The REVEALS model (Sugita, 2007a) is a generalized version of the R value model of Davis (1963). The development of pollen–vegetation modelling from the R value model, via the ERV (extended R value) models of Andersen (1970) and Parsons and Prentice (1981) through to the REVEALS model, is described in detail in numerous earlier papers (Broström et al., 2004; Bunting et al., 2013a; Sugita, 1993, 2007a).

Using simulations, Sugita (2007a) showed that “large lakes” represent regional vegetation; i.e. between-lake differences in pollen assemblages are very small, which was the case for lakes ≥50 ha in the simulations (Sugita, 2007a). Tests using modern pollen data from surface lake sediments have shown that pollen assemblages from lakes ≥50 ha are appropriate to estimate regional plant cover using the REVEALS model (e.g. tests by Hellman et al., 2008a, b, in southern Sweden and by Sugita et al., 2010, in northern America).

The REVEALS model (Eq. 1) calculates estimates of regional vegetation abundance in proportions or percentage cover using fossil pollen counts from large lakes (Sugita, 2007a). 1V^i=ni,k/α^i∫RZmaxgi(z)dz∑j=1mnj,k/α^j∫RZmax⁡gj(z)dz=ni,k/α^iKi∑j=1m(nj,k/α^jKj) The assumptions of the REVEALS model are listed in Sugita (2007a). Using simulations Sugita (2007a) demonstrated that, in theory, the model can also be applied to pollen records from multiple “small lakes” (<50 ha), i.e. lakes for which between lake differences in pollen assemblages can be large. However, the REVEALS estimates using pollen records from small lakes generally have larger standard errors (SEs) than those based on pollen data from large lakes. The latter was demonstrated for empirical pollen records from large lakes versus small sites (lakes and bogs) by Trondman et al. (2016) in southern Sweden and Mazier et al. (2012) in the Czech Republic. Although the application of the model to pollen data from bogs violates the model assumption that no plants grow on the basin, REVEALS can be applied using models of pollen dispersal and deposition for lakes or bogs. The Prentice model (Prentice, 1985, 1988) describes deposition of pollen at a single point in a deposition basin and is suitable for pollen records from bogs. Sugita (1993) developed the “Prentice–Sugita model” that describes pollen deposition in a lake, i.e. on its entire surface with subsequent mixing in the water body before deposition at the lake bottom. The original versions of both models use the Sutton model of pollen dispersal, i.e. a Gaussian plume model from a ground-level source under neutral atmospheric conditions (Sutton, 1953). A Lagrangian stochastic model (LSM) of dispersion has also been introduced as an alternative for the description of pollen dispersal in models of the pollen–vegetation relationship in general and in the REVEALS model in particular (Theuerkauf et al., 2012, 2016). It is difficult, in both theory and practice, to eliminate the effects of pollen coming from plants growing on sedimentary basins (e.g. Poaceae and Cyperaceae in bogs) on regional vegetation reconstruction. Previous studies have assessed the impacts of the violation of this assumption on REVEALS outcomes (Mazier et al., 2012; Sugita et al., 2010; Trondman et al., 2016, 2015). An empirical study in southern Sweden (Trondman et al., 2016) indicated that REVEALS estimates based on pollen records from multiple small sites (lakes and/or bogs) are similar to the REVEALS estimates based on pollen records from large lakes in the same region. The results also suggested that increasing the number of pollen records significantly decreased the standard error of the REVEALS estimates, as expected based on simulations (Sugita, 2007a). It is therefore appropriate to use pollen records from small bogs to increase the number of pollen records included in a REVEALS reconstruction following the protocol of the first-generation REVEALS reconstruction for Europe (Mazier et al., 2012; Trondman et al., 2015). However, REVEALS estimates of plant cover using pollen assemblages from large bogs should only be interpreted with great caution (Mazier et al., 2012; see also Sect. 4, “Discussion”).

The inputs needed to run the REVEALS model are original pollen counts, relative pollen productivity estimates (RPPs) and their standard deviation, fall speed of pollen (FSP), basin type (lake or bog), size of basin (radius), maximum extent of regional vegetation, wind speed (m s-1), and atmospheric conditions. FSP can be calculated using measurements of the pollen grains and Stokes' law (Gregory, 1973). RPPs of major plant taxa can be estimated using datasets of modern pollen assemblages and related vegetation and the extended R value model (e.g. Mazier et al., 2008). RPPs exist for a large number of European plant taxa, and syntheses of FSPs and RPPs were published earlier by Broström et al. (2008) and Mazier et al. (2012). The latter was used in the “first-generation” REVEALS reconstruction (Trondman et al., 2015). A new synthesis of European RPPs was performed for this “second-generation” reconstruction (Appendices A, B and C). Preparation of data from individual pollen records and the values of model parameters used are described below (Sect. 2.2 and 2.3).

