Pollen productivity is a key parameter to quantify past plant abundances and vegetation openness. In this study we explore how changes in land management influence pollen productivity. We study pollen deposition in largely annually laminated sediments from Lake Tiefer See in the Northeastern German lowlands deposited between AD 1880 and 2010. During this period, land use intensity has increased predominantly through the widespread introduction of artificial fertilizers, herbicides and heavy machinery mainly since the 1950s. Although land use statistics show that overall vegetation openness remained largely constant, non-arboreal pollen deposition (from herbs and grasses) sharply declined over the study period. This decline can be partly explained by a shift towards crops that emit little pollen such as wheat and oilseed rape. Furthermore, intensified grassland management, including drainage, also contributed to lower pollen deposition because of the decline of Plantago lanceolata and Rumex from grassland communities. However, the most important effect is a decline in pollen productivity of grasses of about 60%, which most likely is a response to earlier and more frequent mowing, although changes in grass species composition may also have played a role. Our results show that the type and intensity of land use have a strong effect on pollen productivity of grasses (and smaller effects on further crops). Since grass pollen deposition is a main proxy for vegetation openness and grasses are the common reference taxon in most PPE studies, variations in the pollen productivity of grasses introduce so far neglected errors in the reconstruction of the past vegetation cover. Our study provides a first estimate about the magnitude of this effect.

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.