dataesr/scientific-paragraphs-categorization huggingface dataset:
For instance, more work has been undertaken on RPPs of temperate and boreal taxa than those that characterize the Mediterranean region. RPPs and their standard deviations exist for more than 131 Northern Hemisphere taxa, with the longest research effort in Europe, and several syntheses of RPPs have been published  [28, 31, [34] [35] [36]. As the REVEALS model assumes that RPP values are constant within the region of interest and through time  [27] , studies working at the sub-continental scale have calculated mean RPPs considering all available RPP values. This can overcome the variability of RPP estimates within one taxon. Mazier et al. (2012)  produced the first RPP-means dataset for Europe  [28] , comprising 25 pollen taxa that were used in the "first generation" of REVEALS reconstruction for Europe  [33].
Githumbi et al. (2022)  published an updated RPP-mean dataset for 50 taxa from Europe  [31]. Thirty-nine of the taxa were from boreal and temperate Europe, and for the first time, 11 taxa characteristic of Mediterranean Europe were included. The first RPP-mean dataset for Europe was used to evaluate the effect of entomophilous taxa on gridded REVEALS estimates for the Czech Republic  [28]. The authors showed that entomophilous taxa tend to affect the REVEALS estimates because the REVEALS model assumes that all pollen is airborne [27] and justified excluding as many entomophilous taxa as possible from REVEALS reconstructions. Githumbi et al. (2022)  included taxa with mixed wind and insect pollen transport such as Artemisia, Amaranthaceae/Chenopodiaceae, Ericaceae (Calluna excluded), Rubiaceae, and Plantago lanceolata  [31].
This third generation produces grid-based estimates at 1      E, Figures  1  and A1 ) for 25 contiguous time windows across the Holocene. The number of pollen records used (1607) and the area covered (most of Europe) of 539 grid cells represent a significant advance on the results presented by  [31] , which was based on 1128 pollen records for Europe and part of the Eastern Mediterranean-Black Sea-Caspian-Corridor. We used three RPP-means datasets and evaluated the extent to which the selection of a set of RPP-means influences the REVEALS estimates. The three datasets are (i) the RPP-means dataset from  [31]  (RPPs. st1: 31 taxa); (ii) a new synthesis proposed in this study inclusive of a larger number of entomophilous taxa (RPPs. st2: 46 taxa); and (iii) a composite dataset, derived from  [28, 31]  (RPPs.
st3: 31 taxa). Reliable: ≥1 large lake(s), ≥2 small lake(s) and/or small bog(s), mix of ≥1 large lake(s) and ≥1 small lake(s) and/or small bog(s); less reliable: 1 bog (large or small) or 1 small lake. Grey grid cells: less reliable results for all TWs. Colour indicate, for each grid cell, the % of the total number of TWs with reliable REVEALS reconstructions of plant cover. For instance, light yellow grid cells imply that reconstructions are reliable for 8-21% of the TWs, while they are less reliable for 79-92% of the TWs; and dark green grid cells indicate that reconstructions are reliable for 98-100% of the TWs, while they are less reliable for 0-2% of the TWs. The specific aims of this paper are: (1) to improve the accuracy and reliability of REVEALS estimates across all of Europe through significantly increasing the number of pollen records used for the reconstruction (particularly in the Mediterranean region);  (2)  to explore how three different RPP-means datasets impact the model output; (3) to identify the geographical location of the differences between the three REVEALS reconstructions and which plant taxa may explain these differences; and (4) to determine which RPP-means dataset is best to use, by validating REVEALS estimates against recent vegetation cover across Europe.
The specific aims of this paper are:  (1)  to improve the accuracy and reliability of REVEALS estimates across all of Europe through significantly increasing the number of pollen records used for the reconstruction (particularly in the Mediterranean region); (2) to explore how three different RPP-means datasets impact the model output; (3) to identify the geographical location of the differences between the three REVEALS reconstructions and which plant taxa may explain these differences; and (4) to determine which RPP-means dataset is best to use, by validating REVEALS estimates against recent vegetation cover across Europe. Due to the large number of abbreviations in the paper, and in order to facilitate reading, a glossary is provided in Supplementary Materials.