A total of 1143 pollen records from 29 European countries and the eastern Mediterranean–Black Sea–Caspian corridor were obtained from databases and individual data contributors. The contributing databases include the European Pollen Database (Fyfe et al., 2009; Giesecke et al., 2014), the Alpine Palynological Database (ALPADABA; Institute of Plant Sciences, University of Bern; now also archived in EPD), the Czech Quaternary Palynological Database (PALYCZ; Kuneš et al., 2009), PALEOPYR (Lerigoleur et al., 2015), and datasets compiled within synthesis projects from the Mediterranean region (Fyfe et al., 2018; Roberts et al., 2019) and the eastern Mediterranean–Black Sea–Caspian corridor (EMBSeCBIO project; Marinova et al., 2018) (see Fig. 1 for map, “Data availability” section for data location and “Team list” for individual pollen data contributors). We followed the protocols and criteria published in Mazier et al. (2012) and Trondman et al. (2015) for selection of pollen records and application of the REVEALS model. Available pollen records were filtered based on criteria including basin type (to exclude archaeological sites and marine records) and quality of chronological control (excluding sites with poor age–depth models or fewer than three radiocarbon dates). This resulted in 1128 pollen records from lakes and bogs, both small and large. The rationale behind the use of pollen records from small sites is based on the knowledge that REVEALS estimates based on pollen records from multiple sites provide statistically validated approximations of the regional cover of plant taxa (e.g. Trondman et al., 2016; see details of the REVEALS model in Sect. 2.1).

The taxonomy and nomenclature of pollen morphological types from the 1128 pollen records were harmonized. The pollen morphological types were then consistently assigned to 1 of 31 RPP taxa (Table 1; see Sect. 2.3 and Appendices A–C for details on the RPP dataset used in this study), following the protocol outlined in Trondman et al. (2015; SI-2 with examples of harmonization between pollen morphological types and RPP taxa). This process takes into account plant morphology, biology and ecology of the species that are included in each pollen morphological type. Consequently, RPP-harmonized pollen count data were produced for each of the 1128 pollen records. It should be noted that the EMBSeCBIO data do not contain pollen counts from cultivars; i.e. pollen from cereals and cultivated trees were deleted from the pollen records (Marinova et al., 2018). Therefore, the cover of agricultural land (represented by cereals in this reconstruction) will always be zero in the eastern Mediterranean–Black Sea–Caspian corridor in grid cells with only pollen records from EMBSeCBIO, even though agriculture did occur in the region from the early Neolithic.

For the application of REVEALS, an age–depth model (in cal yr BP) is required for each pollen record. We used the author's original published model, the model available in the contributing database or, where necessary, a new age–depth model was constructed following the approach in Trondman et al. (2015). The age–depth model for each pollen record is used to aggregate RPP-harmonized pollen count data into 25 time windows throughout the Holocene following a standard time division used in Mazier et al. (2012) and Trondman et al. (2015), which were later adopted by the Past Global Changes (PAGES) LandCover6k working group (Gaillard et al., 2018). The first three time windows (present–100 BP (where present is the year of coring), 100–350 BP, 350–700 BP) capture the major human-induced land-cover changes since the early Middle Ages. Subsequent time windows are contiguous 500-year-long intervals (e.g. 700–1200, 1200–1700, 1700–2200 BP) with the oldest interval representing the start of the Holocene (11 200–11 700 BP). The use of 500-year-long time windows is motivated by the necessity to obtain sufficiently large pollen counts for reliable REVEALS reconstructions. Since the size of the error in the REVEALS estimate partly depends on the size of the pollen count (Sugita, 2007a), the length of the time window should be a reasonable compromise to ensure both a useful time resolution of the reconstruction and an acceptable reliability of the REVEALS estimate of plant cover (Trondman et al., 2015).

