You can recognize a pioneer by the arrows in his back.

Positions available to work within an integrated terrestrial and marine research program addressing fundamental questions on the origin, maintenance, conservation and future of life and biological diversity on Earth. Candidates should have a strong publication record, relevant analytical and data handling skills, and an ability to communicate within a research team. Competitive salaries are offered.

If you’re looking for a quantitatively oriented postdoc in ecology this position with Kiona Ogle is a great opportunity.

A nice piece in the New York Times (via Ecotone).

I had an interesting conversation with someone the other day that made me think I needed one last frequency distribution post in order to avoid causing some people to not move forward with addressing interesting questions.

Many of us have had the feeling that something is not right these days with the peer-review system in science.

A couple of weeks ago we made it possible for folks to subscribe to JE using email. We did this because we realized that many scientists, even those who are otherwise computationally savvy, really haven’t embraced feed readers as a method of tracking information.

This is a table of contents of sorts for five posts on the visualization, fitting, and comparison of frequency distributions. The goal of these posts is to expose ecologists to the ideas and language related to good statistical practices for addressing frequency distribution data. The focus is on simple distributions and likelihood methods.

Summary Likelihood, likelihood, likelihood (and maybe some other complicated approaches), but definitely not r^2 values from fitting regressions to binned data. A bit more nitty gritty detail In addition to causing issues with parameter estimation, binning based methods are also inappropriate when trying to determine which distribution provides the best fit to empirical data.

Summary Don’t bin you’re data and fit a regression. Don’t use the CDF and fit a regression. Use maximum likelihood or other statistically grounded approaches that can typically be looked up on Wikipedia.