Laurent Marquer
email: laurent.marquer.es@gmail.com

Florence Mazier
email: florence.mazier@univ-tlse2.fr

Shinya Sugita
email: sugita@tlu.ee

Didier Galop
email: didier.galop@univ-tlse2.fr

Thomas Houet
email: thomas.houet@univ-rennes2.fr

Élodie Faure

Marie-José Gaillard
email: marie-jose.gaillard-lemdahl@lnu.se

Sébastien Haunold
email: sebastien.haunold@ensat.fr

Nicolas De Munnik
email: nicolas.demunnik@univ-tlse2.fr

Anaëlle Simonneau
email: anaelle.simonneau@univ-orleans.fr

Elodie Faure
email: elo_faure@yahoo.fr

François De Vleeschouwer
email: francois.devleeschouwer@ensat.fr

Gaël Le Roux
email: gael.leroux@ensat.fr

Pollen-based reconstruction of Holocene land-cover in mountain regions: Evaluation of the Landscape Reconstruction Algorithm in the Vicdessos valley, northern Pyrenees, France

Keywords: Vegetation, Land use, Landscape, Open land, REVEALS, LOVE, Holocene, France

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areas (IPCC, 2014). These human-induced changes in climate and land cover will affect undoubtedly species composition and spatial dynamics of plant communities on mountains, including altitudinal shifts in tree lines [START_REF] Leunda | Ice cave reveals environmental forcing of long-term Pyrenean tree line dynamics[END_REF]. All those expected changes in mountain regions will impact on a large range of ecosystem services, including tourism, recreation, water resources [START_REF] Szczypta | Impact of climate and land cover changes on snow cover in a small Pyrenean catchment[END_REF] and loss of traditional land-use. The Pyrenean mountains have been under intense human influence (e.g. pastoralism and deforestation) since the Bronze Age [START_REF] Galop | Long-term human-environment interactions history in the French Pyrenean Mountains inferred from pollen data[END_REF] and have undergone major transformations during the last century. Owing to rural depopulation, traditional pastoral management has declined since the 1950s [START_REF] Macdonald | Agricultural abandonment in mountain areas of Europe: Environmental consequences and policy response[END_REF]. The history of the Vicdessos area in northeastern Pyrenees is closely related to this progressive depopulation and the associated abandonment of former management practices. This has led to fast reforestation of the slopes and replacement of pastoral uplands by forests and heathlands [START_REF] Houet | Caractérisation de la fermeture des paysages dans les Pyrénées depuis les années 1940. Application sur le Haut-Vicdessos[END_REF][START_REF] Houet | Evaluating the spatial uncertainty of future land abandonment in a mountain valley (Vicdessos, Pyrenees-France): insights form model parameterization and experiments[END_REF][START_REF] Vacquié | Adapting grazing practices to limit the reforestation of mountainous summer pastures: A process-based approach[END_REF], as in many European mountains [START_REF] Kozak | Current practices and challenges for modelling past and future land use and land cover changes in mountainous regions[END_REF]. This has further resulted in a loss of plant diversity (Galop et al., 2011) and insect species richness (e.g. hoverflies) in an adjacent valley [START_REF] Herrault | Combined effects of area, connectivity, history and structural heterogeneity of woodlands on the species richness of hoverflies (Diptera: Syrphidae)[END_REF].

Those baseline information on the past mountain landscapes are critical to evaluate the effects of current and future land-use changes on high altitude ecosystems, biodiversity and for sustainable land-management strategies.

Fossil pollen records have provided information on the century-to millennium-scale changes in the past land cover. However, interpretation of pollen data is tricky in mountain regions because of the complex physical and biological conditions and factors that influence the pollen-vegetation relationships in unknown ways (e.g. [START_REF] Ortu | Pollen-inferred palaeoclimate reconstructions in mountain areas: problems and perspectives[END_REF][START_REF] Leunda | The Late-Glacial and Holocene Marboré Lake sequence (2612 m a.s.l., Central Pyrenees, Spain): Testing high altitude sites sensitivity to millennial scale vegetation and climate variability[END_REF][START_REF] Zhang | Surface Pollen Distribution from Alpine Vegetation in Eastern Tibet, China[END_REF]. There are several possible reasons: 1) The amount of long-distance pollen, particularly arboreal pollen types from lower altitudes brought by orographic wind, overwhelms local pollen signals of meadows and parklands in alpine zones [START_REF] Markgraf | Pollen Dispersal in a Mountain Area[END_REF][START_REF] Randall | A study of modern pollen deposition, Southern Alps, South Island, New Zealand[END_REF][START_REF] Fall | Spatial patterns of atmospheric pollen dispersal in the Colorado Rocky Mountains, USA[END_REF][START_REF] David | Altitudinal variation in the response of the vegetation to Late-glacial climatic events in the northern French Alps[END_REF][START_REF] David | Holocene tree limit history in the northern French Alps stomata and pollen evidence[END_REF][START_REF] Ortu | Pollen-inferred palaeoclimate reconstructions in mountain areas: problems and perspectives[END_REF][START_REF] Zhang | Surface Pollen Distribution from Alpine Vegetation in Eastern Tibet, China[END_REF]. 2) Plant communities vary significantly within relatively short distances along mountain slopes and altitudes; accordingly, pollen records from mountain sites tend to reflect several vegetation types together that are closely located [START_REF] Fall | Spatial patterns of atmospheric pollen dispersal in the Colorado Rocky Mountains, USA[END_REF]. 3) Differences in plant phenology between mountains and lowlands, especially flowering timing of plants of the same species, make interpretation of pollen records difficult [START_REF] Fall | Spatial patterns of atmospheric pollen dispersal in the Colorado Rocky Mountains, USA[END_REF][START_REF] Leunda | The Late-Glacial and Holocene Marboré Lake sequence (2612 m a.s.l., Central Pyrenees, Spain): Testing high altitude sites sensitivity to millennial scale vegetation and climate variability[END_REF]. 4) Insectpollinated plants -the dominant type of plants in alpine zones -tend to be low pollen-producers, thus biasing pollen representation of vegetation [START_REF] Markgraf | Pollen Dispersal in a Mountain Area[END_REF][START_REF] Fall | Spatial patterns of atmospheric pollen dispersal in the Colorado Rocky Mountains, USA[END_REF]. 5) Wind gradients and directions are often site-specific in mountains, thus it is difficult to generalize what pollen records represent [START_REF] Markgraf | Pollen Dispersal in a Mountain Area[END_REF]. To reduce the uncertainties associated with these issues, palaeoecologists often use both macro-remains of plants and pollen for reconstruction of the local-to landscape-scale changes in mountain plant communities (e.g. [START_REF] Birks | Future uses of pollen analysis must include plant macrofossils[END_REF]Leunda et al., in press). The present study aims to explore applicability of modeling approaches that overcome some of these difficult issues and evaluate the extent to which models help to improve the interpretation of pollen records in mountain regions. In the next section we first explore feasibility of the Landscape Reconstruction Algorithm (LRA; Sugita, 2007aSugita, , 2007b) -a two-step model-based reconstruction approach -for mountain vegetation, using simple simulations based on the POLLSCAPE models (e.g., Sugita 1994;[START_REF] Broström | Estimating spatial scale of pollen dispersal in the cultural landscape of southern Sweden[END_REF]Gaillard et al., 2008;Hjelle and Sugita, 2012). The LRA has been used widely in many parts of Europe and elsewhere over the last decade with a reasonable success, especially in relatively flat and less complex geomorphological terrains. We create simplified simulated landscapes that represent mountain landscapes on a two-dimensional space, and then evaluate the potential and limitations of the LRA approach in the idealized simulated situations. With insights into the LRA applicability from the simulations at hand, the rest of this paper evaluates empirically the applicability and reliability of the LRA approach around the Vicdessos valley in the Pyrenees. The study area is well suited for this study, because (1) well-dated pollen records from eight sites are available at and above the tree line, and (2) the regional-and local-scale land-cover maps are available in three time periods (i.e., 1960-1970, 1990-2000 and 2000-2013) for comparison with the pollen-based reconstruction of the past land cover. Major assumptions and current important issues related to the LRA are described in detail in the method section, Appendix A and wherever appropriate. We then discuss pros and cons of the LRA-based reconstruction in mountains and address future directions to improve the approach.

