One fun aspect of this is that donors get to be on the “editorial board”, meaning they get to help choose the stories that get commissioned. Donors also get the first few stories free, and various other goodies depending on how much they donate.

Discussion from top US financial/journalism blogger Felix Salmon here (he thinks it’s a great idea). Further discussion from a skeptic (who thinks it won’t be financially sustainable in the long run) here. UPDATE: One of the founders of Matter responds to skepticism about their business plan here.

Even if you’re not a modeller, you should click through to find out what a sheep cyclone is.

One standard answer is to rely on what Jared Diamond (and probably others) have called “natural experiments”.  The basic idea is as follows. If you think that variation in variable A causes variation in variable B, compare the level of B across systems that vary in their level of A. So instead of manipulating A yourself, you’re relying on the “manipulations” (variations) in the level of A that nature happens to provide.

Unfortunately, natural experiments are infamously unreliable, not just compared to “real” experiments but in an absolute sense. As my PhD supervisor Peter Morin liked to say, “The problem with natural experiments is that there’s no such thing as a natural control.” That is, systems that vary in their level of A often vary in lots of other ways as well, some of which probably also affect the level of B. You can of course try to address this by statistically controlling for the levels of those other variables, assuming you can identify them. And you can try to simply collect lots of data from a large range of systems in the hopes that surely some of the among-system variation in variable A will be independent of all confounding variables. And you can try to get rid of any causal feedbacks from B to A by praying to the god of your choice…

Or maybe there’s a better way. Economists have to deal with all the same challenges in inferring causality that ecologists do. If anything, economists have it even worse because doing relevant experiments often is harder in economics than it is in ecology. In response, economists have come up with an interesting and potentially-powerful approach to inferring causality from natural experiments, the method of “instrumental variables” (IV).

Here’s the basic idea (for details, click the link above, which goes to the very good Wikipedia page on IV). An instrumental variable, call it X, is a variable that causally affects B only via its effect on A, and that is not itself causally affected (directly or indirectly) by B or A. Economists summarize the latter assumption by saying that X is “exogenous”. So you can estimate the causal effect of A on B by using, not just any natural variation in A, but only that natural variation in A that can be attributed to natural variation in X. Changes in X are perturbations that propagate to B via only one causal path, that running from A to B, so variation in the instrumental variable X allows you to estimate that strength of that causal path. The approach can be generalized to multiple causal paths, as long as you have multiple instrumental variables.

One thing I find interesting about IV is that they highlight how “more data” is not always helpful. Tempting as it is to think that, if only you had enough data on A from enough different systems, you could reliably infer the causal effect of A on B, it’s not true. What you need is not more data on the variability of A, you need the right sort of data on the variability of A (namely, that generated by an instrumental variable). Indeed, more of the wrong sort of data on variability in A can actually be harmful to inferring the effect of A on B.

The nice thing about the IV method is that it doesn’t require you to know anything about the rest of the system, such as other variables that might affect B while also covarying with A. All you have to know (and this is the hard part) is that X is what economists call a “good instrument”–that it satisfies the assumptions that make it an instrumental variable.

Which may limit the applicability of IV in ecology. In economics, IV are often policy changes. For instance, an increase in cigarette taxes should affect health only via its effect on how much people smoke. So you can use changes in cigarette taxes to estimate the effect of smoking on health, thereby getting around the fact that lots of factors may affect both health and smoking, and that people’s health may affect their inclination to smoke. Weather events like droughts also tend to make good instruments in economics.

I’m unsure whether ecologists will often have good instruments available to them. Weather is exogenous to ecological systems as well as to economic systems. But the problem is that weather changes typically affect any variable of interest via multiple causal pathways. And many policy changes certainly have ecological as well as economic effects. But the problem with many policy changes affecting ecological variables is that they’re not exogenous–the policy changes are made in response to observed changes in the variable which the policy change is intended to affect. So if ecologists want to use policy changes as instrumental variables, they may want to focus on policies with unintended ecological consequences. And even there you still might have the problem of unintended consequences propagated via multiple causal paths.  But we won’t know if IV can be useful in ecology if we don’t try them out.

And if you do try out IV and get them to work, I hope you’ll submit the paper to Oikos.

It was also the biggest week ever just counting non-syndicated views (2848), even though our many of our non-syndicated views have been replaced by syndicated views since we started putting full posts rather than teasers in our RSS feed.

It’s a bit of a pain to add up the syndicated views since you have to do it by hand from the stats on individual posts. But assuming that the proportion of syndicated views this week was typical (it’s actually probably a bit higher than usual since we had a bunch of posts this week, but whatever), then in a typical week we’re getting 2800-3400 views, or well over 400/day.

Thanks for reading everybody!

***********************************

ELME is a summer educational program at the Kellogg Biological Station devoted to Enhancing Linkages between Mathematics and Ecology.

ELME 2012 will be a sequence of three courses covering: Week 1) Maximum Likelihood Estimation, week 2) Structural Equation Modeling, and week 3) Matrix Population Modeling. In this hands-on environment, students will learn the basics in a lecture setting and cement their knowledge with independent and collaborative modeling projects using the computer program R.

Dates: June 4-22, 2012

Hours: Mon-Fri 9-5

Instructors: Week 1) Colin Kremer (Michigan State University), week 2) Don Schoolmaster (National Wetlands Research Center / USGS), and week 3) Hal Caswell (Woods Hole Oceanographic Institute)

Target audience: 12-18 graduate students or exceptional undergraduates

Prerequisites: At least one semester of statistics, undergraduate calculus, and familiarity with basic matrix manipulations Previous exposure to theoretical ecology and R useful but not required.

Format: A mixture of lecture, guided computer labs, and independent/team projects

To apply, email elme2012@kbs.msu.edu the following:

- your CV

- a statement of research interests and why you’d benefit from the course (< 1 page)

- a statement of relevant educational/research experience, including related coursework (< 1 page)

- the name of a reference who you’ve asked to email a letter of support

Deadline for applications: March 15, 2012

Preference will be given to students interested in all three courses.

Financial support to cover room and board and help defray transportation costs is available. Let us know if this is not necessary.

Academic credit is available, students of MSU and affiliated schools are encouraged to enroll.

For more info see <http://www.kbs.msu.edu/education/elme> or email elme2012@kbs.msu.edu.

Seriously, what are you doing still reading this blog? Click the links already!

The course runs Apr. 30-May 18, 2012.

You have to apply to be admitted. Application deadline is Mar. 1. To apply, go here.

The course description is below. It sounds awesome. I especially like how the course is suitable for students from both empirical and theoretical backgrounds. And Mark is one of the very best people in the world at linking math and data, as well as a great teacher and a great guy. So if you want to learn how to do ecology the way I for one think it should be done, this is the course for you.

**************

This course develops the methods, models and tools for quantitative ecology. Students learn to formulate, analyse, parameterize, and validate quantitative models for ecological processes and data. Applications include population dynamics, species interactions, movement, and spatial processes. Approaches involve classical hypothesis testing, computer simulation, differential equations, individual-based models, least squares, likelihood, matrix equations, Markov processes, multiple working hypotheses, and stochastic processes. A computer lab covers simulation and programming methods. Course discussion entails evaluation and appraisal of current literature. This course is open to graduate and undergraduate students.

Prerequisites: Introductory calculus, and statistics/biostatistics, or permission of the instructor(s).

This course is suitable both for field-based biology students and for mathematical/theoretical students who are interested in learning about how to connect models to data in an applied ecological setting.

HT Jarrett Byrnes (via Twitter)

The linked paper is the best paper I’ve read in a long time. It’s essential reading for everyone who does science, from undergraduates on up. It’s about experimental psychology, but it applies just as much to ecology, perhaps even more so. It says something I’ve long believed, but says it far better than I ever could have.

One partial solution to the problems identified in this paper is for all of us to adhere a lot more strictly to the rules of good frequentist statistical practice that we all teach, or should teach, our undergraduates. Rules like “decide the experimental design, sampling procedure, and statistical analyses in advance”, “don’t chuck outliers just because they’re ‘outliers’”, “separate exploratory and confirmatory analyses, for instance by dividing the data set in half”, “correct for multiple comparisons”, etc. Those rules exist for a very good reason: to keep us from fooling ourselves. This is not to say that judgment calls can ever be eliminated from statistics–indeed, another one of my favorite statistical papers makes precisely this point. But those judgments need to be grounded in a strong appreciation of the rules of good practice, so that the investigator can decide when or how to violate the rules without compromising the severity of the statistical test.

Basically, what I’m suggesting is that, collectively, our standards about when it’s ok to violate the statistical “rules” may well be far too lax. Of course, if they were less lax, doing science would get a lot harder. Or rather, it would seem to get a lot harder. In fact, doing science that leads to correct, replicable conclusions would remain just as hard as it always has been. It would only seem to get harder because we’d stop taking the easy path of cutting statistical corners. And then justifying the corner cutting by making excuses to ourselves about the messiness of the real world and the impracticality of idealized textbook statistical practice.

The linked paper discusses another solution: to report all judgment calls and exploratory analyses, so that reviewers can evaluate their effects on the conclusions. Sounds like a great idea to me. They also note, correctly, that simply doing Bayesian stats is no solution at all. The paper is emphatically not a demonstration of inherent flaws in frequentist statistics.

Further commentary from Andrew Gelman here.