last 20 years that could be added to the database but haven’t been – simply for reasons of obscurity (to us) or time (for us). Thus, when I was asked this year to help write the Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES) Global Assessment, I realized we needed to do a major database expansion and overhaul. Hence, over the past half year or so we (especially my new student Sarah Sanderson) have been accumulating studies, extracting data on phenotypic changes, and adding them to the database. Although we have added many new studies, it is clear that many more are still out there – yet they are sometimes hard to find.

This need, then, is the impetus for this blog post – we would like your help in finding as many relevant studies as possible. Sarah generated a list of all studies in the database (we have some others still being entered), which we provide here. If you study phenotypic change in contemporary time, it would be fantastic if you could skim this list to see if your study is included. If not, we would love it if you could contact us to tell us about your study so that it can be included. Importantly, the database will – when reasonably finished – be provided online for all to use, which can only increase its (and your study’s) value to the scientific community. We hope that you will join us in this endeavor as it has been fun and profitable for me – and it can be for you to.

Cheers and thanks,


Note: we are looking for studies of changes in quantitative traits (body size, morphology, phenology, etc.) over known time frames. These can come from information on the same population in different years (allochronic) or from different populations that had a common ancestor at a known time in the past (synchronic). It is also not necessary to have confirmation of the genetic basis for the change as people are also interested in plasticity - and, regardless, these aspects are coded for each study in the database. 

Sunday, June 11, 2017

How to Tweet

I was a reluctant convert to social media. Ten years ago, I didn’t see the point of it. I didn’t have facebook, twitter, Instagram, a blog, or a youtube channel. Now I have all of these and am quite active on several. I will first outline my social media development as a background for the advice I will then suggest about social media for scientists. This advice needs to be tempered by the fact that I am not a social media icon – nor that I am especially good at it. However, I have found it very useful professionally, and often entertaining personally. (My instagram and flickr accounts are used for showing nature photos I have taken over the years, and so are not really discussed here.)

The transition started about eight years ago when my newer students convinced me that a blog would be a good idea as it was an increasingly common way for people to get scientific information. I started the blog but, at first, just kind of did it lip service without much effort and resented the need to have to generate content on a regular basis. But then people started reading and responding to some of my posts and their value became clear. Then, a few years ago, I started a series of “How To” posts that provided advice for young scientists. This series became surprisingly popular (see the table below) and useful to people to the point that I have numerous people come up to me at meetings to say they follow my blog and find it helpful. It was even featured twice in Nature magazine.

A couple of years into this blogging adventure, it became clear that twitter was a great way to promote the blog – and so I signed on in March 2014. Again, I didn’t really pay much attention at first but, as time went on, I realized its usefulness at both send and receiving information and quickly gained a reasonable number of followers. I don’t have any viral tweets – not that I seek them – but several have been retweeted more than 1000 times (an example is below). My favorite current use of twitter is to promote my book through a fun #PeopleWhoFellAsleepReadingMyBook thread, storified here.

At about the same time as I started on twitter, I initiated a YouTube channel. The goal was not to be a famed “youtuber” but rather to simply share some of my wildlife and nature videos, mostly with family and friends but also with colleagues and with students in my classes. I then added a number of teaching-oriented videos, especially the drunkard’s walk, and some personal climbing parody videos. (In retrospect, it would have been better to keep the professional and personal youtube channels separate.) Again, I am very surprised by how many times some of these quick productions have been viewed, such as those shown below.

In short, social media has become an integral part of my professional life and I think it is a great way to provide information and advice to a much broader range of people than otherwise possible. Hence, my goal in the rest of this post is to make a case for the value of social media for scientists and to provide some suggestions on how to engage, with some of these suggestions being different from those you might hear elsewhere.

Why use social media?

