When I was in Finland last year, I had the pleasure of meeting Tomas Roslin and hearing him describe his Finland-wide citizen-science project on dung beetles. What impressed me most was that it completely flipped my general opinion about citizen science and showed me that the process can be useful.
I’m not trying to sound arrogant or scientifically elitist here – I’m merely stating that it was my opinion that most citizen-science endeavours fail to provide truly novel, useful and rigorous data for scientific hypothesis testing. Well, I must admit that I still believe that ‘most’ citizen-science data meet that description (although there are exceptions – see here for an example), but Tomas’ success showed me just how good they can be.
So what’s the problem with citizen science? Nothing, in principle; in fact, it’s a great idea. Convince keen amateur naturalists over a wide area to observe (as objectively) as possible some ecological phenomenon or function, record the data, and submit it to a scientist to test some brilliant hypothesis. If it works, chances are the data are of much broader coverage and more intensively sampled than could ever be done (or afforded) by a single scientific team alone. So why don’t we do this all the time?
If you’re a scientist, I don’t need to tell you how difficult it is to design a good experimental sampling regime, how even more difficult it is to ensure objectivity and precision when sampling, and the fastidiousness with which the data must be recorded and organised digitally for final analysis. And that’s just for trained scientists! Imagine an army of well-intentioned, but largely inexperienced samplers, you can quickly visualise how the errors might accumulate exponentially in a dataset so that it eventually becomes too unreliable for any real scientific application.
So for these reasons, I’ve been largely reluctant to engage with large-scale citizen-science endeavours. However, I’m proud to say that I have now published my first paper based entirely on citizen science data! Call me a hypocrite (or a slow learner).
Last year I was approached by my friend (and now, colleague), Professor Chris Daniels from the University of South Australia and Mount Lofty Ranges Natural Resource Management Board who is most well-known for his work in urban ecology. Together with Philip Roetman (UniSA) and Andrew Baker (CSIRO), he designed and implemented South Australia’s first Great Koala Count in 2012 – a citizen-science project aiming to quantify the distribution and abundance of koalas mainly in the Adelaide region. Of course, the Great Koala Count was also designed to inform the general public a little more about the koalas (literally) in their back gardens.
What really made the difference with this project was that a special smartphone app was designed to record the data during the 1-day survey. All the citizen scientist had to do was download the app (for iPhone or Android) and take a photo of any koalas seen on the day of the survey. The app would generate a coordinate and the data (including many ancillary questions) were sent to a central server managed by the Atlas of Living Australia. The app itself provided excellent location data as well as a confirmatory photo that could be followed up for quality control.
But what does one do with a whole heap of location data for a single species? If you are an ecologist, you generally would create a species distribution model to estimate the habitat suitability of the species in question across its sampled range (and perhaps beyond). Species distribution models are excellent tools for abundance estimates, monitoring range changes and even predicting how a species might fair in the future as habitats change due to human changes to the landscape (including climate change).
So with this excellent, first-time-ever, South Australian koala database, Chris and colleagues asked me to assist in analysing the data and writing the paper. With the help of my former PhD student, Ana Sequeira (who led the analysis and writing), we have just published the paper online in the journal Ecology and Evolution (Distribution models for koalas in South Australia using citizen science-collected data). In the true spirit of citizen science, the paper is also open-access (i.e., free, thanks to Phil for paying the open-access fees).
The species distribution model arising gives a pretty good prediction of the current and potential distribution of koalas in South Australia, plus a preliminary (albeit wide-ranging) population estimate for the Mount Lofty Ranges (about 100,000 koalas, give or take about 70,000).
Of course, the app couldn’t solve all problems such as biased sampling regimes (most people only looked near roads), lack of ‘absence’ data (people generally only wanted to report when they saw a koala, not when they didn’t) and only a single (very hot) day for the survey, the statistical techniques we employed helped alleviate some of the problems the database contained. Given this experience, we’ve learned a few things about how future surveys – and hopefully there will be another one next year – could be improved to maximise the scientific potential of the data collected.
So thanks to Chris, Philip, Andrew and especially Ana, for their hard work and excellent endeavour. I hope to be involved in the next iteration of the Great Koala Count, and will be certainly open to examining other citizen-science databases.