RESUMO
The initial peopling of the remote Pacific islands was one of the greatest migrations in human history, beginning three millennia ago by Lapita cultural groups. The spread of Lapita out of an ancestral Asian homeland is a dominant narrative in the origins of Pacific peoples, and although Island New Guinea has long been recognized as a springboard for the peopling of Oceania, the role of Indigenous populations in this remarkable phase of exploration remains largely untested. Here, we report the earliest evidence for Lapita-introduced animals, turtle bone technology and repeated obsidian import in southern New Guinea 3,480-3,060 years ago, synchronous with the establishment of the earliest known Lapita settlements 700 km away. Our findings precede sustained Lapita migrations and pottery introductions by several centuries, occur alongside Indigenous technologies and suggest continued multicultural influences on population diversity despite language replacement. Our work shows that initial Lapita expansion throughout Island New Guinea was more expansive than previously considered, with Indigenous contact influencing migration pathways and island-hopping strategies that culminated in rapid and purposeful Pacific-wide settlement. Later Lapita dispersals through New Guinea were facilitated by earlier contact with Indigenous populations and profoundly influenced the region as a global centre of cultural and linguistic diversity.
Assuntos
Tartarugas , Animais , Nova Guiné , OceaniaRESUMO
We describe an investigation into how Massey University's Pollen Classifynder can accelerate the understanding of pollen and its role in nature. The Classifynder is an imaging microscopy system that can locate, image and classify slide based pollen samples. Given the laboriousness of purely manual image acquisition and identification it is vital to exploit assistive technologies like the Classifynder to enable acquisition and analysis of pollen samples. It is also vital that we understand the strengths and limitations of automated systems so that they can be used (and improved) to compliment the strengths and weaknesses of human analysts to the greatest extent possible. This article reviews some of our experiences with the Classifynder system and our exploration of alternative classifier models to enhance both accuracy and interpretability. Our experiments in the pollen analysis problem domain have been based on samples from the Australian National University's pollen reference collection (2,890 grains, 15 species) and images bundled with the Classifynder system (400 grains, 4 species). These samples have been represented using the Classifynder image feature set. We additionally work through a real world case study where we assess the ability of the system to determine the pollen make-up of samples of New Zealand honey. In addition to the Classifynder's native neural network classifier, we have evaluated linear discriminant, support vector machine, decision tree and random forest classifiers on these data with encouraging results. Our hope is that our findings will help enhance the performance of future releases of the Classifynder and other systems for accelerating the acquisition and analysis of pollen samples.