The last experiment I did as a graduate student was one where I wanted to experimentally test the effect of predation on parasitism. To do this, I set up large (5,000 L) whole water column enclosures (more commonly called “bags”) in a local lake. These are really labor intensive, meaning I could only have about 10 experimental units. I decided to use a replicated regression design, with two replicates of each of five predation levels. These were going to be arranged in two spatial blocks (linear “rafts” of bags), each with one replicate of each predation level treatment.
Left: two experimental rafts; right: a close up of one of the rafts, showing the five different bag enclosures
As I got ready to set up the experiment, my advisor asked me how I was going to decide how to arrange the bags. I confidently replied that I was going to randomize them within each block. I mean, that’s obviously how you should assign treatments for an experiment, right? My advisor then asked what I would do if I ended up with the two lowest predation treatments at one end and the two highest predation treatments at the other end of the raft. I paused, and then said something like, “Um, I guess I’d re-randomize?”
This taught me an important experimental design lesson: interspersing treatments is more important than randomizing them. This is especially true when there are relatively small numbers of experimental units*, which is often the case for field experiments. In this case, randomly assigning things is likely to lead to clustering of treatments in a way that could be problematic.
My recent post on building confidence, building resilience, and building CVs got me thinking a lot about rejection, including what is the “right” amount of rejection. There’s no clear answer to that question, but I think there are extremes that would not feel right for me. If every manuscript got accepted at the first journal to which I submitted it, I’d suspect I was playing it too safe in my journal choices. But I also definitely would not want every manuscript rejected from multiple journals before it was accepted!
I originally was going to do a poll asking about what percentage of manuscripts you think you should get rejected, versus what percent actually are rejected. But I think that would be easy to guess at, but that probably it would be hard to estimate well. And I realized that it’s probably more interesting to get some sense of what is actually going on. So, instead, I am going to ask about the three most recent papers on your CV. (Three is an attempt to balance not having one weird paper dominate a response with not wanting the number so high that only senior folks could answer the poll.) This will take a little time to answer, I think – I personally would have to think a bit about each of my three most recent papers and to think of their submission histories. If you’re used to plowing through quizzes, this one might take longer.
When I was at the biology19 meetings recently, someone said something to me that I can’t stop thinking about: a student’s first manuscript should get sent to a journal where it will be accepted without much of a struggle; the second submission should be more of a struggle, but should get accepted at the first journal to which it was submitted; the third should go somewhere where it gets rejected. The person who said this, Hanna Kokko, acknowledged this was somewhat tongue-in-cheek, and that many factors will end up influencing where someone submits a given manuscript; her real approach is to respect the first author’s own wishes, after a discussion of the pros and cons of different options. But her tongue-in-cheek recommendation is motivated by the recognition that rejections can be a huge hit to one’s confidence, especially when someone is just starting out. I’ve seen (and personally experienced) the enormous confidence hit that can come from serial rejections of a manuscript, again, especially when one is just starting out. So, trying to figure out a strategy to reduce the potential for a big ego blow (while learning to deal with rejection too—but not before one has succeeded twice) makes a lot of sense to me.
As I worked on a manuscript recently, I wanted to add a reference to a paper by John K. Gilbert on concepts, misconceptions, and alternative conceptions and how they relate to science education (Gilbert & Watts, 1983). As I scrolled through my EndNote library, I was surprised by how many papers I had in there by the rotifer biologist John J. Gilbert—I felt like I was scrolling a long time to make it past Gilbert, J.J. in the database. This got me wondering: who else is surprisingly well-represented in my EndNote library? And who is in yours? (Feel free to substitute your preferred reference manager for “EndNote”, or to replace “EndNote library” with pdf library or, if you’re old school, folders in your filing cabinet.)
Last Monday, I faced a post-travel inbox filled with emails that needed replies. Some of them were invitations for things that would take up my time, but that seemed interesting or important or valuable or all three. And, then, of course, there were all the other things I needed to do as part of my job – editing manuscripts, writing letters of recommendation, sending emails to get people access to the lab, analyzing data, etc. And it was also the day where my post on seeing a therapist appeared, which led to lots of interactions on social media, via text, and through email. All of that led me to revisit a question that I am constantly asking myself, and that I surely will never stop asking myself: how should I spend my work time?
I couldn’t get this out of my head, and, as I walked to daycare, I realized that there are three questions I should consider as I evaluate whether to do something:
- Is it officially part of my job?
- Am I particularly good at it?
- Do I enjoy doing it?
I thought about how, ideally, I should try to prioritize things where the answer would be “yes” for all three. And I thought about how I spend a lot of time on things where the answer to all three of those questions is “no”.