FEASIBILITY OF LRA-BASED RECONSTRUCTION OF VEGETATION IN MOUNTAIN REGIONS: EVALUATION WITH SIMULATED LANDSCAPES

Besides the complex nature of the wind field on mountains and juxtaposition of plant communities within short distances along short elevational gradients, a systematic selection of sites restricted only to mountain tops and plateaus also potentially biases pollen representation of vegetation, thus affecting the accuracy of the LRA. It is a common practice to select pollen sites located only in sub-alpine or alpine zones because of the availability of sediments free from humanrelated disturbances (Simonneau et al., 2013). This site-selection scheme would influence the evaluation of long-distance pollen from lower altitudes -an important step for objective reconstruction of local vegetation on mountains (Sugita, 2007a,b). The current section uses the POLLSCAPE approach (sensu Sugita, 2013) to evaluate the extent to which this site-selection scheme would affect the entire LRA process. In the POLLSCAPE simulations we regard mountain tops and high-altitude plateaus as "landscape islands" on a flat twodimensional terrain, 80 km x 80 km in size, for the sake of simplicity (as in Sugita, 1994;Sugita et al., 1997Sugita et al., , 1999;;Bunting et al., 2004;[START_REF] Broström | Pollen productivity estimates for the reconstruction of past vegetation cover in the cultural landscape of southern Sweden[END_REF][START_REF] Broström | Estimating spatial scale of pollen dispersal in the cultural landscape of southern Sweden[END_REF]Gaillard et al., 2008;Hjelle and Sugita, 2012). Although generalized, the simulated landscapes depict the characteristics of the spatial structure and species composition of plant communities in high-altitude plateaus and the regional vegetation around the Vicdessos valley (see section 3.1.). Figure 1a The LRA consists of two steps: the first to estimate regional vegetation within <50-100 km with the REVEALS model and the second to reconstruct the local-and landscape-scale changes in vegetation within the relevant source area of pollen (RSAP; sensu Sugita, 1994). Each step requires pollen counts from multiple sites that are ideally selected randomly within the targeted area of interest.

For either REVEALS or LOVE calculation, one of the patches of Patch Type A is selected randomly in the central part of the plot, and a lake is placed within the area of the patch. For REVEALS applications a lake 500-m in radius is placed at the center of the patch selected, and for LOVE applications a lake 50-m in radius is located randomly within the patch. In each simulation run, we repeat this process thirty times to obtain pollen counts from 30 lakes for REVEALS and LOVE, separately. Pollen loading on lakes, with which pollen counts are simulated, is calculated by Sugita's model for pollen deposition on lakes (Sugita, 1993). Pollen transport in air is approximated by both a Gaussian Plume model (GPM) (Prentice, 1985;Sugita, 1993Sugita, , 1994Sugita, , 2007aSugita, , 2007b) ) and a Lagrangian Stochastic model (LSM) of Kuparinen et al. (2007) and Theuerkauf et al. (2013Theuerkauf et al. ( , 2016)). Pollen productivity estimates -important parameters for the pollen loading calculation -are after Mazier et al. (2012). We assume that the simulated pollen records are independently obtained at each site. These records are used as inputs for the LRA-based reconstruction of vegetation around each site. The LOVE results are expressed as area proportions within the RSAP for three categories: conifers (i.e. (Sugita, 2007b;Sugita et al., 2010). Appendix A provides a brief description and sets of parameters necessary for the POLLSCAPE simulations and the LRA calculation. (Sugita, 2007b;Sugita et al., 2010) demonstrates that the RSAP is consistent in the simulations regardless of the pollen dispersal models employed; the RSAP is within 310 m and 325 m with the GPM and LSM options, respectively. With the LSM option, the variation in vegetation reconstruction among sites, as well as the standard error estimate at each site, appears larger than that with the GPM option for the three categories of land cover. (1) Accuracy of the regional estimates of vegetation and land cover matters for reliable LRA applications in mountains as in any other regions. Ideally, the REVEALS application requires pollen records from randomly-selected sites >100 ha in both mountains and lowland areas in the region of interest. It is hard to achieve this scenario in reality, however. The simulation results suggest that, as long as multiple pollen sites are available, the application of REVEALS and LOVE appears to provide reasonable estimates of the regional and local vegetation even if the sites are systematically selected only in the "mountain patches". Of course, large sites (e.g. 500 m in radius -ca. 78 ha -in the simulations) would be preferable to reduce biases and uncertainties of the REVEALS results (Figure 1b); if only small sites 50 m in radius are used, we expect larger uncertainties for the regional estimates, resulting in less reliable LRA outcomes (Appendix A).

(2) Differences in the LRA results between using the GMP and LSM as a pollen dispersal model appear negligible in the simulation runs. Recent studies have shown that the LSM option produces more realistic results in REVEALS-based reconstruction of regional vegetation, especially for plants with heavy and light pollen types (Theuerkauf et al., 2013(Theuerkauf et al., , 2016;;[START_REF] Mariani | How old is the Tasmanian cultural landscape? A test of landscape openness using quantitative land-cover reconstructions[END_REF] than the GPM option does. Considering more advanced understanding of wind field and particle transport in air (Kuparinen et al. 2007), the LSM option has advantages over the GPM option, especially for describing long-distance pollen from regional sources. As the simulation results (Figure 1b) suggest, however, consistency in dispersal-model selection for REVEALS and LOVE in the LRA process helps provide robust LRA estimates for reconstruction of the local-to landscape-scale vegetation in spatially and topographically complex terrains (e.g. [START_REF] Hjelle | From pollen percentage to vegetation cover: evaluation of the Landscape Reconstruction Algorithm in western Norway[END_REF].

MATERIALS AND METHODS

Geographical setting, site characteristics and pollen data collection

We use pollen records from eight small sites: three lakes and five bogs (Table 1, Fig. 2) situated between 1600 and 2000 m a.s.l. Seven sites are located on the plateau of Bassiès (Fig. 2) and one site (ARBU) 6 km to the north. ARBU belongs to another small watershed that is also part of the Vicdessos area. All sites are small, varying from 6 to 40 m in radius for bogs and from 60 to 113 m in radius for lakes (Table 1). Mean annual temperature in this area (over the 2000-2012 period) is around 7°C and mean annual precipitation about 1640 mm [START_REF] Quintana-Seguí | Analysis of Near-Surface Atmospheric Variables: Validation of the SAFRAN Analysis over France[END_REF]. Snow season generally starts in November/December and ends in April/May. Winds are mostly coming from the west and northwest; wind speed varies from 0 to 4 m.s -1 [START_REF] Szczypta | Impact of climate and land cover changes on snow cover in a small Pyrenean catchment[END_REF][START_REF] Claustres | Répartition des éléments traces potentiellement toxiques dans les zones de montagne : Rôle et part des facteurs naturels et anthropiques à l'échelle des temps pédologiques[END_REF]. 6km north of Bassiès (see Table 1 for the labels of the sites). Red dots are bogs and red stars are lakes. Raw radiocarbon dates, dated materials and details on the age-depth models at individual sites are given in Appendix B. Age-depth models of sediments are constructed by using both 210 Pb CRS (Constant Rate of Supply) model (Appleby, 2002) and CLAM radiocarbon age depth model [START_REF] Blaauw | Methods and code for 'classical' age-modelling of radiocarbon sequences[END_REF]. In order to build a robust age-depth model at each site, uncertainties of the 210 Pb ages calculated using Matlab with Monte Carlo simulations according to [START_REF] Binford | Calculation and uncertainty analysis of 210Pb dates for PIRLA project lake sediment cores[END_REF] 1960-1970, 1990-2000 and 2000-2013; those are the time windows for which both the regional and local land-cover maps are available. Total sum of pollen counts used for data analysis varies from 463 (SIG) to 2164 (FOUZ), 544 (EM) to 2530 (W1652), and 1143 (ARBU) to 5812 (OT) for the periods 1960-1970, 1990-2000 and 2000-2013, respectively.

Observed land-cover maps

For evaluation of the LRA estimates, this study obtains the regional-and local/landscapescale estimates of plant composition for the areas within a 50-km radius from the plateau of Bassiès and within a ca. 2-km radius from each site, respectively.

Observed regional land-cover

The CORINE Land Cover raster-based data (https://www.eea.europa.eu/data-and-maps/data) for 1990 and 2006 are selected as a source of the observed regional land-cover for time windows of 1990-2000and 2000-2013 (Fig. 3 (Fig. 3). The digitized vegetation map of Foix (oeuvre collective CNRS, 77 Foix, 1964, Henry Gaussen) in 1964 is used for the time window 1960-1970 (Fig. 3). Note that this 1964 vegetation map does not cover the entire area in the 50-km radius. For this study the landcover maps from three time windows (1964, 1990 and 2006) are harmonized to be comparable to each other.

The regional land-cover in 2006 is characterized by broadleaved forest (27%) and conifer forest (16%) and grasslands (24%); arable and agricultural lands (12%) are mainly located in the northernmost corner (Fig. 3). Oak and beech are the main components of the broadleaved forests, associated with birch, hazel, lime-tree, willow and ash. Pine and fir characterize the conifer forests.

