Submitting abstracts for conferences without having the data


I’ve developed a mechanism to make sure that I stay productive: when I submit abstracts for meetings, I promise data that I haven’t finished collecting. Of course when I give a talk, I can say almost whatever I want. Nobody’s going to cut me off if my talk doesn’t match the program.

I just realized that I always have been in the habit of submitting abstracts for projects that are so fresh, I haven’t even gotten all the numbers, much less run analyses. In grad school, that was the only option, because at one point I didn’t have anything else to say. Now, even when I have other newish finds that I’ve yet to present, I submit abstracts for projects that still lack a rudimentary answer. I do this at least once a year, writing a check for results that aren’t yet in the bank. Continue reading

More backyard reflections: connections between farming and fieldwork


The weekend was beautiful and I spent a good portion of it in the backyard digging up grass. The plan is to have a small raised garden for vegetables, nothing too extensive but enough to plant a few things and enjoy them straight from the earth. You can’t get more local than that. As happens when doing something physical, my mind wandered. I had some “help” from my 4 year old but she would quickly bore of the repetitive nature of the task at hand so I was often left to my own devises.

Beginning of the vegetable plot. Looks easy but my body can attest to the amount of digging it took!

Beginning of the vegetable plot. Looks easy but my body can attest to the amount of digging it took!

Not surprisingly, digging in the dirt got me thinking about the summer I turned 20 and spent 5 months on an organic farm. It was an interesting summer, where I learned a lot but I had no idea I was preparing for a future as a field ecologist. That summer I was a bit lost. I had gone to university for a single semester before dropping out (finances being a major factor) and spent the next year or so working at various service jobs in Vancouver. I knew those weren’t things I wanted to do forever but I wasn’t sure what it was that I wanted. So I headed back across the country to Nova Scotia to live and work on a farm very near where I had spent some of my childhood. The memories of exactly how this plan came to be are foggy for me now (think my mother subtlety encouraged the Nova Scotia angle) but however it came about I ended up living on an organic farm, working for $50/week with three other exploring (or lost depending on how you want to look at it) young women.

Before working on a farm I had a romantic notion that maybe farming was one of things I’d want to do with my life. Farming cured that even though I absolutely loved the summer doing it1. What I saw though was the stress of worrying about the weather, the pests and all the other things that can go wrong. The funny thing is that I face lots of the same problems these days, just in a different context. I’ve lost experiments to deer browsing, mowing and bad weather. One major lesson I took from those farming days is to diversify and protect the truly important “crops” (experiments). I usually have a few field experiments/a few more replicates/etc running ‘just in case’2. A lot of the ‘just in case’ also makes good ecological sense. It is important to know, for example, if the patterns you see are consistent in different populations. It also helps when the deer eat all your plants in one of the populations; at least you still have some data to work with. Protection like fencing is also sometimes a critical part of ecological experiments. If you want to examine plant-insect interactions for example then it doesn’t help if the deer eat everything. If you want to eat the tasty vegetables you plant and know there is at least one hare that prowls your yard, fencing it is.

In plant ecology, often experiments require planting out particular populations or communities. There is the raising of the seeds, planting of the individuals, harvesting of the data and the stress of choosing the right time to do all these things. Sometimes you get it wrong. I always loved this story of a large planting that got hit by a frost; smart and experienced researchers don’t throw up their hands when the frost kills half your plants. If they’re lucky there is variation in survival and they write a paper about that instead.3 However, these decisions aren’t without consequence. While I was a grad student, I witnessed another’s unfortunate loss of an entire experiment to frost shortly after planting one summer.4 So the stress that I thought I was turning away from when I finished at the farm is actually a regular part of my summers. Maybe my income isn’t so directly tied to the harvest as on a farm but if experiments and papers are the currency that allows me to keep going as a scientist, then I’ve definitely paid the price of random events throughout the years.

I learned a lot that summer but probably most things were really about me. I learned I had stamina and that I could push my body and mind to keep going. I learned that I could tolerate bad weather and good to get the job done.5 I learned to laugh at rain and hailstorms and freezing weather and heat that makes you feel like passing out every time you get up.6 I learned that no matter how well you prepare, sometimes you just need to drop everything and change directions. Perhaps most importantly I learned that I liked being out there each day and being proud of what we accomplished. And I learned that some of the best friendships come from sharing the good and the bad of fieldwork (/farm work).

These days I don’t spend 5 months outside maintaining plants and collecting data but when I get to get outside, it is often reminiscent of those farm days. But perhaps that is only since I’ve found myself doing a lot of work in old fields…

And perhaps since I’m not outside toiling in the fields all summer, I have the opportunity/energy to grow my own garden. I know my little garden isn’t enough to even provide for our family. It is really a luxury hobby. But I am growing it because I also want my daughter to have a sense of what it takes to grow food. I want her to be able to recognise what the plants many vegetables come from look like, not just what vegetables look like presented in the store. She’ll probably not grow up to be an ecologist but I want her to appreciate the living world around her, both the wild bits and the tamed.


Ecological Life Lessons:

1Try something before you decide! Seriously, think you want to be an ecologist? Then go work in a lab, if you can’t do that, volunteer. Or if volunteering/work aren’t options, take as many courses as you can that expose you to research experience and get on board for a research project/honours/whatever they call it at your institution. The important thing is to get exposure to what ecologists are really doing on a day-to-day basis. Of course, this advice applies to anyone looking to invest a lot of time in training for a job, not just ecologists. But familiarity of the process of research is a really good thing before you start a masters/PhD program.

2The opposite lesson is to avoid spreading yourself too thin. My PhD student has been collecting data like mad and has a lot of really good hints at what is going on in her system but this year we’ve decided that she needs to do less of the different kinds of things and concentrate on a few key studies that will wrap up her experiments nicely. Right now there is a lot of data but often not sufficient to truly say what is going on. Sometimes this is hard to avoid (e.g. we didn’t know that the variation in the things we’re looking at is so great that it is making it hard to detect whether there is a signal in the data) and she’s also had her fair share of run-ins with the deer and mowers.

3I haven’t yet had the opportunity to turn a disaster into an opportunity at this scale but I certainly look at my failed experiments to see if anything is there.

4Learn from other’s misfortune, as well as your own. As a grad student, you’re actively learning how to run your own research but you’re also surrounded by a bunch of people doing the same thing. Talk to them! Hearing about their successes and failures can be just as important as doing the things yourself. This can apply to teaching, writing, analyses, fieldwork, labwork and the list goes on. These days if I know someone who’s done something that is new to me I ask them for advice. There is always so many tricks that make life simpler, once you’ve figured them out.

5Fieldwork is often not for the faint of heart. Know your limitations. I know I need sleep and I don’t function very well without it. More than that, I work pretty poorly at night. So I won’t ever take up a project looking at night pollination. Cool stuff but I know that it would drain me in ways that super strenuous work during the day never would.

6When things get tough you basically have two options: laugh or cry (or get really sour and unpleasant and take it out on those around you). I prefer to laugh (or at least try to), makes for a better field season.

Backyard science


Spring is springing in Sweden and I’m finally out from under my grant writing load. It is pretty easy to complain about writing grants and I am not innocent in this respect. But it is also an opportunity to explore new ideas and topics. This year I decided to try at the more applied government funding agency which I haven’t attempted before.

I generally do basic science. Sure some of my research might one day shed light on a practical problem but I’m in it trying to understand the world around me. So in previous years I haven’t felt my research fit with the more applied funding sources and didn’t want to jam a square peg into a round hole as it were. If I don’t see a real way that my research fits into a funding agencies goals then I didn’t see the point of sending something there. But this year was different because I started thinking about research questions that interested and excited me and were directly relevant to a more applied grant.

So here’s the steps that have lead me to thinking about a new field and exploring the possibility for grant funding. To begin, last year we bought our first house. I have always wanted to have my own garden and it is a true delight. We moved in mid-summer so we didn’t change so much last year but I was actively adding bee-friendly plants and pondering how to get rid of more grass. The former owners left us with a number of lovely flowerbeds that are starting their spring routine now but there is still an abundance of lawn. At the same time as I was contemplating increasing diversity in our backyard, I was also looking for a system to study here in Sweden. I want to work with nectar-rewarding flowers and was looking around for possibilities.

I started noticing fireweed popping up here and there in my travels. I knew the plant from living and working in North America (it is the study system of my master’s committee member, Brian Husband) and a fair amount is known about its nectar production. Perfect. But when I was looking and asking around for potential locations for populations, I wasn’t finding any local large populations. Instead I was seeing patches in and around the towns I live and work. This got me to thinking about the ecology of these urban dwellers. How does natural selection on floral traits work in an urban context? There are a number of flowering plants that thrive and reproduce in urban environments and this got me thinking about all the same kinds of questions I usually apply to ‘wild’ populations.

I causally started looking into the literature to see what was known about flowers and plant-pollinator interactions in urban landscapes. As I read, I discovered that there is a fair amount known about the ecology of these interactions (hence ‘urban ecology’ as a field of study) but much less is understood about how urbanization affects evolution. So I had fun exploring a new body of literature and saw a niche where my skill set could provide some answers.

