Novel methods of estimating relative pollen productivity: A key parameter for reconstruction of past land cover from pollen records

Abstract

Reconstructing land cover from pollen data using mathematical models of the relationship between them has the potential to translate the many thousand pollen records produced over the last 100 years (over 2300 radiocarbon-dated pollen records exist for the UK alone) into formats relevant to ecologists, archaeologists and climate scientists. However, the reliability of these reconstructions depends on model parameters. A key parameter is Relative Pollen Productivity (RPP), usually estimated from empirical data using ‘Extended R Value analysis’ (ERV analysis). Lack of RPP estimates for many regions is currently a major limitation on reconstructing global land cover. We present two alternatives to ERV analysis, the Modified Davis Method and an iteration method, which use the same underlying model of the relationship between pollen and vegetation to estimate RPP from empirical data, but with different assumptions. We test them in simulation against ERV analysis, and use a case study of a problematic empirical dataset to determine whether they have the potential to increase the speed and geographic range of RPP estimation. The two alternative methods are shown to perform at least as well as ERV analysis in simulation. We also present new RPP estimates from southeastern sub-tropical China for nine taxa estimated using the Modified Davis Method. Adding these two methods to the ‘toolkit’ for land cover reconstruction from pollen records opens up the possibility to estimate a key parameter from existing datasets with less field time than using current methods. This can both speed up the inclusion of more of the globe in past land cover mapping exercises such as the PAGES Landcover6k working group and improve our understanding of how this parameter varies within a single taxon and the factors control that variation.

Compilation of relative pollen productivity (RPP) estimates and taxonomically harmonised RPP datasets for single continents and Northern Hemisphere extratropics

“…Data from the study of Qin et al (2020) have been rejected as they had very high values for most taxa compared to other studies, which we assume was a systematic problem of the study. The study of Fang et al (2019) was excluded because it was designed to test different methods for RPP estimation and was carried out in patchy vegetation without enough sites.…”

Pollen productivity estimates strongly depend on assumed pollen dispersal II: Extending the ERV model

“…ERV is not suited for landscapes with large vegetation patches (>several km grain size) or with vegetation gradients. In such landscapes, PPEs may instead be produced with approaches that do not rely on the assumption of uniform regional pollen deposition, like the optimization approaches of Theuerkauf et al (2013) or Fang et al (2019). These approaches best work with pollen data from lakes and require mapped vegetation data from much larger areas, for example, from a 30 km radius in Theuerkauf et al (2013).…”

“…Using this type of “regional” pollen counts requires estimating plant abundances on a large scale to arrive at good PPE estimates, a “large lake” approach will require plant cover data in a 30–50 km radius around each sample site to arrive at robust PPEs. PPEs in this case can be calculated with iterative approaches (Fang et al, 2019; Theuerkauf et al, 2013) or by using the inverse REVEALS model (Kuneš et al, 2019). The most critical step in the calculations is the appropriate distance weighting of the plant abundance data.…”

Comparison of settlement-era vegetation reconstructions for STEPPS and REVEALS pollen–vegetation models in the northeastern United States

“…Methodological work mainly focuses on the effects of pollen dispersal models (e.g., Sutton model and Lagrangian models; Theuerkauf et al, 2012) and use of parameters describing atmospheric conditions (turbulent vs. stable) on estimates of pollen productivity and vegetation reconstructions. Research is also carried out in the field of parameter estimation with comparisons of different estimation methods such as extended R -value models and methods based on iterative approaches (e.g., Theuerkauf et al, 2012; Fang et al, 2019). In its current form, REVEALS does not treat pollen records as time series and assumes that pollen assemblages within a sediment core are independent, nor does it account for temporal persistence of forest composition.…”