ROPES – Quantitative reconstructions without pollen productivity estimates

While playing with a high resolution pollen record from lake Tiefer See, John and I developed the idea of a new method to reconstruct past vegetation composition. It is now published as “ROPES” in Frontiers of Earth Science. ROPES stands for ‘Reveals withOut PpES’. So, like Shinya Sugitas REVEALS method, ROPES aims to translate pollen data from large lakes (or peatlands) into regional vegetation composition. Other than REVEALS, ROPES does not require pollen productivity estimates (such as PPE or RPPs).

Traditional use of ropes in Greifswald – Treideln of the “Weiße Düne” (that’s the boat) from the city harbour towards the Bay of Greifswald. (© Melanie Asse)


Why is it useful?
All pollen records from lakes and peatlands are biased by differences between single plant taxa in the pollen production and dispersal (to name the main problem, there are more). As already the founders of palynology recognised, pollen assemblages can therefore not simply be translated into past vegetation composition. Today, two general approaches exist to reduce the bias: the modern analogue technique (MAT) and correction factor approaches. MAT first determines modern relationships between pollen assemblages and vegetation cover and then applies these relationships to palaeo-assemblages to infer past vegetation cover. The main limitation of MAT is the lack of modern analogues. In Europe, for example, the forests that covered the continent during the mid-Holocene have largely disappeared. So, surface samples that represent these forests of the past are virtually not existing as modern analogues. Hence, reconstructions of these forests are problematic, as e.g. the paper of Zanon et al. (2018) clearly shows.

More than 30% of mid-Holocene pollen samples from European pollen records have no modern analgue in 2526 surface samples from across continent. These numbers are based on a threshold value T of 0.3. With the more strict value of 0.2 that Davis et al. 2015 proposed for a similar data set, the proportion of missing modern analogs would be even higher.

Correction factor approaches like REVEALS or STEPPS (Dawson et al. 2016) account for the production bias with taxon specific pollen productivity estimates – with the advantage that success does not depend on vegetation composition. (So, Zanon et al. 2018 actually use REVEALS to re-calibrate their MAT results). However, calibrating pollen productivity requires comprehensive sets of surface pollen samples and vegetation data. The calculations are far from simple and require a good understanding of pollen dispersal. Therefore, reliable pollen productivity estimates are thus far available from a few selected areas only. Furthermore, pollen productivity of plants is not constant but may change substantially in response to variable factors, e.g. climate, soils, land management and stand structure. So, correction factor approaches are limited through the availability and the stability of the correction factors.

ROPES translates pollen data from large lakes (or peatlands) into regional vegetation composition without any modern reference. The method is not affected by the just mentioned limitations of MAT and correction factor approaches. It therefore has potential to enable reconstructions for areas and periods, for which the existing approaches are not applicable.

How does it work?
To arrive at reconstructions from the pollen data alone, ROPES combines pollen counts and pollen accumulation rate data (PAR). Modern PARs appear to vary too strongly between sites to interpret them in a quantitative way. Still, changes in PARs through time in a single record should be meaningful: if for example the PAR of pine pollen doubles at some point, that probably means that the cover of pine has doubled (we discuss this assumption below). So while PAR values are difficult to interpret in terms of past plant abundances, they can well be interpreted in terms of changes in plant abundances. To arrive at actual cover values, ROPES combines the analysis of PAR values with REVEALS. The idea is that a REVEALS reconstruction should show the same changes as the PAR values, like the doubling in pine. This information about the past vegetation allows for applying REVEALS without pre-defined pollen productivity estimates. We instead search for those estimates that give the best solution, i.e. the solution in which the resulting changes in the cover of each taxon best match the changes in PAR values. In other words, we search for the pollen productivity estimates, with which for each taxon, the ratio between the REVEALS reconstructed cover and PAR values is (close to) constant all along the record.

Assumptions of ROPES
The fundamental assumption of ROPES is that changes in PAR values are linearly related to changes in plant abundances (the doubling of pine example…). This assumption is probably robust in stable sedimentary environments, i.e. stable lakes. Thomas Giesecke and Sonja Fontana have shown very stable pollen accumulation in three Swedish lakes over the Holocene. Changes in the sedimentary conditions due to changes in e.g. shape, size or depth of a lake may change pollen deposition at a lakes bottom and thus violate the assumption. Lakes that experience(d) such changes are not suited for ROPES.
Beyond that, ROPES also relies on all the underlying assumption of REVEALS. Sites should receive only atmospheric pollen deposition, and the surrounding vegetation has to be largely homogeneous.

2 thoughts on “ROPES – Quantitative reconstructions without pollen productivity estimates

    1. theuerkaum Post author

      This is a rather complex question which requires a very long answer. The most important difference is that peatlands are covered by vegetation, which may disturbe the regional pollen signal, e.g. if grasses occur on the peatland. More details we should discuss directly.


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