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Recent work which combines methods from linguistics and evolutionary biology has been fruitful in discovering the history of major language families because of similarities in evolutionary processes. Such work opens up new possibilities for language research on previously unsolvable problems, especially in areas where information from other sources may be lacking. I use phylogenetic methods to investigate Tasmanian languages. Existing materials are so fragmentary that scholars have been unable to discover how many languages are represented in the sources. Using a clustering algorithm which identifies admixture, source materials representing more than one language are identified. Using the Neighbor-Net algorithm, 12 languages are identified in five clusters. Bayesian phylogenetic methods reveal that the families are not demonstrably related; an important result, given the importance of Tasmanian Aborigines for information about how societies have responded to population collapse in prehistory. This work provides insight into the societies of prehistoric Tasmania and illustrates a new utility of phylogenetics in reconstructing linguistic history.
Bayes theorem, Human Migration, Population Dynamics, migration, phylogeny, 410, Tasmania, Keywords: algorithm, population dynamics, anthropology, Phylogeny, Language, language, algorithm, article, Australia, linguistics, Bayes Theorem, Linguistics, phylogenetics, Phylogen Evolutionary anthropology, Algorithms, cluster analysis
Bayes theorem, Human Migration, Population Dynamics, migration, phylogeny, 410, Tasmania, Keywords: algorithm, population dynamics, anthropology, Phylogeny, Language, language, algorithm, article, Australia, linguistics, Bayes Theorem, Linguistics, phylogenetics, Phylogen Evolutionary anthropology, Algorithms, cluster analysis
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