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Comparison of settlement-era vegetation reconstructions for STEPPS and REVEALS pollen–vegetation models in the northeastern United States

Published online by Cambridge University Press:  07 April 2020

Mathias Trachsel Open the ORCID record for Mathias Trachsel [Opens in a new window] ,
Andria Dawson ,
Christopher J. Paciorek ,
John W. Williams ,
Jason S. McLachlan ,
Charles V. Cogbill ,
David R. Foster ,
Simon J. Goring ,
Stephen T. Jackson  and
W. Wyatt Oswald
...Show all authors
Mathias Trachsel*
Affiliation:
Department of Geography, University of Wisconsin–Madison, Madison, Wisconsin53706, USA
Andria Dawson
Affiliation:
Department of General Education, Mount Royal University, Calgary, AlbertaT3E 6K6, Canada
Christopher J. Paciorek
Affiliation:
Department of Statistics, University of California, Berkeley, Berkeley, California94720, USA
John W. Williams
Affiliation:
Department of Geography, University of Wisconsin–Madison, Madison, Wisconsin53706, USA Center for Climatic Research, University of Wisconsin–Madison, Madison, Wisconsin53706, USA
Jason S. McLachlan
Affiliation:
Department of Biological Sciences, University of Notre Dame, South Bend, Indiana46556, USA
Charles V. Cogbill
Affiliation:
Harvard Forest, Harvard University, Petersham, Massachusetts01366, USA
David R. Foster
Affiliation:
Harvard Forest, Harvard University, Petersham, Massachusetts01366, USA
Simon J. Goring
Affiliation:
Department of Geography, University of Wisconsin–Madison, Madison, Wisconsin53706, USA Center for Climatic Research, University of Wisconsin–Madison, Madison, Wisconsin53706, USA
Stephen T. Jackson
Affiliation:
Department of the Interior Southwest Climate Adaptation Science Center, U.S. Geological Survey, Tucson, Arizona, USA; and Department of Geosciences, University of Arizona, Tucson, Arizona85721, USA
W. Wyatt Oswald
Affiliation:
Harvard Forest, Harvard University, Petersham, Massachusetts01366, USA Institute for Liberal Arts and Interdisciplinary Studies, Emerson College, Boston, Massachusetts02116, USA
Bryan N. Shuman
Affiliation:
Department of Geology and Geophysics, University of Wyoming, Laramie, Wyoming. USA
*
*Corresponding author at: Talstrasse 10, 3174Thoerishaus, Switzerland. E-mail address: [email protected] (M. Trachsel).
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Abstract

Reconstructions of prehistoric vegetation composition help establish natural baselines, variability, and trajectories of forest dynamics before and during the emergence of intensive anthropogenic land use. Pollen–vegetation models (PVMs) enable such reconstructions from fossil pollen assemblages using process-based representations of taxon-specific pollen production and dispersal. However, several PVMs and variants now exist, and the sensitivity of vegetation inferences to PVM selection, variant, and calibration domain is poorly understood. Here, we compare the reconstructions, parameter estimates, and structure of a Bayesian hierarchical PVM, STEPPS, both to observations and to REVEALS, a widely used PVM, for the pre–Euro-American settlement-era vegetation in the northeastern United States (NEUS). We also compare NEUS-based STEPPS parameter estimates to those for the upper midwestern United States (UMW). Both PVMs predict the observed macroscale patterns of vegetation composition in the NEUS; however, reconstructions of minor taxa are less accurate and predictions for some taxa differ between PVMs. These differences can be attributed to intermodel differences in structure and parameter estimates. Estimates of pollen productivity from STEPPS broadly agree with estimates produced for use in REVEALS, while comparison between pollen dispersal parameter estimates shows no significant relationship. STEPPS parameter estimates are similar between the UMW and NEUS, suggesting that STEPPS parameter estimates are transferable between floristically similar regions and scales.

Keywords

BayesianPollenVegetationFossilForestCalibrationPrediction
Type
Research Article
Information
Quaternary Research , Volume 95 , May 2020 , pp. 23 - 42
Copyright
Copyright © University of Washington. Published by Cambridge University Press, 2020

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Footnotes

§

These authors contributed equally to this work.

References

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