For the purpose of this study, a new synthesis of the RPP values available for European plant taxa was performed in 2018–2019 based on the work by Mazier et al. (2012) and additional RPP studies published since then (Appendices A–C). This synthesis provides new alternative RPP datasets for Europe, including or excluding plant taxa with dominant entomophily and with the important addition of plant taxa from the Mediterranean area (Appendix A, Table A1). The selection of RPP studies, RPP values (shown in Appendix B, Tables B1 and B2), and calculation of mean RPP and their standard error (SD) for Europe are explained in Appendix C. The location of studies included in the RPP synthesis is shown in Fig. C1, and related information is provided in Table C1. The synthesis includes a total of 54 taxa for which RPP values are available (Tables B1 and B2); 39 taxa from studies in boreal and temperate Europe; and 15 taxa from studies in Mediterranean Europe, of which 7 include exclusively sub-Mediterranean and Mediterranean taxa: Buxus sempervirens, Carpinus orientalis, Castanea sativa, Ericaceae (Mediterranean species), Phillyrea, Pistacia and evergreen Quercus type (t.). RPP values are available from both boreal or temperate and Mediterranean Europe for seven taxa: i.e. Poaceae (reference taxon), Acer, Corylus avellana, Apiaceae, Artemisia, Plantago lanceolata and Rubiaceae (Table B2). Table A1 presents the new RPP dataset for the 54 plant taxa and, for comparison, the mean RPP values from Mazier et al. (2012) and from the recent synthesis by Wieczorek and Herzschuh (2020). Moreover, comparison with the RPP values of three studies not used in our synthesis is shown in Table A2. For the REVEALS reconstructions presented in this paper, we excluded strictly entomophilous taxa, which resulted in a total of 31 taxa (Table 1). The excluded taxa are Compositae (Asteraceae) SF Cichorioideae, Leucanthemum (Anthemis) t., Potentilla t., Ranunculus acris t. and Rubiaceae. We included entomophilous taxa that are known to be characterized by some anemophily, e.g. Artemisia, Amaranthaceae/Chenopodiaceae, Rubiaceae and Plantago lanceolata. We excluded plant taxa with only one RPP value except Chenopodiaceae, Urtica, Juniperus and Ulmus and the seven exclusively sub-Mediterranean and Mediterranean taxa mentioned above.

The FSP values (Tables 1 and A1) for boreal and temperate plant taxa were obtained from the literature (Broström et al., 2008; Mazier et al., 2012); these values were in turn extracted from Gregory (1973) for trees and calculated based on pollen measurements and Stokes' law for herbs (Broström et al., 2004). FSPs for Mediterranean taxa (Buxus sempervirens, Castanea sativa, Ericaceae (Mediterranean species), Phillyrea, Pistacia and Quercus evergreen type) were obtained by using pollen measurements and Stokes' law (Mazier et al., unpublished); the FSP of Carpinus betulus (Mazier et al., 2012) was used for Carpinus orientalis (Grindean et al., 2019).

The site radius was obtained from original publications where possible. Sites in the EMBSeCBIO were classified as small (0.01–1 km2), medium (1.1–50 km2) or large (50.1–500 km2). These were assigned radii of 399, 2921 and 10 000 m, respectively. Where a site's radius could not be determined from publication, it was geolocated in Google Earth, and the area of the site was measured. A radius value was extracted assuming that a site shape is circular (Mazier et al., 2012). A constant wind speed of 3 m s-1, assumed to correspond approximatively to the modern mean annual wind speed in Europe, was used following Trondman et al. (2015). Zmax (maximum extent of the regional vegetation) was set to 100 km. Zmax and wind speed influence on REVEALS estimates have been evaluated earlier in simulation and empirical studies (Gaillard et al., 2008; Mazier et al., 2012; Sugita, 2007a), which support the values used for these parameters. Atmospheric conditions are assumed to be neutral (Sugita, 2007a).

