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      Stochastic models support rapid peopling of Late Pleistocene Sahul

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          Abstract

          The peopling of Sahul (the combined continent of Australia and New Guinea) represents the earliest continental migration and settlement event of solely anatomically modern humans, but its patterns and ecological drivers remain largely conceptual in the current literature. We present an advanced stochastic-ecological model to test the relative support for scenarios describing where and when the first humans entered Sahul, and their most probable routes of early settlement. The model supports a dominant entry via the northwest Sahul Shelf first, potentially followed by a second entry through New Guinea, with initial entry most consistent with 50,000 or 75,000 years ago based on comparison with bias-corrected archaeological map layers. The model’s emergent properties predict that peopling of the entire continent occurred rapidly across all ecological environments within 156–208 human generations (4368–5599 years) and at a plausible rate of 0.71–0.92 km year −1. More broadly, our methods and approaches can readily inform other global migration debates, with results supporting an exit of anatomically modern humans from Africa 63,000–90,000 years ago, and the peopling of Eurasia in as little as 12,000–15,000 years via inland routes.

          Abstract

          Advanced ecological modelling reveals how Sahul (Australia and New Guinea) was first peopled, suggesting the most probable routes and surprisingly rapid early settlement of this continent by anatomically modern humans starting 50,000 to 75,000 years ago.

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          Most cited references75

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          A working guide to boosted regression trees.

          1. Ecologists use statistical models for both explanation and prediction, and need techniques that are flexible enough to express typical features of their data, such as nonlinearities and interactions. 2. This study provides a working guide to boosted regression trees (BRT), an ensemble method for fitting statistical models that differs fundamentally from conventional techniques that aim to fit a single parsimonious model. Boosted regression trees combine the strengths of two algorithms: regression trees (models that relate a response to their predictors by recursive binary splits) and boosting (an adaptive method for combining many simple models to give improved predictive performance). The final BRT model can be understood as an additive regression model in which individual terms are simple trees, fitted in a forward, stagewise fashion. 3. Boosted regression trees incorporate important advantages of tree-based methods, handling different types of predictor variables and accommodating missing data. They have no need for prior data transformation or elimination of outliers, can fit complex nonlinear relationships, and automatically handle interaction effects between predictors. Fitting multiple trees in BRT overcomes the biggest drawback of single tree models: their relatively poor predictive performance. Although BRT models are complex, they can be summarized in ways that give powerful ecological insight, and their predictive performance is superior to most traditional modelling methods. 4. The unique features of BRT raise a number of practical issues in model fitting. We demonstrate the practicalities and advantages of using BRT through a distributional analysis of the short-finned eel (Anguilla australis Richardson), a native freshwater fish of New Zealand. We use a data set of over 13 000 sites to illustrate effects of several settings, and then fit and interpret a model using a subset of the data. We provide code and a tutorial to enable the wider use of BRT by ecologists.
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            Human occupation of northern Australia by 65,000 years ago

            The time of arrival of people in Australia is an unresolved question. It is relevant to debates about when modern humans first dispersed out of Africa and when their descendants incorporated genetic material from Neanderthals, Denisovans and possibly other hominins. Humans have also been implicated in the extinction of Australia's megafauna. Here we report the results of new excavations conducted at Madjedbebe, a rock shelter in northern Australia. Artefacts in primary depositional context are concentrated in three dense bands, with the stratigraphic integrity of the deposit demonstrated by artefact refits and by optical dating and other analyses of the sediments. Human occupation began around 65,000 years ago, with a distinctive stone tool assemblage including grinding stones, ground ochres, reflective additives and ground-edge hatchet heads. This evidence sets a new minimum age for the arrival of humans in Australia, the dispersal of modern humans out of Africa, and the subsequent interactions of modern humans with Neanderthals and Denisovans.
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              A genomic history of Aboriginal Australia.

              The population history of Aboriginal Australians remains largely uncharacterized. Here we generate high-coverage genomes for 83 Aboriginal Australians (speakers of Pama-Nyungan languages) and 25 Papuans from the New Guinea Highlands. We find that Papuan and Aboriginal Australian ancestors diversified 25-40 thousand years ago (kya), suggesting pre-Holocene population structure in the ancient continent of Sahul (Australia, New Guinea and Tasmania). However, all of the studied Aboriginal Australians descend from a single founding population that differentiated ~10-32 kya. We infer a population expansion in northeast Australia during the Holocene epoch (past 10,000 years) associated with limited gene flow from this region to the rest of Australia, consistent with the spread of the Pama-Nyungan languages. We estimate that Aboriginal Australians and Papuans diverged from Eurasians 51-72 kya, following a single out-of-Africa dispersal, and subsequently admixed with archaic populations. Finally, we report evidence of selection in Aboriginal Australians potentially associated with living in the desert.
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                Author and article information

