Brian, Meg, and I will all have March For Science posts later this week. In the meantime, here’s an open thread. What do you think of the March? Did you attend one, or speak at one? Have you seen any pieces on the March that you think are particularly worth reading? What do you think happens next, or should happen next? Looking forward to hearing from you.
We’ll have a more serious post on the March For Science next week, but in the meantime here’s a compilation of some of the best signs, where “best” is operationally defined as “signs I really liked”. Whether because they pithily summarized what I think are good messages for the March to send, or just because they were funny. Share your favorites in the comments!
Robert Trivers, the world’s foremost living evolutionary theorist, is retiring from Rutgers University. Last year, he published his memoir, Wild Life: Adventures of an Evolutionary Biologist. Here’s my review.
Who pays the publication fee for your papers, when there is one?
When the authors are all members of the same lab, I assume the PI ordinarily pays the fee if there is one. That’s certainly what I do.
Just recently I published an author-pays open access paper with a grad student whom I co-supervised with a colleague, and there’s a second such paper in the works. I had been hoping to split the publication fees with my colleague. But it may come down to whoever has the most grant money.
What about papers by working groups or other big collaborations? Who pays the publication fee then? Does whatever funding source paid for the working group also pay the publication fee? Or does some working group member pay the fee from one of their grants, or from some other source available to them such as an institutional open access fund? What if more than one person in the working group has the ability to pay? In that case I guess the first author, or the first author’s PI, would pay?
Same questions for the data hosting fees charged by some depositories, when depositing data associated with a publication.
ht to a correspondent for suggesting this post idea.
Scientific ideas can have various virtues. Most obviously, they can be correct. But they can also be clever, surprising, elegant, etc.
One important but difficult-to-pin-down virtue is fruitfulness. A scientific idea is fruitful if it leads to a lot of further research, especially if that research retains long-term value (it wasn’t just a trendy bandwagon or whatever). Fruitfulness overlaps a lot with influence.
Fruitfulness or influence covaries positively with correctness, but not perfectly. It would be nice if the covariance were perfect. It’s unfortunate when an influential idea turns out to be wrong, because the work that grew out of that idea often loses at least some of its value, and because there’s an unavoidable opportunity cost to building on ideas that turn out to be wrong. Andrew Hendry has a compilation of ecological and evolutionary ideas that inspired a lot of research despite being (in Andrew’s view) wrong, or at least not all that important.
In this post I’m interested in the flip side of incorrect-but-influential ideas: ideas that were correct but not influential. Somebody said something true–but nobody else cared. Correct but non-influential ideas are the proverbial tree falling in a forest that doesn’t make a sound.
What are your favorite examples of correct-but-uninfluential ideas in ecology? In all of science?
Also this week: NSF Waterman award winners, Hungary vs. its best university, critter cams, keep lectures live, what the pyramid of passive and active learning methods REALLY looks like, the Godfather vs. grade-grubbers, and more.
Regression through the origin is when you force the intercept of a regression model to equal zero. It’s also known as fitting a model without an intercept (e.g., the intercept-free linear model y=bx is equivalent to the model y=a+bx with a=0).
Every time I’ve seen a regression through the origin, the authors have justified it by saying that they know the true intercept has to be zero, or that allowing a non-zero intercept leads to a nonsensical estimated intercept. For instance, Vellend et al. (2017) say that when regressing change in local species richness vs. the time over which the change occurred, the regression should be forced through the origin because it’s impossible for species richness to change if no time passes. As another example, Caley & Schluter (1997) did linear and nonlinear regressions of local species richness on the richness of the regions in which the localities were embedded. They forced the regressions through the origin because by definition regions have at least as many species as any locality within them, so a species-free region can only contain species-free localities.
Which is wrong, in my view. Ok, choosing to fit a no-intercept model isn’t always a big deal (and in particular I don’t think it’s a big deal in either of the papers mentioned in the previous paragraph). But sometimes it is, and it’s wrong. Merely knowing that the true regression has to pass through the origin is not a good reason to force your estimated regression to do so.
One way among many others by which a theoretician might develop a mathematical model of one scenario is by analogy with some other scenario that we already know how to model.
The effectiveness of this approach depends in part on how loose the analogy is. At the risk of shameless self-promotion, I’ll highlight a physical analogy that my own work draws on (the analogy isn’t originally mine): dispersal synchronizes spatially-separated predator-prey cycles for the same reason that physical coupling synchronizes physical oscillators. Here’s a standard, and very cool, demonstration involving metronomes sitting on a rolling platform. The analogy between the ecological system and the physical system is actually fairly close, though for reasons that might not be immediately apparent (how come coupling via dispersal works like coupling via a rolling platform?) The closeness of the analogy is why it works so well (Vasseur and Fox 2009, Fox et al. 2011, Noble et al. 2015, and see Strogatz and Stewart 1993 for a non-technical review of coupled oscillators in physics, chemistry, and biology).
But it’s more common for physical analogies in ecology to be quite loose, justified only by verbal argument. Hence my question (and is is an honest question, not a rhetorical one): can you think of any examples in ecology in which models based on loose physical analogies have worked, for any purpose? Sharpening of intuition, quantitative prediction, generation of hypotheses that are useful to test empirically, etc.? Because I can’t.
Also this week: don’t save your R workspace, tell me again why the peer review system is in crisis, what economists (and ecologists?) don’t know, thought leaders vs. public intellectuals, William Carlos Williams vs. email, Jeremy channels his inner early-90s self, and more. Including an extra-large helping of silliness!