Fossil Pollen Records: Data Compilation and Preparation A total of 1607 pollen records (923 and 684 sites from bogs and lakes, respectively) (see TERRANOVA_metadata in https://doi. org/10. 48579/PRO/J5GZUO, accessed on 24 April Land 2023, 12, 986 9 of 31 2023) was compiled from 41 countries covering all European countries, the western part of Russia and the eastern Mediterranean-Black Sea-Caspian corridor (Appendix A, Figure  A1 ). This work benefited from earlier efforts and projects (Landclim I and II), which compiled pollen datasets from open-access databases and individual data contributors  [31, 33]. The Landclim II pollen dataset includes pollen data from the European Pollen Database  [50, 51] , the Alpine Palynological Database (ALPADABA; Institute of Plant Sciences, University of Bern), the Czech Quaternary Palynological Database (PALYCZ;  [52] ), the Pyrenean Paleoenvironmental Database (PALEOPYR;  [53] ), and datasets compiled within synthesis projects from the Mediterranean region  [7, 11]  and the eastern Mediterranean-Black Sea-Caspian corridor (EMBSeCBIO project;  [54] ).
Most of the cited datasets are now archived in NEOTOMA  [55]. The 479 new records that have been used here are either datasets added to Neotoma or the EPD until end of 2020 or collated from individual data contributors that fulfill the protocols applied in the Landclim projects. Pollen records are from natural terrestrial basins (lakes or bogs) with calibrated chronologies based on ≥3 dates. Where necessary new age-depth models expressed as (calibrated) calendar years before the present (i. e. , cal BP = before 1950 CE, hereafter referred to as BP) were established in collaboration with the data contributors or database managers using the R-package clam  [56]. Site radius information was obtained from original publications where possible. 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.
We test the inclusion or exclusion of plant taxa with dominant entomophily and the effect of RPP values on the grid-based REVEALS estimates (Gb-RVest). The selection of RPP studies, RPP values, and calculation of mean RPP and their standard deviation (SD) for Europe (Table  1 ) are explained in Appendix B, Table  A1. This paper uses three alternative RPP-means datasets (Table  1 ) to evaluate the effect of RPP selection on REVEALS results. RPPs. st1 (31 taxa) is the European RPP-means dataset from  [31]. It includes plant taxa from boreal, temperate, and Mediterranean Europe for the calculation of the RPP-mean values. In this selection, most entomophilous herbs are excluded, except the most common taxa with mixed wind and insect transport, such as Amaranthaceae/Chenopodiaceae, Fi-lipendula, Rumex acetosa t.
, and Plantago lanceolata. Note that the entomophilous tree Tilia and partly entomophilous tree/shrub Salix are included. RPPs. st2 (46 taxa) is a new synthesis proposed in this study, inclusive of a larger number of entomophilous taxa. It uses the same 31 taxa and mean values of RPP used in the first dataset  [31]  and 13 additional entomophilous taxa (some with mixed wind and insect transport), i. e. , Empetrum, Acer, Sambucus nigra t. , Fabaceae, Apiaceae, Compositae SF. Cichorioideae, Comp. Leucanthemum (Anthemis) t. , Plantago media, Plantago montana, Ranunculus acris t. , Potentilla t. , Rubiaceae, and Trollius, as well as Populus and Urtica (mainly anemophilous). For these additional taxa, the mean was calculated using all available European RPP values (Appendix B, Table  A1 ) based on standard 2 from  [28].
We excluded values that were not significantly different from zero considering the lower bound of its SD (e. g. , Empetrum, 0. 07) and values assumed to be outliers or unreliable in the original publications. The RPPs. st2 is used to test the sensitivity of the REVEALS model to the use of pollen types from entomophilous plant taxa, although the model assumes that all pollen is transported by wind (see Section 2. 1). RPPs. st3 (31 taxa) is a composed dataset that compiled 24 plant taxa and their RPP values selected by  [28, 33]  and 7 Mediterranean plant taxa: Amaranthaceae/Chenopodiaceae, Buxus sempervirens, Carpinus orientalis, Castanea sativa, Phillyrea and evergreen Quercus t. [31]. RPPs. st3 differs from RPPs. st2 and RPPs. st1 for 19 RPPs values of the following taxa: Picea, Pinus, Ericaceae, Betula, Corylus avellana, Fraxinus, deciduous Quercus t.
