Advanced research computing for ecology

In a few weeks, I will be giving a talk at the Association Francophone pour le Savoir annual meeting in McGill University, about how advanced research computing (aka high performance computing) can accelerate discoveries in biodiversity sciences and ecology. Collecting data on any ecosystem, no matter how small, is painstaking. It is long. It is expensive. And as a result, we have a relatively small amount of data. So what could advanced research computing possibly deliver?

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Ecological modeling is world building

I remember the first time I have been surprised by a model. I was working on the conditions under which a mutualist can protect its host from a pathogen, and in particular whether the mutualist can persist or will be displaced by the pathogen (unless there are multiple populations connected by dispersal, the answer is no). What surprised me was how, in the end, the answer to this question depended on the relative value of three parameters. Of course, nothing in modeling should be surprising, because the model encompasses the entirety of its own rules, and so of course the answer is in here, waiting to be found. But where do the models come from?

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