Little bit of politics...

Yesterday in the UK, large numbers of public sector workers went on strike to protest about the changes that the government wants to make to their pensions. I was catching up with this by watching Newsnight, and a peculiar rhetoricical tactic became apparent from the government representative. Apparently unrelated to this, I have recently started what I hope will be a fruitful collaboration with a pure mathematician. In a meeting the other day, I was struck once again by the different prism through which mathematicians see the world, apparent through the questions that my colleague asks about ecological phenomena.

In the spirit of seeing things mathematically, I thought I would have a go at applying some basic set theory to the Newsnight debate. Well, that’s probably a little too grand – basically, I’ve produced a pair of Venn diagrams.

This first diagram represents government rhetoric: it is unfair for the generous public sector pensions to be funded by taxpayers (implicitly: public sector workers ⊄ taxpayers). And more specifically, those who suffer most during the strikes – especially those by teachers – are that (tautological, as I am now acutely aware) demographic most beloved of politicians of all stripes: ‘hard-working parents’ (i.e., teachers ⊄ hard-working parents).

In reality, of course all public sector workers are also taxpayers (in fact, about 20% of those working in the UK are public sector employees; PDF of the Office for National Statistics source for this here). And do you know what? Plenty of public sector employees are also parents. Some of these are even – shock! – teachers. And I don’t doubt that at least a few of these work hard. (NB – the incomplete union of hard-working parents with taxpayers is intentional, but more to capture the cash economy than the wealthy tax dodgers!)

I don’t pretend to understand pensions, I can barely muster the interest to open the annual statements I get regarding mine, and certainly I’m not in a position to offer any kind of analysis over whether we really are heading for a crisis that can only be averted by making us all work longer for a lower pension at the end. But, setting up false dichotomies to make political points ought not to pass without challenge.

And with that – I’m off on my hols!

Vital Statistics

Statistical analysis divides bioligists. There are those of us who like nothing more than to dive in to a sparkly new method, and who feel a day has been wasted if it has not involved the interrogation of at least one little model. And then there are normal biologists, for whom stats are, at best, a necessary evil, an arduous process that has to be endured in order to convince others of what you already know to be true. The kind who got into biology to get away from maths, and who have been resentful of its intrusion into their working lives ever since. But, given that biological research without statistics is not really biological research, every tree-hugging, dolphin-bothering biologist must at some point roll up their sleeves and learn some stats. And the question that faces people like me, who have to teach them this stuff that they find difficult and pointless, is: what does a biologist need to know about statistics?

So I was interested to come (indirectly, via the excellent R-bloggers site) across a blog by Ewan Birney entitled Five statistical things I wished I had been taught 20 years ago. Making lists like these is always going to set you up for a fall, and if I disagree with some of Ewan’s points, that’s not to say I don’t admire him for posting them. And some of what he has to say I agree with entirely.

Of his five, for example, I would stick a big tick next to nos. 2 (learn R) and 4 (understand the difference between a P value and an effect size, with (especially relevant for a bioinformatician) reference to sample size), and I also agree with 3 that permutation / randomisation can be a very effective way of making sense of large and badly-behaved datasets.

Regarding number 5, on learning linear models and Principal Components Analysis, I kind of agree. In as much as, yes, properly understanding the basic linear model is probably the single most important piece of statistical knowledge that you can obtain. So much else flows from it: from General to Genearlised Linear Models, on to Generalised Additive (Mixed) Models, mixed effects models of many different flavours, Generalised Least Squares, and most other things you will ever need. On the way, cutting out a lot of confusing terminology too, by conceptually uniting things like regression, Anova and t-tests that most of us are taught as stand-alone tests. PCA, on the other hand, is a useful little tool for visualising multivariate data, and potentially then for feeding in to more interesting analyses, not much more.

But I can’t agree with number 1, on the importance of non-parametric statistics. In my experience, people tend to use these because they don’t really understand the assumptions of parametric methods like linear models: almost always, these make assumptions about the residuals, rather than the raw data – so one of your variables not being ‘normal’ is not necessarily cause for alarm – and almost always, there is a parametric technique which has been designed to cope with the specific odd residual distribution that you have, and which will (incidentally) be considerably more powerful than its non-parametric (typically rank-based) equivalent. If not, randomisation will generally be more informative than rank-based statistics. In addition, people tend not to have a good understanding of the assumptions made by non-parametric tests (often relating to the variance distribution, the very thing which may have driven you to non-parametrics in the first place!).

