RT Journal Article
SR Electronic
T1 Quantifying pollen-vegetation relationships to reconstruct ancient forests using 19th-century forest composition and pollen data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 039073
DO 10.1101/039073
A1 Andria Dawson
A1 Christopher J. Paciorek
A1 Jason S. McLachlan
A1 Simon Goring
A1 John W. Williams
A1 Stephen T. Jackson
YR 2016
UL http://biorxiv.org/content/early/2016/02/09/039073.abstract
AB Mitigation of climate change and adaptation to its effects relies partly on how effectively land-atmosphere interactions can be quantified. Quantifying composition of past forest ecosystems can help understand processes governing forest dynamics in a changing world. Fossil pollen data provide information about past forest composition, but rigorous interpretation requires development of pollen-vegetation models (PVMs) that account for interspecific differences in pollen production and dispersal. Widespread and intensified land-use over the 19th and 20th centuries may have altered pollen-vegetation relationships. Here we use STEPPS, a Bayesian hierarchical spatial PVM, to estimate key process parameters and associated uncertainties in the pollenvegetation relationship. We apply alternate dispersal kernels, and calibrate STEPPS using a newly developed Euro-American settlement-era calibration data set constructed from Public Land Survey data and fossil pollen samples matched to the settlement-era using expert elicitation. Models based on the inverse power-law dispersal kernel outperformed those based on the Gaussian dispersal kernel, indicating that pollen dispersal kernels are fat tailed. Pine and birch have the highest pollen productivities. Pollen productivity and dispersal estimates are generally consistent with previous understanding from modern data sets, although source area estimates are larger. Tests of model predictions demonstrate the ability of STEPPS to predict regional compositional patterns.