The need for quantification of land cover from pollen data has led to the development of a Landscape Reconstruction Algorithm (LRA). The LRA includes several models of which the REVEALS model estimates regional vegetation abundance using pollen assemblages from large sites (lakes or bogs). In this paper we explore the effects of selection and number of pollen samples, and choice of pollen productivity estimates on the REVEALS results. The effect of the size of vegetation surveys is also tested. The results suggest that the differences between two sizes of vegetation surveys have little effect on the model validation. The "characteristic radius" of regional vegetation in southern Sweden was estimated as 200 km. However, the vegetation composition in a 100 x 100 km(2) square matches well with that estimated by REVEALS. Whether 25, 20 (outliers excluded) or 4 pollen samples are used does not change the REVEALS reconstructions much although the error estimates are larger when outliers are included, and very large when only four samples are used. Therefore validation of the REVEALS model and REVEALS reconstructions of past vegetation can be performed using a limited number of pollen samples, although with caution. The use of many pollen samples from multiple sites is always better whenever possible. REVEALS reconstructions are closer to the actual vegetation when the Danish Pollen Productivity Estimates (PPEs) are used instead of the Swedish PPEs for Cereals, Rumex acetosa/acetosella, Plantago lanceolata and Calluna, indicating that the Danish PPEs are more reliable than the Swedish ones for those taxa. It is recommended to test more than one set of PPEs in validation and applications of the REVEALS model for a better evaluation of the results.

Relevant source area of pollen (RSAP) and pollen productivity for 11 key taxa characteristic of the pasture woodland landscape of the Jura Mountains, Switzerland, were estimated using pollen assemblages from moss polsters at 20 sites. To obtain robust pollen productivity estimates (PPEs), we used vegetation survey data at a fine spatial-resolution (1 x 1 m(2)) and randomized locations for sampling sites, techniques rarely used in palynology. Three Extended R value (ERV) submodels and three distance-weighting methods for plant abundance calculation were applied. Different combinations of the submodels and distance-weighting methods provide slightly different estimates of RSAP and PPEs. Although ERV submodel 1 using 1/d (d = distance in meters) best fits the dataset, PPE values for heavy pollen types (e.g. Abies) were sensitive to the method used for distance-weighting. Taxon-specific distance-weighting methods, such as Prentice's model, emphasize the intertaxonomic differences in pollen dispersal and deposition, and are thus theoretically sound. For the dataset obtained in this project, Prentice's model was more appropriate than other distance-weighting methods to estimate PPEs. Most of the taxa have PPEs equal to (Fagus, Plantago media and Potentilla-type), or higher (Abies, Picea, Rubiaceae and Trollius europaeus) than Poaceae (PPE = 1). Acer, Cyperaceae, and Plantago montana-type are low pollen producers. This set of PPEs will be useful for reconstructing heterogeneous, mountainous pasture woodland landscapes from fossil pollen records. The RSAP for moss polsters in this semi-open landscape region is ca. 300 m.

Information on the spatial distribution of past vegetation on local, regional and global scales is increasingly used within climate modelling, nature conservancy and archaeology. It is possible to obtain such information from fossil pollen records in lakes and bogs using the landscape reconstruction algorithm (LRA) and its two models, REVEALS and LOVE. These models assume that reliable pollen productivity estimates (PPEs) are available for the plant taxa involved in the quantitative reconstructions of past vegetation, and that PPEs are constant through time. This paper presents and discusses the PPEs for 15 tree and 18 herb taxa obtained in nine study areas of Europe. Observed differences in PPEs between regions may be explained by methodological issues and environmental variables, of which climate and related factors such as reproduction strategies and growth forms appear to be the most important. An evaluation of the PPEs at hand so far suggests that they can be used in modelling applications and quantitative reconstructions of past vegetation, provided that consideration of past environmental variability within the region is used to inform selection of PPEs, and bearing in mind that PPEs might have changed through time as a response to climate change. Application of a range of possible PPEs will allow a better evaluation of the results.

The REVEALS model was developed to reconstruct quantitatively regional vegetation abundance (in a 10⁴–10⁵ km² area) from pollen assemblages in large lakes (≥100–500 ha). This model corrects for biases in pollen percentages caused by inter-taxonomic differences in pollen productivity and dispersal. This paper presents the first case study to validate REVEALS, using empirical data from southern Sweden. Percentage cover of modern regional vegetation in Skåne and Småland, two contrasting vegetation regions, was predicted with REVEALS for 26 key taxa, using pollen assemblages from surface sediments in 10 large lakes, and compared to the actual vegetation within 10⁴ km² compiled from satellite data, forestry inventories, crop statistics, aerial photographs, and vegetation inventories. REVEALS works well in predicting the percentage cover of large vegetation units such as total trees (wooded land), total herbs (open land), total conifers and total broad-leaved trees, and it provides reasonable estimates for individual taxa, including Pinus, Picea, Betula, Corylus, Alnus, Tilia, Salix spp., Juniperus, Poaceae, Cyperaceae, Cerealia and Secale. The results show great potential for REVEALS applications, including (1) quantitative reconstructions of past regional land cover important for palaeoclimatology and nature conservation, and (2) local-scale reconstruction of vegetation (<1 km² up to ∼ 5 km² area) relevant for palaeoecology and archaeology.