Refine
Document Type
- Article (2)
Language
- English (2)
Has Fulltext
- yes (2)
Is part of the Bibliography
- no (2)
Keywords
- pollen productivity estimates (2) (remove)
Institute
- Institut für Botanik und Landschaftsökologie & Botanischer Garten (2) (remove)
Publisher
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-analog situations of the past. ROPES is available within the R package DISQOVER.
Pollen productivity estimates (PPEs) are a key parameter for quantitative land-cover reconstructions from pollen data. PPEs are commonly estimated using modern pollen-vegetation data sets and the extended R-value (ERV) model. Prominent discrepancies in the existing studies question the reliability of the approach. We here propose an implementation of the ERV model in the R environment for statistical computing, which allows for simplified application and testing. Using simulated pollen-vegetation data sets, we explore sensitivity of ERV application to (1) number of sites, (2) vegetation structure, (3) basin size, (4) noise in the data, and (5) dispersal model selection. The simulations show that noise in the (pollen) data and dispersal model selection are critical factors in ERV application. Pollen count errors imply prominent PPE errors mainly for taxa with low counts, usually low pollen producers. Applied with an unsuited dispersal model, ERV tends to produce wrong PPEs for additional taxa. In a comparison of the still widely applied Prentice model and a Lagrangian stochastic model (LSM), errors are highest for taxa with high and low fall speed of pollen. The errors reflect the too high influence of fall speed in the Prentice model. ERV studies often use local scale pollen data from for example, moss polsters. Describing pollen dispersal on his local scale is particularly complex due to a range of disturbing factors, including differential release height. Considering the importance of the dispersal model in the approach, and the very large uncertainties in dispersal on short distance, we advise to carry out ERV studies with pollen data from open areas or basins that lack local pollen deposition of the taxa of interest.