Forested areas have increased in the 1964-2013 period. [START_REF] Houet | Caractérisation de la fermeture des paysages dans les Pyrénées depuis les années 1940. Application sur le Haut-Vicdessos[END_REF]. The meanings of the labels of each pollen record are given in Table 1. Note that white color corresponds to the absence of data.

Observed local land-cover

Local land-cover maps of the plateaus of Bassiès, which were compiled and created for 1962for , 1993for and 2003 (Fig. 3) (Fig. 3), are suitable for the data-model comparison in the three time windows of our interest: 1960-1970, 1990-2000 and 2000-2013. These maps are based on a semi-automatic geographic object-based image analysis technique and visual interpretation of airborne imageries [START_REF] Houet | Caractérisation de la fermeture des paysages dans les Pyrénées depuis les années 1940. Application sur le Haut-Vicdessos[END_REF].

The plateaus of Bassiès is covered by heather and rhododendron heathlands and grasslands.

Small clumps of short conifer trees are spread out over the plateaus unevenly. Forested areas are located on the neighboring slopes; conifer trees are mostly pine and fir, and broadleaved trees mostly beech, birch and hazel.

Plant composition in individual land-cover types of the maps

Twenty major plant/pollen types (Table 2), which are well presented in both the pollen records and land-cover maps in the study region, are selected for data analysis. Plant composition in each land-cover type is approximated according to a local botanical expert (N. de Munnik, personal communication) and botanical survey on the plateaus of Bassiès (S. Haunold, unpublished). Details about the plant composition in each land-cover type are given in Appendix C.

Table 2 Taxa and groups of taxa used in this study. Relative pollen productivity estimates (RPPs) of individual taxa and their standard errors, and fall speed of pollen are listed. RPP values are relative to that of Gramineae. Botanical nomenclature follows Flora Europaea (Tutin et al., 1964(Tutin et al., -1980)).

Evaluation of the LRA-based vegetation reconstruction

Testing strategy

This study uses pollen records from eight sites for the LRA-based vegetation reconstruction in the regional-and local/landscape-scales. Three categories of land-cover types are considered for evaluation: areas covered by conifers, broadleaved trees, and open land characterized by Ericaceous shrubs, graminoids and other herbaceous plants. The results -both the REVEALS-based estimates of regional vegetation and the LRA-based estimates of local vegetation -are evaluated against the composition of those three categories of land cover regionally and locally. Both the GPM and LSM options are applied in the LRA as the pollen dispersal functions (as in section 2). Figure 4 summarizes the testing scheme described in this section, and Table 3 the nomenclature for the models, data sets used, and model outcomes.

Since pollen data are available only from small-sized sites < 4 ha (Table 1), we use a leaveone out strategy (LOO; [START_REF] Efron | An Introduction to the Bootstrap[END_REF]Sugita et al., 2010;Mazier et al., 2015) to avoid circularity in the LRA process: that is, selection of one site for local vegetation reconstruction with LOVE and the other seven sites for regional vegetation reconstruction with REVEALS. This process is repeated for all the eight sites in the study area. Theoretically and empirically the REVEALS results

prove to be robust even when pollen data from several small-sized lakes and bogs are used (Sugita, 2007a;Sugita et al., 2010;Fyfe et al., 2013;Trondman et al., 2016;[START_REF] Hjelle | Long-term changes in regional vegetation cover along the west coast of southern Norway: The importance of human impact[END_REF]; and others), although error estimates would be large. Hereafter, the REVEALS-based estimates of regional vegetation is referred to as RVs_LOO.

Another set of estimates for the regional vegetation composition within a 50-km radius from individual sites is compiled from the land-cover maps (section 3.2) and used as inputs for the LOVE application, independently and separately from RVs_LOO; hereafter, the data set is referred to as RVs_MAP50. Both RVs_LOO and RVs_MAP50 are then applied for reconstruction of the local/landscape-scale vegetation composition within the relevant source area of pollen (RSAP) with the LOVE model for comparison. The inverse modeling of LOVE (Sugita, 2007b;Sugita et al., 2010) is applied for estimating the RSAP with RVs_MAP50 around the plateaus of Bassiès in the three time windows.

In addition, RVs_MAP50 is compared to the REVEALS results using pollen records from all the eight sites together, referred to as RVs, to evaluate the extent to which the pollen-based reconstruction reflects the observed vegetation composition of the region for the sake of simplicity.

The differences between RVs and individual RVs_LOO are negligible.

The LOVE results with either RVs_LOO or RVs_Map50 are evaluated against the plant composition data extracted from the observed local land-cover maps in each time window. The proportions of the areas (m²) occupied by each land-cover type are estimated in successive concentric rings at 20-m increment out to 2 km from the center of each site. Cumulative estimates of plant composition within a 2-km radius are calculated, using a distance weighting method (either with the LSM or GPM option) and fall speed of pollen for the 20 taxa as listed in Table 2. For all the calculations this study uses ArcGIS 10.2.2, a script in R (Binet, unpublished) and "DWPAcalculator.v7.3a" (Sugita, unpublished).

LRA-MAP50

Local vegetation estimates for a RSAP of 2-km radius with RVs-MAP50 as regional background of pollen

Assumptions and parameter setting for the LRA calculation

We use the pollen count data from three lakes and five bogs as inputs to the LRA. Our preliminary trials (not shown) show that the selection of basin type (i.e. lake or bog) does not affect the outcomes significantly; accordingly all the REVEALS and LOVE applications assume that the pollen data used are from bogs. For model calculations this study uses a set of RPPs and fall speed of pollen from Mazier et al. ( 2012) (Table 2) that is used in the LANDCLIM project [START_REF] Trondman | First pollen-based quantitative reconstructions of Holocene regional vegetation cover (plant functional types and land-cover types) in Europe suitable for climate modelling[END_REF][START_REF] Marquer | Holocene changes in vegetation composition in northern Europe: why quantitative pollen-based vegetation reconstructions matter[END_REF][START_REF] Marquer | Quantifying the effects of land-use and climate on Holocene plant composition and vegetation change in Europe[END_REF] and other projects in Europe.

A previous study (Mazier et al., 2015) shows that rare taxa can influence the LRA and RSAP estimates. We therefore apply a threshold of 1 % to select the major pollen taxa recorded in both the pollen and observed vegetation data sets. Accordingly, eighteen taxa are selected. Two additional taxa (fir and juniper), locally important for the vegetation history of the Vicdessos valley, are added; twenty taxa in total (Table 2). These plant/pollen types include both anemophilous and entomophilous taxa.

We use "LRA.REVEALS.v6.2.2.exe" and "LRA.LOVE.v6.2.3.exe" for data analysis (Sugita, unpublished). When the GPM option is used, pollen dispersal in a neutral atmospheric condition is assumed with wind speed of 3 msec -1 (although the local wind speed varies mostly between 0 and 4 msec -1 [START_REF] Szczypta | Impact of climate and land cover changes on snow cover in a small Pyrenean catchment[END_REF][START_REF] Claustres | Répartition des éléments traces potentiellement toxiques dans les zones de montagne : Rôle et part des facteurs naturels et anthropiques à l'échelle des temps pédologiques[END_REF]). Z max , the maximum spatial extent of the regional vegetation from the centre of the study sites, is set to 50 km as in Mazier et al. (2012Mazier et al. ( , 2015)). When the LSM option is applied, Z max is automatically set to 100 km; all other conditions and parameters specific to the model are the same as those in Kuparinen et al. (2007) and Theuerkauf et al. (2016).

The REVEALS and LOVE results are expressed in proportion cover for three land-cover types: conifers, broadleaved trees, and open-land taxa (Table 2); their standard errors are obtained according to the delta method [START_REF] Stuart | Kendall's advanced theory of statistics[END_REF]).

The regional plant composition based on the CORINE maps -RVs_MAP50 -do not have error estimates (SEs), and these estimates are needed as inputs for the LOVE runs. We therefore have are too low to be plot in Figure 5.

RESULTS

Estimates of the regional vegetation composition

Relevant source area of pollen (RSAP)

The RSAP estimates for the three time windows vary from 663 to 1038 m with the GPM option and from 1933 to 5113 m with the LSM option (Table 4); the former is consistently lower than the latter in each time window. In four cases out of six, the RSAP estimates is lower than 2 km; the two extreme values >4.5 km occur in the time windows of 1960-1970 and 2000-2013 with the LSM option. Hereafter we assume that the area within a 2-km radius from each site is appropriate as the RSAP and the reasonable spatial scale for the local/landscape scale reconstruction of vegetation in the area. Possible reasons for the two extreme estimates of the RSAP will be considered in the discussion section.