I’m not sure that I’ll convince the funding agency to give me the money to do so but I have convinced myself that urban evolutionary ecology is a topic I’d like to explore further. I have some pilot projects planned for this year and I’ll see where they lead. I also have another grant application exploring the more basic questions of evolution of signals and reward in fireweed, so in some ways the funding gods will decide which way my research focus goes for the next few years. One of the outcomes for me is that I am more seriously thinking that applying for grants can be the motivation for thinking in new ways or on new topics. Maybe a little desperation (for funding, the next position, etc) can be a good thing and maybe for me I can find some of the answers in my own backyard. For now I’m happy that major grant writing can be set aside for a bit and I can enjoy the spring.

Academic House Cleaning


Around our house, the weekend usually means catching up. There is catching up on sleep, downtime and relaxing, exercise and getting outside, and, of course, chores. I’ve heard about those super-organised people who do their house cleaning on a weeknight so they can leave the weekend free for other, more fun things. It seems like a great plan but it isn’t one that we’ve managed to institute. And although we do a lot of maintenance through the week, we definitely need to take some time out to give the place a once over on the weekend.

Coming off of the past weekend got me thinking about my academic chores, and whether I should start having a ‘chore day’ there too. I’m partly inspired by my decision to clean up my reference files and pdfs. I’m starting a few review/meta-analyses projects with collaborators and it seems like a good time to get my house in order. When I started doing research, my advisor shared Endnote with me. Also a research assistant, I remember doing some cleaning up my advisors references. I think I was ensuring that the filing cabinet (a literal physical cabinet) had all the references that were in Endnote and vice versa. Modeling after that system, I started my own collection of printed pdfs. Somewhere in the course of my PhD, I stopped printing out files and instead read them on my computer. By that time, I never (rarely) needed to make the trip to the library to photocopy anything. When I moved to Sweden, I finally let go and recycled the alphabetized pdfs I’d carried from Vancouver to Guelph to Ithaca.

Right now my system for pdfs and citations needs an overhaul. I have many pdfs saved to a single folder and it is easy to find one, if it is indeed there. But some things existed as printouts (now recycled) and I haven’t downloaded them. Or I did, but didn’t save it to the master folder. Without going into too many boring details about my citations (or maybe I already crossed that line?), I’ve decided that now is the time to clean up the whole system.

For now, I’m linking pdfs to citations in Endnote and discussing with my collaborators what we should use to facilitate database use across Mac and PC. I might be behind the curve on this one but my aim is to have one place that I can go to search citations, link to the pdf and use for writing manuscripts. Right now it is a chore I’m doing in the evenings or when my brain has slowed down and more creative/thinking things are not efficient. The activity is strongly reminiscent of helping my advisor as an undergraduate assistant. You’d think I’d have learned my lesson from that! But unfortunately the Endnote version I started with didn’t have an option to link pdfs and there has never been a good time update by adding links….so here I am. I’d like to get to a state where I can just maintain my library  (as I’d been attempting), but I might need a spring-cleaning every now and again.

Cleaning up my pdfs and citation software is just one example of an academic chore. I know labs that have lab clean-up events and there are a lot of other little tasks that need doing as an academic. I’ve mostly been cleaning up as I go but I’m starting to consider whether I should have a ‘chore day’. Of course, this wouldn’t be a whole day or anything but maybe a good thing to do Friday afternoon after the departmental fika (Swedish for coffee break). At home, I know that even though we clean up through the week, without setting aside time to do laundry, pick up those things that got left out and whatnot, our house would quickly descend into a place we wouldn’t want to live. Sometimes my desktop (literal and computer) gets so piled up with things that it is impossible to find anything. I don’t have my own lab space these days so it is important to ensure that things in the common area get cleaned up right after use. But I wonder about getting in the habit of doing some chores every week for the other aspects of my job; cleaning up my desktops, emptying out my download folder, organising my inbox, etc. Maybe if I set aside time each week, I wouldn’t get into a state where a real overhaul is necessary. Although I am pretty good at keeping most things organised, it would even better if more things were.

Fig. 1 My messy desk and full download folder.

Fig. 1 My messy desk and full download folder.

Do you have a weekly routine for academic chores? Overall I suspect that it may make me more efficient at my job but there is the balance of not getting too caught up with chores and doing those little tasks instead of the big ones, like writing a paper or grant. I don’t want academic chores to just be a form of procrastination for getting ‘real’ work done!

How all ecology grad students can benefit from an OTS course


If you’ve only just started grad school, or if you’re getting ready to finish, there are a ton of great reasons to take the OTS course this summer. The Organization for Tropical Studies courses aren’t just for tropical biologists, and the experience is useful for all ecology grad students.

  • Breadth of research methods — Gain experience in running experiments in a great variety of biomes, fields, and taxa. No matter your speciality, it can be useful and important to know how to mark insects, do biogeochemistry and microbial ecology, dissect flowers and do pollination experiments, mist net birds and bats, make and analyze sound recordings, and much, much more.
  • Making connections — You will work very closely with a large number of faculty from universities all over the United States and elsewhere. More important, you’re in the course with a bunch of other grad students who are typically fun-loving and academically talented. The course is work hard-play hard environment and you’ll go back home with new friends and colleagues, some of whom you’ll stay in touch with for the remainder of your career. You want to emerge from grad school with a network that goes well beyond your own institution. This is a great way to make that happen.
  • Experimental design — This course will have you designing and conducting experiments at many different sites in small groups. This really helps you learn how to develop the right questions, design the most appropriate experiments and that you’ve had the best analysis in mind the whole time.
  • Data analysis — Because you are involved in so many experiments, you gain experience with may kinds of analysis. The course has expert faculty including well-recognized statistical gurus who communicate in common English. You’ll get training in R to give you the tools that you need.
  • Science communication skills — Learn how to produce media that communicate your science with the public, by working with PhD scientists/filmmakers. Here are the tremendous results from a brief science communication project on the OTS course, from a post on the National Geographic Explorers Journal. The course runs its own blog and you have an opportunity to create podcasts and posts.
  • Experience with conservation in action — You’ll have the chance to interact with land managers and conservation professionals on the sites of ongoing projects. If you’re thinking about getting into the this aspect of the ecology business, you’ll have experiences and opportunities with making connections.
  • Tropical nature — If you haven’t ever spent time in the tropics, the biological diversity is stunning compared to the meager biota of the temperate zone. You get to see these biomes in the company of researchers who are experts in this environment and conduct a number of experiments. If you want to learn natural history and biodiversity, this is a chance to be in the field with the experts who can show you what you what to learn.
  • Units — You get six credit hours from the University of Costa Rica that (typically) count towards the coursework requirements of your program. So, there’s that, too.

Speaking just from my own experience, the course gave me so many skills — and ideas — that have been useful in many unpredictable ways. I’ve yet to meet anybody who has taken the course who has said it is anything short of incredibly useful, and I think everybody has rated it as a spectacular experience. In the course of your graduate career, it definitely is worth your time.

Here’s a pdf flyer with more info.

Here is the link to the course for summer 2014, with its list of great faculty and remarkable sites the course visits, and instructions on how to apply. The deadline for applications is just over a week away, but then there are rolling admissions afterwards.

Does being a “Jack-of-all-Trades” impede or facilitate an early-stage academic career?


This is a guest post by Andrea Kirkwood. She is an Assistant Professor in the Faculty of Science at UOIT, and you can follow on twitter at @KirkwoodLab

Jack-of-all-Trades, Master of Nothing

Recently a topic near and dear to my heart came up on Twitter. Allison Barner (@algaebarnacle) was live-tweeting from the Western Society of Naturalists meeting, and posted the following tweet:

This tweet caught my attention, because it touched on an issue that caused some anxiety for me as I completed my doctoral degree. At the time, I thought my graduate school training was too broad, straddling several disciplines (ecology, phycology and microbiology) across very different systems (lakes vs. wastewater lagoons). Some may view this is a strength, bringing to mind the classical view of what a Doctor of Philosophy should be. Yet at the time I was completing my Ph.D. (circa 2002), I suspected that my skill-set was viewed as old-fashioned, and was being supplanted by the next-wave of sexy techniques. I also felt like I knew a little about a lot of things, but an expert in nothing. Sound familiar? I attempted to rectify this by choosing to do my first post-doc in a lab where I could learn a sexy technique (i.e., applying molecular methods in phylogeny and diversity assessment). Becoming adept at a special skill had to be the right move because several of my peers were getting faculty positions based on their “special skills”. You’re a quantitative ecologist, we’ll hire you! You use the latest molecular technique, we’ll hire you! It seemed that if you had a specific, timely skill-set, you were highly marketable. The message was that Jack-of-all-Trades (JOAT) need not apply.

Still, I wasn’t personally satisfied with just learning the latest sexy tools. When an opportunity came up to do something completely different for my next post-doc, I jumped at the chance. Not only would I get to work in a new system (rivers of the Canadian Rockies), but learn more about theoretical ecology. Here I was expanding my academic repertoire yet again to the detriment of specialization. One could blame my lengthy sojourn as a post-doc (5.5 years) on not having an obvious niche research area. Nonetheless, my academic breadth made it possible to apply to a broad swath of jobs, and end up on interview short-lists. Based on some feedback though, it was apparent that search committees either found it difficult to pigeon-hole my research area, or didn’t think I quite fit the specialization they were looking for.

