Women Get Cited Less. What Can We Do About It?

A few weeks ago, a paper came out about the fate of research papers in ecology and evolution (my field!) pre- and post-publication, comparing outcomes between male and female authors.

I want to focus on just one aspect of their nuanced analysis (you can read the whole paper here for free). In this section, the authors gathered citation data on over 100,000 papers published in 142 journals in our field.

They found a slight, but significant, difference in how often papers by men and by women got cited. On average, controlling for the impact factor (a proxy for quality[1]), papers by women accrued 2% less citations.

A hundred thousand papers! That’s a lot, and it’s why the authors could detect this small difference. The sample size allowed them to do a quite powerful analysis.

It also revealed some interesting interactions. For example, papers with female last authors (which often indicates seniority or leadership of the research group) were cited less than those with male last authors at high impact journals, but the effect was reversed at low-impact journals.

Because more people cite papers in high-impact journals, this meant that overall, women were cited less often. [2]

I guess this shouldn’t have been a surprise, but it’s not something I had seen data about before, and consequently something I hadn’t really thought about.

But it is something that matters. Like it or not, citations are a metric of success that is easy to measure, and therefore whether others cite your work is a good piece of evidence that you’re a valuable scientist when you’re up for a job position, tenure, or an award. It’s not great for women if there is bias preventing them from doing well on this metric. [3]

Sounds About Right

Like a lot of research and headlines about challenges facing women in science, this got me mad. I’m a feminist, and this stuff pisses me off.

I see the experiences of my female friends and colleagues, and see when they are treated differently than their male peers. Not always, of course, but enough to make a pattern of our own anecdotal experience.

Then there’s the fact that in my study topics, almost every giant in the field is a man. The ones that defined the discipline and published the equations in a top journal? Men. There are women there, doing great work, but they are less famous.

When data on various aspects of academic life backs up our experience it’s like, “yup, sounds about right.”

Academia is harsh on most people; it’s a place with a lot of rejection, high standards, low pay, job insecurity, and so many power dynamics. The fact that academic science is even harder on women is just not fair.

And that’s not getting into the more complex and devastating nature of the structural problems in academia, which are even worse for other minorities, especially minority women. This paper found that on average women are cited 2% less than men; how much less often are minority women cited?

Anyway, I read this paper, and I was mad.

What I Did Next

Being mad doesn’t accomplish all that much. I tried to think about what we, in our daily lives as scientists, could do to work against this problem.

As an individual early-career scientist, I can only do a very little bit about peer review outcomes or paper acceptances.

But citations? That’s different. I am always writing papers, and I am always citing others’ work. I realized that this was a small step I could take to try to contribute to supporting female scientists. It sounds trivial, but I can cite their work. Could we all do this?

I brought this idea to our lab group, and was curious what they would think.

Some colleagues immediately recognized that this was a problem, and something we don’t think about enough. There are a handful of big names in our subfield, and we mostly cite them over and over. But do we need to do that? Are their papers that we cite every time actually the most relevant? Maybe not. There is probably a lot of work being done around the world we could cite that instead, or at least in addition to the now-traditional canon.

Another common reaction was for someone to say that they don’t think about the gender of authors when they search for papers, read them, or cite them. Here it diverged: a few people said, “I don’t think about it and now I realize that I should.” Others said, “I don’t think about the gender of authors when I’m citing them, therefore I’m not part of this problem.”

I challenged this statement. Does that really mean you’re not part of the problem? Maybe not, I said – and if not, that’s great for you, good work! But instead of assuming that not consciously citing more papers by men means no harm is done, check your reference list on the paper you’re writing. What’s the author breakdown? Do you think this strategy is really working?

I don’t think I was popular for making this callout.

Rubber Meets the Road

As I mentioned in a tweet a few weeks ago, “caring means walking the walk.”

In my paper-reading project, I track the gender of the authors I read, and I found that I am reading fewer papers by women than by men. I was curious about what the ratio might be of the papers I end up citing.

I went through the references section of my current manuscript with a blue and a pink highlighter (I know, supporting stereotypes, etc., it was lazy). The results were not pretty.

I was frankly surprised at how few of the papers I had cited were by women. I knew it wouldn’t be 50/50 for a lot of reasons (discussed below), but I didn’t think it would be that bad.

I basically proved to myself that in order to cite women, you have to do it on purpose. Just not intentionally excluding them isn’t enough.

It’s like how colorblind policies don’t work. Not intentionally doing harm is not the same thing as not doing harm. As Evelyn Carter writes about claiming to be colorblind with respect to race, “if you ‘don’t see’ race, but you say you care about inclusion, how can you advance inclusion efforts that will effectively target communities of color?”

There is a lot of unconscious bias and systematic barriers that lead us to contribute to inequality. Working for equality and to recognize contributions made by women and minorities means actively working to overcome those unconscious biases and systematic barriers.

Shifting the Balance

After my discouraging experiment with the highlighters, I went through the paper and looked at the places I had cited different work. In some places, I was able to find a paper by a woman that I could cite instead – and often even one that supported my point even better than the paper I had cited originally.

That was the most delightful aspect of this task I had set out for myself: I discovered papers in my core area of study, that were by women and that I had never read. And they were really good and very interesting!

The point of reading and citing work by women isn’t just to check boxes and give women a fair shot at career metrics. The main reason is to do better science. Science is creative; it involves having ideas, being exposed to new things, thinking outside whatever box you’re in. Reading work by more different people will necessarily help that process.

I’m really glad that I found these new-to-me papers.

As I discussed with a different colleague later, a lot of this issue comes down in part to poor citation practice, which is endemic across academia. We cite something because everyone else cites it, even though maybe we haven’t read the whole paper. Or we cite something because it’s already in our reference library and we are in a hurry. I’m completely guilty of this, and I’m quite sure everyone else I work with is, too.

If we spent more time reading, and more time looking for and getting familiar with other work that’s related to what we’re doing, I think all of our papers would have much more diverse author lists – at least in terms of evenness and not being dominated by the famous people in our field.

Our papers might also just simply be better, because of all the ideas we would be having.

Don’t Worry, I’m Still Citing Men

After this citation overhaul, my reference list was still majority male first authors. You need to cite what is relevant, and in a lot of cases this work is by men. One reason is that over the history of ecology, there is a lot more work published by men than by women, especially the farther back you go. [4]

Plus, if you need to cite a classic paper, it doesn’t matter who it’s by. That’s the one you need to cite. That’s a second thing. [5]

And likewise, if you need to cite something about your specific study organism or system, there might be only a handful (or a few handfuls) of people in the world who publish on this very specialized niche. They are who they are. If you need to cite peer-reviewed literature, you may have limited choices, and you need to cite the best and/or most relevant work out of that array. [6]

So there are a lot of reasons you can’t just take your reference list and manipulate it towards 50/50 gender equality. I want to make it completely clear: I am not advocating for a departure from citing good and relevant science!

When I mentioned this idea to colleagues – that citing women was a seemingly small, basic thing that we could do in our everyday lives as scientists to make a difference in structural biases – some were deeply uncomfortable that if they sought out work by women, then they would have to leave out other papers.

Listen: there is so much research out there, it boggles the mind. It’s growing exponentially. It’s insane. And in no paper do we cite all the possibly important research on a topic. We’re going to leave things out anyway. It’s just that now, we seem to be structurally leaving out work by women. Again, to overcome this bias is going to require intentionality. This is not a problem that we can just hope will go away because we are good people and don’t mean to cause harm.

No matter what we do, we’re going to keep citing a lot of great research by men.

Tokenism?

Among women, there was another layer. Many of the women I talked to did not want to be cited just because they were women and someone needed to move their reference list towards gender parity. They wanted to be cited because someone genuinely thought their research was the best and most relevant.

And I get that. I was invited to give a talk once because the organizer was looking for a replacement female speaker after the originally-invited woman couldn’t participate. I was so excited for this opportunity, but at the same time it felt weird to get it explicitly because they were looking for a woman.

