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Driving Factors in the Colonization of Oceania: Developing Island-Level Statistical Models to Test Competing Hypotheses

Published online by Cambridge University Press:  20 January 2017

Adrian V. Bell ,
Thomas E. Currie ,
Geoffrey Irwin  and
Christopher Bradbury
Adrian V. Bell
Affiliation:
Department of Anthropology, University of Utah, 270 S. 1400 E. Room 102, Salt Lake City, UT 84112 (adrian.bell@anthro.Utah.edu)
Thomas E. Currie
Affiliation:
Centre for Ecology & Conservation, College of Life & Environmental Science, University of Exeter, Penryn Campus, Cornwall, United Kingdom TR 10 9FE (T.Currie@exeter.ac.uk)
Geoffrey Irwin
Affiliation:
Department of Anthropology, University of Auckland, Private Bag 92019, Auckland, New Zealand (g.irwin@auckland.ac.nz)
Christopher Bradbury
Affiliation:
Department of Geology & Geophysics, Univesity of Utah, Frederick Albert Sutton Building, 115 S. 1450 E., Room 383, Salt Lake City, UT 84112-0102 (geophysicistchris@gmail.com)
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Abstract

Migration is a key driver of human cultural and genetic evolution, with recent theoretical advances calling for work to accurately identify factors behind early colonization patterns. However, inferring prehistoric migration strategies is a controversial field of inquiry that largely relies on interpreting settlement chronologies and constructing plausible narratives around environmental factors. Model selection approaches, along with new statistical models that match the dynamic nature of colonization, offers a more rigorous framework to test competing theories. We demonstrate the utility of this approach by developing an Island-Level Model of Colonization adapted from epidemiology in a Bayesian model-selection framework. Using model selection techniques, we assess competing colonization theories of Near and Remote Oceania, showing that models of exploration angles and risk performed considerably better than models using inter-island distance, suggesting early seafarers were already adept at long-distance travel. These results are robust after artificially increasing the uncertainty around settlement times. We show how decades of thinking on colonization strategies can be brought together and assessed in one statistical framework, providing us with greater interpretive power to understand a fundamental feature of our past.

Résumé

Résumé

La migración es un factor clave de la evolución cultural y genética humana. Recientamente, los avances teóricos que requieren esfuerzos para identificar los factores que respaldan los modelos de la colonización primaria. Sin embargo, la investigación para infer ir estrategias de migración es un tema polémico que se basa en gran parte en la interpretación de las cronologías de asentamiento y la creación de narrativas plausibles en torno a factores ambientales. Los criterios de selección de modelo,junto con los nuevos modelos estadísticos que responden a la naturaleza dinámica de la colonización, ofrecen un método más riguroso para poner a prueba las teorías en pugna. Demostramos la utilidad de este enfoque mediante el desarrollo de un Modelo de Colonización de Islas, adaptado de la epidemiología, con una especiftcación bayesiana. Usando técnicas de selección de modelo, evaluamos teorías divergentes de colonización de la Oceanίa Cercana y Lejana, y demostramos que los modelos de los ángulos de exploración y riesgo son considerablemente más respaldados que los modelos que utilizan la distancia entre las islas, lo que sugiere que los primeros marinos ya eran más adeptos a los viajes de larga distancia. Estos resultados son robustos aun después de un aumento artificial de la incertidumbre en torno a los tiempos de la colonización. Este anólisis muestra cómo décadas de teoría sobre las estrategias de colonización pueden ser compiladas y evaluadas en un enfoque estadístico que nos ofrece una mayor capacidad de interpretación de las características fundamentales de nuestro pasado.

Type
Reports
Information
American Antiquity , Volume 80 , Issue 2 , April 2015 , pp. 397 - 407
Copyright
Copyright © The Society for American Archaeology 2015

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