, Carpinus betulus, Fagus, Tilia, Salix, Calluna vulgaris, Artemisia, Cyperaceae, Filipendula, Plantago lanceolata, Rumex acetosa t. , and Secale cereale. The following 12 plant taxa share the same mean value of RPP for all three datasets: Abies, Juniperus, Phillyrea, Pistacia, evergreen Quercus t. , Buxus sempervirens, Carpinus orientalis, Castanea sativa, Ulmus, Amaranthaceae/Chenopodiaceae, Poaceae, and Cerealia t. Fall speed of pollen (FSP) values are listed in  [31]. Table  1. Land-cover types (LCTs) and their corresponding pollen morphological types. Fall speed of pollen (FSP) and the mean relative pollen productivity estimates (RPPs) for three different RPP-means datasets (RPPs. sts), with their standard deviations (SDs) in brackets (see text for more explanations). We highlighted the values that remain fixed across the RPPs.
GFC-Trees was derived from the global forest change dataset [57]. This is based on the analysis of Landsat 7 Enhanced Thematic Mapper Plus data at a 30-m spatial resolution to characterize forest extent, loss, and gain from 2000 to 2012. We used tree cover data for the year 2000 that expresses tree cover (defined as vegetation taller than 5 m in height) as a percentage per output grid cell. All forms of natural forests or plantations across a range of canopy densities are considered. Broadleaved and coniferous trees are not differentiated. Original tree cover data are viewable and downable at full resolution at http://earthenginepartners. appspot. com/science-2013-global-forest, accessed on 21 March 2023. RV-Trees values are based on the Gb-RVest results from the core top samples (-45 to -55 BP, i.
e. , 1995 AD to 2005 AD). GB-RVest results for taxa in the summer green trees and evergreen tree groups (Table  1 ) have been summed. The number of core top records from large lakes is limited in Europe (N = 9), and thus we have included Gb-RVest, which is based on multiple small-sized sites within our evaluation of RPP-mean datasets. The total number of grid cells used is 111. This results in 36 grid cells with lakes and 75 grid cells with both lakes and bogs (of which 47 with only ≥1 bog(s)) of all radii. Therefore, the modern pollen dataset covered 20 European countries from the Mediterranean to the boreal vegetation zones, with a tree-cover gradient from 1% to 80% (Figure  2 ). The same pollen dataset was used for the comparison between raw pollen data (RW-data) and GFC-Trees for 31 taxa and 46 taxa.
REVEALS Run and Data Analysis The REVEALS function within the LRA R-package  [58]  was used to produce gridbased REVEALS estimates (Gb-RVest). In this study, we selected the Gaussian Plume model to describe pollen dispersal as selected RPP values are derived from the GPM model. Depending on the type of site, the REVEALS function used a different deposition model, Sugita's model for lakes and ponds  [40]  or Prentice's model for bogs and mires  [43, 44]. Pollen records from all sites, regardless of their size, are used to maximize the number of pollen records within each 1° × 1° grid cell across the studied area. For the grid cells that include pollen data from both lakes and bogs, we apply REVEALS separately for the lake and bog data and then combine results to produce a single mean Gb-RVest and its standard error (SE) for each taxon.
REVEALS Run and Data Analysis The REVEALS function within the LRA R-package  [58]  was used to produce gridbased REVEALS estimates (Gb-RVest). In this study, we selected the Gaussian Plume model to describe pollen dispersal as selected RPP values are derived from the GPM model. Depending on the type of site, the REVEALS function used a different deposition model, Sugita's model for lakes and ponds  [40]  or Prentice's model for bogs and mires  [43, 44]. Pollen records from all sites, regardless of their size, are used to maximize the number of pollen records within each 1 • × 1 • grid cell across the studied area. For the grid cells that include pollen data from both lakes and bogs, we apply REVEALS separately for the lake and bog data and then combine results to produce a single mean Gb-RVest and its standard error (SE) for each taxon.
When running REVEALS, neutral atmospheric conditions and wind speed of 3 m. s -  as in [27,  28, 31, 33]  are assumed. Z max , the maximum spatial extent of the regional vegetation from the centre of the site, is set to 50 km, roughly corresponding to a 1 • × 1 • grid cell  [28, 31, 33]. REVEALS results are extracted by time window, producing 25 matrices of mean Gb-RVest and 25 matrices of corresponding mean SEs for each of the RPP taxa and each grid cell. As three RPP-means datasets are tested, three REVEALS results are produced per time window. The taxon-based Gb-RVest are then grouped into land-cover types (LCTs, Table  1 ), hereafter named Gb-RVest-LCTs. For the modern time window (-45 to -55 BP), as the RV-Trees do not separate the contributions of evergreen and summer green species, the sum of the two Gb-RVest-LCTs was calculated for comparison with GFC-Trees (Table  1 ).
