How to find fossils

30 03 2016

Many palaeontologists and archaeologists might be a little put out by the mere suggestion that they can be told by ecologists how to do their job better. That is certainly not our intention.

Like fossil-hunting scientists, ecologists regularly search for things (individuals of species) that are rare and difficult to find, because surveying the big wide world for biodiversity is a challenge that we have faced since the dawn of our discipline. In fact, much of the mathematical development of ecology stems from this probabilistic challenge — for example, species distribution models are an increasingly important component of both observational and predictive ecology.

IMG_1277But the palaeo types generally don’t rely on mathematical models to ‘predict’ where fossils might be hiding just under the surface. Even I’ve done what most do when trying to find a fossil — go to a place where fossils have already been found and start fossicking. I’ve done this now with very experienced sedimentary geologists in the Flinders Rangers looking for 550 million year-old Ediacaran fossils, and most recently searching for Jurassic fossils (mainly ammonites) on the southern coast of England (Devon’s Jurassic Coast). My prized ammonite find is shown in the photo to the left.

If you’ve read anything on this blog before, you’ll probably know that I’m getting increasingly excited about palaeo-ecology, with particular emphasis on Australia’s late-Pleistocene and early Holocene mass-extinction of megafauna. So with a beautiful, brand-new, shiny, and quality-rated megafauna dataset1, we cheekily decided to take fossil hunting to the next level by throwing mathematics at the problem.

Just published2 in PloS One, I’m happy to announce our newest paper entitled Where to dig for fossils: combining climate-envelope, taphonomy and discovery models.

Of course, we couldn’t just treat fossil predictions like ecological ones — there are a few more steps involved because we are dealing with long-dead specimens. Our approach therefore involved three steps: 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 »





Pickled niches

2 08 2011

Another fine contribution from Salvador Herrando-Pérez (see previous posts here, here, here and here).

Sometimes evolution fails to shape new species that are able to expand the habitat of their ancestors. This failure does not rein in speciation, but forces it to take place in a habitat that changes little over geological time. Such evolutionary outcomes are important to predict the distribution of groups of phylogenetically related species.

Those who have ever written a novel, a biography, or even a court application, will know that a termite-eaten photo or an old hand-written letter can help rebuild moments of our lives with surgical precision. Likewise, museums of natural sciences store historical biodiversity data of great value for modern research and conservation1.

A notable example is the study of chameleons from Madagascar by Chris Raxworthy and colleagues2. By collating 621 records of 11 species of the tongue-throwing reptiles, these authors subsequently concentrated survey efforts on particular regions where they discovered the impressive figure of seven new species to science, which has continued to expand3 (see figure below). The trick was to characterise the habitat at historical and modern chameleon records on the basis of satellite data describing climate, hydrology, topography, soil and vegetation, then extrapolate over the entire island to predict what land features were most likely to harbour other populations and species. This application of species distribution models4 supports the idea that the phenotypic, morphological and ecological shifts brought about by speciation can take place at slower rates than changes in the habitats where species evolve – the so-called ‘niche conservatism’ (a young concept with already contrasting definitions, e.g.,5-7).

Read the rest of this entry »