1. A lot of scientists not on social media think it is a triviality for which they “do not have time.” I don’t agree with this proposition; indeed, I am sure I don’t have any more time than they do. The reason I disagree is that, for me, social media has simply replaced other time-wasting activities. When working on my computer, I get bored just like anyone else and I used to procrastinate by going to cnn or espn or cbc or whatever other traditional media sites. Now, I simply replace most of that procrastination time by looking at twitter or instagram, which is much more useful as it is related to my work.

2. Some scientists are of the opinion that social media is just filled with a bunch of uninteresting stuff that will swamp them with nonsense. This proposition is certainly true if you aren’t careful and selective. However, the great benefit of social media is just the opposite – you can tailor your own news feed, making it much more targeted and useful to you than more traditional forms of media. For instance, I follow colleagues, students, journals, universities, departments, and so on. If the information I am getting isn’t useful, I simply unfollow that person. Indeed, I how have my twitter feed whittled down to a great set of complementary news sources.

3. Social media is a good way to get scientific information. If you tailor your feed appropriately, you can get lots of pointers to good new papers that are coming out that you wouldn’t otherwise see. In the old days, you could pretty much cover ALL the relevant literature by just skimming the contents of a dozen or so journals. Now there is an order of magnitude more science out there, making it impossible to find everything – but the right feed can reveal to you some of the best stuff coming out now.

4. Social media helps you reach a broader audience – and, hence, better promote your work and ideas. As just noted above, it is impossible to keep up with all the publications of others. For the same reason, your own papers are likely to be lost under in the avalanche of information that is out there. It is not enough to simply publish your work any more – you need to promote it. Social media is one way to do so as students and colleagues in your field will often follow you and see your posts.

How to use social media?

The following suggestions are based on my experiences over the past five years or so. Again, these are just my opinions, with which others will not necessarily agree. I tend to refer mostly to twitter below, but the same basic points apply to other platforms such as facebook, instragram, blogging, youtube, and so on. I also image they will be most useful to people who have not already “found their own way” on social media – but perhaps a few will be interesting even to experienced folks.

1. Be selective in who you follow. Some people will argue that the “contract” of social media is to follow back – that is, when someone follows you, you immediately reciprocate. However, this approach doesn’t work for me. To not be overwhelmed, and to be sure to see the stuff I want to see, I keep the number of accounts I follow to a manageable number – about 100 on twitter. The alternative is to follow more but “mute” them. However, the muting strategy seems to me rather dishonest. If I follow you, then I don’t mute you.

2. Be selective in what you tweet. Analyses have been done about how often one should tweet/post. I don’t know the details of those analyses but I am sure that they all say that too-frequent posting is not beneficial. Instead, retweet/post those things that are truly interesting and that you think would be interesting to your followers.

3. Re-tweet interesting posts from diverse people and feeds. Although I don’t follow everyone that follows me, I do periodically check out the feeds of my followers to find interesting posts of theirs to re-tweet. I think this help can be especially helpful to graduate students who don’t yet have a large following on social media.

4. Add a visual. Almost every one of my tweets includes an image of some sort. Images are much more likely to catch the eye of someone who is skimming quickly and thus inspire them to stop and actually read it. This comes partly from personal experience – I generally don’t pay attention to tweets that are text only. This isn’t a philosophy or anything – it is just the practicality of not having time to read everything and, while a tweet is only 140 characters, a picture is worth 1000 words!

5. Acknowledge, either by tagging or through a link, the source of the material – especially images – that you put on social media. And, along the same lines, don’t retweet people who don’t acknowledge their sources. For example, many of the aggregating feeds that post cool pictures don’t credit the original photographer. Don’t retweet them.

6. Be apolitical, at least usually, unless your goal is to be political in your professional life. I subscribe to the perspective that scientists should be as objective as possible if we are to maintain our credibility as experts. Many social media users, by contrast, spend tons of time criticizing universities, journals, funding agencies, colleagues, conference organizers, etc. I have no use or time for these diatribes. (Of course, I do retweet the occasional well-thought out or funny piece about idiotic and dangerous demagogues.)