REVEALS was implemented using the REVEALS function within the LRA R package of Abraham et al. (2014) (see “Code availability”, Sect. 5). The function enables the use of deposition models for bogs (Prentice's model) and lakes (Sugita's model) and two dispersal models (a Gaussian plume model and a Lagrangian stochastic model taken from the DISQOVER package; Theuerkauf et al., 2016). Within this study, the Gaussian plume model was applied. The REVEALS model was run on all pollen records within each 1∘ × 1∘ grid cell across Europe. The REVEALS function is applied to lake and bog sites separately within each 1∘ × 1∘ grid cell and combines results (if there is more than one pollen record per cell) to produce a single mean cover estimate (in proportion) and mean standard error (SE) for each taxon. The formulation of the SE is found in Appendix A of Sugita (2007a). The REVEALS SE accounts for the standard deviations of the relative pollen productivities for the individual pollen taxa (Table 1) and the number of pollen grains counted in the sample (Sugita, 2007a). The uncertainties in the averaged REVEALS estimates of plant taxa for a grid cell are calculated using the delta method (Stuart and Ord, 1994) and expressed as the SEs derived from the sum of the within- and between-site variations in the REVEALS results in the grid cell. The delta method is a mathematical solution to the problem of calculating the mean of individual SEs (see Appendix C in Li et al., 2020, for formula and further details). Results of the REVEALS function are extracted by time window, producing 25 matrices of mean REVEALS land-cover estimates and 25 matrices of corresponding mean SEs for each of the 31 RPP taxa and each grid cell. The 31 RPP taxa are also assigned to 12 plant functional types (PFTs) and 3 land-cover types (LCTs) (Table 1), and their mean REVEALS estimates were calculated. These PFTs follow Trondman et al. (2015), with the addition of two PFTs for Mediterranean vegetation not reconstructed in earlier studies: Mediterranean shade-tolerant broadleaved evergreen trees (MTBE) and Mediterranean broadleaved tall shrubs, evergreen (MTSE). The mean SE for LCTs and PFTs including more than one plant taxon are calculated using the delta method (Stuart and Ord, 1994), as described above.

To illustrate the information that the new REVEALS reconstruction provides, we present and describe (Sect. 3) maps of the REVEALS estimates (per cent cover) and their associated SEs for the three LCTs (Figs. 2 to 4) and five taxa for eight selected time windows: the five taxa are Cerealia t. and Picea abies (Figs. 5 and 6), Calluna vulgaris, deciduous Quercus type (t.), and evergreen Quercus t. (Figs. D1–D3). The selection of the five taxa and eight time windows is motivated essentially by notable changes in the spatial distribution of these taxa through time, with higher resolution for recent times characterized by the largest and most rapid human-induced changes in vegetation cover. For visualization purposes, the estimates are mapped in nine per cent cover classes. These fractions are the same for the three LCTs (Figs. 2–4), and the mapped output can therefore be directly compared. In contrast, the colour scales used for the five taxa vary between maps depending on the abundance of the PFT or taxon (Figs. 5 and 6, D1–D3). Different taxa thus have different scales, and maps cannot be directly compared. We visualize uncertainty in our data by plotting the SE as a circle inside each grid cell; it is the coefficient of variation (CV; i.e. the standard error divided by the REVEALS estimate). Circles are scaled to fill the grid cell if the SE is equal to or greater than the mean REVEALS estimate (i.e. CV ≥1). Grid-based REVEALS results that are based on pollen records from just one large bog or single small bogs or lakes provide lower-quality results (see Sect. 2.1 on the REVEALS model and Sect. 4.1, “Data reliability”). The quality of REVEALS land-cover estimates by grid cell and time window is provided in Table GC_quality_by_TW (see Sect. 6, “Data availability”). The percentage scale ranges we use here are different from those used in the maps of Trondman et al. (2015), and therefore the data visualization cannot be directly compared.

The three land-cover types are evergreen trees (ETs), summer-green trees (STs) and open land (OL). ETs include six PFTs which are composed of nine pollen morphological types (hereinafter referred to as taxa). STs include 3 PFTs which are composed of 12 taxa, while OL includes 3 PFTs that are in turn composed of 10 taxa (Table 1).

In terms of PFTs, Cerealia type (t.) is assigned to agricultural land (AL), Picea abies to shade-tolerant evergreen trees (TBE1: Picea abies is the only taxon in this PFT), Calluna vulgaris to low evergreen shrubs (LSE: Calluna vulgaris is the only taxon in this PFT), deciduous Quercus t. to shade-tolerant summer-green trees (TBS) and evergreen Quercus t. to Mediterranean shade-tolerant broadleaved evergreen trees (MTBE) (Table 1).