Table 4 The relevant source areas of pollen (RSAP) based on LOVE runs using RVs_MAP50 as regional vegetation background in LOVE. For all RSAP calculations both pollen dispersal model options have been tested, GPM and LSM. Although expected, those results reaffirm that the LRA-based results are more realistic than pollen proportions alone in reconstruction of vegetation and land cover in mountains such as our study area.

RVs_MAP50_2006

Evaluation of the LRA-based reconstruction of vegetation within a 2-km radius

Relationship between the spatial structure of plant composition and the LRA results

The LRA estimates and factors affecting their reliability in three time windows

Against the cumulative DWPA-based estimates of vegetation cover within a 2-km radius, LRA_MAP50 is a better choice for all the land-cover types than LRA_LOO in the three time windows (Figure 7). Relative to the LRA_LOO results (the bottom row of Figure 7), the LRA_MAP50 estimates tend to have smaller standard errors at individual sites, to have smaller site-to-site variations for each land-cover type, and to be closer to the 1:1 relationship against the cumulative DWPA-based estimates (the middle row of Figure 7). These patterns are apparent for the open-land cover estimates in particular. For example, the differences between the highest and lowest LRA_MAP50 estimates are 32, 23 and 30%, and 42, 46 and 49% when LRA_LOO is concerned, in time windows 2013-2000, 2000-1990 and 1970-1960, respectively. The site-to-site variation of the LRA estimates does not change significantly among the three time-windows when LRA_MAP50 is considered; when LRA_LOO is concerned, however, the older the time window, the larger the site-to-site variation of the LRA estimates for each land-cover type.

This study shows that selection of a pollen dispersal model, either the GPM or the LSM option, does not affect significantly the LRA-based reconstruction of vegetation cover within the RSAP since the 1960s (Figure 7) as long as the model selection is consistent in the entire LRA process. However, the selection appears influencing the magnitude of uncertainties. With the LSM option, the SEs of LRA_LOO for open land vary from 10.5-57 %; on the other hand, those vary from 5.2 to 26.1% with the GPM option. When LRA_MAP50 is concerned, the differences influenced by the selection of the dispersal model are negligible: the SEs vary from 7.4 to 25.2% with the LSM option and from 5.2 to 26.1 % with the GPM option. As suggested in the simulations in section 2, the systematic selection of pollen sites in mountains, which is the case for RVs_LOO (and thus affecting LRA_LOO), is a suspect for those results on the differences in the SEs.

DISCUSSION

Pollen proportions are notoriously difficult to use for reconstruction of mountain vegetation [START_REF] Markgraf | Pollen Dispersal in a Mountain Area[END_REF][START_REF] Fall | Spatial patterns of atmospheric pollen dispersal in the Colorado Rocky Mountains, USA[END_REF][START_REF] Birks | Future uses of pollen analysis must include plant macrofossils[END_REF][START_REF] Ortu | Pollen-inferred palaeoclimate reconstructions in mountain areas: problems and perspectives[END_REF][START_REF] Leunda | The Late-Glacial and Holocene Marboré Lake sequence (2612 m a.s.l., Central Pyrenees, Spain): Testing high altitude sites sensitivity to millennial scale vegetation and climate variability[END_REF]. This study demonstrates that the LRA approach improves reconstruction of vegetation and land cover in mountains greatly over pollen percentages alone. The assumptions and conditions for the LRA are too simple for landscapes with complex topography and wind field in mountain regions. However, the simulations (section 2) and the application results in the Vicdessos area show that some of the difficult issues associated with pollen-based reconstruction of vegetation in mountains can be addressed and overcome.

Effects on the LRA results of systematic selection of pollen sites on mountain

Pollen-based reconstruction of mountain vegetation often selects small-sized pollen sites only in mountains and high-altitude plateaus. This site-selection strategy is useful for evaluating sitespecific correlational relationships between pollen assemblages and the surround vegetation; however, it makes the LRA-based quantitative reconstruction of vegetation difficult. The REVEALS-based estimates of regional vegetation -an important outcome from the first step of the LRA-are critical for evaluating the abundance of long-distance pollen from the regional sources that are assumed to be consistent among small-sized sites for the LOVE applications. However, when several small-sized sites are selected above the tree line on the same land cover type, we expect biased estimates of the regional vegetation within a 50-100 km radius. This is the case in our study in the Vicdessos area (Figure 5); the regional components of the open land cover are overestimated by REVEALS. Because pollen records from small-sized sites tend to represent local vegetation (Prentice, 1985;Sugita, 1994),

we expect that the REVEALS results based on pollen records from those sites underestimate the abundances of tree covers in the region. One of the solutions to reduce the biases described above is to use large lakes (known as regional reliable pollen archives; Sugita, 2007a;Trondman et al., 2016) and/or several small sites (lakes and bogs) located in various valleys, slopes and altitudes in the region, although reliable pollen sites in mountains are mainly small and located in sub-alpine or alpine zones (Simonneau et al., 2013).

Abraham et al. ( 2017) used pollen records from small sites located above and below the treeline in low mountain ranges of NE Czech Republic and obtained reasonable REVEALS estimates of past regional vegetation. [START_REF] Hjelle | From pollen percentage to vegetation cover: evaluation of the Landscape Reconstruction Algorithm in western Norway[END_REF] have used pollen samples from one large lake and twentyeight small lakes located in different types of vegetation in western Norway. They found a good relationship between the REVEALS estimates and CORINE land-cover data, although the REVEALS model tends to overestimate open land cover against the CORINE land-cover. Hjelle et al. further show that the higher the number of small lakes, the more reliable the REVEALS estimates.

Thus, establishing a proper site-selection scheme is the important step for REVEALS applications, although it is hard to find a large number of suitable pollen sites in mountain regions.

As expected, the LRA results using RVs_MAP50 are better matched with the map-based DWPAs within a 2-km radius than those using RVs_LOO (Figure 7). Standard errors of LRA_MAP50s are smaller than those of LRA_LOOs, in general. Those results demonstrate the importance of the accuracy and reliability of the regional estimates of vegetation and land cover for the LRA approach. Even so, it's also encouraging that the RVs_LOOs are usable to provide robust estimates of LRA_LOOs using our data set.

types with high fall speed of pollen, than the LSM does (Kuparinen et al., 2007;Theuerkauf et al., 2013), and in our study this is the cause of the underestimation of Abies and Fagus based on LSM, and thereby of the overestimation of regional open land based on LSM. Size of pollen-sampling sites may be one of the possible reasons why the LRA estimates with the LSM option do not improve significantly against those with the GPM option. Indeed, the LSM would be a better option for pollen dispersion and deposition in large lakes than the GPM; pollen dispersion and deposition in such lakes are greatly affected by turbulent flows and updrafts [START_REF] Jackson | Pollen dispersal models in Quaternary plant ecology: assumptions, parameters, and prescriptions[END_REF], thus the LSM is more appropriate than the GPM. However, short-distance dispersal of pollen is better described by the GPM than by the LSM (Theuerkauf et al., 2016); accordingly, the GPM option would be better suited for reconstruction of vegetation using pollen records from small bogs and lakes like in the Vicdessos, where asymmetric airflows would play a minor role locally. As a consequence, selection of a pollen dispersal model would affect the LRA results little, when small sites are used. Standard errors are higher using LSM than using GPM, however, as the simulations (section 2) demonstrate. Another important issue is the availability of the estimates of relative pollen productivity, RPP, for the constituent taxa used for data analysis. This study uses RPPs that were compiled from previous studies in NW Europe; those studies used the Extended R-value models to obtain RPPs, assuming that pollen dispersal follows the GPM (e.g. Sugita, 1994;[START_REF] Broström | Pollen productivity estimates of key European plant taxa for quantitative reconstruction of past vegetation: a review[END_REF]Mazier et al., 2012).

Ideally, when the LSM is used for the LRA, RPPs need to be obtained from other data sets by assuming the LSM as a pollen dispersal model for consistency. Theuerkauf et al. (2013Theuerkauf et al. ( , 2016) ) obtained RPPs of selected taxa in lowlands in North Germany; however, Asteraceae and Ericaceae, important taxa in our study, are not included. [START_REF] Mariani | Testing quantitative pollen dispersal models in animal-pollinated vegetation mosaics: An example from temperate Tasmania, Australia[END_REF][START_REF] Mariani | How old is the Tasmanian cultural landscape? A test of landscape openness using quantitative land-cover reconstructions[END_REF] estimated RPPs for selected taxa in Tasmania. Thus far, the LSM-based estimates of RPPs are available from one study only in NE Germany; considering their variations caused by various environmental and anthropogenic factors, it is necessary to expand the database of the LSM-based RPPs for further applications of the LSM in the LRA reconstruction. All things considered, it is justified for this study to use RPPs for the selected taxa from NW Europe (Mazier et al., 2012;[START_REF] Trondman | First pollen-based quantitative reconstructions of Holocene regional vegetation cover (plant functional types and land-cover types) in Europe suitable for climate modelling[END_REF][START_REF] Marquer | Holocene changes in vegetation composition in northern Europe: why quantitative pollen-based vegetation reconstructions matter[END_REF][START_REF] Marquer | Quantifying the effects of land-use and climate on Holocene plant composition and vegetation change in Europe[END_REF].

