Hypotheses are over-rated. Don’t let them ruin your writing.

It starts, like all great stories do, with Lavender. When I was a PhD student, one of my projects was to study time-series of bacteria-bacteriophage infection networks in the soil. We had a little plot of soil, about 10 cm by 50 cm, in one of the remote university “green” spaces (Montpellier in the summer is the brownish kind of green). On day 1, we got our five soil samples, and started isolating them in the lab. Then after a week, we thought it was time for another sample. Took a fork and some falcon tubes (we were good at improv science), headed down the stairs. No more soil plot. It had been replaced by the loveliest arrangement of rocks, fresh soil, and lavender plants I had ever seen. No more plot, no more sampling, no more project, no more paper.

Continue reading Hypotheses are over-rated. Don’t let them ruin your writing.

Two grants should be enough.

Since I am still waiting for my immune system to win its week-long fight with some viruses (go cytokines go!), I figured I would deviate from the planning and write something related to, not ecology directly, but how to mislead people with statistics. And it involves the logistic curve, so it is basically population dynamics anyways.

Continue reading Two grants should be enough.

Approximate Bayesian Computation and tiny data in ecology

Every time I hear about Big Data in ecology, I cringe a little bit. Some of us may be lucky enough to have genuinely big data, but I believe this is the exception rather than the norm. And this is a good thing, because tiny data are extremely exciting – in short, they offer the challenge of isolating a little bit of signal in a lot of noise, and this is a perfect excuse to apply some really fun tools. And one of my favorite approaches for really small data is ABC, Approximate Bayesian Computation. Let’s dig in!

Continue reading Approximate Bayesian Computation and tiny data in ecology