So if you’re an academic and have written or read recommendation letters for your students and care at all about gender equality in academia, you’ve probably come across this handy poster:
Most of this advice is pretty nuanced and thought-provoking, and will get most of us to write better, more balanced letters for all our students. But I cannot express how angry I get every time I see the section titled “Stay away from stereotypes”, in which it is suggested that letter writers stay away from using adjectives like “caring”, “compassionate”, and “helpful” in letters for women, because these sorts of words “are used more frequently in letters for women and can evoke gender stereotypes which can hurt a candidate.” A more detailed list below expands this list of words to avoid, including “tactful”, “dependable”, and “diligent”. The words that the poster recommends that letter writers retain include “ambitious”, “confident”, and “intellectual.”
You’ll notice right away that the words in the two lists are not at all equivalent. It’s not like the words to avoid are slightly less impactful versions of the words to include. The two lists of adjectives tell us fundamentally different things about a person, and words we’re being told to avoid actually describe traits that I hope we all want in our colleagues. These two sets of adjectives are far from mutually exclusive, but if they were, I know I’d choose to work with someone who is hard-working and compassionate over someone who is ambitious and successful. The colleagues I most enjoy working with, regardless of their gender, embody the best of both lists.
So what this poster is effectively, and not even subtly, telling us is that we should avoid using words that provide useful information simply because they are coded as female. Like so many other purported solutions to gender bias, this advice furthers patriarchal values instead of subverting them. If we follow the advice in this poster, and then turn around to complain that academia is full of tactless, uncaring, and selfish people, we are being hypocrites.
But the poster also points the way forward. Turns out that recommendation letters for men are 16% longer than letters for women. So use that extra space to fully describe your female students who are both hard-working and accomplished as such, and cut out some instances of “excellent” and “intellectual” to do the same for your male students. It’s then up to the search committees to decide what they value in their colleagues. If these search committees would rather hire someone who is “excellent” over someone who is “excellent” AND “hardworking”, the problem lies there, and not in the letters of recommendation.
[2019 Update: what about adding text like this to all of your letters?]
My very first task in the lab as an undergrad was to pull layers of fungus off dozens of cups of tomato juice. My second task was PCR, at which I initially excelled. Cock-sure after a week of smaller samples, I remember confidently attempting an 80-reaction PCR, with no positive control. Every single reaction failed. Which is to say that science doesn’t let you go for long without failure.
I’ve been thinking a lot about this PCR, partly because I’m returning to molecular work after a seven year gap. But mostly, I’ve been thinking about how my bosses responded to that failed reaction. I don’t remember the precise details but I suspect my immediate mentors, other lab members, regaled me with their own “shit happens” stories. I vividly recall a flash of disappointment across the face of one of my PIs, probably mourning all that wasted Taq. That combination—“this happens to all of us, but it really would be best if it didn’t happen again”—was exactly what I needed to keep going and to be more careful.
In the time since I was a college freshman, I’ve learnt how widely varied different academic mentoring styles are. And my overwhelming feeling in the face of what I’ve learnt is gratitude at having dodged the “tough love” bullet. “Tough love” is the idea that because doing science requires a tough skin, it is a mentor’s role to provide the stimulus for that skin toughening. I know that I would have wilted under such mentoring, and I know plenty of others who feel similarly.
But “tough love” mentoring in science seems to persist so much as to be mostly unremarked upon. Take, for example, this excerpt from Hope Jahren’s widely acclaimed new memoir, Lab Girl:
Either you think this is business-as-usual in a science lab, or, like me, you read this and worry that all you are destined to be is an adequate scientist, that you are one of the many who will be weeded out of science because you aren’t tough enough, aren’t dedicated enough, and do consider your time (literally the only thing we’re guaranteed in this world) of some value.
If my first experiences in science had been like this, I doubt I would be doing a Ph.D. I took a quick poll of about 15 of my grad student and postdoc friends, asking them to imagine how they’d react in that situation. Not a single one of them believed that they’d have stayed that extra hour. Of course, your mileage may vary—maybe something like this was your first experience in science, and you thrived because of it. And having mentored a grand total of four undergrads, it obviously isn’t my place to tell anyone else how to mentor their students. But I do know how I’d respond as a mentee, and I do wonder who ends up being excluded from science when tests of this manner are devised for students to “pass”.
As my failed 80-reaction PCR and many subsequent failures have shown me, science is tough enough without the hurdles placed before us, consciously or unconsciously, by our peers and superiors. As a community, we need to figure out which of the tasks we require of scientists-in-training are vital to making us good scientists, and which serve simply as hoops to jump through, excluding anyone who isn’t a very particular type of person. This is especially vital because the track record of who is consistently underrepresented in science is clear.
We’ll likely never all agree on how much tough love is the right amount to prepare someone for a career in science. Our disagreement is a good thing, and will provide a range of mentoring environments in which a range of people will thrive. But students entering science need to know that this range exists, that tough love isn’t the only way that scientists are trained. There are alternatives to being thrown in the deep end, and it’s possible to have the time and space to learn to swim, to gradually grow a tougher skin, before you sink. There is no single story on the basis of which you should decide not to enter the water at all.