I’m not sure what to do with this concern, but I think it comes back to proper citation practice. Is your work relevant to the topic, and being cited correctly? Then you deserve to be cited. If papers by women are accepted less often and cited less often, then part of the reason you are not currently being cited might be simply because someone isn’t familiar with your work.

And The Inevitable Question, Does Quality Limit Equality

Finally, we had some conversations about whether this might be because women’s work just wasn’t as good as men’s.

It’s pretty hard to assess that (although the paper I started this blog post with tried to, by using journal impact factor as a covariate).

I have two thoughts. One is that I doubt it’s true. There was an interesting graphic and paper that went around Twitter – about economics, not ecology – showing that papers by women are better written than those by men, that women incorporate reviewer comments more often, and that they improve at presenting information over the course of their careers.

It’s provocative and I’m not sure if it’s also true in my field or not, but I believe it. I think we are culturally trained from a young age to try to please, and so we might be likely to try to pacify reviewers, and to make a revision extra perfect if it was rejected the first time around.

Also, in the sciences, women have to be better to be evaluated as being as qualified as men.

Second, about the content. If it is possible that the data and experiments presented by women are less strong, why might that be?

It is interesting to think about how structural problems would lead to this. Could it be because women get less funding to do their research, and thus might have less resources or support? Could it be that women are asked to do more departmental service and teaching, and have less time to do research?

If the research really is worse – which I’m not convinced it is, but there’s little way to objectively assess, at least not for a dataset of any size – this very well might be a result of the same structural issues that cause the citation patterns.

What Next

What do you think about this? Are there any other ways to work on this problem?

Notes

[1] Impact factor is not a perfect measure of journal quality. It is an estimate of how often work there gets cited, which is a traditional metric for how important it is. For any individual paper, the impact factor of the journal it is published in doesn’t say much about the quality of that paper. However, I think it was the best covariate the authors could find to control for the differences in quality between papers when comparing men’s and women’s outcomes. Also, for better or for worse scientists pay attention to impact factors, so it may affect citation practice even if it’s not an actual metric of “quality” per se.

[2] I have an interesting, harebrained idea about this: if papers by women are less likely to be accepted, do good papers with women as last authors end up in lower-impact journals? That could explain why the better-cited papers from those journals are by women. Totally spitballing here.

[3] Citations shouldn’t define one’s value as a scientist, colleague, or employee anyway, but… that’s a discussion for another day.

[4] Is it really though? Women probably contributed to many important ideas. They typed the manuscripts. Maybe they did some of the work. There are tons of cases in science where people had the same idea independently, and one person got famous, and the other didn’t. Sometimes both these people were white men. I’m guessing there’s a lot of times when some of these people were women, minorities, scientists from outside Europe and North America, and people who were/are otherwise excluded from the elite science community.

[5] Given what I just wrote, I think I – and we all – need to put more effort into finding papers that were written around the same time as the famous ones that “came up with” ideas, and represent contributions of the community-building of those ideas by other people.

[6] When you’re looking for very specific things, there might be a lot of important and relevant data in theses and lower-impact papers by students who did not continue in science. This is valuable literature to search for, and might expand what you think the contributions of women and minorities are, given that these people are less likely to continue in academic careers either by choice or by exclusion.

From #fieldworkfail to Published Paper

Amphipods are, unfortunately, not very photogenic. But here you can see some of my study organisms swimming around in a mesocosm in the laboratory, shredding some leaf litter like it’s their business (because it is).

It can be intimidating to try to turn your research into an academic paper. I think that sometimes we have the idea that a project has to go perfectly, or reveal some really fascinating new information, in order to be worth spending the time and effort to publish.

This is the story of not that kind of project.

One of my dissertation chapters was just published in the journal Aquatic Ecology. You can read it here.

The project originated from a need to show that the results of my lab experiments were relevant to real-world situations. To start out my PhD, I had done several experiments with amphipods – small crustacean invertebrates common to central European streams – in containers, which we call mesocosms. I filled the mesocosms with water and different kinds of leaves, then added different species and combinations of amphipods. After a few weeks, I saw how much leaf litter the amphipods had eaten.

We found that there were some differences between amphipod species in how much they ate, and their preferences for different kinds of leaves based on nutrient content or toughness (that work is here). But the lab setting was quite different than real streams.

So I worked with two students from our limnoecology course (which includes both bachelors and masters students) to develop a field experiment that would test the same types of amphipod-leaf combinations in streams.

We built “cages” out of PVC pipe with 1-mm mesh over the ends. We would put amphipods and leaf litter inside the cages, zip tie them to a cement block, and place the cement block in a stream. We did this in two places in Eastern Switzerland, and with two different species of amphipod.

After two weeks, we pulled half the cement blocks and cages out. After four weeks, we pulled the other half out. Moving all those cement blocks around was pretty tough. I think of myself as strong and the two students were burly Swiss guys, but by the time we pulled the last cement block up a muddy stream bank I was ready to never do this type of experiment again.

Elvira and our two students, Marcel and Denis, with an experimental block in the stream. This was the stream with easy access; the other had a tall, steep bank that was a real haul to get in and out of.

Unfortunately, when I analyzed the data, it was clear that something had gone wrong. The data made no sense.

The control cages, with no amphipods in them, had lost more leaf litter than the ones with amphipods – which shouldn’t be the case since they only had bacteria and fungi decomposing them, whereas the amphipod cages had shredding invertebrates. And the cages we had removed after two weeks had lost more leaf litter than the ones we left in the stream for four weeks.

These are not the “results” you want to see.

We must have somewhere along the way made a mistake in labeling or putting material into cages, though I couldn’t see how. I tried to reconstruct what could have gone wrong, if labels could have gotten swapped or material misplaced. I don’t have an answer, but the data weren’t reliable. I couldn’t be sure that there was some ecological meaning behind the strange pattern. It could have just been human error.

I felt bad for the students I was working with, because it can be discouraging to do your first research project and not find any interesting results. It wasn’t the experience I wanted to have given them.

My supervisor and I agreed, with regret, that we had to redo the experiment. I was NOT HAPPY. I wasn’t mad at him, because I knew he was right, but I really didn’t want to do it. I’ve never been less excited to go do fieldwork.

But back out into the field I went with my cages and concrete blocks (and no students this time). In case we made more mistakes, we designed the experiment a bit differently. We had one really well-replicated timepoint instead of two timepoints with less replicates, and worked in one stream instead of two.

Begrudgingly, we hauled the blocks to the stream and then hauled them back out again.

Cages zip-tied to cement blocks and deployed in the stream. You can see the brown leaf litter inside the enclosure.

And then for 2 ½ years I ignored the data, until my dissertation was due, at which point I frantically analyzed it and turned it into a chapter.

The draft that I initially submitted (to the journal and in my dissertation) was not what I would call my best work. My FasterSkier colleague Gavin generously offered to do some copy-editing, and I was ashamed at how many mistakes he found. I hope he doesn’t think less of me. A fellow PhD student, Moritz, also read it for me, and had a lot of very prescient criticisms.

But through all of that and peer review, the paper improved. Even though it is not going to change the course of history, I’m glad that I put together the analyses and published it, because we found two kind of interesting things.

The first was about species differences. I had used two amphipod species in the experiment (separately, not mixed together). Per capita, one species ate a lot more/faster than the other… but that species was also twice as big as the other! So per biomass, the species had nearly identical consumption rates.

The metabolic theory of ecology is a powerful framework that explains a lot of patterns we see in the world. One of its rules is that metabolism does not scale linearly with body size (here’s a good blog post explainer of the theory and data and here’s the Wikipedia article). That is, an organism twice as big shouldn’t have twice the metabolic needs of a smaller organism. It should need some more energy, but not double.

This relates to my results because the consumption of leaf litter was directly fueling the amphipods’ metabolism. They may have gotten some energy and resources from elsewhere in the cages, but we didn’t put any plant material or other food sources in there. So we could expect to roughly substitute “consumption” for “metabolism” in this body size-metabolism relationship.

Metabolic theory was originally developed looking across all of life, from tiny organisms to elephants, so our twofold size difference among the two amphipod species isn’t that big. That makes it less surprising that the two species have the same per-biomass food consumption rates. But it’s still interesting.