The LRA-based reconstruction of vegetation composition with insect-pollinated plants

Plant communities at the mountain tops in our study region are characterized by high abundance of insect-pollinated taxa such as Ericaceae (e.g. Rhododendron spp. and Vaccinium spp.)

with semi-dominant grass and Calluna (heather). Ericaceae plants have low pollen production (see Table 2) relative to the wind-pollinated taxa in general (Mazier et al., 2012); then fall speed of pollen being similar among the constituent taxa, the LRA estimates of Ericaceae, thus the open land cover, tend to increase relative to the other taxa and land cover types. Mazier et al. (2012) excluded zoophilous taxa from the REVEALS reconstruction, mainly because the model assumes that all pollen grains are coming via wind transport. However, the majority of the upland plant taxa common on mountain tops are zoophilous. It is ecologically important to include zoophilous taxa in pollen-based reconstruction of vegetation. [START_REF] Mariani | How old is the Tasmanian cultural landscape? A test of landscape openness using quantitative land-cover reconstructions[END_REF] used both wind-and animal-pollinating plant taxa together for the REVEALS applications in Tasmania successfully. In the Pyrenees, this study also demonstrates that the LRA approach appears working reasonably well with zoophilous taxa for reconstruction of the vegetation and land cover in the local-and landscape-scales. This suggests that entomophilous taxa that are preserved in sediments might also be transported by wind.

Relevant source area of pollen (RSAP) and LRA estimates of local vegetation in mountains

In theory and practice the RSAP is the smallest spatial scale possible for pollen-based reconstruction of vegetation (Sugita, 1994(Sugita, , 2007b(Sugita, , 2013;;Sugita et al., 2010). In the current LRA form (Sugita, 2007a(Sugita, , 2007b)), the LRA result is expressed in proportions of the total cumulative sum of plant abundance at the RSAP (e.g., in biomass, plant cover, foliage mass, basal area, etc.) that is measured in a distance-weighted fashion for all the constituent taxa. Interpretation of the LRA results can be confusing, however. A cumulative sum of the DWPA is a way how pollen records reflect the surrounding vegetation (Prentice, 1984(Prentice, , 1988;;Sugita, 1994;Sugita et al., 2010), and the LRA results are based on the cumulative DWPA at the RSAP (Sugita, 2007b). According to this view on the pollen-vegetation relationship, the site-specific spatial structure of vegetation within the RSAP is impossible to assess quantitatively. Figure 6 is an attempt to visualize how different the spatial structure of the surrounding vegetation at individual sites could be, even when the LRA results are relatively similar among most of the sites.

The RSAP estimates (Table 3), which are obtained with an inverse modeling of LOVE (Sugita, 2007b;Sugita et al., 2010), are significantly smaller with the GPM option than those with the LSM option at all time windows. This is contradictory to the simulation results in section 2; the LRA-based estimate of the RSAP with the GPM option (i.e. 310 m) is only slightly smaller than that with the LSM option (i.e. 325 m). The GPM describes pollen dispersal in such a way that pollen grains, especially heavy pollen types such as Ericaceae fall to the ground in shorter distances than the LSM does (Kuparinen et al., 2007;Theuerkauf et al., 2016; see also section 5.2). On the other hand, the LSM predicts higher abundance of long-distance pollen from the regional source, including arboreal plants growing abundantly in lower altitudes, into mountain sites than the GPM does. All these factors, as well as others (e.g., the spatial structure of mountain vegetation, non-random selection of pollensite location, and the lack of RPP estimates considering LSM) would interact and affect the LRAbased estimates of the RSAP in unknown ways. Further studies are necessary. Among the six RSAP estimates listed in Table 3, four estimates are close to or smaller than 2 km. The other two, which are obtained with the LSM option, are >4.5 km. Most of the previous studies also show that the RSAP estimates are smaller than 2.8 km in radius, when pollen sites are small-to medium-sized (Sugita et al., 2010;[START_REF] Overballe-Petersen | Quantitative vegetation reconstruction from pollen analysis and historical inventory data around a Danish small forest hollow[END_REF][START_REF] Cui | The role of tree composition in Holocene fire history of the hemiboreal and southern boreal zones of southern Sweden, as revealed by the application of the Landscape Reconstruction Algorithm: implications for biodiversity and climatechange issues[END_REF][START_REF] Cui | Historical land-use and landscape change in southern Sweden and implications for present and future biodiversity[END_REF][START_REF] Poska | The Verijärv area, South Estonia over the last millennium: a high resolution quantitative land cover reconstruction based on pollen and historical data[END_REF][START_REF] Hjelle | From pollen percentage to vegetation cover: evaluation of the Landscape Reconstruction Algorithm in western Norway[END_REF]Mazier et al., 2015;Abraham et al., 2017). In practice, the local vegetation maps are available only within a 2-km radius around each site for comparison, as well. All things considered, the area within a 2-km radius is appropriate to be used as the RSAP for the study, with which all the results in Figures 6 and 7 are obtained.

Figure 6 shows that the areas close to the sampling sites are mostly unforested; tree cover (mostly broadleaved trees) increases at and beyond 270-1600 m depending on the sites. The DWPA results depict that, with either the GPM or LSM option, the open-land cover is dominant consistently.

Except for the EM site, the LRA_LOOs and LRA_MAP50s within a 2-km radius are reasonable against the DWPAs; these results assure the relevance of the LRA approach in this area. At the same time it is clear that the spatial structure of vegetation within the RSAP varies among sites significantly, even though the site-to-site variation of the LRA results is small. It's hard to speculate the site-specific spatial structure of vegetation within the RSAP based on the LRA approach or something else.

When the GPM option is selected, the radii of the LRA-based RSAP vary from 663 m (1960-1970) to 888 m (1990-2000) to 1038 m (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013), indicative of a shift in the "grain size" of the spatial structure of the surrounding vegetation around the sites (Sugita, 1994;Bunting et al., 2004).

It is reasonable to assume that changes in climate regimes and anthropogenic forcing in the area have affected the spatial structure of vegetation and land cover constantly.

CONCLUSIONS

The main conclusions from this study are:  The LRA approach is a significant improvement in pollen-based reconstruction of mountain vegetation in the local-and landscape-scales over pollen percentages alone.  Both the simulations and empirical model-data comparison in the Pyrenees demonstrate that accuracy of the regional estimates of vegetation matters for reconstruction of the local/landscape scale reconstruction of mountain vegetation in the LOVE applications.  The REVEALS-based estimate of regional vegetation -the first step of the LRA -is influenced by selection of pollen dispersal model and the systematic selection of pollen sites on mountains. Historical land-cover maps are effective as an alternative source for the regional vegetation in the last couple of centuries.  Area within a 2-km radius appears to be a relevant spatial scale for the LRA reconstructions in the Vicdessos area in the Pyrenees in the three time windows evaluated.  Insect-pollinated plant taxa, often important to characterize vegetation in higher altitudes, can be included and informative for the LRA-based vegetation reconstruction in mountains.  Further clarification is necessary to better understand the pros and cons of two dispersal models -the Lagrangian Stochastic Model and Gaussian Plume model -for the LRA applications in mountain environments.

The present work provides an important basis for reliable palaeoecological reconstruction in the Pyrenees and for advances in research about the past and recent intense human impact in the region. Further studies will help improve some of the modelling approaches for a better understanding of the spatial and temporal distribution of plant taxa within a catchment (Plancher et al., in prep.) and the reconstruction of local paleorainfall by using the erosion processes (Allen et al., in press). Simulations place different-sized and circular-shaped lakes -500 m and 50 m in radiusonly in patches of Patch Type A. Pollen data from 500-m radius lakes are used for REVEALS modeling in the LRA (Sugita, 2007a) to estimate the mean vegetation composition within a 100-km radius from the center of individual lakes; those from 50-m radius lakes are applied for LOVE modeling (Sugita, 2007b) to evaluate the relevant source area of pollen (RSAP; sensu Sugita, 1994) and estimate the local vegetation composition within the RSAP. A 500-m radius lake is set at the center of a patch of Patch Type A that is randomly selected in the central part of the plot; we repeat the same process thirty times to obtain thirty sets of pollen loading and proportions independently.