7. Don’t troll/shame people – unless you want to be known as a troll. Sure, trolls tend to get the most followers, reminding of me of when David Houle wrote in a book review that “Negative reviews often give a frisson of pleasure to the reader.” In my opinion, however, negative comments reflect more on the commenter than the commentee. Instead, generate occasional and thoughtful positive criticism that helps move things forward in a constructive way.

8. Don’t blindly retweet everything you see about social/scientific equality. For instance, many white male profs seem to retweet everything they see about the disadvantages facing women and people of color. Those disadvantages are real, and need our recognition. However, the more someone not in those categories tweets about them, the less sincere that person seems - at least to me. Instead, periodically retweet THE BEST posts about social/scientific equality. Your sincerity and credibility will, I think, benefit from it. Here is the blog post I wrote about my own “Subtle Sexism Self-Evaluation.”

My most retweeted tweet.

And finally

If you goal is to be a scientist, then tweeting will not do the job for you. You need to publish your own research, which you can then supplement and promote with social media. A social media profile will not replace a real research portfolio – only enhance it. Of course, if your goal is to forge a career in science communication, then social media would take on greater importance.

Yikes, I (red dot) appear to be close to being a scientific Kardashian! Original paper here.

Monday, May 22, 2017

The zombie grant

Not to the swift, the race: 
Not to the strong, the fight: 
Not to the righteous, perfect grace: 
Not to the wise, the light. 

But often faltering feet
Come surest to the goal; 
And they who walk in darkness meet
The sunrise of the soul.

Excerpt from ‘Reliance’ by Henry Van Dyke

They say ‘good things come to those who wait’. I’m writing today to say that this is at least sometimes true. We waited. And waited. And waited. And something really good, that we are really proud of, has finally arrived. To be more precise, Katie Peichel and Andrew Hendry and I wrote a grant (2009), and rewrote it (2010), and rewrote it (2011), and rewrote it (2011), and were all set to give up when we got funded. Then we hired [postdoc Yoel Stuart, most significantly], planned, did the field work, collected the data, analyzed the data, wrote the paper. We bounced it across several good journals. And now, the core paper from the study has come out. We are pretty excited, but also a bit stunned by the start-to-finish time. To put this in perspective, the idea for this project was conceived when my first child was this old:
Me, with more hair and a 4-month-old, just before the Evolution meeting in Minnesota in 2008 where we hatched our plan
and I had a lot more hair. The resulting paper was born nine years later when she was old enough to play piano, build her own spectroscope, and get 1st place in the Austin Regional Elementary School Science Fair for figuring out the elemental composition of the sun:
That 4-month old can now build a homemade spectroscope and interpret Fraunhaufer Lines to infer elemental composition of the sun. This picture was taken a couple months before our paper was accepted.

Grad students, maybe that helps you feel like your PhD isn’t taking so long now, after all.

My goal for this blog post isn’t to delve into the science of our paper: you can read the paper, or read Yoel Stuart’s blog post on  for that. My goal is to tell the story of the process. A story of perseverance that maybe will make you feel better when you get that 4th grant proposal submission rejected yet again (for this, also see the Heard-Hendryblog on serial rejection). And maybe a broader lesson about the glacial pace of science and what we might be able to do about it.

There’s a recent trend towards Zombie remakes of classic literature. I’m thinking specifically of Pride and Prejudice and Zombies, a Zom-Com-Rom take on Jane Austen’s classic book. In several real senses, this is a Zombie story too. A Sci-Zom. You’ll see why soon.

I. Zombie films sometimes involve some initial chance encounter
Andrew Hendry and I had been doing field work on stickleback in the same general area of Vancouver Island, for over 5 years, when we finally crossed paths in 2006.