The second interesting result had to do with how the two species fed when they were offered mixtures of different kinds of leaves. Some leaves are “better”, with higher nutrient contents, for example. Both species had consumed these leaves at high rates when they were offered those leaves alone, and had comparatively lower consumption rates when offered only poor-quality leaves.

In the mixtures, one species ate the “better” leaves even faster than would be expected based on the rates in single-species mixtures. That is, when offered better and worse food sources, they preferentially ate the better ones. The other species did not exhibit this preferential feeding behavior.

I thought this was mildly interesting, but I realized it was even cooler based on a comment from one of our peer reviewers. (S)he pointed out that this meant that streams inhabited by one species or the other might have different nutrient cycling patterns, if it was the species that preferentially ate all of the high-nutrient leaves, or not. We could link this to neat research by some other scientists. It was a truly helpful nudge in the peer review process.

So, while I had hated this project at one point, it’s finally published. And I think it was worth pushing through.

It was not a perfect project, but projects don’t have to be perfect for it to be worth telling their stories and sharing their data.

Planoiras Part 2: Seeking Confidence and Resilience

Note: This is the second of two posts about my racing in Lenzherheide, Switzerland, this weekend. For the first post, click here.

Saturday morning I woke up to one of those emails you don’t want to get at the start of the weekend. A paper I had submitted was rejected. Argh!

This happens all the time if you are an academic, and I think I have generally gotten slightly better at dealing with it. I was able to find some positives: the paper did go out for review (rather than getting rejected by the editor without review, something that is quite common), and all of the reviewers and editors agreed the premise was interesting. It’s not like they were telling me I, or my science, was garbage.

But it was still very disappointing. It was the chapter of my dissertation that I felt the most ownership over: the thing I felt like I had come up with all by myself and then convinced my supervisor and co-author to pursue, and that had turned out to have really interesting results. I had sent it to one of the journals I admire most in my field, and to have it published there would have felt like an incredible milestone.

Luckily, I was meeting some friends for a ski that morning, so after reading the reviewer comments over breakfast I hopped on the train and got some beautiful, sunshiney snow time. Glide. Good therapy.

Later in the day, I skyped with Steve, who is traveling for work. We chatted about a bunch of different things before I even remembered to mention the paper rejection. Then he asked if I was ready for my ski race the next day.

“I’m trying to be,” I said. “But it’s hard. The weather is going to be pretty terrible. It’s just blah.”

“You’re paper got rejected and now everything is painted gray,” he responded. “I know how you will be. The weather is gray and I don’t like it. The skiing is gray. This breakfast is gray, yuck. Gray gray gray.”

I laughed, because he was right, kind of. I definitely get that way. Sometimes when one bad thing happens, it leads me right down a chain of negativity until everything seems overwhelming, bad, and unsolvable. I can’t seem to see anything good in the world.

But I also laughed because it’s something I’m working on. For Christmas I bought myself Kara Goucher’s new book, “Strong.” It’s about building confidence. Some of the presentation is a little too girly for me, but there are aspects of the book that I love. It all works because Goucher is completely honest about her struggles, and she’s easily convincing when she relates how mental training helped her.

One section is about reframing negative thoughts and turning them into strengths, and this is something I really liked.

Here’s an example. These days when I go to a ski race, I’m aware that I probably don’t train as much as most of the people who are around me – people who look all pro in their shiny suits, who own the newest skis and boots and poles, and who probably poured a couple hundred Francs into their wax jobs. I certainly don’t have as much time on snow, because I live in Zurich, and most of them live much closer to the mountains, if not actually in the mountains.

As I see all these people warming up and putting their skis on the line, sometimes I feel like a complete imposter. What am I doing here!? These people are so much better prepared than me! Look how fit they all look!

And, well, some of them are better trained. But physical preparation is not the only thing that makes you go fast. You could have done the best training this year, but if you show up at a race and don’t work hard, you’re probably not going to reach your goals.

I work really, really hard in races in order to make up for my lack of ski-specific (or some years even total…) training. I try to target my effort in the ways that will help the most, take advantage of my love of downhills and corners, and attempt to finish the race having spent every bit of energy I have.

And so when there was an exercise in “Strong” to write down a common negative thought you have and reframe it, this is what I picked.

“Everyone here has done better training than you,” I wrote down for the negative thought.

“You know how to get the most out of the training you’ve done,” I wrote down as a new mantra.

I hadn’t really thought about things that way before, but it felt good.

Did it help me in my race on Sunday? I don’t know. The race still wasn’t that fun, but I did stay focused even though I was performing worse than I had hoped. N=1. Maybe I would have anyway.

A few days later, I was listening to the Science of Ultra podcast when an episode came on about mental training. The host describing the RISE approach: recognize, identify, switch, and execute. His example for recognizing your emotions hit home.

“First, recognize the thoughts you’re having. Be aware of negative, unhelpful, and destructive thoughts…. maybe you’re going much slower than expected, and disappointed that you’re not going to make your goal time, or embarrassed that so many people are passing you.”

As I wrote in part 1 of this blog post, I need to clarify why it is that I race. Skiing doesn’t really have goal times (one of the things I love about it!) and you never know who will show up at a given marathon. Setting results-based goals seems particularly futile when you’re in a field of competitors you don’t know anything about, and I wouldn’t say that I am driven to race because I think I’ll do “well”. I don’t train full time. I’m getting worse at skiing. I know that.

And yet, that embarrassment when lots of people pass me is real. That’s something I need to recognize. Even though results are not the main reason I do this, it feels bad.

What’s funny about all of this is that I have been thinking about mental resilience a lot lately, but not because of sports. Instead, I’ve been thinking about it in my life as a scientist.

Finishing my dissertation was really hard, and I still don’t feel like I’m fully recovered. It took a lot out of me intellectually and emotionally. Two months after handing it in, I sit down at the computer to write on one of the other papers I owe my boss and I just can’t. The words don’t come out. The ideas I had disappear.

And even before that, sometimes I get into these negative spirals. Everything gets painted gray. Science has highs and lows and sometimes I feel like I’m swinging wildly between them from one day to the next. Going through something like a dissertation doesn’t help you deal with all the “normal” lows like getting a paper rejected.

I love science, and I want to keep doing it. But I need to do everything I can to be healthy.

And so when I was at the British Ecological Society annual meeting in Birmingham, England, in December, I headed to a lunchtime workshop about mental resilience in academia.

I was relieved to see that the room was full of people. I wasn’t weak for thinking I needed help in this department. Apparently, this was something that everyone thought sounded like a lifeline. Including people I recognized and admired.

Some things we talked about I already knew. Others I hadn’t thought about, or not in the same way. One of the latter was the instruction to recognize and accept your emotions.

“Sometimes we think that resilience is bouncing back, getting over it and soldiering on,” the workshop organizer said. “But there’s a danger in that. You need to recognize and deal with your emotions, with how you feel about the bad things you’re experiencing. If you bury them in an effort to just ‘soldier on’, that’s not going to work in the long run. That’s not resilience.”

All of these things – confidence, recognition, resilience – seem tied together for me, even though I’m not doing a good job of explaining why. But even though I’m exhausted by my PhD and frequently overwhelmed, I think that thinking about all these things has made me more balanced in the last month or so.

Kara Goucher’s book is about keeping a confidence journal. The premise is that every day, you write down something specific, that you will remember immediately, and that will make you feel more confident when you go back and read it later.

I’ve enjoyed keeping a confidence journal so far. I always write something about the training/exercise session I did each day (or what was good about resting instead of training), and some days I write about science, too. Both sides of my life are places where I need to go back and find some extra confidence sometimes.

My weekend started off with a rejection, but it didn’t have to end that way. I recognized my disappointment and frustration with racing, but found the positive side in my journal entry.

My Ford Sayre ski coach, Scottie Eliassen, always had us talk about one thing that went well and one thing to improve on for next time after every race. This is what I channeled.

