GIS has been an integral part of epidemiological research for more than a decade and its roles in this particular field of research have been diverse: the mapping of disease incidence and prevalence, modeling of patterns of spread, correlation of morbidity and mortality to specific geographical, climatic or political zones. It has often also been used in projection modeling – for example to attempt to estimate the changes in disease vector range in response to climate change. McGill’s own Dr. Lea Berrang Ford’s work is a prime example of the modern applications of GIS in public health science.
Another field where GIS has penetrated quite rapidly was that of biodiversity and conservation. Neither of these disciplinary partnerships are particularly surprising, considering the strong spatial component of both areas of research.
Never the less, it is always possible to count on GIS to surprise us with its potential to drastically change the direction of a long-lived scientific debate, methodology or paradigm. As I [MP] was browsing through New Scientist during one of those procrastination moments typical of undergraduate midterm period, I stumbled upon an article that did just that. It was about microbial diversity. Whenever was microbial diversity a subject for debate? Oh, only since the very beginnings of evolutionary science. The problem is an interesting one: considering the thousands of species that can be found in a mere 30g of soil (usually, this is defined by bacteria that differ in more than 30% of their genome), the diversity of microbial life on earth must be staggering. It is also incalculable – one cannot sample all bacteria found in soil. At best, we can only extrapolate. When biologists do, they tend to place their estimates of bacterial diversity to about 1011 species worldwide. That’s 1011 different types of organisms, fulfilling myriads of different functional roles, living in myriads of different environments. How do we study their response to human processes like agriculture, or their response to phenomena like climate change? How do we integrate them in disease spread models? How do we know when a keystone organism has gone extinct?
David Wilkinson attempted to answer exactly these questions: how do we tackle quantifying the diversity of microbes and what does it imply for humans around the globe? He correlated microbial diversity to the organism’s ease of transfer across geographical space. This is a fundamental evolutionary principle: organisms with many fractioned ranges are more diverse as they tend to diverge genetically through time.
You probably see where I am getting at by now: to explore this principle further, Wilkinson and his research team borrowed a computer model designed to study the dispersal of dust particles in earth system’s sciences. They found that microbes smaller than 20 microns in diameter could travel thousands of kilometres. Those less than 9 micrometers could spread from the southern tip of South America all the way to Australia. This is interesting from the point of view of micro-organism diversity, as it makes it less likely that their total global diversity is made up of an incredibly large number of indigenous and different species.
The result of this computer modeling, if replicated, could have enormous implications for disease spread models. Namely, it helps to explain another rather interesting pattern in the field of epidemiology – the association of dust storms with certain disease outbreaks. This model could also be used to explore microbial genome hybridizations and the spread of antibiotic resistance. In any case, it brings into epidemiology yet another spatial component that can only make our projective models more accurate. This is thus just one of the many ways a GIS-related discovery can be incredibly cool from a biology-geek’s perspective, all the while changing the way an entire field thinks about and analyses spatial components in research.
From MP, Intro to GIS