I was very fortunate to be hired by a new university in Canada that didn’t have the luxury of hiring specialists. They needed someone who could teach a broad selection of biology courses, as well as have an applied research angle to fulfill their STEM mandate. This was an extremely rare kind of faculty hire at a Canadian research university. In the United States, this is apparently the typical hiring emphasis for small, teaching-focused universities as vouched by Terry McGlynn (@hormiga) on Twitter:

Although I breathed a sigh of relief upon securing a tenure-track faculty position, I then had to fret about the views of research-funding committees. I know several colleagues who were denied funding, in part, because they could not convince the reviewers they were expert enough. To add to my angst, the Great Recession had begun and the current government was slashing and burning research funding. Ironically, it was these dire funding circumstances that showcased the strengths of being an academic JOAT. I quickly discovered that I could access a broader pool of funding sources compared to my specialist colleagues. I secured grants in ecology, conservation, and biotechnology. Now one could argue this approach allows funding agencies to direct your research program (i.e., tail wagging the dog), which is true to some extent. Ultimately, the researcher has to decide to what degree they will chase money this way, and perhaps only use this funding model during the lean years. In my mind, if it can keep your lab running and let you and your students continue to do science, it certainly has its merits.

So admittedly up to this point, I have provided a narrative that asserts my credentials as a card-carrying JOAT. What does that mean exactly? Am I really a Master of Nothing? The very nature of grad school is to become a specialist at something, at least compared to the general population. Along the way, grad students and post-docs acquire specialized skills in their fields, some more than others. Serendipitously, I became a leading expert on Didymosphenia geminata (aka “Didymo” or “rock snot”) during my second post-doc, and will unabashedly credit D. geminata for saving my career (a blog entry for another day). Does this mean I lose the right to claim the JOAT label at all?

Brett Favaro (@brettfavaro) sums it up nicely by stating:

Brett cites an interesting opinion piece by Parsons (2012) in the Journal of Environmental Studies and Sciences that certainly extolls the virtues of a broad skill-set in an interdisciplinary field like Conservation. However, it is not enough to know about a lot of things, but to also have a deep understanding of them too. Thus, one needs to have a complement of specialized skills, perhaps at the “expert-lite” level and not necessarily “leading-expert” level. Would you accept your oncologist or cardiologist being “expert-lite” in your treatment options? Probably not, but I think this approach lends itself to disciplines such as ecology and environmental science, where a broad and somewhat deep skill-set can be an asset in research collaboration and communication.

Offering an intriguing new layer to this discussion on the JOAT phenomena in academia, Britt Koskella (@bkoskella) pondered on Twitter:

Britt cites an article by Wang et al. (2013) that assesses the role of gender in influencing career choices in STEM vs. non-STEM fields. The authors determined that individuals with high ability in both math and verbal skills tended not to pursue STEM careers. In contrast, individuals who had high math skills, but moderate verbal skills tended to choose STEM careers. This suggests that having a broad-skill set (i.e., being a JOAT) offers more career options, and thus an increased capacity to choose a career outside of STEM. What is also notable about these findings is that the group with high math and verbal ability included more females. This raises interesting questions about the underlying cause(s) of fewer women than men entering STEM careers. Is it ultimately about inherent freedom to choose a career path rather than ability?

Overall, I think it is clear that there is no clear-cut answer to the question posed in the title of this blog post. In my own personal experience, I can say that being an academic JOAT likely helped or hindered me at different points along my career path. Based on the unique experiences of every academic, I imagine there is a multitude of views on the JOAT phenomenon, and whether it matters or even exists. I think it exists, but is defined by perception on a sliding-scale.


Parsons, E. C. (2012). You’ll be a conservationist if…. Journal of Environmental Studies and Sciences, 1-2.

Wang, M. T., Eccles, J. S., & Kenny, S. (2013). Not Lack of Ability but More Choice Individual and Gender Differences in Choice of Careers in Science, Technology, Engineering, and Mathematics. Psychological Science, 24(5), 770-775.

On creating your own path through life


This weekend, I took my kid to a Mythbusters live show. When I left, I was inspired.

The source of inspiration wasn’t the world’s most impressive paintball gun, modified from an anti-aircraft machine gun. Though that was pretty cool.

I was inspired by learning about the wandering path taken by Jamie Hyneman. He’s the quieter Mythbuster that always wears a beret and has walrus-y facial hair. A one-page bio of Hyneman was in the big glossy program connected to the show.

As long as it’s all true, it seems that Jamie Hyneman has led one hell of a life. I’ll try to capture his trajectory, based on what I learned from his bio as well as his Q&A session during the show. The timing of all these things is rather vague to me, but here are highlights:

  • He grew up in a small Midwestern town. When he was 14, at his request, his parents sent him to a hardcore wilderness survival training school in Wyoming.
  • When he graduated from high school he bought a pet shop, and then sold it after a few years.
  • He went to college and got a degree in Russian. At one point along the line, he worked as a librarian for the United Nations in Geneva.
  • He worked as crew on sailing vessels in the Caribbean. He eventually bought his own ship, and got all the certifications to be a captain, and sailed around for a living.
  • He was interested in the various creative challenges with movie effects, so he left for New York City, where he started working in entry-level jobs in movie production, working to gain new skills.
  • He moved to San Francisco to access more exciting movie production work, and when his company folded he bought up the shop and went into business for himself. One of the guys he hired along the line was Adam Savage. At some point he asked Adam to join him in a pilot for Mythbusters, and you can figure out the trajectory for the following ten years up to the present.

I hear far too often, “What can I do with a degree in X?” This question comes with a false assumption: what you do after college must directly follow from the undergraduate degree. When a premed asks me what’s a good major, I say: “What do you find interesting? Since you’re going to be a doctor for your whole life, then what do you want to do before you get trained as a doctor? Art? Philosophy? Economics? Cell Biology? Music?”

Jamie Hyneman became a Mythbuster, with a degree in Russian. One of my siblings became a financial manager with a degree in Art. Another became a middle school special education teacher with a degree in Theater. A friend of mine became an FBI agent with a degree in Biology.

We chart our own paths in life. Far too often, we let our past decisions dictate our future directions far more than necessary.

The way that academics discuss their jobs in the university, they make it sound like we are captive to our disciplines. Tenure has been called the golden handcuffs. That’s pretty much the silliest notion ever. You can study — and do — whatever you want with tenure.

Linus Pauling, a tenured protein chemist, won a goddamn Nobel Peace Prize because of his social activism. This didn’t happen because he was handcuffed to the laboratory. Then again, nobody ever used the term “golden handcuffs” in the day of Linus Pauling. Nobody told him he couldn’t be both a scientist and social activist.

Jamie Hyneman could have made a go at his Indiana pet shop until retirement, or he could have stayed on as a Russian librarian, or he could have been running a sailing business in the Caribbean for his career. Or he could have kept to movie effects and never made a TV pilot. Mythbusters isn’t the culmination of his life. It’s just one chapter, albeit a very public one. He’s chosen an exciting and rewarding route.

All of our lives are short, and from the looks of it, Jamie Hyneman is making the most of his.

My trajectory is as linear as Hyneman’s has been circuitous. I went to high school, then college. Then I farted around for a year before grad school. Then I did a postdoc, visiting faculty, assistant professor, associate professor. I’ve lived in different places but I have been a scientist since the age of 20, and I enjoy science so much, that I’ll just keep doing it.

I have a very rare gift – tenure – and I don’t want to waste it. I have the opportunity to attempt the extraordinary, and am able to keep my stable job and pension in my back pocket the whole time. I’d like to think that what I am doing, on a day-to-day basis, is a part of this attempt. This blog is part of the attempt, and the continued effort to provide opportunities to my students is part of that attempt. This attempt at the extraordinary means that I will continue to pursue high-risk experiments that might not work but could turn out to be exciting. I’d like to think that with less personal security, I’d be just as inclined to take chances. I don’t know how true that would be.

The most extraordinary endeavors can also appear, on the outside, to be the most mundane. Being a parent, and spouse, is a special responsibility and joy. Sometimes the most extraordinary thing is making waffles for my family on a weekend morning. That might seem like an odd take-home message from a night out with the Mythbusters. But if Jamie can give up his gig as a Russian librarian to become a movie special effects wiz, then I can be, and do, far more than the stereotyped professor, husband and father.

This purposefulness about living an intentional life did not emerge in isolation. Overwhelming anything related to Mythbusters, this weekend my family experienced a loss that was was simultaneously sudden and gradual. I’ve been freshly reminded of the brevity and preciousness of life.

Perhaps the best way to honor those that have given us life is to make utility of this life as much as possible. It can be entirely workable that inspiration for our own utility can come from unconventional sources.

Collected observations from travels among universities


Invited seminars and job interviews offer a unique opportunity to learn (and remember) what grad school is like and how universities work. You get to have a lot of intentional sit-down conversations on a wide variety of topics. Spending time meeting new people and learning new stuff rocks. And when you chat with other people about themselves, and their work, labs and universities, you have a chance to put your own way of doing things in perspective.

I’ve had a few such opportunities in the past month. There were a number of recurring conversational themes and undercurrents. During these visits, you get to have conversations to learn not just about all kinds of research, but about how people chose the directions that led to their current trajectories. And, you often learn about how personal lives shape our research directions and priorities, both by design and by hap.

