I’m revising a paper about my older work with plasmids. One of the reviewers, a theoretician (they wrote their review in TeX), thinks the paper “is really lacking a serious mathematical and statistical modeling effort”. And here I was happy to finally have a paper with no math in it! They don’t think it’s clear that my data support the conclusions I make, though it seems obvious enough to me. Plus, they want me to use a specific modelling method I think is seriously questionable.

At first I was upset at having to spend a bunch of time and effort responding to reviewer comments that were wrong and weren’t going to improve the paper. But then, in the process of doing some math to address a question from the other (more sensible) reviewer, I realized I could extend the math and show, quantitatively, how competing evolutionary hypotheses make different predictions about what should happen in my experiments. In the end, not only am I able to show that my data reject one hypothesis but are consistent with another, but I’m also able to explain the specific shape of my data—something I’d never even attempted to do. I’m actually surprised it fits so well.

So there you go. Score another one for peer review. It’s even better than the peers doing the reviewing.

jeff smith, J. David Van Dyken, & Peter C. Zee (2010) “A generalization of Hamilton’s rule for the evolution of microbial cooperation” Science 328: 1700-1703. [Link]

Here are some questions I ask myself when reading a paper:

• Why should anyone read this paper? What is the paper’s main point, the thing that everybody should take away from it? Is this point important?
• Is the paper right? How do the results support the main point? What are some alternative hypotheses for the results? Did the authors do the right controls? Did they do the right statistical tests?
• How is this paper wrong? All papers are wrong somehow. If you don’t find something wrong with a paper, you haven’t read closely enough. Some papers are more wrong than others. How do the errors in this one affect its main point? Are they fatal flaws or only minor quibbles?
• Is the paper well written? Is the data presented clearly? How could the writing be improved?
• What does the paper do right? What about this paper is worth emulating in your own work?
• If you were a reviewer, what would you recommend to an editor: accept as is, accept with minor revisions, send back for major revisions and re-review, or reject outright?

Scientists have opinions, and they are most interesting when they are controversial.  I have little patience for the pretense of a “fair and balanced view,” when we all know that balance comes out of discussions and disagreements among peers, not from the point of view of a single individual. (Pigliucci 2007 Science 31:317)

I more or less agree with this. But the same time, I think it’s important to try to recognize your own biases and try to look past them, to the extent that’s possible. I’ve always been impressed by scientists that actively seek out alternative explanations for their own data and then test those hypotheses with more data. Curt Lively is really good at this.

Why does this come up? Recently I’ve become interested in nonadaptive processes in evolution: the parts of evolution that aren’t natural selection. No, I shouldn’t say interested. More like: I’m mainly interested in selection, but if I’m going to spend all my time studying some particular phenotype of microbes, I want to know if that phenotype is a result of selection, and how.

Thinking about nonadaptive processes doesn’t come natural to me. I first got into evolution through behavioral ecology, a field that’s almost entirely about selection. But not considering the alternatives can lead you astray. The classic work here is “The spandrels of San Marco and the Panglossian paradigm: a critique of the adaptationist programme” by Gould and Lewontin. The basic idea is that not all phenotypes are adaptations; they might just be side effects of other things that are. More recently, Mike Lynch has shown that that many aspects of genome evolution have less to do with selection and more to do with mutation, recombination, and genetic drift.

So I may know that there’s more to evolution than selection, but it’s hard to just decide to think differently. And now that I come to think of it, my experience pretty much illustrate’s Pigliucci’s point. I only started thinking seriously about nonadaptive processes after talking with colleagues who have different backgrounds and different biases.

But we did have decent luck with reviewers.  Their questions, comments, and suggestions helped improve the paper, even if we didn’t always agree with them.  Two of the three were totally on board with what we were trying to do and how we were doing it.  The third mainly took issue with some of our stronger statements knocking Hamilton’s rule.  Worse would’ve been a reviewer antagonistic to our research program or a reviewer that just didn’t get the point, for whatever reason.  In the end, we clarified the problems we had with Hamilton’s rule, toned down our rhetoric somewhat, and that was enough.  I get the impression that many papers in these journals go through a similar cycle — they start out boldly stated to make it to full review (“An accurate replacement for…”), then get toned down on revision (“A generalization of…”).