When I got to daycare, I knew I wanted to think about this more, and was worried I would forget it. So, I pulled out my notebook in the daycare lobby, propped it on top of the stroller, and drew this:
If you here require a practical rule of me, I will present you with this: Whenever you feel an impulse to perpetrate a piece of exceptionally fine writing, obey it—whole-heartedly—and delete it before sending your manuscript to press. Murder your darlings.
– Arthur Quiller-Couch, “On Style”, 1914
“Murder your darlings” and its variants is common writing advice.* But what do you do if you’re not quite sure you’re ready to part with those darlings? My strategy is the same as Ethan White’s:
I suspect this is a common strategy (certainly the twitter responses suggest it is), though I don’t think it’s one that gets discussed much.
Last week, I had the honor of being a plenary speaker at the biology19 conference in Zurich. This is an annual meeting of Swiss organismal biologists, where most of the attendees are Swiss graduate students and postdocs. When I first thought about my talk, I debated whether to use the last part to talk about mental health in academia, especially since I am on sabbatical this year and some of my sabbatical projects relate to student mental health. But, when I prepared my talk, I decided to just stick with my normal research.
On the first day of the meeting, I had several conversations with people that veered towards student mental health, which made me wonder if I should have included mental health in my talk. Then, the afternoon plenary on the first day was given by Virpi Lummaa. She gave a really interesting talk about her research, but pivoted at the end to talk more about the human side of science. It was inspirational. So inspirational that I went back to the hotel and changed the end of my talk to focus on mental health in academia. When I decided to make that change, I made another decision: I would admit to a room full of hundreds of my colleagues that I see a therapist regularly, and that doing that is essential to my ability to do everything I do, including my science.
As I’ve blogged about a few times recently, I have been working with a couple of collaborators, Susan Cheng and JW Hammond, on a project aimed at understanding student views on climate change. As part of this, I’ve been thinking about what we teach and how we teach it, and also about a common challenge faced by instructors who teach about climate change: how do we convey the severity of climate change without leaving students feeling depressed and hopeless?
As I was working on the manuscript describing the first set of our results, I typed a sentence to that effect, and then just sat and stared at the computer for a bit, wondering “Is it my responsibility as a biology instructor to leave students empowered and with a sense of purpose?”
Some ecologists start their careers planning to study climate change, and others make a decision to pivot towards that line of research. But something I find fascinating is that there are ecologists, myself included, who didn’t necessarily set out to study climate change, but who are accidental climate change biologists. To give just one example: if you work on a time series on natural populations, communities, or ecosystems that extends more than a few years, chances are you’ve found that climate change is now a part of what you’re studying.
I’ve thought about this over the years as projects we work on that started out as basic research into host-parasite interactions end up relating to climate change. Some links are obvious—wanting to understand how temperature influences host-parasite interactions leads pretty naturally to thinking about how climate change will influence host-parasite interactions. Some links are less obvious—for example, we wondered whether the light environment might be influencing when and where we saw parasite outbreaks. As I recall, our initial interest in this was not related to climate change. But lakes are getting browner, in part due climate change, so any work we do on how lake light levels influence disease naturally links with climate change. And we now have some data on host-parasite interactions in lakes that spans 1-2 decades. Once you’re into decadal time scales, you have to consider the impact of climate change on what you’re seeing.
I’ve also thought about this in terms of some projects I didn’t work on. When I started grad school, one of the projects I was thinking of working on related to what was going on under the ice in lakes in winter, and how things like snow cover influenced that. So, when I saw news articles about a new study showing that there will be an “extensive loss of lake ice…within the next generation”, I thought back to those grad school plans to work on lake ice & snow cover. My recollection is that my interest in that project was mainly wanting to understand the basic biology of lakes, but clearly it would have ended up being a study of climate change if I’d pursued it.
Based on conversations with colleagues, I know I’m not alone in coming to realize that I am an accidental climate change biologist.
So, I’m curious: for my fellow accidental climate change scientists, when did you realize you were studying climate change?
Last week, I wrote a post where I talked about how my training in evolutionary ecology led me to try reaction norms (that is, paired line plots) for plotting paired Likert data. I had already tried a few other options, but didn’t include them in that post, and I got some feedback on that post that gave me more ideas. There was also a request for code on how to actually generate those plots. So, this post shows four different ways of visualizing individual-level responses to paired Likert-scale questions (paired line plots, dot plots, mosaic plots, and heat maps). It does that for two different comparisons, leading me to the conclusion that the type of plot that works best will depend on your data. I’d love to hear which ones you think work best — there are polls where you can vote for your favorite! And, if you’re working on similar data and want to see code, there’s an associated Github repo, but it comes with the disclaimer that my code is good enough, but definitely not elegant.