The SEs of each Gb-RVest-LCT and overall tree cover were calculated using the delta method  [59]. We use here major axis (MA) as the regression method (see Appendix C)  [60, 61]  to explore the bivariate relationships between two pairs of variables, or data series: among different RPPs. sts over the Holocene (RPPs. st2 vs. RPPs. st1 and RPPs. st3 vs. RPPs. st1) and between modern vegetation and REVEALS results. Further, pairs of Gb-RVest-LCTs (RPPs. st1 vs. RPPs. st2, RPPs. st1 vs. RPPs. st3) for all time windows together were compared calculating the difference between the values in each grid cell, geolocalizing in the map of Europe the negative and positive values.
Effect of Type and Number of Pollen Taxa in RPP-Means Datasets on Gb-RVest-LCTs In this section, we evaluate how Gb-RVest-LCTs, using RPPs. st1 as a reference, compare to RPPs. st2 and RPPs. st3 (Figure  3 ) and formulate hypotheses on the nature of these relationships and how they vary across RPP-means datasets. The strongest association in the MA regression results is between Gb-RVest. st1 and Gb-RVest. st2, with R 2 values of 0. 834 (ET), 0. 876 (OL), and 0. 953 for (ST). The R 2 values of the comparison between Gb-RVest. st1 and Gb-RVest. st3 are 0. 569 (ET), 0. 784 (OL), and 0. 858 (ST). ET-RV. st2 values are generally lower than ET-RV. st1 (r = -0. 109), as are OL-RV. st3 compared to OL-RV. st1 (r = -0. 094), ST-RV. st2 compared to ST-RV. st1 (r = -0. 134), and ST-RV. st3 compared to ST-RV.
Geographical Pattern of the Gb-RVest-LCTs Differences between RPP-Means Datasets Differences between pairs of Gb-RVest-LCTs were mapped using the positive and negative results of the differences (i. e. , diff. A, Figure  4a ,b; diff. B, Figure  5a, b ). Furthermore, the negative and positive values of the differences are explain by maximum values of the most representative taxa over all time windows (Figures  4c-f  and 5c,d ). These taxa influence the over-or under-representation of LCT in the analyses. The greatest differences between Gb-RVest-LCTs. st1 and Gb-RVest-LCTs. st3 (diff. A) (Figure  4a, b ) are found within ET and OL. ST values are broadly comparable. The greatest differences between Gb-RVest-ET. st3 and Gb-RVest-ET. st1 are located in Spain, Portugal, southern France, central Italy, and the U.
Discussion Our discussion focuses on (i) proposing a robust RPP-means dataset, through validation, for a reliable representation of vegetation for the last 11,700 years BP at a European scale, (ii) the influence of different RPP-means datasets on Gb-RVest as a test of the sensitivity of the REVEALS approach, (iii) the evaluation of some challenging taxa, and (iv) the importance of the number of pollen records for high-quality Gb-RVest to capture transient vegetation change at a sub-millennial time scale through the Holocene.
New Insight after Validation Testing the reliability of REVEALS-based reconstructions relies on comparison with different datasets, such as remote sensing data, and here we have used the global forest change dataset (GFC). Neither RVest nor GFC provides a completely accurate reflection of the "actual" vegetation. Both are subject to a number of potential sources of errors, as already observed in  [62]  in the correspondence between CORINE  [63]  and pollen-based land-cover classes. This study shares some similar challenges for comparing estimates of vegetation based on the remotely sensed and pollen-inferred land cover with  [62] , which might have influenced the validation as a whole. These include: (1) georeferencing inaccuracies: misplaced pollen site locations can affect both GFC (by extracting the wrong forest cover data for sites) and RVest (by placing sites in the wrong grid cells); (2) misclassification of land cover as remote sensing techniques make it difficult to differentiate between land-cover types (e.
g. , the determination of different forest types) and not all land-cover types are detectable via remote sensing; (3) the normalization factor applied to both RVest and GFC to make the datasets comparable leads to loss of some details. However, our comparison differs from that of  [62]  for several reasons, which are: (1) the approach used to transform pollen data into records of land-cover change, as we have been able to compare quantified values in both RVest and GFC, rather than compare classification results (via biomization techniques used in  [62] ), (  2 ) the modern vegetation used, (3) the use of modern surface pollen samples. The global forest change dataset is >80% accurate  [57]. It is the most widely used forest cover product for global and regional analyses due to its high resolution (30 m), standardized classes, yearly updates, and convenient and cost-free use  [64].