Here are some of the highlights. None of these observations are shocking news by any measure. But I was struck by the obviousness of these ideas and the frequency with which they emerged, even when I wasn’t looking for them:

  • Research universities are no longer primarily oriented towards training excellent scientists. They are now primarily oriented towards teaching students how to publish and to get grants. If a grad student develops the desire to become an excellent practitioner of science, this is probably going to emerge from the undergraduate experience.
  • Anybody currently building a future in the quantitative sciences needs to learn how to write code to promote their own research success. Being able to manage and analyze super-duper huge datasets (bioinformatics) is really useful.
  • High quantity data will never be a substitute for high quality data.
  • People need to get off their goddamn phones.
  • Genomics is now at the point when all flavors of biologists are in a practical position to figure out heritable mechanisms accounting for phenomena involving organisms in nature. For many kinds of questions, any species can now be a model system.
  • Most ecological theories are ephemeral, and are either myopic or wrong. The parenting of popular, ephemeral and myopic theories is the prevailing route to success.
  • It’s difficult to maintain the presence of mind to recognize the power of one’s own authority.
  • In ecology and evolutionary biology, women fall out of academic careers most heavily in the transition phase between from Ph.D. to faculty. Lots of parties are at fault, but the ones that seem to be the most significant are some senior faculty (of both genders) and some spouses. Deans have many opportunities to proactively make positive changes, but that rarely happens.
  • The number of students who want to do serious, long-term, field biology in the service of contemporary research questions has sharply declined. This limits our potential to answer some major wide open questions in biology.
  • Universities that maintain a strong faculty actively keep their professors from going on the market in search of greener pastures. Universities would not lose valued faculty members as often as they do, if they actually supported faculty commensurate with the degree to which they are valued. Once someone is driven to look for a new faculty job on the market, then it’s impossible to not take a great offer seriously, even when there are many good reasons to not move.
  • The beauty of life – both in biodiversity and our relations with fellow humans – is immense beyond words. Humanity might be ugly, but people are gorgeous.

A snapshot of the publication cycle


I was recently asked:

Q: How do you decide what project you work on?

A: I work on the thing that is most exciting at the moment. Or the one I feel most bad about.

In the early stages, the motivator is excitement, and in the end, the motivator is guilt. (If I worked in a research institution, I guess an additional motivator would be fear.)

Don’t get me wrong: I do science because it’s tremendous fun. But the last part – finessing a manuscript through the final stages – isn’t as fun as the many other pieces. How do I keep track of the production line from conception to publication, and how do I make sure that things keep rolling?

At the top center of my computer desktop lives a document entitled “manuscript progress.” I consult this file when I need to figure out what to work on, which could involve doing something myself or perhaps pestering someone else to get something done.

In this document are three categories:

  1. Manuscript completed
  2. Paper in progress
  3. In development projects

Instead of writing about the publication cycle in the abstract, I thought it might be more illustrative to explain what is in each category at this moment. (It might be perplexing, annoying or overbearing, too. I guess I’m taking that chance.) My list is just that – a list. Here, I amplify to describe how the project was placed on the treadmill and how it’s moving along, or not moving along. I won’t bore those of you with the details of ecology, myrmecology or tropical biology, and I’m not naming names. But you can get the gist.

Any “Student” is my own student – and a “Collaborator” is anybody outside my own institution with whom I’m working, including grad students in other labs. A legend to the characters is at the end.

Manuscript completed

Paper A: Just deleted from this list right now! Accepted a week ago, the page proofs just arrived today! The idea for this project started as the result of a cool and unexpected natural history observation by Student A in 2011. Collaborator A joined in with Student B to do the work on this project later that summer. I and Collab A worked on the manuscript by email, and I once took a couple days to visit Collab A at her university in late 2011 to work together on some manuscripts. After that, it was in Collab A’s hands as first author and she did a rockin’ job (DOI:10.1007/s00114-013-1109-3).

Paper B: I was brought in to work with Collab B and Collab C on a part of this smallish-scale project using my expertise on ants. I conducted this work with Student C in my lab last year and the paper is now in review in a specialized regional journal (I think).

Paper C: This manuscript is finished but not-yet-submitted work by a student of Collab D, which I joined in by doing the ant piece of the project. This manuscript requires some editing, and I owe the other authors my remarks on it. I realize that I promised remarks about three months ago, and it would take only an hour or two, so I should definitely do my part! However, based on my conversations, I’m pretty sure that I’m not holding anything up, and I’m sure they’d let me know if I was. I sure hope so, at least.

Paper D: The main paper out of Student A’s MS thesis in my lab. This paper was built with from Collab E and Collab F and Student D. Student A wrote the paper, I did some fine-tuning, and it’s been on a couple rounds of rejections already. I need to turn it around again, when I have the opportunity. There isn’t anything in the reviews that actually require a change, so I just need to get this done.

Paper E: Collab A mentored Student H in a field project in 2011 at my field site, on a project that was mostly my idea but refined by Collab A and Student H. The project worked out really well, and I worked on this manuscript the same time as Paper A. I can’t remember if it’s been rejected once or not yet submitted, but either way it’s going out soon. I imagine it’ll come to press sometime in the next year.

Manuscripts in Progress

Paper F: Student D conducted the fieldwork in the summer of 2012 on this project, which grew out of a project by student A. The data are complete, and the specific approach to writing the paper has been cooked up with Student D and myself, and now I need to do the full analysis/figures for the manuscript before turning it off to StudentD to finish. She is going away for another extended field season in a couple months, and so I don’t know if I’ll get to it by then. If I do, then we should submit the paper in months. If I don’t, it’ll be by the end of 2014, which is when Student D is applying to grad schools.

Paper G: Student B conducted fieldwork in the summer of 2012 on a project connected to a field experiment set up by Collab C. I spent the spring of 2013 in the lab finishing up the work, and I gave a talk on it this last summer. It’s a really cool set of data though I haven’t had the chance to work it up completely. I contacted Collab G to see if he had someone in his lab that wanted to join me in working on it. Instead, he volunteered himself and we suckered our pal Collab H to join us in on it. The analyses and writing should be straightforward, but we actually need to do it and we’re all committed to other things at the moment. So, now I just need to make the dropbox folder to share the files with those guys and we can take the next step. I imagine it’ll be done somewhere between months to years from now, depending on how much any one of us pushes.

Paper H: So far, this one has been just me. It was built on a set of data that my lab has accumulated over few projects and several years. It’s a unique set of data to ask a long-standing question that others haven’t had the data to approach. The results are cool, and I’m mostly done with them, and the manuscript just needs a couple more analyses to finish up the paper. I, however, have continued to be remiss in my training in newly emerged statistical software. So this manuscript is either waiting for myself to learn the software, or for a collaborator or student eager to take this on and finish up the manuscript. It could be somewhere between weeks to several years from now.

Paper I: I saw a very cool talk by someone a meeting in 2007, which was ripe to be continued into a more complete project, even though it was just a side project. After some conversations, this project evolved into a collaboration, with Student E to do fieldwork in summer 2008 and January 2009. We agreed that Collab I would be first author, Student E would be second author and I’d be last author. The project is now ABM (all but manuscript), and after communicating many times with Collab I over the years, I’m still waiting for the manuscript. A few times I indicated that I would be interested in writing up our half on our own for a lower-tier journal. It’s pretty much fallen off my radar and I don’t see when I’ll have time to write it up. Whenever I see my collaborator he admits to it as a source of guilt and I offer absolution. It remains an interesting and timely would-be paper and hopefully he’ll find the time to get to it. However, being good is better than being right, and I don’t want to hound Collab I because he’s got a lot to do and neither one of us really needs the paper. It is very cool, though, in my opinion, and it’d be nice for this 5-year old project to be shared with the world before it rots on our hard drives. He’s a rocking scholar with a string of great papers, but still, he’s in a position to benefit from being first author way more myself, so I’ll let this one sit on his tray for a while longer. This is a cool enough little story, though, that I’m not going to forget about it and the main findings will not be scooped, nor grow stale, with time.

Paper J: This is a review and meta-analysis that I have been wanting to write for a few years now, which I was going to put into a previous review, but it really will end up standing on its own. I am working with a Student F to aggregate information from a disparate literature. If the student is successful, which I think is likely, then we’ll probably be writing this paper together over the next year, even as she is away doing long-term field research in a distant land.

Paper K: At a conference in 2009, I saw a grad student present a poster with a really cool result and an interesting dataset that came from the same field station as myself. This project was built on an intensively collected set of samples from the field, and those same samples, if processed for a new kind of lab analysis, would be able to test a new question. I sent Student G across the country to the lab of this grad student (Collab J) to process these samples for analysis. We ran the results, and they were cool. To make these results more relevant, the manuscript requires a comprehensive tally of related studies. We decided that this is the task of Student G. She has gotten the bulk of it done over the course of the past year, and should be finishing in the next month or two, and then we can finish writing our share of this manuscript. Collab J has followed through on her end, but, as it’s a side project for both of us, neither of us are in a rush and the ball’s in my court at the moment. I anticipate that we’ll get done with this in a year or two, because I’ll have to analyze the results from Student G and put them into the manuscript, which will be first authored by Collab J.

Paper L: This is a project by Student I, as a follow-up to the project of Student H in paper E, conducted in the summer of 2013. The data are all collected, and a preliminary analysis has been done, and I’m waiting for Student I to turn these data into both a thesis and a manuscript.

