@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}
}
@article{CaoTianTelfordetal.2017,
  author    = {Cao, Xianyong and Tian, Fang and Telford, Richard J. and Ni, Jian and Xu, Qinghai and Chen, Fahu and Liu, Xingqi and Stebich, Martina and Zhao, Yan and Herzschuh, Ulrike},
  title     = {Impacts of the spatial extent of pollen-climate calibration-set on the absolute values, range and trends of reconstructed Holocene precipitation},
  series = {Quaternary science reviews : the international multidisciplinary research and review journal},
  volume    = {178},
  journal   = {Quaternary science reviews : the international multidisciplinary research and review journal},
  publisher = {Elsevier},
  address   = {Oxford},
  issn      = {0277-3791},
  doi       = {10.1016/j.quascirev.2017.10.030},
  pages     = {37 -- 53},
  year      = {2017},
  abstract  = {Pollen-based quantitative reconstructions of past climate variables is a standard palaeoclimatic approach. Despite knowing that the spatial extent of the calibration-set affects the reconstruction result, guidance is lacking as to how to determine a suitable spatial extent of the pollen-climate calibration-set. In this study, past mean annual precipitation (P-ann) during the Holocene (since 11.5 cal ka BP) is reconstructed repeatedly for pollen records from Qinghai Lake (36.7 degrees N, 100.5 degrees E; north-east Tibetan Plateau), Gonghai Lake (38.9 degrees N, 112.2 degrees E; north China) and Sihailongwan Lake (42.3 degrees N, 126.6 degrees E; north-east China) using calibration-sets of varying spatial extents extracted from the modern pollen dataset of China and Mongolia (2559 sampling sites and 168 pollen taxa in total). Results indicate that the spatial extent of the calibration-set has a strong impact on model performance, analogue quality and reconstruction diagnostics (absolute value, range, trend, optimum). Generally, these effects are stronger with the modern analogue technique (MAT) than with weighted averaging partial least squares (WA-PLS). With respect to fossil spectra from northern China, the spatial extent of calibration-sets should be restricted to radii between ca. 1000 and 1500 km because small-scale calibration-sets (<800 km radius) will likely fail to include enough spatial variation in the modern pollen assemblages to reflect the temporal range shifts during the Holocene, while too broad a scale calibration-set (>1500 km radius) will include taxa with very different pollen-climate relationships. (C) 2017 Elsevier Ltd. All rights reserved.},
  language  = {en}
}