Paper Of The Week: Buschke et al. 2015. Simple mechanistic models can partially explain local but not range-wide co-occurrence of African mammals

13 May

When doing GIS, sometimes you can get so caught up in all the fancy things that you can do with it, that you forget that, just like statistics, while GIS can do many amazing things, it’s still just a tool which you can use to answer research questions which interest you. This is why some of my favourite examples of the practical application of GIS to biological research don’t necessarily contain huge amounts of complex GIS-based data manipulations, but instead just use some basic GIS processing to bring data together so they can be analysed to help investigate interesting questions.

In the case of this week’s paper of the week, the interesting question being investigated is: why are variations in species diversity often linked with variations in climate? It seems a straight-forward enough question, but it’s one that we really don’t yet have a satisfactory explanation for. This is where a recent study by Buschke et al. 2015 comes in. In this study, titled ‘Simple mechanistic models can partially explain local but not range-wide co-occurrence of African mammals‘ (Global Ecology and Biogeography doi: 10.1111/geb.12316), the authors sought to explore this very issue (a summary of this paper, containing some nice animations, written by the lead author, can be found here on the Solitary Ecologist blog).

As is often the case, the GIS work involved in this is well-hidden within the methodology, but it involves calculating species richness within individual grid cells so that the local diversity of mammals can be compared to local climatic conditions. As such, while the actual spatial processing is relatively basic and straight-forward, without GIS, this study would not have been possible. However, the term GIS isn’t even mentioned once. I point this out not as a criticism of the authors, but rather to highlight how the importance of GIS to individual studies, and to biology as a whole, can often be overlooked in preference to detailed descriptions and discussions of statistical analyses.

So, with that personal bugbear out of the way, what did the study actually find? Well, they found that the evidence suggests that climate doesn’t influence the ranges of individual species, which in turn determines species richness. Rather, they suggest that climate may influence how many species can persist in a local area, and it is this that then determines the variations in local biodiversity linked to variations in climate.

This is an interesting idea, and one which is worthy of further investigation, especially with other taxa, in parts of the world and in environments (this study only looked at terrestrial mammals in Africa). In particular, it would be interesting to see how this hypothesis transfers into the marine environment, and whether patterns of biodiversity in marine mammals follow the same rules as their terrestrial relatives. In marine mammals, there are clear relationships between biodiversity and water temperature, but no one has really come up with a decent explanation as to why and maybe this hypothesis could provide one.

Does this matter? Well, quite frankly, yes. If we’re to understand how climate change will affect local diversity of marine mammals (or indeed any other taxa), we need to understand why species diversity is linked to climate in the first place.  So, if there are any students out there looking for an interesting GIS-related project, this would be one that, following the methods laid out in Buschke et al., would be both relatively easy to conduct, and that could potentially play an important role in understanding how climate change is likely to impact marine mammals.

Dr Colin D. MacLeod,
Founder, GIS In Ecology


2 Responses to “Paper Of The Week: Buschke et al. 2015. Simple mechanistic models can partially explain local but not range-wide co-occurrence of African mammals”

  1. Falko Buschke 13/05/2015 at 12:54 #

    Wow, thanks for the excellent write-up of our work, Colin. I’m a little peeved that you managed to explain my research more clearly than I could!

    You’re right to point out that we didn’t mention GIS at all. However, I’d argue that this is how it ought to be. Barring very specialised work, GIS is mostly ‘just’ a tool for doing research, not the centrepiece of the research.

    I suppose an optimist can claim that the failure to explicitly mention GIS is a sign that these tools and techniques are so embedded in the research environment that they are taken for granted. I think this is indeed the case in macroecology. Everyone just accepts that large-scale spatial analyses need GIS that they don’t even feel to mention it explicitly…

    For interested readers, our analyses were carried out using the R programming language. The command scripts and data are available online at FigShare and GitHub

    • GIS In Ecology 13/05/2015 at 13:54 #

      Hi Falko,

      I’m glad you liked my write up of your excellent paper. I only really mentioned the GIS side of things because my blog is primarily about GIS, but there is a more serious point here as well.

      While you’re right about GIS being a tool (and I am very much of this school of thought), if the GIS methods aren’t explicitly included, in the same manner that the statistical ones usually are, it becomes difficult for others to use the same methodologies to produce comparable results with different taxa/in different study areas.

      In particular, there can be a number of ways to do the same things in GIS, each of which can give slightly different outcomes. This is especially problematic when people try to repeat things using different GIS software packages and they don’t realise that the data are being processed in slightly different ways in each one, and that this is the reason they are getting different results.

      Anyway, as I said, this is just a GIS-biased perspective of things, and none of it detracts in any way from your very interesting work, and thanks for posting the link to your R code for your data processing and analysis. Good luck with your future research.

      All the best,


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