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Finding karstic caves and rockshelters in the Inner Asian mountain corridor using predictive modelling and field survey
The area of the Inner Asian Mountain Corridor (IAMC) follows the foothills and piedmont zones around the northern limits of Asia’s interior mountains, connecting two important areas for human evolution: the Fergana valley and the Siberian Altai. Prior research has suggested the IAMC may have provide...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7816991/ https://www.ncbi.nlm.nih.gov/pubmed/33471843 http://dx.doi.org/10.1371/journal.pone.0245170 |
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author | Cuthbertson, Patrick Ullmann, Tobias Büdel, Christian Varis, Aristeidis Namen, Abay Seltmann, Reimar Reed, Denné Taimagambetov, Zhaken Iovita, Radu |
author_facet | Cuthbertson, Patrick Ullmann, Tobias Büdel, Christian Varis, Aristeidis Namen, Abay Seltmann, Reimar Reed, Denné Taimagambetov, Zhaken Iovita, Radu |
author_sort | Cuthbertson, Patrick |
collection | PubMed |
description | The area of the Inner Asian Mountain Corridor (IAMC) follows the foothills and piedmont zones around the northern limits of Asia’s interior mountains, connecting two important areas for human evolution: the Fergana valley and the Siberian Altai. Prior research has suggested the IAMC may have provided an area of connected refugia from harsh climates during the Pleistocene. To date, this region contains very few secure, dateable Pleistocene sites, but its widely available carbonate units present an opportunity for discovering cave sites, which generally preserve longer sequences and organic remains. Here we present two models for predicting karstic cave and rockshelter features in the Kazakh portion of the IAMC. The 2018 model used a combination of lithological data and unsupervised landform classification, while the 2019 model used feature locations from the results of our 2017–2018 field surveys in a supervised classification using a minimum-distance classifier and morphometric features derived from the ASTER digital elevation model (DEM). We present the results of two seasons of survey using two iterations of the karstic cave models (2018 and 2019), and evaluate their performance during survey. In total, we identified 105 cave and rockshelter features from 2017–2019. We conclude that this model-led approach significantly reduces the target area for foot survey. |
format | Online Article Text |
id | pubmed-7816991 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-78169912021-01-28 Finding karstic caves and rockshelters in the Inner Asian mountain corridor using predictive modelling and field survey Cuthbertson, Patrick Ullmann, Tobias Büdel, Christian Varis, Aristeidis Namen, Abay Seltmann, Reimar Reed, Denné Taimagambetov, Zhaken Iovita, Radu PLoS One Research Article The area of the Inner Asian Mountain Corridor (IAMC) follows the foothills and piedmont zones around the northern limits of Asia’s interior mountains, connecting two important areas for human evolution: the Fergana valley and the Siberian Altai. Prior research has suggested the IAMC may have provided an area of connected refugia from harsh climates during the Pleistocene. To date, this region contains very few secure, dateable Pleistocene sites, but its widely available carbonate units present an opportunity for discovering cave sites, which generally preserve longer sequences and organic remains. Here we present two models for predicting karstic cave and rockshelter features in the Kazakh portion of the IAMC. The 2018 model used a combination of lithological data and unsupervised landform classification, while the 2019 model used feature locations from the results of our 2017–2018 field surveys in a supervised classification using a minimum-distance classifier and morphometric features derived from the ASTER digital elevation model (DEM). We present the results of two seasons of survey using two iterations of the karstic cave models (2018 and 2019), and evaluate their performance during survey. In total, we identified 105 cave and rockshelter features from 2017–2019. We conclude that this model-led approach significantly reduces the target area for foot survey. Public Library of Science 2021-01-20 /pmc/articles/PMC7816991/ /pubmed/33471843 http://dx.doi.org/10.1371/journal.pone.0245170 Text en © 2021 Cuthbertson et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Cuthbertson, Patrick Ullmann, Tobias Büdel, Christian Varis, Aristeidis Namen, Abay Seltmann, Reimar Reed, Denné Taimagambetov, Zhaken Iovita, Radu Finding karstic caves and rockshelters in the Inner Asian mountain corridor using predictive modelling and field survey |
title | Finding karstic caves and rockshelters in the Inner Asian mountain corridor using predictive modelling and field survey |
title_full | Finding karstic caves and rockshelters in the Inner Asian mountain corridor using predictive modelling and field survey |
title_fullStr | Finding karstic caves and rockshelters in the Inner Asian mountain corridor using predictive modelling and field survey |
title_full_unstemmed | Finding karstic caves and rockshelters in the Inner Asian mountain corridor using predictive modelling and field survey |
title_short | Finding karstic caves and rockshelters in the Inner Asian mountain corridor using predictive modelling and field survey |
title_sort | finding karstic caves and rockshelters in the inner asian mountain corridor using predictive modelling and field survey |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7816991/ https://www.ncbi.nlm.nih.gov/pubmed/33471843 http://dx.doi.org/10.1371/journal.pone.0245170 |
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