Paper M: This is a project by Student L, building on prior projects that I conducted on my own. Fieldwork was conducted in the summer of 2012, and it is in the same place as Paper K, waiting for the student to convert it into a thesis and a manuscript.

Paper N: This was conducted in the field in summer 2013 as a collaboration between Student D and Student N. The field component was successful and now requires me to do about a month’s worth of labwork to finish up the project, as the nature of the work makes it somewhere between impractical and unfeasible to train the students to do themselves. I was hoping to do it this fall, to use these data not just for a paper but also preliminary data for a grant proposal in January, but I don’t think I’ll be able to do it until the spring 2014, which would mean the paper would get submitted in Fall 2014 at the earliest, or maybe 2015. This one will be on the frontburner because Students D and N should end up in awesome labs for grad school and having this paper in press should enhance their applications.

Paper O: This project was conducted in the field in summer 2013, and the labwork is now in the hands of Student O, who is doing it independently, as he is based out of an institution far away from my own and he has the skill set to do this. I need to continue communicating with this student to make sure that it doesn’t fall off the radar or doesn’t get done right.

Paper P: This project is waiting to get published from an older collaborative project, a large multi-PI biocomplexity endeavor at my fieldstation. I had a postdoc for one year on this project, and she published one paper from the project but as she moved on, left behind a number of cool results that I need to write up myself. I’ve been putting this off because it would rely on me also spending some serious lab time doing a lot of specimen identifications to get this integrative project done right. I’ve been putting it off for a few years, and I don’t see that changing, unless I am on a roll from the work for Paper N and just keep moving on in the lab.

Paper Q: A review and meta-analysis that came out of a conversation with Collabs K and L. I have been co-teaching field courses with Collab K a few times, and we share a lot of viewpoints about this topic that go against the incorrect prevailing wisdom, so we thought we’d do something about it. This emerged in the context of a discussion with L. I am now working with Student P to help systematically collect data for this project, which I imagine will come together over the next year or two, depending on how hard the pushing comes from myself or K or L. Again it’s a side project for all of us, so we’ll see. The worst case scenario is that we’ll all see one another again next summer and presumably pick things up from there. Having my student generating data is might keep the engine running.

Paper R: This is something I haven’t thought about in a year or so. Student A, in the course of her project, was able to collect samples and data in a structured fashion that could be used with the tools developed by Collab M and a student working with her. This project is in their hands, as well as first and lead authorship, so we’ve done our share and are just waiting to hear back. There have been some practical problem on their side, that we can’t control, and they’re working to get around it.

Paper S: While I was working with Collab N on an earlier paper in the field in 2008, a very cool natural history observation was made that could result in an even cooler scientific finding. I’ve brought in Collab O to do this part of the work, but because of some practical problems (the same as in Paper R, by pure coincidence) this is taking longer than we thought and is best fixed by bringing in the involvement of a new potential collaborator who has control over a unique required resource. I’ve been lagging on the communication required for this part of the project. After I do the proper consultation, if it works out, we can get rolling and, if it works, I’d drop everything to write it up because it would be the most awesome thing ever. But, there’s plenty to be done between now and then.

Paper T: This is a project by Student M, who is conducted a local research project on a system entirely unrelated to my own, enrolled in a degree program outside my department though I am serving as her advisor. The field and labwork was conducted in the first half of 2013 – and the potential long-shot result come up positive and really interesting! This one is, also, waiting for the student to convert the work into a thesis and manuscript. You might want to note, by the way, that I tell every Master’s student coming into my lab that I won’t sign off on their thesis until they also produce a manuscript in submittable condition.

Projects in development

These are still in the works, and are so primordial there’s little to say. A bunch of this stuff will happen in summer 2014, but a lot of it won’t, even though all of it is exciting.


I have a lot of irons in the fire, though that’s not going to keep me from collecting new data and working on new ideas. This backlog is growing to an unsustainable size, and I imagine a genuine sabbatical might help me lighten the load. I’m eligible for a sabbatical but I can’t see taking it without putting a few projects on hold that would really deny opportunities to a bunch of students. Could I have promoted one of these manuscripts from one list to the other instead of writing this post? I don’t think so, but I could have at least made a small dent.

Legend to Students and Collaborators

Student A: Former M.S. student, now entering her 2nd year training to become a D.P.T.; actively and reliably working on the manuscript to make sure it gets published

Student B: Former undergrad, now in his first year in mighty great lab and program for his Ph.D. in Ecology and Evolutionary Biology

Student C: Former undergrad, now in a M.S. program studying disease ecology from a public health standpoint, I think.

Student D: Undergrad still active in my lab

Student E: Former undergrad, now working in biology somewhere

Student F: Former undergrad, working in my lab, applying to grad school for animal behavior

Student G: Former undergrad, oriented towards grad school, wavering between something microbial genetics and microbial ecology/evolution (The only distinction is what kind of department to end up in for grad school.)

Student H: Former undergrad, now in a great M.S. program in marine science

Student I: Current M.S. student

Student L: Current M.S. student

Student M: Current M.S. student

Student N: Current undergrad, applying to Ph.D. programs to study community ecology

Student O: Just starting undergrad at a university on the other side of the country

Student P: Current M.S. student

Collab A: Started collaborating as grad student, now a postdoc in the lab of a friend/colleague

Collab B: Grad student in the lab of Collab C

Collab C: Faculty at R1 university

Collab D: Faculty at a small liberal arts college

Collab E: Faculty at a small liberal arts college

Collab F: International collaborator

Collab G: Faculty at an R1 university

Collab H: Started collaborating as postdoc, now faculty at an R1 university

Collab I: Was Ph.D. student, now faculty at a research institution

Collab J: Ph.D. student at R1 university

Collab K: Postdoc at R1 university, same institution as Collab L

Collab L: Ph.D. student who had the same doctoral PI as Collab A

Collab M: Postdoc at research institution

Collab N: Former Ph.D. student of Collab H.; postdoc at research institution

Collab O: Faculty at a teaching-centered institution similar to my own

By the way, if you’re still interested in this topic, there was also a high-quality post on the same topic on Tenure, She Wrote, using a fruit-related metaphor with some really nice fruit-related photos.

Model systems don’t work at teaching universities


Many research strategies, developed inside large research institutions, don’t work well in small teaching-centered institutions.

One of these strategies, I suggest, is the use of a biological model system.

What do I mean by model systems? Any system, used in multiple laboratories, that has been optimized for a broad variety of lab investigations. I’m thinking of zebrafish, Drosophila, mice, C. elegans, E. coli, Arabidopsis, and honey bees. For the ant people, this includes Temnothorax.

It might seem counterintuitive that model systems can prevent you from getting research done in a teaching-centered institution. After all, these model systems were developed, in part, to make research easier and remove methodological barriers experienced by those working in a broad variety of organisms.

Even though model organisms are easy to work with, have lots of standardized methods, and a massive bank of potential collaborators, the costs and risks of working with a model system far outweigh the benefits and the opportunities.

My initial thought on this topic came from observation. I don’t see much research on model systems getting done within teaching institutions. I also have seen a variety of people, who work on model systems in teaching institutions, experience substantial challenges that have kept them from getting work done. Here’s a list of problems that I’ve seen crop up for those using model systems in teaching institutions.

  • The low-hanging fruit has been picked. In model systems, the things which are a combination of easy, obvious and trivial have already been done. That only leaves things which are either difficult, requiring particularly deep insight, or trivial.
  • Model systems have intense competition. In model systems, many labs compete rather than collaborate. A small lab in a teaching institution isn’t built to deal with this kind of competition. This doesn’t mean that you should avoid competition by working on minutia, but it also means that you should choose an avenue that has high pressure competition. As any ecologist will tell you, competition is an interaction which has a negative effect on both parties. (An economist will tell you the same thing, though it’s better for the consumer).
  • You might get scooped. The probability that someone is doing exactly what you are working on is higher in a model system.
  • Collaborating is difficult. The labs that run model systems typically have lots of routine methods happening like clockwork, run by technicians and students who have become specialized in the model system. If you’re working on this model system, you are not likely to be able to contribute anything of substance to a big lab, which could do the same thing much more quickly than you could offer. Collaboration is easier if you have a specific resource or skills that others want or need, and that’s harder from a teaching institution if you work in a model system.
  • You may become isolated. In the community of people working in model systems, if you have a small undergraduate lab you’re more likely to be overlooked by your peers. I’m not sure how or why this is the case, but it’s what I’ve observed on several occasions. Everyone I know who has worked on a model system wasn’t well integrated into the community of frenemies that works on the system. It’s hard to have a scientific impact if you are unrecognized by your peers.
  • The bar for evidence is very high. Peer review is more likely to be rough and demanding. People with model systems tend to be territorial, and especially if you’re not perceived as a competitor that can bite back with teeth, then at least one reviewer is likely to go the extra mile to find something wrong with what you’ve done.
  • The minimum publishable unit is greater. If you’re working in a model system, odds are that the big labs working in the field are able to heavily replicate their experiments, and sandwich multiple experiments together into a paper. This establishes a standard for a large volume of data in a paper. I’m not arguing that you should seek to publish as soon as you have enough data for any kind of publication. However, if you have a genuine, interesting, and cool finding and want to get it out for everybody to read, it’s a bummer to have others telling you that you that it’s ready for publication because you don’t have a mountain of data.
  • Maintaining colonies takes continuous effort. Mustering any kind of consistent student work in the lab is a success. You don’t want your success in long-term student research to be squandered on the maintenance of lab colonies of a model system. You want to work on a system in which students can dive in and collect actual data, rather than spending all of their time keeping critters fed, clean, reproductive and healthy. Also, you want to be free to disappear for long periods of time, and not chained to the lab to maintain your organisms.

