Abstract
Quantitative reconstructions of past vegetation cover commonly require pollen productivity estimates (PPEs). PPEs are calibrated in extensive and rather cumbersome surface-sample studies, and are so far only available for selected regions. Moreover, it may be questioned whether present-day pollen-landcover relationships are valid for palaeo-situations. We here introduce the ROPES approach that simultaneously derives PPEs and mean plant abundances from single pollen records. ROPES requires pollen counts and pollen accumulation rates (PARs, grains cm-2 year-1). Pollen counts are used to reconstruct plant abundances following the REVEALS approach. The principle of ROPES is that changes in plant abundance are linearly represented in observed PAR values. For example, if the PAR of pine doubles, so should the REVEALS reconstructed abundance of pine. Consequently, if a REVEALS reconstruction is ‘correct’ (i.e. ‘correct’ PPEs are used) the ratio ‘PAR over REVEALS’ is constant for each taxon along all samples of a record. With incorrect PPEs, the ratio will instead vary. ROPES starts from random (likely incorrect) PPEs, but then adjusts them using an optimization algorithm with the aim to minimize variation in the ‘PAR over REVEALS’ ratio across the record. ROPES thus simultaneously calculates mean plant abundances and PPEs. We illustrate the approach with test applications on nine synthetic pollen records. The results show that good performance of ROPES requires data sets with high underlying variation, many samples and low noise in the PAR data. ROPES can deliver first landcover reconstructions in regions for which PPEs are not yet available. The PPEs provided by ROPES may then allow for further REVEALS-based reconstructions. Similarly, ROPES can provide insight in pollen productivity during distinct periods of the past such as the Lateglacial. We see a potential to study spatial and temporal variation in pollen productivity for example in relation to site parameters, climate and land use. It may even be possible to detect expansion of non-pollen producing areas in a landscape. Overall, ROPES will help produce more accurate landcover reconstructions and expand reconstructions into new study regions and non-analogue situations of the past. ROPES will be available within the R package DISQOVER.
Highlights
ObjectivesThe field of pollen analysis was established 100 years ago, following the presentation of first pollen diagrams by Swedish geologist Lennart von Post (von Post, 1918)
The results show that good performance of ROPES requires data sets with high underlying variation, many samples and low noise in the pollen accumulation rates (PAR) data
Existing methods of quantitative vegetation reconstruction correct for the productivity bias in pollen data with correction factors such as pollen productivity estimates (PPEs) that are calibrated in the present day cultural landscape
Summary
The field of pollen analysis was established 100 years ago, following the presentation of first pollen diagrams by Swedish geologist Lennart von Post (von Post, 1918). Used for stratigraphic purposes, the power of pollen analysis to reconstruct past landcover was soon recognized—and it has remained the most powerful tool in that field until today. Reconstructing past landcover from the pollen record is far from simple, . The most obvious limitations arise from the production bias and the dispersal bias: pollen productivity as well as pollen dispersal differ among plant taxa. Pollen deposition at each site is composed of pollen arriving from the vicinity of the sample site as well as of pollen arriving from farther away. Because nearby pollen sources contribute more pollen than distant ones, the pollen record represents the surrounding vegetation in a distance weighted manner
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