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.
There is a very important family of models in ecology based around describing the flows and fluxes of quantity across “boxes”. This can be biomass across species, alleles across spatial patches, population size, individuals across age classes, etc. Almost all of these models are based on ordinary differential equations, and they use parameters to express ecological processes. And the more quantities you want to model, the more parameters you need to link them together. As a result, complexity of the models often increase in a non-linear way with regard to the size of the problem.
In this follow-up to the previous part of the manuscript on computational ecology, I explore some of the ways to facilitate collaborations between data users and data producers. You can read the first part to get up to speed, and then feel free to comment and give feedback.
Deep down, I feel like there are two types of person in me when I am confronted to a science problem. First, there’s the artist. The big-picture, pie-in-the-sky guy, with creativity, and moxie, and a “We’ll sweat the details later” kind of attitude. And then, there’s the craftsman. The stickler for details, the one in charge of making sure that everything is clear, the one who understand the tools and knows how to use them. This is maybe especially important for computational research, but we need to understand how these two persons-in-us, the artist and the craftsperson, interact.