Writing in Plos One, Carmel et al. report on trends in the subject matter and methods of ecological research over the last 30 years. They sampled 25 articles/year from 1981-2010 from the ecological literature as a whole (136 journals), and another 25/year from eight leading journals published throughout that time period. Among the headline numbers: about 2/3 of all studies are of single species, and that fraction hasn’t changed much in 30 years. Studies of “climate change” and “biodiversity” have increased in frequency. In the literature as a whole, studies of genetics have increased in frequency while studies of physiology and behavior have declined in frequency. The most common method in both the literature as a whole and in leading journals is observational (!), with experiments a distant second, though the gap is smaller in leading journals. That surprises me. But this doesn’t: only 12% of studies in the literature as a whole and in leading journals are modeling studies, and that fraction has hardly changed since 1981. Meta-analyses are increasing in frequency in the literature as a whole, though not in leading journals, but still comprise only a small minority of all studies. And the frequency of studies focused on solving applied problems has increased substantially over time. I think it’s great to have data like this, it’s a reality check for our own biases and faulty memories. For instance, Lindenmayer and Likens’ recent complaints about ecology becoming dominated by math and meta-analysis are mostly baseless. (HT Don Schoolmaster, in the comments)
Philosopher Nancy Cartwright has an accessible piece in medical journal The Lancet on the virtues and limitations of randomized controlled trials as means of identifying the most effective medical treatments. Everything she has to say applies just as much to randomized controlled experiments in ecology. She begins with a very clear, lucid explanation for why randomized controlled experiments remain the gold standard for demonstrating causality, as compared to, e.g., methods like “instrumental variables” and structural equation modeling. She then goes on to discuss the challenge of generalizing from randomized controlled experiments. For instance, in ecology we might wonder if the experiment would come out differently if conducted at some other place or time or with some other species. The challenge is to gain knowledge of “causal capacities”: under what range of circumstances does treatment X cause effect Y? One way to address this issue is via brute inductive force: repeat the experiment under many different conditions. NutNet is an ecological example. But there may be other ways. Anyway, probably nothing super-new here, at least not to many of you, but it is an especially clear discussion. And it’s perhaps heartening (or depressing?) to know that we ecologists aren’t the only ones who struggle with the challenges Cartwright discusses. And just for fun, here’s Cartwright’s piece in cartoon form.
Terry McGlynn on how avoiding mathematical modeling helped him make an important discovery about ant life histories. I suspect I may surprise a few readers when I say I like Terry’s post very much. On this blog, I often argue for the value of mathematical modeling and try to clarify why it’s valuable. But I would never argue that all good science starts with modeling! Indeed, as I noted in the comments on Terry’s post, I and my labmates have done theory-free pattern-discovery of the sort Terry describes.
I thought about doing a whole post on this one, but chickened out and decided to bury it in the Friday linkfest to minimize the controversy: Economist John Whitehead notes an interview with the EiC of American Economic Review (AER; the leading journal in all of economics) in which the EiC admits that “prestige of the author” is a factor in deciding which papers to accept at AER. Which is something that non-prestigious people probably suspected, but it’s unusual to see it openly admitted. As to the relevance of this to ecology, I leave it to you to discuss as you see fit! Because I don’t dare (even though I have some relevant first hand information…) (HT Economist’s View)
Of course there’s a wiki devoted to explaining every xkcd comic. (HT Brad DeLong)