| dc.description.abstract | Quantitative vegetation reconstruction aims to provide information about past vegetation cover.
One method is using pollen diagrams that are supposed to reflect the relative abundance of taxa in
the surrounding vegetation. However, the relationship between pollen and vegetation is affected by
production and dispersal bias. Therefore, a need for methods arose that could correct for these
biases. To quantify the effect of production bias, estimates for pollen productivity (PPEs) were
developed. Secondly, to account for dispersal bias, accurate pollen dispersal models that described
the movement pattern of pollen grains with different physical characteristics were needed.
This review first presents an overview of the development of dispersal and deposition
models from the basic idea of the R-value model by Margaret Davis to the Extended R-value model
by Colin Prentice that was further developed by Shinya Sugita. It continues with a reconstruction of
the development of PPEs and dispersal models that should correct for production and dispersal bias
is given. Lastly, three current quantitative vegetation reconstruction approaches (the Landscape
Reconstruction Algorithm (LRA), the Multiple Scenario Approach (MSA) and the Extended
Downscaling Approach (EDA)) are compared in the context of a theoretical intermediate scale
landscape reconstruction in the Netherlands for the Lateglacial and the Holocene.
While the LRA has been often applied on regional-continental scales and sporadically on
local scales, the MSA and EDA have been only applied on local scale reconstructions. The LRA
reconstructs regional landscapes based on the pollen assemblages of large lakes (REVEALS) and local
landscapes on the pollen assemblages of small lakes (LOVE). The MSA and EDA reconstruct
landscapes based on the input of abiotic landscape parameters and the simulation of theoretical
pollen assemblages that are then compared to the empirical assemblage. The MSA can yield multiple
likely landscape scenarios, whereas the EDA only reconstructs one scenario using an optimization
key.
In the light of a potential landscape reconstruction of the Dutch Lateglacial and Holocene,
application of the LRA could be impractical due to the low availability of large lakes (100-500ha). The
availability of high-quality abiotic landscape information makes the application of the MSA, and the
EDA seem quite suitable, but both methods have not yet been applied on intermediate scales and
scaling up may be bothered by high simulation times, especially for the MSA. | |
