Job: Koala Data Research Technician

6 03 2017

koalaIf you live in South Australia, and in Adelaide especially, you would have had to be living under a rock not to have heard of the Great Koala Counts 1 and 2. So I’m not really writing this for those sotto pietra types. If you are a regular reader of CB.com, you’ll also know that I’ve been involved in helping analyse the data from GKC1, as well as improving the design of the GKC2.

8037320-3x2-940x627Well, the data are in for GKC2 and we need help to analyse them. Just as a little reminder, the GKCs are designed to provide better data to estimate the distribution and density of koalas in South Australia (especially in the Mount Lofty Ranges). We’ve already written one scientific article from GKC1, but we now have a more expansive and quality-controlled dataset, so it’s now time to write the second. Read the rest of this entry »





Putting the ‘science’ in citizen science

30 04 2014
How to tell if a koala has been in your garden. © Great Koala Count

How to tell if a koala has been in your garden. © Great Koala Count

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). Read the rest of this entry »