So why would one ever work with a model organism? One great reason would be to provide lots of valuable hands-on laboratory experiences to students. But in my observation, this kind of approach is a poor recipe for completing original research in a teaching institution.

In my opinion, if we are giving students research training, that means we’re giving them research training. Which means that they’re doing research, and research is something that ends up in new findings that are shared with the scientific community. If you want to give a genuine research experience to students, then you need to regularly publish the work that you are doing. From my vantage point, it’s very difficult to do this while working with a model system in a teaching institution.

I have a couple other alternative explanations for the (perceived) phenomenon that researchers working on model systems in teaching institution have a harder time publishing their work. The first alternative is that I’m just wrong, and that my experiences or perceptions aren’t representative or accurate.

The second alternative is that less research is completed because of the use of strategies from R1 labs that don’t translate to a teaching institution. Most people at teaching institutions, who work on model systems, are continuing with their dissertation system, I think. The research approaches from a dissertation lab probably don’t translate well to a teaching institution. I haven’t spent much time inside the labs of teaching institution professors using model systems, so I don’t know how they run things on a day-to-day basis, and am not too sure about this.

Do you think that it’s hard to get research done on a model system at a teaching institution? Did you switch into one, or out of one, to get research done? Thinking about it?

On specialization: don’t research your way into obscurity


A current conversation about model systems and the benefits of being a scientific specialist or generalist has been interesting, and I thought I’d join in. (This post was written for a while, but now’s a good moment to actually share it.)

Here’s my main point : Becoming a specialist on a narrow topic or taxon is a bad idea, because it narrows your opportunities and decreases both your visibility and your impact. If you’re in a position to choose your research trajectory and degree of specialization, I have two specific prescriptions near the end of the post.

When building a research program within teaching institutions, I’ve seen junior faculty advised by senior colleagues to avoid competing with big research labs. They are advised to find a specialized topic, not subject to competition, and develop expertise. For example, I’ve seen people whose entire research career, at a teaching institution, is built on the phylogenetic reconstruction of a single genus.

The notion behind this strategy is that you can be the world expert in something if nobody else is studying it. This route can allow you to retain a niche in academia, even if you are busy enough teaching that you can’t penetrate a more broader or more mainstream research agenda.

This sounds like a horrible idea.

If you specialize on an obscure topic that nobody is interested in studying, that means that you’ve become an expert in a mostly useless topic. What good is that?

If you are convinced that your particular obscure field should is truly important, and that the whole world is wrong, then that’s a different matter. If you think that understanding the detailed phylogeny of a single genus is important because of the unrealized significance of this group and the broad and general lessons to be learned from this model taxon, then fine, go ahead and spend your career working on it. However, if you choose a specialization for the purpose of defending a narrow niche, then you’re not allowing yourself the academic freedom that produces the best scholarship. And, you’ve intentionally chosen a route to obscurity.

Let’s just say that your obscure specialty suddenly matters to everyone? Let’s say that your model genus simultaneously emerges as an invasive species, produces a cancer-curing chemical and is the secret to carbon-free energy production. Are you going to have a great impact? No.

You’ll get more attention at first, but then you’ll then be shuffled back into obscurity when the big names in the field move into your pet taxon. Then, you won’t even own your little niche anymore, and your work will be seen as even more obscure.

Here’s a true anecdote: I once worked with someone who worked in a tiny niche, without collaborating. Coming up for tenure at a teaching institution, this person focused work on a single niche project over a few years. Shortly after the project was finished, less than a year before turning in the tenure dossier, this person was scooped by a bigger lab. This bigger lab just knocked out this work as a side project with some spare time. This unfortunate choice to overspecialize, without collaboration, meant that this person came up for tenure short of research expectations.

You won’t be a famous scientist working at a teaching institution. So, if you’re going to be a successful scientist, you need to be productive and successful one without the fame. Success in research is often based in collaboration, and people won’t be seeking you out for your narrow expertise in an obscure topic – they’ll be seeking you out because you are good and have something useful to offer for research on an existing question of interest.

Why would someone work in an obscure niche? Maybe it could be a personal passion, which would be a great reason, just like any other hobby. Other than that, the only reason I can think of is that a noncompetitive niche enables someone to continue to consistently publish a large number of papers that few people read. Some have made the argument that working on an obscure topic is the only reliable road to a position in the academic community, for those who are working in teaching institutions. It’s the road to being recognized as a scholar on your campus, but that’s not our real academic community.

Doing this work on an obscure topic buys you a place at the table. That might be true, with a caveat: Working on minutia buys you a place at the kiddie table.

What is a good attitude towards developing a research program at a teaching institution? It should take advantage of the fact that the productivity or prestige of your research program isn’t so important to your institution. I agree with Chris Richardson of Young Harris College, who uses his job at a teaching institution to give him the freedom to do research on anything that interests him:

There is… much less pressure on me to be the expert in one particular niche, leaving the research questions I can pursue much more open.

What is the best approach towards the generalization/specialization continuum as a researcher at a teaching institution? I have two specific prescriptions:

  1. Do whatever you want! Be free! Use the freedom that have.
  2. You’re best off if you general work on a diversity of questions, but within a framework that allows you expertise that will be of collaborative utility.

I’m not an ideal example, but at least I’m familiar with myself. Nearly everything I do involves ants. Moreover, it’s on one particular location that’s only 15 km2 in area. It’s easy to argue that this is obscure. However, the questions that I have asked include behavior, community ecology, some ecosystem ecology and a little chemical ecology sprinkled in. I have a set of experimental techniques and data that allow me to have specific, and long-term, information that is very difficult to acquire in any system. So you could say that I only study ants, in this one tiny place. Or you could say that I’m a generalist who works on all kinds of questions, with this one particular system.

What has this approach done for me? I’ve chosen the place where I work carefully, and it is a place where a lot of other people work. The kinds of information that I have generated can be useful to others, and my experience allows me to be of substantial use as a collaborator. Actually, I’m not even close to being the main expert of my taxon of choice at my field site. I am, however, a guy who works there consistently and broadly on a variety of questions, and is prepared to engage with collaborators on new questions.

I’m not required to follow a path that necessitates continuous external funds, marketing my doctoral students and publishing well every year. This is the ideal academic job – I am not required to satisfy anybody with my research program, other than myself. Hopefully the research will be broadly useful to all, but I can design and run it as I wish, based on the funding, timescale and focus that suits my needs.

I’m not wedded to my study system, nor to the single place where I’ve conducted most of my field-based research for my career. But, it works for me, and I really enjoy it, and this depth of experience in this one system and location gives me an avenue to ask lots of questions in all kinds of fields that I wouldn’t be able to do if I switched to a different taxon or location with which I was less familiar.

So, I’m a geographical and taxonomic specialist, but a conceptual generalist. To me, this is the most fun way to do science, and it lets me give great opportunities to students as well as to collaborators. I think this approach merges the benefits of generalization and specialization but minimizes the drawbacks.

Ant science: how avoiding modeling led to a cool discovery


Here’s a specific example, from my own work, of how the avoidance of mathematical modeling led to a fundamental discovery that eluded modelers and experimentalist for decades.

At least, that’s how I see it when I’m not feeling humble. It’s about resource allocation in ants, not the grand unified theory, after all.

For context, for those newer to the site, consider this post as a coda to an ongoing series (and discussion of sorts with Dynamic Ecology) about approaches to designing a research program. I have advocated that exploration by tinkering with unexplained curiosities within natural systems often leads to the best discoveries as well as the most consequential research programs. This post from a few weeks ago provides a good summary of that series. Another precursor to this post is a discussion about the relationship between mathematical modeling, hypothesis development, and how much math you need to become a scientist.  That is also a precursor to this post, though it is a “long read,” for those averse to verbiage.

The subject of this post — the scientific discovery — came out in a paper last year (go read it if you wish), which I wrote with Sarah Diamond and Rob Dunn. In short, we discovered a fundamental pattern that could have been obvious to everyone, if anybody just looked in that direction. This pattern explains many unanswered ideas, going back to theories that E.O Wilson developed in the 1970s, along with George Oster.

A twig nest of Pheidole sensitiva. Photo: Benoit Guénard

A twig nest of Pheidole sensitiva.
Photo: Benoit Guénard

Oster & Wilson set out to understand what regulates the varying levels of investment into the different members of ant colonies. Most inhabitants of ant colonies are functionally sterile, and in some species, there are multiple physical castes of sterile ants.

The genus Pheidole is the most species rich ant genus, and they’re found pretty much everywhere. All Pheidole (aside from a few exceptions) do something that isn’t found in many other lineages: they have two discrete sterile worker casts. They make big-headed soldiers and tinier minor workers, both of which do a variety of work for the colony. Some think that this dimorphic worker caste, and potentially the flexibility tied to its production, has enabled these ants to not only become ecologically successful but also to diversify.