“I didn’t go fast, but dang I worked hard. My threshold HR is 177 and my average for the 25 k race was 175. Despite the snowstorm and feeling bad, I hit my process goal of not getting complacent and giving up. I kept pushing.”

Next time I’m about to race and I begin worrying that everyone is more fit than I am, maybe reading that message will help. I’ve been doing this for a long time and I know how to get the best out of the training I’ve done.

Book Review: Spying On Whales by Nick Pyenson

My aunt sends us books for Christmas every year, a.k.a. the best kind of holiday presents. In this year’s box was Spying on Whales by Nick Pyenson, and I sat down to start reading it that very night.

In some ways, I’m the perfect audience for this book. I’m interested in nature, natural history, and evolution, but I actually didn’t know much of anything about whales.

img_3489

Whale territory. You just can’t see them because they are under the water – as Pyenson discusses, this is one of the things that make these giant animals so mysterious and compelling.

In November I had gone on a whale-watching boat ride in northern Norway, and more or less everything I knew came from the Ukrainian guide on the boat. It wasn’t much, but before and after the trip, I thought whales were cool, when I thought about them at all.

If you are already a little obsessed with whales, then I think you’ll like the book too. All the facts are there for you to nerd out on.

9780735224568But Pyenson writes impressively well, and the book is never bogged down in this nerdery. Instead, it’s fun and adventurous. Chapters sometimes end in cliffhangers – I certainly couldn’t stop reading. And at times, it’s quite a moving read.

In the prologue, Pyenson describes how the Voyager 1 and Voyager 2 spacecraft are carrying whalesong into the outer reaches of the solar system. We may not understand what whales are saying, but we intuitively feel that they are intelligent and powerful – and that their communication is not only beautiful but probably important.

“They’re so compelling that we imagine aliens might find them interesting,” Pyenson writes of whales.

And yet, he continues, we know very little about whales, either the ones that live in our oceans today or the whales of the past.

I’ll admit: I didn’t actually really realize that.

Pyenson fills in details that I never knew I didn’t know, and certainly didn’t realize were so recently discovered. He discusses what things we still don’t know, and what mysteries will be exciting to solve. This book is filled with behavior, anatomy, and evolutionary history, as well as discussions of how all three connect to make a modern whale.

“Nothing in biology makes sense except in the light of evolution”, foundational biologist Theodosius Dobzhansky wrote as the title of one of his essays.

In my fields – ecology and evolutionary biology – this is tossed back and forth. “Nothing in ecology makes sense except in the light of evolution,” an evolutionary biologist will say, scoffing at ecology as boring (yes, this has happened to me, in person).

“Well, nothing in evolution makes sense except in the light of ecology,” a ecologist might retort.

Pyenson’s book is a great demonstration that both are true. In trying to piece together – literally, based on fossil fragments – how whales went from living on dry land to being the biggest animals in the oceans and indeed on the planet, ever, both ecology and evolution are needed. The two are woven together throughout the book as Pyenson describes working with one collaborator after another.

“The trip was an opportunity to push at the questions sitting on the edges of our disciplines, at the intersection of Ari’s understanding of behavior and local ecology, Jeremy’s grasp of physiology and biomechanics, and my background in paleontology and Earth history,” Pyenson writes at one point. “The basic questions about how whales do that they do require all these fields of understanding, and I’ve always thought the best way to answer them was through this Venn diagram of disciplines and personalities.”

Now that’s a scientific approach I admire, and he demonstrates that it’s fun, too. Minus the part at the whale slaughterhouse.

How does this approach deliver answers? Here’s an example question. If whales have gradually evolved to be bigger and bigger, why aren’t they even bigger still? That evolutionary question has an ecological answer.

The first two-thirds of the book are about the past and the present of whales. But my favorite part was the last third, about the future of whales, and of us. Whales live a very long time, sometimes two hundred years. There is a beautiful description of all that a 200-year-old whale has seen in its lifetime.

And then, a description of what today’s whales might see in the future, things that you and I will not live to see.

“Any bowheads living today do so in a liminal gap between a familiar past and a potentially unrecognizable future,” Pyenson writes of climate change in the Arctic. “A bowhead calf born today will live in an Arctic that, by the next century, will be a different world than that experienced by all of its ancestors.”

Maybe that sounds trivial – like, duh, climate change is happening fast – but after reading a hundred pages about all of the different environments that whales have encountered through their long evolutionary history, it is powerful when you reach this point.

And that is one of the things that kept me reading this book. It is peppered with fun facts – large baleen whales grow at a rate of a hundred pounds a day before reaching maturity, dang, that seems impossible! – but also with profound observations and good writing.

Finally, as a field ecologist, I really enjoyed Pyenson’s descriptions of fieldwork trips. He takes us to Chile, Panama, Iceland, and Antarctica, as well as more familiar-to-Americans spots like California and North Carolina. All of his descriptions of the challenges and successes of fieldwork, the camaraderie between colleagues, and even that one wonderful-but-definitely-weirdo collaborator (we all have at least one), feel authentic.

If you like science and nature writing, and compelling nonfiction in general, I think you’ll like this book. I did. Thanks Lizzie!

My #365papers Experiment in 2018

This year, based on initiatives by some other ecologists in the past, I embarked on the #365papers challenge. The idea of the challenge is that in academia, we end up skimming a lot of material in papers: we jump to the figures, or look for a specific part of the methods or one line of the results we need. Instead, this challenge urged people to read deeper. Every day, they should read a whole paper.

(Jacquelyn Gill and Meghan Duffy launched the initiative and wrote about their first years of it. But #365papers is now not just in ecology, but in other academic fields. Some of the past recaps I read were by Anne Jefferson, Joshua Drew, Elina Mäntylä, and Caitlin MacKenzie. Caitlin’s was probably the post that catalyzed m to do the challenge.)

I knew that 365 papers was too ambitious for me, and that I wouldn’t (and didn’t want to!) read on the weekends, for example. I decided to try nevertheless to read a paper every weekday in 2018, which would be 261 days total.

In the end, I clocked in at 217 papers (I read more than that, but see below for what I counted as a “paper” for this challenge) – not bad! I tweeted links to all the papers, so you can see my list via this Twitter search. I can confidently say that I have never read so many papers in a year.

In fact, I am guessing that this is more papers than I have read in their entirety (not skimming or extracting, as mentioned above), in my total career before 2018. That’s embarrassing to admit but I am guessing it’s not that unusual. (What do you think, if we’re all being honest here?)

This was a great exercise. I learned so much about writing, for one thing – there’s no better way to learn to write than to read a lot.

But the thing that was most exciting was that I read a lot more, and a lot of fun pieces. I had gotten to a place where there were so many papers that I felt I had to read for my own work, that I would just look at the pile, blanche, and put it off for later. Reading had become a chore, not something fun.

Titles_wordle

A Wordle of the paper titles. On my website it says I am a community and ecosystem ecologist, and I guess my reading choices reflect that! (I’d be interested to make a Wordle based on the abstracts, to see if there are more diverse words than the ones we choose for titles – but I didn’t make time to extract all the abstracts for that.)

That’s not a great way to do research, and luckily the challenge changed my reading status quo. If I was reading every day, I reasoned, then not every paper had to be directly related to my work as a community ecologist. There would be ample time for that, but I could also read things that simply looked interesting. And I did! I devoured Table of Contents emails from journals with glee and read about all sorts of things – evolution, the physical science of climate change, remote sensing.

These papers, despite seeming like frivolous choices, taught me a lot about science. Just because they were not about exactly what I was researching does not mean they did not inform how I think about things. This was incredibly valuable. We get stuck in our subfields, on our PhD projects, in our own little bubbles. Seeing things from a different angle is great and can catalyze new ideas or different framing of results. Things that didn’t make sense might make sense in a different light.

But I also did read lots of papers directly related to what I was working on. I think I could only do that because it no longer felt like a chore, like a big stack of paper sitting on the corner of my desk glaring at me. This challenge freed me, as strange as that sounds given the time commitment!

