A methodological framework for assessing and reducing temporal uncertainty in paleovegetation mapping from late-Quaternary pollen records |
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| Authors: | Jessica L. Blois John W. Williams Eric C. Grimm Stephen T. Jackson Russell W. Graham |
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| Affiliation: | 1. Department of Geography and the Center for Climatic Research, University of Wisconsin-Madison, 1225 W, Dayton St., Madison, WI 53706, USA;2. Research and Collections Center, Illinois State Museum, Springfield, IL 62703, USA;3. Department of Botany and Program in Ecology, University of Wyoming, Laramie, WY 82071, USA;4. Earth and Mineral Sciences Museum, Pennsylvania State University, University Park, PA 16802, USA;1. Department of Ecosystem and Landscape Dynamics, University of Amsterdam, Science Park 904, 1098XH, Amsterdam, the Netherlands;2. Faculty of Archaeology, Leiden University, the Netherlands;3. School of Geography and Environmental Sciences, University of Southampton, Southampton, UK;4. Palaeoecology, Department of Physical Geography, Utrecht University, Princetonlaan 8a, 3584 CB, Utrecht, the Netherlands;5. Instituto Tecnológico de Santo Domingo and Museo del Hombre Dominicano, Dominican Republic;1. Key Laboratory of Tibetan Environment Changes and Land Surface Processes (TEL), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China;2. CAS Center for Excellence in Tibetan Plateau Earth Sciences, Beijing 100101, China;3. Institute of Geography, Friedrich-Schiller-University Jena, Loebdergraben 32, 07743 Jena, Germany;4. School of Marine Science and Technology, Tokai University, 3-20-1 Orido, Shimizu, Shizuoka 424-0902, Japan;5. Center for Chronological Research, Nagoya University, Furo-cho, Chikusa, Nagoya 464-8601, Japan;1. Departamento de Geología, Museo Nacional de Ciencias Naturales, CSIC, José Gutiérrez Abascal 2, 28006 Madrid, Spain;2. Departamento de Geología, Facultad de Ciencias, Universidad de Salamanca, 37008 Salamanca, Spain;3. Departamento de Estratigrafía, Universidad Complutense de Madrid, 28040 Madrid, Spain;4. Departamento de Ciencias Analíticas, Facultad de Ciencias, Universidad Nacional de Educación a Distancia, 28040 Madrid, Spain;5. Departamento de Geología, Edificio Ciencias, Universidad de Alcalá, 28871 Alcalá de Henares, Spain;6. Université du Québec à Montréal, GEOTOP-UQAM, Montréal, QC, Canada H3C 3P8;7. Área de Geografía Física, Facultad de Humanidades, Universidad de Huelva, 21007 Huelva, Spain;8. Departamento de Edafología, ETSI, Agrónomos, Universidad Politécnica, 28040 Madrid, Spain;9. Estación Volcanológica de Canarias, CSIC, Avenida Astrofísico Sánchez 3, 38206 La Laguna, Tenerife, Spain;1. Botany Department, School of Natural Sciences, Trinity College Dublin, College Green, Dublin 2, Ireland;2. York Institute for Tropical Ecosystems, Environment Department, Wentworth Way, University of York, Heslington, York, YO10 5NG, United Kingdom;3. Ecology and Evolutionary Biology, Yale University, New Haven, USA;4. Département de Géographie, Université de Montréal, Montréal, Canada;5. Institute of Plant Sciences & Oeschger Centre for Climate Change Research, University of Bern, Altenbergrain 21, CH-3013, Bern, Switzerland;6. Centre National de la Recherche Scientifique (CNRS), Environnements et Paléoenvironments Océaniques et Continentaux (EPOC), Unité Mixte de Recherche (UMR) 5805, Université de Bordeaux, 33615 Pessac Cedex, France;7. School of Forestry and Environmental Studies, Yale University, New Haven, CT, USA;8. The University of Utah, 260 S Campus Drive, Room 270, Orson Spencer Hall, Salt Lake City, UT, USA;9. CNRS, Chrono-environnement Laboratory, Université de Franche-Comté, 16 route de Gray, F-25030, Besançon Cedex, France;10. SKLLQG and Key Lab of Aerosol Chemistry & Physics, Institute of Earth Environment, Chinese Academy of Sciences, Xi''an, 710061, China;11. Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research, Atmospheric Environmental Research, 82467 Garmisch-Partenkirchen, Germany;12. Geosciences and Environmental Climate Change Science Center, U.S. Geological Survey, Denver Federal Center, Lakewood, CO, 80225, USA;13. University, New Haven of North Carolina at Charlotte, Charlotte, NC, USA;14. École Pratique des Hautes Études (EPHE), & Lab for Ecology of Natural and Man-made Hydrosystems (UMR5023, CNRS), Université Lyon 1, F-69622, Villeurbanne, France;15. Montana State University, Department of Ecology, Bozeman, MT, USA;p. National Museums of Kenya, Earth Sciences Department, Palaeobotany and Palynology Section, PO Box 40658, 00100, Nairobi, Kenya;1. Long Term Ecology Laboratory, Biodiversity Institute, Oxford Martin School, Department of Zoology, University of Oxford, Oxford OX1 3PS, UK;2. Department of Biology, University of Bergen, Allégaten 41, N-5007 Bergen, Norway;3. Royal Botanic Gardens, Kew, Richmon, TW9 3AE, UK |
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| Abstract: | Mapping past vegetation dynamics from heterogeneous databases of fossil-pollen records must face the challenge of temporal uncertainty. The growing collection of densely sampled fossil-pollen records with accurate and precise chronologies allows us to develop new methods to assess and reduce this uncertainty. Here, we test our methods in the context of vegetation changes in eastern North America during the abrupt climate changes of the last deglaciation. We use the network of fossil-pollen records in the Neotoma Paleoecology Database (www.neotomadb.org) and data contributed by individual investigators. Because many of these records were collected decades before the current generation of 14C and age-model technologies, we first developed a framework to assess the overall reliability of 14C chronologies by systematically evaluating individual 14C ages and associated chronologies. We developed a qualitative ranking scheme for individual 14C ages that combines information about their accuracy and precision. ‘Benchmark’ pollen records were defined to have at least one 14C age with an accuracy within 250 years and a precision less than 500 years that is within 1000 years of the time interval of interest, and at least five pollen samples per 1000 years across this time period. Only 22 of >350 late-Pleistocene pollen cores in eastern North America met the benchmark criteria.We then used Bayesian change-point analysis to identify widespread ecological events (Picea decline, Quercus rise, and Alnus decline), and interpolated the ages of these events from the benchmark sites to non-benchmark sites. Leave-one-out cross-validation analyses with the benchmark sites indicated that the spatial error associated with interpolation was less for inverse distance-weighting (IDW) than thin-plate splines (TPS) and was about 500 years for the three biotic events. By comparison, the difference between the original ages of events at poorly constrained sites and the biostratigraphic ages interpolated from the benchmark sites was close to 1000 years, suggesting that the use of biostratigraphic ages can significantly improve the age models for poorly constrained sites. Overall, these analyses suggest that the temporal resolution of multi-site syntheses of late-Pleistocene fossil-pollen data in eastern North America is about 500 years, a resolution that allows analysis of ecological responses to millennial-scale climate change during the last deglaciation. |
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