Gerbault, P., Allaby, R., Boivin, N., Rudzinski, A., Grimaldi, I.M., Pires, C., et al. 2014. Storytelling and story testing in domestication. Proceedings of the National Academy of Sciences. PDF
Storytelling and story testing in domestication
Pascale Gerbault1, Robin Allaby2, Nicole Boivin3, Anna Rudzinski1, Ilaria Maria Grimaldi3, J. Chris Pires4, Cynthia C. Vigueira5, Keith Dobney6, Kristen J. Gremillion7, Loukas Barton8, Manuel Arroyo-Kalin9, Michael Purugganan10,11, Rafael Rubio de Casas12, Ruth Bollongino13, Joachim Burger13, Dorian Q. Fuller17, Daniel G. Bradley15, David J. Balding16, Peter Richerson17, M. Thomas P. Gilbert18, Greger Larson19, Mark G. Thomas1
Our knowledge of the domestication of animal and plant species comes from a diverse range of disciplines, and interpretation of patterns in data from these disciplines has been the dominant paradigm in domestication research. However, such interpretations are easily steered by subjective biases that typically fail to account for the inherent randomness of evolutionary processes, and which can be blind to emergent patterns in data. The testing of explicit models using computer simulations, and the availability of powerful statistical techniques to fit models to observed data, provide a scientifically robust means of addressing these problems. Here we outline the principles and argue for the merits of such approaches in the context of domestication-related questions.
The domestication of plants and animals marks one of the most significant transitions in human, and indeed global, history. Traditionally, study of the domestication process was the exclusive domain of archaeologists and agricultural scientists; today it is an increasingly multidisciplinary enterprise that has come to involve the skills of evolutionary biologists and geneticists. Although the application of new information sources and methodologies has dramatically transformed our ability to study and understand domestication, it has also generated increasingly large and complex datasets, the interpretation of which is not straightforward. In particular, challenges of equifinality, evolutionary variance, and emergence of unexpected or counter-intuitive patterns all face researchers attempting to infer past processes directly from patterns in data. We argue that explicit modeling approaches, drawing upon emerging methodologies in statistics and population genetics, provide a powerful means of addressing these limitations. Modeling also offers an approach to analyzing datasets that avoids conclusions steered by implicit biases, and makes possible the formal integration of different data types. Here we outline some of the modeling approaches most relevant to current problems in domestication research, and demonstrate the ways in which simulation modeling is beginning to reshape our understanding of the domestication process.