The field crew gathering in 2006. Left-to-right: Kate Hudson, On Lee Lau, Dan Bolnick, Andrew Hendry (crouching), Ann-Catherine Grandchamp, Jean-Sebastian Moore, Daniel Berner (crouching),  Credit: Tania Tasneem (not pictured)    
His crew showed up at the cabin we rent on Vancouver Island (Roberts Lake Resort), and crashed on our floor for about a week. We got a bottle of good scotch in return, which Andrew drank most of (then proceeded to build a duct-tape spiderweb in the middle of the cabin at 3 AM). Between doing our respective field tasks, we spent a lot of time playing McGill-vs-UTAustin ultimate games, and talking science late into the night. We came up with some grant proposal idea, sketched out on the wobbly dining table, wrote it up that fall, submitted it in early 2007, and got it soundly rejected later that year. The wobbly table may have been a metaphor. Oh well. I don’t even quite recall what we proposed to do. Something about a continuum of ecological speciation, or lack thereof.

II. The main characters hatch a plan to go to the woods. Always a bad idea in Zombie stories.
Before the 2008 Evolution meeting in Minneapolis, I emailed Katie Peichel and Andrew Hendry to say that it would be fun to develop a collaborative project between the three of us. Something on lake-stream evolution that fused Katie’s genetics know-how, with Andrew’s experience with the lake-stream system. Not sure what I contributed, really.We met after the BBQ social and sat and talked for many many many hours. What emerged was a plan to make a plan. The general outline was clear: we wanted to know whether ‘parallel’ evolution of lake-stream stickleback was a reflection of parallel environments, driving parallel selection, causing changes at the same genes, leading to heritable lake-stream differences in the same traits, in each of many replicate lake-stream pairs (pictured). 
Comida Lake and its outlet stream. Photo by Thor Veen

We wanted to go big, with many lake-stream pairs. And we wanted to fuse QTL mapping, genome-wide association mapping, field measures of selection, quantitative genetics, and ecology. With this, we could ask whether the loci that diverge most strongly & repeatedly from lake to stream (GWAS) are also loci linked to traits (QTL) that are highly divergent (survey data) and under divergent selection in the wild (field selection experiment). Many different layers of data would be needed: environmental data, diet data, parasite data, trait data, functional performance measures, genotypes, and fitness. We left the Evolution meeting with two definite plans. First, we would meet again in December 2008 to write a grant. Second, Andrew’s postdoc Renaud Kaeuffer would genotype six lake-stream pairs at several hundred microsatellite loci, test for lake-stream differences in allele frequencies, and determine whether there is evidence for any genetic parallel evolution. This could be preliminary data for our vaguely-defined grant idea.

III.  The idyllic gathering in a guest house in the woods. Or rather, at a winery.
In December 2008 I was in San Francisco, looking after my 1½-year-old daughter while my wife attended the American Anthropological Association conference. At the end of the conference, Katie Peichel picked me up in a rental car and we drove up to Napa, to the Hendry Winery. Andrew was spending a sabbatical there, in the guest house on his uncle’s and brother’s winery. The three of us (plus Andrew’s postdoc Renaud Kaeuffer) spent three days there. We brainstormed the first day, expanded details the second, and wrote outlines and some text the third day. We worked hard all day, pausing for a long jog around the vineyard each morning. Around dinner time, we would receive a delivery of spare bottles of wine, left over from tastings earlier in the day. The best science of course happened in the evening after those deliveries. You might say, we were on a Mission.

Renaud Kaeuffer, Andrew Hendry, Dan Bolnick, Katie Peichel (left to right), in Napa in 2008
 By the end of the three days, we had the skeleton of a grant. I should also note that this vineyard meeting led to two other papers as well ***see footnotes.

Yesterday, I dug up some old files from those meetings: a five-page file I wrote out a couple weeks before our Napa meeting, to start the conversation, and a document I wrote during the meeting, summarizing our brain-storming effort. These contained the following seed of our subsequent work:

Is evolution repeatable and deterministic, or idiosyncratic and stochastic? This question, elegantly framed by Gould’s ‘tape of life’ quote, has remained a major puzzle for biologists. At its most expansive, the question is of course unanswerable, because we are faced with a single replicate of life's history on earth. However, we can begin to address Gould's challenge by shifting to smaller temporal and spatial scales, for which replication is feasible.

