Why do we need quantitative methods in palynology?

Pollen from lakes, mires and other deposits is the most comprehensive record of past vegetation.  However, we cannot easily translate pollen into past vegetation composition, mainly for three reasons:

  1. Production bias: the amount of pollen produced differs between plant taxa by orders of magnitude
  2. Dispersal bias: some pollen types may be more easily dispersed than others
  3. Homogeneity bias:  pollen records include pollen both from nearby and far away sources that usually differ in vegetation composition.

To interpret pollen data in terms of past plant abundances, they have to be corrected for theses biases.

Early developments

Ad-hoc attempts to correct over- and underrepresentation of plant taxa in the pollen record have a long history. The first well-known formalized approach is the R-value approach by Davis (1963), later refined to the extended R-value approach by Parsons and Prentice (1981). The R-value approach uses a taxon-specific correction factor (the ratio of R-values) to correct for production and dispersal bias at the same time. R-values are therefore basin specific: they have to be calibrated separately for each basin type.

Palynology after Y2K

The REVEALS model (Sugita 2007) overcomes this limitation by correcting the production bias and the dispersal bias separately. It uses pollen productivity estimates (PPEs) to account for the production bias and pollen fall speeds and the associated ‘pollen dispersal-deposition coefficient’ or K-factor to account for the dispersal bias. The K-factor represents how much pollen of a taxon is deposited in a lake or peatland with a known diameter compared to the amount of pollen deposited in a basin with a zero diameter; K is 1 in a basin with zero diameter and declines with increasing basin size. K-factors are calculated with a specific pollen dispersal model. The REVEALS model uses regional pollen deposition and aims at reconstructing vegetation composition in a large area surrounding the sample point.

The LOVE model (Sugita 2007b) instead aims at reconstructing vegetation composition on a local stand scale. To that end, LOVE combines pollen deposition from large and small sites. LOVE requires REVEALS output and both models are combined in the Landscape Reconstruction Algorithm (LRA). Like the LRA, the MARCO POLO model (Spangenberg 2008, Mrotzek 2015) reconstructs local vegetation composition by combining pollen data from large and small sites. Unlike the LRA, MARCO POLO does not rely on a pollen dispersal model, but distinguishes pollen from nearby and far away  using manipulations of the pollen sum.

REVEALS is applied with the assumption that the vegetation cover of the region is homogeneous, an assumption that is rarely met in reality. The problem is most obvious in the disturbing effects that shore vegetation can have on the pollen record found in a lake. For example, high pollen values of Alnus in a lake may solely derive from a small fringe of Alnus trees around the lake. A REVEALS reconstruction would instead detect Alnus as an important element of the regional vegetation.

Therefore, in situations where regional vegetation is expected to be patchy, approaches that do not rely on homogeneity are preferable. For a single site, the multiple scenario approach aims to detect vegetation mosaics on the basis of known landscape patterns (Fyfe 2006; Bunting et al. 2008). The extended downscaling approach (EDA) follows the same goal, but uses many sites in an forward modelling approach (Theuerkauf et al. 2014).


Bunting MJ, Twiddle CL and Middleton R (2008) Using models of pollen dispersal and deposition in hilly landscapes: Some possible approaches. Palaeogeography, Palaeoclimatology, Palaeoecology 259(1): 77–91: doi:10.1016/j.palaeo.2007.03.051.

Davis MB (1963) On the theory of pollen analysis. American Journal of Science 261: 897–912.

Fyfe RM (2006) GIS and the application of a model of pollen deposition and dispersal: a new approach to testing landscape hypotheses using the POLLANDCAL models. Journal of Archaeological Science 33(4): 483–493: doi:10.1016/j.jas.2005.09.005.

Mrotzek A (2015) Paläobotanische Untersuchungen zur Vegetationsgeschichte der Insel Vilm. In: Gehlhar U and Knapp HD (eds) Erste Ergebnisse der Naturwaldforschung im NaturwaldreservatInsel Vilm. BfN-Skripten 390, Bundesamt für Naturschutz, 53–73.

Parsons RW and Prentice IC (1981) Statistical approaches to R-values and the pollen—vegetation relationship. Review of Palaeobotany and Palynology 32: 127–152.

Spangenberg A (2008) 2000 Jahre Waldentwicklung auf nährstoff- und basenreichen Standorten im mitteleuropäischen Jungpleistozän – Fallstudie Naturschutzgebiet Eldena (Vorpommern Deutschland). .

Sugita S (2007a) Theory of quantitative reconstruction of vegetation I: pollen from large sites REVEALS regional vegetation composition. The Holocene 2: 229–242.

Sugita S (2007b) Theory of quantitative reconstruction of vegetation II: all you need is LOVE. The Holocene 2: 243–258.

Theuerkauf M, Bos JAA, Jahns S, Janke W, Kuparinen A, Stebich M, et al. (2014) Corylus expansion and persistent openness in the early Holocene vegetation of northern central Europe. Quaternary Science Reviews 90: 183–198: doi: