Abstract
There is a long history of archaeologists and forensic scientists using pollen found in a dust sample to identify its geographic origin or history. Such palynological approaches have important limitations as they require time-consuming identification of pollen grains, a priori knowledge of plant species distributions, and a sufficient diversity of pollen types to permit spatial or temporal identification. We demonstrate an alternative approach based on DNA sequencing analyses of the fungal diversity found in dust samples. Using nearly 1,000 dust samples collected from across the continental U.S., our analyses identify up to 40,000 fungal taxa from these samples, many of which exhibit a high degree of geographic endemism. We develop a statistical learning algorithm via discriminant analysis that exploits this geographic endemicity in the fungal diversity to correctly identify samples to within a few hundred kilometers of their geographic origin with high probability. In addition, our statistical approach provides a measure of certainty for each prediction, in contrast with current palynology methods that are almost always based on expert opinion and devoid of statistical inference. Fungal taxa found in dust samples can therefore be used to identify the origin of that dust and, more importantly, we can quantify our degree of certainty that a sample originated in a particular place. This work opens up a new approach to forensic biology that could be used by scientists to identify the origin of dust or soil samples found on objects, clothing, or archaeological artifacts.
Highlights
In the Sherlock Holmes mysteries the author Arthur Conan Doyle repeatedly has Holmes solve crimes by identifying the geographic origin of mud on a shoe, a pair of pants or some other material [1]
The sequence-based methods yielded a database of 38,473 fungal taxa with 72.4% of taxa found in < 10 samples, 96.1% found in < 100 samples, and an average of 727 fungal taxa per individual dust sample
This endemism is at the root of our ability to make predictions about the geographic origins of samples
Summary
In the Sherlock Holmes mysteries the author Arthur Conan Doyle repeatedly has Holmes solve crimes by identifying the geographic origin of mud on a shoe, a pair of pants or some other material [1]. Such work has been valuable in both archaeological and criminal investigations and has been used to ascertain the origin of dust or soil found on artifacts [1], on skin, in lungs [2], on clothes, on a document [5] or contraband in a shipment [6] or even on the grill of a car Such investigations can be based on the abiotic characteristics of the soil or dust [7], as Sherlock described, but they are frequently based on analyses of the types of plant pollen present in such samples. We do not know if fungi can effectively be used to determine the geographic origin of dust samples and the utility of such an approach will require combining a broad-scale analysis of dust-associated fungi with rigorous statistical analyses to assess the probability that a sample has come from a particular habitat or region. The resulting model successfully predicts the origin of samples to within a great-circle distance of 230 km, on average, with high probability
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