When I give seminars about my work and when I write grant proposals, I often talk about how tractable it is – the hosts are see-through, allowing you to see internal parasites! You can maintain individual genotypes in the lab for years! Population dynamics are fast, so you can watch evolution in a natural population over the course of a single season!
That’s all true, but it ignores something else: even tractable systems are hard. I think of this especially at this time of year because we’re in the midst of field season (our field season runs from July through November) and because there are new folks in the lab who need to be trained. I feel like I talk a lot about the cool things we can do with our system when I’m recruiting folks to the lab, so then I feel a little bad when they arrive, eager to get involved in our field survey, only to find out that they can’t help with counting samples.* In order to be able to count field samples, the person needs to be able to identify seven different species of host (and Daphnia are literally a textbook example of phenotypic plasticity), plus a whole suite of parasites (there are ~8-10 that we see routinely, plus various other ones that we see less commonly). That’s a lot to learn how to identify all at once. Actually, it’s too much to learn how to identify all at once, and, as a result, grad students (and technicians) usually spend a full year in the lab before they can count any of the field samples. And, as I’ve written about before, lab experiments often have lots of failures, sometimes for inexplicable reasons. And, when piloting new experiments, sometimes things that should be really straightforward don’t work, and it can take years to figure out why. (As one example, we’ve spent the past two years troubleshooting something that seemed like a slam-dunk experiment and still haven’t figured out the problem.**)
I am not claiming that my study system is especially hard. I think everyone’s systems have advantages and disadvantages, even if it’s easy to focus more on the advantages and disadvantages and end up with system envy when you see someone’s polished talk or publication. But I sometimes wonder if I’ve oversold the tractability of my system. In the end, I have to remind myself that, unfortunately, tractable is different than easy.
*I do say this during the recruitment process, too.
** But I have a new hypothesis for something that might be causing the problem! Hope springs eternal.
Dynamic Ecology 
Preach on Meg! Everything you say totally jives with my own experience with protist microcosms. And I’ve wondered the same thing as you–whether the fact that we emphasize the tractability of these systems in our papers, plus the fact that our papers only report experiments that worked, is what gives people who don’t work in these systems a false sense that it’s easy.
Maybe we can somehow arrange for everyone in the world to try one of these systems out and fail abysmally at them? 🙂 Over the years, I’ve known several people who’ve been trying out protist microcosms totally fail before coming to me or another protist microcosm person for advice. Sometimes because they can’t grow the organisms, but more commonly because they don’t have the experience and background knowledge to make reasonable guesses about how the organisms will grow and behave. So they end up choosing inappropriate species for whatever experiment they’re trying to do.
Although my system may be easier than yours, in that I can train students to identify and count several different protist species in a few weeks. Though the plasticity of some protists can make that difficult sometimes.
And you didn’t even mention how your organisms are sometimes killed by their own habitat. 🙂
Your comment relates to my system-based research post. All the natural history knowledge that comes from working with a system for a while is really crucial and hard to easily transfer to someone else.
I was just emailing with someone who I’ll send some genotypes to, and warned him that they will try to beach themselves in the surface film if he doesn’t use cetyl alcohol. But I also warned him that you can’t use too much, or they’ll die from that. See? Tractable != easy
“All the natural history knowledge that comes from working with a system for a while is really crucial and hard to easily transfer to someone else.”
Yup. I wonder how much people who don’t work in these systems realize that there *is* natural history knowledge involved. That is, it’s not just that they don’t know the natural history–they don’t realize that there *is* natural history to know.
Of course, part of what we’re calling “natural history” here is “knowing from experience what will happen under artificial conditions X”. Like “the Daphnia will beach themselves in the surface film of a lab culture unless you add cetyl alcohol”. Which maybe isn’t the sort of thing that field ecologists would ordinarily think of as “natural history” knowledge. Because they probably think of natural history as having to do with close observation and detailed knowledge of specific organisms in nature, as opposed to in artificial conditions. But I think the term is apt.
Apparently this kind of “tacit knowledge” is increasingly regarded as a big deal in molecular biology too. Apparently it’s sometimes impossible to describe your methods in sufficient detail that someone else could just read your methods section and repeat your work. They have to basically watch you do it so they can do *exactly* as you do. This has led to interest in video methods sections.
I think you’d find Stephanie Hampton’s comments on microscopic natural history as part of the Natural Histories Project a breath of fresh air with respect to the common assumption that “natural history” = “being outdoors.”
http://naturalhistoriesproject.org/conversations/the-microscopic-world
“I would argue that my connection with plankton is just as strong as somebody else’s connection with a forest. And that happened in a lab.”
I need to put a plug in for even “tractable theory” being hard. I thought I understood the way a particular approach worked–an approach a few prominent ecologists have been plugging far and wide–but it is failing in some textbook cases. It would be nice to understand why so that I can at least write a deeply unfun paper saying, “Extreme sensitivity to assumption X renders the approach useless for most real systems,” but so far, it’s just a lot of head-banging.
What I find hardest, as a new PI, is not turning these failures into a referendum on my own suitability for science.
Thanks for this reminder. I needed it today.
Ugh, this sounds very frustrating!
I think a lot of field ecologists have the idea that you just whip out a theory in a few days. In my experience good theory development has its own rhythms, goes in fits and starts, and often a really worthwhile result takes a year or two for the most important insights to pop out. It may not be explicitly tied to the seasons where you know you will need exactly X years to get the data collected like some field projects, but good theory takes time.
In two specific cases I have pounded away at something (off and on) for 5 years because I felt it was important, and I never got all the way to where I wanted to get, so I just published what to me is a partial result in the hopes others can go further. So far in both of the cases I am thinking of I have gotten really positive feedback for my “partial results” so sometimes I do think I am setting the bar too high for what counts as a publishable solution.
I have an old post where I talked about my most embarrassing moments in science. The most embarrassing of which came because of a bit of theory that took me years to work out–but that I mistakenly *thought* I’d worked out after “only” a year of work:
https://dynamicecology.wordpress.com/2014/05/08/my-most-embarrassing-moments-in-academia/
I’m also rethinking what should be “publishable” here. It would be so satisfying to have an analytic solution that defines the cases in which this thing works, because my answer so far is “systems with not too much and not too little of X.” I’m sure this would still be a contribution, though, and it might even help the approach get properly applied! But I can’t help feeling like it has too many loose ends and is sure to get partially corrected. That, I guess, is science.
The challenge for me is figuring out those “failsafe” in silico experiments that will lead to an interesting answer in finite time no matter what. Holding out for Certain Fundamental Insight Into Everything is my natural inclination, and it leads to long delays between publications. I think in bench and field work, you have to force yourself to think harder about experiments and results in a modular way, and I need to adopt this more systematic approach in theory. It’s a lot better than thinking, “Damn, six months on this problem and I still don’t really get what’s going on.”
This conversation has me thinking back to Peter Adler’s old guest post on how doing good science never gets any easier:
https://dynamicecology.wordpress.com/2013/08/21/it-doesnt-get-any-easier-guest-post/
And see also the comments on that post, where Peter says that he used to think modeling was faster/easier than field work, but now he thinks the opposite.
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