Vital statstics for biologists, then? Learn your linear models (some good books). And basic probability. But then judging risk and probability is a vital skill for everyone, if you ask me.

Or, you can follow the advice that I read in a ‘Statistics for Social Scientists’ book (and I don’t mean to bash the social sciences here, at all, I know they drive some excellent stats, that did just happen to be the book I read this advice in): If the data are not unanimous in support of your hypothesis – change your hypothesis…

Shifting baselines of happiness

I’ve just read a fascinating piece on happiness by the evolutionary ecologist Hanna Kokko, in the latest issue of the British Ecological Society’s Bulletin. Hanna was reporting her impressions of a multidisciplinary happiness conference, Is more always better?, at which she had been the only ecologist. Now, I’ve been sort of vaguely aware of this kind of research for a while, things like the pervasive role of income disparity, rather than income per se_, in determining how happy we are; the fact that happiness only correlates with wealth up to a (rather low) threshold (i.e., once you can afford food and shelter, additional wealth has little impact on (average) happiness levels); and that partly this is due to the fact that, as Hanna quotes neuroscientist Morten Kringelbach, money ‘never evokes satiety’ in the brain – no-one ever feels like they have enough (hence the prevalence of tax-dodging among the super rich, no doubt). (Most of my previous understanding, by the way, derives from Clive Hamilton’s excellent and thought-provoking Growth Fetishfetish, which questions the predominance of growth in GDP as the single underlying policy of most modern political parties.)

But what intrigued me particularly in Hanna’s article was the reference to the role of ecology, or perhaps more specifically ‘nature’ (with a small ‘n’, note!), in making us happy. Things like birdsong, green space, clean rivers, ancient trees all make us happy (most of us, anyway) in ways that probably don’t need to be quantified, but can be nonetheless. A big new initiative in the UK, the Valuing Nature Network, is trying to do just that, so that nature’s value can be properly entered into future planning and development discussions.

The corollary of us valuing nature is that losing nature generally reduces the sum of human happiness. The uncomfortable question that Hanna raises is, Yes, but for how long?

Some years ago in Beijing it struck me how many millions of its inhabitants go on about their daily lives without having much of the chance to hear birds sing… They did not seem to wake up utterly devastated by this every morning… Humans appear pretty resilient, thus when we’re trying to protect charismatic species for the delight of future generations, how should we react to the news that they will be relatively indifferent to our failures?… Of course we’d all prefer that the dodo still existed, but on a daily basis, none of us is actively outraged by its extinction.

In other words, the baseline of what we consider acceptable shifts, certainly over generations, but also perhaps even within a generation. I mentioned Daniel Pauly’s work on shifting baselines in an earlier post, in a fisheries context, and there are lots of interesting case studies. For example, when quizzed by researchers, older Mexican artisinal fishermen were far more pessimistic about the state of fishing grounds than were their sons and grandsons (and the questions were structured in such a way to avoid ‘grumpy old man’ artefacts!). And for a terrestrial example, think how different life would have been in those parts of North America overflown by 300-mile long flocks of passenger pigeons. But that folk memory has gone.

Closer to (my) home, how many visitors to the North Yorkshire seaside resort of Scarborough leave disappointed that they were not able to go sport fishing for bluefin tuna during their week’s holiday? And yet, less than a century ago, this used to happen.

So what’s to be done? Should we just append Joni Mitchell’s You don’t know what you’ve got till it’s gone with ‘(and then after a while, you forget about it anyway)’? Well, maybe. But I strongly believe that one of the things we should be doing as ecologists (in collaboration with historians, visual artists, virtual reality experts, whoever it takes) is to be recreating a view of nature that is gone, but attainable; to draw some lines in the sand, to fix at least some baselines; to create a collective imaginative vision of what nature could be, and so to inspire people to conserve and restore what we have left.