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

Modern pollen samples provide an invaluable research tool for helping to interpret the quaternary fossil pollen record, allowing investigation of the relationship between pollen as the proxy and the environmental parameters such as vegetation, land-use, and climate that the pollen proxy represents. The European Modern Pollen Database (EMPD) is a new initiative within the European Pollen Database (EPD) to establish a publicly accessible repository of modern (surface sample) pollen data. This new database will complement the EPD, which at present holds only fossil sedimentary pollen data. The EMPD is freely available online to the scientific community and currently has information on almost 5,000 pollen samples from throughout the Euro-Siberian and Mediterranean regions, contributed by over 40 individuals and research groups. Here we describe how the EMPD was constructed, the various tables and their fields, problems and errors, quality controls, and continuing efforts to improve the available data

In this paper, we estimate the Relevant Source Area of Pollen (RSAP) in past hypothetical landscapes of the Middle and Late Holocene in southern Sweden, in order to explore the possible effects of past changes in vegetation composition, openness and structure in terms of patch size and spatial distribution. The RSAP of small basins (bogs or lakes) in the past has to be estimated if quantitative reconstruction of past vegetation at the local spatial scale is to be achieved using Sugita's Landscape Reconstruction Algorithm (LRA). In this study we apply a forward modelling approach to estimate past RSAP using the computer simulation model HUMPOL. The landscape designs are based on past landscape maps produced using a combination of palaeobotanical, archaeological and historical data, and the area's geology and soil characteristics. Four time windows characterised by different landscape/land-use were selected, i.e. Early Neolithic, Late Bronze Age, Viking Age, and Middle Ages. We found that RSAP estimates for hypothetical past landscapes in Skåne differ by ca. 600 m to 1200 m between the selected time periods, whatever the size of the basin (lake or bog, 25-250 m radius). The most probable explanation for the differences in RSAP between time slices is variable patch size and spatial distribution of patches in the landscape. The RSAPs vary between ca. 1200 and 2300 m for small basins (25 m and 70 m radius), and between ca. 2000 and 3000 m for larger basins (250 m radius). These values are within the range of earlier estimates of modern and past RSAPs for southern Scandinavia obtained using simulated or empirical data. These results suggest that, given the type of setting of that region in terms of taxa composition and traditional land-use, the RSAP for small-size lakes (25-250 m radius) will generally be in the range ca. 1200-3000 m. The forward modelling approach is found to be useful to assess the possible effects on RSAP of changes in vegetation/landscape characteristics between different periods of the past. Moreover, comparison of RSAP estimates obtained using both the forward and backward modelling approaches will be important to identify the most credible RSAP estimates for the past.

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