The REVEALS results are reliant on the quality of the input datasets, namely pollen count data, chronological control for sequences, and the number and reliability of RPP estimates used (see discussion on RPPs in Sect. 4.2). The standard errors (SEs) can be considered a measure of the precision of the REVEALS results and of reliability and quality (Trondman et al., 2015). Where SEs are equal to or greater than the REVEALS estimates (represented in the maps of Figs. 2–6 and D1–D3 as a circle that fills the grid), caution should be applied when using the REVEALS estimates as it implies that they are not different from zero when taking the SEs into account. Whilst this is possible within an algorithmic approach that includes estimates of uncertainty, it is conceptually impossible to have negative vegetation cover. If SEs ≥ mean REVEALS value, it is therefore uncertain whether the plant taxon has cover within the grid cell. Either the cover may be very low, or the taxon may be absent within the region (grid cell in this case).

The size of pollen counts impacts the size of REVEALS SEs (Sugita, 2007a); larger counts result in smaller SEs. Aggregation of samples from pollen records to longer time windows results in larger count sizes and thus lower SEs (see Sects. 2.2 above and 4.2 below). Our input dataset includes more than 59 million individual pollen identifications, organized here into 16 711 samples from 1128 sites, where a sample is an aggregated pollen count for RPP taxa for a time window at a site. A total of 77 % of samples have count sizes in excess of 1000, which is deemed most appropriate for REVEALS reconstructions (Sugita, 2007a). The mean count size across all samples is 3550. Samples with count sizes lower than 1000 are still used but result in higher SEs. More than half of the pollen records used in the study were sourced from databases (see Sect. 2.2). Note that the EMBSeCBIO taxonomy has been pre-standardized, and the data compilers have removed Cerealia type (t.). This means that for grid cells within the eastern Mediterranean–Black Sea–Caspian corridor, caution is advised in the interpretation of Cerealia type. Nevertheless, pollen from ruderals that are often related to agriculture, for example, Artemisia, Amaranthaceae/Chenopodiaceae and Rumex acetosa type, are included in the land-cover type open land (OL); therefore, changes in OL cover in the eastern Mediterranean–Black Sea–Caspian corridor may be related to changes in agricultural land (see also discussion below, Sect. 4.3, “agricultural land” PFT).

Aggregation of pollen counts to time windows depends on age–depth models. We have used the best age–depth models available to us, based on the chronologies presented in Giesecke et al. (2014) for EPD sites and through liaison with data contributors. Nevertheless, future REVEALS runs may draw on improvements to age–depth modelling, which may result in some original pollen count data being assigned to different time windows.

The REVEALS results presented here are provided for 1∘ × 1∘ grid cells across Europe. The size and number of suitable pollen records is an important factor in the quality of the REVEALS estimates for each grid cell. The REVEALS model was developed for use with large lakes (≥50 ha; Sugita, 2007a) that represent regional vegetation. Grid cells with multiple large lakes will thus provide results with the highest level of certainty and reflect the regional vegetation most accurately. These grid cell results comprised of one or more large lakes, several small sites (lake or bog), or a mix of large site(s) and small sites are considered “high-quality” (dark-grey grids in Fig. 1b). It has been shown both theoretically (Sugita, 2007a) and empirically (Fyfe et al., 2013; Trondman et al., 2016) that pollen records from multiple smaller (<50 ha) lakes will also provide REVEALS estimates that reflect regional vegetation. However, SEs may be larger if there is high variability in pollen composition between records. We therefore also consider grid cells with multiple sites “high-quality”. Application of REVEALS to pollen records from large bogs violates assumptions of the model (see Sect. 2.1 above). Therefore, REVEALS estimates for grid cells including large bogs or single small sites (lake or bog) may not be representative of regional vegetation, particularly in areas characterized by heterogeneous vegetation. We consider such estimates to be “lower-quality” (light-grey grids in Fig. 1b), although they may still provide first-order indications of vegetation cover and represent an improvement in pollen percentage data (Marquer et al., 2014). Our results provide REVEALS estimates for a maximum of 420 grid cells per time window. The number and type of pollen records in a grid cell can change between time windows: not all pollen records cover the entire Holocene. To assess the reliability of individual results it is important to consider not just the number and type of pollen records in the total dataset, but how these change between the time windows. Results for a maximum of 143 grid cells are based on 3 or more sites, 65 on 2 sites, and a minimum of 212 grid cells on a single site. The results of a maximum of 67 grid cells are based on single small bogs (<400 m radius), 68 on single small lakes (<400 m radius), and 82 on single large bogs.