It also has the advantage of being a time series of changes in tree cover  [65]. Nevertheless, a number of sources of error are specific to GFC. In  [65, 66] , accuracy issues for this dataset are reported. GFC is less accurate in mountainous regions due to a combination of intense cloudiness and topographic shadowing  [67]. GFC is also unable to distinguish between natural forest cover and agricultural tree crops  [66]. Lower accuracy is also reported in regions with sparse and variable tree cover due to the background signal or seasonal variability in phenology or cloud cover. Another limiting factor can be ascribed to the rescaling process that limits the ability of the dataset to capture tree canopy cover values on the ground at 30 × 30 m as it is derived from a coarser-resolution product  [64].
In this study, the number of available modern pollen datasets with top cores samples was limited. For the sake of complete Europe-wide validation, as many sites (191) as possible were considered, including small lakes and bogs. A total of 41% of the grid cells (111 out of 539) had top core samples useful for comparison. Our comparison revealed that RPPs. st1 is the most suitable to represent modern vegetation in Europe, both using bogs and lakes, or only lakes, as suggested by  [31]. The REVEALS model improved the accuracy of vegetation reconstruction significantly over the pollen proportions alone. Both RV-Trees and uncorrected pollen-Trees over-represented forest cover compared to GFC-Trees; however, the best match was found between RV-Trees and GFC-Trees. Trees in GFC are defined as plants taller than 5 m.
Some common European trees begin to produce and disperse pollen even before reaching a height of 5 m (e. g. , Betula and Alnus), particularly in regions where tree growth might be more stunted or there are lots of shrubby trees  [68] [69] [70]. This may explain a greater representation of trees in RVest than in GFC. On the other hand, a young Pinus woodland may not produce substantial volumes of pollen but will appear in the remote sensing dataset as a forest. Thus, it is important to bear in mind the characteristics of the plant taxa and take into consideration their flowering age (i. e. , the number of years a particular plant taxon needs before it produces a significant amount of pollen), location in the landscape (within or outside a woodland), or location within a woodland (with flowering parts below or within the woodland higher canopy) in order to better interpret the pollen-based reconstructions of plant cover.
Influence of RPPs. sts on REVEALS Model Sensitivity and Pattern of Difference at the Spatial Level The second aim of this paper was to explore how different RPP-means datasets impact the model output. It has previously been shown that RVest is strongly influenced by the choice of RPPs that are used  [39, 71, 72]. The RPPs values for a given taxon may, in some cases, differ between study areas, although it was less clear whether differences were related to environmental factors (e. g. , climate, soils, land-use practices) or field methodologies in pollen sampling and vegetation survey  [35, 73, 74]. The solution to variable RPPs between studies has been the calculation of mean RPP values that are applied within single studies (e. g. ,  [28, 31, 33] ), and these have facilitated comparison between studies.
We explored in our analysis the impact that different RPP-mean datasets may have on the results of analysis, with a particular focus on the inclusion of entomophilous taxa and through experimentation with taxa that have very different values depending on the environment from which they derive. This is the case in particular for Ericaceae (see Appendix B, Table  A1 ), which, to date, has only two extreme values available. The first, from central Sweden, is 0. 07  [75] , and the second one from Mediterranean France of 4. 265  [31] , all values are relative to Poaceae. The results of our analysis show that Gb-RVest is more sensitive to changes in RPP values (when the comparison sets have the same number of RPP taxa) than the addition of taxa. The addition of a larger number of entomophilous taxa did not significantly impact the overall results, despite REVEALS assuming that the major agent of pollen transport is wind  [27].
The main differences in Gb-RVest are found between runs using the RPPs. st1 and RPPs. st3 datasets (Figure  3 ), which are caused by differences in RPP values for 19 taxa (see Table  1 ). Experimentation using different RPPs. sts, and careful comparison to modern forest cover data, has enabled us to test different values for the same taxa by observing which taxa are responsible for the over-or under-representation of the Gb-RVest-LCTs. We have shown in our experimentation that uncertainty in RPPs for two challenging taxa (Ericaceae and Empetrum) has the greatest impact on our Gb-RVest. This is mainly due to factors that influence the RPP values.