dataesr/scientific-paragraphs-categorization huggingface dataset:
The increase in the spatial coverage of sites and temporal scale to the entire Holocene to capture transient vegetation change at sub-millennial timescales is vital to capture information on the transformation of the biosphere by human actions. Europe has a deep history of pollen data production  (Edwards et al. , 2017)  and an open-access repository for pollen records (the European Pollen Database, EPD) as well as regional pollen repositories (list of databases and access links in Sect. 2. 2 and the "Data availability" section). These data repositories result in abundant pollen records that can be used for data-driven reconstructions of past vegetation patterns at continental scales. Pollen-based vegetation reconstructions for Europe have used community-level approaches  (Huntley, 1990) , biomization methods  (Davis et al.
As with the  Trondman et al. (2015)  reconstruction, this new dataset is specifically designed to be used in climate modelling. It is performed at a spatial scale of 1 • × 1 • (ca. 100 km × 100 km) across 30-75 • N, 25 • W-50 • E (Europe and part of the eastern Mediterranean-Black Sea-Caspian corridor) (Fig. 1 ). The number of pollen records used (1128), the area covered and time length (entire Holocene) are a significant advancement for the results presented in  Trondman et al. (2015) , which used 636 pollen records covering NW Europe (including Poland and the Czech Republic but excluding western Russia and the Mediterranean area) and produced estimates for five time windows (in cal yr BP, hereafter abbreviated BP): 6200-5700, 4200-3700, 700-350 and 350-100 BP and 100 BP to present. Marquer et al.
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 individ-ual pollen records and the values of model parameters used are described below (Sect. 2. 2 and 2. 3).
Pollen records -data compilation and preparation 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 (PA-LYCZ;  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.
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.
Model parameter setting 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.
Results The complete REVEALS land-cover reconstruction dataset includes mean REVEALS values (in proportions) and their related mean SE for 31 individual tree and herb taxa, 12 PFTs, and 3 LCTs for each grid cell in 25 consecutive time windows of the Holocene (11. 7 cal kyr BP to present). Here, results are illustrated by maps of the three LCTs (Figs. 2 3 4 ) and five taxa (Figs. 5-6, D1-D3). The presented maps are not part of the published dataset archived in the PANGAEA online public database (see "Data availability", Sect. 6); they are examples of how the data can be visually presented and what they can be used for.
Data reliability 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 RE-VEALS 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 EMB-SeCBIO 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.
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.
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 borealtemperate 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 Eu-rope 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.
Use of the REVEALS land-cover reconstruction results 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 Land-Cover6k. LandClim is a Swedish Research Council project studying the difference in the biogeophysical effect of landcover change on climate at 6000, 2500 and 200 BP  (Fyfe et al. , 2022; Githumbi et al. , 2019; Strandberg et al. , 2014 Strandberg et al. , , 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 RE-VEALS land-cover reconstruction  (Marquer et al. , 2014 (Marquer et al. , , 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 (Pirzamanbein et al. , , 2018 (Pirzamanbein et al. , , 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 (Marquer et al. , , 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.
Code availability REVEALS was implemented using the REVEALS function within the LRA R package  (Abraham et al. , 2014) , available at https://github. com/petrkunes/LRA (last access: 5 April 2022). Example code for data preparation and implementation of REVEALS, using two grid cells from SW Britain, is available at https://github. com/rmfyfe/landclimII (last access: 5 April 2022;  Abraham et al. , 2014).
Data availability All data files reported in this work which were used for calculations and figure production are available for public download at https://doi. org/10. 1594/PANGAEA. 937075 (Fyfe et al. , 2022). The data available in Pangaea in-clude (1) REVEALS reconstructions and their associated SE for the 25 time windows; (2) metadata of the 1128 pollen records used; (3) LandClimII contributors listing the data contributors, collectors and databases; (4) the list of FSP and RPP values used for the reconstructions; and (5) grid-cell-quality information (in terms of available pollen data, which influence the resulting quality: mean REVEALS estimate of plant cover) for all grid cells. Pollen data were extracted from ALPADABA (https://www. neotomadb. org/, last access: 5 April 2022), EMBSECBIO (https://doi.
org/10. 17864/1947. 109; Harrison and Marinova, 2017) , EPD (http://www. europeanpollendatabase. net/index. php, last access: 5 April 2022), LandClimI, PALYCZ (https: //botany. natur. cuni. cz/palycz/, last access: 5 April 2022) and PALEOPYR (http://paleopyr. univ-tlse2. fr/, last access: 5 April 2022).
e. study land use as a climate forcing  (Gaillard et al. , 2010a (Gaillard et al. , , 2018;; Harrison et al. , 2020; Strandberg et al. , 2014). Local reconstructions (LOVE) can be a complementary approach to archaeological surveys as fine-scale human use of the landscape cannot be distinguished using REVEALS (regional estimates). The LOVE model requires that regional plant cover is known: the REVEALS reconstructions are therefore needed for this purpose as well, and gridded reconstructions may be a way to perform LOVE reconstructions, although other strategies can be chosen  (Cui et al. , 2013; Mazier et al. , 2015). Questions aiming to understand the degree of vegetation openness through the Holocene in Europe or regarding changes in the relationship between summer-green and evergreen tree cover through time can now and in the future be answered and validated with fossil pollen data via the REVEALS approach.
