A continental-scale chironomid training set for reconstructing Arctic temperatures
A continental-scale chironomid training set for reconstructing Arctic temperatures
We present chironomid species assemblage data from 402 lakes across northern North America, Greenland, Iceland, and Svalbard to inform interpretations of Holocene subfossil chironomid assemblages used in paleolimnological reconstruction. This calibration-set was developed by re-identifying and taxonomically harmonizing chironomids in previously described surface sediment samples, with identifications made at finer taxonomic resolution than in original publications. The large geographic coverage of this dataset is intended to provide climatic analogues for a wide range of Holocene climates in the northwest North Atlantic region and North American Arctic, including Greenland. For many of these regions, modern calibration data are sparse despite keen interest in paleoclimate reconstructions from high latitudes. A suite of chironomid-based temperature models based upon this training set are evaluated here and the best statistical model is used to reconstruct late glacial (Allerød and Younger Dryas) and Holocene paleotemperatures at five non-glacial lakes representing a wide range of climate zones across Greenland. The new continent-scale training set offers more analogues for the majority of Greenland subfossil assemblages than existing smaller training sets, with many in Iceland and northern Canada. We find strong agreement between chironomid-based reconstructions derived from the new model and independent glacier-based evidence for multi-millennial Holocene temperature trends. Some of the new Holocene reconstructions are very similar to published data, but at a subset of sites and time periods we find improved paleotemperature reconstructions attributable both to the new model's finer taxonomic resolution and to its expanded geographic/climatic coverage, which resulted in improved characterization of species optima. In the late glacial, the new model's finer taxonomic resolution yields a unique ability to resolve temperatures of the Allerød from colder temperatures of the Younger Dryas, although the magnitude of that temperature difference may be underestimated. This study demonstrates the value of geographically and climatically broad paleoecological training sets. The large, taxonomically harmonized dataset presented here should be useful for a wide range of future investigations, including but not limited to paleotemperature reconstructions across the Arctic.
Arctic, Chironomidae, Greenland, Midges, Paleolimnology, Paleotemperature models, Training set
Medeiros, Andrew
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Chipman, Melissa
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Francis, Donna
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Hamerlik, Ladislav
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Langdon, Peter
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Puleo, Peter
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Schellinger, Grace
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Steigleder, Regan
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Walker, Ian
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Woodroffe, Sarah
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Axford, Yarrow
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15 October 2022
Medeiros, Andrew
37f103d7-31c9-4042-bc60-6908b98dd6e9
Chipman, Melissa
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Francis, Donna
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Hamerlik, Ladislav
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Langdon, Peter
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Puleo, Peter
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Schellinger, Grace
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Steigleder, Regan
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Walker, Ian
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Woodroffe, Sarah
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Axford, Yarrow
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Medeiros, Andrew, Chipman, Melissa, Francis, Donna, Hamerlik, Ladislav, Langdon, Peter, Puleo, Peter, Schellinger, Grace, Steigleder, Regan, Walker, Ian, Woodroffe, Sarah and Axford, Yarrow
(2022)
A continental-scale chironomid training set for reconstructing Arctic temperatures.
Quaternary Science Reviews, 294, [107728].
(doi:10.1016/j.quascirev.2022.107728).
Abstract
We present chironomid species assemblage data from 402 lakes across northern North America, Greenland, Iceland, and Svalbard to inform interpretations of Holocene subfossil chironomid assemblages used in paleolimnological reconstruction. This calibration-set was developed by re-identifying and taxonomically harmonizing chironomids in previously described surface sediment samples, with identifications made at finer taxonomic resolution than in original publications. The large geographic coverage of this dataset is intended to provide climatic analogues for a wide range of Holocene climates in the northwest North Atlantic region and North American Arctic, including Greenland. For many of these regions, modern calibration data are sparse despite keen interest in paleoclimate reconstructions from high latitudes. A suite of chironomid-based temperature models based upon this training set are evaluated here and the best statistical model is used to reconstruct late glacial (Allerød and Younger Dryas) and Holocene paleotemperatures at five non-glacial lakes representing a wide range of climate zones across Greenland. The new continent-scale training set offers more analogues for the majority of Greenland subfossil assemblages than existing smaller training sets, with many in Iceland and northern Canada. We find strong agreement between chironomid-based reconstructions derived from the new model and independent glacier-based evidence for multi-millennial Holocene temperature trends. Some of the new Holocene reconstructions are very similar to published data, but at a subset of sites and time periods we find improved paleotemperature reconstructions attributable both to the new model's finer taxonomic resolution and to its expanded geographic/climatic coverage, which resulted in improved characterization of species optima. In the late glacial, the new model's finer taxonomic resolution yields a unique ability to resolve temperatures of the Allerød from colder temperatures of the Younger Dryas, although the magnitude of that temperature difference may be underestimated. This study demonstrates the value of geographically and climatically broad paleoecological training sets. The large, taxonomically harmonized dataset presented here should be useful for a wide range of future investigations, including but not limited to paleotemperature reconstructions across the Arctic.
Text
Medeirosa-et-al-2022-8-22-22_revision2_clean
- Accepted Manuscript
More information
Accepted/In Press date: 23 August 2022
e-pub ahead of print date: 16 September 2022
Published date: 15 October 2022
Additional Information:
Funding Information:
This work was supported by the U.S. National Science Foundation's Office of Polar Programs (awards 2002515 and 1454734 to Axford) and Dalhousie University . We thank Steve Brooks and the Natural History Museum (London) for hosting a meeting on taxonomic harmonization. Jamie McFarlin and Eleanor Maddison contributed to discussions on subfossil taxonomy. We thank Kirsten Christoffersen, University of Copenhagen, for providing additional original sample data used in this study, as well as Klaus P. Brodersen, Konrad Gajewski, David Porinchu, Marie-Claude Fortin, and the many scientists and funding agencies who enabled collection of the valuable original data in the source publications used in this study. Permits and permissions required for the collection of previously published data can be found in the original publications used in this study. Tim Coston, Aaron Hartz, Laura Larocca, and G. Everett Lasher assisted with field work at Lake N14, with support from Polar Field Services and Jacky Simoud/Blue Ice Explorer. WHOI NOSAMS and Beta Analytic analyzed Lake N14 radiocarbon samples. Lake N14 samples were collected under Scientific Survey License VU-00160 and Export Permit 025/2019 from Naalakkersuisut, the Government of Greenland. We thank the people of Kalaallit Nunaat (known in English as Greenland) and the Kujalleq municipality for granting access to Greenland lakes including Lake N14. We also thank the three reviewers and editor of Quaternary Science Reviews for their detailed comments which improved this manuscript.
Keywords:
Arctic, Chironomidae, Greenland, Midges, Paleolimnology, Paleotemperature models, Training set
Identifiers
Local EPrints ID: 471965
URI: http://eprints.soton.ac.uk/id/eprint/471965
ISSN: 0277-3791
PURE UUID: 846de212-6de3-49a2-8bf9-72ba805d4bc7
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Date deposited: 23 Nov 2022 17:31
Last modified: 16 Sep 2024 04:01
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Contributors
Author:
Andrew Medeiros
Author:
Melissa Chipman
Author:
Donna Francis
Author:
Ladislav Hamerlik
Author:
Peter Puleo
Author:
Grace Schellinger
Author:
Regan Steigleder
Author:
Ian Walker
Author:
Sarah Woodroffe
Author:
Yarrow Axford
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