And if it’s true that mentoring in science is, at present, overwhelmingly tough love-ish, is that something we want to change? Yes, if we’re at all committed to making science accessible to people from varied backgrounds, and ensuring that they (we) have the space to thrive.
Thanks to the eleven people, spanning a range of career stages, who read this post over and offered comments/suggestions before it went public.
P.S. Letting fungus grow on tomato seeds mixed in tomato juice them is a clever way to get the gelatinous coat off the tomato seeds, which helps the seeds germinate without rotting
P.P.S. Dr. Jahren has a couple of questions for me (and you) in response to this post:
Amongst theoreticians (as opposed to — or perhaps in addition to — laboratory or field experimentalists), it is very common to require a graduate student to take difficult course(s) in higher math, coding, statistics etc., without any guarantee that the techniques taught will be directly useful or even invoked during the dissertation project, under the expectation that what the student learns from struggling with the material is a useful general enrichment. For some students, these types of courses serve as roadblocks to progressing. Does this at all relate to the tough-love mentoring phenomenon you name and describe?
The extremely competitive funding situation in science research has a direct effect on the amount of productivity that must be proposed and then delivered in order to “make ends meet” in the laboratory for each 3-year cycle (e.g., the numbers-budget breakdown that I offered in Part 2 is illustrative). To what extent does this provide a structural constraint on the amount of time and energy available for the trial and error process that is so important during learning? Would changing the availability and mechanism of funding for students have an effect on tough-love mentoring?
I’ll be pondering these questions; in the meanwhile, chime in with your thoughts in the comments below!
Note: this is blogpost number 100! A relatively meaningless milestone, as it includes 25+ articles for The Hindu BLink, some re-blogs, and some housekeeping posts, but a milestone nonetheless. To mark it, here is the second half of an essay that I wrote recently about the role of competition and cooperation in biology and among biologists. The first half of the essay was adapted from this post. The second half of the essay is about the recent controversy surrounding the evolution of extreme cooperation and what, if anything, we learn from bitter scientific disputes. If you already understand the textbook versions of kin selection and eusociality, skip ahead to the section titled “Is Cooperation on Shaky Ground?“. Hope you like it!
If helping someone else helps you too, then cooperation can be pragmatic. Many examples of cooperation in both the natural and the human worlds take this sensible form.
In contrast, the evolution of altruism seems paradoxical—an altruistic individual helping someone else doesn’t, by definition, gain anything in return. Under the principles of natural selection, an organism should do everything it can to maximize its own survival and reproduction (or “fitness,” for short). Being altruistic, however, benefits someone else’s fitness, while harming one’s own. How then could altruism evolve?
But altruism does evolve. Some of the most abundant animals on the planet exhibit an especially extreme form of altruism called eusociality. In a eusocial colony, some individuals forego all their fitness by not reproducing; instead, they work to help a single colony member produce an enormous number of offspring. All sorts of insects (ants, wasps, bees, termites, and even some beetles), a few shrimp, and even two species of mole rat, live in colonies of this sort.
From the point of view of a worker in a eusocial colony, who will never bear offspring of her own, eusociality must seem like the ultimate exploitation. But if you think, instead, about the genes that encode the information from which organisms are built, altruism and eusociality can begin to look consistent with natural selection. An allele, defined as a particular variant of a gene, can be said to succeed if there are more copies of it in future generations than there are at present. For the most part, an allele’s fitness increases if it confers some advantage to the individuals that carry it. This leads the individual it lives in to have lots of offspring, some of which will carry the allele as well.
Let’s assume that the allele we’re talking about causes its bearers to cooperate, and consider two siblings who both have the allele. The frequency of the allele will increase equally if, say, one of its bearers has four offspring and another has none, or if both bearers have two offspring each. Cooperation among these two siblings to ensure that one of them has more offspring is, from the perspective of the allele’s success, not inconceivable. And if the allele confers an extra advantage to cooperating siblings—suppose cooperating individuals have a total of six offspring instead of four—then the evolution of cooperation may even be likely.
This logic is captured in a simple equation1 proposed by W.D. Hamilton in 1964, according to which cooperation between two individuals can be favoured by natural selection if the following relationship holds between the costs (c) and benefits (b) of cooperation, and how closely the two individuals are related to one another (R):
Hamilton’s Rule: R > c/b
This equation suggests that, when thinking about the evolution of cooperation from the perspective of an individual, we cannot restrict our measurement of fitness to just the individual’s own offspring. Fitness must stretch to include the offspring of one’s relations as well, weighted by the appropriate measure of relatedness.This expanded version of fitness is described as “inclusive”. So an individual with no offspring of its own, and thus with no direct fitness, can still have inclusive fitness via its relatives. And a behaviour that spreads due to its inclusive fitness benefits is said to evolve by the process of kin selection. So, according to this equation, we expect that for a worker in a eusocial colony, the benefits of bearing her own children are far outweighed by the costs. She instead works to maximize her inclusive fitness by helping raise sisters, directly benefitting her mother’s fitness. Through the lens of kin selection, eusociality is the ultimate collaboration.
