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.
Last week, I was part of a very interesting discussion about how data sharing in ecology has, so far, failed. Up to 64% of archived datasets are made public in a way that prevents re-use, but this is not even the biggest problem. We are currently sharing ecological data in a way that is mostly useless.
In the last part, I discussed ways to respond to the associate editor, and now it is time to discuss how to actually write the replies to the reviewers. This is a frustrating exercise, but one that can be made constructive if you try to find, in each response, a way to make your article better. Let’s dig in!
One of the thing that made publishing easier for me was to learn how to reply to reviewer comments adequately. This can easily be overlooked, and yet understanding how it works and how to react makes a significant difference. A good response can make you skip a round of review, or can convince the editor to not reject the paper in the reviewers have strong criticisms. Everything that follows is what worked for me, and it might not work for you, and you may want to approach the problem differently. Feel free to discuss in the comments.
Thanks to support from the Canadian Wildlife Federation, the Québec Centre for Biodiversity Sciences, and the Federation of Students Associations at the Université de Montréal, we have organized a science bioblitz at the Laurentians biology field station, operated by the Université de Montréal. Now that a good fraction of the data are online, I wanted to have a look at the results.
A little while ago, I gave a talk about the promises and challenges of high performance computing for biodiversity sciences. Because I wanted to go beyond “having more cores means we can run more model replicates”, I started by discussing the availability of data on Canadian’s biodiversity, and how we can do data-driven research. Long story short, unless we like birds, we can’t.
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.
By the time this blog post will be online, I will be back from a two days bioblitz, during which experts will have inventoried part of the biodiversity in our field station. In the months leading up to this, and in part because part of my own research depends on data collected by citizens, I have been thinking about Citizen Science a lot. And I am not entirely sure of what this is exactly, besides a cost-effective way of getting data.