The key questions we identified were:
1)    To what extent is phenotypic divergence (between lake and stream stickleback) parallel between replicated lake-stream pairs (in different watersheds).
2)    To what extent is the genetic basis of this divergence parallel?
a.      To be addressed by:
                                               i.     QTL mapping
                                              ii.     GWAS across replicated clines
                                            iii.     Common garden assays of heritability
3)    To what extent is selection parallel across lake/stream pairs?
4)    How do mate choice and habitat choice contribute to lake-stream divergence?

After the meeting, I went home with a new outline and proceeded to write.  By January we had a proposal. I budgeted $560,000 for field experiments and genotyping (SNP array at the time) of many wild-caught fish. Katie budgeted $940,000 for doing a bunch of replicate QTL maps. Andrew would share in my budget for field work and a common-garden heritability study at McGill. We were pretty excited by the ideas, but felt that the budget was a bit big. We couldn’t see a way to get it smaller without compromising the science. After some worrying, we justified the budget as appropriate for the large-scale that we planned to do. We submitted the proposal in January 2009 (this was in the days before pre-proposals).

IV. In which the hero of our tale (the grant proposal) dies. Several times.
It was rejected.  I have the reviews on hand (1 Excellent,  2 Excellent/Very Good, 2 Very Good, 1 Very Good/Good). Basically, they all wanted us to do even more: more sampling, more lake-stream pairs, more environmental and phenotypic measures, and more genetic data. And it was too expensive. Do more, for less.

We resubmitted in January 2010. The budget was about the same. We proposed to do more, for less money. We got 2Excellent,  1 Excellent/Very Good, 4 Very Good, and 1 Good. Concerns this time mostly centered on our plan to do genomics across replicate clines, whether we were adequately measuring predation, and whether the cages for our field fitness experiment would alter the environment appreciably. All fair concerns, so we made some changes. In conversations with the program officer, it was also clear that two of us PIs were part of the trouble. I had both a Packard Foundation fellowship and a Howard Hughes Medical Institute Early Career Scientist position. Although my last NSF grant had expired three years earlier, they saw me as over-funded already. I ended up going 5 years without any federal grant funding, relying on private foundations instead.  And Katie had NIH grants which to NSF meant she was considered ‘rich’ as well. In fact, as a faculty member at the Fred Hutchinson Cancer Research Institute she was expected to bring in a lot of grants to pay her own salary, and was barely scraping by.

We resubmitted in January 2011. My budget went up to $722,000 (some things were more expensive by then, and we added some features to appease reviewers), Katie’s was flat at $940,000.  The reviewers still didn’t like the clinal genomics plan, and still were worried that field enclosures would modify flow regimes. For the first time, the panel specifically recommended that we split the proposal up, carving off some features for another grant. Said one reviewer, “Quite simply, this proposal is far too expansive and ambitious”. And again, private conversations made it clear that NSF saw us as not needing more money.  

Katie thought it wasn’t worth trying again. I insisted.  We resubmitted in July 2011, roughly the same budget. On this fourth try, our scores came back:  (5 Excellent, 1 Excellent/VeryGood).  Only one other time have I had such uniformly positive reviews (6 Excellents).  Reviewers wrote things like: “This is a tremendous proposal. The PIs are absolutely correct that no one has done anything like it.”.  “this is an outstanding proposal on all fronts 
and in my view it is not worth picking on reamaining minor 
details that may have been overlooked in order to try 
being negative. 
Related to the inlecctual [sic] merit, both the main objective and related specific objectives of this proposal are at the very forefront of the most pressing question in our field”. “tour de force”;  “This proposal will likely be the most extensive quantitative characterization of morphological parallelism/non-parallelism to date”.  Declined.  (incidentally, my 6-E CAREER proposal was also declined). Why? We had other funding. Now, I’m a pretty left-wing guy, and believe strongly in fair distribution of resources. But I also believe that a proposal with 5 or 6 ‘Excellents’ deserves funding. We were pretty demoralized, and decided that until we were research paupers, we probably couldn’t do this project. So we basically decided to dig a hole and bury it. Cue the violins. 