Anyhow, Oster & Wilson made a number of predictions about the adapability of the ratio of soldiers to minor workers in Phediole colonies. One of their big testable predictions, or perhaps it could be seen as model to be falsified, is that the colonies actively adjust the ratio of soldiers to workers in response to environmental challenges.

It entirely makes sense. If a Pheidole colony is in an environment that requires more soliders, they would make more soldiers. Right? The problem is, despite a lot of looking carefully at Pheidole colonies, this wasn’t found. Finally in the mid ’90s it was found in the lab of Luc Passera, that P. pallidula colonies made more soldiers when they were exposed, without contact, to neighboring colonies. When I say it was found in the lab of Passera, I mean it happened physically in his lab. These were captive colonies.

A similar thing was found in the field in 2002, when I and Jeb Owen published a paper showing adaptive soldier production in another Pheidole species. (Also, my labmate Samantha Messier did the same thing before the Passera group, in a field experiment involving Nasutitermes termites and a machete.) Our studies were done in the field. In my experiment, when I put clumps of supplemental food in the field for months on end, the food was defended by soldiers, and in a short time colonies made more soldiers.

One thing I didn’t mention at the time, though, was that I didn’t find adaptive soldier production in a whole bunch of other species. However, I had less statistical power, and it was the most common species that showed this pattern. Maybe the less common ones did, but it was harder to detect.

If you were to ask around and dig into the literature, you’d see that it’s pretty clear that most species of Pheidole actually do not overtly shift their caste ratios when you mess around with their environment. Not every colony produces the same ratio, but a systemic environmental manipulation doesn’t cause an increase. Other than the two papers I just mentioned, I don’t think anybody else has found adaptive caste ratios in Pheidole. Others have looked, but it hasn’t emerged very clearly.

So, if most species just don’t ramp up and ramp down soldier production in response to the environment, what controls soldier production? For decades, there has been a consistent amount of work asking this question from behavioral, physiological and developmental angles. In the course of all of this excellent work (a lot of it being done by Diana Wheeler, Fred Nijhout, and their associates), we’ve made a lot of progress in understanding how colonies regulate their activity and how development is regulated through genetic, biochemical and physiological mechanisms.

One thing that I’ve always wondered about is, why do some species produce more soldiers than others? I’ve cracked open lots of twigs, and the numbers of soldiers are highly variable. And my experiments have shown that most species don’t obviously change their soldier production in response to environmental changes. There has been lots of great work to understand variation within a single species, but interspecific comparisons have been scant.

I can understand why there hasn’t been much comparative work. Measuring caste ratios of entire colonies can be hard. Find a Pheidole colony in the back yard and compare the number of soldiers and workers. See, not easy, huh? You’ve got to dig them up. Unless, of course, your backyard is a rainforest. In that case, you just pick up twigs. Over the years, I estimate that I and my students have picked up over 106 twigs over the years. Thousands of these have had Pheidole colonies inside. The rainforest is diverse, so I have data on many species. How do they compare?

Well, I learned that the caste ratios were different among species. Some species produced way more soldiers than others. Considering that we know so little about the natural history of these species, there wasn’t a great basis for comparing many of these species to one another. But one thing we could examine, quite easily, was body size. And, as it turned out, that was super-duper predictive of solider investment. Smaller species produced more soldiers than larger ones. When this pattern emerged on my laptop, it was one of those moments of elation that are very cool, but then you don’t have anybody with whom to share.

Then, I dug through the literature so see if the information that we had about caste ratios and body size shows the same pattern that I found in my rainforest. It turns out that the relationship is as identical as you can get. Our local scale pattern recapitulated Pheidole from around the world, and across the phylogeny.

Now, if you ask someone, what controls soldier production in Pheidole? You can say the answer is quite clearly body size. How and why does body size control this? There is some cool work that’s been done on this intraspecifically, that presumably is a mechanism that works more broadly.

How did my discovery of this generalized relationship come about from avoiding models? If you look at the work on soldier production, ever since Oster & Wilson published their monograph in the 1970s, there’s been a strong emphasis on modeling the mechanisms that trigger and regulate soldier production. Meanwhile, nobody before me bothered to step back to look at the big picture and ask, “how are species different and what is predictive of that?” If they did, then they would have found the caste ratio data in the literature as I had, and looked at the most obvious predictor: body size. Others were modeling solider production. I was merely trying to find a pattern.

I’m not claiming that the discovery of this pattern is earthshaking or that it explains mechanistically how colonies make more or fewer soldiers at the proximate level. The main take-home message from this paper is that many of the differences we find are driven by constraints rather than by adaptation, or that selection on body size is coupled with selection on soldier production. This leads to a lot of exciting thoughts about community structure, which we’re now working on.

This work by no means diminishes all of the careful experiments that others have done over the years on Pheidole. Though I’m not a developmental biologist nor as much of a behaviorist, I was able to find something that will be (or at least, I think should be) at the basis of future conversations about the evolution of caste ants.

This is why my choice is to keep asking “What is the pattern?” rather than attempting to model patterns.

Tinkering around is the best way to do research


On my desktop sits a file, as a reminder. It’s the log of a Skype text chat dated 24 October 2007.

My desktop isn’t usually tidy, but this file always sits there in a corner. I haven’t read it in years, but its existence is, in itself, a reminder.

This article is third in a series of four. A couple weeks ago I wrote about whether or not we should try to develop new theories or to test existing ones by hopping on theoretical bandwagons.

Last week I wrote why theoretical bandwagons are good for, or at least well suited to, big labs and that small labs should avoid them. (You might want to read those over, if you haven’t yet, before reading the present post. Or not. Your call.)

This week, I’m explaining the kind of research that I choose to do in my own small lab.

This chat took place with a deep friend of mine as we both were undergoing career transitions, both of us starting out in new (and radically different) faculty positions. (It’s great when your friends are your role models, and when your role models are your friends, even if you only see one another in a long while. It’s not too often that you connect with others whose values and priorities are well calibrated to match your own, and it’s a pleasant confluence.)

Darwin was a tinkerer. (Richmond's portrait of Darwin is from 1840)

Darwin was a tinkerer. (Richmond’s portrait of Darwin is from 1840)

I had just moved to a new position, back to my hometown. This change involved a massive shift in pretty much everything. I was wondering what kind of questions I should be pursuing, and how I should go about it. My friend was settling into a tenure-track position at a research institution and his lab was growing exponentially.

We were wondering what I was going to work on next. At this point, I wasn’t sure. I had a number of big questions that I wanted to tackle, each of which would involve a major direction for my lab. Up until this point, I had been doing a series of one-off projects (which essentially is what my dissertation was as well).

So, I threw out a bunch of ideas. I want to work on X, I want to work on Y, and Z looks interesting too. I said I didn’t want my work to get lost as ephemera, addressing theories-of-the moment.

Then, at the same moment, we independently stumbled on the term that describes the work that I enjoy most, and also has had the greatest impact.


My best work has happened whenever I’ve found some little natural history curiosity that has piqued my interest, and then I designed an experiment (observational or manipulative) to tinker around with the system to figure out what’s going on. It was my doctoral advisor who first introduced me to “experimental natural history.” (Sorry about the paywall, damn JSTOR)

This leads to both the stuff that is most cool, interesting, and in the long term useful to other people. I think that good science happened because my approach was most likely to lead to discovery, even if discovery was not the goal.

Research is supposed to result in new knowledge.

What are the odds that you’re going to make a big discovery or formulate a grand theory as long as you’re working on the same ideas that other people are? How much are you pushing the frontiers of science when there are other people out there doing the same thing? If you’re working for a specific applied aim – an HIV vaccine, cancer prevention, et cetera, then I can understand that a massive push in one direction, like against a two-ton piece of stone, is what can make the stone move.

I’m not in the business of inventing vaccines for rapidly evolving viruses or building pyramids. I’m doing basic research. I’m just trying to understand how the world works. There is so little that is known, that I want to mine into directions that that are entirely mysterious. The world is still fundamentally mysterious.

I posit that there are two distinct philosophies that scientists have about the nature of our knowledge, with little middle ground. On one side are people who think that we have learned a lot in the fields that we have studied, and that research is filling in the gaps and discovering new fields that we have yet to understand. On the other side are people who think that we are still vastly ignorant about the world, and even the things that we have studied really heavily remain mysterious and what we think we know may in fact be wrong.

Is this a fair dichotomy? Does one of these describe you or do you fit in the middle somewhere?

I’m in the latter group (or at one end of the spectrum if it’s not a dichotomy). I suspect that a number of ecologists might fall into that group as well. For all the work that we’ve done, we’ve only scratched the surface, and that surface is probably deceiving. Some classic major concepts, such as “competitive exclusion,” are so simplistic that they don’t even begin to describe nature.

The one thing that students seem to learn in school about evolution is that Lamarck was wrong, and this lesson comes with a certain example involving a giraffe. It’s taken us a couple centuries to figure out that, to a certain extent, Lamarck was quite right about the inheritance of acquired characteristics after all. He just didn’t know the mechanism was epigenetic, just as Darwin wasn’t aware of the particulate inheritance mechanisms described by Mendel. Jerry Coyne addressed this a score of moons ago.

In short, some things we think we fundamentally understand, we really don’t. This is particularly the case for complex phenomena that are explained by theories requiring mechanisms that can’t be readily measured in nature. Natural selection is very straightforward and observable, and we have that one locked down. But many more intricate concepts in ecology? I wouldn’t buy stock in them.