                Contributors
                corey.bradshaw@flinders.edu.au
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                29 April 2021
                29 April 2021
                2021
                : 12
                : 2440
                Affiliations
                [1 ]GRID grid.1014.4, ISNI 0000 0004 0367 2697, Global Ecology, College of Science and Engineering, , Flinders University, ; Adelaide, SA Australia
                [2 ]ARC Centre of Excellence for Australian Biodiversity and Heritage, Wollongong, NSW Australia
                [3 ]GRID grid.1007.6, ISNI 0000 0004 0486 528X, Centre for Archaeological Science, School of Earth, Atmospheric and Life Sciences, , University of Wollongong, ; Wollongong, NSW Australia
                [4 ]GRID grid.1011.1, ISNI 0000 0004 0474 1797, College of Arts, Society and Education, , James Cook University, ; Cairns, QLD Australia
                [5 ]GRID grid.1005.4, ISNI 0000 0004 4902 0432, Climate Change Research Centre, School of Biological, Earth and Environmental Sciences, , University of New South Wales, ; Sydney, NSW Australia
                [6 ]EMM Consulting, St Leonards, NSW Australia
                [7 ]GRID grid.1003.2, ISNI 0000 0000 9320 7537, School of Social Science, , University of Queensland, ; Brisbane, QLD Australia
                [8 ]GRID grid.469873.7, ISNI 0000 0004 4914 1197, Max Planck Institute for the Science of Human History, ; Jena, Germany
                [9 ]GRID grid.1007.6, ISNI 0000 0004 0486 528X, Faculty of Science, Medicine and Health, , University of Wollongong, ; Wollongong, NSW Australia
                [10 ]GRID grid.507621.7, UR 1052, French National Institute for Agricultural Research (INRA), ; Montfavet, France
                [11 ]GRID grid.1011.1, ISNI 0000 0004 0474 1797, College of Science and Engineering, , James Cook University, ; Cairns, QLD Australia
                [12 ]GRID grid.29857.31, ISNI 0000 0001 2097 4281, Department of Anthropology, , Pennsylvania State University, ; University Park, PA USA
                [13 ]GRID grid.1001.0, ISNI 0000 0001 2180 7477, Department of Archaeology and Natural History, School of Culture, History and Language, , Australian National University, ; Canberra, ACT Australia
                [14 ]GRID grid.1010.0, ISNI 0000 0004 1936 7304, School of Biological Sciences, Environment Institute, , University of Adelaide, ; Adelaide, SA Australia
                [15 ]GRID grid.1001.0, ISNI 0000 0001 2180 7477, National Centre for Indigenous Genomics, , Australian National University, ; Canberra, ACT Australia
                [16 ]GRID grid.410445.0, ISNI 0000 0001 2188 0957, Department of Oceanography, School of Ocean and Earth Science and Technology, , University of Hawai’i at Manoa, ; Honolulu, Hawai’i USA
                [17 ]GRID grid.1012.2, ISNI 0000 0004 1936 7910, Archaeology and the Centre for Rock Art Research and Management M257, School of Social Sciences, , University of Western Australia, ; Crawley, WA Australia
                [18 ]GRID grid.412690.8, ISNI 0000 0001 0663 0554, Department of Anthropology and Sociology, , University of Papua New Guinea, ; Port Moresby, Papua New Guinea
                Author information
                http://orcid.org/0000-0002-5328-7741
                http://orcid.org/0000-0001-6653-9963
                http://orcid.org/0000-0002-8938-8974
                http://orcid.org/0000-0001-6834-2646
                http://orcid.org/0000-0001-5424-5837
                http://orcid.org/0000-0002-0128-4119
                http://orcid.org/0000-0003-1801-8703
                http://orcid.org/0000-0001-5802-6535
                http://orcid.org/0000-0001-9381-078X
                http://orcid.org/0000-0002-5550-9176
                http://orcid.org/0000-0001-7324-4100
                http://orcid.org/0000-0002-1717-6390
                http://orcid.org/0000-0002-4612-8304
                http://orcid.org/0000-0002-5040-3911
                Article
                21551
                10.1038/s41467-021-21551-3
                8085232
                33927195
                ec363ad9-9862-4057-bffb-77a234854e29
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 20 April 2020
                : 2 February 2021
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100000923, Department of Education and Training | Australian Research Council (ARC);
                Award ID: CE170100015
                Award ID: FT150100138
                Award ID: CE170100015
                Award ID: FL130100116
                Award ID: CE170100015
                Award ID: FT180100407
                Award ID: CE170100015
                Award ID: FT170100448
                Award Recipient :
                Categories
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                Custom metadata
                © The Author(s) 2021

                Uncategorized
                ecology,ecological modelling,geography
                Uncategorized
                ecology, ecological modelling, geography

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