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 It had been replaced by the loveliest arrangement of rocks, fresh soil, and lavender plants I had ever seen.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.
A few months ago, I was invited to an event organized by the Mozilla Science Labs, and I gave a short presentation on How to train data-driven ecologists. One of the key points in my presentation was the importance of community over institution, or broadly speaking, how to leverage existing training material that has been developed by the community.
I read, review, edit (and write) a lot of papers describing software for ecological research. So much so that I ended up developing a taste for it, and I thought it may be relevant to share it. This may also be the last blog post until September, so enjoy!
A good figure is worth a thousand words. But some figures can change the world in very profound ways. Today, I would like to discuss what I think is the most important scientific figure ever drawn.
There is a whole sub-genre of the ecological network literature working on elucidating “the structure” of bipartite networks (parasite/host, pollinator/plant, …). I am, of course, guilty of contributing a few papers to this genre. The premise is that, by putting together enough data from different places, we may be able to infer some of the general mechanisms that shape different aspects of the structure.
I was talking with a friend about a conversation they had, where someone questioned the fact that “community ecology” was a field/concept/thing. My own opinion of this, as a sometimes self-described community ecologist, is obviously “yes it is”. But let’s entertain the idea that it is not, and justify its existence.
In what is going to be the most technical note so far, I will try to reflect on a few years of using the Julia programming language for computational ecology projects. In particular, I will discuss how multiple dispatch changed my life (for the better), and how it can be used to make ecological analyses streamlined. I will most likely add a few entries to this series during the fall, leading up to a class I will give in the winter.