ACKNOWLEGEMEMNTS

For a 50-m radius lake the same sampling scheme is used, except for the location of the lake that is placed randomly within a patch of Patch Type A; then we repeat the process thirty times. Each trial, either for a 500-m radius lake or a 50-m radius lake, is run independently from the other runs. Mean plant abundance (i.e. the area occupied by each plant type per unit area) is calculated in consecutive concentric rings 5-m in width from the lake shore out to 100 km. For plant abundance data beyond the 80 km x 80 km plot border, simulations assume the outside-plot vegetation as homogeneous and the plant composition the same as that within the plot for the constituent taxa (the last column in Pollen dispersal and deposition is modeled by both the GPM and LSM options implemented in POLLSCAPE2017.v3.4.exe (Sugita, unpublished). The LSM option uses a look-up table of distance-dependent pollen deposition from a single point source based on the LSM model of Kuparinen et al. (2007) and Theuerkauf et al. (2016); the table was obtained from M. Theuerkauf.

This implementation assumes the pollen source area to be within a 100-km radius from each lake.

The GPM option, based on Sutton (1953), has been applied in palynology since the 1960s [START_REF] Tauber | Differential pollen dispersion and the interpretation of pollen diagrams[END_REF]. Those dispersal models are used to obtain the distance-weighted plant abundance (DWPA) of the surrounding vegetation, using the mean plant-abundance data at every 5-m increment out to 100 km at each site. In theory, pollen loading is linearly related to the DWPA for each of the constituent plant taxa (Prentice, 1985;Sugita, 1993Sugita, , 1994Sugita, , 2007aSugita, , 2007b)); the slope of the relationship represents pollen productivity of the corresponding taxon. Pollen productivity estimates (PPEs) relative to that of Poaceae are listed in Table A -2 (after Mazier et al., 2012). Those estimates were compiled from previous studies in various parts of northwestern Europe [START_REF] Broström | Pollen productivity estimates of key European plant taxa for quantitative reconstruction of past vegetation: a review[END_REF]many others). Those studies cited use the Extended R-Value (ERV) model for obtaining PPEs, assuming that pollen dispersal and deposition follows the GPM (Prentice, 1985;Sugita, 1994). With the LSM option the number of European plant/pollen types, for which PPEs are available, is still limited, except for PPEs obtained in Theuerkauf et al. (2013). This study uses the estimates in Table A-2 as inputs to simulation runs with either the GPM or the LSM, for the sake of consistency. Table A-2 also includes estimates of the fall speed of pollen in air -an important parameter for either the GPM or the LSM -for individual taxa (after Mazier et al., 2012).

2010)

The Landscape Reconstruction Algorithm (LRA) consists of two steps: estimating regional vegetation using the REVEALS model (Sugita, 2007a) and local vegetation using the LOVE model (Sugita, 2007b). REVEALS outcomes provide the mean vegetation composition, not distance weighted, within a 50-100 km radius. With the REVEALS results as inputs, LOVE calculates the local vegetation composition in a distance-weighted fashion within the relevant source area of pollen (RSAP) -the smallest spatial scale possible for local vegetation reconstruction (Sugita, 1994(Sugita, , 2007b;;Sugita et al., 2010). Both the GPM and the LSM options are used for the LRA applications for comparison. Relative PPEs and fall speed of pollen in Table A-2 are entered to all the LRA runs.

All the LOVE-based estimates of local vegetation within the RSAP are obtained using pollen data from lake 50-m in radius. An inverse modeling approach of LOVE (Sugita, 2007b;Sugita et al., 2010) is applied for the RSAP estimate for all the LRA runs. The basic assumptions and conditions described in the POLLSCAPE section above are mostly the same for the Landscape Reconstruction Algorithm (LRA) applied in the simulation runs here.

Ideally pollen records from large sites >100 ha are preferable for REVEALS applications (Sugita, 2007a). The main conclusions of this simulation study (i.e. Figure 1 in the main text) are drawn from the result based on the REVEALS runs with pollen data from 500-m radius lakes. When large sites are unavailable as in most mountain regions, pollen records from a number of small sites could potentially be used for both the REVEALS and LOVE applications; however, uncertainties of the regional vegetation estimates in particular are expected to increase in unknown ways (Sugita, 2007a;Sugita et al., 2010;Fyfe et al., 2013;Mazier et al., 2015;Trondman et al., 2016).

To evaluate uncertainties and accuracy of the LRA results when pollen records from small sites are used for REVEALS in mountain situations, we conducted additional simulation runs, assuming that the REVEALS estimates of regional vegetation composition are obtained using pollen records from 50-m radius lakes (i.e. those are the same as those created for the LOVE calculations in the main simulation runs). significantly the accuracy of vegetation reconstruction over pollen percentages alone, the simulation results demonstrate that regional vegetation reconstruction using pollen records from "large sites" is preferred whenever possible to reduce impacts on the LRA results and reliability of the systematic selection of pollen sites only on mountain tops (Sugita, 2007a).

1 Highlights-

 1 Pollen-based reconstruction of Holocene land-cover in mountain regions -The LRA approach is effective in mountain regions -The REVEALS-based estimates are influenced by the systematic selection of pollen sites -Pollen proportions alone are not useful for the local reconstruction -New horizons for answering questions about human impact on upland vegetation 1. INTRODUCTION Mountain landscapes in Europe are expected to change significantly over the next few decades because of the ongoing climate change and reforestation caused by the abandonment of agro-pastoral

  illustrates the general scheme of the simulated landscapes with randomly located patches of specific plant-community types within the Matrix. Seven plant/pollen types are included for simulations: Betula, Calluna, Ericaceae, Fagus, Quercus, Poaceae and Pinus. The Matrix and Patch Type B represent plant communities in lower altitudes in mountain valleys and flat plains, characterized by forests of mixed broadleaved and conifer trees (Fagus, Quercus and Pinus) and grass (Poaceae)-dominated open areas, respectively. Patch Type A symbolizes mountain tops that are characterized by Ericaceae plants (including Rhododendron spp. and Vaccinium spp.) with semi-dominant grass and Calluna (heather). In Patch Type A, small-patches of two types of heather heathlands are placed to fine-tune the local spatial structure of vegetation: Patch Type C with heather, grass and Ericaceae and Patch Type D with heather, grass, Ericaceae and Pinus.

  Figure1bshows the LRA results against the DWPA of the three land-cover categories within

Figure 1a )

 1a Figure 1a) Simulated mountain landscape that mimics the one of the Vicdessos valley to estimate pollen loadings on 30 lakes systematically located on mountain tops and plateaus. Mountain tops and plateaus correspond to Patch type A that includes two small patch types (Patch types C and D) of heather heathlands. The Matrix and Patch type B represent plant communities in lower altitudes in mountain valleys and flat plains. The simulated study plot is of 80 km x 80 km. More details about the simulation are given in the text and Appendix A. b) Results of LRA comparing distance weighted plant abundance (DWPA) based on observed vegetation (i.e. landscape design around each lake) against pollen proportion (untransformed pollen data; red dots) and two sets of LRA estimates (blue dots), one using GPM and the other LSM as pollen dispersal model. Each point represents a pollen record from each lake. Pollen proportion and LRA estimates are reasonable representation of observed vegetation when pollen records are close to the dashed line. Standard errors are also shown.

Figure 2

 2 Figure 2 Location of the study area and pollen archives in the Vicdessos area (French Pyrenees). Yellow color corresponds to open land (above the tree line), green to forests and grey to mineral surfaces. Isolines of 100 m are shown. Seven pollen records (SIG, ESC, LEG, W1652, FOUZ, OT and EM) are located on the plateau of Bassiès and one (ARBU)

  are combined with the radiocarbon uncertainties in CLAM. The pollen counts are pooled together for each 10-year time window between 1960 and 2013; the most recent time window is 2000-2013. For this study we select three time windows to assess the accuracy of the LRA-based reconstruction of vegetation:

Figure 3

 3 Figure3Observed regional land-cover maps of a 50-km radius from the plateaus of Bassiès and local land-cover maps of an agglomerated 2-km radius around each lakes and bogs (pollen archives). Regional maps are based on CORINE datasets for 2006 and 1990 and from the CNRS vegetation map "Foix 1964" for 1964. Local maps are based on series of aerial imageries[START_REF] Houet | Caractérisation de la fermeture des paysages dans les Pyrénées depuis les années 1940. Application sur le Haut-Vicdessos[END_REF]. The meanings of the labels of each pollen record are given in Table1. Note that

Figure 4

 4 Figure4Flow chart of the methodological approach. LOO: Leave One Out approach. DWPA: Distance weighted plant abundance.