If your research program is oriented towards testing theories, then you’re less likely to stumble on a new perspective.

When I design experiments, I “tinker” with natural systems by tweaking them in small ways to see what happens. I do this because I find something that’s curious to me, and I want to understand what’s happening in that system. I don’t pretend that what I find will answer a grand theory or unify different branches of our disciplines. I just want to get a little answer about a little thing that’s curious. My suspicion, that might approach something resembling belief, is that this kind of work will help us learn more about the world than most theoretically-driven research. I think that most of our major advances came from this kind of approach as well.

You’lll find some mildly unflattering things said about this approach, over at Dynamic Ecology. This is a healthful disagreement of opinion. (Heck, there might even be a claim that it wasn’t unflattering!) I recognize that what I’m writing goes against current dogma, that if your work isn’t driven by theory, then it’s not of much value. I can respectfully disagree, but then again, there’s no major concept or principle with my name on it, either, so I can’t push my point too firmly.

If you take a walk through a rainforest, a few hundred curiosities, with no known answers, should slap you in the face very quickly. This happens during a walk during the desert, as well, though with lower frequency as there’s less biomass.

When I walk through the rainforest, I see something new every time I step out. Among the things that visibly move under their own power, ants are clearly the dominant feature of rainforests. If I want to be able to ask a whole bunch of questions, and had to pick a taxon, ants are a good way to go. (A well known and true event is that Bert Hölldobler and Ed Wilson spent two weeks together at what is now my field site; it resulted in three very cool publications based on what they found.) One major unexplored frontier is the leaf litter of tropical rainforests. Nearly all of the the primary production of the forest ends up on this thin layer between the sky and the earth, as Jack Longino once said, and we know so little about it and its denizens. It’s a big linkage in food webs that is a huge black box with respect to most fields of ecology (aside from ecosystem ecology, though this is still not as well known as it could be in this respect).

Now you can see why I have trouble assembling an elevator talk.

I propose a taxonomy of research goals, with three domains:

  • Discovery. Finding or creating something brand new – a species, a theory, a mechanism.
  • Improving ideas. This is the theoretical bandwagon – amassing evidence to flesh out, support, refute or modify existing theories.
  • Tinkering. There’s a little something that doesn’t make sense and you want to figure it out. Your goal is not to create a new theory or to test specific hypotheses.

Obviously the third category wouldn’t sit well with funding agencies. That’s not keeping me from adopting this approach as my primary orientation. From reading my papers, you wouldn’t necessarily be able to tell which primary goal led to a particular manuscript, though it’s almost always the result of tinkering. You can’t sell tinkering to well-read journals in the current environment. They want you start your story as if your experiment was always designed to test one very specific hypothesis, even if everybody knows that isn’t true.

When I’m wondering what project I want to do next, I do a few things. I weigh a bunch of factors – what’s fundable, what’s do-able, and what’s publishable.

Then I notice the file on my desktop, and I toss all of that crap aside.

I do that little thing that’s always been nagging: “Answer me!” Then, I go off and do that project. My only problem is that the list of nagging questions is far too long for me to answer in one lifetime.

You might be asking, “How’s that working out for ya?” I’ll get to that next week with some specific examples.

Making ideas or evaluating them? Climbing aboard theoretical bandwagons


It’s no mere coincidence that both Darwin and Wallace figured out natural selection at roughly the same time. The basic facts at the foundation of the mechanism of natural selection seem to have been established for a couple millennia. They didn’t converge until the Victorian era of natural philosophers. Before that time, some false assumptions about the nature of existence stood in the way.

In a similar vein, both Newton and Leibniz independently developed calculus at the same time as one another.

Likewise, Verhulst created the logistic equation. Then, it took almost 100 years for someone to come upon this again, by Pearl and by Lotka who did this independently of one another.

At the start of the 1900s, people were attempting to build a heavier-than-air machine capable of controlled flight. There was a convergence of technology and ideas that allowed these things to develop on three continents, at just about the same moment in human history. That’s no mere coincidence. History was ripe for that to happen, though it took a special vision, and plenty of hard work applied in just the right way, to put things together. The Wright Brothers were were perpetual tinkerers. They were also driven by data, experimentation and critical analysis of their findings, allowing them to figure out the actually fatal errors of their predecessors. (It’s worth a visit to Dayton, I had the chance to visit a couple months ago. Their bicycle shop looks and feels a lot like a lab you’d find at a small teaching school. It’s mighty inspiring.)

For every Darwin and Newton, whose ideas had contemporary shadows, there are many more innovators that go it alone. If their ideas were not developed, then we have to wonder if they ever would have happened. Some people say that about the smartphone. It’s hard to say how often this is true. Regardless, there is a reward to the first to figure out an important idea, when these ideas spur progress. (I have to admit that the copy of Kuhn’s Structure of Scientific Revolutions on my shelf is dusty and not fully read. I think more people have made it through Ulysses.)

I got to take a vacation to Iceland a couple years ago. It was enlightening. And there was a Penis Museum in Húsavík, too. For a millennium, Iceland’s subsistence living, and whatever mediocre export economy that could be mustered, depended heavily on sheep. While farmers in Europe were using the spinning wheel for centuries, Icelandic farmers were still spinning wool with feeble handspools. Moreover, back in the day they made shoes out of hide, but never figured out how to make leather. A long journey would require several pairs of shoes for long journeys because they would wear out so quickly. Some contemporary roads are named after the number of pairs of shoes it used to take to make the journey along the road. I don’t mean to pick on the Viking ancestors of contemporary Icelanders, as they withstood the little ice age far better than I ever could have. I don’t know if, while spinning wool on a handspool, I would have been the one to independently invent a spinning wheel separate from outside influence. I’d like to think I could have been that resourceful, though I might have been too busy to take the days off to work on it.

As contemporary Icelanders can tell you, the development of new ideas matters.

Orville and Wilbur Wright invented the plane. Now, without consulting Wikipedia, can you tell me who else was critical in the development of early airplanes?

Many people did great and important work on early flight. Their contributions were critical, even if we can’t recall many of their names. Heck, I’ve been surrounded by aviation history for more than a decade (on account of my spouse’s job and the location of my campus) and I can’t name more than a handful of the pioneers of early flight.

from wikimedia commons

from Wikimedia Commons

Here’s why we can’t remember those other guys (and, it seems they indeed were all men) who turned early planes into something workable for society: their jobs were interchangeable.

I posit that anybody with the training in engineering, math and workmanship skills could have followed through on the first principles developed by the Wright Brothers to grow the field of aviation. Much of it was done by the Wrights themselves, but they had many colleagues and competitors. Flight wouldn’t have taken off (heh heh) unless there was the labor and brain juice expended by many people at the time.

When a new idea comes out, which is more important, the development of the idea or the fleshing out of the idea? Clearly, more glory comes with the former. Both are important. I think it’s silly to say that one is more important than the other because both are essential components. When a great idea comes around, someone’s got to put meat on those bones. It take a whole community of researchers to do that.

For example, some have said that E.O. Wilson is one of the most important scientists of the past century. Why do people say that? Because he created the kernels of many ideas. He put them out into the world, and then many people pursued them. These include the taxon cycle, island biogeography, the social regulation of caste in social insects, sociobiology. He fleshed out the ideas enough to get others to test them out in great detail. He never really lingered on these ideas once he put them out there.

The community of scientists is principally composed of people who are testing theories and fleshing them out. After someone figured out the spinning wheel, then there were many people who worked on the design to make it better. That task of filling-in-the-details is the currently bulk of work in science.

Humor me while I bring out a couple more examples.

In the field of ecology, Hubbell’s formulation of neutral theory was a major progress as a null model that was entirely lacking in community ecology. In the field of behavior, Hamilton’s conception of inclusive fitness revolutionized how we think about the evolution of social groups. After these ideas were formalized, small armies of researchers have pursued these questions to hammer out details, question theoretical foundations, and understand how things can be generalized and how things might not occur. Regardless of how significant kin selection is 100 years from now (I am not invested into it either way), the formulation of the idea by Hamilton was successful in spurring a scientific revolution, which is still spinning to this day (and Wilson even stepped into the fray as a gadfly).

Many of my friends and colleagues have done great work, with much of their careers invested, on the details of kin selection and many of its subtheories and corollaries. So, I hope I don’t hurt any feelings when I suggest the idea that a lot of this work could have been done by interchangeable scientists. (I’m open to being convinced otherwise.) The work required brilliance, perseverance and specialized training. However, if any one person didn’t make some of the contributions, then the gaps would have filled in by the others. As a group, the entire endeavor was significant and as a community, researchers of social animals learned a ton. I greatly value their contributions, and some of them are a model for how I run my own lab in a number of ways.

Who should be a part of that workforce ? Does it matter? Who is best suited to it?

Who is suited to making big new concepts, and who is suited to that kind of fleshing-out-of-ideas science, to test existing theories, and build upon these to make new subtheories? Moreover, what kinds of research labs are suited to each kind of option? My little undergraduate lab probably shouldn’t follow the same path of a lab with multiple doctoral students and postdocs.

So, I don’t choose that path.  I mean: I don’t like either option. I choose option C.

What’s option C? That requires a taxonomy of research goals. That’s a set of posts within the next month.