Our grant

V.  The zombie grant
Grant proposals, like zombies, sometimes emerge from the cold hard ground when the main characters of our story least expect it. Our fourth submission, which got such great reviews, was rejected in December of 2011. And we decided to leave it dead. But, on April 18th 2012 I got a phone call out of the blue from NSF. Two things happened. They found some money stuffed into someone’s old shoe, or that had fallen behind some couch cushions. Something like that. Apparently this is not uncommon. Also, they realized that Katie, despite her NIH grant, was in dire need of funding because of how FHCRC pays its faculty. These two factors brought the grant-corpse back to partial life. We were getting funded. Not our whole $1.7 million 4-year request, mind you, but just over half that. This is a zombie story, after all. Not a complete resurrection. But we weren’t about to protest at a $900,000 budget for a 3-year project. $450,000 for Katie, and the balance mostly to my lab but some as a sub-contract to Andrew. And just like zombies are missing limbs, jaws, etc, our partly revived project was missing some important parts because we had to slice out nearly half the budget. No more field measurements of selection (which reviewers were both critical of and especially excited by). No more GWAS surveys along replicated lake-stream clines (I did that anyway at a smaller scale with HHMI money; the first of several papers from that is Weber et al 2017 Evolution). Scaled-back common-garden rearing. Fewer QTL populations.
The funding started July 1 2012 (just after our usual field season, sadly). That first summer of funding, my soon-to-be graduate student Brian Lohman spent three weeks scouting out lake-stream pairs all over Vancouver Island, giving us a set of 16 study sites. In January 2013 we had a small working group meeting at my house in Austin, with Katie and Andrew, Rowan Barrett, Dieta Hansen, and an incoming postdoc Yoel Stuart, who was just finishing his PhD with Jonathan Losos at Harvard. Yoel would be the glue the bound the project together and the engine that made everything happen. We talked, ate great tacos, drank scotch, read childrens’ books, played Cards Against Humanity (Yoel won every time), and went for a climbing excursion to Reimer’s Ranch.

Andrew lectures Dan on some of the finer points of parallel evolution. 

Andrew climbing in Dan’s backyard bouldering gym.
Andrew and Katie climbing in parallel.  Get it? Parallel?    

The Austin 2012 meeting ended in a climbing trip at Reimer’s Ranch

 Yoel and I assembled a team of field helpers, and planned the field season. As field crew, he’d have PhD student Brian Lohman, K-12 teachers Tania Tasneem and Andrew Doggett, and undergraduates Rebecca Izen and Cole Thompson. Andrew and Katie contributed people as well (PhD student Dieta Hansen, Undergraduates Elena Motivans and Mingsha Zhou), and Rowan Barrett joined in for some field work. From Seattle, we were joined by Katie’s team including Matt Dubin, Susannah  Halbrook, and high school teacher Carole Tanner. There was also a team of 12 people from my lab doing a different project (another NSF grant that was also a back-from-the-dead funding situation in 2012). At our peak we had 28 people in the field at once. I was basically just a glorified travel agent that year.

The 2013 field crews (missing a few!). Left to right, approximately: Dan Bolnick, Andrew Hendry, Cole Thompson, Katie Peichel, Dieta Hansen, Yoel Stuart, Matt Dubin, Kelsey Jiang, Connor French, Hollis Woodard, Alicia sp., Brian Lohman, Amy Doan, Rebecca Izen, Chase sp., Racine Rangel,  Kim Hendrix (HS teacher), Travis Ingram, Carole Tanner (HS teacher), Susannah Halbrook,  Gina Conte, Rowan Barrett,  Ruger is the dog. Yes, as in the gun. He handles security at Roberts Lake Resort. The mountain lions respect him.    
 In the summer of 2013 Yoel and his team collected environmental data and stickleback specimens from 16 lakes, 16 adjacent streams, and 4 estuaries, yielding roughly 80 fish from each of 36 locations. This was a lot of hard work, schlepping traps and YSI monitors through thick forest and deep muck. It was not without incident. Cole Thompson, a University of Texas Undergraduate, punctured his foot when bush-wacking through the brush beside a remote lake, when a branch on a fallen tree penetrated the sole of his waders. Aside from that day in the emergency room, much fun was had.