  When the forested (i.e. conifers and broadleaved trees together) and unforested(i.e. open land) covers are concerned, the REVEALS results (RVs) are largely overestimating open land, in particular the ones using the LSM, in comparison to the observed data (RVs_MAP50); on the other hand, forested areas are overestimated by pollen proportions alone (the left graph in Figure5). In general, RVs (with pollen data from all sites) using REVEALS with either the GPM or the LSM are more dissimilar to the CORINE-map based estimates (RVs_MAP50) than pollen proportions alone.The right graph of Figure5depicts the results along three axes of land cover types: open land, conifers and broadleaved trees. Conifers and broadleaved trees are overestimated in pollen proportions in comparison to RVs_MAP 50. Open land cover is overestimated in RVs relative to RVs_MAP50. RVs with the GPM tend to be more similar to RVs_MAP50 than RVs with the LSM.

Figure 5

 5 Figure5Differences between RVs_MAP50 (i.e. observed regional land-covers for2006, 1990 and 1964), pollen proportions (untransformed pollen data) and REVEALS (RVs) using all local pollen records together for the three time windows,2013-2000, 2000-1990 and 1970-1960. REVEALS estimates have been calculated using both pollen dispersal model alternatives, GPM and LSM. Results are shown for tree-versus open land-cover and groups of taxa (conifer trees, broadleaved trees and open land taxa). The error bars are shown for the tree-versus open land-covers.

Figure 6

 6 Figure 6 Spatial distribution of observed vegetation proportion based on local maps at a scale of incremental 20-m rings from the center of each site. Results are shown for the three groups of taxa, i.e. open land, broadleaved and conifer trees. Mean pollen proportion (untransformed pollen data) and LRA and DWPA estimates are given at a distance 2 km for each site. Note that pollen proportion, LRA and DWPA estimates are means over the whole 2 km. Results from all alternative scenarios we have used in this study are shown as well as the standard errors, for open land category. All data presented in this figure correspond to the time window 2013-2000. See Table 1 and Figure 2 for labels and site locations.

Figure 7

 7 Figure 7 Comparison distance weighted plant abundance based on observed local land-cover maps against pollen proportion (untransformed pollen data) and two sets of LRA estimates, LRA_MAP50 and LRA-LOO (see text for further explanation). The results are shown for the three studied time windows. Each point represents a site. Pollen proportions and LRA estimates are reasonable representation of observed local land-cover (expressed here as distance weighted plant abundance) when pollen records are close to the dashed line.

Figure 6

 6 Figure6summarizes the spatial distribution of the three land-cover types in proportion from

  This work is part of the TRAM (Trace metal legacy on mountains aquatic ecogeochemistry) and MODE-RESPYR (Modeling Past and future land cover changes in the Pyrenees) projects (supported by The French National Research Agency) coordinated respectively by G. Le Roux and T. Houet, the PEPS CNRS POPEYE « Du POllen aux PalEopaYsagEs montagnard, vers un développement méthodologique » project coordinated by F. Mazier and the Observatoire Hommes-Milieux Pyrénées Haut Vicdessos (Labex DRIIHM) research program coordinated by D. Galop. //www.pastglobalchanges.org/ini/wg/landcover6k/intro) coordinated by M.J. Gaillard. S. Sugita specific, making it hard to model in a general way. Although the simulated plots with flat terrains are simplistic, it captures the main characteristics of the spatial structure and species composition of plant communities both in high plateaus and the regional vegetation around the Vicdessos valley (Figure 1a in the main text). Table A-1 shows the parameter setting for the simulated landscapes, including the mean plant composition in proportion for individual patch types and the matrix in 80 km x 80 km plots. The Matrix and Patch Type B represent plant communities in lower altitudes including mountain valleys and flat plains. The Matrix is characterized by forests consisted of broadleaved and conifer trees (Fagus, Quercus and Pinus) with small amounts of grasses (Poaceae) and Ericaceous plants (Calluna and other Ericaceae taxa). Patch type B represents Poaceae-dominated unforested/open areas. Patch Type A symbolizes mountain tops that are characterized by Ericaceae plants (including Rhododendron spp. and Vaccinium spp.) with semi-dominant grass and Calluna (heather). Within the areas occupied by Patch Type A, small-patches of two types of heather heathlands are placed to create a localized mosaic of mountain plant communities: Patch Type C with heather, grass and Ericaceae and Patch Type D with heather, grass, Ericaceae and Pinus. Patch Type A occupies 30% of the total area in the 80-km x 80-km plots, and the Matrix and Patch Type B 50% and 20%, respectively. Within the "mountain" areas covered by Patch Type A, Patch Types C and D occupy 20% and 10% in area respectively; the rest (i.e. 70% in mountain area) is characterized by plant composition of Patch Type A. For all the patch types circular-shaped patches are placed randomly without overlap; patch sizes (±SE) for Patch Types A, B, C and D are 314 (±15) ha, 10 (±1) ha, 2 (±0.2) ha and 1 (±0.1) ha, respectively.

  Figure A-1 shows the simulation result.

Figure A- 1 .

 1 Figure A-1. LRA-based estimates of local vegetation against DWPA-based vegetation proportion within RSAP, assuming all the pollen records are from 50-m radius lakes in Patch Type A. Blue dots and error bars indicate the LRA estimates and their SEs against vegetation proportions at 30 sites; red dots represent pollen proportions at 30 sites. RSAP is the area within a 705-m radius when GPM is used and within a 945-m radius when LSM is used.

Figure B- 2 .

 2 Figure B-2. Zoom on the outcomes of the Clam age-depth model for the upper sediment layers of Legunabens.

Figure B- 3 .

 3 Figure B-3. Clam age-depth model for Fouzes.

  Figure B-5 corresponds to a zoom on the outcomes of the Clam age-depth model for the upper sediment layers.

Figure B- 1 .

 1 Figure B-1. Clam age-depth model for Arbu.

Figure B- 2 .

 2 Figure B-2. Zoom on the outcomes of the Clam age-depth model for the upper sediment layers of Arbu.

Table 1

 1 Metadata of the pollen archives.

	Site

name Lat.DD Long.DD Elevation (m) Site radius (m) Basin type Age/depth model and dating

	Sigriou (SIG)	42.754	1.422	2000	66	Lake	Simonneau et al. (2013)
	Legunabens (LEG) 42.764	1.431	1680	58	Lake	Appendix B
	Arbu (ARBU)	42.821	1.437	1740	113	Lake	Appendix B
	Escale (ESC)	42.761	1.430	1630	40	Bog	Hansson et al. (2017)
	Fouzes (FOUZ)	42.766	1.410	1720	13	Bog	Appendix B
	W1652a (W1652) 42.763	1.416	1660	6	Bog	Hansson et al. (2017)
	Etang Mort (EM) 42.766	1.419	1670	36	Bog	Hansson et al. (2017)
	Orry de Théo (OT) 42.766	1.415	1680	31	Bog	Galop et al. (2011)

Table 3 Nomenclature

 3

	Model data/name	Meaning
	LRA	Landscape Reconstruction Algorithm: REVEALS + LOVE models
	REVEALS	Model for regional land-cover reconstruction
	LOVE	Model for local land-cover reconstruction
	POLLSCAPE	Model for landscape simulation
	DWPA	Distance Weighted Plant Abundance
	RSAP	Relevant Source Area of Pollen
	RPP	Relative Pollen Productivity estimates
	GPM	Gaussian Plume Model as pollen dispersal model
	LSM	Lagrangian Stochastic Model as pollen dispersal model
	RVs	The eight pollen archives are used with REVEALS
	RVs-LOO	Seven pollen archives are used with REVEALS; LOO: Leave One Out approach
	RVs-MAP50	Regional land-cover reconstruction based on the CORINE map
	SE	Standard errors
	LRA-LOO	Local vegetation estimates for a RSAP of 2-km radius with RVs-LOO as regional background of pollen

  calculated SEs for RVs_MAP50 by taking into account both SEs that were previously obtained from RVs and both GPM and LSM options as follow:

						𝑆𝐸 𝑖 =	𝑆𝐸 𝑅𝑉 𝐺𝑃𝑀 𝑖 𝑅𝑉 𝐺𝑃𝑀 𝑖	+ 2	𝑆𝐸 𝑅𝑉 𝐿𝑆𝑀 𝑖 𝑅𝑉 𝐿𝑆𝑀 𝑖	× 𝑅𝑉 𝑀𝐴𝑃50 𝑖
	where	𝑆𝐸 𝑅𝑉 𝐺𝑃𝑀 𝑖	and	𝑆𝐸 𝑅𝑉 𝐿𝑆𝑀 𝑖	are the standard errors for species i of RVs using the GPM (	𝑅𝑉 𝐺𝑃𝑀 𝑖
	) and LSM (	) option, respectively. 𝑅𝑉 𝐿𝑆𝑀 𝑖	𝑅𝑉 𝑀𝐴𝑃50 𝑖	is RVs_MAP50 for the species i.

Table A -

 A

1).

