TY - JOUR
T1 - Storytelling and story testing in domestication
AU - Gerbault, Pascale
AU - Allaby, Robin G.
AU - Boivin, Nicole
AU - Rudzinski, Anna
AU - Grimaldi, Ilaria M.
AU - Pires, J. Chris
AU - Climer Vigueira, Cynthia
AU - Dobney, Keith
AU - Gremillion, Kristen J.
AU - Barton, Loukas
AU - Arroyo-Kalin, Manuel
AU - Purugganan, Michael D.
AU - De Casas, Rafael Rubio
AU - Bollongino, Ruth
AU - Burger, Joachim
AU - Fuller, Dorian Q.
AU - Bradley, Daniel G.
AU - Balding, David J.
AU - Richerson, Peter J.
AU - Gilbert, M. Thomas P
AU - Larson, Greger
AU - Thomas, Mark G.
PY - 2014/4/29
Y1 - 2014/4/29
N2 - 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.
AB - 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.
KW - Agriculture
KW - Evolution
KW - Inference
KW - Model
KW - Neolithic
UR - http://www.scopus.com/inward/record.url?scp=84899618260&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84899618260&partnerID=8YFLogxK
U2 - 10.1073/pnas.1400425111
DO - 10.1073/pnas.1400425111
M3 - Article
C2 - 24753572
AN - SCOPUS:84899618260
SN - 0027-8424
VL - 111
SP - 6159
EP - 6164
JO - Proceedings of the National Academy of Sciences of the United States of America
JF - Proceedings of the National Academy of Sciences of the United States of America
IS - 17
ER -