And finally, I tweeted each paper, and tagged the authors if possible. This helped me make some new connections and, often, learn about even more cool research. It helped me put faces to names at conferences and gave me the courage to strike up conversations. The social aspect of this challenge was fun and also probably pretty useful in the long run.

For all of the reasons I just mentioned, I would highly recommend this challenge to other academics. (It’s not just in ecology – if you look at the #365papers hashtag on Twitter, there are a lot of different people in different fields taking up the challenge.) Does 365 or 261 papers sound like too many? Set a different goal.  But make it ambitious enough that you are challenging yourself. For me, I found that making it a daily habit was key, because then it doesn’t feel like something you have to schedule (or something you can put off) – you just do it. Then sit down and read a whole paper, with focus and attention to detail. If you like it, why is that? Is the topic of interest to you? The writing good? The analyses particularly appropriate and well-explained? Is it that the visuals add a lot to the paper? Are the hypotheses (and alternative hypotheses) identified clearly, making it easier to follow? Or, if you don’t like it, why is that? Is it the science, or the presentation? What would you do differently?

One thing I didn’t nail down was how to keep notes. I read on paper, so I would highlight important or relevant bits or references to look up. But I don’t have a great system for how to transfer this to Evernote (where I keep papers’ abstracts linked to their online versions, each tagged in topic categories). In the beginning I was adding photos of each part of the paper I had highlighted to its note, but this was too time-consuming and I gave up. In the end it was like, if I had time, I would manually re-type my reading notes into Evernote, and if not, I wouldn’t. I do think the notes are valuable and important to have searchable, so this probably limits the utility of all that reading a little bit. It’s something I will think how to improve for next year. The biggest challenge is time.

In addition to reading a lot, I kept track of some minimal data about each paper I read. I’ll present that below, in a few sections:

  • Where (journals) and when the papers were published
  • Who wrote them – first authorship (gender, nationality, location)
  • A few thoughts about last authorship
  • Grades I assigned the papers when reading – potential biases (had I eaten lunch yet!?) and the three papers I thought were best at the time I read them

I plan to try this challenge again next year, and the data that I summarize will probably inform how I go about it. I’ll discuss that a little at the very end.

What Did I Count as One of My 365  261  217 Papers?

First, some methodological details. For this effort, I didn’t count drafts of papers that I was a co-author on, although that would have upped the number or papers quite a bit because I have been working on a lot of collaborative projects this year. I also didn’t count reading chapters of colleagues’ theses, or a chapter of a book draft. And I didn’t count book chapters, although I did read a few academic books, among them Dennis Chitty’s Do Lemmings Commit Suicide, Mathew Leibold & Jonathan Chase’s Metacommunity Ecology, Andy Friedland and Carol Folt’s Writing Successful Science Proposals, and a book about R Markdown. I started but haven’t finished Mark McPeek’s Evolutionary Community Ecology.

I did count manuscripts that I read for peer review.

Where The Papers Were Published

I didn’t go into this challenge with a specific idea of what I wanted to read. I find papers primarily through Table of Contents alerts, but also through Twitter, references in other papers, and searches for specific topics while I was working on my dissertation or on research proposals. This biases the papers I read to be more likely to be by people I’m already aware of or in journals I already read. Not entirely, but substantially.

We also have a “journal club” in our Altermatt group lab meeting which doesn’t function like a standard one, but instead each person is assigned one or two journals to “follow” and we rotate through each person summarizing the interesting papers in “their” journals once every few months (the cycle length depends on the number of people in the lab at a given time). That’s a good way to notice papers that might be good to read, and since we are a pretty diverse lab in terms of research topics, introduces some novelty. I think it’s a clever idea by my supervisor, Florian.

Given that I wasn’t seeking out papers in a very systematic way, I wasn’t really sure what the final balance between different journals and types of journals would be at the end of the year. The table below shows the number of papers for each of the 63 (!) journals that I read from. That’s more journals than I was expecting! (Alphabetical within each count category)

In addition, I read one preprint on BioRxiv.

I don’t necessarily think that Nature papers are the best ecology out there; that’s not why it tops the list. Seeing EcologyOikos, and Ecology Lettersas the next best-represented journals is probably a better representation of my interests.

But, I do think that Nature (and Science, which had just a few fewer papers) papers get a lot of attention and must have been chosen for a reason (am I naive there?). There are not so many of them in my field and I do try to read them to gauge what other people seem to see as the most important topics. I also read them because it exposes me to research tangential to my field or even entirely in other fields – which I wouldn’t find in ecology journals, but which are important to my overall understanding of my science.

I’m pleased that Ecology & Evolution is one of my top-read journals, because it indicates (along with the rest of the list) that I’m not only reading things for novelty/high-profile science, but also more mechanistic papers that are important to my work even if they aren’t so sexy per se. A lot of the journals pretty high up the list are just good ecology journals with a wide range of content.

There are a lot of aquatic-specific journals on the list, which reflects me trying to get background on my specific research. But there are also some plant journals on the list, either because I’m still interested in plant community ecology despite being in freshwater for the duration of my PhD, or because they are about community ecology topics that are useful to all ecology. It will be interesting to see if the aquatic journals stay well-represented when I shift to my next research project in a postdoc.

Society journals (from the Ecological Society of America, Nordic Society Oikos, British Ecological Society, and American Society of Naturalists, among others) are well represented. Thanks, scientific societies!

When The Papers Were Published

The vast, vast majority of papers I read were published very recently. Or, well, let’s be honest, because this is academic publishing: who knows when they were written? I didn’t systematically track this, but definitely noticed some were accepted months or maybe even a year before final paginated publication. And they were likely written long before that. But you get the point. As for the publication year, that’s below.

year_published

This data was not a surprise from me as a fair amount of my paper choices come from seeing journal table of contents alerts. I probably should read more older papers though.

Who Wrote the Papers: First Authors

Okay, on to the authors. Who were they? As I mentioned for journals, I didn’t systematically choose what I was reading, so I was curious what the gender and geographic breakdown of the authors would be. Since I didn’t very consciously try to read lots of papers authored by women, people of color, or people outside of North America and Europe, I guess I expected that the gender of first authors to be male-skewed, white, and from those continents. I wasn’t actively trying to counteract bias in this part of the process, so I expected to see evidence of it.

I did my best to find the gender of all first authors. Of those for which this was deducible based on homepages, Twitter profiles listing pronouns, in-person meetings at conferences, etc.,:

  • 59 first authors were women
  • 155 first authors were men
  • 2 papers had joint first authors
  • 1 paper I peer-reviewed was double-blind (authorship unknown to me)

I’m fairly troubled by this. I certainly wasn’t going out of my way to read papers by men, and I didn’t think it would be this skewed when I did a final tally. If I want to support women scientists by reading their work – and then citing it, sharing it with a colleague, contacting them about it, starting discussions, etc. – I am going to have to be a lot more deliberate. I want to learn about other women scientists’ ideas! They have a lot of great ones. I’m going to try harder in the future. Or, really, I’m going to try in the future – as mentioned, I was not intentionally reading or not reading women this past year.

I initially tried to track whether authors were people of color, but it’s just too fraught for me to infer from Googling. I don’t want to misrepresent people. But I can say that the number of authors who were POC was certainly quite low.

I did, however, take some geographic stats: where (to the best of my Googling abilities) authors originally came from, and where their primary affiliation listed on the paper was located.

For the authors for whom I could identify nationality based on websites, CVs, etc., 31 countries were represented.

FA nationality

The authors were numerically dominated by native English speakers, but those had relative geographic diversity, coming from the US, Canada, the UK, Ireland, Australia, and New Zealand (I’m not sure if English is the first language of the South African author). 15 different European nationalities were represented. There were a number of authors from Brazil, and one each from Chile, Colombia, and Ecuador, as well as Central America being represented by a Guatemalan. Maybe a surprise was that Chinese authors were underrepresented, either from Chinese institutions (see below) or those outside China; there were just five. There are many countries from which there are great scientists which are not represented in this dataset.

When it came to institutional country, the field narrowed to 24 countries plus the Isle of Man.