Table A -

 A 1. Parameter setting for simulated landscapes

							Nested patches
							within Patch Type A
				Matrix	Patch Type A	Patch Type B	Patch Type C	Patch Type D
	Landscape	design	Areal proportion Patch size (SE) in ha	0.50	0.30 31415	0.20 101	0.20 20.2	0.10 10.1
									Overall proportion
									in the entire plot
			Poaceae	0.03	0.15	0.94	0.21	0.30	0.256
	Plant composition in	each patch type and the matrix	Pinus Betula Quercus Fagus Calluna Ericaceae	0.40 0.05 0.30 0.20 0.01 0.01	0.02 0.01 0.01 0.01 0.10 0.70	0.01 0.01 0.01 0.01 0.01 0.01	0.01 0.01 0.01 0.01 0.55 0.20	0.05 0.01 0.01 0.01 0.40 0.22	0.208 0.030 0.155 0.105 0.073 0.173
			SUM	1.00	1.00	1.00	1.00	1.00	1.000

Table A -

 A 2. Relative pollen productivity and fall speed of pollen used in simulations (afterMazier et al., 2012) in pollen proportions of the constituent taxa at each lake, calculated from the pollen loading data relative to that of a reference taxon. Pollen counts are then obtained by back calculation in such a way that the total sum becomes 1000 grains at each site.

		Relative pollen	Fall speed of
		productivity	pollen [m sec -1 ]
	Poaceae	1.00	0.035
	Pinus	6.38	0.031
	Betula	3.09	0.024
	Quercus	5.83	0.035
	Fagus	2.35	0.057
	Calluna	0.82	0.038
	Ericaceae	0.07	0.038
	POLLSCAPE simulations result

A-

2. Landscape Reconstruction Algorithm (LRA -Sugita, 2007a, 2007b; Sugita et al.,

Selection of pollen dispersal models for mountain situations

The differences in the LRA results with the LSM and GPM options are negligible in the simulations and the empirical study at the Vicdessos area (Figure 7). A LSM scheme is more advanced and realistic in description of atmospheric airflow and long-distance particle dispersal than a GPM scheme (Kuparinen et al., 2007). Comparisons between REVEALS_LSM and REVEALS_GPM (Theuerkauf et al., 2013(Theuerkauf et al., , 2016) ) using pollen records from lakes in NE Germany showed the superiority of the use of the LSM of Kuparinen et al. (2007) over the GPM traditionally used in pollen-based vegetation modelling. [START_REF] Mariani | How old is the Tasmanian cultural landscape? A test of landscape openness using quantitative land-cover reconstructions[END_REF] came to the same conclusion based on their study in Tasmania. We expected that applications of the LSM would improve the LRA results, in particular reducing biases on the long-distance pollen from the regional source, caused by complex topography and wind field in mountains. However, our results show that the REVEALS_GPM-based estimates of the regional vegetation cover tend to be closer to the observed regional land-cover than those by REVEALS_LSM (Figure 5); the LSM and GPM options do not influence significantly the LOVE results. The LSM takes into account higher updraft velocities in air via turbulence better than the GPM (Kuparinen et al., 2007); thus, heavier pollen types, such as Abies and Fagus, tend to be modeled to disperse further with the LSM than with the GPM (Sutton, 1953). The consequence is that GPM gives greater importance to pollen arriving from shorter distances, especially for pollen was partially supported by a grant from the Estonia Research Council (IUT18-09: the ENCHANTED project). We thank ARTEMIS facility (CNRS-Gif sur Yvette, France) for radiocarbon dating of the samples, and P. van Beek and M. Souhaut at the LAFARA underground laboratory where the sediment samples have been analyzed for 210 Pb and 137 Cs to establish chronologies.

APPENDICES

Appendix A. POLLSCAPE simulation and evaluation of the Landscape Reconstruction Algorithm for mountain regions

A-1. POLLSCAPE simulations (Sugita 1994(Sugita , 2013;;Sugita et al., 1999;Gaillard et al., 2008) and parameter setting POLLSCAPE is a simple simulation approach to evaluate factors and mechanisms that affect pollen-vegetation relationships in patchy and heterogeneous landscapes (Sugita, 1994(Sugita, , 2013)).

Effects on pollen assemblages of various combinations of specific assumptions and conditions can be assessed; those factors include inter-taxonomic differences in productivity and fall speed of pollen, basin size, spatial patterns of plant distribution and land cover, and atmospheric conditions (e.g., wind speed and turbulence that simultaneously influence pollen dispersal and deposition). This modeling approach has been informative to provide plausible scenarios about how pollen assemblages from lakes and mires would reflect the surrounding vegetation (e.g., Sugita et al., 1997Sugita et al., , 1999;;Bunting et al., 2004;[START_REF] Bunting | Equifinality and uncertainty in the interpretation of pollen data: the Multiple Scenario Approach to reconstruction of past vegetation mosaics[END_REF][START_REF] Caseldine | Pollen modelling, palaeoecology and archaeology: virtualization and/or visualisation of the past?[END_REF][START_REF] Fyfe | GIS and the application of a model of pollen deposition and dispersal: A new approach to testing landscape hypotheses using the POLLANDCAL models[END_REF]Gaillard et al., 2008;[START_REF] Hellman | Effects of the sampling design and selection of parameter values on pollen-based quantitative reconstructions of regional vegetation: a case study in southern Sweden using the REVEALS model[END_REF]Hjelle and Sugita, 2012).

Major basic assumptions and conditions of the POLLSCAPE approach in this study are as follows:

 The sampling basin, either a lake or mire, is a circular opening in the vegetation canopy on a flat two-dimensional plain; heights of source plants for pollen are assumed to be at the ground level.

 Each taxon has a constant pollen productivity in the entire simulated plot.

 Wind above canopy is the dominant agent of pollen transport.

 Wind direction is even in all directions. Accordingly the spatial distribution of source plants for each taxon is described as a function of distance from a point at the center of the basin.

 Pollen dispersal is approximated either by a Gaussian Plume Model (GPM) of small particles from a ground-level source under various atmospheric conditions (Sutton, 1953;[START_REF] Tauber | Differential pollen dispersion and the interpretation of pollen diagrams[END_REF]Prentice, 1985Prentice, , 1988;;Sugita, 1993Sugita, , 1994) ) or by a Lagrangian Stochastic Model (LSM) under more realistic wind-fields and atmospheric turbulence conditions (Kuparinen et al., 2007;Theuerkauf et al., 2016).

 When pollen assemblages from lakes are concerned, Sugita's model (Sugita, 1993) is used for approximation of pollen deposition on the entire lake surface either by the GPM or the LSM.

For the sake of simplicity this simulation study assumes mountain tops and high plateaus as "landscape islands" on a flat two-dimensional terrain 80 km x 80 km in size. Wind fields and turbulence in mountain regions are complex in reality; thus, pollen transport is area-and region-

Appendix B. Dated materials and details on age-depth models of sedimentary archives.

Lake and peat cores were dated by using radiocarbon and 210 Pb ages together. Age-depth models were based on all available age dating information and the entire cores, although the present study focused on the last 60 years. Radiocarbon dating were obtained from previouslyidentified above-ground plant remains selected from 1 cm 3 of bulk peat/sediment at EcoLab and GEODE laboratories following [START_REF] Mauquoy | Late Holocene climatic changes in Tierra del Fuego based on multiproxy analyses of peat deposits[END_REF]. Sample pretreatments included acidalkali-acid washing (to remove bacterial CO 2 , carbonate and humic-fulvic acids), drying, combustion and graphitization. These pretreatments and the radiocarbon measurements by acceleration mass spectrometer were performed at Beta Analytic (London, UK), Poznan

Radiocarbon Laboratory (Poznan, Poland) and ARTEMIS facility (CNRS-Gif sur Yvette, France). 210 Pb measurements were achieved by using peat powder and Gamma spectrometry [START_REF] Van Beek | LAFARA: A new underground laboratory in the French Pyrénées for low-background gamma spectrometry[END_REF]Appleby, 2002), except Orry de Théo peatland for which Alpha spectrometry was used [START_REF] Sanchez-Cabeza | 210 Pb and 210 Po analysis in sediments and soils by microwave acid digestion[END_REF].

Table B-1 summarizes the dating information. The age-depth models of Legunabens, Arbu and Fouzes that were not published so far are given below.

LEGUNABENS

The age-depth model of Legunabens sediment core is based on five radiocarbon measurements from macrofossils. Three radiocarbon ages are calibrated by using the NH IntCal09 calibration curve. The results from the two upper sediment layers are used as postbomb ages [START_REF] Goodsite | High-resolution AMS 14 C dating of post-bomb peat archives of atmospheric pollutants[END_REF]. The Clam age-depth model is shown in figures B-1 and B-2 and all details about radiocarbon measurements are described in Table B-2 below.