Yoel's in a boat, y'all

That log isn't totally stable
Rebecca Izen processing stickleback in the truck bed to stay dry

Andrew tries out Dan's boat. It is fun.

Dieta Hansen catching fish

First day, training newcomers

It rains a lot there

Cluexewe Estuary campground is spectacular

Videos of field work:

            For the next two years, Yoel and a group of really dedicated undergraduates worked on collecting detailed data from samples of fish from each of the 36 sites, including >70,000 SNPs (via ddRAD, with help from Jesse Weber) on 24 fish per site,  86 morphological characters (fin shape, body shape, gill rakers, armor and spine traits, eye traits, jaw biomechanical traits, brain morphology), diet data, parasite data. They processed mud and water samples for environmental invertebrate abundances.
            Data curation was also a massive undertaking. The resulting dataset spanned site-level data (eg., elevation, surface area), microhabitat data taken at each trap (substrate, flow rates), morphology, diet, parasites, and genetics. Thor Veen joined the team when he contributed an excellent GitHub data structure and helped with analyses. Analysis itself took a long time. For instance, we recruited Mark Ravinet to contribute Approximate Bayesian Simulations to estimate population genetic history parameters (divergence time, migration rates, colonization order) for all 16 lake-stream pairs. That computationally intensive process took months.
Finally, we had results, and a paper. The usual rounds of edits, then we sent it off. Rejected (Nature).  Rejected (Science).  And finally to Nature Ecology & Evolution, where we went through three rounds of review including a remarkable >100-day wait for the second reviews after our first revision. It was finally accepted at Nature E&E, and came out today. Co-authors include two K-12 science teachers, and multiple undergraduates. Some of the basic science insights are summarized in a nice blog post, by Yoel Stuart, on Nature Ecology and Evolution's website.

A few weeks ago, I made corrections on the proofs to our paper. I’ve always enjoyed proofs. We set the paper aside for weeks or months for the journal to process, then get to see our work as if with entirely new eyes. And I was really proud of this work. It pulls together many ideas, and an absolutely massive amount of data, to ask some pretty cool questions. The basic punch-line is, there’s less parallel evolution in stickleback than you thought, and we can explain why, at least in part. If you want to know more than that, well… go read the paper (email me [danbolnick <> utexas <.> edu] if you can't access it). Look: it took us 9 years of dealing with zombies to deliver this to you, don’t tell me you can’t take half an hour to look it over. To put it in perspective, it is shorter than this blog post.

The project also spun off other papers as well. There’s an incomplete list at the end of this post (***footnote). Analysis and writing of the QTL work is ongoing, and there’s a lot more that we are pulling out of the genomic and phenotypic data. Look for more papers to come.

Looking back

To conclude, I want to reflect on some lessons learned.

First, science is really slow. From idea (early summer 2008) to paper (late spring 2017), was enough time for my infant daughter to grow big enough to read The Hobbit and to play Fur Elise on the piano (nicely, at that). Almost half that time was waiting for money. This bothers me**.