For the longest while, I accepted all of the above as fact, unaware of the uncertainty among scientists about how exactly kin selection and inclusive fitness work. But this uncertainty rumbled below science’s surface, out of most biologists’ sight, until the publication in 2010 of an explosive paper by Martin Nowak, Corina Tarnita, and E.O. Wilson2. From the fallout from this explosion, it has become clear that we don’t understand cooperation in the natural world all that well
Is Cooperation on Shaky Ground?
In their paper, Nowak et al.2 claim that the contributions of kin selection and inclusive fitness to biology “must be considered meagre.” Provocatively, they suggest that the inclusive fitness theory hasn’t even been tested properly, and that it probably won’t be, because actually calculating inclusive fitness is not straightforward. This is especially true in populations where interactions among individuals are so complicated that their effects on fitness cannot simply be added together. Imagine you and your friend, separately, do something good for me—I can add your contributions together to measure the total benefit I’ve received. But now suppose you and your brother work together with me to help my cousin, but that you refuse to similarly help my uncle—adding together the benefits and costs that I’ve received from you and your brother becomes much trickier.
Additive interactions, Nowak et al.2 argue, are rare in nature, so “inclusive fitness theory…only works for very restrictive scenarios.” In other words, Nowak and colleagues think that studying inclusive fitness isn’t worth the trouble.
But measurement problems are just one entry in the list of Nowak et al.’s2 complaints. A crucial question that Nowak et al.2 pose is about causality. Did the close relatedness among members of a group of ancestral bees, for example, cause them to evolve eusocial cooperation? Or was the close relatedness among members of a hive simply “the consequence rather than the cause of eusociality”? Nowak et al.2 claim the latter is true, and this claim formed the centre of several critiques of their paper, which set into motion a seemingly endless exchange of responses.
The Frustration of Learning from an Argument
Reading the protracted back and forth between the challengers and defenders of kin selection is like watching a tennis match in which the ball abruptly changes shape, size, colour, and direction every time it crosses over the net. Five comments3-7 written by over a hundred biologists were published in response to Nowak et al.’s2 paper, accompanied by a response8 from the original authors. Then, in 2015, Xiaoyun Liao, Stephen Rong, and David Queller published a paper9 that served as an extended comment on Nowak et al.’s2 original; a response from Nowak and Ben Allen10 followed, as well as a response to the response11.
You’d hope that reading this lengthy conversation would help to clarify the points of dispute among these scientists, but it doesn’t. As insect biologist and photographer Alex Wild put it in an essay12 responding to the original paper, “You might think that scientists who study cooperation ought [to] show signs of being good at it themselves. But you’d be mistaken.” Consider, for example, the discussion surrounding how to test the hypothesis that high relatedness causes the evolution of eusociality.
In an idealized version of the scientific method, one advances science by testing hypotheses about how the world works. And, implicitly or explicitly, each hypothesis is tested by comparing it to an alternative—we discover what is true by gradually figuring out if one explanation is more true than another, and rejecting the explanation that is less true. Nowak et al.2 contend that biologists studying of inclusive fitness theory have failed to “consider multiple competing hypotheses.” Instead, “when the data do not fit, elaborations of inclusive-fitness theory can be constructed that make them fit.” But Nowak et al.’s2 scathing critique of the absence of alternative hypotheses does not seem to extend to themselves. In constructing mathematical models to understand the evolution of eusociality, Nowak et al.2 don’t factor in variation in relatedness at all. They therefore don’t actually test the hypothesis that high relatedness can cause eusociality to evolve. As Carl Zimmer analogizes13, “it would be as if a team of researchers carried out a study on the effects of diet and exercise on health. Their subjects get different amounts of exercise but stay on the same diet. In the end, the experiment might show that exercise makes people more healthy. But it would not make any sense to also conclude that diet plays no role.” In fact, Nowak et al.2 did the equivalent of feeding their participants the healthiest possible diet—they assumed that relatedness within their hypothetical groups was as high as it could possibly be. As a result, critics9 conclude that Nowak et al.’s2 models “have nothing to say about the importance of relatedness in the evolution of eusociality,” while Nowak et al.2 themselves argue emphatically that “relatedness does not drive the evolution of eusociality.”
Liao et al.9 respond to this problem directly by modifying Nowak et al.’s2 models to incorporate variation in relatedness, and find that eusociality evolves more readily in groups with higher relatedness among its members than in groups with low relatedness. Liao et al.9 see this as a win for the majority opinion, saying that Nowak et al.’s2 “modelling strategy, properly applied, actually confirms major insights of inclusive fitness studies of kin selection.” But Nowak and Allen10 respond by saying that Liao et al.9 “fail to analyse their own models with inclusive fitness theory” and that Liao et al.’s9 work has “no relevance for evolution of eusociality,” because only the unlikeliest of biological scenarios could lead to relatedness varying in the way that Liao et al.9 describe.
