For those readers who have met me, it will come as no surprise that I was a bit of a geek when I was doing my undergraduate studies.  And that was long before geek was in any way sexy.  Sheldon (from ‘The Big Bang Theory’ and not @ben_sheldon_EGI) probably hadn’t been born.  However, one day one of the cool gang of undergraduates did talk to me.  She wondered whether she could use my results from a practical she had been ‘unable’ to attend.  I wanted to help but I was also concerned she’d copy my data and I’d end up being the one hauled over the coals for plagiarism.  So I came up with a cunning plan.  I wrote some code on the VAX (look it up online if you’re under 45) that took my data and generated a pseudo random dataset with many of the same statistical properties as the dataset I had collected.  It took me most of the night.  Nicole seemed happy, but not sufficiently so to come for a drink with me. Continue reading “Chi square I’ve met you before. A belated Valentine’s blog”

A few years ago, someone with an interest in dynamical systems devised a complex financial product that allowed banks and other larger corporations to achieve a good return on their investments at limited risk. But it turned out that their money was not as safe as they thought, and things went belly up. Banks lost money hand over fist, some ended up bankrupt, while the taxpayer bailed others out. The global economy took a nosedive, and countries ended up being much more in debt than they would have liked. As the next general election approaches, we are told that things are improving in the UK, but the deficit is large, and it is not coming down as quickly as expected. This is a serious problem, and something that will take time to sort out. There must have been many very difficult meetings in Whitehall, with departments told they need to spend less money and, where possible, generate money. Whether one agrees with this strategy or not, the logic behind it is straightforward to follow: we need to pay off our debts so we should spend less money and generate more of it. Continue reading “My life as a wealth generation asset”

In his thought-provoking blog, Tim asks a fundamental question every ecologist has to think about occasionally: how many terms should I include in my model? Tim argues that models with a high heuristic value include only a few parameters; models like Verhulst’s logistic model of population dynamics and Lotka-Volterra’s predator-prey model. Tim also advises that ecologists in the quest of universal laws should limit the number of parameters in their models to as few as necessary to get the job done. However, I shall argue that the devil is in the detail! Continue reading “How complex should models used by ecologists be?”

One of the things that I enjoy most about the science I do is collaborating with both field biologists who know their systems inside out, and theoreticians who’s specialist expertise is abstraction and equations. One thing I have learned from these collaborations is that every field or laboratory system exhibits some oddities. The Trinidadian guppy system is the latest, wonderful, system I have begun collaborating on, and it exhibits numerous quirks. One of my favourites is what we affectionately term ‘zombie males’. Because females store sperm, males can sire offspring after death. Such behavior is, of course, not particularly unusual, but this is the first time I have had to ponder whether it is necessary to incorporate such a life history ‘quirk’ in models, and if so, how. These system-specific oddities make me take issue with a quote from a theoretician colleague. It goes something like this: ‘reality is just a special case, and not a particularly interesting one’. Reality is, in fact, very interesting.   However, the oddities of each system do generate certain challenges for the modeler. Should they always be incorporated into models? Continue reading “Modelers to the left of me, field biologists to the right; here I am, stuck in the middle with you”

Forty years. That’s at least how long an academic career can last, if you start at 30 and retire at 70. There is no mandatory retirement age (at least in the UK and US) and, unlike most people, tenured academics rarely lose their jobs. For older academics (say over 50) increased longevity can be accompanied by the right to work as long as one wants.

The usual career pattern – always sideways or up, rarely down – means that academics spend 20 years at near-maximum salary and with a tight grip on institutional power and hiring practices. This isn’t actually bad for productivity since studies convincingly show that aging doesn’t affect productivity. And the ability to work into old age is attractive to researchers whose salaries often lag the business sector. Continue reading “Down the up staircase: longevity and academics”