REVEALS and the homogeneity bias in pollen data

When Lennart von Post presented the first pollen diagrams and thus laid the foundations of palynology in 1916, the limitations of the method were immediately recognised. Plants produce pollen in very different amounts, so there is a production bias in pollen data. Pollen types differ in fall speed, so there is a dispersal bias in pollen data. Thirdly, plants growing near a site contribute more pollen than plants growing at greater distance. This problem, first raised by Hesselman, may be called the homogeneity bias because it is only a problem if vegetation composition near the site is different from vegetation composition further away. The problem reflects the difference between the ‘world as a pollen record sees it’ and the ‘world as humans see it’. Pollen samples provide a distance weighted assessment of the relative cover of various taxa, whereas humans see vegetation patches at various distances. These two views only match when the vegetation cover is homogeneous.

The three biases have hampered the obviously necessary translation of pollen data into past plant abundances for many decades. Only in 2007, Shinya Sugita presented a solution: the REVEALS model. REVEALS reduces (ideally eliminates) the production bias in pollen percentage data with pollen productivity estimates (PPEs) and the dispersal bias with the K factor. K factors are calculated with a dispersal model. Since then, REVEALS has become an important, regularly applied instrument in palynology. And we hope that our R implementation with a state-of-the art pollen dispersal modelling helps to further broaden its application.

It is often ignored, however, what REVEALS cannot achieve: it does not account for the homogeneity bias. REVEALS output can only be interpreted in terms of mean regional vegetation composition, but this interpretation is only solid if vegetation composition is homogeneous across the pollen source area. A simple example illustrates the problem. Assume a pollen sample from a large lake with 50% pollen of taxon A (in green) and 50% pollen of taxon B (in grey). For simplicity, both have similar pollen productivity and fall speed of pollen (3 cm s-1). The lake has a radius of 500 m, is nicely circular and has no inflow. Pollen dispersal follows the LS model, which predicts with the given settings that 50% of total pollen deposition arrives from within 25 km distance and 95% from within 100 km distance. So to arrive at 50% of total pollen deposition, taxon A may cover all the area within 25 km distance (case 1), or half of the full source area (case 2), or all the area between 25 km and somewhat more than 100 km distance (case 3).

Absolute cover of taxon A would be ~2000 km² in the first case, 16,000 m² in the second case or 30,000 km² in the third case: very different cover but still the same pollen deposition.

This example is certainly extreme and will rarely apply in reality. It still illustrates that the inherent postulation of homogeneity may not be ignored in REVEALS modelling. Whether regional vegetation was indeed homogeneous can be validated by comparing the pollen records of a number of sites. Similar pollen records will prove homogeneity. Different pollen records indicate that vegetation composition was not homogeneous across the region – and that REVEALS output can be misleading in terms of representing actual vegetation composition. In such situations, ever so common in a Central European context, the EDA will help explore the vegetation pattern.

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