Juxtaposed thus, the extent of mutual misunderstanding is remarkable. How can the two groups of scientists, who in principle are united in their goal of understanding eusociality, disagree on something as fundamental as whether their own models are at all relevant to eusociality? Equally baffling disagreements of similar scope pervade the rest of this discussion as well.
As a relatively un-invested reader, I found it nearly impossible to understand if either side was worth listening to. It seemed that both sides were making problematic assumptions, but the consequences of these assumptions came to light only when pointed out by their rivals. And equally frustratingly, neither side engaged with their rivals’ most damning criticisms. Remember the objection, raised by Nowak et al.2, about being unable to measure inclusive fitness in situations complex enough that we can’t simply add bits of fitness up? Not one of the kin selection defenders addresses this criticism properly. More generally, any sort of objectivity, any generous engagement with the opposite side’s views, was hard to come by. I found it difficult to escape the conclusion that this bitter dispute was fuelled mostly by the desire of both sides to win the competition. The unwritten rules of this competition seemed to indicate that winning depends on not conceding any of one’s own weaknesses or mistakes. Winning the competition did not seem to involve much learning, and our understanding of cooperation in the natural world had stagnated as a result.
And I’m not alone in finding this sort of competition futile. “The partisans have become more interested in discrediting the other side than in advancing mutual understanding,” wrote Alex Wild in his exasperated essay12 about this dispute, “small statements are taken out of context and destroyed in straw-man arguments, studies are cherry-picked for rhetorical effect, quotes are mined, and the result is a downwardly tribalistic spiral as frustration grows and everyone starts to hate everyone else. A fine pickle for cooperation research if you ask me.”
Recasting the Impasse
I was all but ready to give up on competition among scientists as a source of good in scientific advancement, when a paper written by two philosophers, called “Kin Selection and Its Critics,” came to my attention14. I was encouraged by both the title, which indicated a consideration of both sides, and the authors’ distance from the dispute, which hinted at the possibility of some objectivity.
And the paper did not disappoint. Beyond simply summarizing the last five years of argument, Jonathan Birch and Samir Okasha14 diagnose why the two sides seem incapable of fruitful communication—they are, in fact, talking about very different things.
In the half century since its formulation1, Hamilton’s Rule has taken on different meanings to different people. The differences between these versions have to do with what exactly the parameters c and b mean in this equation:
R > c/b
In the special version of Hamilton’s Rule, the parameters c and b represent the exact costs and benefits to individuals in a population. The special version applies to a population only when these costs and benefits can be added up easily (when you and your friend, separately, do something good for me, for example). It’s this special version of Hamilton’s Rule, which rarely holds in natural populations, that Nowak and colleagues are arguing against.
But the version that almost every other biologist is defending is a far more general one. The general version can apply to all sorts of situations (like when you and your brother work together with me to help my cousin, but refuse to similarly help my uncle), because all the complexities of different types of interactions get subsumed into the values of b and c. This makes b and c impossibly difficult to actually calculate. However, the general form of Hamilton’s Rule has the mathematical advantage of always being true.
It’s no wonder then, that the two sides of this debate are incapable of finding any common ground. One side is attacking a version of kin selection that is rarely true. The other side is defending a version of kin selection that is always true, but that may not be useful.
But why does the difference between these two versions of Hamilton’s Rule matter to the rest of us? What does it say about how we understand the evolution of cooperation? The answer lies in the gap between our intuition and reality. For most of us, our intuitive understanding of how kin selection works depends on being able to talk about individual costs and benefits (flip back to my initial description of kin selection and inclusive fitness, if you don’t believe me). It’s how we are taught about it, it’s how we read about it in textbooks, and it’s probably how we will teach our students about it. But this dependence on talking about individual costs and benefits means that our intuition describes the special version of Hamilton’s Rule. Our intuition is therefore usually wrong. Birch and Okasha’s14 clarification of the kin selection dispute tells us that we can retain our intuition about kin selection or believe that kin selection applies broadly to the natural world—we cannot have both.
One would hope that any future argument about kin selection between biologists takes heed of their conceptual misunderstandings as well as their biological disagreements. As Birch and Okasha14 say in their paper’s conclusion, “progress is achievable if rival camps of researchers are able to communicate and cooperate” But any communication or cooperation would be impossible without conceptual clarity, and these philosophers offer us that. They also offer us a model for engagement, if we can imitate their “even-handed approach that identifies what both critics and defenders of kin selection have got right.”
So I suppose I’m beginning to see that there may be purpose to a bitter scientific competition after all, if it brings attention to a subject that would otherwise languish, unclarified. I asked both Birch and Okasha about the extent to which the protracted, high-profile dispute influenced their decisions to study kin selection and inclusive fitness. For Birch, it was a direct influence. “I already had an interest in kin selection for other reasons,” he said in an email to me, “but I think it was the controversy that persuaded me that kin selection merited philosophical attention in its own right.”
For Okasha, pondering the controversy has led him to reconsider his views. “I was originally of the view that Nowak et al. were ‘surely wrong’, but more recently have come to the conclusion that they actually made some good points, albeit rather overstated,” he wrote. And though Okasha signed one of several critical responses3 to Nowak et al.2, along with more than a hundred other biologists, he said that “in retrospect, I would not have signed the letter.” If a dispute can spur anyone to change their mind after careful consideration, then the dispute certainly has some value.
Birch also agrees that this sort of competition can be productive, “in so far as it causes the underlying philosophical assumptions of different research programmes to be brought to the surface, potentially giving philosophers of science and other impartial observers a clearer picture of how the two programmes differ and how they might be reconciled. This can then feed back into the science in a productive way, provided philosophers of science make an effort to make their work visible to scientists (and provided scientists pay attention to it!).” Nevertheless, Birch seems doubtful of his impact so far. “It’s hard for me to tell whether I’ve succeeded in influencing either side.”
Hamilton, W.D. 1964. The genetical evolution of social behaviour. Journal of Theoretical Biology 7: 1–52.
Nowak, M.A., C.E. Tarnita, and E.O. Wilson. 2010. The evolution of eusociality. Nature 466: 1057–1062.
Abbot, P, et al. 2011. Inclusive fitness theory and eusociality. Nature 471: E1 – E4.
Boomsma, J.J., M. Beekman, C.K Cornwallis, A.S. Griffin, L. Holman, W.OH. Hughes, L. Keller, B.P. Oldroyd, and F.L.W. Ratnieks. 2011. Only full-sibling families evolved eusociality. Nature 471: E4–E5.
Strassmann, J.E., R.E. Page Jr., G.E. Robinson, and T.D. Seeley. 2011. Kin selection and eusociality. Nature 471: E5–E6.
Ferriere, R., and R.E. Michod. 2011. Inclusive fitness in evolution. Nature 471: E6–E7.
Herre, E.A., and W.T. Wcislo. 2011. In defence of inclusive fitness theory. Nature 471: E8–E9.
Nowak, M.A., C.E. Tarnita, and E.O. Wilson. 2011. Nowak et al. reply. Nature 471: E9–E10.
Liao, X., S. Rong, D.C. Queller. 2015. Relatedness, conflict, and the evolution of eusociality. PLoS Biology 13: e1002098.
Nowak, M.A., and B. Allen. 2015. Inclusive fitness theorizing invokes phenomena that are not relevant for the evolution of eusociality. PLoS Biology 13: e1002134.
Queller, D.C., S. Rong, and X. Liao. 2015. Some agreement on kin selection and eusociality? PLoS Biology 13: e1002133.
With the nostalgia that invariably accompanies year-endings, I’ve been looking over my writing in 2015, trying to pick out the pieces I like best. My personal favourite, by a long distance, is this post I wrote for Anole Annals, titled “Are Brown Anoles in Florida Really Driving Green Anoles to Extinction?” Here’s the first paragraph, just to give you a sense of what it’s about:
Tell almost anyone in Florida that you’re doing research on brown anoles (Anolis sagrei), and they’ll express some distaste for your study organism. “I don’t like them,” they’ll say, “they’re invasive. Aren’t they driving the native green anoles extinct?”* Everyone—literally everyone who has lived in Florida for a while—will tell you how their backyards used to be full of green anoles (Anolis carolinensis). Today, they report, these green anoles have disappeared and been replaced by the invading browns.
The rest of the post goes on to discuss why these “backyard tales” may be unfounded. The main takeaway of the post is that, rather than going extinct, it is possible that green anoles have simply shifted upwards out of sight in many habitats where they co-occur with brown anoles. I present some data from an informal, small-scale mark-recapture study we conducted in 2015, and make inferences from both the number and the sex ratio of the green anoles we caught to suggest that the green anoles in that site, and likely elsewhere, are still around.
Why do I like this post so much? Because it combines data and logic and story telling to challenge a rather prevalent notion, namely the “usual alarmist hysteria [about] green anoles being pushed to extinction” by brown anoles. Because it was born from observing animals in their natural habitats. Because it spurred comments from biologists and non-biologists, plus an accompanying post from Jonathan Losos adding an evolutionary dimension to the argument that green and brown anoles can coexist. But most of all, I like the post because it appears in the one location where people who are interested in this question are most likely to find it—a blog dedicated to the biology of Anolis lizards, a blog that is followed by a large number of professional and amateur Anolis enthusiasts.
That got me thinking about the best thing to do with datasets like the one I wrote about. Could it have been published as a short note in a natural history journal? Possibly, but only after much more effort from me into manuscript preparation and formatting, and months in review, demanding further effort from editors and reviewers. Does a study this small, this tentative, need peer review? Not really, and when published in a place like Anole Annals, readers are free to post comments clarifying or criticizing the methodology and conclusions. Would its reach have been wider, its impact stronger, as a published paper? Almost certainly not. Whether a blog post or a paper, people will reach it via a Google Search. Does any of this make these data inconsequential? No. I know my post is veryfar from earth-shattering, but it’s a thought-provoking dataset to people who care about Anolis lizards, and in it’s current location and format, it reaches those people efficiently. Of course, Anole Annals didn’t emerge overnight—I know that it’s taken time and effort from many contributers to establish and run—but I suspect that effort pays high dividends.