FA_inst

While there were 78 American first authors, 90 first authors came from American universities/institutions. In Europe, Denmark, Sweden and Switzerland gobbled up some of the institutional affiliations despite having low numbers of first authors originally from those countries (this is very consistent with my experience in those places).

(Note: it would have been really nice to make a riverplot showing how authors moved between countries, but I was too lazy to build a transition matrix. Sorry.)

This isn’t really surprising, the consolidation into fewer countries. It reflects that while small countries have great scientists, they often don’t have as many resources to have great research funding or many universities. Some places, even those with traditionally strong academic institutions, are simply going through austerity measures. I think of many Europeans I know who decided that leaving their countries – Portugal, Spain, the Baltics and Balkans, and other places – was their best bet to be able to do the research they wanted to do, and have a job. I think of others, notably a friend in Slovenia, who is staying there because he loves it, but whose opportunities are probably curtailed because of that.

I’d like to read more widely in terms of institutional location and author nationality, but it’s a bit overwhelming to make a solid plan. Reading more women is fairly straightforward. But when I think of all the places with good science but where I didn’t read a single paper, there are a lot of them. I can only read so many papers! So part of it will be recognizing that I can make an effort to read more diversely but I’m not going to solve bias in science just with my reading project. I need to make an effort that is meaningful, and then be okay with what it doesn’t accomplish.

Also, I don’t always know the gender, race, or nationality of an author before I Google them – this past year, I only did that after I read the papers. I might need to sometimes reverse that process, perhaps?

Do you have other ideas of how to tackle this? I’d love suggestions if anyone has them.

One thought is to more deliberately read from the non-North American, non-European authors in the journals I already read from. I already know I like the papers those editorial teams select. This would probably be the least amount of extra work required to diversify my reading, because I could stick to the same method of choosing papers (table of contents alerts), but execute differently on those tables of contents.

And a Bit About the Last Authors

I did not collect as detailed information about the last authors of each paper, but I did collect some. A big topic in academia is that women get fewer and fewer the higher you go in the academic hierarchy. I wondered if that was true in the papers I was reading.

There were fewer last authors because some papers were single-author. Of those that were multi-author, I filtered the dataset to look at only those where last-authorship seemed to denote seniority (based on author contribution statements, lab composition and relationships between authors, etc.) rather than being alphabetical or based on something else (on some papers with very many authors, all the senior authors were listed at the front of the author list). Of these,

  • 19 senior last authors were women
  • 105 senior last authors were men

Yikes!

That’s all one can say! Yikes!

Like the first authors, the last authors came from 31 different countries… but some different ones were represented (Venezuela, Serbia, India). They represented institutions in a few more places than the first authors, 28 different countries vs. 24 for first/single authors. I’m not sure what to make of that, especially since this is from a smaller subset of papers (since the single-author papers were removed), but obviously collaborative research and writing is alive and well.

Ratings and Favorite Papers

Right after I read each paper, I assigned it a letter grade. Looking back through my record keeping, I am less and less convinced that this is really meaningful. I think it had to do a lot with my mindset at the time, among other things. Did I just have a stressful meeting? Was I impatient to finish my reading and go home? Was I tired? Maybe I was less receptive to what I was reading. Or conversely, maybe if I was tired and a little distracted I was less likely to notice flaws in the paper. Who knows. Anyway, “B” was the grade I most frequently assigned.

grades

I didn’t keep detailed notes of why I felt different grades were merited, but I can make a few generalizations. Quite a number of the papers I gave poor grades were because I didn’t find methods to be well enough explained. I either couldn’t follow what the authors did, or maybe important statistical information wasn’t even included (or only in the supplementary information when I thought it was so essential to understanding the work that it really needed to be brought to the center). In particular this included some papers using fancy and cutting edge methods… just using those statistics or analysis techniques doesn’t make your paper magic. You still need to say what those analyses show and what they mean ecologically, and convince me that the fancy stats actually lead to a better understanding of what’s going on!

In some ways this is not authors’ faults – journals are often pressing for shorter word counts, and some don’t even publish methods in the main text, which is a total pain if you’re a reader. Also, it’s one of the biggest things I struggle with when writing – you know perfectly well what you did, and it can be hard to see that for an outsider your methods description seem incomplete. I get it! Reading papers where you don’t understand the methods is always a good cue to think about how you present your own work.

I assigned three papers grades of “A+”. Were they better than the ones I deemed “A”s? I’m not sure, but at the time, whether because of my general mood or their true brilliance, I sure thought they were great. They were:

I read a lot of other great papers too! But looking back, I can say that these were among my favorites, all for different reasons. I could go and add more papers to a “best-of” list but I’ll just leave it at that.

Recap!

Besides all the great reasons to do this challenge that I mentioned in the opening, this was pretty interesting data to delve into. I think I will try to keep doing the challenge in 2019, and I am currently thinking about how I choose which papers to read and if there are good strategies to read more diverse authors. I’m happy with the diversity of research that I read, but I would be happier if the voices describing that research were more diverse, to reflect the diversity of scientists in our world.

Do you have ideas about that? Comment below.

This was the final year of my PhD, and so in some ways a great time to do a reading challenge. It probably would have been more helpful if I had done this in the first year of my PhD, but hey, too late now. This year I wasn’t doing lab work, just writing and analyzing, so it was easy to fit in a lot of reading. It’s not good to stare at a screen writing all day, and I prefer to read on paper, so it was often a welcome break.

I don’t know what my work life will be like next year, so I will see how many papers I end up reading. It could be more, as I start a new project and need to get up to speed on a new subfield. Or it could be less as my working habits change. I’ll just do my best and adapt.

Finally, I’m thinking about whether there’s additional data I should track for next year’s challenge. Whether there is a difference between first and corresponding authors might be interesting. I’d welcome other suggestions too, but only if they don’t take much work to extract!

Understanding our values of nature

Above: a view from the Lägern forest outside of Baden, Switzerland, one of the research sites for Katie Horgan’s PhD work. I ran the Lägern ridge while I was training for a marathon this fall.

I’ve always been interested in conservation. Okay, maybe in the beginning it wasn’t a choice. My mother worked for (and now runs) a land conservation nonprofit, and I grew up kicking around the office and volunteering at events.

Some of my first research and jobs in ecology were about topics related to global change: invasive species, contamination, climate change. I was motivated to take these jobs because I cared about the issues and thought that doing research related to them would help.

But these projects were all very much ecology. The grants weren’t written and the studies weren’t designed with an outcome in mind that could be translated to stakeholders or implemented as policy. They were pure basic science: what is happening? Hey, that’s cool (or alarming, or boring, or …. ), and it’s good that we know it now!

Even though so many of our ecology results are framed in the context of global change, including sentences and maybe even paragraphs talking about implications for land management, I’ve never studied conservation biology. Neither have most of my collaborators.

Recently, I have begun to understand how my good intentions and environmentalism don’t help the environment that much. I’ve been doing ecology in a vacuum: I care about “issues”, but those issues are separate from my science (even if they are merged on my Twitter feed). I really like community ecology and understanding species interactions, and that’s what drives my research questions.

I’m not sure that in and of itself is problematic. But what is certainly problematic is the extent to which I didn’t realize what I was doing. It’s hard to expect your science to connect to and inform conservation and planning if you don’t really understand conservation and planning.

One of the things that made me more cognizant of this was the defense of a fellow University of Zurich PhD student, Katie Horgan, last week. Katie’s dissertation was highly interdisciplinary, and it exploded my conceptions of how we think about nature and ecosystems.

Embarrassingly, the big thing that I realized should be obvious: how we value nature depends more on us, than on nature.

(That might seem like a bit of a jump from my first few paragraphs, but I promise I will link it together at the end.)

When I write out my “big realization”, it looks stupid. I find myself thinking that I already knew that. I interact with people every day who think about the outdoors in a different way than I do.

My boyfriend illustrates this perfectly. When I go for a long run, I want to go somewhere new and see a new view. Steve? He would be happy running exactly the same loop around Uetliberg, the local ridge, that he ran the previous week.

“Why do you need to take the train for two hours?” he asks me. “It’s still just a forest.”

