Ecology vs Math: do we need to pick sides?

Like many other young ecologists, I chose this career because I cared about the Earth and because I wanted a job that gave me the thrill of discovery every day. Whether it’s seeing a new ecosystem for the first time, sighting a wild plant or animal species I’ve never seen before, coming up with a novel theory, methodology, or sampling technique, or finally ‘getting’ the statistical analysis I’ve been struggling with for weeks – I get to play explorer every day, and I love it.

Sadly, in some countries, it is a field that struggles to convince a large number of graduates to stay in a research career. This is mostly because of funding issues, but can also come from confusion after 4-5 years of being pushed and pulled between too many stimulating sub-disciplines and inspiring mentors.

Many students are bombarded throughout their degree with promotion of multiple sub-disciplines of ecology as “the one that rules them all”. As a naive undergraduate with a lot to learn about the industry and the world in general, this can influence which career path they take.

So it’s heartening when the more experienced generation encourage aspiring scientists to follow their passion and intuition and stick with science (particularly ecology and the natural sciences), even if they don’t fit into the apparent intellectual “norms”. E.O. Wilson’s recent piece in The Wall Street Journal is just that.

Wilson is one of this era’s most well-known and influential ecologists. Like all of us, he’s not perfect and has probably made one or two mistakes in his career. But contrary to his critics, I don’t believe this piece is one of them.

I have lamented before ecology’s current obsession with mathematical modelling. Don’t get me wrong, I absolutely love maths (well, physics to be precise) and always have. But just having passion sometimes isn’t enough. My mental capacity is geared toward creative, philosophical, right-brain pursuits and I’ve sweated many a headache in my life, desperately trying to get my mental claws into things like this:

Einstein's blackboard, 16 May 1931. University of Oxford 'Bye bye blackboard' exhibition
Einstein’s blackboard, 16 May 1931. University of Oxford ‘Bye bye blackboard’ exhibition

…to no avail.

It’s a fact of life that some of us comprehend complicated mathematics and some don’t. It’s also a fact that some of those people who don’t understand complicated math, do understand ecology and the natural world (and lots of other amazing things!).

Yet ecological science’s apparent focus on modelling and metrics can be very, very daunting to those people. I spent much of my undergraduate degree feeling inadequate and seriously believing that I would never succeed in research because I struggled with statistics. Many of the textbooks and lecturers I encountered in my degree corroborated this – formulae, symbols, theories and predictive graphs are often given more credence in ecology than the intuitive processes that discover how the natural world actually ‘works’.

But I’m glad I bit the bullet and signed up for the ride – I have taught myself more about ecological theory and statistics in the last year than I learned in four years of university. There is a lot that I still don’t understand, and most likely never will, but I now know I have the ability to learn enough to keep my passion and creativity alive, and ensure I keep contributing to my field. If I had believed all the voices claiming that ecology can’t exist without maths and modelling, I would not be here.

We need more voices echoing E.O. Wilson’s, especially experienced and well-respected ones. Far from being “damaging” to the field (as some of his critics have claimed), Wilson’s piece merely points out that being only “semi-literate” in maths and statistics can be enough for you to make it in science, provided your passion, intelligence and intuition is strong enough to keep you focused and willing to expand your mind. And coming from someone of his standing, that is enough fuel to convince a wavering novice.

As I’ve discussed before, modelling and predictions are great (and indeed extremely helpful in some cases), but they are just that…predictions. Predictions cannot be facts until the predicted event has happened under natural circumstances, with little or no manipulation or interference by human hands (and don’t forget, predictions can sometimes be wrong!).

Just like a map can tell you how to get to a destination, but can’t tell you what condition the road is in…just like a recipe can tell you what and how many ingredients to put in your cake, but not how it will taste to you…and just like boiling an egg depends on a lot of specific environmental variables, mathematical predictions are not facts, they are educated maps for potential discovery.

To say that great ecological research cannot happen without maths, statistics, modelling or manipulative experiments is denying all the great discoveries that have happened in the natural world, some of them purely serendipitous! It also denies the intellectual power of just thinking about a natural system purely in terms of its ecological condition, history and natural processes. (The educational capacity of this approach is invaluable too – Ian Lunt’s popular series of quiz posts about his current study system are a great example).

So to any aspiring ecologist or scientist who is passionate about researching and discovering the natural world, but has nightmares about mathematical symbols dressed in white coats – don’t give up! You will make great ecological discoveries without fully understanding maths and statistics, or without conducting manipulative lab-based experiments all the time.

You will need to be receptive to maths, and you will need to collaborate closely with mathematicians and statisticians – you may even learn so much in the process that you fall in love with math, and become a purely theoretical ecologist.

But, in the meantime, don’t give up – semi-literacy (however small) in maths and statistics is just enough to keep your mind open to the wondrous discoveries of the natural world, and motivated enough to expand your knowledge.

© Manu Saunders 2013

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