As a natural history enthusiast, I love the possibilities that a blog like Anole Annals affords for changing how we go about collecting and disseminating the natural history observations that field biologists accrue. But anoles are a special beast—most genera of organisms do not have such an ardent following. Can this model be scaled upwards in any way? I wondered aloud about this on Twitter a while ago, and the consensus was that the Encyclopaedia of Life, or something like it, was our best bet (thanks to Felicity Muth for the suggestion!)
I don’t think I’m suggesting that we do away with natural history journals entirely, because there is certainly a need for more comprehensive and substantial natural history research, for which publication in a journal (and the associated credit it brings) makes sense. But I know that many of us field biologists have far more observations and datasets that don’t get submitted as papers to natural history journals. It seems a shame not to share these at all—if and when I stop studying lizards, I know I’ll miss the chance to talk about my study organisms’ natural history at a venue like Anole Annals.
*Fun aside: the quote isn’t made up; it’s from a conversation with the talented tattoo artist, Rich Mal, from Anthem Tattoo in Gainesville. I recommend that establishment highly, in case you’re interested.
For me, 2015 has been a year of indulging in hobbies. I’ve dabbled in a range of things, sampling from things I’ve always done, stuff I’ve always wanted to try, and things I used to do a while ago. Among the hobbies that have resurfaced, reading poetry is one that seems to be sticking.
Curiously, though, the reason for the return of poetry to my life is not because poetry provides an escape from my work as a behavioural ecologist, but because the two are so very deeply connected. In particular, I’ve found special resonance between my world–the world of an ecologist–and the world of Richard Wilbur‘s nature poems.
Wilbur writes about nature often, and these nature poems reveal a way of looking at the world that is startlingly similar to the mindset I’ve been trying to cultivate when I spend time outdoors. It’s a mixture of careful observation of the details in nature that often get missed and exploring the possibility that these details can have giant consequences. It rests on making outrageous connections, which in turn rests on proposing, interrogating and, often, shooting down metaphors. This mindset is a lofty goal to aspire to–I’m nowhere near as close to it as I want to be. But in trying to construct this approach to nature, I know that Richard Wilbur’s poetry will be one of my favourite blueprints.
To see what I mean, here are two of my favourite Wilbur poems. First, a poem that will sound familiar to any ecologist who thinks about competitive exclusion or niche partitioning:
Some would distinguish nothing here but oaks,
Proud heads conversant with the power and glory
Of heaven’s rays or heaven’s thunderstrokes,
And adumbrators to the understory,
Where, in their shade, small trees of modest leanings
Contend for light and are content with gleanings.
And yet here’s dogwood: overshadowed, small,
But not inclined to droop and count its losses,
It cranes its way to sunlight after all,
And signs the air of May with Maltese crosses.
And here’s witch hazel, that from underneath
Great vacant boughs will bloom in winter’s teeth.
Given a source of light so far away
That nothing, short or tall, comes very near it,
Would it not take a proper fool to say
That any tree has not the proper spirit?
Air, water, earth, and fire are to be blended,
But no one style, I think, is recommended.
Next, a poem that, to me, nearly perfectly describes both the good and bad parts of how we do science
The August 1st issue of The Hindu BLink carried a review I wrote a while ago of the book Sex on Earth: A Celebration of Animal Reproduction by Jules Howard. There have been some issues getting the review online, so in the meanwhile, here it is:
Of the many routes you might take into the lives of the animals, a route that takes you through the varied, weird, and thoroughly entertaining world of animal reproduction is perhaps one that you may not admit taking. But embarrassment surrounding the subject of animal sex is unwarranted. Because almost all creatures depend on it to reproduce persist, sex has been tremendously important in shaping the natural world, and any attempt to understand animals depends upon understanding sex. Contemplating animal sex can lead you to think about some of the most puzzling questions in biology, so set aside your prudishness for a while, and go read Sex on Earth: A Celebration of Animal Reproduction by Jules Howard.
Howard doesn’t travel far or wide to bring you first-hand accounts of the oddest animal sex on the planet, because he doesn’t need to. The woods, gardens, zoos, and museums of his native England are filled with plenty of organisms with fascinating sex lives, and this book includes the stories of ducks with explosive penises and corkscrew vaginas, the sex arenas of retired race horses, and chicks raised by a pair of male flamingos named Carlos and Fernando.
But investigating the “sex lives of the everyday” quickly leads Howard to the limits of our knowledge about animal reproduction. For example, while waiting to catch frogs in the act, Howard raises the question of how exactly male and female frogs decide when to migrate to the breeding ponds in which they mate with each other. Turns out this is a question we don’t really know the answer to. In examining the mysterious lives of bdelloid rotifers, which inhabit almost every corner of the globe and never have sex, he ponders why sex evolved in the first place, a puzzle that scientists have been mulling over for many decades but have only recently begun to solve. By balancing stories of what we do know about animal sex with constant questions about what we don’t know, by talking to and learning from the scientists who spend their lives asking these questions, Sex on Earth shows us how science really works. But don’t expect any large revelations about life on earth or the human condition when you reach the end of this book—it is better read as a series of essays, occasionally repetitive and some decidedly better than others, without a central narrative.
