@article{KlossFischerDurka2011,
  author    = {Kloss, Lena and Fischer, Markus and Durka, Walter},
  title     = {Land-use effects on genetic structure of a common grassland herb a matter of scale},
  series = {Basic and applied ecology : Journal of the Gesellschaft f{\"u}r {\"O}kologie},
  volume    = {12},
  journal   = {Basic and applied ecology : Journal of the Gesellschaft f{\"u}r {\"O}kologie},
  number    = {5},
  publisher = {Elsevier},
  address   = {Jena},
  issn      = {1439-1791},
  doi       = {10.1016/j.baae.2011.06.001},
  pages     = {440 -- 448},
  year      = {2011},
  abstract  = {The most common management practices in European grasslands are grazing by livestock and mowing for silage and hay. Grazing and mowing differ in their potential effects on plant gene flow and resulting population genetic structure. After assessing its breeding system, we investigated the effect of land use on the population genetic structure in the common grassland plant Veronica chamaedrys using 63 study populations on meadows, mown pastures and pastures in three regions in Germany, the so-called Biodiversity Exploratories. We determined plant density and analysed the genetic diversity, differentiation and small-scale genetic structure using amplified fragment length polymorphism (AFLP) markers. The breeding system of V chamaedrys turned out as self-incompatible and outcrossing. Its genetic diversity did not differ among land-use types. This may be attributed to large population sizes and the strong dispersal ability of the species, which maintains genetically diverse populations not prone to genetic drift. Genetic differentiation among populations was low (overall F(ST) = 0.075) but significant among the three regions. Land use had only weak effects on population differentiation in only one region. However, land use affected small-scale genetic structure suggesting that gene flow within plots was more restricted on meadows than on mown and unmown pastures. Our study shows that land use influences genetic structure mainly at the small scale within populations, despite high gene flow.},
  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}
}