Ecology is not a dirty word

Ecologists: where do your research ideas come from? And does this influence the science you do?

This excellent blog post by Stephen Heard illustrates how observation and creative freedom are such an important part of the scientific method. We all know how to run an experiment to ensure the results are actually ‘science’.

But why do we run experiments or collect data? Where do we get the idea to do the experiment in the first place?

We might not know why we start an investigation, in formulaic ‘null hypothesis’ terms. But our knowledge and experience to date, however limited or vague, has given us the idea that collecting these data in that system is worth doing – there is some level of uncertainty in the system that inspires us to investigate further. Often we can’t clarify that uncertainty in words or numbers…so we put it down to ‘I was bored and just wanted to see what happened’.

But if you already know for certain, through knowledge and experience, that testing an idea is pointless, you wouldn’t waste your time, right? You don’t just suddenly say “Hmm, I wonder what would happen if I pee while standing on my head? I’m bored, so I might try it and see…”

Science is founded on ‘experimental’ experiments like Stephen’s mushroom fly village in the forest. A lot of scientists do this kind of thing all the time based on scientific intuition. Especially ecologists.

Far from being ‘flawed’ science, or something embarrassing to hide, isn’t this what ecology is all about? Isn’t this why most of us embarked on this career in the first place? We’re here for the thrill of chasing uncertainties and patterns in the natural world…for curiosity, wonder and looking under rocks, often without really knowing what we’re going to find…to find clues as to why species and communities exist when and where they do.

And yes, we will translate those clues into hypotheses, test predictions and build theories. But we can’t understand processes without understanding patterns first. (And studying natural history gives you a head-start in that department.)

I’ve never met an ecologist who came to the field for the love of data or for the wonder of a p-value. ~ Robin Wall Kimmerer, 2013, Braiding Sweetgrass

To find those patterns, and understand those processes, most field ecologists need the freedom to ‘play’ … to spend time in their system just looking around, getting to know it, running experiments/collecting data about something apparently random because observation and experience suggested something interesting might be found.

The Scientific Method is surprisingly contextual, despite the perpetuated myth that it is a standardised, linear process based on predictions and disproving null hypotheses using models and p values.

In reality, the method used to establish scientific knowledge is a dynamic, ongoing process based on two interlinked elements: observation and experiment (which can be traditional lab-based experiments, microcosm experiments, data modelling, field experiments etc.).

Each element is equally important as the other to the overall advancement of science.

Whittaker’s (1952) study of ‘summer foliage insect communities in the Great Smoky Mountains’ is considered one of the pioneer studies of modern community ecology methods. The very short Introduction starts with the sentence “The study was designed to sample foliage insects in a series of natural communities and to obtain results of ecological significance from the samples.” No “specific research questions” and the hypotheses and predictions don’t appear until the Discussion.

Sadly, Popper’s more recent legacy* has placed too much emphasis on science being all about disproving null hypotheses. This builds a myth that all science can be reduced to a predicted dichotomy, i.e. right vs. wrong, true vs. false.

Yes, in some cases, this may be true…but it’s not certain. The sun rises in the east…but the Earth’s magnetic field shifts every few million years. One day, the sun will rise in the west.

When dealing with interactions between humans, animals and ecosystems (i.e. ecology), very little is certain – context can change anything. Turning up naked would be ‘right’ at a nudist beach, but very ‘wrong’ at a job interview (unless it was for a job as a nudist beach lifesaver). Bumblebees are damaging introduced pests in Australia, but mascots for pollinator conservation in England. etc. etc.

So a focus on dichotomous hypotheses, and a standardised, linear approach to collecting data, is unrealistic in most natural science research. Why? Because this approach doesn’t allow for other possible results: that the situation is a little bit one way, a little bit another, and when the climate does this, or humans do that, then the situation becomes a little bit this other way. And sometimes you may not notice that the situation becomes this other way, until the climate does this, or humans do that.

Most scientists get this, but the administrative and political processes that support and fund scientific research don’t always allow for science by any other method.

Yes, it’s important to have some boundaries in science. Allowing scientists to do whatever they want willy-nilly is not the way to build a reputable foundation for Science. But allowing time for observation and creativity also builds better research. Where is the room for this in a system that emphasises time limits, predicted results, and pre-defined tangible outcomes?

We need funding to go out and find interesting things in the environment at large scales in which environmental change is happening, but we need to create hypotheses to justify the funding though the truly interesting hypotheses can’t emerge until after we’ve looked at a system broadly for patterns that warrant further investigation. As a result, we are forced to state hypotheses that we know to be trivial, or else we risk grant rejection under the criticisms that the proposed work is “merely exploratory” or a “fishing expedition”. ~ Sagarin & Pauchard (2012) Observation and Ecology.

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*The Logic of Scientific Discovery gives some additional insights into Popper’s philosophy above the standard fare taught at school and university. But, overall, he was a fan of deductive reasoning, a “top down” approach to making predictions. He was also a fan of causal explanations (although, to be fair, he clarified the need for their contextual use), which could explain the abundant misuse of causal language in scientific literature today.

© Manu Saunders 2015