There are many, many articles and books about the science of animal reproduction written for people who aren’t biologists, which makes writing yet another one a risky proposition. I don’t think I’d recommend Sex on Earth to you if you know nothing about biology and aren’t willing to learn some of it on the fly—Howard doesn’t define some basic terms (expect to see words like “sexual dimorphism” and “sub-hominin”), and only quickly explains some fairly difficult concepts. What sets this book apart from most popular writing about the science of animal reproduction is Howard’s willingness to voice an opinion on some of the most problematic aspects of how people have studied and talked about animal sex. Foremost among these is our tendency to impose human values and insecurities about sex onto the animal world. Why else has it taken scientists so long to acknowledge that females play as important a role in sex as males? What’s with our abhorrent tendency to describe some animal sex as “rape”? And why do we speculate, on only the shakiest of scientific foundations, about the length of an erect T. rex penis? Howard takes on these questions forcefully but also funnily, and the result is a refreshingly different account of animal reproduction.
I have a piece in the Hindu BLink’s latest themed issue. The theme is loneliness, and my piece is about “lonely” ideas, which I define as “ideas that deserve to have an impact, but don’t,” ideas that are “ahead of their time and threaten established ways of thinking.” Click through to read about two lonely scientific ideas, and how they recently met on my Facebook page.
If you’ve written a scientific paper recently, I’m willing to bet that, during the writing/editing process, someone told you to avoid writing in the passive voice (unless you’d already heard this advice before, and avoided it from the start!). This suggestion is usually mentioned in reference to the Methods section, which seems to be where the passive voice is most often employed.
I’ll admit that my first instinct is to write Methods sections wholly in the passive voice. I’m not sure why this is—most likely, it’s just a habit I’ve picked up from reading scientific papers, or perhaps I was taught to write this way in high school science lab reports. And I suspect that I get annoyed when commanded to write in the active voice partly because I’m stubborn. But for the longest time, I’ve found something about the advice to abandon the passive voice fishy. I recently realised that my doubts lie in the most popular reason I’ve heard for ditching the passive voice—that it’s incompatible with story-telling.
“I hold that good writing is basically good storytelling. To tell a story well, we need to clearly identify our characters and then show the reader what those characters do. The passive voice makes storytelling more difficult because it hides the characters deep in the sentence—if it shows them at all.”
When I first read that the passive voice may hinder storytelling, I grew quite concerned with my unwillingness to abandon it, because I firmly believe in the power of stories. But my concern turned to skepticism when I realised that storytelling in a scientific paper is so different from other types of storytelling that this complaint becomes nearly meaningless. And I think this boils down to an unusual divide, in scientific writing, between the characters we care about (the organisms, the molecules, the ideas…) and the characters that act (the scientists).
The Methods section of a paper has a very definite purpose—to convey the series of actions taken to collect and analyse the data in the paper (or the equivalent series of actions in a theoretical/modelling paper). We don’t spend any time describing the characters taking these actions (the scientists), we don’t even stop to distinguish members of this cast of characters from one another. We don’t care about the characters’ emotional states, memories, or imaginations while they perform these actions, and we don’t care too much about the physical setting in which the actions take place. But these are exactly the sort of details that story-tellers use to write engaging prose in the active voice. To illustrate this point, here are some examples from Purple Hibiscus, a moving novel by Chimamanda Ngozi Adichie, which I happen to have just read twice through.
This novel is narrated in the first person by Kambili, a teenage girl with a rich and influential but oppressive and abusive father. A visit to her aunt’s home changes Kambili’s life, leading her to discover herself and happiness. One of the threads in this story is how Kambili learns to do household chores; at home, she lives according to a strict schedule, imposed by her father, that includes nothing but studies and prayer. In her aunt’s house, however, she is expected to help, and her inexperience only exacerbates an already-tense relationship between Kambili and her cousin, Amaka.
Aunty Ifeoma got one of the huge yams we had brought from home. Amaka spread newspaper sheets on the floor to slice the tuber; it was easier than picking it up and placing it on the counter. When Amaka put the yam slices in a plastic bowl, I offered to help peel them, and she silently handed me a knife.
“You are wasting yam, Kambili,” Amaka snapped. “Ah! Ah! Is that how you peel yam in your house?”
Later on, Kambili returns to her aunt’s home, to recover from a terrifying interaction with her father. Kambili has changed in many ways since her last visit.
I went to the verandah, still coughing. It was clear that I was unused to bleaching palm oil, that I was used to vegetable oil, which did not need bleaching. But there had been no resentment in Amaka’s eyes, no sneer, no turndown of her lips. I was grateful when she called me back later to ask that I help her cut the ugu for the soup. I did not just cut the ugu, I made the garri also. Without her eyes still bearing down on me, I did not pour in too much hot water, and the garri turned out firm and smooth.