The value we place on seeing and experiencing ecosystems is completely different. Running around in the same forest, or similar forests, we pick up different things and take away different experiences.

How does that relate to ecology? In the past few decades, there has been a push to quantify the value of the natural world. We call them ecosystem services – things like clean water, clean air, the provision of fish to eat and wood to build with, nice places to go recreate in. All of these things can be assigned a dollar value and deemed “natural capital.”

This approach can then be used to make policies protecting natural areas. Asking people to identify what is valuable in their landscape helps set conservation priorities. This approach can also demonstrate that neglecting such protection would be economically costly. In some places the ecosystem services approach has worked great, and in others not so much.

Until last week, I have to admit that I kind of saw ecosystem services as black and white: this ecosystem either provides this service, or it doesn’t. This forest provides X and Y. That lake provides X and Z. I saw ecosystem services as something you could measure objectively.

Then I walked into Katie’s defense.

Katie’s research is fascinating. She worked at eight different research sites scattered around the world, from two right here in northeast Switzerland to those in Siberia and Borneo. At each site, she asked people who worked at the conservation areas a series of questions: did they think that ecosystem service X was being provided by this area? What about ecosystem service Y?

This is a seemingly simple dataset and study, but the work and the results are far from simple. Just getting the interview responses, despite cultural and language barriers and all the rest, was a huge feat.

The thing that struck me most from Katie’s talk was that even in ecosystems that seemed in some ways similar – and actually, even at the same ecosystem– the people Katie interviewed had different answers about whether an ecosystem service was provided or not.

In other words: assessing what ecosystem services are provided does depend on the ecosystem. It depends on how people see the ecosystem.

Katie also mined through the responses and deduced how people thought about the value of these conservation areas. Rather than thinking only about an ecosystem service, she classified the responses by what this service corresponded to.

Did people see the service provided as something more utilitarian (“instrumental”)?

Or did they value this service differently, in a more “intrinsic” way – is the service provided something more fundamental, like biodiversity, that just is?

The third type of value is the one that makes me go run in a new place whenever I can – “relational” value, defined by the way that we interact with nature.

Different ecosystem services, which are the metric by which we turn nature into “natural capital”, were valued in a wide range of ways. And importantly, nearly every ecosystem service that Katie asked about was valued in each of the three ways by at least one study participant. In fact, most participants places more than one value on a given service!

Katie’s big question was, what are the things that motivate people to take positive action about biodiversity?

I had been thinking, like a cold scientist, that the natural capital of an area or ecosystem was defined by the ecosystem itself: the biodiversity contained within, the black and white ecosystem services it provided. I thought we had to convince people of that value, and then they would be motivated to protect it.

What Katie illustrated so powerfully was that actually, the value of nature is defined by the people valuing that nature. If we don’t recognize that, then we won’t really succeed with protected areas and conservation policymaking.

And because of that, it’s a little bit stupid for someone like me who leaves human interaction completely out of my research to put in pompous statements about how that same research will inform conservation. The two parts of my brain that think about environmentalism and ecology had skipped a pretty fundamental dialogue that they could be having.

A few days later, I saw the following tweet from Andy Gonzalez, from a presentation by University of Vermont professor Taylor Ricketts at a science conference in Quebec. It distills this point in a different, perfect way.

 

I bet I’m not the only ecologist who needs to mull over that message.

We live and we learn, and I’m trying to become a better scientist and a better person all the time. Part of that is being humble and realizing when you’re being clumsy or just kind of an idiot.

Hearing from inspirational colleagues definitely helps in that process.

The marathons of 2018.

This autumn I ground away at two big goals: finishing my dissertation, and running my first trail marathon.

A number of people told me I was insane to try to do both of these things at the same time. But everyone has different ways of staying happy and maximizing what they are capable of. For me, it’s essential to have more than one thing to focus on. I have a few friends who must live like I do: they said, oh, that’s perfect!

The last few months of dissertation writing were really hard. Although I made a plan with my supervisor about how to get everything done, work didn’t really proceed according to plan. Some things took longer. Other tasks required waiting on collaborators for feedback. Sometimes I simply realized that I had no idea what was expected as a certain output. I tried to start working anyway, only to have my first attempt deemed garbage.

By contrast, my marathon training was straightforward. I won’t say it was easy, but I knew what I had to do.

***

I didn’t sign up for just any marathon; the Transruinaulta in southeastern Switzerland is mostly off-road and features 1,800 meters (~6,000 feet) of climbing, plus the corresponding 1,800 meters of descents. In order to do a race effort I felt good about, I knew I would have to take training seriously.

I bought a training plan from Uphill Athlete, a company and community run by Scott Johnston and Steve House. I have known about Scott for years through the cross-country ski community (though I have never met him), and I respect his work, experience, and philosophy so much. I knew that whatever plan I got from Uphill Athlete would deliver me well-prepared to the start line. It had been seven years since I last followed a training plan, but at last, I was ready to return to intentional, organized training. I dove in and had confidence every step of the way that I was doing the right thing.

“The right thing” involved functional strength training exercises that did more to rehab my ankle from last year’s ruptured ligaments than anything my non-skiing PT had taught me. It included interval sessions that I found I really enjoyed – a surprise, since in those last seven years I had done intervals less than a dozen times annually, and some years probably less than five times.

One week “the right thing” involved a 30-kilometer run/hike one day and a 20 k  run/hike the next day. That was hard, but I planned in advance to head to the Engadin valley for the weekend so that I the spectacular scenery would entice me out the door on Sunday when my body was already tired.

IMG_3088

Enjoying some amazing trail running/hiking around Pontresina.

And maybe the hardest week was when “the right thing” had a 30 k run scheduled on a weekday. I woke up early, took the train to Baden, and ran all the way to the office. I have to admit I wasn’t a very effective worker that day.

But even though it was often hard, I knew what I had to do. Just follow the plan. The plan will get you where you want to go.

Training for a marathon was probably the easiest thing I did this fall.

***

The trauma (there, I said it) of the last month of my dissertation has almost blotted out the months that came before, work-wise. But looking back, I can piece together what they looked like.

I want to be clear that a lot of my problems were self-inflicted. I’m a perfectionist. I hate doing less than the best I could possibly do.

I also have a strong viewpoint that data should not go un-analyzed and un-reported. It’s not good for science if we leave something in a file drawer just because it didn’t turn out to be interesting. That means that someone else will repeat our experiment in the future. And if they also leave it in a file drawer because it turns out to not be interesting, then some unsuspecting third scientist will also decide to tackle it. And so on. You get the picture.

My natural tendency to overwork myself was at some points made worse by my supervisor. Florian is a great supervisor – I would highly recommend working in his lab, and the effusive thanks I eventually wrote in the acknowledgments section of my dissertation were not exaggerations. But he knows how to get the most out of all of us. And at this point, he has known me for four years. He probably knew that if he told me he didn’t think I could do something, that would make me try that much harder to get it done.

All of which is to say that in late August when I sat down with Florian to plan the final few months, I should have been confident that my dissertation would be fine. I had already published three chapters of it as papers, which is a great position to be in. If I had wanted to, I could have coasted in to the finish, writing up one more chapter and calling it a day. Nobody would have said my dissertation wasn’t adequate.

But neither Florian nor I were interested in that option. Instead we planned out three more chapters, plus an introduction and conclusion to the dissertation. I had the data already for all of those chapters, but I still had to analyze it and I still had to do the writing.  I had until mid-November to get all of that done.

And so I made an estimate of how long everything would take. Choosing and learning the appropriate geostatistical method to upscale my survey data: would that take two days, or two weeks? Better just schedule one.

“You can write a paper in a week,” Florian said. I didn’t feel like that was true, but sure, chapter four, let’s schedule a week for the writing.

Inevitably, things didn’t go according to plan. And I also had to apply for postdoc fellowships, too, an exhausting process during which I came up with a research proposal that didn’t even strongly relate to my dissertation. Charging ahead on both of these fronts required shifting between intellectual arenas in my brain.