A key assumption of the REVEALS model is that RPP values are constant within the region of interest and through time (Sugita, 2007a). Nevertheless, it has been suggested that RPPs may vary between regions, with the variation caused by environmental variability (climate, land use), vegetation structure or methodological design differences (Broström et al., 2008; Hellman et al., 2008a; Mazier et al., 2012; Li et al., 2020; Wieczorek and Herzschuh, 2020). Wieczorek and Herzschuh (2020) have shown that inter-taxon variability in RPP values is generally lower than intra-taxon variability, lending support to application of the approach we used in the new synthesis of RPPs for Europe (Appendices A–C), i.e. calculation of mean RPPs using all available RPP values that can be considered to be reliable. Nevertheless, some RPP taxa still present a challenge, for example, Ericaceae, where Mediterranean tree forms have a greater number of inflorescences and hence may have a higher RPP than low-growth-form Ericaceae in central and northern Europe. As we only have a unique RPP value for Ericaceae in boreal–temperate Europe and a unique RPP value in Mediterranean Europe, the large difference in RPP between the two biomes remains to be confirmed with more RPP studies.

Currently there is higher confidence in the boreal and temperate RPP values that are based on a wider set of studies increasing the spread of values and hence reliability of the mean RPP values used (Mazier et al., 2012; Wieczorek and Herschuh, 2020), whilst RPP values for Mediterranean taxa are based on fewer empirical RPP studies. The new RPP datasets for Europe produced for this study (Appendices A–C) can be used in different ways. The RPPs provided in Table A1 can be used for the entire European region, including or excluding entomophilous taxa and including all values from the Mediterranean area or only the values for the strictly sub-Mediterranean and/or Mediterranean taxa. If one uses all RPPs from the Mediterranean area, there will be taxa for which there is both an RPP value obtained in boreal–temperate Europe and an RPP value obtained in Mediterranean Europe. Application of both RPP values in a single REVEALS reconstruction is not straightforward to achieve, because the border between the two regions has shifted over the Holocene. In the REVEALS reconstruction presented in this paper, we chose to use the RPPs from Mediterranean Europe only for the sub-Mediterranean and/or Mediterranean taxa (including Ericaceae) (Tables 1 and A1), and for all other taxa we used the RPPs from boreal/ temperate Europe. The major issue with this choice is the RPP value of Ericaceae. Using only the large value from Mediterranean Europe may lead to an under-representation of Ericaceae (Calluna excluded), in particular in boreal Europe, but perhaps also in temperate Europe. Using only the small value from boreal–temperate Europe may lead to an over-representation of Ericaceae in Mediterranean Europe.

Until we have more RPP values for each taxon, it is not possible to disentangle the effect of all factors influencing the estimation of RPPs and to separate the effect of methodological factors from those of factors such as vegetation type, climate and land use. The only way to evaluate the reliability of RPP datasets is to test them with modern or historical pollen assemblages and related plant cover (Hellman et al., 2008a, b). We argue that RPP values of certain taxa may not vary substantially within some plant families or genera, while they might be variable within others, depending on the characteristics of flowers and inflorescences that may be either very different or relatively constant within families or genera (see discussion in Li et al., 2018). Therefore, we advise to use compilations of RPPs at continental or sub-continental scales rather than compilations at multi-continental scales as the Northern Hemisphere dataset proposed by Wieczorek and Herzschuh (2020). We consider the RPP selection used within this work as the most suitable for Europe to date but expect revised and improved RPP values as more RPP empirical studies are published. Moreover, experimentation in REVEALS applications will allow future studies to evaluate the effects of using different RPP datasets on land-cover reconstructions (e.g. Mazier et al., 2012).

The role of FSP values in the pollen dispersal and deposition function (gi(z) in Eq. 1 of the REVEALS model, Sect. 2.1) has been discussed by Theuerkauf et al. (2012). In this application of REVEALS we used the Gaussian plume model (GPM) of dispersion and deposition as most existing RPP values have been estimated using this model. The GPM approximates dispersal as a fast-declining curve with distance from the source plant, which implies short distances of transport for pollen grain with high FSP compared to other models of dispersion and deposition (Theuerkauf et al., 2012). We have used the FSP values obtained for deciduous Quercus type (t.) (0.035 m s-1) and boreal–temperate Ericaceae (0.037 m s-1) for evergreen Quercus t. and Mediterranean Ericaceae, respectively, although the FSP values of those two taxa were estimated to be 0.015 and 0.051 in the Mediterranean study (Tables 1 and A1). Whether using a lower FSP for evergreen Quercus t. (0.015 m s-1) and a higher FSP for Mediterranean Ericaceae (0.051 m s-1) will have an effect on the REVEALS results is not known and requires further testing.