Both these paragraphs are primarily composed of a series of actions, not too different from a Methods section. But the actions do not themselves make these paragraphs interesting—they’re also about the relationships between the characters taking the actions, and how the characters and their relationships evolve between one paragraph and the next. All of these details help make these paragraphs part of a compelling story. These details also contribute to making these paragraphs good writing. Having the option of incorporating these details into the series of actions allows the author her to vary the structure of her sentences. Variation in sentence structure—in length and rhythm and tone—is essential to lively writing.
Because most of these situation- and character-related details have no place in the Methods section of a scientific paper, the ways in which we can vary sentence structure are severely curtailed. Moreover, the characters central to our scientific stories are, in the Methods section, largely being acted upon by the minor characters (us, the scientists). From the perspective of both good writing and good story-telling, therefore, we should be allowed to use the passive voice in scientific writing. It offers us a tool to vary sentence structure, and allows us to focus attention on the important characters. Of course, a Methods section written entirely in the passive voice will likely be boring, because it will have limited variation in sentence structure. But the same can be said of a Methods section written entirely in the active voice. The solution, clearly, is to abandon all proscriptions and strive for variation.
Also, none of this is to say that we should never care about the scientists who collect or analyse data. Our identities matter, and influence the stories we tell. But this means we should be telling more and varied stories about science, in a wide variety of venues and targeting a range of different audiences. Some of these stories need to be focussed on the science. Others should make visible and celebrate the people involved in science, in ways that are more substantial than writing our Methods sections in the active voice.
Over the last few years, I have found myself giving quite a few prospective EEB graduate students advice on applying to graduate school. I realise this subject is rather popular with academic bloggers, and there are plenty of other places to get advice on the process (here, and here, and here, to begin with). I’m therefore going to restrict my advice to one aspect in particular: how to show people how you think during graduate school interviews.
My memory of my own grad school interviews is starting to get a bit hazy, so I’m not sure which pieces of advice I was given and which pieces I happened upon during the process. In any case, at some point my formula for most interviews became trying to demonstrate to whomever I was talking with that I was capable of thinking. And because most interviews tend to follow one of a few trajectories, this became relatively straightforward to execute, in one of a few ways:
1. If an interviewer asks you what you want to work on in grad school, make sure you have a concrete idea for a potential project that’s related to the research of the lab you are applying to work in. The idea needn’t be big, the plan shouldn’t be detailed, and the project absolutely does not have to be what you will actually work on in graduate school. What this must do, however, is show that you can think of a question whose answers will extend what we know in a certain field or about a particular organism (see here for great advice to grad students on how to come up with project ideas, but remember you’ll probably be held to lower standards at the interview). Ideally this project should fall within whatever you’re claiming your broad research interests are. (Everyone knows these will change too, and that you will tailor your interests a little bit to the lab you’re interviewing at. That said, your interests must be somewhat consistent across interviews—don’t tell whoever you’re talking to that you’re interested in exactly the same things as them).
Your interviewer may push you to elaborate on your idea, perhaps by asking a question or maybe by pointing out a particular flaw with your plan. Hopefully you’ve done enough background research and thinking to begin to respond, but again, the goal isn’t to have a completely thought-out research proposal, so your answer doesn’t have to be perfect. Rather, see this as another opportunity to demonstrate how you think. Don’t be afraid to speculate or think aloud. You can always preface your answer with, “I haven’t thought this out fully, but what about….” That brings you and your interviewer into the territory of an actual back-and-forth conversation about science, which is exactly where you want to be.
2. Sometimes, the conversation doesn’t really take off when you’re talking about your own research interests and plans. In that case, make sure to find a way to ask your interviewer about their own research, but do NOT just sit silently listening to their answer. Ask a question that shows you’re thinking about what they’re saying. You can prepare for this a bit by looking up your interviewers’ research on their websites beforehand and thinking of questions you want to ask, but don’t depend on this—the websites may not be up-to-date, and they might feel like talking about something they’ve only just started thinking about or working on. Again, don’t be afraid to ask questions that you think might be silly—a silly question is better than no question at all, and odds are it isn’t really silly. If you’re worried it might be, preface it with “Just to clarify,…” or “This might be a silly question, but…” Again, the goal is simply to have a thoughtful conversation about science.
3. Some interviewers throw conventional interview formats to the wind, and instead seem to launch straight into a discussion about whatever scientific topic they feel like talking about. Don’t panic if this happens—as suggested above, do your best to ask a question.
A large part of your grad school interview is about fit—assessing whether you want to be a part of the program you’re interviewing at, and for the program to assess if you’ll be a good fit with them. The interview therefore involves lots of mutual information gathering—about the culture of the program and the lab, the place you’ll be living in, your personality, and more. (Here’s a great post from fellow Amherst-and-then-Harvard student, Ben Vincent on the right sort of questions to ask at your interview to gather some of this information.) That part just requires you to be yourself. But if there’s one aspect of whole interview that requires you to perform, that you should strive to get right, it’s showing that you can think like a future scientist. I hope this post makes it a little bit more clear how you can do that!