Spread of Indo-European folktale traditions related to cultural and demic diffusion (using genomic data)

folktale-genomics

New article at PNAS, Inferring patterns of folktale diffusion using genomic data, by Bortoloni et al. (2017).

Abstract:

Observable patterns of cultural variation are consistently intertwined with demic movements, cultural diffusion, and adaptation to different ecological contexts [Cavalli-Sforza and Feldman (1981) Cultural Transmission and Evolution: A Quantitative Approach; Boyd and Richerson (1985) Culture and the Evolutionary Process]. The quantitative study of gene–culture coevolution has focused in particular on the mechanisms responsible for change in frequency and attributes of cultural traits, the spread of cultural information through demic and cultural diffusion, and detecting relationships between genetic and cultural lineages. Here, we make use of worldwide whole-genome sequences [Pagani et al. (2016) Nature 538:238–242] to assess the impact of processes involving population movement and replacement on cultural diversity, focusing on the variability observed in folktale traditions (n = 596) [Uther (2004) The Types of International Folktales: A Classification and Bibliography. Based on the System of Antti Aarne and Stith Thompson] in Eurasia. We find that a model of cultural diffusion predicted by isolation-by-distance alone is not sufficient to explain the observed patterns, especially at small spatial scales (up to ~4,000 km). We also provide an empirical approach to infer presence and impact of ethnolinguistic barriers preventing the unbiased transmission of both genetic and cultural information. After correcting for the effect of ethnolinguistic boundaries, we show that, of the alternative models that we propose, the one entailing cultural diffusion biased by linguistic differences is the most plausible. Additionally, we identify 15 tales that are more likely to be predominantly transmitted through population movement and replacement and locate putative focal areas for a set of tales that are spread worldwide.

I am very interested in folktales and their origins within Proto-Indo-European culture, so the title alone was an immediate click-bait for me. It did, as always, disappoint in its methods and conclusions, but just the idea it proposes is of great interest for future studies.

There are gross limitations in assessing folktales using simply the Aarne-Thompson-Uther Classification without further analysis or explanation, apart from a summary of tales in the supplementary materials.

But their maps and simplistic hypothesized waves of diffusion (‘African origin’, ‘northern Eurasian’, ‘Eastern European’, or ‘Middle-Eastern/Caucasian’) seem to me as if they try to swim with the tide of the current literature regarding the identification of Proto-Indo-European demic diffusion with “steppe admixture” distribution (and ancient language family diffusion in general through admixture), and as such it can only be wrong.

If you just look at actual folktale distribution (black dots) and compare them with prehistoric cultures and ancient Y-DNA distribution, you realize their maps don’t make much sense, and more complex methods (and a clearer idea of what admixture represents) are needed.

If their intention was to get published in a journal of high impact factor, they succeeded, so good for them. I am glad this subject gets more attention. Of course, their conclusions are kept formally in line with the many limitations of their methods, and are the most interesting aspect of the article:

By correcting for the presence of ethnolinguistic barriers, we find that the null model of cultural diffusion predicted by IBD alone cannot explain the observed distribution of folktales across Eurasia. Instead, beyond ~4,000 km, cultural diffusion biased by linguistic barriers exhibits the highest correlation at all geographic bins. At small geographic bins (<4,000 km), population movements and linguistic barriers may be more relevant than geographic proximity, pointing once again at the possible importance of small-scale processes of cultural transmission for testing more specific hypotheses when using genetic evidence. In addition, processes other than simple cultural diffusion may be more relevant for a smaller group of tales shared by pairs of populations that are genetically closer than populations not exhibiting those tales. Looking for smaller packages of tales or individual tales and their variants can be useful to shed light on the formation process of this vast body of popular knowledge. The long-range patterns detected by our analyses may complement this picture by suggesting a more ancient origin of some of these folktales (SI Appendix). On a broader level, these results can be used in the future to infer directional trends of cultural dispersal as well as to test for the emergence of systematic social biases [such as prestige bias, conformism/anticonformism, heterophily, and content-dependent biases] or cultural barriers different from linguistic ones, which have a chronology that may be independently ascertained.

If you are interested in studies about folktales, and especially those related to Indo-European traditions, you can check out the following articles I found interesting in the past:

Related:

Featured image (featured also in the article): Possible focal area and dispersion pattern for tale ATU313 “The Magic Flight,” one the most popular folktales in this dataset, which may have been additionally spread through population movement and replacement. It is interesting to note how this tale reached locations that are far from its putative origin (such as Japan and southeastern Africa), whereas it was not retained by many populations located in between (gray dots).