This second-generation dataset of pollen-based REVEALS land cover in Europe over the Holocene is currently used in two major research projects: LandClim and PAGES LandCover6k. LandClim is a Swedish Research Council project studying the difference in the biogeophysical effect of land-cover change on climate at 6000, 2500 and 200 BP (Fyfe et al., 2022; Githumbi et al., 2019; Strandberg et al., 2014, 2022; Trondman et al., 2015). PAGES LandCover6k focuses on providing datasets on past land cover and land use for climate modelling studies (Dawson et al., 2018; Gaillard et al., 2018; Harrison et al., 2020). The first-generation REVEALS land-cover reconstruction (Marquer et al., 2014, 2017; Trondman et al., 2015) was used to evaluate other pollen-based reconstructions of Holocene tree-cover changes in Europe (Roberts et al., 2018) and scenarios of anthropogenic land-cover changes (ALCCs) (Kaplan et al., 2017) (see also Sect. 1). The Trondman et al. (2015) reconstructions were used to create continuous spatial datasets of past land cover using spatial statistical modelling (Pirzamanbein et al., 2014, 2018, 2020).

Spatially explicit datasets and maps based on this second generation of REVEALS reconstructions are currently being produced within PAGES LandCover6k and used to evaluate and revise the HYDE (Klein Goldewijk et al., 2017) and KK10 (Kaplan et al., 2009) ALCC scenarios. Moreover, LandCover6k archaeology-based reconstructions of past land-use change (Morrison et al., 2021) will be integrated with the datasets of REVEALS land cover. Besides the uses listed above, the second generation of REVEALS reconstruction for Europe offers great potential for use in a large range of studies on past European regional vegetation dynamics and changes in biodiversity over the Holocene (Marquer et al., 2014, 2017) as well as the relationship between regional plant cover, land use and climate over millennial and centennial timescales. Since the reconstructions are of regional plant cover they will have value in archaeological research when impacts are expected at the regional level (e.g. the impact of early mining; Schauer et al., 2019). Archaeological questions and research programmes that require information on local vegetation cover will require the full application of the LRA (REVEALS and LOVE; Sugita, 2007a, b), such as the local vegetation estimates presented from Norway focussing on cultural landscape development (Mehl et al., 2015). The same approach of using the REVEALS results within the LOVE model is necessary for ecological questions that require local vegetation estimates (Cui et al., 2013, 2014; Sugita et al., 2010).

Several papers have discussed in depth the issues that need to be taken into account when interpreting REVEALS reconstructions of past plant cover, in particular Trondman et al. (2015) and Marquer et al. (2017). The interpretation in terms of human-induced vegetation change is one of the major challenges. The cover of open land (OL) may be used to assess landscape openness but is not a precise measure of human disturbance. OL will include plant taxa characterizing both naturally open land and agricultural land that has been created by humans through the course of the Holocene with the domestication of plants and livestock. Natural openness can occur in arctic and alpine areas, in wet regions, in river deltas, and around large lakes as well as in eastern steppe areas. It is a particular challenge in the Mediterranean region, where natural vegetation openness represents a larger fraction of the land cover than in temperate or boreal Europe (Roberts et al., 2019). Agricultural land (AL; Trondman et al., 2015) is the only PFT that includes cultivars; nevertheless, it is restricted to cereal cropping, and many other cultivated crop types that can be identified through pollen analysis do not yet have RPP values (e.g. Linum usitatissimum (common flax), Cannabis (hemp), Fagopyrum (buckwheat), beans). Moreover, the Cerealia t. pollen morphological type includes pollen from wild species of Poaceae, especially when identification relies essentially on measurements of the pollen grain and its pore and does not consider exine structure and sculpture (Beug, 2004; Dickson, 1988).

The maps presented and described in Sect. 3 as an illustration of the results show similar changes in spatial distributions and quantitative cover of plant taxa and land-cover types through time, between 6000 BP and the present, as the results published in Trondman et al. (2015). The much greater potential of the new REVEALS reconstruction resides in its larger spatial extent, covering not only boreal and temperate Europe but also southern and eastern Europe, and its contiguous time windows across the entire Holocene, from 11 700 BP to the present. The quality of results is also higher in a number of grid cells in comparison to Trondman et al. (2015), where new pollen records have been included, which may in several cases decrease the standard error of the REVEALS estimates.