@article{TianHerzschuhTelfordetal.2014,
  author    = {Tian, Fang and Herzschuh, Ulrike and Telford, Richard J. and Mischke, Steffen and Van der Meeren, Thijs and Krengel, Michael},
  title     = {A modern pollen-climate calibration set from central-western Mongolia and its application to a late glacial-Holocene record},
  series = {Journal of biogeography},
  volume    = {41},
  journal   = {Journal of biogeography},
  number    = {10},
  publisher = {Wiley-Blackwell},
  address   = {Hoboken},
  issn      = {0305-0270},
  doi       = {10.1111/jbi.12338},
  pages     = {1909 -- 1922},
  year      = {2014},
  abstract  = {AimFossil pollen spectra from lake sediments in central and western Mongolia have been used to interpret past climatic variations, but hitherto no suitable modern pollen-climate calibration set has been available to infer past climate changes quantitatively. We established such a modern pollen dataset and used it to develop a transfer function model that we applied to a fossil pollen record in order to investigate: (1) whether there was a significant moisture response to the Younger Dryas event in north-western Mongolia; and (2) whether the early Holocene was characterized by dry or wet climatic conditions. LocationCentral and western Mongolia. MethodsWe analysed pollen data from surface sediments from 90 lakes. A transfer function for mean annual precipitation (P-ann) was developed with weighted averaging partial least squares regression (WA-PLS) and applied to a fossil pollen record from Lake Bayan Nuur (49.98 degrees N, 93.95 degrees E, 932m a.s.l.). Statistical approaches were used to investigate the modern pollen-climate relationships and assess model performance and reconstruction output. ResultsRedundancy analysis shows that the modern pollen spectra are characteristic of their respective vegetation types and local climate. Spatial autocorrelation and significance tests of environmental variables show that the WA-PLS model for P-ann is the most valid function for our dataset, and possesses the lowest root mean squared error of prediction. Main conclusionsPrecipitation is the most important predictor of pollen and vegetation distributions in our study area. Our quantitative climate reconstruction indicates a dry Younger Dryas, a relatively dry early Holocene, a wet mid-Holocene and a dry late Holocene.},
  language  = {en}
}
@article{CaoHerzschuhTelfordetal.2014,
  author    = {Cao, Xianyong and Herzschuh, Ulrike and Telford, Richard J. and Ni, Jian},
  title     = {A modern pollen-climate dataset from China and Mongolia: assessing its potential for climate reconstruction},
  series = {Review of palaeobotany and palynology : an international journal},
  volume    = {211},
  journal   = {Review of palaeobotany and palynology : an international journal},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0034-6667},
  doi       = {10.1016/j.revpalbo.2014.08.007},
  pages     = {87 -- 96},
  year      = {2014},
  abstract  = {A modern pollen dataset from China and Mongolia (18-52 degrees N, 74-132 degrees E) is investigated for its potential use in climate reconstructions. The dataset includes 2559 samples, 229 terrestrial pollen taxa and four climatic variables - mean annual precipitation (P-ann): 35-2091 mm, mean annual temperature (T-ann): -12.1-25.8 degrees C, mean temperature in the coldest month (Mt(co).): -33.8-21.7 degrees C, and mean temperature in the warmest month (Mt(wa)): 03-29.8 degrees C. Modern pollen-climate relationships are assessed using canonical correspondence analysis (CCA), Huisman-Olff-Fresco (HOF) models, the modern analogue technique (MAT), and weighted averaging partial least squares (WA-PLS). Results indicate that P-ann is the most important climatic determinant of pollen distribution and the most promising climate variable for reconstructions, as assessed by the coefficient of determination between observed and predicted environmental values (r(2)) and root mean square error of prediction (RMSEP). Mt(co) and Mt(wa) may be reconstructed too, but with caution. Samples from different depositional environments influence the performance of cross-validation differently, with samples from lake sediment-surfaces and moss polsters having the best fit with the lowest RMSEP. The better model performances of MAT are most probably caused by spatial autocorrelation. Accordingly, the WA-PLS models of this dataset are deemed most suitable for reconstructing past climate quantitatively because of their more reliable predictive power. (C) 2014 Elsevier B.V. All rights reserved.},
  language  = {en}
}