A1 New RPP synthesis for Europe The most common method to estimate RPPs involves the application of the extended R value (ERV) model on datasets of modern pollen assemblages and related vegetation cover. A summary of the ERV model and its assumptions and an extensive description of standardized field methods for the purpose of RPP studies are found in  Bunting et al. (2013a). Estimation of RPPs in Europe started with the studies by  Sugita et al. (1999)  and  Broström et al. (2004)  in southern Sweden and  Nielsen (2004)  in Denmark. The first tests of the RPP in pollen-based reconstructions of plant cover using the LRA's REVEALS (Regional Estimates of VEgetation Abundance from Large Sites) model  (Sugita, 2007a)  were published by  Soepboer et al. (2007)  in Switzerland and  Hellman et al.
, 2005) ; therefore, RPPs related to Poaceae were calculated by assuming that the RPP value for Quercus (related to Poaceae; Quercus (Poaceae) ) was the same in this study region as the mean of Quercus (Poaceae) RPPs from all other available studies. The pollen taxonomy and nomenclature follow the system used in the European Pollen Database (EPD;  Fyfe et al. 2009). The ranking of RPPs (relative to Poaceae, RPP = 1) for 23 tree taxa (M: Mediterranean taxa), from the largest (13. 56) to the smallest (0. 240), is as follows (Poaceae included for comparison): Alnus > evergreen Quercus t. (M) > Abies alba > Pinus > Fagus sylvatica > Picea abies > Ericaceae (M) > Betula > deciduous Quercus t. > Carpinus betulus > Populus > Juniperus > Corylus avellana > Castanea sativa > Sambucus nigra t. > Ulmus > Tilia > Salix > Fraxinus > Poaceae (= 1) > Acer > Pistacia (M) > Phillyrea (M) > Carpinus orientalis (M).
(2012). Underlined values are values from the three published studies that are close to the values of the synthesis in this paper. g The original paper does not provide an RPP for Poaceae and SDs for the RPPs. We extracted the RPP values related to Picea from Table  5  in  Twiddle et al. (2012). RPPs related to Poaceae (1. 00 g ) were then calculated by assuming that the RPP of Picea was equal to the mean RPP of Picea in Europe (this synthesis) (in bold). h The RPPs and their SDs are not listed in the original paper; we therefore extracted the values from Fig. 4  in  Bunting et al. (2013b) , and the decimals are approximate. i  Kuneš et al. (2019) : we chose the RPP values that were considered best by the authors, i. e. using the lake dataset (pollen from lake sediment), ERV sub-model 1 and the Lagrangian stochastic model (for details, see "Discussion" section in this paper).
Most of the RPP values of the three studies Twi, Bun and Kun are in the range of the values selected from the studies included in our synthesis (New) except for Urtica, Abies alba, Carpinus and Pinus in Kun. The Lagrangian stochastic model is used in Kun instead of the Gaussian plume model in New, which may be one of the factors behind the lower RPPs in Kun, in particular (but not only) for taxa with heavy pollen grains. Tables B1 (boreal and temperate Europe) and B2 (Mediterranean Europe) list the RPP values from the 16 selected studies according to the information on models used provided in Appendix C (Table  C1 ) with further explanations on selection of RPP studies. We followed similar procedures and rules as  Mazier et al. (2012)  and  Li et al. (2018)  to produce a new standard RPP dataset for Europe.
We consider that there are still too few RPP values per taxon to disentangle variability in the RPP values for a particular taxon due to methodological issues, landscape characteristics, land use or climate. We therefore use the mean of selected RPP values for each taxon in the new standard RPP dataset following  Broström et al. (2008)  and  Mazier et al. (2012). In boreal and temperate Europe, the number of RPP values per taxon varies between one and nine (Betula) (Table  B1 ), and in Mediterranean Europe, there is only one value per taxon (Table  B2 ). In general, all three sub-models of the ERV model were used in the RPP studies. We selected the RPP values obtained with the ERV sub-model considered by the authors to have provided the best results (following the approach of  Li et al. , 2018).
g. Vaccinium for Finland  (Räsänen et al. , 2007) , Pinus for central  Sweden (von Stedingk et al. , 2008) ). Moreover, some RPP values were excluded as they were assumed to be outliers or unreliable based on experts' knowledge on the plants involved, the pollen-vegetation dataset and the field characteristics of the related studies. For example, the RPPs for Cyperaceae, Potentilla t. and Rubiaceae obtained in SW Norway  (Hjelle, 1998)  and those for Salix and Calluna vulgaris from central Sweden  (von Stedingk et al. , 2008)  were assumed to be too low compared to the values obtained in other study areas  (Mazier et al. , 2012). 2. (i) When five or more RPP estimates of pollen productivity (N ≥ 5) were available for a pollen type, the largest and the smallest RPP values (generally outlier values) were excluded, and the mean was calculated using the remaining three or more RPP estimates.