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So here’s a plan I didn’t end up following, like, at all… in fact, the chapters aren’t even the ones that ended up in my dissertation!

Most days I came home from work exhausted, but through early fall, I was making progress. I submitted the fourth chapter to a journal two weeks before we had planned. Things weren’t going exactly as I had thought, but the parts going better than planned seemed to be making up for the areas where I was way behind.

***

In mid-October, with one month until my dissertation was due, I took the train to southeastern Switzerland on a Friday afternoon and got ready to race the next day. I had been tapering, which felt weird. I hadn’t done any competitions I felt strongly enough about to taper for since my only other marathon run, back in 2013 in France. (That one was on the road; I trained for it, but not according to any real plan.)

My friend Annie came down to race too, and was likewise stressed by work. She had been in the field all week, hardly ideal preparation. We went to bed early, and neither of us slept well. We made some overnight oats for breakfast and found a regional bus that would take us to Ilanz, where the race would start.

In the leadup to the race, a lot of people would ask how long I thought it would take. I had no idea what to answer. Five hours? Four hours? There was all that up and down. Plus, though it was clear that the race wouldn’t have much pavement, would the balance be dirt/gravel roads, or singletrack? How technical would the terrain be? This was clearly not a race where you could pick a pace or split and just try to consistently hit it.

Instead, I made a race plan based on heart rate. I wanted to start off easy on for the first few kilometers and then get into an easy but fast groove for the first ten or so kilometers, which looked mostly flat on the course profile. I set limits for the big climbs: don’t let your heart rate go above this. If you have to walk, walk. You’re in this for the long hall and you are not going to make yourself bonk. Downhills are one of my strengths, so I wanted to run every downhill as fast as I sustainably could.

Oh, and I planned to eat as many calories as I could stuff in my face.

I more or less followed this plan. My slow start meant that people poured past me in the opening kilometers (it was an individual-start marathon, weirdly), and I ended up going a little harder than I planned – but still easy enough that I don’t think it taxed me too much. My plan had probably been too conservative.

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First 10 k: whee, this is fun!

After that, my plan worked great. On the climbs that went for kilometer after kilometer, mostly on dirt roads but sometimes on singletrack, I kept up a steady effort hovering just around my anaerobic threshold. The downhills were a blast as I flew past people. Sometimes they would pass me again as I slowed to my steady pace on the uphills, but it paid off.

We hit the high point of the course around 30 k (20 miles) into the marathon, and there was an aid station at the top. One guy who had been running around me – sometimes ahead, sometime behind – staggered over to a picnic table and sat down heavily.

“Scheisse,” he groaned.

I ran through the aid station, stopping only for a few seconds to refill a water flask. I had quite a few kilometers of gradual to steep downhill to look forward to. I hadn’t completely wrecked myself on the uphill, and I started reeling people in. I was flying, catching runners whom I had told myself not to worry about as they went past me on the last climb.

It was pretty fun until a few kilometers to go. We had all been warned that there were three steep hills just before the finish, so to save something. The first one was a reality check after those nice kilometers of downhill, and it was longer than I had guessed, but not so bad.

The second one was short and very steep. I walked. Everyone walked.

The third one: very steep. It was terrible. I mentally cursed the race organizers. I came over what I thought was the top only to see that the hill went on. I felt like I was crawling. My swagger from a few kilometers ago was long gone. But at least from here it would only get easier towards the finish.

Down the other side, around a corner and… what the hell? Another steep hill. Like, really steep, find-something-to-grab-ahold-of steep. There were two retirees by the side of the trail. The runners ahead of me swore out loud this time, and the retirees laughed at them. At us. If I wasn’t so tired I would have fixed them an evil glare as I went by.

By the time I went down the fourth of the three hills, I wasn’t even fast on the downhills anymore. There was a very, very gradual climb to the finish line, back on pavement, which should have felt fast and easy. Instead, I struggled to maintain a jog. But I got to the finish, clocking a time just under five hours.

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The organizers set up this sweet panorama so you could mug and get a cool finish line photo as if you were running on the trail, but I was so beat I didn’t even notice. Whoops!

The sun was shining as we congratulated each other and began to refuel the calories and salt we had lost. Dry clothes felt so good. Sitting down felt good. I was proud of myself – my result was not particularly great, but I had worked hard and followed a plan and, I believe, done the best race I could do on that day. I was just over a year out from a major injury, and another major victory is that I hadn’t hurt myself again. That functional strength had worked: even when I was so tired, my feet nimbly navigated the trails and my ankles stayed stable.

Most importantly, I had a ton of fun and I was already dreaming of what long trail or mountain race to sign up for next year.

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With Annie at the finish: we did it! (Photo: some older lady walking by, who we accosted…)

***

The race hadn’t been easy.

If I’m thinking about the events that cap off my grueling goals, I think my PhD defense – scheduled for January – will be much easier. I like giving presentations, and I am excited to tell my colleagues, friends, and family about what I’ve been working on. I’m sure I will be nervous, but mostly, it will be fun. I’ve been imagining that day for months and months and months.

Compared to a mountainous trail marathon? PhD defense = easy.

But if I’m thinking about the paths that lead to those days, the running was much easier. The day after my marathon, I went for a little walk in the mountains with Annie, because we were already there and the views and mountain feeling are too good to miss even when your legs are jelly.

On Monday I went back to the office, and I didn’t take another day away from my dissertation until I handed it in just over a month later.

Again: that bad, bad situation of overwork, and everything it led to, was somewhat self-inflicted. I could have told myself, look, this is crazy. You don’t even really need six chapters. Florian, I can’t do chapter six. I’m going to take the weekend off and unscramble my brain and work on giving you a great five-chapter dissertation.

But that is not what I did. I wrote for hours at a time. I revised. I formatted. I cried. I ate a lot of cookies (a lot!). I asked colleagues to read terrible drafts. I rarely went running. I kept writing. I slept badly. I complained. I became a bad friend and officemate. I resented Florian. I cried more.

What I lacked was confidence. I was trying to follow the plan we had made, but it wasn’t working. I didn’t have that feeling that if I just did what was on the schedule, everything would be fine. Most days, it felt like there was no way in the world that everything would be fine.

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You think you’re doing okay and then you start correcting your bibliography and it looks like this…

Maybe partly because my training was over and I was rarely exercising, I totally lost perspective. My dissertation seemed like the only thing in my life, in some ways, and it felt like a slow-rolling disaster. Every little setback seemed like the end of the world.

But, on November 19, I handed Florian a printed version of my dissertation.

He made some minor corrections and told me it was very nice. This was classic: he had previously told me that he expected he would make a lot of corrections and there was no way I’d be able to turn it in the next day. But by saying that, he had ensured that I would ruin myself attempting to give him a nearly perfect dissertation.

I made those small corrections, and on November 20, I submitted my dissertation to the University of Zurich. It was anticlimactic. I uploaded a PDF to the online interface, and then walked some paperwork over to the Faculty of Science. The woman at the desk who accepted my registration for a PhD defense didn’t even say congratulations. Nobody had come along to give me a high five or hug, because I hadn’t asked them to.

Instead I went home and, much like after my marathon, lay on the couch. I sank into the leather cushions and felt like maybe I could stay there forever.

***

Recovery began the next day.

If there’s anything that being an athlete has taught me, it is that recovery is important. It’s not something I’m particularly good at, and it’s also something that I didn’t really value for much of my “serious” athletic career. I was interested in too many other things – when I didn’t have to train, I filled that time with something else. I’m pretty sure I would have been a lot faster if I had just taken a nap.

But now I’m some combination of older and wiser, and my body is older, and my brain is older. They need recovery and I fully believe in its value.

I took almost a week off from work, and now I’m back. I’m able to enjoy going to the office again. I’m able to get excited about reading papers, another thing that I almost completely neglected while I was writing. Many of the projects I am working on now, in this time between my dissertation and defense, are collaborative, and that feels great to get back to, too.

And in the back of my mind I can say that no matter what else happened in 2018 – the political, the personal, the stupid stress I put myself under – I accomplished my two big goals. That feels pretty good.