Second, I tend to do a shock-and-awe approach to grant writing: put in an ambitious (but in my mind genuinely feasible) agenda with many interlocking parts. That does not always serve me well, and reviewers wanted us to do something more modest in the end. They told us to cut, and we cut. This ultimately paid off, not just because we got funded. Remember, our requests ended up being $1,700,000, but we had to cut $800,000 off that to get our grant.  As a result, we sadly deleted what we thought was the most exciting component of the study, the field experiments to measure selection in multiple replicate lake-stream pairs. A missing arm of the grant-zombie, meant to test whether parallel phenotypic divergence really reflects parallel selection. Well, zombie arms have a habit of clambering out of the ground on their own, later on. My great postdoc Yoel Stuart turned that cut-out-idea into a new grant proposal. As our first grant came to an end, he got another $1,000,000 funded on his first try (somebody hire the guy!), to do what we had previously eliminated. Actually, it wasn’t funded exactly on the first try; this, too, was a back-from-the-dead proposal. That’s ongoing.

Third lesson: hire a great postdoc for your projects. Yoel Stuart handled a complex web of field, molecular, and morphological data collection and analysis, and wrote what I think is a genuinely good paper. As a corollary, hire Yoel for your faculty.
Dr. Yoel Stuart
Finally, persistence does pay off. Think about those reviews: we went from okay-ish reviews with a mix of good to Excellent, to a really strong set of reviews on our fourth submission. This project ended up being better, more focused, and probably more feasible, thanks to those anonymous reviewers (I’m looking at you, Brian Langerhaans) who worked hard to give us feedback through multiple rounds of submissions. Keep this in mind when you get that rejection from NSF this spring. Try, try again. “But often faltering feet Come surest to the goal;”

 -Dan Bolnick, May 5, 2017

Yoel contemplating evolution

I’ve had several graduate students come up with great project ideas that we’ve turned into grant proposals. Twice, these ideas have been funded. But with lag-times like this, there’s almost no way that a grad student idea can be turned into a full NSF proposal, and from there to funding and work and papers, within the time-span of a PhD. That has huge policy implications to how we train students. If we have to support students as RAs (rather than teaching assistants), then students will typically have to work on my ideas, conceived years before they entered our graduate program, instead of their own ideas. That’s a problem, because my experience is that my students are more innovative than I am. We need rapid response funding for good student ideas that won’t take 4 years to get funded. NSF DDIG grants are a good attempt at that, but have such a limited budget that our students’ imaginations must be kept in check.

A selection of other papers from this project:

UT Austin undergrad Newaz Ahmed has a paper just out in Ecology & Evolution on brain morphology evolution in lake-stream stickleback.  Basically, no parallel evolution there.

UT undergrad Rebecca Izen worked on within-stream phenotypic variation

UT graduate student Kelsey Jiang published several papers on lake versus stream divergence in swimming behavior in flowing water:

UT graduate student Brian Lohman published papers on multivariate lake-stream clines:

UT postdoc Jesse Weber published a paper on clinal lake-stream divergence. This is basically a smaller version of what we had to cut out for budgeting reasons when our grant was cut in half. We did it with HHMI funds instead, and at least one more paper is coming from that dataset. For now, here’s the paper, and a digest about the paper:

Brian also has a paper in review and on BioRXiv about transcriptomic response to novel environments in lake and stream stickleback:

McGill PhD student Dieta Hansen got out a paper on the apparent lack of allochrony-driven reproductive isolation between lake and stream stickleback:

McGill PhD student Krista Oke has a paper on the role of phenotypic plasticity in lake-stream parallel evolution:

Krista also has a fantastic new paper out about parallel evolution in fish more generally:

I have a paper on MHC-parasite associations in three lake-stream pairs:

McGill postdoc Renaud Kaeuffer had a paper that emerged from our Hendry Winery meeting:
with a follow-up paper by Andrew:

The Winery meeting also led to this review paper:

I have a paper in press in Nature on the lack of (detectable) divergent selection in lake-stream stickleback

UT undergrad  Cole Thompson has a revision in re-review at Evolution on evolution of complex biomechanical traits in the lake-stream pairs.

I’m sure I’ve missed a few papers that have been touched by this project, and more will be out in the coming year. We have several people actively working on more analyses and papers. Keep your eyes peeled!